**Dynamic Energy Storage Management for Dependable Renewable Electricity Generation**

Ruddy Blonbou, Stéphanie Monjoly and Jean-Louis Bernard

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/52411

## **1. Introduction**

270 Energy Storage – Technologies and Applications

2009;54(27):7121-7127.

[84] Oono Y, Sounai A, Hori M. Influence of the Phosphoric Acid-Doping Level in a Polybenzimidazole Membrane on the Cell Performance of High-Temperature Proton

[86] Lin HL, Hsieh YS, Chiu CW, Yu TL, Chen LC. Durability and Stability Test of Proton Exchange Membrane Fuel Cells Prepared From Polybenzimidazole/Poly(Tetrafluoro

Ethylene) Composite Membrane. Journal of Power Sources. 2009;193(1):170-174.

Exchange Membrane Fuel Cells. Journal of Power Sources. 2009;189(2):943-949. [85] Modestov AD, Tarasevich MR, Filimonov VY, Zagudaeva NM. Degradation of High Temperature MEA with PBI-H(3)PO(4) Membrane in a Life Test. Electrochimica Acta.

> The administrators of the distribution networks have to face the insertion of the decentralized electricity production of renewable origin. In particular, wind and photovoltaic electricity generation know a fast development supported by satisfying technical maturity, greater environmental concern and political will expressed by financial and statutory incentives.

> However, one of the main handicaps of many renewable energies - and quite particularly wind energy and solar energy - is the temporal variation of the resource and the weak previsibility of its availability. Thus, connecting wind or photovoltaic farms to electrical networks is an important challenge for the administrators of distribution and transport networks of electricity. They impact on the planning, on the operation of distribution networks and on the safety of the electric systems.

> For grid connected renewable generation, as long as the penetration rates of these productions are marginal, the network compensates, within few minutes, for the fast variations thanks to the power reserves, which is for example mobilized hydroelectric power reserve, or gas turbines in rotating reserve (which produce greenhouse gases). These power reserves have a cost that must be take into account in the economic analysis of the deployment of distributed renewable electricity generation.

> On a regional scale, the presence of renewable generators often induces additional costs for network reinforcement to limit the risks of congestion. Indeed, the favorable conditions for wind energy exploitation are often found in remote area (windy coast, offshore) where the network infrastructures are weak or non-existent. Since a regional network is sized according to the maximum transit power, to prevent power congestion, it is necessary to size the network infrastructures to match the total installed capacity. As the load factor of

wind energy is about 25 %, the ratio of the cost for network strengthening over the aggregated energy produced for wind (or solar) energy is higher to that of the other (non intermittent) sources of energy.

Dynamic Energy Storage Management for Dependable Renewable Electricity Generation 273

**2. Energy storage technologies for renewable energy power smoothing** 

intermittent supply base.

Energy-storage technologies are vital for the large-scale exploitation of renewable energies since they could ensure secure and continuous supply to the consumer from distributed and

Many techniques can be used to stored electrical energy [1]. First, it must be transformed into a storable form of energy that could be mechanical, chemical or thermal. Then, there must be a process that gets back the stored energy into a usable form. Within the scope of

The common belief that electricity cannot be stored at a realistic cost comes from the fact that electricity is mainly produced, transmitted and consumed in AC. Today, energy storage capacity roughly represents less than 3% of the total electricity production capacity. However, the emergence of cost effective power electronic solutions that can handle high

The past decade was marked by strong evolution of the technological context in storage of energy [2,3]. At the same time, the static converters knew a strong development in the range of powers from the kW to about few MW, carried in particular by the development of the photovoltaic and wind productions. As shown in Figure 1, pumped hydroelectric storage represents more than 97 % of the total of 120 GW reported storage capacity, followed by the classic compressed air with 440 MW (250 times less). Other technologies adapted for a deployment in the electrical distribution networks comes then, with only some tenth of % of

PHES CAES NaS PbA NiCd FES Li-ion Flow

**Energy Storage Technology**

Redox

this chapter, we will focus on energy storage technologies for electrical applications.

power levels makes it possible to store electricity for grid applications.

which a majority of NAS (sodium-sulfur) batteries.

1

10

100

1000

**Installed Capacity (MW)**

10000

100000

1000000

**Figure 1.** Installed capacity of various energy storage systems (from [2])

The renewable generations capacity will have to take into account the preservation of the reliability and the safety of the networks. The objective is that renewable generators must not entail the degradation of the supply security nor imply dramatic cost increase for the consumers.

Energy storage technologies are identified as key elements for the development of electricity generation exploiting renewable energy sources. They could contribute to remove the technical constraints that limit the contribution of renewables into electrical networks. As mentioned above, these technical limits are present both on the regional scale and on the scale of the whole network.

More generally, energy storage could propose valuable services by reducing the instantaneous variations of the injected power. A simple approach consists in storing a part of the random production that would be add up to the future production to decrease the amplitude of variation of the injected power. That approach, however, does not guarantee the availability of stored energy, nor the level at which the power will be injected to the network. Furthermore, a trade-off must be estimated carefully to ensure the benefits will surpass the cost associated with the deployment of energy storage facilities.

In this chapter, we present an advanced approach that uses power production forecasts to dynamically manage the power flow to and from the battery and the networks for grid connected wind or solar electricity production. The objective is to guarantee, some time in advance and with a predefined error margin, the level of power that will be sent to the network, allowing a more efficient management of these stochastic energy resources and the optimization of the sizing of the storage facility. We also propose, through an in-depth analysis of the wind to power transfer function, a discussion about the power limit setting and the sizing of storage capacity in the context of congestion management.

The chapter will be organized as follow. First, we review the available storage technologies through the lens of their compatibility with the proposed approaches including a short discussion on the envisaged power converter solution for coupling of renewable generations and storage. Then, we demonstrate the advantage of an in-depth analysis of the wind to power transfer function and the use of energy storage for the sake of the optimal sizing of transmission line capacity in the context of the transport of wind-originated electricity. The role of energy storage is emphasized further in the presentation of an advanced power flow and energy storage management scheme. We complete the chapter with the presentation of the results obtained by applying the proposed approach during a simulation using real wind energy production data. The interest of the proposed method is that he permits to guarantee, within a pre-set margin of error, the power that will be sent to the grid by automatically dispatching the power flows between the wind plants, the energy storage facility and the electrical network. We conclude the chapter with a short discussion on energy storage management dynamic strategies and the improvement perspective of such approach.

