5. Simulation for different case studies

the algorithm first runs an initial optimization, under the form of an MILP that minimizes the daily costs, while respecting a set of constraints. The main linear variables for this optimization are either active or reactive powers. In order to quantify the impact of the PQ issues in terms of active and reactive powers, new PQ indices have been created for the purpose of this work (αΔV, αdist, αunb,ab, αunb,bc, and αunb,ca). These new indices have the advantage to be directly computed in the MILP and do not require any load flow (see Section 3.7.2). The MILP optimizations are solved with the software GAMS. The solver typically converges in around 90 itera-

During this first optimization, the parameters αlim,ΔV, αlim,dist, and αlim,unb have been initialized to a very large value, high enough so that the inequalities (32), (35),

Using the results of the MILP, the program OpenDSS [19] will launch a set of harmonic load flows. These load-flows will give all the information about the voltage on the different nodes of the microgrid that is needed to compute the PQ indices, i.e., the voltage deviation in per unit, the voltage THD, and the VUF. If any of these indices does not respect the standards that have been presented in the Section 2, a noninfinite value will be attributed to the corresponding αlim, so that at

Flow chart of the decision process. Green processes are performed by Excel, orange by GAMS, gray by OpenDSS,

and (40) do not restrict the value of αΔV, αdist, αunb,ab, αunb,bc, and αunb,ca.

tions within a relative error of 2 <sup>10</sup>6.

Micro-Grids - Applications, Operation, Control and Protection

Figure 3.

76

and blue by MATLAB.

The conceptual microgrid that has been used in this chapter is shown in Figure 4. It is an alteration of the one presented in [6]. While the properties of the lines, the American voltage levels, and the characteristics of the transformers and the DERs have been kept identical, this microgrid has different kinds of loads and a smaller number of branches. In order to show the diversity of power consumers in a microgrid, three main different loads have been introduced: a residential load, an industrial load, and a commercial load.

The test microgrid has three different levels of voltage, at 11.2 kV, 408, and 207 V. They are interfaced by transformers but do not include any DC bus. The architecture is radial, as usual in distribution systems, and divided into four different branches. The first branch contains two diesel generators genset<sup>1</sup> and genset2. These diesel generators are essentially a backup power supply in stand-alone operations. The second branch has a first bus at 408 V (node 3), which is connected to a small 60 kWp wind turbine and a battery storage system of 80 kWh useful energy. This branch continues to the 207 V voltage to feed a residential load of 48 dwellings, called Load1. The third branch supplies an important industrial load named Load2. Finally, the fourth branch is a bidirectional line that reaches a small office building load, Load3, with a 40 kWp PV installation. The power demands of the different aggregated loads are represented in Figure 5.

The utility grid seen from the PCC at node 4 has been replaced by its Thevenin equivalent. It has a short-circuit power of 1000 MVA and a X/R ratio of 22. All

Figure 4. Architecture of the test microgrid.

Figure 5. Active power demand curves of the three aggregated loads of the conceptual microgrid.

the technical and economical parameters concerning the properties of the lines, the transformers, and the DERs are listed in Appendix. The loss parameters lossP and lossQ are tuned to the upper bound of the ratio between the total active (or reactive) loss with respect to the active (or reactive) total load. After several trials, it has been found that lossP is worth 1.5% and lossQ is worth 13.0%.

The Normal operation scenario depicts how the microgrid operates without PQ issues, when the utility grid is reliable and its cost profile is based on real historical data. Figure 7 shows the results of the simulation concerning the active power operation points of all the DERs and the electricity drawn from the utility grid to cover the total load. The active power losses are not represented in this graph because they are relatively small, usually below 3 kW, and can be neglected. It can be seen that the diesel generators never appear in the electricity mix in this scenario. This is due to the fact that the different costs associated with their operation are quite high compared to the cost of electricity bought on the spot market. The renewable sources are obviously always running because their variable cost is extremely low. Concerning the ESS, it is charged when the grid is the lowest, at 3, 4,

Composition of the price of electricity from the utility grid for MV clients on the March 2, 2018 in the

Power Quality Improvement of a Microgrid with a Demand-Side-Based Energy Management…

DOI: http://dx.doi.org/10.5772/intechopen.83604

5, and 13 h and discharged three times, at 1, 10, and 20 h, when the price of

Active power dispatch during the day for the "normal operation" scenario.

