Power Oscillations and Electrical Infrastructures

Washington, DC: Tech. Rep. DE2010-972306; 2010

Power System Stability

pp. 1-6. DOI: 10.1109/ TDC.2005.1546867

52

[27] Guoqiang H, Renmu H, Huachun Y, et al. Iterative prony method based power system low frequency oscillation mode analysis and PSS design. In: 2005 IEEE/PES Asia and Pacific Transmission and Distribution Conference and Exhibition. Dalian, China: IEEE; 2005.

Chapter 4

Salman Rezaei

presence of series capacitance.

1. Introduction

55

Abstract

Power Oscillation Due to

Subsynchronous Resonance

Power oscillation occurs in electrical network due to variety of phenomena. Subsynchronous resonance (SSR) and ferroresonance are the phenomena that cause power oscillation of rotary systems. Ferroresonance is likely to occur due to traversing capacitance line of the system across nonlinear area of transformer saturation curve due to several configurations like breaker failure, voltage transformer connected to grading capacitor circuit breaker, line and plant outage, etc. It causes misshaping the waveforms and frequency difference between two points in the network. Frequency difference (Δf) results in oscillation of power with a swing frequency which is equal to Δf. During SSR, electrical energy is exchanged between generators and transmission systems below power frequency. It happens due to interaction of a series compensated transmission line with a generator. It results in oscillation in the shaft and power oscillation. In addition, SSR causes the magnitudes of voltage and current to increase. Increasing the voltage causes saturation of iron core of transformer or reactor and consequently occurrence of ferroresonance in the

Keywords: power oscillation, ferroresonance, subsynchronous resonance, series compensation, saturation, frequency difference, Manitoba hydro, Mohave plant

The word ferroresonance was originally expressed in 1920 to explain the phenomenon of two stable fundamental frequency operating points in a series resistor, nonlinear inductor, and capacitor circuit [1]. It has been extensively studied over the past 90 years. Severity of ferroresonance is classified as four categories like fundamental, harmonic, quasi-periodic, and chaotic [2]. Ref [3] explains Conventional configurations which lead in ferroresonance like; voltage transformer (VT) energized through the grading capacitance of open circuit breakers or VT connected to an ungrounded neutral system, circuit breaker failure during opening or closing operation, and power transformer supplied through a long transmission line cable with low short-circuit power [3]. Several methods have been presented to analyze ferroresonance in time and frequency domain. Ref. [4] is concerned with comparing analytical nonlinear dynamics methods with a two-dimensional (2-D) bruteforce bifurcation diagram for displaying safety margins in a 2-D parameter space.

Ferroresonance and

## Chapter 4

## Power Oscillation Due to Ferroresonance and Subsynchronous Resonance

Salman Rezaei

## Abstract

Power oscillation occurs in electrical network due to variety of phenomena. Subsynchronous resonance (SSR) and ferroresonance are the phenomena that cause power oscillation of rotary systems. Ferroresonance is likely to occur due to traversing capacitance line of the system across nonlinear area of transformer saturation curve due to several configurations like breaker failure, voltage transformer connected to grading capacitor circuit breaker, line and plant outage, etc. It causes misshaping the waveforms and frequency difference between two points in the network. Frequency difference (Δf) results in oscillation of power with a swing frequency which is equal to Δf. During SSR, electrical energy is exchanged between generators and transmission systems below power frequency. It happens due to interaction of a series compensated transmission line with a generator. It results in oscillation in the shaft and power oscillation. In addition, SSR causes the magnitudes of voltage and current to increase. Increasing the voltage causes saturation of iron core of transformer or reactor and consequently occurrence of ferroresonance in the presence of series capacitance.

Keywords: power oscillation, ferroresonance, subsynchronous resonance, series compensation, saturation, frequency difference, Manitoba hydro, Mohave plant

## 1. Introduction

The word ferroresonance was originally expressed in 1920 to explain the phenomenon of two stable fundamental frequency operating points in a series resistor, nonlinear inductor, and capacitor circuit [1]. It has been extensively studied over the past 90 years. Severity of ferroresonance is classified as four categories like fundamental, harmonic, quasi-periodic, and chaotic [2]. Ref [3] explains Conventional configurations which lead in ferroresonance like; voltage transformer (VT) energized through the grading capacitance of open circuit breakers or VT connected to an ungrounded neutral system, circuit breaker failure during opening or closing operation, and power transformer supplied through a long transmission line cable with low short-circuit power [3]. Several methods have been presented to analyze ferroresonance in time and frequency domain. Ref. [4] is concerned with comparing analytical nonlinear dynamics methods with a two-dimensional (2-D) bruteforce bifurcation diagram for displaying safety margins in a 2-D parameter space.

Analysis and mitigation of ferroresonant oscillations based on harmonic balance method and bifurcation theory are presented in [5].

Ferroresonance generally occurs in distribution network and is significantly probable in HV systems [6–8]. For instance, plant outage in HV power system is able to change electrical characteristics and parameters in a nonlinear circuit, which may lead in ferroresonance [9].

2. Theoretical approach of ferroresonance and subsynchronous

Power Oscillation Due to Ferroresonance and Subsynchronous Resonance

nonlinear inductance and capacitance in a circuit with low resistance. In

q

VL ¼

It is also a nonlinear function of current as follows:

Ferroresonance is a nonlinear resonance, which occurs in presence of a saturable

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi V2

The inductance in this circuit is a nonlinear element due to the core saturation and hysteresis. The voltage across the capacitance in the circuit is represented as:

where Vh is the total voltage across the circuit. Similarly, the voltage across the

As shown in Figure 1, point of intersection of VL = ωf(I) and VC represents the operating point of the circuit. Changing the capacitance of the system causes change in the slope given by tanα = 1/ωC. On the other hand, changes in inductance result in an interaction with a wide range of circuit capacitances resulting in existence of several stable steady state responses to any given change of parameter. It causes changes in configuration of the circuit, and so capacitance traverses across

nonlinear area of VL = ωf(I) curve and results in occurrence of ferroresonance in the

As was mentioned above, frequency of waveform can be deviated from nominal frequency in ferroresonance so that frequency deviation can be defined as follows

<sup>h</sup> � ð Þ <sup>I</sup>:<sup>R</sup> <sup>2</sup>

þ I

<sup>ω</sup><sup>C</sup> (1)

VL ¼ ωf Ið Þ (2)

VC ¼ I=ωC (3)

VL ¼ V þ ð Þ I=ωC (4)

ferroresonance, the capacitance line crosses inductance characteristic in nonlinear area. It results in presence of abnormally large currents and frequency distortion. A graphical approach is obtained by calculating the parameters of nonlinear circuits in time domain. For instance, in a typical series, RLC circuit inductor

resonance

2.1 Ferroresonance

voltage is calculated as follows [6]:

DOI: http://dx.doi.org/10.5772/intechopen.81724

inductance can also be expressed as:

circuit.

Figure 1.

57

Diagram of parameters in series RLC ferroresonant circuit.

Impact of ferroresonance has been taken into consideration in wind farm. Ref. [10] presents the scenarios that can lead to ferroresonant circuits in Doubly Fed Induction Generator (DFIG)-based wind parks. Transient and sustained ferroresonance phenomenon in wind farms connected to a power distribution system is analyzed in [11]. Occurrence of ferroresonance causes misshaped waveform of magnitudes with different frequencies. It leads in power oscillation between two points with a certain frequency difference in the network [12]. Ferroresonance is typically damped by using several methods. For instance, installation of permanent resistance in the secondary circuit of distribution transformer; furthermore, replacing VT with CVT causes mitigation of ferroresonance in voltage transformer [6].

The term of SSR has been taken into consideration in the power industry since first experienced in 1970. It results in shaft failure of units at Mohave power plant. The second failure was the real cause of failure recognized as SSR in 1971 [13].

Many investigations have been implemented in analysis and mitigation of SSR. By bifurcation analysis, the stable limit cycle bifurcates to a stable torus and an unstable limit cycle, which connects to a stable limit cycle by a supercritical torus bifurcation [14]. Frequency scanning computes the equivalent resistance and inductance, seen looking into the network from a point behind the stator winding of a generator as a function of frequency [15]. Design and implementation details of an artificial neural network-based SSR are presented in [16]. Time frequency distribution algorithm extracts time variable information about frequency contents from the time domain signal [17].

For the mitigation of SSR, several methods have been presented. STATCOM is used in the transformer bus to damp SSR [18]. Application of gate-controlled series capacitors (GCSC) for reducing stresses due to SSR in turbine-generator shaft is presented in [19]. Fuzzy logic and ANFIS controller-based Static Var Compensation (SVC) for mitigating SSR is explained in [20].

SSR has received considerable attention in the wind farm. SSR analysis on DFIGbased wind farm and optimal adaptive controls to mitigate SSR in wind farm are presented in [21, 22].

SSR causes subharmonic components of electrical quantities to interact with natural frequencies of rotary systems due to series capacitance. It leads in torsional oscillation of the turbo-generator shaft. Torsional oscillation in the shaft results in out of step condition of the generator. It increases the magnitudes of voltage and current. Increasing the voltage causes saturation of transformer and probable occurrence of ferroresonance in a series compensated network [23, 24]. Oscillation due to ferroresonance is superimposed on torsional oscillation in SSR.

In this chapter, theoretical approach of ferroresonance and SSR is presented. Manitoba hydro electrical network has experienced ferroresonant states in 1995 [8]. In addition, it includes long transmission line of about 500 km, which is suitable for series compensation studies [6]. Hence, it is a good example of a case study in this field. The state that results in ferroresonance is simulated in Manitoba hydro system by PSCAD/EMTDC. Power oscillation due to SSR and ferroresonance is presented in HV power system, and results of oscillation are analyzed on electrical and mechanical parameters of rotary systems including hydro generator in Grand Rapids station.

## 2. Theoretical approach of ferroresonance and subsynchronous resonance

## 2.1 Ferroresonance

Analysis and mitigation of ferroresonant oscillations based on harmonic balance

Ferroresonance generally occurs in distribution network and is significantly probable in HV systems [6–8]. For instance, plant outage in HV power system is able to change electrical characteristics and parameters in a nonlinear circuit, which

Impact of ferroresonance has been taken into consideration in wind farm. Ref. [10] presents the scenarios that can lead to ferroresonant circuits in Doubly Fed Induction Generator (DFIG)-based wind parks. Transient and sustained ferroresonance phenomenon in wind farms connected to a power distribution system is analyzed in [11]. Occurrence of ferroresonance causes misshaped waveform of magnitudes with different frequencies. It leads in power oscillation between two points with a certain frequency difference in the network [12]. Ferroresonance is typically damped by using several methods. For instance, installation of permanent resistance in the secondary circuit of distribution transformer; furthermore, replacing VT with CVT causes mitigation of ferroresonance in voltage

The term of SSR has been taken into consideration in the power industry since first experienced in 1970. It results in shaft failure of units at Mohave power plant. The second failure was the real cause of failure recognized as SSR in 1971 [13]. Many investigations have been implemented in analysis and mitigation of SSR. By bifurcation analysis, the stable limit cycle bifurcates to a stable torus and an unstable limit cycle, which connects to a stable limit cycle by a supercritical torus bifurcation [14]. Frequency scanning computes the equivalent resistance and inductance, seen looking into the network from a point behind the stator winding of a generator as a function of frequency [15]. Design and implementation details of an artificial neural network-based SSR are presented in [16]. Time frequency distribution algorithm extracts time variable information about frequency contents from

For the mitigation of SSR, several methods have been presented. STATCOM is used in the transformer bus to damp SSR [18]. Application of gate-controlled series capacitors (GCSC) for reducing stresses due to SSR in turbine-generator shaft is presented in [19]. Fuzzy logic and ANFIS controller-based Static Var Compensation

SSR has received considerable attention in the wind farm. SSR analysis on DFIGbased wind farm and optimal adaptive controls to mitigate SSR in wind farm are

SSR causes subharmonic components of electrical quantities to interact with natural frequencies of rotary systems due to series capacitance. It leads in torsional oscillation of the turbo-generator shaft. Torsional oscillation in the shaft results in out of step condition of the generator. It increases the magnitudes of voltage and current. Increasing the voltage causes saturation of transformer and probable occurrence of ferroresonance in a series compensated network [23, 24]. Oscillation

In this chapter, theoretical approach of ferroresonance and SSR is presented. Manitoba hydro electrical network has experienced ferroresonant states in 1995 [8]. In addition, it includes long transmission line of about 500 km, which is suitable for series compensation studies [6]. Hence, it is a good example of a case study in this field. The state that results in ferroresonance is simulated in Manitoba hydro system by PSCAD/EMTDC. Power oscillation due to SSR and ferroresonance is presented in HV power system, and results of oscillation are analyzed on electrical and mechanical parameters of rotary systems including hydro generator in Grand

due to ferroresonance is superimposed on torsional oscillation in SSR.

method and bifurcation theory are presented in [5].

may lead in ferroresonance [9].

Power System Stability

transformer [6].

the time domain signal [17].

presented in [21, 22].

Rapids station.

56

(SVC) for mitigating SSR is explained in [20].

Ferroresonance is a nonlinear resonance, which occurs in presence of a saturable nonlinear inductance and capacitance in a circuit with low resistance. In ferroresonance, the capacitance line crosses inductance characteristic in nonlinear area. It results in presence of abnormally large currents and frequency distortion.

A graphical approach is obtained by calculating the parameters of nonlinear circuits in time domain. For instance, in a typical series, RLC circuit inductor voltage is calculated as follows [6]:

$$V\_L = \sqrt{V\_h^2 - \left(I.R\right)^2} + \frac{I}{o\epsilon \mathcal{C}}\tag{1}$$

It is also a nonlinear function of current as follows:

$$V\_L = o \mathcal{f}(I) \tag{2}$$

The inductance in this circuit is a nonlinear element due to the core saturation and hysteresis. The voltage across the capacitance in the circuit is represented as:

$$V\_C = I/o\!C\tag{3}$$

where Vh is the total voltage across the circuit. Similarly, the voltage across the inductance can also be expressed as:

$$V\_L = V + (I/a\mathcal{C})\tag{4}$$

As shown in Figure 1, point of intersection of VL = ωf(I) and VC represents the operating point of the circuit. Changing the capacitance of the system causes change in the slope given by tanα = 1/ωC. On the other hand, changes in inductance result in an interaction with a wide range of circuit capacitances resulting in existence of several stable steady state responses to any given change of parameter. It causes changes in configuration of the circuit, and so capacitance traverses across nonlinear area of VL = ωf(I) curve and results in occurrence of ferroresonance in the circuit.

As was mentioned above, frequency of waveform can be deviated from nominal frequency in ferroresonance so that frequency deviation can be defined as follows

Figure 1. Diagram of parameters in series RLC ferroresonant circuit.

$$
\Delta f = \left| f\_{fr} - f\_{nom} \right| \tag{5}
$$

Loadability of AC transmission line is defined as follows:

Power Oscillation Due to Ferroresonance and Subsynchronous Resonance

which decrease total line impedance as follows:

DOI: http://dx.doi.org/10.5772/intechopen.81724

resonance frequency is given by the following formula.

defined as follows:

grid.

59

ments in the grid [25].

where ζ is damping ratio given by (7).

ω<sup>2</sup> is damping frequency as follows:

ω<sup>n</sup> is undamped natural frequency as follows:

<sup>P</sup> <sup>¼</sup> VS:VR XT

Series compensation increases transmittable power by adding series capacitors,

Here, S is the compensation degree, which is changed between 0 and 100%

The degree of compensation could be 100% theoretically. It may produce large currents in the presence of small disturbances or faults. In other hand, a high level of compensation highlights the problem in protective relays and in voltage profile during fault condition. In a radial series-compensated power system, the electrical

> ffiffiffiffiffiffi XC XL

i tðÞ¼ K A sin <sup>ω</sup>1<sup>t</sup> <sup>þ</sup> <sup>ψ</sup><sup>1</sup> ð Þþ Be�ζω2<sup>t</sup> sin <sup>ω</sup>2<sup>t</sup> <sup>þ</sup> <sup>ψ</sup><sup>2</sup> ð Þ � � (14)

ffiffiffi C L r

ffiffiffiffiffiffiffiffiffiffiffiffi <sup>1</sup> � <sup>ζ</sup><sup>2</sup> q

ffiffiffiffiffiffi 1 LC r

r

<sup>S</sup> <sup>¼</sup> XC XL

f er ¼ �fs

<sup>ζ</sup> <sup>¼</sup> <sup>R</sup> 2

ω<sup>2</sup> ¼ ω<sup>n</sup>

ω<sup>n</sup> ¼

Subsynchronous current induced in generator produces torque on the turbinegenerator shaft. Subsynchronous torque may coincide with one of the natural frequencies of the rotary system. It causes oscillation of the shaft at some natural frequencies. Subsynchronous resonance can cause catastrophic damage to the

where fs is power system nominal frequency and XL is total inductance of the

Current flowing in the grid circulates to the armature winding of the generator and interacts with turbine-generator rotor as subharmonic and super harmonic frequencies. As shown below, the current includes fundamental component (grid frequency), and another sinusoidal components are determined by existing ele-

sinδ (9)

XT ¼ XL � XC (10) XT ¼ ð Þ 1 � S :XL (11)

� 100% (12)

(13)

(15)

(16)

(17)

where ffr is the frequency of waveform in ferroresonance.

Resulted waveform is decomposed to its number of harmonics using fast Fourier transform (FFT). Measurement is done by evaluation of samples, which are taken in specific sampling interval; hence, discrete Fourier transform (DFT) is used with a certain sampling rate to illustrate harmonic components on harmonic spectrum.

$$V\_{Lk} = \sum\_{n=0}^{N-1} V\_{Ln} e^{-j2\pi k \frac{n}{N}} K = 0...N-1\tag{6}$$

N = number of samples.