## **2. Energy storage technologies for renewable energy power smoothing**

272 Energy Storage – Technologies and Applications

intermittent) sources of energy.

scale of the whole network.

consumers.

wind energy is about 25 %, the ratio of the cost for network strengthening over the aggregated energy produced for wind (or solar) energy is higher to that of the other (non

The renewable generations capacity will have to take into account the preservation of the reliability and the safety of the networks. The objective is that renewable generators must not entail the degradation of the supply security nor imply dramatic cost increase for the

Energy storage technologies are identified as key elements for the development of electricity generation exploiting renewable energy sources. They could contribute to remove the technical constraints that limit the contribution of renewables into electrical networks. As mentioned above, these technical limits are present both on the regional scale and on the

More generally, energy storage could propose valuable services by reducing the instantaneous variations of the injected power. A simple approach consists in storing a part of the random production that would be add up to the future production to decrease the amplitude of variation of the injected power. That approach, however, does not guarantee the availability of stored energy, nor the level at which the power will be injected to the network. Furthermore, a trade-off must be estimated carefully to ensure the benefits will

In this chapter, we present an advanced approach that uses power production forecasts to dynamically manage the power flow to and from the battery and the networks for grid connected wind or solar electricity production. The objective is to guarantee, some time in advance and with a predefined error margin, the level of power that will be sent to the network, allowing a more efficient management of these stochastic energy resources and the optimization of the sizing of the storage facility. We also propose, through an in-depth analysis of the wind to power transfer function, a discussion about the power limit setting

The chapter will be organized as follow. First, we review the available storage technologies through the lens of their compatibility with the proposed approaches including a short discussion on the envisaged power converter solution for coupling of renewable generations and storage. Then, we demonstrate the advantage of an in-depth analysis of the wind to power transfer function and the use of energy storage for the sake of the optimal sizing of transmission line capacity in the context of the transport of wind-originated electricity. The role of energy storage is emphasized further in the presentation of an advanced power flow and energy storage management scheme. We complete the chapter with the presentation of the results obtained by applying the proposed approach during a simulation using real wind energy production data. The interest of the proposed method is that he permits to guarantee, within a pre-set margin of error, the power that will be sent to the grid by automatically dispatching the power flows between the wind plants, the energy storage facility and the electrical network. We conclude the chapter with a short discussion on energy storage

management dynamic strategies and the improvement perspective of such approach.

surpass the cost associated with the deployment of energy storage facilities.

and the sizing of storage capacity in the context of congestion management.

Energy-storage technologies are vital for the large-scale exploitation of renewable energies since they could ensure secure and continuous supply to the consumer from distributed and intermittent supply base.

Many techniques can be used to stored electrical energy [1]. First, it must be transformed into a storable form of energy that could be mechanical, chemical or thermal. Then, there must be a process that gets back the stored energy into a usable form. Within the scope of this chapter, we will focus on energy storage technologies for electrical applications.

The common belief that electricity cannot be stored at a realistic cost comes from the fact that electricity is mainly produced, transmitted and consumed in AC. Today, energy storage capacity roughly represents less than 3% of the total electricity production capacity. However, the emergence of cost effective power electronic solutions that can handle high power levels makes it possible to store electricity for grid applications.

The past decade was marked by strong evolution of the technological context in storage of energy [2,3]. At the same time, the static converters knew a strong development in the range of powers from the kW to about few MW, carried in particular by the development of the photovoltaic and wind productions. As shown in Figure 1, pumped hydroelectric storage represents more than 97 % of the total of 120 GW reported storage capacity, followed by the classic compressed air with 440 MW (250 times less). Other technologies adapted for a deployment in the electrical distribution networks comes then, with only some tenth of % of which a majority of NAS (sodium-sulfur) batteries.

**Figure 1.** Installed capacity of various energy storage systems (from [2])

A summary of the various applications of energy storage aimed to support the electrical network expressly in the case of high rate of intermittent generations is reported in [3-7]. These articles review the characteristic of energy storage system in the scope of electrical networks with high renewable energies penetration rate.

Dynamic Energy Storage Management for Dependable Renewable Electricity Generation 275

400 kWh – 245 MWh

50 kW - >10 MW

4500 at 90 % DoD

loss (up to 20%/days)

€/kW

€/kWh

Low

Up to 100% Up to 98%

environmental impact if reactants are adequately confined

with specific treatment of solid sodium

Commercial units for stationary application (small market)

Due to thermal

100 kWh – 2 MWh

65-70 AC/

1000-2000 at 80 % DoD

1 %/h due to diffusion of dibrome through the membrane

1000-2000 €/kW

600 – 800 €/kWh

Prototypes and few industrial units

Limited environmental impact if reactants are adequately confined. Possible H2 emission to be accounted for.

kW to MW kW to MW

2 MWh – 120 MWh

80%-85% DC 65%-75% AC

>13000 at 100%

Up to 10% due to auxiliary consumption

1750€/kW

215 €/kWh – 450

Prototypes and

Low

environmental impact.

Recycling of the electrolyte

demonstration units. Few industrial units

DoD

Conventional Batteries High temperature Batteries Redox flow Batteries

1/200 sec. 1/200 sec. 1/200 sec. 1/200 sec. 1/200 sec. 1/200 sec.


500€/kWh 200 – 250

Technology PbA Li-ion ZEBRA NaS ZnBr VRB

Up to 100 KWh

5 kW - > 500 kW

3000 at 80 % DoD

Commercial units for embedded applications. Few stationary demonstrators.

Low

environmental impact if reactants are adequately confined

loss (up to 18%/days)

Due to thermal

70% – 85% 80% - 90% 85% - 90% 85% - 90% 75-80 DC

1 Wh – 50 MWh

Up to 7000 at 80% DoD

2% to 10% per month

MW

Typology Electro-chemical energy storage

Rated Energy 1 kWh – 40

Self discharge 1% to 5% per month

Cycle Efficiency

Response time

Cycling tolerance

Power capital

cost

Energy capital cost

Maturity status

Environment al or statutory constraints

Recycling ability

MWh

Rated Power 1W to 10 MW Few W – 50

<1500 at 80% DoD

<50€/kWh (car batt.) to 250€/kWh

Explosion risk if electrolyte gases leak.

Mature technology for large number of applications

<500€/kW 500 – 2000

€/kW

700 – 1500€/kWh

Mature technology for handheld electronic devices. Numerous demonstrators for electric cars and >1 MW stationary units.

Need electronic supervision of each cell for safe operation.

90% Recycling of

the electrode metals

**Table 2.** Characteristics of electrochemical energy storage systems

The potential and opportunities of the storage of energy in the distribution networks is investigated in [2]. This study focuses on the technologies of storage susceptible to be installed on the levels of tension of distribution networks (unit power capacity in the range





Rated Energy 500 MWh – 8000

Self discharge Very low: water

Energy capital

Environmental or statutory constraints

Recycling ability Dismantlement

cost

MWh

Rated Power 10 MW – 1 GW Two plants in the

evaporation for long storage time

Maturity status Mature technology Mature technology

Rely on favorable topology

need to be planned

**Table 1.** Characteristics of energy storage systems

networks with high renewable energies penetration rate.

A summary of the various applications of energy storage aimed to support the electrical network expressly in the case of high rate of intermittent generations is reported in [3-7]. These articles review the characteristic of energy storage system in the scope of electrical

The potential and opportunities of the storage of energy in the distribution networks is investigated in [2]. This study focuses on the technologies of storage susceptible to be installed on the levels of tension of distribution networks (unit power capacity in the range

energy storage

1W – 1 MW

1%/day. Increase with temperature and SoC.

1000-20000 €/kW

Mature technology for embedded systems. Some stationary units reported.