Figure 8 shows the voltages in per unit at the important nodes of the network. The 5% deviation from 1 pu is never exceeded during 24 h. Figure 9 represents the voltage THD evolution at each load node throughout the day. As one can observe, the Load<sup>1</sup> and Load<sup>3</sup> have a low-voltage THD, below 1.5%. For the industrial Load2, it is practically negligible because of the scarcity of nonlinear devices. Since all the

electricity peaks (Figure 6).

Figure 6.

Figure 7.

79

Brussels-Capital region.

The VoLL and the flexibility of each type of load for this test-case microgrid are reported in Table 1, alongside other characteristics from [20–24].

#### 5.1 Normal operation

The first investigated scenario aims to represent the operation of the microgrid under standard technical and economic conditions. In order to establish a realistic price curve for the utility grid electricity throughout the day, the data are directly taken from the hourly values of the day-ahead prices from the Belgian power exchange (BELPEX) on March 2, 2018 [25]. The tariffs for the system operators and regulators and the other public obligations reproduce the one imposed by the Brussels DSO Sibelga on middle-voltage clients [26]. Figure 6 shows the composition of the cost of electricity from the utility grid, translated into dollars with a fixed exchange rate of 1.1942\$/€. The reactive power flow required by the grid at each hour for the voltage support has been fixed arbitrarily, but it remains small with respect to the reactive power from the load and does not impact substantially the results.


#### Table 1. Properties of the types of loads.

Power Quality Improvement of a Microgrid with a Demand-Side-Based Energy Management… DOI: http://dx.doi.org/10.5772/intechopen.83604

#### Figure 6.

the technical and economical parameters concerning the properties of the lines, the transformers, and the DERs are listed in Appendix. The loss parameters lossP and lossQ are tuned to the upper bound of the ratio between the total active (or reactive) loss with respect to the active (or reactive) total load. After several trials, it has been

The VoLL and the flexibility of each type of load for this test-case microgrid are

The first investigated scenario aims to represent the operation of the microgrid under standard technical and economic conditions. In order to establish a realistic price curve for the utility grid electricity throughout the day, the data are directly taken from the hourly values of the day-ahead prices from the Belgian power exchange (BELPEX) on March 2, 2018 [25]. The tariffs for the system operators and regulators and the other public obligations reproduce the one imposed by the Brussels DSO Sibelga on middle-voltage clients [26]. Figure 6 shows the composition of the cost of electricity from the utility grid, translated into dollars with a fixed exchange rate of 1.1942\$/€. The reactive power flow required by the grid at each hour for the voltage support has been fixed arbitrarily, but it remains small with respect to the reactive power from the load and does not impact substantially the

Flex ð Þ % THDI ð Þ % pf Typeratio

HVAC 2 20 8.2 0.98 23.9 16.8 37.3 DHW 3 20 0 1 9.5 14.4 1.8 Lights 5 10 27.1 0.8 9.4 6.5 10.6 Appliances 5 35 43.3 0.65 57.2 7.3 50.3 Motor drives 10 1 0% 1 0 55 0

Load<sup>1</sup> (%) Load<sup>2</sup> (%) Load<sup>3</sup> (%)

found that lossP is worth 1.5% and lossQ is worth 13.0%.

Micro-Grids - Applications, Operation, Control and Protection

5.1 Normal operation

results.

Table 1.

78

Properties of the types of loads.

Type of load

Cload (VoLL) (\$/kWh)

Figure 5.

reported in Table 1, alongside other characteristics from [20–24].

Active power demand curves of the three aggregated loads of the conceptual microgrid.

Composition of the price of electricity from the utility grid for MV clients on the March 2, 2018 in the Brussels-Capital region.