Then, total harmonic distortion is calculated, and so integer harmonics, which obtained from FFT, are considered in the following formula:

$$THD = \sqrt{\sum\_{h=2}^{x} \left(\frac{individual\ (h)}{individual\ (1)}\right)^2} \tag{7}$$

where x is integer harmonics.

In order to determine ferroresonance based on measurement in a logical manner, ferroresonant characteristics must be quantified. THD and Δf are the quantities that are used as criteria to determine ferroresonance of different types (Table 1).

As shown in the table, fundamental ferroresonance is detected when frequency of waveform remains at nominal value (Δf is zero) and the value of THD is more than 50%. Harmonic ferroresonance is detected when frequency of waveform is deviated from nominal value and remains constant (Δf is not zero); furthermore, the value of THD is also more than 50%. In most cases, fundamental and harmonic ferroresonance contain odd harmonics; hence, harmonic spectrum is discrete. Quasi-periodic and chaotic modes are determined when <sup>d</sup>Δ<sup>f</sup> dt is detected and calculated as follow.

$$\frac{d\Delta f}{dt} = T.\frac{\Delta f(t) - \Delta f(t - \Delta t)}{\Delta t} \tag{8}$$

where T is the time constant, t � Δt is the previous time step, and Δt is the time step interval.

Furthermore, the value of THD increases more than 100% where chaotic mode contains a continuous harmonic spectrum. As harmonic spectrum is mostly a qualified characteristic, THD and Δf are used to determine ferroresonance modes.

#### 2.2 Subsynchronous resonance

In this section, theoretical aspects of SSR in AC transmission system are explained.


Table 1. Criteria to determine ferroresonance modes. Power Oscillation Due to Ferroresonance and Subsynchronous Resonance DOI: http://dx.doi.org/10.5772/intechopen.81724

Loadability of AC transmission line is defined as follows:

$$P = \frac{V\_S.V\_R}{X\_T} \sin \delta \tag{9}$$

Series compensation increases transmittable power by adding series capacitors, which decrease total line impedance as follows:

$$X\_T = X\_L - X\_C \tag{10}$$

$$X\_T = (\mathbf{1} - \mathbf{S})X\_L \tag{11}$$

Here, S is the compensation degree, which is changed between 0 and 100% defined as follows:

$$S = \frac{X\_C}{X\_L} \times 100\text{\%} \tag{12}$$

The degree of compensation could be 100% theoretically. It may produce large currents in the presence of small disturbances or faults. In other hand, a high level of compensation highlights the problem in protective relays and in voltage profile during fault condition. In a radial series-compensated power system, the electrical resonance frequency is given by the following formula.

$$f\_{cr} = \pm f\_s \sqrt{\frac{X\_C}{X\_L}}\tag{13}$$

where fs is power system nominal frequency and XL is total inductance of the grid.

Current flowing in the grid circulates to the armature winding of the generator and interacts with turbine-generator rotor as subharmonic and super harmonic frequencies. As shown below, the current includes fundamental component (grid frequency), and another sinusoidal components are determined by existing elements in the grid [25].

$$\dot{a}(t) = K \left[ A \sin \left( a\_1 t + \varphi\_1 \right) + B e^{-\zeta\_2 a\_2 t} \sin \left( a\_2 t + \varphi\_2 \right) \right] \tag{14}$$

where ζ is damping ratio given by (7).

$$
\zeta = \frac{R}{2} \sqrt{\frac{\mathcal{C}}{L}} \tag{15}
$$

ω<sup>2</sup> is damping frequency as follows:

$$
\rho\_2 = \rho\_n \sqrt{1 - \zeta^2} \tag{16}
$$

ω<sup>n</sup> is undamped natural frequency as follows:

$$
\rho\_n = \sqrt{\frac{1}{LC}}\tag{17}
$$

Subsynchronous current induced in generator produces torque on the turbinegenerator shaft. Subsynchronous torque may coincide with one of the natural frequencies of the rotary system. It causes oscillation of the shaft at some natural frequencies. Subsynchronous resonance can cause catastrophic damage to the

Δf ¼ ffr � f nom � � �

Resulted waveform is decomposed to its number of harmonics using fast Fourier transform (FFT). Measurement is done by evaluation of samples, which are taken in specific sampling interval; hence, discrete Fourier transform (DFT) is used with a certain sampling rate to illustrate harmonic components on harmonic spectrum.

�j2πk<sup>n</sup>

Then, total harmonic distortion is calculated, and so integer harmonics, which

In order to determine ferroresonance based on measurement in a logical manner, ferroresonant characteristics must be quantified. THD and Δf are the quantities that are used as criteria to determine ferroresonance of different types (Table 1). As shown in the table, fundamental ferroresonance is detected when frequency of waveform remains at nominal value (Δf is zero) and the value of THD is more than 50%. Harmonic ferroresonance is detected when frequency of waveform is deviated from nominal value and remains constant (Δf is not zero); furthermore, the value of THD is also more than 50%. In most cases, fundamental and harmonic ferroresonance contain odd harmonics; hence, harmonic spectrum is discrete. Quasi-periodic and

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

Δf tðÞ� Δf tð Þ � Δt

where T is the time constant, t � Δt is the previous time step, and Δt is the time

Furthermore, the value of THD increases more than 100% where chaotic mode contains a continuous harmonic spectrum. As harmonic spectrum is mostly a qualified characteristic, THD and Δf are used to determine ferroresonance modes.

In this section, theoretical aspects of SSR in AC transmission system are explained.

Fundamental Zero Zero >50 Discrete Harmonic Constant Zero >50 Discrete Quasi-periodic Variable Not zero >100 Discrete Chaotic Variable Not zero >100 Continuous

� �<sup>2</sup> <sup>s</sup>

individual hð Þ individual ð Þ1

dt is detected and calculated as follow.

<sup>Δ</sup><sup>t</sup> (8)

dt THD (%) Harmonic spectrum

where ffr is the frequency of waveform in ferroresonance.

VLk ¼ ∑ N�1 n¼0

obtained from FFT, are considered in the following formula:

THD ¼

dΔf dt <sup>¼</sup> <sup>T</sup>:

N = number of samples.

Power System Stability

where x is integer harmonics.

chaotic modes are determined when <sup>d</sup>Δ<sup>f</sup>

2.2 Subsynchronous resonance

Criteria to determine ferroresonance modes.

Ferroresonance type Δf <sup>d</sup>Δ<sup>f</sup>

step interval.

Table 1.

58

VLne

∑ x h¼2 � �

� (5)

<sup>N</sup> K ¼ 0…N � 1 (6)

(7)

turbine-generator shaft. SSR is generally divided into transient and steady state which are described as follows [26].

Transient SSR occurs due to occurrence of a short circuit in a system with series compensation. Transient magnitudes include subsynchronous frequencies, which depend on elements in the network. Slip frequency fr in generator is given by (18).

$$f\_r = f\_0 - f\_{er} \tag{18}$$

PT explosion was occurrence of ferroresonance, which is caused by switching procedure. De-energized bus and the associated PTs were being connected to the energized bus B2 through the grading capacitors (5061 pF) of nine open 230 kV circuit breakers. Station service transformer SST2, which is normally connected to bus A2, had been previously disconnected. This arrangement leads in occurrence of

Another ferroresonant state occurred on August 5, 1995, at 14:18. A 4.16 kV breaker failed to latch while attempting to energize a 1500 kW induction motor at the Dorsey Converter Station [28]. It resulted in opening eleven 230 kV breakers to clear bus B2 to which the 230/4.16 kV transformer (SST1) was connected. Noise levels of SST1 were significantly higher than normal state. Figure 3 shows bus arrangement, equivalent source impedance, and capacitances. Misshaped waveforms and voltage increasing near 1.5 pu occurred in bus B2. This is the evidence of

existing a steady-state asymmetric fundamental mode of ferroresonance.

Dorsey converter station 230 kV bus arrangement in closing 4.16 kV circuit breake.

3.2 Power oscillation due to ferroresonance examined in Manitoba hydro

One of the popular configurations, which cause ferroresonance in the network, is transformer-terminated double circuit line. Ferroresonance occurs due to capacitive coupling between double circuit lines. In such configuration, power transformers are connected to de-energized transmission lines of remarkable length,

As shown in Figure 4, in Manitoba hydro network, 230/66 kV transformers in Silver station are supplied from a single transmission line, which is taped from A3R line. A double circuit transmission line with a length of about 200 km comprises A3R-A4D lines, which are routed between Ashern and Rosser station. Grand Rapids is connected to Ashern station by G1A-G2A double circuit transmission line with a

3.1.2 Energizing induction motor by closing 4.16 kV circuit breaker

Dorsey converter station 230 kV bus arrangement in Failure of wound PT.

Power Oscillation Due to Ferroresonance and Subsynchronous Resonance

DOI: http://dx.doi.org/10.5772/intechopen.81724

ferroresonance in phase A and B [6].

which is parallel to another energized line.

system

Figure 3.

61

Figure 2.

In case this frequency coincides with one of natural frequencies of the turbinegenerator rotor (fn), torque amplitude is increased much larger with respect to the system without compensation.

Steady state (self-excitation) SSR is divided into the induction generator effect (IGE) and torsional interaction (TI). IGE considers generator as a rigid mass at constant speed connected to the network. TI considers the turbine generator with multimass shaft, which interacts with the system disturbances at its natural frequencies.

## 3. Power oscillation due to ferroresonance

Catastrophic circumstances and equipment failures in electrical networks are mostly caused by emerging unwanted and unpredicted phenomena in electrical network. Resonance and ferroresonance are those phenomena which have been investigated many years ago. Manitoba hydro 230 kV electrical network has experienced ferroresonant states several times. Such conditions may occur in effect of short circuit, breaker phase failure, transformer energizing, load rejection, accidental or scheduled line disconnection, and plant outage. Power oscillation has been experienced during ferroresonance studies in Manitoba hydro system. In this section, ferroresonant states, which occurred in Manitoba hydro network, are explained, and results are analyzed. The latest studies on ferroresonance in Manitoba hydro system are explained, and power oscillation due to ferroresonance is discussed in this network. In addition to that, impact of ferroresonance on electrical and mechanical parameters of hydro generator is analyzed.

#### 3.1 Ferroresonance accidents in Manitoba hydro system

#### 3.1.1 Failure of wound PT in effect of opening grading capacitance circuit breakers

Manitoba hydro 230 kV electrical network consists of several 230 kV power sources like Vermillion, Dorsey, Ridgway, Rosser, and Grand Rapids station. Furthermore, Ashern station comprises an overvoltage-damping reactor, and Silver station with 2 � 230/66 kV, YNd, 50 MVA transformers, is considered for particular ferroresonant investigations [27]. In order to meet a �50°C low temperature specification, circuit breakers used in Dorsey converter station have been provided by SF6 mixed with CF4 since 1988. In high voltage systems, multiple interrupting chambers are connected in series to break the current and withstand the high recovery voltage. Grading capacitors with the values of 1500–1600 for an SF6 breaker are installed in parallel with each chamber to obtain an equal voltage distribution [28].

As shown in Figure 2, the 230 kV ac bus in Dorsey HVDC converter station consists of four bus sections. At 22:04, May 20, 1995, bus A2 was disconnected for maintenance. At approximately 22:30, a potential transformer (V13F) failed catastrophically. It caused damage to equipment up to 33 m away. The main reason for Power Oscillation Due to Ferroresonance and Subsynchronous Resonance DOI: http://dx.doi.org/10.5772/intechopen.81724

Figure 2. Dorsey converter station 230 kV bus arrangement in Failure of wound PT.

PT explosion was occurrence of ferroresonance, which is caused by switching procedure. De-energized bus and the associated PTs were being connected to the energized bus B2 through the grading capacitors (5061 pF) of nine open 230 kV circuit breakers. Station service transformer SST2, which is normally connected to bus A2, had been previously disconnected. This arrangement leads in occurrence of ferroresonance in phase A and B [6].

#### 3.1.2 Energizing induction motor by closing 4.16 kV circuit breaker

Another ferroresonant state occurred on August 5, 1995, at 14:18. A 4.16 kV breaker failed to latch while attempting to energize a 1500 kW induction motor at the Dorsey Converter Station [28]. It resulted in opening eleven 230 kV breakers to clear bus B2 to which the 230/4.16 kV transformer (SST1) was connected. Noise levels of SST1 were significantly higher than normal state. Figure 3 shows bus arrangement, equivalent source impedance, and capacitances. Misshaped waveforms and voltage increasing near 1.5 pu occurred in bus B2. This is the evidence of existing a steady-state asymmetric fundamental mode of ferroresonance.

## 3.2 Power oscillation due to ferroresonance examined in Manitoba hydro system

One of the popular configurations, which cause ferroresonance in the network, is transformer-terminated double circuit line. Ferroresonance occurs due to capacitive coupling between double circuit lines. In such configuration, power transformers are connected to de-energized transmission lines of remarkable length, which is parallel to another energized line.

As shown in Figure 4, in Manitoba hydro network, 230/66 kV transformers in Silver station are supplied from a single transmission line, which is taped from A3R line. A double circuit transmission line with a length of about 200 km comprises A3R-A4D lines, which are routed between Ashern and Rosser station. Grand Rapids is connected to Ashern station by G1A-G2A double circuit transmission line with a

Figure 3. Dorsey converter station 230 kV bus arrangement in closing 4.16 kV circuit breake.

turbine-generator shaft. SSR is generally divided into transient and steady state

Transient SSR occurs due to occurrence of a short circuit in a system with series compensation. Transient magnitudes include subsynchronous frequencies, which depend on elements in the network. Slip frequency fr in generator is given by (18).

In case this frequency coincides with one of natural frequencies of the turbinegenerator rotor (fn), torque amplitude is increased much larger with respect to the

Steady state (self-excitation) SSR is divided into the induction generator effect (IGE) and torsional interaction (TI). IGE considers generator as a rigid mass at constant speed connected to the network. TI considers the turbine generator with multimass shaft, which interacts with the system disturbances at its natural fre-

Catastrophic circumstances and equipment failures in electrical networks are mostly caused by emerging unwanted and unpredicted phenomena in electrical network. Resonance and ferroresonance are those phenomena which have been investigated many years ago. Manitoba hydro 230 kV electrical network has experienced ferroresonant states several times. Such conditions may occur in effect of short circuit, breaker phase failure, transformer energizing, load rejection, accidental or scheduled line disconnection, and plant outage. Power oscillation has been experienced during ferroresonance studies in Manitoba hydro system. In this section, ferroresonant states, which occurred in Manitoba hydro network, are explained, and results are analyzed. The latest studies on ferroresonance in Manitoba hydro system are explained, and power oscillation due to ferroresonance is discussed in this network. In addition to that, impact of ferroresonance on electrical

fr ¼ f <sup>0</sup> � f er (18)

which are described as follows [26].

Power System Stability

system without compensation.

3. Power oscillation due to ferroresonance

and mechanical parameters of hydro generator is analyzed.

3.1 Ferroresonance accidents in Manitoba hydro system

3.1.1 Failure of wound PT in effect of opening grading capacitance circuit breakers

Manitoba hydro 230 kV electrical network consists of several 230 kV power sources like Vermillion, Dorsey, Ridgway, Rosser, and Grand Rapids station. Furthermore, Ashern station comprises an overvoltage-damping reactor, and Silver station with 2 � 230/66 kV, YNd, 50 MVA transformers, is considered for particular ferroresonant investigations [27]. In order to meet a �50°C low temperature specification, circuit breakers used in Dorsey converter station have been provided by SF6 mixed with CF4 since 1988. In high voltage systems, multiple interrupting chambers are connected in series to break the current and withstand the high recovery voltage. Grading capacitors with the values of 1500–1600 for an SF6 breaker are installed in parallel with each chamber to obtain an equal voltage

As shown in Figure 2, the 230 kV ac bus in Dorsey HVDC converter station consists of four bus sections. At 22:04, May 20, 1995, bus A2 was disconnected for maintenance. At approximately 22:30, a potential transformer (V13F) failed catastrophically. It caused damage to equipment up to 33 m away. The main reason for

quencies.

distribution [28].

length of about 234 km. Several ferroresonant states, which resulted in power oscillation, were experienced by EMTP are fully explained in [6].

New ferroresonance studies have been implemented in Manitoba hydro system to obtain other configurations vulnerable to ferroresonance in the network [9, 12]. These arrangements comprise breaker phase failure, transformer-terminated double-circuit transmission line, and plant outage. Unlike previous investigations, in obtained ferroresonant arrangements due to transformer-terminated double circuit transmission line, both lines are remained energized. Hence, it is concluded that capacitive coupling of double circuit lines is not the only reason of occurrence of ferroresonance. In the following, one of these configurations is explained.

#### 3.2.1 Disconnection of A3R line from Rosser and G2A line from Grand Rapids station

Figure 4 shows a ferroresonant configuration in Manitoba system. In this arrangement, A3R-A4D and G2A transmission lines are energized by G1A line, which is still connected to Grand Rapids station. In addition, Vermillion is disconnected from Ashern station by a 230 kV circuit breaker in Ashern station. By such switching operation, G2A, A4D, and A3R transmission lines are changed to openend lines. As was mentioned in advance, in order to supply transformers in Silver station, a 230 kV single line is taped from A3R line of A3R-A4D double circuit line. In such arrangement, transformers in Silver station are located at the end of an open end line whose voltage is increased.