Limited risk of toxicity, flammability of some used material.

Dependent on material used

€/kWh

5 kWh – 25 kWh 10 kWh

Few kW up to 10

Continuously; completely discharged within

minutes

10 – 20 €/kWh 5 – 70 €/kWh 400 – 800 €/kWh 6800 – 20000

Application dependent

frequency regulation.

Mature technology; numerous units deployed in grids for

No environmental risk. No emission.

100%. No chemical compounds.

MW

Typology Mechanical energy storage Electrostatic

500 MWh – 3000

world: 110 MW (USA) and 290 MW (Germany)

Cycle Efficiency 65% – 80% 70% 85% - 95% 85% - 98%

Response time Minutes Seconds to minutes < 1/50 sec. <1/200 sec. Cycling tolerance 50000 30000 > 10 million 1 million

leaks.

world

Power capital cost 500-1500 €/kW <100 €/kW 400-25000 €/kW

Very low: thermal loss in the storage tank. Pneumatic

but only two plants in operation in the

Rely on favorable topology and availability of natural gas

Dismantlement need to be planned

Technology PHES CAES FES SC

MWh

of 10 to 20 MW in production and less than 40 MW in consumption). The author highlights the technologies that do not present major environmental or statutory constraints that could limit their deployment and that are susceptible to reach both technical and commercial maturity by 2015.

Dynamic Energy Storage Management for Dependable Renewable Electricity Generation 277

a single PHES facility by installing two penstocks as point out in [9]; a double penstock system enables the PHES to store excess wind energy while at the same time providing ancillary services to the grid. The results of the techno-economic studies [9] suggest that, the double penstock system could be economically credible while enable the wind energy penetration to increase above 40%. However, the economic value of PHES is sensitive to

The terminology "batteries" encompasses electrochemical storage cellular technologies that consist of an arrangement (in series or in parallel) of cell units. Each cell is made of two electrodes and an electrolyte secured into a sealed container. Batteries store chemical energy and generate electricity by a reduction-oxidation (redox) reaction. Batteries energy storage systems have been studied for almost 150 years, most research effort now aimed at cost reduction and high power application. The following section proposes a description of some promising batteries technologies. An overview of electrochemical energy storage systems is

Lead-Acid batteries are the most used devices for low to medium scale energy storage application. Lead-acid batteries have a low-cost (\$300–600/kW), high reliability, high power ramp capabilities and efficiency in the range (65%–80%). However, the performance of Lead-Acid battery will deteriorate quickly in the case of frequent charge-discharge cycles. The weak tolerance to high number of cycles limits the use of PbA batteries in application

changes in fuel prices, interest rates, and total annual wind production.

**Figure 2.** A double penstock PHES system

**4. Batteries** 

given in [10].

**4.1. Lead-acid batteries** 

such as wind variations smoothing.

In this chapter, we consider energy storage technologies to tackle congestion relief and to smooth wind power variations on short time scales (up to several minutes). We are treating applications where energy storage systems are required to inject or absorb power during period of time in the order of minutes. Through these specific applications, we aimed to demonstrate the advantage of dynamic management of energy storage to raise the acceptance level of variable renewable energy sources for electricity generation.

Several criteria have to be analyzed to identify the storage technologies that are pertinent for the aforementioned applications. These applications require storage technologies with high power, short discharge period and good resilience to high number of charge-discharge cycles. Tables 1 and 2 report the main characteristics of a selection of energy storage technologies.

## **2.1. Tables nomenclatures**

PHES: Pumped Hydro energy storage CAES: Compressed Air Energy Storage FES: Flywheel energy system SC: Supercapacitors PbA: Lead-Acid Li-ion: Lithium-Ion ZEBRA: Sodium Nickel Chlorides VRB: Vanadium-Vanadium ZnBr: Zinc – Bromine NaS: Sodium – Sulphur

## **3. Pumped hydroelectric energy storage**

Pumped hydroelectric storage (PHES) systems exploit gravitational potential energy. Energy is stored by pumping water from a lower reservoir to an upper reservoir. The amount of stored energy is proportional the volume of water in the upper reservoir. When needed, water flows from the upper reservoir to the lower reservoir to release the stored energy with round trip efficiency in the range of 70% to 80%. PHES is the major energy storage technology; it account for 97% of the world total storage capacity [2]. The energy can be stored several days and high power ramp can be achieved during both the charge and discharge phases (0–1800 MW in 16 s at the Dinorwig pumping station for example, [8]). The PHS technology suffers low modularity and can only be installed on site with particular topology. PHES is a key asset for wind energy as it enables the grid to operate securely while incorporating high wind penetrations. There may be additional benefits when using PHES that can charge and discharge at the same time (see Figure 2). This can be achieved in a single PHES facility by installing two penstocks as point out in [9]; a double penstock system enables the PHES to store excess wind energy while at the same time providing ancillary services to the grid. The results of the techno-economic studies [9] suggest that, the double penstock system could be economically credible while enable the wind energy penetration to increase above 40%. However, the economic value of PHES is sensitive to changes in fuel prices, interest rates, and total annual wind production.

**Figure 2.** A double penstock PHES system

## **4. Batteries**

276 Energy Storage – Technologies and Applications

**2.1. Tables nomenclatures** 

FES: Flywheel energy system

ZEBRA: Sodium Nickel Chlorides VRB: Vanadium-Vanadium ZnBr: Zinc – Bromine NaS: Sodium – Sulphur

**3. Pumped hydroelectric energy storage** 

SC: Supercapacitors PbA: Lead-Acid Li-ion: Lithium-Ion

PHES: Pumped Hydro energy storage CAES: Compressed Air Energy Storage

maturity by 2015.

of 10 to 20 MW in production and less than 40 MW in consumption). The author highlights the technologies that do not present major environmental or statutory constraints that could limit their deployment and that are susceptible to reach both technical and commercial

In this chapter, we consider energy storage technologies to tackle congestion relief and to smooth wind power variations on short time scales (up to several minutes). We are treating applications where energy storage systems are required to inject or absorb power during period of time in the order of minutes. Through these specific applications, we aimed to demonstrate the advantage of dynamic management of energy storage to raise the

Several criteria have to be analyzed to identify the storage technologies that are pertinent for the aforementioned applications. These applications require storage technologies with high power, short discharge period and good resilience to high number of charge-discharge cycles. Tables 1 and 2 report the main characteristics of a selection of energy storage technologies.

Pumped hydroelectric storage (PHES) systems exploit gravitational potential energy. Energy is stored by pumping water from a lower reservoir to an upper reservoir. The amount of stored energy is proportional the volume of water in the upper reservoir. When needed, water flows from the upper reservoir to the lower reservoir to release the stored energy with round trip efficiency in the range of 70% to 80%. PHES is the major energy storage technology; it account for 97% of the world total storage capacity [2]. The energy can be stored several days and high power ramp can be achieved during both the charge and discharge phases (0–1800 MW in 16 s at the Dinorwig pumping station for example, [8]). The PHS technology suffers low modularity and can only be installed on site with particular topology. PHES is a key asset for wind energy as it enables the grid to operate securely while incorporating high wind penetrations. There may be additional benefits when using PHES that can charge and discharge at the same time (see Figure 2). This can be achieved in

acceptance level of variable renewable energy sources for electricity generation.