The Normal operation scenario depicts how the microgrid operates without PQ issues, when the utility grid is reliable and its cost profile is based on real historical data. Figure 7 shows the results of the simulation concerning the active power operation points of all the DERs and the electricity drawn from the utility grid to cover the total load. The active power losses are not represented in this graph because they are relatively small, usually below 3 kW, and can be neglected. It can be seen that the diesel generators never appear in the electricity mix in this scenario. This is due to the fact that the different costs associated with their operation are quite high compared to the cost of electricity bought on the spot market. The renewable sources are obviously always running because their variable cost is extremely low. Concerning the ESS, it is charged when the grid is the lowest, at 3, 4, 5, and 13 h and discharged three times, at 1, 10, and 20 h, when the price of electricity peaks (Figure 6).

Figure 8 shows the voltages in per unit at the important nodes of the network. The 5% deviation from 1 pu is never exceeded during 24 h. Figure 9 represents the voltage THD evolution at each load node throughout the day. As one can observe, the Load<sup>1</sup> and Load<sup>3</sup> have a low-voltage THD, below 1.5%. For the industrial Load2, it is practically negligible because of the scarcity of nonlinear devices. Since all the

18 h. This first observation can be seen in Figure 10. The voltage at the node 8, where the Load<sup>3</sup> is connected, is only worth 0.9307 in per units at 12 h. To address this problem, an iteration process is started and activates the constraint (31), with a parameter λΔ<sup>V</sup> equal to 0.9. The value of this sensitivity parameter has been found by a trial-and-error process. It comes from a trade-off between the speed and the accuracy of the convergence. The next graph on Figure 11 shows the reduction of the voltage drop after the third iteration. It can be concluded that the EMS manages to keep the voltage above 0.95 pu after only two iterations. The voltage obtained at 12 h after the first iteration is 0.9350 and then 0.9551 after the second iteration. Still for this particular hour, the DSM scheme has reduced the HVAC consumption in Load<sup>3</sup> by 20% and the appliance share by 11.41%. The appliances have indeed a low fundamental-frequency power factor, which leads to a higher reactive power

Power Quality Improvement of a Microgrid with a Demand-Side-Based Energy Management…

demand and a worse effect on the voltage drop.

DOI: http://dx.doi.org/10.5772/intechopen.83604

Voltage in per unit at the different nodes for the initial stage.

Voltage magnitude in per unit after the first optimization for the different nodes after two iterations of the EMS

Figure 10.

Figure 11.

81

algorithm regulation loop.

#### Figure 8.

Evolution of the voltage in per unit for the scenario "normal operation."

Figure 9. Evolution of the voltage THD in percent for the scenario "normal operation."

aggregated loads are assumed perfectly balanced in this scenario, the VUF is equal to 0 at every node.

Since none of the PQ norms are violated in this scenario, the constraints in (32), (35), and (40) are not activated and there is no need for iterations between the MILP and the harmonic load flows.

#### 5.2 Voltage drop issue

To illustrate the action of the EMS algorithm when a power quality issue is detected, a scenario is created concerning a potential voltage drop. Other scenarios on harmonic distortion and phase unbalance have been conducted in a longer version of this study and essentially show similar results in terms of accuracy and speed of convergence.

In this scenario, the load curves of Load<sup>1</sup> and Load<sup>3</sup> are increased by 10% in comparison with the Table 1. This scenario was created to test the resilience of the microgrid toward voltage deviations. After the initial optimization, the EMS detects with the load flows that the increase of the total power requested by the first load causes several voltage drops under the 0.95 bound, at 10 h, from 12 till 16 h, and at

Power Quality Improvement of a Microgrid with a Demand-Side-Based Energy Management… DOI: http://dx.doi.org/10.5772/intechopen.83604

18 h. This first observation can be seen in Figure 10. The voltage at the node 8, where the Load<sup>3</sup> is connected, is only worth 0.9307 in per units at 12 h. To address this problem, an iteration process is started and activates the constraint (31), with a parameter λΔ<sup>V</sup> equal to 0.9. The value of this sensitivity parameter has been found by a trial-and-error process. It comes from a trade-off between the speed and the accuracy of the convergence. The next graph on Figure 11 shows the reduction of the voltage drop after the third iteration. It can be concluded that the EMS manages to keep the voltage above 0.95 pu after only two iterations. The voltage obtained at 12 h after the first iteration is 0.9350 and then 0.9551 after the second iteration. Still for this particular hour, the DSM scheme has reduced the HVAC consumption in Load<sup>3</sup> by 20% and the appliance share by 11.41%. The appliances have indeed a low fundamental-frequency power factor, which leads to a higher reactive power demand and a worse effect on the voltage drop.