This experiment is represented by two different conditions for a time of about 10 s. In one condition, transformers in Silver station are considered nonsaturated. As shown in Figure 5, the magnitude of voltage is increased up to 304 kVpick prim (pick value in primary side) in Silver station due to voltage increment in open-end line, whereas this value is 209 kVpick prim in normal status. It must be noted that all mentioned voltage and current magnitudes in this paper are phase to neutral values. Oscillograph shows that voltage and current waveforms are not misshaped and remained in sinusoidal form; hence, no ferroresonance occurs in the system. In this experiment, damping reactor in Ashern station is disconnected from the network. In addition, grading capacitor circuit breakers are open in Dorsey converter station.

In another experiment, transformer core is saturated with specified values of magnetizing parameters. Oscillograph shows occurrence of ferroresonance due to

> transformer saturation core. As shown in Figure 6, in addition to misshaping the waveforms, the magnitude of voltage and current is increased up to maximum values of about 383 kVpick prim and 1.21 kApick prim, respectively, at the beginning of

> In fact, the main reason of occurrence of ferroresonance is existing nonlinear elements in the circuit. Consequently, the voltage and current values are nonlinear with respect to each other due to nonlinear characteristic of elements. It results in misshaping waveforms, and so that they are deviated from sinusoidal form. In such conditions, waveforms comprise orders of harmonics. It definitely depends on nonlinear characteristic of elements in the network. Frequency of total waveform is deviated from nominal frequency due to existing number of harmonics. The resulted frequency of waveform in ferroresonance depends on phase and values of orders with respect to fundamental. Reciprocally, in such conditions, frequency of

3.3 Analysis of power oscillation in effect of ferroresonance

Voltage and current waveform of saturated core (ferroresonance).

ferroresonance.

Figure 6.

63

Figure 5.

Voltage and current waveform of nonsaturated core.

DOI: http://dx.doi.org/10.5772/intechopen.81724

Power Oscillation Due to Ferroresonance and Subsynchronous Resonance

Figure 4. Manitoba hydro network under study in PSCAD/EMTDC.

Power Oscillation Due to Ferroresonance and Subsynchronous Resonance DOI: http://dx.doi.org/10.5772/intechopen.81724

Figure 5. Voltage and current waveform of nonsaturated core.

#### Figure 6.

length of about 234 km. Several ferroresonant states, which resulted in power

3.2.1 Disconnection of A3R line from Rosser and G2A line from Grand Rapids station

Figure 4 shows a ferroresonant configuration in Manitoba system. In this arrangement, A3R-A4D and G2A transmission lines are energized by G1A line, which is still connected to Grand Rapids station. In addition, Vermillion is disconnected from Ashern station by a 230 kV circuit breaker in Ashern station. By such switching operation, G2A, A4D, and A3R transmission lines are changed to openend lines. As was mentioned in advance, in order to supply transformers in Silver station, a 230 kV single line is taped from A3R line of A3R-A4D double circuit line. In such arrangement, transformers in Silver station are located at the end of an open

This experiment is represented by two different conditions for a time of about 10 s. In one condition, transformers in Silver station are considered nonsaturated. As shown in Figure 5, the magnitude of voltage is increased up to 304 kVpick prim (pick value in primary side) in Silver station due to voltage increment in open-end line, whereas this value is 209 kVpick prim in normal status. It must be noted that all mentioned voltage and current magnitudes in this paper are phase to neutral values. Oscillograph shows that voltage and current waveforms are not misshaped and remained in sinusoidal form; hence, no ferroresonance occurs in the system. In this experiment, damping reactor in Ashern station is disconnected from the network. In addition, grading capacitor circuit breakers are open in Dorsey converter station. In another experiment, transformer core is saturated with specified values of magnetizing parameters. Oscillograph shows occurrence of ferroresonance due to

New ferroresonance studies have been implemented in Manitoba hydro system to obtain other configurations vulnerable to ferroresonance in the network [9, 12]. These arrangements comprise breaker phase failure, transformer-terminated double-circuit transmission line, and plant outage. Unlike previous investigations, in obtained ferroresonant arrangements due to transformer-terminated double circuit transmission line, both lines are remained energized. Hence, it is concluded that capacitive coupling of double circuit lines is not the only reason of occurrence of ferroresonance. In the following, one of these configurations is explained.

oscillation, were experienced by EMTP are fully explained in [6].

end line whose voltage is increased.

Power System Stability

Figure 4.

62

Manitoba hydro network under study in PSCAD/EMTDC.

Voltage and current waveform of saturated core (ferroresonance).

transformer saturation core. As shown in Figure 6, in addition to misshaping the waveforms, the magnitude of voltage and current is increased up to maximum values of about 383 kVpick prim and 1.21 kApick prim, respectively, at the beginning of ferroresonance.

## 3.3 Analysis of power oscillation in effect of ferroresonance

In fact, the main reason of occurrence of ferroresonance is existing nonlinear elements in the circuit. Consequently, the voltage and current values are nonlinear with respect to each other due to nonlinear characteristic of elements. It results in misshaping waveforms, and so that they are deviated from sinusoidal form. In such conditions, waveforms comprise orders of harmonics. It definitely depends on nonlinear characteristic of elements in the network. Frequency of total waveform is deviated from nominal frequency due to existing number of harmonics. The resulted frequency of waveform in ferroresonance depends on phase and values of orders with respect to fundamental. Reciprocally, in such conditions, frequency of

the waveform may remain in nominal value in another side of the network or can get different nonlinear characteristic. Consequently, a frequency difference between two points in the network is resulted. The main factor, which results in power oscillation, is frequency difference in the network. Frequency of power oscillation is a function of frequency difference between two points.

sequentially, whereas the same envelope of power covers both negative and positive sides in each period of swing. In addition, in case of power swing in effect of out of step condition in no ferroresonant state, waveform of voltage and current mostly includes fundamental waveform. Consequently, power oscillation in case of pole

ferroresonance. It must be noted that, in some cases, electrical parameters in case of

Figure 8 shows frequency difference between Grand Rapids and Silver station during ferroresonance. As it is inferred from diagram, the frequency difference is not constant in the time of ferroresonance. Consequently, period of power oscilla-

slipping in the network can be distinguished from oscillation in effect of

according to dynamics of generator and power system.

Power Oscillation Due to Ferroresonance and Subsynchronous Resonance

tion is variable in such conditions.

DOI: http://dx.doi.org/10.5772/intechopen.81724

hydro generator

Figure 9.

65

Electrical parameters of hydro generator during ferroresonance.

pole slipping in generator get different magnitudes and harmonic distortion

3.4 Impact of power oscillation in effect of ferroresonance on operation of

In order to analyze impact of power oscillation in effect of ferroresonance on operation of hydro generator, Grand Rapids station in Manitoba hydro system is simulated as both equivalent circuit and hydro-generator mode in the software. In

As was shown in Figure 6, voltage and current oscillate with specific period in effect of ferroresonance. Envelope of misshaped waveforms due to existing number of harmonics in ferroresonance oscillates with a frequency of about 10 Hz. It is resulted from frequency difference of about 10 Hz between Grand Rapids and Silver station.

There is significant difference between power oscillation experienced in ferroresonance and a typical oscillation in case of pole slipping in the generator. As shown in Figure 7, in a typical pole slipping in the generator, envelope of voltage swing advances by 180° with respect to the envelope of current swing or vice versa, whereas envelope of voltage swing is the same phase with respect to the envelope of current swing in power oscillation due to ferroresonance. Furthermore, the envelope of power oscillation due to ferroresonance covers negative or positive side

#### Figure 7.

Typical status of voltage and current in case of pole slipping in generator.

Figure 8. Δf between Grand Rapids and Silver station.

## Power Oscillation Due to Ferroresonance and Subsynchronous Resonance DOI: http://dx.doi.org/10.5772/intechopen.81724

sequentially, whereas the same envelope of power covers both negative and positive sides in each period of swing. In addition, in case of power swing in effect of out of step condition in no ferroresonant state, waveform of voltage and current mostly includes fundamental waveform. Consequently, power oscillation in case of pole slipping in the network can be distinguished from oscillation in effect of ferroresonance. It must be noted that, in some cases, electrical parameters in case of pole slipping in generator get different magnitudes and harmonic distortion according to dynamics of generator and power system.

Figure 8 shows frequency difference between Grand Rapids and Silver station during ferroresonance. As it is inferred from diagram, the frequency difference is not constant in the time of ferroresonance. Consequently, period of power oscillation is variable in such conditions.

## 3.4 Impact of power oscillation in effect of ferroresonance on operation of hydro generator

In order to analyze impact of power oscillation in effect of ferroresonance on operation of hydro generator, Grand Rapids station in Manitoba hydro system is simulated as both equivalent circuit and hydro-generator mode in the software. In

Figure 9. Electrical parameters of hydro generator during ferroresonance.

the waveform may remain in nominal value in another side of the network or can get different nonlinear characteristic. Consequently, a frequency difference between two points in the network is resulted. The main factor, which results in power oscillation, is frequency difference in the network. Frequency of power

As was shown in Figure 6, voltage and current oscillate with specific period in effect of ferroresonance. Envelope of misshaped waveforms due to existing number of harmonics in ferroresonance oscillates with a frequency of about 10 Hz. It is resulted from frequency difference of about 10 Hz between Grand Rapids and

There is significant difference between power oscillation experienced in ferroresonance and a typical oscillation in case of pole slipping in the generator. As shown in Figure 7, in a typical pole slipping in the generator, envelope of voltage swing advances by 180° with respect to the envelope of current swing or vice versa, whereas envelope of voltage swing is the same phase with respect to the envelope of current swing in power oscillation due to ferroresonance. Furthermore, the envelope of power oscillation due to ferroresonance covers negative or positive side

oscillation is a function of frequency difference between two points.

Typical status of voltage and current in case of pole slipping in generator.

Silver station.

Power System Stability

Figure 7.

Figure 8.

64

Δf between Grand Rapids and Silver station.

order to compare behavior of the station in both operation mode and prevent instability due to sudden energizing at the beginning of simulation, the station is changed from equivalent circuit to hydro generator with constant speed along with exciter and PSS in the time of 0.5 s from the beginning of simulation. When the condition is stable, generator is changed to full-blown machine where governor and multi-mass torsional shaft model are also released in the time after 0.1 s. Voltage and current waveforms follow the same oscillations, as well as previous state with a lower magnitude of about 320 kVpick prim and 0.380 kApick prim, respectively, in generator mode.

Hydro generator will be in unstable condition due to oscillation of parameters, which follows power oscillation in the network. Load angle oscillates as well as real power and increases up to maximum 14°; however, the value is far from power stability limit, which is defined as 90°. Mechanical parameters of the generator have increasing manner in the time of ferroresonance. Torque on shaft from generator to stage one of hydro turbine increases from 0.12 to 0.16 pu in a duration of 1.5 s. Mechanical displacement of stage 1 of hydro turbine with respect to generator increases from 1.4 to 2°. In addition, mechanical speed difference of the generator with respect to rated speed increases up to 3.6 rpm at the same time. Consequently,

The phenomenon of subsynchronous resonance on alternating self-excited power oscillation in series compensation line was first treated in the technical literature in the early of 1943. As was discussed in advance, two shaft failures occurred at the Mohave Generating Station in Southern Nevada. In this section, occurrence of SSR in Mohave power plant is explained, and test results are analyzed. Manitoba hydro system with long transmission line of about 500 km is under series compensation studies in 230 kV level. In addition to that, impact of SSR on

frequency oscillates from 59.8 to 60.2 Hz in Grand Rapids station.

Power Oscillation Due to Ferroresonance and Subsynchronous Resonance

DOI: http://dx.doi.org/10.5772/intechopen.81724

4. Power oscillation due to subsynchronous resonance

electrical and mechanical parameters of hydro generator is analyzed.

4.1 Occurrence of subsynchronous resonance in Mohave power plant

units. Figure 11 shows arrangement of one unit. The high-pressure (HP)

Figure 11.

67

Mohave generating station 909 MVA cross compound units.

Power generation in Mohave plant includes two 909 MVA cross-compound

generators comprise two-pole, 483 MVA, 22 kV machines. The low-pressure (LP) generators are four-pole, 426-MVA machines each driven by LP turbine. The cross-compound units are connected to the Mohave 500 kV bus through 825 MVA, 525/22 kV transformer bank. The generation plant and its associated transmission are included into the 500 kV system, which is designed with 70% series compensation. Unit No. 2 Mohave power plant was exposed to failure in the shaft section between the generator and exciter at the main generator collector due to torsional fatigue. The mechanical strain cycling which involved plastic deformations caused the shaft to heat up to temperatures, which resulted in the breakdown of the insulation between the collector rings and the shaft. The heavy current flow that resulted from the positive and negative generator field short circuit eroded large pockets of metal from the shaft and the collector ring. Analysis of line current oscillogram taken during the disturbance on the line indicated the presence of appreciable currents of subsynchronous frequency (lower than 60 Hz).

Frequency difference between Grand Rapids and Silver station is about 10 Hz when station is in service as equivalent circuit. In such condition, power oscillates from 179 to 212 MW with a frequency of about 10 Hz in the time before 0.5 s.

In the time after 0.5 s (Grand Rapids in generator mode), power oscillation due to ferroresonance affects the stability of hydro generator. As shown in Figure 9, ferroresonance causes envelope of power with a frequency of about 16 Hz. Power oscillates increasingly from 93 to 309 MW in the time of about 3 ms in each period of envelope. Hence, frequency of power oscillation in each envelope is 166 Hz.

Electrical and mechanical parameters of hydro generator are shown in Figures 9 and 10, respectively. The parameters increase significantly during ferroresonance.

Figure 10. Mechanical parameters of hydro generator during ferroresonance.

Power Oscillation Due to Ferroresonance and Subsynchronous Resonance DOI: http://dx.doi.org/10.5772/intechopen.81724

Hydro generator will be in unstable condition due to oscillation of parameters, which follows power oscillation in the network. Load angle oscillates as well as real power and increases up to maximum 14°; however, the value is far from power stability limit, which is defined as 90°. Mechanical parameters of the generator have increasing manner in the time of ferroresonance. Torque on shaft from generator to stage one of hydro turbine increases from 0.12 to 0.16 pu in a duration of 1.5 s. Mechanical displacement of stage 1 of hydro turbine with respect to generator increases from 1.4 to 2°. In addition, mechanical speed difference of the generator with respect to rated speed increases up to 3.6 rpm at the same time. Consequently, frequency oscillates from 59.8 to 60.2 Hz in Grand Rapids station.

## 4. Power oscillation due to subsynchronous resonance

The phenomenon of subsynchronous resonance on alternating self-excited power oscillation in series compensation line was first treated in the technical literature in the early of 1943. As was discussed in advance, two shaft failures occurred at the Mohave Generating Station in Southern Nevada. In this section, occurrence of SSR in Mohave power plant is explained, and test results are analyzed. Manitoba hydro system with long transmission line of about 500 km is under series compensation studies in 230 kV level. In addition to that, impact of SSR on electrical and mechanical parameters of hydro generator is analyzed.

#### 4.1 Occurrence of subsynchronous resonance in Mohave power plant

Power generation in Mohave plant includes two 909 MVA cross-compound units. Figure 11 shows arrangement of one unit. The high-pressure (HP) generators comprise two-pole, 483 MVA, 22 kV machines. The low-pressure (LP) generators are four-pole, 426-MVA machines each driven by LP turbine. The cross-compound units are connected to the Mohave 500 kV bus through 825 MVA, 525/22 kV transformer bank. The generation plant and its associated transmission are included into the 500 kV system, which is designed with 70% series compensation. Unit No. 2 Mohave power plant was exposed to failure in the shaft section between the generator and exciter at the main generator collector due to torsional fatigue. The mechanical strain cycling which involved plastic deformations caused the shaft to heat up to temperatures, which resulted in the breakdown of the insulation between the collector rings and the shaft. The heavy current flow that resulted from the positive and negative generator field short circuit eroded large pockets of metal from the shaft and the collector ring. Analysis of line current oscillogram taken during the disturbance on the line indicated the presence of appreciable currents of subsynchronous frequency (lower than 60 Hz).

Figure 11. Mohave generating station 909 MVA cross compound units.

order to compare behavior of the station in both operation mode and prevent instability due to sudden energizing at the beginning of simulation, the station is changed from equivalent circuit to hydro generator with constant speed along with exciter and PSS in the time of 0.5 s from the beginning of simulation. When the condition is stable, generator is changed to full-blown machine where governor and multi-mass torsional shaft model are also released in the time after 0.1 s. Voltage and current waveforms follow the same oscillations, as well as previous state with a lower magnitude of about 320 kVpick prim and 0.380 kApick prim, respectively, in

Frequency difference between Grand Rapids and Silver station is about 10 Hz when station is in service as equivalent circuit. In such condition, power oscillates from 179 to 212 MW with a frequency of about 10 Hz in the time before 0.5 s.

In the time after 0.5 s (Grand Rapids in generator mode), power oscillation due to ferroresonance affects the stability of hydro generator. As shown in Figure 9, ferroresonance causes envelope of power with a frequency of about 16 Hz. Power oscillates increasingly from 93 to 309 MW in the time of about 3 ms in each period of envelope. Hence, frequency of power oscillation in each envelope is 166 Hz.

Electrical and mechanical parameters of hydro generator are shown in Figures 9 and 10, respectively. The parameters increase significantly during ferroresonance.

generator mode.

Power System Stability

Figure 10.

66

Mechanical parameters of hydro generator during ferroresonance.

Subsynchronous currents flow in the generator armature and react with the main flux of the generator to increase torque on the shaft at the slip frequency between the rotating main generator flux and the subsynchronous current flowing in the electrical network. The slip frequency following the disturbance, which causes the Mohave failures, coincided with the second flexible torsional natural frequency of the turbine-generator rotor system. It results in amplifying the magnitude of the shaft response torque. For this mode of oscillation, maximum twist occurs in the shaft span between the generator and exciter [29, 30].