The terminology "batteries" encompasses electrochemical storage cellular technologies that consist of an arrangement (in series or in parallel) of cell units. Each cell is made of two electrodes and an electrolyte secured into a sealed container. Batteries store chemical energy and generate electricity by a reduction-oxidation (redox) reaction. Batteries energy storage systems have been studied for almost 150 years, most research effort now aimed at cost reduction and high power application. The following section proposes a description of some promising batteries technologies. An overview of electrochemical energy storage systems is given in [10].

## **4.1. Lead-acid batteries**

Lead-Acid batteries are the most used devices for low to medium scale energy storage application. Lead-acid batteries have a low-cost (\$300–600/kW), high reliability, high power ramp capabilities and efficiency in the range (65%–80%). However, the performance of Lead-Acid battery will deteriorate quickly in the case of frequent charge-discharge cycles. The weak tolerance to high number of cycles limits the use of PbA batteries in application such as wind variations smoothing.

## **4.2. Lithium-ion batteries**

Lithium-ion batteries are ideal for portable applications; they are widely use in mobile phone and in almost any other electronic portable device. They tolerate over 3000 cycles, have 95% efficiency at 80% depth of discharge and have high power ramp capability. Nowadays, the emergence of electric cars drives numerous researches on Li-Ion technology and materials to obtain reliable high power system [11]. Since their lifetime is related to the cycles' depth of discharge, Li-Ion should not be use in application where they may be fully discharged. In addition, Li-Ion technology must be operated with a protection circuit to ensure safe voltage and temperature operation ranges.

Dynamic Energy Storage Management for Dependable Renewable Electricity Generation 279

NaS PbA Li-ion VRB

Figure 12 illustrates one major advantage of flow batteries. The maximum number of cycles tolerated during the lifetime of the batteries is plotted versus the depth of discharge for four technologies; PbA, Li-ion, NaS and VRB. The tolerable number of cycles decreases for PbA,

Battery systems do not seems fully adequate for smoothing wind or solar power applications due to their limited tolerance to large number of charge - discharge cycles. Super capacitors (SC) or ultracapacitors, are electrochemical capacitor with remarkable high energy density, as compared to conventional capacitors, and high power density as compared to batteries. Moreover SC tolerate over a million charge – discharge cycles [13,14]. However, the voltage of an ultracapacitor tends to decrease during discharge. This affects the efficiency of the subsequent power converter and can undermine the energy utilization of the capacitor. In [15], parallel-series ultracapacitor shift circuits are employed to improve the energy utilization and minimize the voltage drop. The principal drawback of SC is its

0 20 40 60 80 100 120

**DoD (%)**

**7. Power conversion solutions for coupling of renewable generations and** 

The present chapter deals with the combination of renewable electricity generation with energy storage system for the sake of renewable power smoothing. The preceding section focuses on the appropriate storage technologies. For wind or solar power smoothing, the storage technology should tolerate high number of cycles with partial DoD, be capable of

Li-ion and NaS but remain constant at a relatively high value for VRB.

**Figure 3.** Cycling ability of various energy storage systems (from [2])

**6. Super capacitors** 

100

1000

10000

**Cycling limits**

100000

1000000

high cost (up to 20000€/kWh).

**storage** 

## **4.3. Sodium-sulphur batteries**

NaS batteries are one of the most promising options for high power energy storage applications. The anode is made of sodium (Na), while the cathode is made of sulphur (S). The electrolyte enables the transfer of sodium ions to the cathode where they combine with sulphur anions and produce sodium polysulphide (NaSx). During the charge cycle, the opposite reaction occurs; sodium polysulphide is decomposed into sodium and sulphur. NaS batteries have good resilience to cycling (up to 4500 cycles), and can discharge quickly at high power [2-4]. NaS technology is modular; a single unit's rated power starts from 50 kW. Additionally, NaS batteries have low self-discharge and require low maintenance. However, the operating temperature must be kept at about 350°C.

## **5. Flow batteries**

Flow batteries store at least one of its electrolytes in an external storage tank. During operation, the electrolytes need to be pumped into the electrochemical cell to produce electricity. Unlike conventional batteries, the power capacity of flow batteries is independent of the storage energy capacity and self-discharge is almost inexistent. The energy capacity depends on the stored volume of electrolyte and the power delivered depends only on the dimension of the electrodes and the number of cells. Additionally, flow batteries have a very short response time, can be fully discharged without consequences and are able to store energy over long period of time. Compared to conventional batteries, flow batteries have an unlimited life in theory and no memory effects. However, the necessity to control the electrolytic flows induces high operating cost.

## **5.1. Vanadium redox-flow batteries (VRB)**

Among the various redox-flow batteries technology (Zinc Bromine, Polysulfide Bromide, Cerium-Zinc, …), VRB exhibits the best potentiality, thanks to its competitive cost, its simplicity and since it contains no toxic materials [12]. Energy is stored in two reservoirs; a catholytic reservoir and an anolytic reservoir. VRB low specific energy, <35 Wh/kg, limits its use in non-stationary applications.

Figure 12 illustrates one major advantage of flow batteries. The maximum number of cycles tolerated during the lifetime of the batteries is plotted versus the depth of discharge for four technologies; PbA, Li-ion, NaS and VRB. The tolerable number of cycles decreases for PbA, Li-ion and NaS but remain constant at a relatively high value for VRB.

**Figure 3.** Cycling ability of various energy storage systems (from [2])

## **6. Super capacitors**

278 Energy Storage – Technologies and Applications

**4.3. Sodium-sulphur batteries** 

**5. Flow batteries** 

ensure safe voltage and temperature operation ranges.

However, the operating temperature must be kept at about 350°C.

control the electrolytic flows induces high operating cost.

**5.1. Vanadium redox-flow batteries (VRB)** 

use in non-stationary applications.

Lithium-ion batteries are ideal for portable applications; they are widely use in mobile phone and in almost any other electronic portable device. They tolerate over 3000 cycles, have 95% efficiency at 80% depth of discharge and have high power ramp capability. Nowadays, the emergence of electric cars drives numerous researches on Li-Ion technology and materials to obtain reliable high power system [11]. Since their lifetime is related to the cycles' depth of discharge, Li-Ion should not be use in application where they may be fully discharged. In addition, Li-Ion technology must be operated with a protection circuit to

NaS batteries are one of the most promising options for high power energy storage applications. The anode is made of sodium (Na), while the cathode is made of sulphur (S). The electrolyte enables the transfer of sodium ions to the cathode where they combine with sulphur anions and produce sodium polysulphide (NaSx). During the charge cycle, the opposite reaction occurs; sodium polysulphide is decomposed into sodium and sulphur. NaS batteries have good resilience to cycling (up to 4500 cycles), and can discharge quickly at high power [2-4]. NaS technology is modular; a single unit's rated power starts from 50 kW. Additionally, NaS batteries have low self-discharge and require low maintenance.