Figure 10. Voltage in per unit at the different nodes for the initial stage.

#### Figure 11.

Voltage magnitude in per unit after the first optimization for the different nodes after two iterations of the EMS algorithm regulation loop.

aggregated loads are assumed perfectly balanced in this scenario, the VUF is equal

(35), and (40) are not activated and there is no need for iterations between the

To illustrate the action of the EMS algorithm when a power quality issue is detected, a scenario is created concerning a potential voltage drop. Other scenarios on harmonic distortion and phase unbalance have been conducted in a longer version of this study and essentially show similar results in terms of accuracy and

In this scenario, the load curves of Load<sup>1</sup> and Load<sup>3</sup> are increased by 10% in comparison with the Table 1. This scenario was created to test the resilience of the microgrid toward voltage deviations. After the initial optimization, the EMS detects with the load flows that the increase of the total power requested by the first load causes several voltage drops under the 0.95 bound, at 10 h, from 12 till 16 h, and at

Since none of the PQ norms are violated in this scenario, the constraints in (32),

to 0 at every node.

Figure 9.

Figure 8.

5.2 Voltage drop issue

speed of convergence.

80

MILP and the harmonic load flows.

Evolution of the voltage in per unit for the scenario "normal operation."

Micro-Grids - Applications, Operation, Control and Protection

Evolution of the voltage THD in percent for the scenario "normal operation."

## 6. Conclusion and perspectives

Security of electricity supply, flexibility, cost-effectiveness, and renewable sources integration are some of the motivations that lead private or public entities to consider the implementation of grid-connected microgrid. The local production and storage systems allow some microgrids to ensure the supply of electricity for their loads in case of a frequency or voltage outage on the traditional grid. However, studies have shown that PQ disturbances can be difficult to tackle in small-scale microgrids, due to the lower stiffness of the distributed power generation.

Lines Voltage (kV) Type r (Ω km�<sup>1</sup>

DOI: http://dx.doi.org/10.5772/intechopen.83604

Table 3.

Table 4.

Table 5.

Table 6.

Table 7.

83

Technical properties of the DGs.

Economic properties of the DGs.

Technical properties of the ESS.

Current harmonic spectrum of the types of load.

Properties of the lines.

L1, L2, L3, L4 0.48 3 phase/4 wire 0.049 0.027 L5, L6 0.207 3 phase/4 wire 0.06 0.03

Power Quality Improvement of a Microgrid with a Demand-Side-Based Energy Management…

DG Voltage (kV) Pmin (kW) Pmax (kW) Qabs,max (kVAR) Qgen,max (kVAR) genset1, genset2 0.48 50 200 150 150 WT 0.48 0 60 45 45 PV 0.207 0 40 30 30

DG CDG,on (\$/h) CDG,var (\$/kWh) CDG,ST (\$) genset1 100 0.90 300 genset2 100 0.91 300 PV 0 0.01 0 WF 0 0.01 0

ESS Voltage Pchar=dis,max Qabs=gen,max Nominal energy Init SOC Roundtrip efficiency

HVAC (%) DHW (%) Lights (%) Appliances (%) Motor drives (%)

ESS1 0.48 kV 40 kW 40 kVAR 80 kWh 50% 81%

 5.0 0 21.1 29.9 0 6.0 0 11.9 23.3 0 2.3 0 11.8 15.7 0 1 0 2.0 10.8 0 0 0 1.0 8.2 0 THDI 8.2 0 27.1 43.3 0

Harmonic order Current harmonic magnitude (%fund.)