Natural frequencies of the rotor are calculated by eigenvalue analysis and catego-

Silver station. Compensation level is set to 75% for all three sections. The fault occurs in the time of 1.2 s from energizing, and then it is removed after 0.2 s, where

Analysis of SSR is implemented by applying a three-phase fault at 230 kV side in

rized in six torsional modes as shown in Figure 12 [31–32].

DOI: http://dx.doi.org/10.5772/intechopen.81724

Power Oscillation Due to Ferroresonance and Subsynchronous Resonance

Torsional modes of hydro generator in Grand Rapids station.

Frequency spectrum of current of generator in Grand Rapids station.

Grid parameters during short circuit in noncompensated system.

Figure 12.

Figure 13.

Figure 14.

69

## 4.2 Power oscillation due to SSR examined in Manitoba hydro system

As was shown in advance, Manitoba hydro system comprises G1A-G2A double circuit transmission line, which connects Ashern station to Grand Rapids station with a length of about 234 km. In another side, Ashern station is connected to Rosser station by A3R-A4D double circuit transmission line with a length of about 200 km. Transformers in Silver station are supplied from a single line, which is tapped from A3R line in a distance of about 50 km from Ashern station. As the line comprises three sections, compensation is applied at the beginning of the line in each section individually (Figure 4).

Electrical resonance frequency (fer) generated by the elements of the network including series capacitance produces a frequency in hydro generator in Grand Rapids station. It may coincide with one of natural frequencies of the hydro generator. Rotor shaft comprises a 4-section turbine, generator, and exciter. Natural frequencies of the rotor are categorized in six torsional modes. Table 2 shows electrical specifications of series compensation in each section. Fer generated by the elements of the network produce fr, which may coincide with one of natural frequencies (fn) of 480-MW hydro generator in Grand Rapids station. Inertia constant, shaft stiffness, and natural frequencies of rotor shaft are shown in Table 3.


#### Table 2.

Specifications of series compensation in the network.


#### Table 3.

Values of inertia, stiffness, and natural frequencies of 480 MW hydro generator.

Power Oscillation Due to Ferroresonance and Subsynchronous Resonance DOI: http://dx.doi.org/10.5772/intechopen.81724

Natural frequencies of the rotor are calculated by eigenvalue analysis and categorized in six torsional modes as shown in Figure 12 [31–32].

Analysis of SSR is implemented by applying a three-phase fault at 230 kV side in Silver station. Compensation level is set to 75% for all three sections. The fault occurs in the time of 1.2 s from energizing, and then it is removed after 0.2 s, where

#### Figure 12.

Subsynchronous currents flow in the generator armature and react with the main flux of the generator to increase torque on the shaft at the slip frequency

between the rotating main generator flux and the subsynchronous current flowing in the electrical network. The slip frequency following the disturbance, which causes the Mohave failures, coincided with the second flexible torsional natural frequency of the turbine-generator rotor system. It results in amplifying

the magnitude of the shaft response torque. For this mode of oscillation, maximum twist occurs in the shaft span between the generator and exciter

4.2 Power oscillation due to SSR examined in Manitoba hydro system

As was shown in advance, Manitoba hydro system comprises G1A-G2A double circuit transmission line, which connects Ashern station to Grand Rapids station with a length of about 234 km. In another side, Ashern station is connected to Rosser station by A3R-A4D double circuit transmission line with a length of about 200 km. Transformers in Silver station are supplied from a single line, which is tapped from A3R line in a distance of about 50 km from Ashern station. As the line comprises three sections, compensation is applied at the beginning of the line in

Electrical resonance frequency (fer) generated by the elements of the network including series capacitance produces a frequency in hydro generator in Grand Rapids station. It may coincide with one of natural frequencies of the hydro generator. Rotor shaft comprises a 4-section turbine, generator, and exciter. Natural frequencies of the rotor are categorized in six torsional modes. Table 2 shows electrical specifications of series compensation in each section. Fer generated by the elements of the network produce fr, which may coincide with one of natural frequencies (fn) of 480-MW hydro generator in Grand Rapids station. Inertia constant, shaft stiffness, and natural frequencies of rotor shaft are shown in Table 3.

G1A-G2A 234 km A3R-A4D 50 km A3R-A4D 150 km Comp. % XL (Ω) C(μf) Comp. % XL (Ω) C(μf) Comp. % XL (Ω) C(μf) 100 50.3 53 100 10.6 250 100 32 83 75 37.5 71 75 7.95 330 75 24 110 50 25.1 105 50 5.3 500 50 18 155 25 12.5 210 25 2.65 990 25 8 330

Inertia Value (s) Stiffness Value (pu. T/rad) Fn (Hz) Mode JT1 0.0830 KT1-2 18.0858 51.94 5 JT2 0.1451 KT2-3 33.1075 43.15 4 JT3 0.7864 KT3-4 51.3650 33.31 3 JT4 0.7945 KT4-Gen 68.0483 0.00 0 JGen 0.7859 KGen-Exc 2.117 20.86 1 JExc 0.0284 24.97 2

Values of inertia, stiffness, and natural frequencies of 480 MW hydro generator.

[29, 30].

Power System Stability

Table 2.

Table 3.

68

each section individually (Figure 4).

Specifications of series compensation in the network.

Torsional modes of hydro generator in Grand Rapids station.

#### Figure 13.

Frequency spectrum of current of generator in Grand Rapids station.

Figure 14.

Grid parameters during short circuit in noncompensated system.

Figure 15. Grid parameters during short circuit in series-compensated system.

Grand Rapids station is in generator mode. Current in hydro generator stator in Grand Rapids station consists of subharmonic 27 with a value of about 50% (Figure 13). This is fer, which is generated by the network elements at the instance of short circuit. fer is induced in the generator rotor and generates fr ¼ 60 � 27 ¼ 33 Hz. The slip frequency of fr coincides with natural frequency (Figure 12, mode 3) of rotor shaft in Grand Rapids station.

Figures 14 and 15 compare impact of series compensation on grid parameters with respect to noncompensated line during short circuit. As shown in Figure 14

> (noncompensation), voltage increases up to 285 kVpick in two periods and suppresses immediately. Current includes DC component, which is decayed gradually. In Figure 15, (series compensation) voltage increases up to 1 MVpick. It causes saturation of transformer core and occurrence of ferroresonance. Current waveform is misshaped and includes subharmonics, which flow in the network. Electrical parameters of hydro generator are shown in Figure 16. Active power and load angle increase and oscillate along with increasing and oscillation of voltage and current after removing the fault and power oscillation begins with a frequency of about 1.5 Hz. Envelope of current grows up to 2.66 times greater than nominal current. As shown in Figure 17, oscillation of active power in Grand Rapids station causes increasing and oscillation of mechanical parameters of the hydro generator. Torsional strength from turbine to generator increases significantly in the time after 9 s. Direction of torque on the shaft and mechanical displacement between turbine to generator changes alternatively and increases at the same time. In addition, speed

Figure 17.

71

Mechanical parameters of hydro generator during SSR.

Power Oscillation Due to Ferroresonance and Subsynchronous Resonance

DOI: http://dx.doi.org/10.5772/intechopen.81724

of the masses increases significantly with respect to rated speed.

Figure 16.

Electrical parameters of hydro generator during SSR.

Power Oscillation Due to Ferroresonance and Subsynchronous Resonance DOI: http://dx.doi.org/10.5772/intechopen.81724

Figure 17. Mechanical parameters of hydro generator during SSR.

(noncompensation), voltage increases up to 285 kVpick in two periods and suppresses immediately. Current includes DC component, which is decayed gradually. In Figure 15, (series compensation) voltage increases up to 1 MVpick. It causes saturation of transformer core and occurrence of ferroresonance. Current waveform is misshaped and includes subharmonics, which flow in the network. Electrical parameters of hydro generator are shown in Figure 16. Active power and load angle increase and oscillate along with increasing and oscillation of voltage and current after removing the fault and power oscillation begins with a frequency of about 1.5 Hz. Envelope of current grows up to 2.66 times greater than nominal current. As shown in Figure 17, oscillation of active power in Grand Rapids station causes increasing and oscillation of mechanical parameters of the hydro generator. Torsional strength from turbine to generator increases significantly in the time after 9 s. Direction of torque on the shaft and mechanical displacement between turbine to generator changes alternatively and increases at the same time. In addition, speed of the masses increases significantly with respect to rated speed.

Grand Rapids station is in generator mode. Current in hydro generator stator in Grand Rapids station consists of subharmonic 27 with a value of about 50%

fr ¼ 60 � 27 ¼ 33 Hz. The slip frequency of fr coincides with natural frequency

of short circuit. fer is induced in the generator rotor and generates

(Figure 12, mode 3) of rotor shaft in Grand Rapids station.

Grid parameters during short circuit in series-compensated system.

Figure 15.

Power System Stability

Figure 16.

70

Electrical parameters of hydro generator during SSR.

(Figure 13). This is fer, which is generated by the network elements at the instance

Figures 14 and 15 compare impact of series compensation on grid parameters with respect to noncompensated line during short circuit. As shown in Figure 14

Power System Stability

## Author details

Salman Rezaei Kerman Power Generation Management Company, Kerman, Iran

\*Address all correspondence to: rezaiesalman@gmail.com

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

References

827-828

Schneider; 1998

17(3):865-871

2014. pp. 211-216

[1] Boucherot P. Éxistence de deux régimes en ferrorésonance. Rev. Gen. de L'Éclairage Électrique. 1920;8(24):

DOI: http://dx.doi.org/10.5772/intechopen.81724

Power Oscillation Due to Ferroresonance and Subsynchronous Resonance

differential protection in presence of SVC in electrical network. IET Generation, Transmission and Distribution. 2017;11(7):1671-1682

[10] Karaagac U, Mahseredjian J, Cai L. Ferroresonance conditions in wind parks. Electric Power Systems Research.

[11] Siahpoosh MK, Dorrel D, Li L, Ferroresonance assessment in a case study wind farm with 8 units of 2 MVA DFIG wind turbines. 20th International Conference on Electrical Machines and Systems (ICEMS). 2017. pp. 1-5

[12] Rezaei S. Impact of ferroresonance on protective relays in Manitoba hydro 230 kV electrical network. In: Proc. IEEE 15th Int. Conf. on Environment and Electrical Engineering. Rome, Italy;

[13] Ballance JW, Goldberg S. Sub synchronous resonance in series compensated transmission lines. IEEE Transactions on Power Apparatus and Systems. 1973;PAS-92:1649-1658

[14] Varghese M, Wu FF, Varaiya P. Bifurcations associated with subsynchronous resonance. IEEE

Transactions on Power Systems. 1998;

[15] Gupta S, Moharana A, Varma RK. Frequency scanning study of subsynchronous resonance in power systems. In: 26th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). 2013. pp. 1-6

[16] Nagabhushana; Chandrasekharaiah BS, Lai LL, Vujatovic D. Neural network approach to identification and control of sub-synchronous resonance in series compensated systems. In: Proceedings of the IEEE Int. Conf. on Power Electronics and Drive Systems. PEDS '99. Vol. 2.

2016;38:41-49

2015. pp. 1694-1699

13(1):139-144

1999. pp. 683-687

[2] Ferracci P. Ferroresonance-Cahier Technique Schneider no. 190; Groupe

[3] Valverde V, Buigues G, Mazón AJ, Zamora I, Albizu I. Ferroresonant configurations in power systems. In: International Conference on Renewable

Energies and Power Quality (ICREPQ'12); 28-30 March 2012, Santiago de Compostela, Spain. 2012

[4] Jacobson DAN, Lehn Peter W, Menzies W. Robert: Stability domain calculations of period-1 ferroresonance in a nonlinear resonant circuit. IEEE Transactions on Power Delivery. 2002;

[5] Kováč M, Eleschová Ž, Heretík P, Koníček M. Analysis and mitigation of ferroresonant oscillations in power system. Proceedings of the 15th International Scientific Conference on Electric Power Engineering (EPE).

[6] Jacobson DAN. Field testing, modeling, and analysis of

[7] Scott LH. A case study of

[8] Jacobson DAN, Menzies RW. Investigation of station service

73

ferroresonance in a high voltage power system [Ph.D. dissertation]. Dept. elect. and comp. Eng., Univ. Manitoba; 2000

ferroresonance in a CCVT secondary circuit and its impact on protective relaying. In: WPRC; 17-19 October 2006; Spokane, Washington. 2006

transformer ferroresonance in Manitoba Hydro's 230 kV Dorsey Converter Station. In: Proc. IPST conf. Rio de Janeiro; 2001

[9] Rezaei S. Impact of plant outage on ferroresonance and mal operation of

Power Oscillation Due to Ferroresonance and Subsynchronous Resonance DOI: http://dx.doi.org/10.5772/intechopen.81724

## References

[1] Boucherot P. Éxistence de deux régimes en ferrorésonance. Rev. Gen. de L'Éclairage Électrique. 1920;8(24): 827-828

[2] Ferracci P. Ferroresonance-Cahier Technique Schneider no. 190; Groupe Schneider; 1998

[3] Valverde V, Buigues G, Mazón AJ, Zamora I, Albizu I. Ferroresonant configurations in power systems. In: International Conference on Renewable Energies and Power Quality (ICREPQ'12); 28-30 March 2012, Santiago de Compostela, Spain. 2012

[4] Jacobson DAN, Lehn Peter W, Menzies W. Robert: Stability domain calculations of period-1 ferroresonance in a nonlinear resonant circuit. IEEE Transactions on Power Delivery. 2002; 17(3):865-871

[5] Kováč M, Eleschová Ž, Heretík P, Koníček M. Analysis and mitigation of ferroresonant oscillations in power system. Proceedings of the 15th International Scientific Conference on Electric Power Engineering (EPE). 2014. pp. 211-216

[6] Jacobson DAN. Field testing, modeling, and analysis of ferroresonance in a high voltage power system [Ph.D. dissertation]. Dept. elect. and comp. Eng., Univ. Manitoba; 2000

[7] Scott LH. A case study of ferroresonance in a CCVT secondary circuit and its impact on protective relaying. In: WPRC; 17-19 October 2006; Spokane, Washington. 2006

[8] Jacobson DAN, Menzies RW. Investigation of station service transformer ferroresonance in Manitoba Hydro's 230 kV Dorsey Converter Station. In: Proc. IPST conf. Rio de Janeiro; 2001

[9] Rezaei S. Impact of plant outage on ferroresonance and mal operation of

differential protection in presence of SVC in electrical network. IET Generation, Transmission and Distribution. 2017;11(7):1671-1682

[10] Karaagac U, Mahseredjian J, Cai L. Ferroresonance conditions in wind parks. Electric Power Systems Research. 2016;38:41-49

[11] Siahpoosh MK, Dorrel D, Li L, Ferroresonance assessment in a case study wind farm with 8 units of 2 MVA DFIG wind turbines. 20th International Conference on Electrical Machines and Systems (ICEMS). 2017. pp. 1-5

[12] Rezaei S. Impact of ferroresonance on protective relays in Manitoba hydro 230 kV electrical network. In: Proc. IEEE 15th Int. Conf. on Environment and Electrical Engineering. Rome, Italy; 2015. pp. 1694-1699

[13] Ballance JW, Goldberg S. Sub synchronous resonance in series compensated transmission lines. IEEE Transactions on Power Apparatus and Systems. 1973;PAS-92:1649-1658

[14] Varghese M, Wu FF, Varaiya P. Bifurcations associated with subsynchronous resonance. IEEE Transactions on Power Systems. 1998; 13(1):139-144

[15] Gupta S, Moharana A, Varma RK. Frequency scanning study of subsynchronous resonance in power systems. In: 26th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). 2013. pp. 1-6

[16] Nagabhushana; Chandrasekharaiah BS, Lai LL, Vujatovic D. Neural network approach to identification and control of sub-synchronous resonance in series compensated systems. In: Proceedings of the IEEE Int. Conf. on Power Electronics and Drive Systems. PEDS '99. Vol. 2. 1999. pp. 683-687

Author details

Power System Stability

Kerman Power Generation Management Company, Kerman, Iran

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

\*Address all correspondence to: rezaiesalman@gmail.com

provided the original work is properly cited.