Flow batteries store at least one of its electrolytes in an external storage tank. During operation, the electrolytes need to be pumped into the electrochemical cell to produce electricity. Unlike conventional batteries, the power capacity of flow batteries is independent of the storage energy capacity and self-discharge is almost inexistent. The energy capacity depends on the stored volume of electrolyte and the power delivered depends only on the dimension of the electrodes and the number of cells. Additionally, flow batteries have a very short response time, can be fully discharged without consequences and are able to store energy over long period of time. Compared to conventional batteries, flow batteries have an unlimited life in theory and no memory effects. However, the necessity to

Among the various redox-flow batteries technology (Zinc Bromine, Polysulfide Bromide, Cerium-Zinc, …), VRB exhibits the best potentiality, thanks to its competitive cost, its simplicity and since it contains no toxic materials [12]. Energy is stored in two reservoirs; a catholytic reservoir and an anolytic reservoir. VRB low specific energy, <35 Wh/kg, limits its

**4.2. Lithium-ion batteries** 

Battery systems do not seems fully adequate for smoothing wind or solar power applications due to their limited tolerance to large number of charge - discharge cycles. Super capacitors (SC) or ultracapacitors, are electrochemical capacitor with remarkable high energy density, as compared to conventional capacitors, and high power density as compared to batteries. Moreover SC tolerate over a million charge – discharge cycles [13,14]. However, the voltage of an ultracapacitor tends to decrease during discharge. This affects the efficiency of the subsequent power converter and can undermine the energy utilization of the capacitor. In [15], parallel-series ultracapacitor shift circuits are employed to improve the energy utilization and minimize the voltage drop. The principal drawback of SC is its high cost (up to 20000€/kWh).

## **7. Power conversion solutions for coupling of renewable generations and storage**

The present chapter deals with the combination of renewable electricity generation with energy storage system for the sake of renewable power smoothing. The preceding section focuses on the appropriate storage technologies. For wind or solar power smoothing, the storage technology should tolerate high number of cycles with partial DoD, be capable of high power ramp and short response time while keeping high efficiency. In addition, a chain of power conversion is necessary to pair the energy storage system with renewable energy sources and to adapt the voltage output of the ensemble to the network's voltage. The Figure 4 shows the structure of a DC-coupled hybrid power system with renewable sources and energy storage along with its control chain.

Dynamic Energy Storage Management for Dependable Renewable Electricity Generation 281

1. *The Switching Control Unit (SCU)* is the active interface between the power converters and the control units of higher level. The SCU opto-couplers and the modulation

2. *The Automatic Control Unit (ACU)*. The ACU's control algorithms calculate the modulation indexes of each power converter in accordance with the reference values set

3. *The Power Control Unit (PCU)* performs the instantaneous power balancing of the entire hybrid power system. The PCU's algorithm calculates the values of the parameters for the regulation of the voltages and the currents in accordance with the power reference

4. *The Mode Control Unit (MCU)* supervises the entire power system. The MCU sets the operating mode and the power references by taking into account the grid requirements from the network operator and the state of the power system. The state of the power system may include: the renewable energy generation capacity that is a function of the local climate, the SoC of the energy storage system and the grid operating condition at

The extent of the functions to be performed by the control chain and the level of complexity depend on the considered application and more specifically, on the typology of the storage system. Including for example, an imperative supervision at the level of every element in the case of Li-ion. In every case, this real time supervision of the storage unit is useful to the

**8. Sizing the storage capacity for the management of wind power induced** 

This sub-section discusses the sizing of transmission line capacity in the context of the transport of wind-originated electricity. A regional network is sized according to the maximal power that could transit through it. To prevent power congestion, it is necessary either, to size the network infrastructures to match the maximal expected power production or to limit the level of power that could transit through the

This last strategy calls for judicious arbitration between the loss of income due to the power limitation and the associated infrastructure cost reduction. A refine analysis of the production on a given site allows the developer to size sensibly the power level limit above which excess production will be rejected. To reduce energy waste, the excess of energy could be stored and re-injected later, during periods of low production. The aim here is mainly to avoid congestion while reducing the costs linked to infrastructures reinforcement

As the load factor of wind energy is about 25 %, the ratio of the cost for network strengthening over the aggregated energy produced for wind (or solar) energy is higher to

modules generate the power converters' transistors ON/OFF signal.

the injection point (voltage and frequency measurements).

and maximizing the energy output of the installation.

that of the other (non intermittent) sources of energy.

diagnosis in case of default or for the anticipation of needs in maintenance.

by the PCU.

**congestion.** 

transmission lines.

values set by the MCU.

DC-coupled structures are flexible since they can accommodate with different type of energy sources and energy storage technology [12]. In a DC-coupled structure, the renewable energy sources and the energy storage devices are generally connected through static power converters to a DC bus. These power converters can be either:


Power flows to the electrical grid from the DC-bus through a DC/AC inverter and a grid transformer.

**Figure 4.** DC-coupled renewable and energy storage power conversion system (source [16]) The structure of the control chain involves four different levels explained below:

1. *The Switching Control Unit (SCU)* is the active interface between the power converters and the control units of higher level. The SCU opto-couplers and the modulation modules generate the power converters' transistors ON/OFF signal.

280 Energy Storage – Technologies and Applications

such as supercapacitors.

speed, such as flywheel.

transformer.

sources and energy storage along with its control chain.

high power ramp and short response time while keeping high efficiency. In addition, a chain of power conversion is necessary to pair the energy storage system with renewable energy sources and to adapt the voltage output of the ensemble to the network's voltage. The Figure 4 shows the structure of a DC-coupled hybrid power system with renewable

DC-coupled structures are flexible since they can accommodate with different type of energy sources and energy storage technology [12]. In a DC-coupled structure, the renewable energy sources and the energy storage devices are generally connected through

DC/DC buck-boost converters; to control the voltage variations of DC energy sources

AC/DC inverters; for storage devices requiring a mechanical training with variable

Power flows to the electrical grid from the DC-bus through a DC/AC inverter and a grid

**Figure 4.** DC-coupled renewable and energy storage power conversion system (source [16])

The structure of the control chain involves four different levels explained below:

static power converters to a DC bus. These power converters can be either:


The extent of the functions to be performed by the control chain and the level of complexity depend on the considered application and more specifically, on the typology of the storage system. Including for example, an imperative supervision at the level of every element in the case of Li-ion. In every case, this real time supervision of the storage unit is useful to the diagnosis in case of default or for the anticipation of needs in maintenance.

## **8. Sizing the storage capacity for the management of wind power induced congestion.**

This sub-section discusses the sizing of transmission line capacity in the context of the transport of wind-originated electricity. A regional network is sized according to the maximal power that could transit through it. To prevent power congestion, it is necessary either, to size the network infrastructures to match the maximal expected power production or to limit the level of power that could transit through the transmission lines.