<sup>Þ</sup> <sup>x</sup> (<sup>Ω</sup> km�<sup>1</sup>

Þ

The purpose of this chapter is to tackle several of these PQ issues that occur in steady state, namely voltage drop, harmonic distortion, and phase unbalance, by acting on the demand level of certain types of electrical devices in the microgrid. First, it has been considered that any load at a node of the network can be represented as the aggregation of several smaller loads that account for a particular type of end use. Consequently, every type of load has its own physical and economical properties. A demand-side management framework has been implemented, so the demand level can be regulated within a certain flexibility range. The DSM mechanism can be launched whenever the cost of electricity peaks, in offgrid situations, or to mitigate a PQ issue that violates the standards. This process is integrated under the form of constraints inside an optimization-based EMS algorithm that minimizes the overall daily costs. The algorithmic structure of this tool consists in a regulation loop between an MILP optimization and harmonic load flows. In order to reflect the magnitude of the PQ issues in the MILP optimization, new power quality indices that rely on active and reactive powers have been introduced. The designed algorithm has been simulated according to different scenarios on a test-case microgrid. The results show that the production, the storage, and the consumption in the microgrid can adapt efficiently to the price of electricity from the traditional grid and that the different power quality standards can be met after few iterations. This algorithm can be used for grid-connected microgrids at low or medium voltage that possess an efficient communication framework between the different consumers and producers of electricity.

Further research on the topic of power-quality-supporting EMS algorithms could include the possibility to add electric vehicles (EV) management, combined heat and power (CHP) generation units, or other kinds of electrical equipment inside the microgrid.

### A. Appendix

Tables 2–7 gather the data used to run the simulations on the test-case microgrid presented in the fifth section.


Table 2. Properties of the transformers. Power Quality Improvement of a Microgrid with a Demand-Side-Based Energy Management… DOI: http://dx.doi.org/10.5772/intechopen.83604


#### Table 3.

6. Conclusion and perspectives

Micro-Grids - Applications, Operation, Control and Protection

Security of electricity supply, flexibility, cost-effectiveness, and renewable sources integration are some of the motivations that lead private or public entities to consider the implementation of grid-connected microgrid. The local production and storage systems allow some microgrids to ensure the supply of electricity for their loads in case of a frequency or voltage outage on the traditional grid. However, studies have shown that PQ disturbances can be difficult to tackle in small-scale microgrids, due to the lower stiffness of the distributed power generation.

The purpose of this chapter is to tackle several of these PQ issues that occur in steady state, namely voltage drop, harmonic distortion, and phase unbalance, by acting on the demand level of certain types of electrical devices in the microgrid. First, it has been considered that any load at a node of the network can be

represented as the aggregation of several smaller loads that account for a particular type of end use. Consequently, every type of load has its own physical and eco-

implemented, so the demand level can be regulated within a certain flexibility range. The DSM mechanism can be launched whenever the cost of electricity peaks, in offgrid situations, or to mitigate a PQ issue that violates the standards. This process is integrated under the form of constraints inside an optimization-based EMS algorithm that minimizes the overall daily costs. The algorithmic structure of this tool consists in a regulation loop between an MILP optimization and harmonic load flows. In order to reflect the magnitude of the PQ issues in the MILP optimization, new power quality indices that rely on active and reactive powers have been introduced. The designed algorithm has been simulated according to different scenarios on a test-case microgrid. The results show that the production, the storage, and the consumption in the microgrid can adapt efficiently to the price of electricity from the traditional grid and that the different power quality standards can be met after few iterations. This algorithm can be used for grid-connected microgrids at low or medium voltage that possess an efficient communication framework

Further research on the topic of power-quality-supporting EMS algorithms could include the possibility to add electric vehicles (EV) management, combined heat and power (CHP) generation units, or other kinds of electrical equipment

Tables 2–7 gather the data used to run the simulations on the test-case microgrid

Transformers Voltage (kV) Base MVA Connections %Z X/R T1 0.48/11.2 2 Δ-Y 5.75 6 T2, T3, T4 11.2/0.48 0.5 Δ-Y 5.75 6 T5, T6 0.48/0.207 0.25 Δ-Y 5.75 3

nomical properties. A demand-side management framework has been

between the different consumers and producers of electricity.

inside the microgrid.

presented in the fifth section.

A. Appendix

Table 2.

82

Properties of the transformers.

Properties of the lines.


#### Table 4.

Technical properties of the DGs.


#### Table 5.

Economic properties of the DGs.


#### Table 6.

Technical properties of the ESS.


#### Table 7.

Current harmonic spectrum of the types of load.

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