Salman Rezaei

[17] Yousif N, Al-Dabbagh M. Timefrequency distribution application for sub-synchronous resonance analysis in power systems. In: International Power Engineering Conference. Vol. 2. 2005. pp. 771-775

[18] Khalilinia H, Ghaisari J. Improve sub-synchronous resonance (SSR) damping using a STATCOM in the transformer bus. In: IEEE EUROCON. 2009. pp. 445-450

[19] Umre BS, Helonde JB, Modak JP, Renkey S. Application of gate-controlled series capacitors (GCSC) for reducing stresses due to sub-synchronous resonance in turbine-generator shaft. In: IEEE Energy Conversion Congress and Exposition. 2010. pp. 2300-2305

[20] Lak A, Nazarpour D, Ghahramani H. Novel methods with fuzzy logic and ANFIS controller based SVC for damping sub-synchronous resonance and low-frequency power oscillation. In: 20th Iranian Conference on Electrical Engineering (ICEE2012). 2012. pp. 450-455

[21] Mohammad pour HA, Santi E. Subsynchronous resonance analysis in DFIG-based wind farms: Definitions and problem identification—Part I. In: IEEE Energy Conversion Congress and Exposition (ECCE). 2014. pp. 812-819

[22] Mohammad pour HA, Santi E. Optimal adaptive sub-synchronous resonance damping controller for a series-compensated doubly-fed induction generator-based wind farm. IET Renewable Power Generation. 2015; 9(6):669-681

[23] Gagnon R, Viarouge P, Sybille G, Tourkhani E. Identification of ferroresonance as the cause of SVC instability in a degraded series compensated network. In: IEEE Power Engineering Society Winter Meeting. Vol. 2. 2000. pp. 1377-1382

[24] Woodford DA. Solving the ferroresonance problem when compensating a Dc converter station with a series capacitor. IEEE Transactions on Power Systems. 1996; 11(3):1325-1331

[25] Anderson PM, Agrawal BL, Van Ness JE. Sub synchronous resonance in power systems. In: IEEE PES Book. 1926. p. 9

[26] Leon AE, Solsona JA. Sub synchronous interaction damping control for DFIG wind turbines. IEEE Transactions on Power Systems. 2015; 30(1):419-428

[27] EMTP works version 2.0.2, examples, Ferro-Demo, Data case given by Jacobson DAN

[28] Jacobson DAN. Examples of ferroresonance in a high voltage power system. In: Proc. IEEE PES General Meeting. 2003. pp. 1206-1212

[29] Walker DN, Bowler CEJ, Jackson RL, Hodges DA. Results of subsynchronous resonance test at Mohave. IEEE Transactions on Power Apparatus and Systems. 1975;94(5):1878-1889

[30] Tweedie R, Abrams HW. Electrical features of the 1590 MW coal-slurrysupplied Mohave generating station. IEEE Transactions on Power Apparatus and Systems. 1971;PAS 90(2):725-735

[31] Rezaei S. Impact of sub synchronous resonance on operation of protective relays and prevention method. In: IEEE/ IAS 53rd Industrial and Commercial Power Systems Technical Conference (I&CPS); 2017; Niagara Falls, ON, Canada. 2017. pp. 1-9

[32] Rezaei S. An adaptive algorithm based on sub harmonic and time domain analysis to prevent mal operation of overcurrent relay during sub synchronous resonance. IEEE Transactions on Industry Applications. 2018;54(3):2085-2096

**75**

**Chapter 5**

**Abstract**

*Daniel Burillo*

**1. Introduction**

ally, changing climates as well.

Effects of Climate Change in

Electric Power Infrastructures

Climate change mitigation and adaptation has been a major driving force to modernize electric power infrastructure and include more renewable energy systems. This chapter explains several ways in which electric power infrastructure has contributed to climate change, how climate change affects electric power infrastructure, mitigation options, and adaptation options. Electricity infrastructure categories include power generation technologies, transmission lines, substations, and building loads. Climate change categories include atmospheric greenhouse gas concentration levels, rising sea levels, changes in precipitation patterns and river flows, as well as more extreme air temperatures. Specific quantitative case studies are provided to estimate vulnerabilities from heat waves in the US desert southwest, including long-term forecasting of infrastructure performance, as well as, various supply-side and demand-side strategic options to maintain reliable operations.

**Keywords:** climate change, risk management, demand forecast, load volatility,

Climate change occurs because of both natural and human causes. A geographic area that has a particular prevailing weather condition is said to have a particular climate [1, 2]. Over the course of time, earth has gone through several global climate changes, including the asteroid that killed the dinosaurs [3], the ice ages, and the warm period that we are in now [4]. Specific regions of the earth have also gone through local climate changes due to large storms, earthquakes, and volcanic eruptions that mostly only affect the target locations [5, 6]. Since human civilizations started intelligently designing ecosystems by channeling water, doing agriculture, building cities, and so on—we have been intentionally, and sometimes unintention-

Civilization arguably did not start contributing to climate change at a global scale until after the industrial revolution with the proliferation of coal-powered steam engines and the burning of fossil fuels into the air [7]. The portable energy transformation device was revolutionary; the abundance with which humans lived and moved increased dramatically. Then, in 1896, Swedish chemist Svante Arrhenius estimated that the long-term effects of coal burning would enhance the natural greenhouse effect, and that a doubling of carbon dioxide in the atmosphere would warm the earth a few degrees Celsius. Modern-day climate models have maintained Arrhenius's conclusion, and only added more specifics to the predictions, with

vulnerability, failure prediction, outage prediction, long-term planning

## **Chapter 5**

[17] Yousif N, Al-Dabbagh M. Timefrequency distribution application for sub-synchronous resonance analysis in power systems. In: International Power Engineering Conference. Vol. 2. 2005.

[24] Woodford DA. Solving the ferroresonance problem when compensating a Dc converter station

with a series capacitor. IEEE

[26] Leon AE, Solsona JA. Sub synchronous interaction damping control for DFIG wind turbines. IEEE Transactions on Power Systems. 2015;

11(3):1325-1331

30(1):419-428

DAN

Transactions on Power Systems. 1996;

[25] Anderson PM, Agrawal BL, Van Ness JE. Sub synchronous resonance in power systems. In: IEEE PES Book. 1926. p. 9

[27] EMTP works version 2.0.2, examples, Ferro-Demo, Data case given by Jacobson

[29] Walker DN, Bowler CEJ, Jackson RL, Hodges DA. Results of subsynchronous

[30] Tweedie R, Abrams HW. Electrical features of the 1590 MW coal-slurrysupplied Mohave generating station. IEEE Transactions on Power Apparatus and Systems. 1971;PAS 90(2):725-735

[31] Rezaei S. Impact of sub synchronous resonance on operation of protective relays and prevention method. In: IEEE/ IAS 53rd Industrial and Commercial Power Systems Technical Conference (I&CPS); 2017; Niagara Falls, ON,

[32] Rezaei S. An adaptive algorithm based on sub harmonic and time domain analysis to prevent mal operation of overcurrent relay during sub synchronous resonance. IEEE

Transactions on Industry Applications.

Canada. 2017. pp. 1-9

2018;54(3):2085-2096

[28] Jacobson DAN. Examples of ferroresonance in a high voltage power system. In: Proc. IEEE PES General Meeting. 2003. pp. 1206-1212

resonance test at Mohave. IEEE Transactions on Power Apparatus and Systems. 1975;94(5):1878-1889

[18] Khalilinia H, Ghaisari J. Improve sub-synchronous resonance (SSR) damping using a STATCOM in the transformer bus. In: IEEE EUROCON.

[19] Umre BS, Helonde JB, Modak JP, Renkey S. Application of gate-controlled series capacitors (GCSC) for reducing stresses due to sub-synchronous

resonance in turbine-generator shaft. In: IEEE Energy Conversion Congress and Exposition. 2010. pp. 2300-2305

[20] Lak A, Nazarpour D, Ghahramani H. Novel methods with fuzzy logic and ANFIS controller based SVC for damping sub-synchronous resonance and low-frequency power oscillation. In: 20th Iranian Conference on Electrical Engineering (ICEE2012). 2012.

[21] Mohammad pour HA, Santi E. Subsynchronous resonance analysis in DFIG-based wind farms: Definitions and problem identification—Part I. In: IEEE Energy Conversion Congress and Exposition (ECCE). 2014.

[22] Mohammad pour HA, Santi E. Optimal adaptive sub-synchronous resonance damping controller for a series-compensated doubly-fed induction generator-based wind farm. IET Renewable Power Generation. 2015;

[23] Gagnon R, Viarouge P, Sybille G, Tourkhani E. Identification of ferroresonance as the cause of SVC instability in a degraded series

compensated network. In: IEEE Power Engineering Society Winter Meeting.

Vol. 2. 2000. pp. 1377-1382

pp. 771-775

2009. pp. 445-450

Power System Stability

pp. 450-455

pp. 812-819

9(6):669-681

74

## Effects of Climate Change in Electric Power Infrastructures

*Daniel Burillo*

## **Abstract**

Climate change mitigation and adaptation has been a major driving force to modernize electric power infrastructure and include more renewable energy systems. This chapter explains several ways in which electric power infrastructure has contributed to climate change, how climate change affects electric power infrastructure, mitigation options, and adaptation options. Electricity infrastructure categories include power generation technologies, transmission lines, substations, and building loads. Climate change categories include atmospheric greenhouse gas concentration levels, rising sea levels, changes in precipitation patterns and river flows, as well as more extreme air temperatures. Specific quantitative case studies are provided to estimate vulnerabilities from heat waves in the US desert southwest, including long-term forecasting of infrastructure performance, as well as, various supply-side and demand-side strategic options to maintain reliable operations.

**Keywords:** climate change, risk management, demand forecast, load volatility, vulnerability, failure prediction, outage prediction, long-term planning

## **1. Introduction**

Climate change occurs because of both natural and human causes. A geographic area that has a particular prevailing weather condition is said to have a particular climate [1, 2]. Over the course of time, earth has gone through several global climate changes, including the asteroid that killed the dinosaurs [3], the ice ages, and the warm period that we are in now [4]. Specific regions of the earth have also gone through local climate changes due to large storms, earthquakes, and volcanic eruptions that mostly only affect the target locations [5, 6]. Since human civilizations started intelligently designing ecosystems by channeling water, doing agriculture, building cities, and so on—we have been intentionally, and sometimes unintentionally, changing climates as well.

Civilization arguably did not start contributing to climate change at a global scale until after the industrial revolution with the proliferation of coal-powered steam engines and the burning of fossil fuels into the air [7]. The portable energy transformation device was revolutionary; the abundance with which humans lived and moved increased dramatically. Then, in 1896, Swedish chemist Svante Arrhenius estimated that the long-term effects of coal burning would enhance the natural greenhouse effect, and that a doubling of carbon dioxide in the atmosphere would warm the earth a few degrees Celsius. Modern-day climate models have maintained Arrhenius's conclusion, and only added more specifics to the predictions, with

details such as less average freezing at the earth's poles, higher sea level, more forceful storms, and various different weather patterns in particular geographies [8, 9]. Oil spills, trash barges, mass pavement, deforestation, various air-borne pollutants, and so on have also affected earth's ecosystems and climates [10].

Climate change is now affecting infrastructure systems by changing the weather conditions in which they must operate. The United States Department of Homeland Security has defined 16 critical infrastructure sectors that are considered vital to the "security, national economic security, and national public health or safety" of the country [11]. These critical infrastructure sectors are: chemicals, commercial facilities, communications, critical manufacturing, dams, defense, emergency services, energy, financial services, food and agriculture, government facilities, healthcare and public health, information technology, nuclear, transportation, and water and wastewater systems [11]. Across these infrastructure sectors, climate change will impact physical assets, operations, and use [12, 13]. As public awareness of the risks of climate change has risen, vulnerability assessments and adaption planning studies have been rapidly emerging in recent years too [13–16].

Climate is typically considered in infrastructure system designs by using several years' recent weather conditions to specify tolerances. This can be problematic for two reasons. First, because weather is not exactly the same every year, and more robust hardware is typically costlier, investors are often faced with tough risk management problems for low-probability high-impact events. Second, climates are changing. Thanks to advancements in global climate modeling, researchers are now able to forecast changes in future climate conditions and plan for extreme weather conditions with higher confidence. Climate change assessments generally rely on scenarios standardized by the Intergovernmental Panel on Climate Change (IPCC) [17]; however other considerations are made as well for factors such as the anthropogenic change in urban environments [18, 19]. The IPCC standard scenarios are referred to as Representative Concentration Pathways (RCPs), and are numbered corresponding to the amount of radiation forcing increase from the sun associated with the greenhouse gas effect relative to pre-industrial times, for example, RCP 4.5 and RCP 8.5 (4.5 and 8.5 W/m<sup>2</sup> ) [20, 21].

The newest technological advancements in climate change modeling and long-term weather forecasting include high-resolution spatial projections based on "downscaling" techniques. These downscaling techniques aim to improve the geographic and temporal resolution of specific weather projections, including air temperature, wind speed, solar radiation, precipitation, snowpack, and hydrology for specific geographic regions [22–27]. However, challenges still exist in incorporating climate change data into practice [28–30]. These challenges range from a lack of understanding of what parameters to use in complex models, to the methods used in the models, to what to do about the results. Significant literature is emerging to disentangle the contribution of different mechanisms to the response patterns, yielding more transparent models and results [31]. Further solutions to these challenges are expected to be met through ongoing collaboration between climate scientists and engineers, which we have included examples for in this chapter for electricity infrastructure and heat waves.

### **2. Electricity infrastructure vulnerabilities to climate change**

Electric power infrastructure broadly consists of three systems: generation, delivery, and demand. In terms of the physical processes, electrical power is created by generators to meet demand via delivery hardware. In terms of functionality however, it is the demand for electric power that drives the development of the

**77**

*Effects of Climate Change in Electric Power Infrastructures*

ated impacts on the power sector, adapted from [33].

well as flooding and storm-gusty winds in general [33].

Delivery systems can be affected by climate change due to higher temperatures causing higher demand, reduced capacity, and congestion; wildfires that can render power lines inoperable due to ionized air; and large storms that can cause physical damage via flooding and high winds that make trees fall on lines [41]. Delivery systems physically consist of various types of power lines that transport energy, transformers which convert the power to different voltage levels, quality devices for efficiency and reliability, and protection devices that interrupt power flows during hazardous conditions. Climate change can cause failures via physical hardware damage or create operational conditions that exceed hardware tolerances. Higher temperatures can cause individual components to become inoperable because protection devices will cut them off if power flow is too high for the weather

other two systems. Reliable electric power is central to urban development, and is a critical service in modern cities as almost all other major infrastructure and services rely on it: commerce, communication, manufacturing, defense, emergency, finance, agriculture, healthcare, information technology, transportation, and water [32]. Climate change can affect energy trade over time in ways that are significant to economics and natural resource consumption. For example, more extreme summer and winter temperatures necessarily result in more demand for cooling and heating, respectively. Climate change can also affect electric service reliability. A shortage of electric power generation, or sequence of faults in the delivery network, can result in an interruption in service at any second. This is why generation and delivery systems are built with multiple redundancies, such that individual component outages can occur safely. Unless there are multiple simultaneous outages, the infrastructure system can still deliver power to buildings and other loads without an interruption in service. **Table 1** provides a summary of major climate variables and their associ-

Generation is vulnerable to flooding, reduced streamflow, warmer water, and warmer air temperatures, which can all cause a shortage of power supply in the system [34]. There are many ways to physically generate electric power, but to evaluate the effects of climate change we have chosen to broadly categorize them as those that use water, and those that do not as follows. Conventional hydroelectric and water-cooled turbine generators (e.g., nuclear, coal-fired, and some natural gas) use water, and so are vulnerable to changes in three ways. First, flooding can damage physical hardware of above and below ground equipment if that hardware is not sufficiently shielded [35]. For example, sea level is projected to rise by 1–1.4 m by the end of the century, and if that is the case, then 25 coastal plants in California will be at risk of flooding during 1-in-100 year high-tide events [36]. Second, if the water levels in natural sources are too low (e.g., low river flow during droughts), then production capacity can be dependent upon priority level in access rights or reduced to zero if the water level physically goes below the intake pipe [37]. Third, some once-through generators are vulnerable to increases in water temperature in coastal plants, as a certain amount of temperature rise is necessary to cool the generators. Environmental regulations prevent expelling of water that is too hot to be safe for the ecosystem [38]. In August of 2015, the Pilgrim Nuclear Power Station in Massachusetts cut its power because the temperature of sea water used as influent was too high [39]. Power generators that do not use water include dry combustion natural gas and solar photovoltaics. These types of "dry" power generators are generally inland and could be at risk of flooding if they are located in a basin-like landscape that would collect water from a storm. Dry power generators also operate less efficiently under higher ambient air temperatures, which mean they also have lower production capacity to meet peak demand [40]. Dry generators are also vulnerable to changes in humidity that can affect their air circulation systems, as

*DOI: http://dx.doi.org/10.5772/intechopen.82146*

#### *Effects of Climate Change in Electric Power Infrastructures DOI: http://dx.doi.org/10.5772/intechopen.82146*

*Power System Stability*

details such as less average freezing at the earth's poles, higher sea level, more forceful storms, and various different weather patterns in particular geographies [8, 9]. Oil spills, trash barges, mass pavement, deforestation, various air-borne pollutants,

Climate change is now affecting infrastructure systems by changing the weather conditions in which they must operate. The United States Department of Homeland Security has defined 16 critical infrastructure sectors that are considered vital to the "security, national economic security, and national public health or safety" of the country [11]. These critical infrastructure sectors are: chemicals, commercial facilities, communications, critical manufacturing, dams, defense, emergency services, energy, financial services, food and agriculture, government facilities, healthcare and public health, information technology, nuclear, transportation, and water and wastewater systems [11]. Across these infrastructure sectors, climate change will impact physical assets, operations, and use [12, 13]. As public awareness of the risks of climate change has risen, vulnerability assessments and adaption planning stud-

Climate is typically considered in infrastructure system designs by using several years' recent weather conditions to specify tolerances. This can be problematic for two reasons. First, because weather is not exactly the same every year, and more robust hardware is typically costlier, investors are often faced with tough risk management problems for low-probability high-impact events. Second, climates are changing. Thanks to advancements in global climate modeling, researchers are now able to forecast changes in future climate conditions and plan for extreme weather conditions with higher confidence. Climate change assessments generally rely on scenarios standardized by the Intergovernmental Panel on Climate Change (IPCC) [17]; however other considerations are made as well for factors such as the anthropogenic change in urban environments [18, 19]. The IPCC standard scenarios are referred to as Representative Concentration Pathways (RCPs), and are numbered corresponding to the amount of radiation forcing increase from the sun associated with the greenhouse gas effect relative to pre-industrial times, for example, RCP 4.5

and so on have also affected earth's ecosystems and climates [10].

ies have been rapidly emerging in recent years too [13–16].

) [20, 21].