This last strategy calls for judicious arbitration between the loss of income due to the power limitation and the associated infrastructure cost reduction. A refine analysis of the production on a given site allows the developer to size sensibly the power level limit above which excess production will be rejected. To reduce energy waste, the excess of energy could be stored and re-injected later, during periods of low production. The aim here is mainly to avoid congestion while reducing the costs linked to infrastructures reinforcement and maximizing the energy output of the installation.

As the load factor of wind energy is about 25 %, the ratio of the cost for network strengthening over the aggregated energy produced for wind (or solar) energy is higher to that of the other (non intermittent) sources of energy.

In [17], the authors proposed an in-depth analysis of the wind speed variations and the related electrical power variations, based on a probabilistic approach that gives, for a specified wind speed range, the distribution of the expected wind farm power output. This method is used here to evaluate with more precision, the load factor of wind energy, in order to size the level of power curtailing and to estimate the required storage capacity to avoid energy waste.

Dynamic Energy Storage Management for Dependable Renewable Electricity Generation 283

������������(�)�������������(�) (1)

���(�) <sup>=</sup> |������������(�)|�

**Figure 6.** Wind power and wind speed power spectral densities

**Figure 7.** Magnitude square coherence between wind speed and electrical power

It is equal to the cross spectrum of *Vwind* and *Pcluster* divided by the product of the power spectra of *Vwind* and *Pcluster*. This quotient is a real number between 0 and 1 that measures the correlation between *Vwind* and *Pcluster* at the frequency *f*. The plot of Figure 7 shows the

Concerning the wind speed to electrical power conversion, many studies have investigated wind turbines response to wind variations. Figures 5 shows the plots of a two-months (61 days) sequence of wind speed and associated wind farm power output. Under the influence of meteorological conditions wind speed fluctuates over time. These variations occur on different time scales: from seconds to years. The response of a wind turbine, in term of power output variations, depends on the wind turbine technology [18,19]. Some smoothing effect can also be obtained due to the turbine inertia and size. For a group of turbines, further smoothing can be expected due to the spatial distribution of the turbine within the area. For large area, wind energy overall variability can be much lower than the variability of a single wind turbine since the meteorological fluctuations do not affect each wind cluster at the same time.

**Figure 5.** A two-month sample of wind speed and wind power.

To further investigate the wind speed – electrical power relationship, various tools from the Fourier analysis can be used. The plots in Figure 6 are the power spectral density of a sample of wind speed and electrical power produced by a cluster of wind turbines. The spectra are plotted for the frequency range between 1.3x10-4 Hz and 2x10-2 Hz using the periodogram method. Within this frequency range, no frequency peak can be observed from the two spectra. Moreover, we calculated the magnitude square coherence between the wind velocity *Vwind* and the power signal *Pcluster*. The magnitude square coherence is defined by:

**Figure 6.** Wind power and wind speed power spectral densities

avoid energy waste.

at the same time.

**Figure 5.** A two-month sample of wind speed and wind power.

In [17], the authors proposed an in-depth analysis of the wind speed variations and the related electrical power variations, based on a probabilistic approach that gives, for a specified wind speed range, the distribution of the expected wind farm power output. This method is used here to evaluate with more precision, the load factor of wind energy, in order to size the level of power curtailing and to estimate the required storage capacity to

Concerning the wind speed to electrical power conversion, many studies have investigated wind turbines response to wind variations. Figures 5 shows the plots of a two-months (61 days) sequence of wind speed and associated wind farm power output. Under the influence of meteorological conditions wind speed fluctuates over time. These variations occur on different time scales: from seconds to years. The response of a wind turbine, in term of power output variations, depends on the wind turbine technology [18,19]. Some smoothing effect can also be obtained due to the turbine inertia and size. For a group of turbines, further smoothing can be expected due to the spatial distribution of the turbine within the area. For large area, wind energy overall variability can be much lower than the variability of a single wind turbine since the meteorological fluctuations do not affect each wind cluster

To further investigate the wind speed – electrical power relationship, various tools from the Fourier analysis can be used. The plots in Figure 6 are the power spectral density of a sample of wind speed and electrical power produced by a cluster of wind turbines. The spectra are plotted for the frequency range between 1.3x10-4 Hz and 2x10-2 Hz using the periodogram method. Within this frequency range, no frequency peak can be observed from the two spectra. Moreover, we calculated the magnitude square coherence between the wind velocity

*Vwind* and the power signal *Pcluster*. The magnitude square coherence is defined by:

**Figure 7.** Magnitude square coherence between wind speed and electrical power

It is equal to the cross spectrum of *Vwind* and *Pcluster* divided by the product of the power spectra of *Vwind* and *Pcluster*. This quotient is a real number between 0 and 1 that measures the correlation between *Vwind* and *Pcluster* at the frequency *f*. The plot of Figure 7 shows the

$$F(P \mid \mathcal{V}\_{\text{Wind}}) = \text{prob}(\mathcal{V}\_{\text{Cluster}} \le P \mid \mathcal{V}\_{\text{Wind}}) \tag{2}$$

The objective is to optimize the use of a transmission line by reducing its capacity while avoiding line saturation. Therefore, we set the transmission line capacity to 55% ofܲܣܯܺଶ௧௦. A generic energy storage system is used to store all or part of the excess energy. We tested different level of storage capacity. For the tests, we set the storage system efficiency to 75% and limit the depth of discharge (DoD) to 80%.

Dynamic Energy Storage Management for Dependable Renewable Electricity Generation 287

**Figure 11.** Time evolution of the storage system SoC and power during the test.

**Figure 12.** Iso-percentages of the conditional cumulative distribution function of the wind farm output power as a function of wind velocity. The power threshold levels are expressed as percentage of the

Efficient forecasting scheme that includes some information on the likelihood of the forecast and based on a better knowledge of the wind variations characteristics along with their influence on power output variation is of key importance for the optimal integration of wind energy in power system. In [20], the author has developed a short-term wind energy prediction scheme that uses artificial neural networks and adaptive learning procedures based on Bayesian approach and Gaussian approximation. We propose to illustrate how such

curtailed transmission line capacity which, here, equals 55%.

If, during the test, the state of charge (SoC) of the storage system reaches 100%, the subsequent excess energy will be rejected as long as the SoC remains at 100%. To limit this event, as long as the SoC remains above 20%, the stored energy will be discharged whenever the instantaneous wind farm output power drops below 50% ofܲܣܯܺଶ௧௦. This contributes to keep the storage SoC below 100% as often as possible, allowing more excess energy to be stored and redirected through the grid.

Table 3 gives the percentage of energy effectively sent through the transmission line in respect with ܧܱܶܶܮܣଶ௧௦, as a function of the storage system capacity. These elements added with a cost analysis of power transmission lines, enable calculations to investigate the profitability of such a power curtailment scheme.

The plots of Figure 11 show the evolution of the storage system SoC and its temporal derivative (storage's charge and discharge power) during a test conducted with a capacity of storage equalsͳǤ͵͵ܯܹ݄, which represents 5% ofܧ݁ݔܿ݁ݏݏଶ௧௦. These results demonstrate the benefit of this approach. With a relatively limited storage capacity, it is possible to exploit 99.92% of the energy produced by the wind farm while limiting the transmission line capacity at 55% of the maximum wind power observed during the two-months test period.