**2. Electricity infrastructure vulnerabilities to climate change**

Electric power infrastructure broadly consists of three systems: generation, delivery, and demand. In terms of the physical processes, electrical power is created by generators to meet demand via delivery hardware. In terms of functionality however, it is the demand for electric power that drives the development of the

The newest technological advancements in climate change modeling and long-term weather forecasting include high-resolution spatial projections based on "downscaling" techniques. These downscaling techniques aim to improve the geographic and temporal resolution of specific weather projections, including air temperature, wind speed, solar radiation, precipitation, snowpack, and hydrology for specific geographic regions [22–27]. However, challenges still exist in incorporating climate change data into practice [28–30]. These challenges range from a lack of understanding of what parameters to use in complex models, to the methods used in the models, to what to do about the results. Significant literature is emerging to disentangle the contribution of different mechanisms to the response patterns, yielding more transparent models and results [31]. Further solutions to these challenges are expected to be met through ongoing collaboration between climate scientists and engineers, which we have included examples for in this chapter for

and RCP 8.5 (4.5 and 8.5 W/m<sup>2</sup>

electricity infrastructure and heat waves.

**76**

other two systems. Reliable electric power is central to urban development, and is a critical service in modern cities as almost all other major infrastructure and services rely on it: commerce, communication, manufacturing, defense, emergency, finance, agriculture, healthcare, information technology, transportation, and water [32]. Climate change can affect energy trade over time in ways that are significant to economics and natural resource consumption. For example, more extreme summer and winter temperatures necessarily result in more demand for cooling and heating, respectively. Climate change can also affect electric service reliability. A shortage of electric power generation, or sequence of faults in the delivery network, can result in an interruption in service at any second. This is why generation and delivery systems are built with multiple redundancies, such that individual component outages can occur safely. Unless there are multiple simultaneous outages, the infrastructure system can still deliver power to buildings and other loads without an interruption in service. **Table 1** provides a summary of major climate variables and their associated impacts on the power sector, adapted from [33].

Generation is vulnerable to flooding, reduced streamflow, warmer water, and warmer air temperatures, which can all cause a shortage of power supply in the system [34]. There are many ways to physically generate electric power, but to evaluate the effects of climate change we have chosen to broadly categorize them as those that use water, and those that do not as follows. Conventional hydroelectric and water-cooled turbine generators (e.g., nuclear, coal-fired, and some natural gas) use water, and so are vulnerable to changes in three ways. First, flooding can damage physical hardware of above and below ground equipment if that hardware is not sufficiently shielded [35]. For example, sea level is projected to rise by 1–1.4 m by the end of the century, and if that is the case, then 25 coastal plants in California will be at risk of flooding during 1-in-100 year high-tide events [36]. Second, if the water levels in natural sources are too low (e.g., low river flow during droughts), then production capacity can be dependent upon priority level in access rights or reduced to zero if the water level physically goes below the intake pipe [37]. Third, some once-through generators are vulnerable to increases in water temperature in coastal plants, as a certain amount of temperature rise is necessary to cool the generators. Environmental regulations prevent expelling of water that is too hot to be safe for the ecosystem [38]. In August of 2015, the Pilgrim Nuclear Power Station in Massachusetts cut its power because the temperature of sea water used as influent was too high [39]. Power generators that do not use water include dry combustion natural gas and solar photovoltaics. These types of "dry" power generators are generally inland and could be at risk of flooding if they are located in a basin-like landscape that would collect water from a storm. Dry power generators also operate less efficiently under higher ambient air temperatures, which mean they also have lower production capacity to meet peak demand [40]. Dry generators are also vulnerable to changes in humidity that can affect their air circulation systems, as well as flooding and storm-gusty winds in general [33].

Delivery systems can be affected by climate change due to higher temperatures causing higher demand, reduced capacity, and congestion; wildfires that can render power lines inoperable due to ionized air; and large storms that can cause physical damage via flooding and high winds that make trees fall on lines [41]. Delivery systems physically consist of various types of power lines that transport energy, transformers which convert the power to different voltage levels, quality devices for efficiency and reliability, and protection devices that interrupt power flows during hazardous conditions. Climate change can cause failures via physical hardware damage or create operational conditions that exceed hardware tolerances. Higher temperatures can cause individual components to become inoperable because protection devices will cut them off if power flow is too high for the weather


**79**

*Effects of Climate Change in Electric Power Infrastructures*

• Damaged infrastructure • Disrupted supply chains and offshore activity • Damage to facilities related to soil erosion

*Summary of key climate drivers and possible impacts to power systems.*

**Key impacts Impacted** 

**segment**

Generation Delivery-Transmission and Distribution **Adaptation strategies**

• Concrete-sided buildings instead of metal • Implement more rigorous structural standards

• Implement porous materials for

• Increased decentralized energy

• Cite infrastructure away from heavily wooded areas/rigor-

• Same as above

better wind flow

ously prune trees

generation

conditions [42]. Additionally, higher temperatures can result in reduced capacity for above ground power lines to safely carry electricity. If too many components are offline or the capacity of the system is significantly reduced, then power may not be available when it is needed causing cascading failures and blackouts as happened in the US in 2003 and 2011 [43, 44]. Alternatively, if protection devices are not properly calibrated, then components can overheat. This has happened to hundreds of distribution-level transformers during recent record breaking heat waves in the US southwest [45]. Moreover, lines can sag to the point that they permanently deform. Not coincidentally, during these record-breaking heat waves, the air is very dry, and the risk of wildfires is high. If wildfires burn under power lines, then those components can fail as well due to air ionization. Like generators, substations are vulnerable to rising sea levels and storm floods near the coast and in basin-like land areas [36]. Flooding can erode or short the hardware in substations and underground power lines [33]. Lastly, severe storms can blow trees, and other things, into

Electric power demand is primarily susceptible to higher air temperatures, which can increase both total energy consumption and the peak demand in regions with significant electric air conditioning [40, 46, 47]. Demand is typically planned for at city- and state-level geographies based on seasonal weather usage patterns, daily weather usage patterns, and local use patterns. In warm to hot climates, the peak electricity demand is usually in the late afternoon during the summer when businesses are still operating and people are coming home and turning on air conditioners [48]. Historically, preparing for higher peak demand means building additional generation and delivery capacity, but policies aimed at natural resource conservation have targeted building and appliance energy efficiency standards which also offset increases in peak demand [40]. In terms of climate change, higher average temperatures and higher maximum temperatures mean more demand for AC usage, which could mean more energy usage over time, higher power demand for ACs to operate at hotter temperatures, and more installations of ACs total in moderately warm climates. The combined effects could be a significant increase in per capita demand [40]. This may be more than local delivery infrastructure are capable of supporting without systemic or

*DOI: http://dx.doi.org/10.5772/intechopen.82146*

**Climate hazard**

**Table 1.**

More frequent/ severe extreme events (floods, typhoons, drought, high winds, etc.)

power lines and cause outages.

network-wide investments [49].

*Effects of Climate Change in Electric Power Infrastructures DOI: http://dx.doi.org/10.5772/intechopen.82146*


#### **Table 1.**

*Power System Stability*

**Key impacts Impacted** 

• Lower generation efficiency

• Reduced carrying capacity of lines and transformers

• Increased losses in lines and transformers

• Increased peak demand and total energy demand

for cooling

• Reduced combustion efficiency due to increased moisture content of coal

• Damaged power lines from snow and ice • Flooding of underground infrastructure • Damaged towers due to

• Decreased availability of freshwater for thermal

erosion

cooling

• Flooding/damage to coastal/low-lying infrastructure

• Decreased coal-to-gas conversion efficiency • Decreased combined cycle gas turbine efficiency • Decreased solar PV efficiency

**segment**

Delivery-Transmission & Distribution

Demand-End Use

Delivery-Transmission & Distribution

Generation/ delivery-Transmission and Distribution Demand-End use **Adaptation strategies**

• Site new generation in cooler

• Underground hardware • Use more heat-resistant

• Implement more effective cooling for transformers

• Switch to fuel that is more moisture resistant (e.g., natural

• Improved flood protection for equipment at ground level • Use covered and/or insulated

• Include lightning protection (e.g., earth wires, spark gaps) in the distribution network

• Switch to more "waterefficient" fuels (e.g., natural

• Increase volume of water treat-

• Implement flood control (dams, dikes, reservoirs,

• Improve coastal defenses (seawalls, bulkheads, etc.) • Build in and/or relocate to less exposed locations • Raise structure levels • Improved drainage systems • Protect fuel storage

gas, wind, solar)

ment system • Restore/reforest land

polders, etc.)

• AC energy efficiency • Building thermal efficiency

• Peak load shifting

Generation • Protect coal stockpiles

gas)

conductors

Generation • Switch to recirculating or dry cooling

Generation • Implement air chillers or more efficient chillers

locations

materials

**Climate hazard**

Increased air temperatures

Increase in precipitation

Decrease in precipitation

Sea level rise/ increased storm surge during hurricanes and tropical storms/ increased nuisance flooding during high tides

**78**

*Summary of key climate drivers and possible impacts to power systems.*

conditions [42]. Additionally, higher temperatures can result in reduced capacity for above ground power lines to safely carry electricity. If too many components are offline or the capacity of the system is significantly reduced, then power may not be available when it is needed causing cascading failures and blackouts as happened in the US in 2003 and 2011 [43, 44]. Alternatively, if protection devices are not properly calibrated, then components can overheat. This has happened to hundreds of distribution-level transformers during recent record breaking heat waves in the US southwest [45]. Moreover, lines can sag to the point that they permanently deform. Not coincidentally, during these record-breaking heat waves, the air is very dry, and the risk of wildfires is high. If wildfires burn under power lines, then those components can fail as well due to air ionization. Like generators, substations are vulnerable to rising sea levels and storm floods near the coast and in basin-like land areas [36]. Flooding can erode or short the hardware in substations and underground power lines [33]. Lastly, severe storms can blow trees, and other things, into power lines and cause outages.

Electric power demand is primarily susceptible to higher air temperatures, which can increase both total energy consumption and the peak demand in regions with significant electric air conditioning [40, 46, 47]. Demand is typically planned for at city- and state-level geographies based on seasonal weather usage patterns, daily weather usage patterns, and local use patterns. In warm to hot climates, the peak electricity demand is usually in the late afternoon during the summer when businesses are still operating and people are coming home and turning on air conditioners [48]. Historically, preparing for higher peak demand means building additional generation and delivery capacity, but policies aimed at natural resource conservation have targeted building and appliance energy efficiency standards which also offset increases in peak demand [40]. In terms of climate change, higher average temperatures and higher maximum temperatures mean more demand for AC usage, which could mean more energy usage over time, higher power demand for ACs to operate at hotter temperatures, and more installations of ACs total in moderately warm climates. The combined effects could be a significant increase in per capita demand [40]. This may be more than local delivery infrastructure are capable of supporting without systemic or network-wide investments [49].

## **3. How heat waves can result in service interruptions**

The fault tree in **Figure 1** shows the terminal event of a service interruption on the right, and the power- and material/hardware-based-failures that can lead to a service interruption logically proceeding from the left [50]. Hardware failures feedback into the event triggers as their loss of functionality results in a loss of powerflow that could cause an interruption. System operators generally maintain an n − 1 redundancy standard in design at the high-voltage transmission level meaning that the single largest generator, transmission line branch, or substation can fail

**81**

*Effects of Climate Change in Electric Power Infrastructures*

at any time without any interruption in service [51]. These n − 1 redundancies are represented by octagon boxes and logical AND gates in the figure. Service interruptions due to major component failures only occur when more than one individual component fails at the same time. Such events can lead to cascading failures including blackouts as in the 2011 Arizona-California blackout [52]. The pathway for high demand is colored red because it is a critical condition for a service interruption in a

The two ways that a service interruption can occur as a function of purely rising air temperatures are that there is either not enough total generation to meet total demand, or particular power lines and substations do not have sufficient capacity to deliver power to loads. The following list explains how increases in ambient air temperatures can trigger failures leading to service interruptions consistent with

a. High air temperatures can result in loss of generation capacity and loss of efficiency in the transmission and distribution (T&D) network [36, 53]. If the system is also in high demand, (B), then load can exceed generation. If there are insufficient generation reserves, then there will be a service interruption.

b.High air temperatures can result in higher demand, especially during the already hot summer months due to increased burden on building air conditioning systems [53].

c.High air temperatures result in less capacity in T&D lines and transformers [36, 53]. If a circuit is in high demand, then power flow can result in components' tempera-

i.If protection devices function correctly, then they will trip (open) the circuit under excessive loading and power flow will be instantaneously redistributed to parallel T&D components [52]. If there is insufficient capacity in parallel branches to deliver power to the load, then there will be a service

ii.If a protection device fails to trip and a circuit is over loaded, then excess heat accelerates the chemical degradation rate of sensitive materials and can result in mechanical failure (E) [54, 55]. Protection devices can fail because they are not accurately designed or calibrated for local climate conditions or other reasons [56]. Depending on the type and location of overload failure, a generator, transmission line, substation, quality device, or other protection device can fail. If a generator fails, then the system state goes to (A) as the system now has less generation. If a line or transformer fails, then the system goes to (C) as the T&D network operates at lower efficiency and or has less power flow capacity. If a power quality device fails, then it goes to (A) or (C) again or directly to excessive loading depending on the circumstances. If another redundant protection device fails, then the cycle of potential failures

d.High air temperatures can result in a protection device failing to trip [56]. The device could be calibrated to a certain power rating that should be lower for the actual air temperature. If that occurs during high loading, then a component can

e.High air temperatures can result in an accelerated physical material degradation rate, which can result in accelerated failures for any electrical devices [57]. The

repeats for additional components on connected circuits.

become overloaded and fail as in (ii).

*DOI: http://dx.doi.org/10.5772/intechopen.82146*

system protected with multiple redundancies.

tures exceeding safe operating temperatures [52].

the lettering in **Figure 1**.

interruption [52].

**Figure 1.**

*Fault tree from heat wave to service interruption.*

## *Effects of Climate Change in Electric Power Infrastructures DOI: http://dx.doi.org/10.5772/intechopen.82146*

*Power System Stability*

**3. How heat waves can result in service interruptions**

The fault tree in **Figure 1** shows the terminal event of a service interruption on the right, and the power- and material/hardware-based-failures that can lead to a service interruption logically proceeding from the left [50]. Hardware failures feedback into the event triggers as their loss of functionality results in a loss of powerflow that could cause an interruption. System operators generally maintain an n − 1 redundancy standard in design at the high-voltage transmission level meaning that the single largest generator, transmission line branch, or substation can fail

**80**

**Figure 1.**

*Fault tree from heat wave to service interruption.*

at any time without any interruption in service [51]. These n − 1 redundancies are represented by octagon boxes and logical AND gates in the figure. Service interruptions due to major component failures only occur when more than one individual component fails at the same time. Such events can lead to cascading failures including blackouts as in the 2011 Arizona-California blackout [52]. The pathway for high demand is colored red because it is a critical condition for a service interruption in a system protected with multiple redundancies.

The two ways that a service interruption can occur as a function of purely rising air temperatures are that there is either not enough total generation to meet total demand, or particular power lines and substations do not have sufficient capacity to deliver power to loads. The following list explains how increases in ambient air temperatures can trigger failures leading to service interruptions consistent with the lettering in **Figure 1**.

	- i.If protection devices function correctly, then they will trip (open) the circuit under excessive loading and power flow will be instantaneously redistributed to parallel T&D components [52]. If there is insufficient capacity in parallel branches to deliver power to the load, then there will be a service interruption [52].
	- ii.If a protection device fails to trip and a circuit is over loaded, then excess heat accelerates the chemical degradation rate of sensitive materials and can result in mechanical failure (E) [54, 55]. Protection devices can fail because they are not accurately designed or calibrated for local climate conditions or other reasons [56]. Depending on the type and location of overload failure, a generator, transmission line, substation, quality device, or other protection device can fail. If a generator fails, then the system state goes to (A) as the system now has less generation. If a line or transformer fails, then the system goes to (C) as the T&D network operates at lower efficiency and or has less power flow capacity. If a power quality device fails, then it goes to (A) or (C) again or directly to excessive loading depending on the circumstances. If another redundant protection device fails, then the cycle of potential failures repeats for additional components on connected circuits.

same failure scenarios can occur as described above, with the addition of an undesired trip of a protection device. If a protection device fails with an undesired trip, and there is no redundant power flow, then a service interruption occurs.

## **4. Case study 1: quick estimate of peak demand for record-breaking heat waves**

How can we know how much electricity demand there will be if weather conditions are more severe than they have ever been in the past? With no past records, how can we know what the future will be? How do we know if there will be sufficient generation resources to meet the demand? These are not straightforward questions to answer as demand and generation are, at the city scale, rather complicated with millions of moving parts. In this case study, adapted from Burillo et al. [40], we show how analysts can produce a reasonable city-scale predictive model using basic computational tools, and simple publicly available data for buildings, electricity demand, and air temperature.

We will consider Phoenix, Arizona and Los Angeles, California as regions because they have summertime peak demands with significant air conditioner (AC) penetration, and are expected to have higher air temperatures in the future with climate change [58–60]. A simple approach to predicting peak demand for future temperatures would be to plot daily peak electricity demand against daily maximum air temperatures, *Tmax*, and draw a straight line, but doing so would be an oversimplification as it results in an overestimate of demand. Overestimating peak demand would be very costly from a planning perspective because it would inflate delivery capacity and resource adequacy requirements. Instead, we are going use regression techniques to fit the structural equation model (SEM) developed in [40] using the number of residential and commercial utility customers, daily peak demand data for those customers, the air conditioning penetration percent from the county assessor's office, and daily *Tmax*.

A full explanation of the theory and equations are in [40], but the concept in brief has two main parts as follows. First, at a micro-scale, as outdoor thermal forces (sunlight and air temperature) increase, the work that individual ACs do increases and so does their electrical load. In our prior study, we found that the most common AC units (split indoor-outdoor dry air-cooled) have an increase in active load of 1.33% kW per 1°C ± 0.35%. Second, at a macro scale, AC duty cycles increase proportional to the ratio of incoming and outgoing building thermal energy at the thermostat set point. At higher *Tmax*, the number of ACs simultaneously active in a region during the peak period increases as well up to a theoretical limit of 100%. This behavior can be effectively modeled in the form of an s-curve.

The results for the peak demand SEM are shown in **Figure 2** compared to a straight-line approach. Peak demand in Phoenix is more sensitive to average historical seasonal changes in air temperatures than Los Angeles, but results show that *marginal* changes in peak demand are more significant in Los Angeles than in Phoenix at summertime highs. Peak demand for Phoenix increased more in its historical range because its 90th percentile, *T90*, was relatively higher, and Phoenix has higher AC penetration. From historical *T90* to the highest projected *Tmax* however, peak demand increased more in Los Angeles than Phoenix, another 3.9 GW vs. 1.2 GW or 34 vs. 16%. In this case, the larger increase in peak demand in Los Angeles was due to the larger relative difference between historical *T90* and future *Tmax*. As shown in **Figure 2b**, Phoenix's ACs expect to already be running at nearly 100% duty cycle at its *T90*, whereas Los Angeles's ACs expect to only run at about 60% duty cycle at its *T90*. Thus, the potential for a record-breaking heat wave to affect peak demand is higher in Los Angeles than Phoenix.

**83**

**Figure 2.**

*Effects of Climate Change in Electric Power Infrastructures*

**5. Case study 2: using downscaled climate data to inform long-term** 

*(a) Peak demand. (b) Peak demand SEM factors. (a) Shows SEM approach results in solids. Straight-line approach shown in dotted lines. (b) Shows the two s-curved lines are expected values. T90 ranges represent the* 

While city-scale blackouts often make for bigger headlines in the news, neighborhood-scale outages are much more common. In the last case study, we saw how forecasting peak electricity demand was critical for planning generation resource capacity, and how those efforts could be enhanced with better quantitative understanding of climate change. In this case study, based on [61], we incorporate climate change projections at the next level in electricity infrastructure planning and consider the highly complex problem of siting and sizing delivery component capacities. It is not enough to simply have generation resources in the same city as loads. There must also be lines, substations, and transformers to get the power from the generators to the users, and each of those have their own capacities which are a function of how hot the devices can safely operate at. Where and how should we build immovable field assets with 30–70-year useable life spans? How do we know what the urban landscape,

**demand forecasts and capital infrastructure planning**

*range of 90th percentile values for the locations sampled.*

*DOI: http://dx.doi.org/10.5772/intechopen.82146*

*Effects of Climate Change in Electric Power Infrastructures DOI: http://dx.doi.org/10.5772/intechopen.82146*

**Figure 2.**

*Power System Stability*

**heat waves**

electricity demand, and air temperature.

same failure scenarios can occur as described above, with the addition of an undesired trip of a protection device. If a protection device fails with an undesired trip, and there is no redundant power flow, then a service interruption occurs.

**4. Case study 1: quick estimate of peak demand for record-breaking** 

How can we know how much electricity demand there will be if weather conditions are more severe than they have ever been in the past? With no past records, how can we know what the future will be? How do we know if there will be sufficient generation resources to meet the demand? These are not straightforward questions to answer as demand and generation are, at the city scale, rather complicated with millions of moving parts. In this case study, adapted from Burillo et al. [40], we show how analysts can produce a reasonable city-scale predictive model using basic computational tools, and simple publicly available data for buildings,

We will consider Phoenix, Arizona and Los Angeles, California as regions because they have summertime peak demands with significant air conditioner (AC) penetration, and are expected to have higher air temperatures in the future with climate change [58–60]. A simple approach to predicting peak demand for future temperatures would be to plot daily peak electricity demand against daily maximum air temperatures, *Tmax*, and draw a straight line, but doing so would be an oversimplification as it results in an overestimate of demand. Overestimating peak demand would be very costly from a planning perspective because it would inflate delivery capacity and resource adequacy requirements. Instead, we are going use regression techniques to fit the structural equation model (SEM) developed in [40] using the number of residential and commercial utility customers, daily peak demand data for those customers, the air conditioning penetration percent from the county assessor's office, and daily *Tmax*. A full explanation of the theory and equations are in [40], but the concept in brief has two main parts as follows. First, at a micro-scale, as outdoor thermal forces (sunlight and air temperature) increase, the work that individual ACs do increases and so does their electrical load. In our prior study, we found that the most common AC units (split indoor-outdoor dry air-cooled) have an increase in active load of 1.33% kW per 1°C ± 0.35%. Second, at a macro scale, AC duty cycles increase proportional to the ratio of incoming and outgoing building thermal energy at the thermostat set point. At higher *Tmax*, the number of ACs simultaneously active in a region during the peak period increases as well up to a theoretical limit of 100%.

This behavior can be effectively modeled in the form of an s-curve.

affect peak demand is higher in Los Angeles than Phoenix.

The results for the peak demand SEM are shown in **Figure 2** compared to a straight-line approach. Peak demand in Phoenix is more sensitive to average historical seasonal changes in air temperatures than Los Angeles, but results show that *marginal* changes in peak demand are more significant in Los Angeles than in Phoenix at summertime highs. Peak demand for Phoenix increased more in its historical range because its 90th percentile, *T90*, was relatively higher, and Phoenix

has higher AC penetration. From historical *T90* to the highest projected *Tmax* however, peak demand increased more in Los Angeles than Phoenix, another 3.9 GW vs. 1.2 GW or 34 vs. 16%. In this case, the larger increase in peak demand in Los Angeles was due to the larger relative difference between historical *T90* and future *Tmax*. As shown in **Figure 2b**, Phoenix's ACs expect to already be running at nearly 100% duty cycle at its *T90*, whereas Los Angeles's ACs expect to only run at about 60% duty cycle at its *T90*. Thus, the potential for a record-breaking heat wave to

**82**

*(a) Peak demand. (b) Peak demand SEM factors. (a) Shows SEM approach results in solids. Straight-line approach shown in dotted lines. (b) Shows the two s-curved lines are expected values. T90 ranges represent the range of 90th percentile values for the locations sampled.*

## **5. Case study 2: using downscaled climate data to inform long-term demand forecasts and capital infrastructure planning**

While city-scale blackouts often make for bigger headlines in the news, neighborhood-scale outages are much more common. In the last case study, we saw how forecasting peak electricity demand was critical for planning generation resource capacity, and how those efforts could be enhanced with better quantitative understanding of climate change. In this case study, based on [61], we incorporate climate change projections at the next level in electricity infrastructure planning and consider the highly complex problem of siting and sizing delivery component capacities.

It is not enough to simply have generation resources in the same city as loads. There must also be lines, substations, and transformers to get the power from the generators to the users, and each of those have their own capacities which are a function of how hot the devices can safely operate at. Where and how should we build immovable field assets with 30–70-year useable life spans? How do we know what the urban landscape, the buildings, the appliance technology, the population, and so on will look like that far into the future? Doing this well is a highly coordinated effort with many steps and iterations across multiple planning departments, as we shall get a taste for below.

If we are going to attempt to model a map of the future infrastructure requirements with any accuracy, then first we need a model that produces an accurate map of current conditions. The full details of our approach are described in Ref. [61], and the concept at brief is as follows. We used high-resolution (2 km2 ) data for daily maximum ambient air temperatures (*Tmax*), residential and commercial building models calibrated for the region, a geographic map of the buildings' locations, and a geographic map of lines and substations. With these tools and data we are able to validate a map of the base period (2010) electric power demand and infrastructure loading in Los Angeles County, California as shown in **Figure 3**.

With a reasonably accurate and verified model of base period electricity demand, and initial loading on delivery hardware, we can use historical climate data to estimate overloading risks in the base period. We do this by re-running our models with the composite image of the highest temperature values that historically occurred in any location at any historical period in time. We also use that temperature image to estimate the reduced capacity on infrastructure hardware. Combining the two together, we can compute the thermally de-rated load factors on hardware as shown in **Figure 4** for substations with corresponding definitions in **Table 2**.

**Figure 3.** *Map of Los Angeles County, California. (a) Peak demand and (b) substation loading in base period.*

**85**

**Table 2.**

*Substation derated load factor risk metrics.*

**Figure 4.**

**Load factor**

0.01– 0.5

0.51– 0.85

0.86– 1.00

1.01– 1.20

1.21– 2.00

*Effects of Climate Change in Electric Power Infrastructures*

*Map of Los Angeles County, California substation risks in base period.*

**Risk level Reference Description**

n/a Unknown n/a Substation(s) exists in this space according to national

factor data were unavailable

parallel/redundant configuration

Very safe Assumption Negligible thermal wear, probably n − 2 reliable if in

Caution 15% rule Some thermal wear, probably not n − 1 reliable

>2 Outage [4] Extreme thermal wear, switchgear will automatically trip to

Safe 15% rule Very low thermal wear, probably n − 1 reliable if in parallel/ redundant configuration

Warning [3, 4] Moderate thermal wear, component overloaded, automatic

Emergency [3, 4] Significant thermal wear, component very overloaded,

upon switch gear settings

database [1], but not shown in SCE DERiM [2], so load

switching may occur within 24 h to 30 days if loading continues at this level depending upon switch gear settings

automatic switching may occur within 30 min depending

prevent combustion and permanent hardware damage

*DOI: http://dx.doi.org/10.5772/intechopen.82146*

*Effects of Climate Change in Electric Power Infrastructures DOI: http://dx.doi.org/10.5772/intechopen.82146*

*Power System Stability*

the buildings, the appliance technology, the population, and so on will look like that far into the future? Doing this well is a highly coordinated effort with many steps and iterations across multiple planning departments, as we shall get a taste for below. If we are going to attempt to model a map of the future infrastructure requirements with any accuracy, then first we need a model that produces an accurate map of current conditions. The full details of our approach are described in Ref. [61],

maximum ambient air temperatures (*Tmax*), residential and commercial building models calibrated for the region, a geographic map of the buildings' locations, and a geographic map of lines and substations. With these tools and data we are able to validate a map of the base period (2010) electric power demand and infrastructure

With a reasonably accurate and verified model of base period electricity demand, and initial loading on delivery hardware, we can use historical climate data to estimate overloading risks in the base period. We do this by re-running our models with the composite image of the highest temperature values that historically occurred in any location at any historical period in time. We also use that temperature image to estimate the reduced capacity on infrastructure hardware. Combining the two together, we can compute the thermally de-rated load factors on hardware as shown in **Figure 4** for substations with corresponding definitions in **Table 2**.

*Map of Los Angeles County, California. (a) Peak demand and (b) substation loading in base period.*

) data for daily

and the concept at brief is as follows. We used high-resolution (2 km2

loading in Los Angeles County, California as shown in **Figure 3**.

**84**

**Figure 3.**

**Figure 4.** *Map of Los Angeles County, California substation risks in base period.*


#### **Table 2.** *Substation derated load factor risk metrics.*

**Figure 5.** *Map of Substation Risks by 2060.*

We can now forecast into the future for a variety of factors. In this case, we considered rising air temperatures, population growth, building stock turnover, housing densification, air conditioning penetration, and air conditioning efficiency. All of these factors were technically specified in either the building energy models or at the census block group in making spatial allocations to the maps. The results are shown for two population growth scenarios, and two energy efficiency scenarios for substation loading in **Figure 5**. This is what the peak hour could look like during a heat wave in 2060 with the same infrastructure as in the base period. Specific cities and neighborhoods are identified as being at risk of overloading and outages as shown in **Figure 5**.

## **6. Climate change risk mitigation and adaptation options in the electric power sector**

There are many ways to maintain stability in electric power systems in light of climate change. Several mitigation and adaptation options are listed in **Figure 6** for our case studies of insufficient supply-side resources during rising air temperatures, with effects on stability and other factors important for consideration as well. We categorically consider several options in the form of technology implementations, market

**87**

**Figure 6.**

**6.1 Electrical systems: resources**

*Climate change risk mitigation and adaptation options and effects.*

*Effects of Climate Change in Electric Power Infrastructures*

incentives, and building stock. We consider load variance as an effect explicitly because less variance means more consistent load, more capacity for contingencies, and lower operations and maintenance costs [49]. We also identified effects of those options on several other complex interdependent factors that are priorities for stakeholders too. This discussion should not be considered exhaustive nor advocate any particular option, but simply present several options as we have identified so far in a structured manner.

The major tradeoffs between generation technologies—distributed solar PV (with storage and power quality controls) and centralized systems—in meeting demand are: land space requirements, delivery congestion relief, water usage, air

*DOI: http://dx.doi.org/10.5772/intechopen.82146*

*Power System Stability*

**86**

**power sector**

**Figure 5.**

*Map of Substation Risks by 2060.*

We can now forecast into the future for a variety of factors. In this case, we considered rising air temperatures, population growth, building stock turnover, housing densification, air conditioning penetration, and air conditioning efficiency. All of these factors were technically specified in either the building energy models or at the census block group in making spatial allocations to the maps. The results are shown for two population growth scenarios, and two energy efficiency scenarios for substation loading in **Figure 5**. This is what the peak hour could look like during a heat wave in 2060 with the same infrastructure as in the base period. Specific cities and neighborhoods are identified as being at risk of overloading and outages as shown in **Figure 5**.

**6. Climate change risk mitigation and adaptation options in the electric** 

There are many ways to maintain stability in electric power systems in light of climate change. Several mitigation and adaptation options are listed in **Figure 6** for our case studies of insufficient supply-side resources during rising air temperatures, with effects on stability and other factors important for consideration as well. We categorically consider several options in the form of technology implementations, market



#### **Figure 6.**

*Climate change risk mitigation and adaptation options and effects.*

incentives, and building stock. We consider load variance as an effect explicitly because less variance means more consistent load, more capacity for contingencies, and lower operations and maintenance costs [49]. We also identified effects of those options on several other complex interdependent factors that are priorities for stakeholders too. This discussion should not be considered exhaustive nor advocate any particular option, but simply present several options as we have identified so far in a structured manner.

## **6.1 Electrical systems: resources**

The major tradeoffs between generation technologies—distributed solar PV (with storage and power quality controls) and centralized systems—in meeting demand are: land space requirements, delivery congestion relief, water usage, air emissions, and marginal capital costs. Solar PV can be installed on building roofs, whereas centralized systems require their own dedicated land footprint and delivery infrastructure [62–65]. When implemented at the distribution level, solar PV can power load directly without going through delivery components that are necessary for central systems. The net effect is a relative decrease in load from the perspective of the grid relative to demand. Yet these distributed systems beg the question of storage given peak demand occurs once the PV systems decline in production of power. At the same time, this will be an important metric to monitor for reliability purposes going forward—if storage is included—as those two values have historically been one and the same. The most prominent fast-ramping central generation technology is combined cycle natural gas plants, which both consume water and emit various gasses into the atmosphere. Combustion-only natural gas plants could be implemented, which would not use water, but would be more sensitive to rising air temperatures, as well as less fuel-efficient, and therefore more costly and emissions intensive per kWh. While levelized costs of solar PV are now at or below parity with bulk generation plants on a per kWh basis, the combined costs of solar PV with storage to provide 24/7 dispatchable energy and regulation services are still higher than traditional central generation plants [66]. Thus, the best options for new resource procurement across competing objectives, will be those that consider the current and future state of the delivery infrastructure.

Implementing DER with new buildings may be the most cost-effective way to meet demand associated with growth in areas where delivery infrastructure are already over capacity during extreme heat waves. In such areas, some substations may be able to be adapted with improved heat sinks, forced air, or water cooling systems to increase capacity. But some may not, and overhead power line capacity will still be limited to convective cooling. The cost of increasing delivery infrastructure capacity necessary to meet demand through central generation, or long-distance imported power could be quite significant at \$10–130 million USD per substation and \$1–3 million USD per mile of line length leading all the way out of the urban center [67, 68].

Future work for vulnerable neighborhoods should consider implementation of adaptation options by considering 24-h load profiles on distribution-level circuits, the total Watt-hours of necessary storage capacity to complement solar PV capacity, and opportunity for network aggregation in supplying ancillary grid services. Circuits with higher portion of commercial and industrial loads may be preferable for the installation of DERs, as their load profiles may more closely match the PV generation profile (peaking at mid-day) allowing for more storage efficiency. Effective implementation of energy storage would reduce load variance by charging during off-peak hours and discharging during peak hours, resulting in a more consistent load, which is more readily manageable by system operators, and therefore has lower operations and maintenance costs [49]. This could occur through some kind of automated and networked market incentives that are available for wholesale markets as of February 2018 [69].

Implementation of new bulk generation systems and delivery infrastructure may be more valuable in the northern areas of San Fernando and Antelope Valley. The areas are relatively less developed there and so land should be more readily available for construction. Future studies should consider the reliability and security benefits of redundant central and distributed energy systems, and determine what amount of each, including storage, is optimal for different outage risk tolerances.

#### **6.2 Electrical systems: loads**

More energy efficient appliances can reduce use-phase load, load variance, and thus provide benefits to power systems' stability. To mitigate risks from heat waves

**89**

stationary extreme heat events.

hybrid designs.