**Table 3.** Percentage of energy sent through the transmission line as a function of the storage system capacity

The ideal storage system characteristics could be deduced from this analysis. For the presented case, the adequate storage system should have a rated power of ʹǡͷܯܹ, a rated energy of ͳǡͷܯܹ݄ and efficiency around 75%.

## **9. Dynamic energy storage management for wind electricity injection into electrical grids**

In the application presented above, the power variations induced by the wind's fluctuations are not anticipated for. We propose here to consider the potential benefit of wind energy production forecast to improve the reliability of wind-originated electricity.

**Figure 11.** Time evolution of the storage system SoC and power during the test.

The objective is to optimize the use of a transmission line by reducing its capacity while avoiding line saturation. Therefore, we set the transmission line capacity to 55% ofܲܣܯܺଶ௧௦. A generic energy storage system is used to store all or part of the excess energy. We tested different level of storage capacity. For the tests, we set the storage system

If, during the test, the state of charge (SoC) of the storage system reaches 100%, the subsequent excess energy will be rejected as long as the SoC remains at 100%. To limit this event, as long as the SoC remains above 20%, the stored energy will be discharged whenever the instantaneous wind farm output power drops below 50% ofܲܣܯܺଶ௧௦. This contributes to keep the storage SoC below 100% as often as possible, allowing more excess

Table 3 gives the percentage of energy effectively sent through the transmission line in respect with ܧܱܶܶܮܣଶ௧௦, as a function of the storage system capacity. These elements added with a cost analysis of power transmission lines, enable calculations to investigate the

The plots of Figure 11 show the evolution of the storage system SoC and its temporal derivative (storage's charge and discharge power) during a test conducted with a capacity of storage equalsͳǤ͵͵ܯܹ݄, which represents 5% ofܧ݁ݔܿ݁ݏݏଶ௧௦. These results demonstrate the benefit of this approach. With a relatively limited storage capacity, it is possible to exploit 99.92% of the energy produced by the wind farm while limiting the transmission line capacity at 55% of the maximum wind power observed during the two-months test period.

(%ܧ݁ݔܿ݁ݏݏଶ௧௦) *MWh (%*ܧܱܶܶܮܣଶ ௧௦*) GWh*  0% (no storage) 0 98,81% 2,2353 1% 0,269 99,40% 2,2486 5% 1,34 99,92% 2,2603 10% 2,68 99,98% 2,2617

**Table 3.** Percentage of energy sent through the transmission line as a function of the storage system

**9. Dynamic energy storage management for wind electricity injection** 

production forecast to improve the reliability of wind-originated electricity.

In the application presented above, the power variations induced by the wind's fluctuations are not anticipated for. We propose here to consider the potential benefit of wind energy

The ideal storage system characteristics could be deduced from this analysis. For the presented case, the adequate storage system should have a rated power of ʹǡͷܯܹ, a rated

Storage system capacity Energy sent through the transmission line

efficiency to 75% and limit the depth of discharge (DoD) to 80%.

energy to be stored and redirected through the grid.

profitability of such a power curtailment scheme.

energy of ͳǡͷܯܹ݄ and efficiency around 75%.

capacity

**into electrical grids** 

**Figure 12.** Iso-percentages of the conditional cumulative distribution function of the wind farm output power as a function of wind velocity. The power threshold levels are expressed as percentage of the curtailed transmission line capacity which, here, equals 55%.

Efficient forecasting scheme that includes some information on the likelihood of the forecast and based on a better knowledge of the wind variations characteristics along with their influence on power output variation is of key importance for the optimal integration of wind energy in power system. In [20], the author has developed a short-term wind energy prediction scheme that uses artificial neural networks and adaptive learning procedures based on Bayesian approach and Gaussian approximation. We propose to illustrate how such


## **11. The dynamic power-scheduling algorithm**

Once the predictor anticipated the energy production of the wind farms ahead in time. A scheduling algorithm is able to calculate ������. In this chapter, we report the result of tests were ������ estimates the power level at which electrical energy will be delivered to the grid, throughout the future time interval �� � ���������� � � �� ��������. Therefore, the grid operator will have, �� ������� ahead in time, valuable information about the availability of wind energy. The Figure 15 gives a sketch of the power-scheduling plan.

Dynamic Energy Storage Management for Dependable Renewable Electricity Generation 291

The Figures 16 and 17 below, show the injected and scheduled power plots superimposed to the actual wind power plot. This nine-hours demonstration started with zero energy in the energy storage system. The dynamic scheduling algorithm manages to maintain the injected power within the +/- 5% of ������interval while maintaining the energy reserve strictly above zero as shown by the plot of Figure 17. Notice that the required energy capacity remains relatively low, around 3% (less than ��� ��ℎ) of the total energy supplied by the wind farms during the whole duration of the demonstration (about �� ��ℎ supplied in 9 ℎ). The required charge or discharge power of the storage system is estimated at ��� ��.

**Figure 16.** Scheduled and injected electrical power superimposed to the actual wind power

**Figure 17.** Evolution of the energy reserve of the storage system

To comply with the power assignment ������, the electrical energy will come from the wind farm, supported if needed, by an energy storage system. The value of the scheduled power ������ is calculated under the following constraints:


**Figure 15.** Dynamic power-scheduling

At any given time �, the scheduling algorithm evaluates ������ from the predicted energy, the current storage reserve level and the previously observed deviation between the actual and predicted energy.

For the calculation of ������, the algorithm takes into account the stored energy only at the level of 50 % in case the previously observed deviation between the actual and predicted energy is positive and 0 % if the deviation is negative. The objective is to ensure there is some energy left in the storage system to compensate the power shortage and possible prediction errors at a later time.

The Figures 16 and 17 below, show the injected and scheduled power plots superimposed to the actual wind power plot. This nine-hours demonstration started with zero energy in the energy storage system. The dynamic scheduling algorithm manages to maintain the injected power within the +/- 5% of ������interval while maintaining the energy reserve strictly above zero as shown by the plot of Figure 17. Notice that the required energy capacity remains relatively low, around 3% (less than ��� ��ℎ) of the total energy supplied by the wind farms during the whole duration of the demonstration (about �� ��ℎ supplied in 9 ℎ). The required charge or discharge power of the storage system is estimated at ��� ��.

290 Energy Storage – Technologies and Applications

interval around ������.

**Figure 15.** Dynamic power-scheduling

prediction errors at a later time.

and predicted energy.

**11. The dynamic power-scheduling algorithm** 

power ������ is calculated under the following constraints:

We set the discharging efficiency to 85%.

wind energy. The Figure 15 gives a sketch of the power-scheduling plan.