**6.3 Market incentives: supply side and utilities**

*Effects of Climate Change in Electric Power Infrastructures*

however, focus should be directed towards air conditioner units. Differences in lighting and other appliance efficiencies only affected peak demand in the models by 2% in California, but that state already has aggressive energy efficiency policies, so other areas around the world could benefit more. AC units generally accounted for 60–70% of summertime peak demand within residential buildings, and higher air temperatures resulted in a 3–7% increase in demand per 1°C (1.8°F). Los Angeles County currently has only 45% AC penetration in its residential buildings, meaning that peak demand in just over half of the current building stock does not increase

Policies that would guide new or replacement ACs based on different performance constraints or different technologies would aid in reducing the risk of excessive peak demand during extreme heat events. It is possible to design AC units that are more efficient under the hottest conditions or that utilize thermal storage to achieve 'flat' efficiency curves that do not degrade at the hottest temperatures [70, 71]. For example, developing a new 'peak performance rating' for ACs at 50°C (122°F) could be useful to mitigate peak load during extreme heat waves. Doing so could provide incentive for ACs to be optimally engineered for more efficient performance at or near such extreme temperatures. Current standards, SEER and EER [72], are primarily for temperatures at or below 35°C (95°F). The current SEER standard, SEER 13, is already optimized to the point that improvements in SEER ratings in the model up to SEER 21 only affected peak demand by a few percent and were slightly counter-effective in some instances where temperatures exceeded 45°C (113°F) due to tradeoffs in engineering design optimization. Water-based evaporative cooling systems are another option that uses much less electric power, but requires water to operate, and are often not accepted by users as the sole-source of air conditioning due to insufficient comfort levels when the weather is both hot and humid [73, 74]. Further study may be useful to identify the practicality of

Some studies suggest that the traditional utility business model, that couples energy sales to profits, is not compatible with certain energy efficiency goals or large amounts of DER [85]. The former issue is because utility revenues are primarily dependent on total energy sales, but the costs of providing reliable infrastructure are primarily dependent on capital expenditures, operations, and maintenance [75]. Profits increase with volumetric energy sales, and costs are relatively flat. Therefore, financial incentives must exist to be relatively inefficient in some processes. Hence, public regulatory commissions exist to oversee the prices set for ratepayers. The alternative business model is referred to as a "decoupled" market, where "excess" profits are carried forward and accounted for in adjusting the following years' prices [76, 77]. When utility profits are decoupled from energy sales, load serving entities can implement effective conservation programs without violating fiduciary responsibility to shareholders [78]. This structure has been implemented in several states with positive effects on energy efficiency. For example, California's per-capita annual energy consumption has remained relatively flat since decoupling was implemented in the 1980s, whereas many other states' has steadily risen [79]. Market design determines rules by which participants must play [80]. If utilities' profits were a function of key reliability precursors, such as smaller load variance, then utilities would have a direct incentive to reduce peak load (including shifting it to off-peak hours), resulting in less congestion, higher utilization of lower-cost base-load bulk generation resources, and more contingency capacity for non-

with air temperature. By 2060 almost all buildings could have AC.

*DOI: http://dx.doi.org/10.5772/intechopen.82146*

#### *Effects of Climate Change in Electric Power Infrastructures DOI: http://dx.doi.org/10.5772/intechopen.82146*

*Power System Stability*

emissions, and marginal capital costs. Solar PV can be installed on building roofs, whereas centralized systems require their own dedicated land footprint and delivery infrastructure [62–65]. When implemented at the distribution level, solar PV can power load directly without going through delivery components that are necessary for central systems. The net effect is a relative decrease in load from the perspective of the grid relative to demand. Yet these distributed systems beg the question of storage given peak demand occurs once the PV systems decline in production of power. At the same time, this will be an important metric to monitor for reliability purposes going forward—if storage is included—as those two values have historically been one and the same. The most prominent fast-ramping central generation technology is combined cycle natural gas plants, which both consume water and emit various gasses into the atmosphere. Combustion-only natural gas plants could be implemented, which would not use water, but would be more sensitive to rising air temperatures, as well as less fuel-efficient, and therefore more costly and emissions intensive per kWh. While levelized costs of solar PV are now at or below parity with bulk generation plants on a per kWh basis, the combined costs of solar PV with storage to provide 24/7 dispatchable energy and regulation services are still higher than traditional central generation plants [66]. Thus, the best options for new resource procurement across competing objectives, will be those that consider

Implementing DER with new buildings may be the most cost-effective way to meet demand associated with growth in areas where delivery infrastructure are already over capacity during extreme heat waves. In such areas, some substations may be able to be adapted with improved heat sinks, forced air, or water cooling systems to increase capacity. But some may not, and overhead power line capacity will still be limited to convective cooling. The cost of increasing delivery infrastructure capacity necessary to meet demand through central generation, or long-distance imported power could be quite significant at \$10–130 million USD per substation and \$1–3 million USD per mile of line length leading all the way out

Future work for vulnerable neighborhoods should consider implementation of adaptation options by considering 24-h load profiles on distribution-level circuits, the total Watt-hours of necessary storage capacity to complement solar PV capacity, and opportunity for network aggregation in supplying ancillary grid services. Circuits with higher portion of commercial and industrial loads may be preferable for the installation of DERs, as their load profiles may more closely match the PV generation profile (peaking at mid-day) allowing for more storage efficiency. Effective implementation of energy storage would reduce load variance by charging during off-peak hours and discharging during peak hours, resulting in a more consistent load, which is more readily manageable by system operators, and therefore has lower operations and maintenance costs [49]. This could occur through some kind of automated and networked market incentives that are available for wholesale markets as of February 2018 [69]. Implementation of new bulk generation systems and delivery infrastructure may be more valuable in the northern areas of San Fernando and Antelope Valley. The areas are relatively less developed there and so land should be more readily available for construction. Future studies should consider the reliability and security benefits of redundant central and distributed energy systems, and determine what amount

of each, including storage, is optimal for different outage risk tolerances.

More energy efficient appliances can reduce use-phase load, load variance, and thus provide benefits to power systems' stability. To mitigate risks from heat waves

the current and future state of the delivery infrastructure.

of the urban center [67, 68].

**6.2 Electrical systems: loads**

**88**

however, focus should be directed towards air conditioner units. Differences in lighting and other appliance efficiencies only affected peak demand in the models by 2% in California, but that state already has aggressive energy efficiency policies, so other areas around the world could benefit more. AC units generally accounted for 60–70% of summertime peak demand within residential buildings, and higher air temperatures resulted in a 3–7% increase in demand per 1°C (1.8°F). Los Angeles County currently has only 45% AC penetration in its residential buildings, meaning that peak demand in just over half of the current building stock does not increase with air temperature. By 2060 almost all buildings could have AC.

Policies that would guide new or replacement ACs based on different performance constraints or different technologies would aid in reducing the risk of excessive peak demand during extreme heat events. It is possible to design AC units that are more efficient under the hottest conditions or that utilize thermal storage to achieve 'flat' efficiency curves that do not degrade at the hottest temperatures [70, 71]. For example, developing a new 'peak performance rating' for ACs at 50°C (122°F) could be useful to mitigate peak load during extreme heat waves. Doing so could provide incentive for ACs to be optimally engineered for more efficient performance at or near such extreme temperatures. Current standards, SEER and EER [72], are primarily for temperatures at or below 35°C (95°F). The current SEER standard, SEER 13, is already optimized to the point that improvements in SEER ratings in the model up to SEER 21 only affected peak demand by a few percent and were slightly counter-effective in some instances where temperatures exceeded 45°C (113°F) due to tradeoffs in engineering design optimization. Water-based evaporative cooling systems are another option that uses much less electric power, but requires water to operate, and are often not accepted by users as the sole-source of air conditioning due to insufficient comfort levels when the weather is both hot and humid [73, 74]. Further study may be useful to identify the practicality of hybrid designs.

## **6.3 Market incentives: supply side and utilities**

Some studies suggest that the traditional utility business model, that couples energy sales to profits, is not compatible with certain energy efficiency goals or large amounts of DER [85]. The former issue is because utility revenues are primarily dependent on total energy sales, but the costs of providing reliable infrastructure are primarily dependent on capital expenditures, operations, and maintenance [75]. Profits increase with volumetric energy sales, and costs are relatively flat. Therefore, financial incentives must exist to be relatively inefficient in some processes. Hence, public regulatory commissions exist to oversee the prices set for ratepayers. The alternative business model is referred to as a "decoupled" market, where "excess" profits are carried forward and accounted for in adjusting the following years' prices [76, 77]. When utility profits are decoupled from energy sales, load serving entities can implement effective conservation programs without violating fiduciary responsibility to shareholders [78]. This structure has been implemented in several states with positive effects on energy efficiency. For example, California's per-capita annual energy consumption has remained relatively flat since decoupling was implemented in the 1980s, whereas many other states' has steadily risen [79]. Market design determines rules by which participants must play [80]. If utilities' profits were a function of key reliability precursors, such as smaller load variance, then utilities would have a direct incentive to reduce peak load (including shifting it to off-peak hours), resulting in less congestion, higher utilization of lower-cost base-load bulk generation resources, and more contingency capacity for nonstationary extreme heat events.

## **6.4 Market incentives: demand side and ratepayers**

One philosophy for evaluating public policy is to consider whether the rules are equitable, efficient, transparent, administratively simple, and support achieving greater policy goals [81]. Current retail electricity rate schedules in Los Angeles and Phoenix generally meet these criteria via monthly energy billing with tiered and time of use rates [82–85]. Charging residential ratepayers monthly based on total energy use is simple, transparent, equitable, and promotes energy efficiency. Higher electricity prices during peak hours helps to reduce peak load by incentivizing ratepayers to take action to shift flexible or non-critical usage to off-peak hours when electricity can be generated at lower cost [86]. Incentivizing ratepayers to turn off loads in the form of demand response relieves congestion on the grid; however, the majority of that relief comes from industrial customers who often switch to onsite diesel power or natural gas combined heat and power units [87]. Rebates are available in some localities for ratepayers to obtain solar PV, storage, or demand management technologies [88]. One-time rebates for building energy efficiency enhancements have also been found to reduce demand and peak load [89]. Overall, these incentives generally reflect the philosophy that electricity is both a critical infrastructure necessity and a non-critical commodity. Electricity is critical for powering infrastructure systems such as water, transportation, food, fuel, communications, and finance [32]. It is also critical in residential buildings for lighting, cooking, cleaning, and climate control.

## **6.5 Building stock**

Population growth will increase peak demand, but where and by how much will be significantly influenced by decisions relating to the management of urban systems. Housing demand in less developed areas can be met through either single family or multi-family dwelling units. To meet population growth through densification however, most housing demand would need to be met through new multifamily dwelling. In addition to conserving land space, the benefits of building new multi-family unit residential housing can be as much as a 50% lower peak demand per capita than single-family detached units. Those benefits are due to reduced volume and shared walls, which significantly reduce exposure to extreme heat. In addition, street albedo and widths should be considered for urban heat island impacts.

## **7. Conclusions**

Climate change is a broad term used to describe ecosystem disruptions that result in long-term changes in weather patterns. Industrial processes have had various impacts on ecosystems over time, and for the most part business peace treaties have been effective in the form of government regulations to limit climate-altering emissions that are harmful to human health. While electric power generation is not the only contributor to climate change, it has historically been a major one with various emissions regulations developed for the solid, liquid, and air-borne wastes of different processes. Carbon-dioxide emissions, once thought relatively harmless, are now understood to be the primary contributor to higher solar energy retention by the earth's atmosphere, and thus lower average annual ice formation, higher sealevels, warmer air temperatures, and various related effects around the globe. As public understanding of the risks of climate change have increased in recent years, several advancements in technology, analytics, and regulations have been piloted

**91**

provided the original work is properly cited.

Independent Scholar, Glendale, Arizona, USA

\*Address all correspondence to: danieltb@gmail.com

*Effects of Climate Change in Electric Power Infrastructures*

to reduce carbon emissions as well. Through advancements in weather forecasting tools, analysts are better able to characterize extreme weather conditions, and support electric power systems planning to forecast peak demand, resource adequacy requirements, delivery infrastructure capacity, and avoid outages during heat waves. While public understanding of the risks of climate change has increased, little knowledge exists of the value of low-cost energy available to the public nor the public risk of unstable power systems. As power systems around the world undergo transformation to lower-emissions technology standards, analysts can use the techniques demonstrated in this chapter to clearly define other risky climate conditions and support development of tools, regulations, and implementations that manage

risks of other power stability issues in conjunction with climate change.

The content in this chapter was adapted from the published PhD dissertation of its author [90], which was supported by the California Energy Commission under grant number CEC EPC-15-007, Climate Change in Los Angeles County: Grid Vulnerability to Extreme Events, and the National Science Foundation under grant number 1360509, 2014-2017, Advancing Infrastructure and Institutional Resilience to Climate Change for Coupled Water-Energy Systems. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or the

*DOI: http://dx.doi.org/10.5772/intechopen.82146*

**Acknowledgements**

California Energy Commission.

**Conflict of interest**

None.

**Author details**

Daniel Burillo

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

*Effects of Climate Change in Electric Power Infrastructures DOI: http://dx.doi.org/10.5772/intechopen.82146*

to reduce carbon emissions as well. Through advancements in weather forecasting tools, analysts are better able to characterize extreme weather conditions, and support electric power systems planning to forecast peak demand, resource adequacy requirements, delivery infrastructure capacity, and avoid outages during heat waves. While public understanding of the risks of climate change has increased, little knowledge exists of the value of low-cost energy available to the public nor the public risk of unstable power systems. As power systems around the world undergo transformation to lower-emissions technology standards, analysts can use the techniques demonstrated in this chapter to clearly define other risky climate conditions and support development of tools, regulations, and implementations that manage risks of other power stability issues in conjunction with climate change.

## **Acknowledgements**

*Power System Stability*

**6.5 Building stock**

impacts.

**7. Conclusions**

**6.4 Market incentives: demand side and ratepayers**

lighting, cooking, cleaning, and climate control.

One philosophy for evaluating public policy is to consider whether the rules are equitable, efficient, transparent, administratively simple, and support achieving greater policy goals [81]. Current retail electricity rate schedules in Los Angeles and Phoenix generally meet these criteria via monthly energy billing with tiered and time of use rates [82–85]. Charging residential ratepayers monthly based on total energy use is simple, transparent, equitable, and promotes energy efficiency. Higher electricity prices during peak hours helps to reduce peak load by incentivizing ratepayers to take action to shift flexible or non-critical usage to off-peak hours when electricity can be generated at lower cost [86]. Incentivizing ratepayers to turn off loads in the form of demand response relieves congestion on the grid; however, the majority of that relief comes from industrial customers who often switch to onsite diesel power or natural gas combined heat and power units [87]. Rebates are available in some localities for ratepayers to obtain solar PV, storage, or demand management technologies [88]. One-time rebates for building energy efficiency enhancements have also been found to reduce demand and peak load [89]. Overall, these incentives generally reflect the philosophy that electricity is both a critical infrastructure necessity and a non-critical commodity. Electricity is critical for powering infrastructure systems such as water, transportation, food, fuel, communications, and finance [32]. It is also critical in residential buildings for

Population growth will increase peak demand, but where and by how much will be significantly influenced by decisions relating to the management of urban systems. Housing demand in less developed areas can be met through either single family or multi-family dwelling units. To meet population growth through densification however, most housing demand would need to be met through new multifamily dwelling. In addition to conserving land space, the benefits of building new multi-family unit residential housing can be as much as a 50% lower peak demand per capita than single-family detached units. Those benefits are due to reduced volume and shared walls, which significantly reduce exposure to extreme heat. In addition, street albedo and widths should be considered for urban heat island

Climate change is a broad term used to describe ecosystem disruptions that result in long-term changes in weather patterns. Industrial processes have had various impacts on ecosystems over time, and for the most part business peace treaties have been effective in the form of government regulations to limit climate-altering emissions that are harmful to human health. While electric power generation is not the only contributor to climate change, it has historically been a major one with various emissions regulations developed for the solid, liquid, and air-borne wastes of different processes. Carbon-dioxide emissions, once thought relatively harmless, are now understood to be the primary contributor to higher solar energy retention by the earth's atmosphere, and thus lower average annual ice formation, higher sealevels, warmer air temperatures, and various related effects around the globe. As public understanding of the risks of climate change have increased in recent years, several advancements in technology, analytics, and regulations have been piloted

**90**

The content in this chapter was adapted from the published PhD dissertation of its author [90], which was supported by the California Energy Commission under grant number CEC EPC-15-007, Climate Change in Los Angeles County: Grid Vulnerability to Extreme Events, and the National Science Foundation under grant number 1360509, 2014-2017, Advancing Infrastructure and Institutional Resilience to Climate Change for Coupled Water-Energy Systems. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or the California Energy Commission.

## **Conflict of interest**

None.

## **Author details**

Daniel Burillo Independent Scholar, Glendale, Arizona, USA

\*Address all correspondence to: danieltb@gmail.com

© 2018 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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## *Edited by Kenneth Eloghene Okedu*

The target readers for this book are academics and engineers working in universities, research institutes and industry sectors wishing to enhance their knowledge about power system stability. Readers of this book should gain technical ideas and special experience with detailed information about small signal stability, dynamics, modeling, power oscillations and electrical power infrastructures relating to power system stability. The contents of this book provide many solutions to problems that can be integrated into larger research findings and projects. The book addresses some power system stability studies such as an overview of power systems and stability criteria, applications of the trajectory sensitivity theory to small signal stability, power system small signal stability in grid connected smart park, power system dynamics and modeling. The book also describes some recent developments in power oscillations due to ferroresonance, sub synchronous resonance and effects of climate change in electric power infrastructures.

Published in London, UK © 2019 IntechOpen © Nenov / iStock

Power System Stability

Power System Stability

*Edited by Kenneth Eloghene Okedu*