Once the predictor anticipated the energy production of the wind farms ahead in time. A scheduling algorithm is able to calculate ������. In this chapter, we report the result of tests were ������ estimates the power level at which electrical energy will be delivered to the grid, throughout the future time interval �� � ���������� � � �� ��������. Therefore, the grid operator will have, �� ������� ahead in time, valuable information about the availability of

To comply with the power assignment ������, the electrical energy will come from the wind farm, supported if needed, by an energy storage system. The value of the scheduled

1. Energy must be delivered to the grid at power levels that remain within the +/- 5%

2. If the actual instantaneous wind power is above the top of this interval, the energy excess is sent to the energy storage. The algorithm takes into account the charging

3. If the actual instantaneous wind power falls below the bottom of this interval, energy discharged from the energy storage compensates the energy shortage, as long as the energy in the storage system is larger than a pre-set level to account for the lower DoD limit. The algorithm also takes into account the storage system's discharging efficiency.

At any given time �, the scheduling algorithm evaluates ������ from the predicted energy, the current storage reserve level and the previously observed deviation between the actual

For the calculation of ������, the algorithm takes into account the stored energy only at the level of 50 % in case the previously observed deviation between the actual and predicted energy is positive and 0 % if the deviation is negative. The objective is to ensure there is some energy left in the storage system to compensate the power shortage and possible

efficiency of the storage system. We set the charging efficiency to 85%.

**Figure 16.** Scheduled and injected electrical power superimposed to the actual wind power

**Figure 17.** Evolution of the energy reserve of the storage system

The interest of the proposed method is that he permits to guarantee the power at which energy will be sent to the grid by automatically dispatching the power flows between the wind plants and the energy storage facility.

Dynamic Energy Storage Management for Dependable Renewable Electricity Generation 293

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## **12. Conclusion**

Energy storage technologies are identified as key elements for the development of electricity generation exploiting renewable energy sources. In this chapter, we have illustrated, through two simulations cases, how they could contribute to remove the technical constraints that limit the contribution of renewables energy sources into electrical networks.

The sector of energy storage technologies sees new solutions to emerge every day. The arrival on the market of electric vehicles contributes largely to this profusion of innovation. Our objective was to show that a dynamic approach of the management of the charge and the discharge at the level of the energy storage system insures a good quality of service (energy efficient power curtailment, power smoothing and uncertainty reduction) with a reduced storage capacity.

We illustrated our proposed approaches by dealing with the case of wind energy. However, the proposed methods are immediately transposable to the case of the photovoltaic electricity production given preliminary preprocessing of the solar insolation and photovoltaic-production data.

## **Author details**

Ruddy Blonbou\* , Stéphanie Monjoly and Jean-Louis Bernard *Geosciences and Energy Research Laboratory, Université des Antilles et de la Guyane, Guadeloupe, France* 

## **Acknowledgement**

This work was performed, in part in the frame of the ERA/Alizeole Project (Contract No. 1/1.4/32315) and funded by the European Commission. Acknowledgments are due to, the Vergnet Caraïbes and Aerowatt companies, for providing technical help and EDF for providing the power data for this work. Special thanks to the Regional Council of Guadeloupe for its financial support.

## **13. References**

[1] Hall P J, Bain E J (2008) Energy-storage technologies and electricity generation. Energy Policy 36: 4352–4355

<sup>\*</sup> Corresponding Author

[2] Delille G, (2007) Contribution du stockage à la gestion avancée des systèmes électriques. PhD thesis, Université Lille Nord-de-France.

292 Energy Storage – Technologies and Applications

**12. Conclusion** 

reduced storage capacity.

photovoltaic-production data.

**Author details** 

**Acknowledgement** 

**13. References** 

Corresponding Author

 \*

Guadeloupe for its financial support.

Policy 36: 4352–4355

Ruddy Blonbou\*

*France* 

wind plants and the energy storage facility.

The interest of the proposed method is that he permits to guarantee the power at which energy will be sent to the grid by automatically dispatching the power flows between the

Energy storage technologies are identified as key elements for the development of electricity generation exploiting renewable energy sources. In this chapter, we have illustrated, through two simulations cases, how they could contribute to remove the technical constraints that limit the contribution of renewables energy sources into electrical networks. The sector of energy storage technologies sees new solutions to emerge every day. The arrival on the market of electric vehicles contributes largely to this profusion of innovation. Our objective was to show that a dynamic approach of the management of the charge and the discharge at the level of the energy storage system insures a good quality of service (energy efficient power curtailment, power smoothing and uncertainty reduction) with a

We illustrated our proposed approaches by dealing with the case of wind energy. However, the proposed methods are immediately transposable to the case of the photovoltaic electricity production given preliminary preprocessing of the solar insolation and

*Geosciences and Energy Research Laboratory, Université des Antilles et de la Guyane, Guadeloupe,* 

This work was performed, in part in the frame of the ERA/Alizeole Project (Contract No. 1/1.4/32315) and funded by the European Commission. Acknowledgments are due to, the Vergnet Caraïbes and Aerowatt companies, for providing technical help and EDF for providing the power data for this work. Special thanks to the Regional Council of

[1] Hall P J, Bain E J (2008) Energy-storage technologies and electricity generation. Energy

, Stéphanie Monjoly and Jean-Louis Bernard

	- [20] Blonbou R (2011) Very Short Term Wind Power Forecasting with Neural Networks and Adaptive Bayesian learning. Renewable Energy 36(3): 1118-1124.

**Chapter 12** 

© 2013 Sarasua et al., licensee InTech. This is an open access chapter 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.

© 2013 Sarasua et al., licensee InTech. This is a paper 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.

**Dynamic Modelling of Advanced Battery** 

In the last decade, power generation technology innovations and a changing economic, financial, and regulatory environment of the power markets have resulted in a renewed interest in on-site small-scale electricity generation, also called distributed, dispersed or decentralized generation (DG) (Abdollahi Sofla & Gharehpetian, 2011). Other major factors that have contributed to this evolution are the constraints on the construction of new transmission lines, the increased customer demand for highly reliable electricity and concerns about climate change (Guerrero et al, 2010). Along with DG, local storage directly coupled to the grid (aka distributed energy storage or DES) is also assuming a major role for balancing supply and demand, as was done in the early days of the power industry. All these distributed energy resources (DERs), i.e. DG and DES, are presently increasing their penetration in developed countries as a means to produce in-situ highly reliable and good

Incorporating advanced technologies, sophisticated control strategies and integrated digital communications into the existing electricity grid results in Smart Grids (SGs), which are presently seen as the energy infrastructure of the future intelligent cities (Wissner, 2011). Smart grids allow delivering electricity to consumers using two-way (full-duplex) digital technology that enable the efficient management of consumers and the efficient use of the grid to identify and correct supply-demand imbalances. Smartness in integrated energy systems (IESs) which are called microgrids (MG) refers to the ability to control and manage energy consumption and production in the distribution level. In such IES systems, the gridinteractive AC microgrid is a novel network structure that allows obtaining the better use of

**Grid-Tied AC Microgrid Applications** 

Marcelo Gustavo Molina and Pedro Enrique Mercado

**Energy Storage System for** 

Additional information is available at the end of the chapter

quality electrical power (Kroposki et al, 2008).

Antonio Ernesto Sarasua,

http://dx.doi.org/10.5772/52219

**1. Introduction** 


**Chapter 12** 
