Wind Generation and Electric Vehicles coordination in Microgrids for Peak Shaving purposes

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Available online at www.sciencedirect.com Energy Procedia 00 (2017) 000–000

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Energy Procedia 00 (2017) 000–000

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Energy (2017) 000–000 407–416 EnergyProcedia Procedia119 00 (2017)

International Conference on Technologies and Materials for Renewable Energy, Environment and www.elsevier.com/locate/procedia Sustainability, TMREES17, 21-24 April 2017, Beirut Lebanon International Conference on Technologies and Materials for Renewable Energy, Environment and Wind Generation and TMREES17, Electric Vehicles in Microgrids Sustainability, 21-24 April coordination 2017, Beirut Lebanon

for Peak Shaving purposes Wind Generation and Electric Vehicles coordination in Microgrids The 15th International Symposium on District Heating and Cooling a a Anestis G. Anastasiadis *, Georgios A.Shaving Vokas , Stavros A. Konstantinopoulosb, Georgios for Peak purposes Assessing of using the dheat demand-outdoor P. Kondylisb, the Tonifeasibility Khalilc , Apostolos Polyzakis , Konstantinos Tsatsakisb a a b Anestis G. Anastasiadis *, Georgios A. Vokas A.250, Konstantinopoulos , Georgios Department of Electronics Engineering, TEI Piraeus,,P.Stavros Ralli & Thivon 12244 Aigaleo, Greece temperature function for a long-term district heat demand forecast b c d b 0F

a

P. Kondylis , Toni Khalil , Apostolos Polyzakis , Konstantinos Tsatsakis I. Andrić *, A. Pina , P. Ferrão , J. Fournier ., B. Lacarrière , O. Le Corre

School of Electrical and Computer Engineering, National Technical University of Athens, Heroon Polytechniou 9, 15780 Zografou, Greece c Fanar Campus, a,b,c Faculty of Science, a Lebanese University, a b 90656 Jdeidet, Lebanon c c d a Department of Mechanical Engineering, TEI of TEI Western Greece, Megalou Alexandrou 1, Koukouli, Department of Electronics Engineering, Piraeus, P. Ralli & Thivon 250, 12244 Aigaleo, Patras, Greece Greece b School of Electrical and Computer Engineering, National Technical University of Athens, Heroon Polytechniou 9, 15780 Zografou, Greece a IN+ Center for Innovation, c Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal bFaculty of Science, Lebanese University, Fanar Campus, 90656 Jdeidet, Lebanon Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France d cDepartment of Mechanical Engineering, TEI of Western Greece, Megalou Alexandrou 1, Koukouli, Patras, Greece Département Systèmes Énergétiques et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Abstract b

0F

Nowadays, the urgent need to reduce emissions has led to the development of Renewable Energy Sources (RES). Wind energy via Abstract Wind Generators is a very common RES, which, however, is characterized by great intermittency. Consequently, wind energy Abstract production may vary from zero to excessive (way above the load demand), which makes wind power plants’ penetration very Nowadays, the urgentneed need to reduce emissions has led to the development of Renewable Energy Sources (RES). Wind energyand via difficult, storageaddressed essential. other hand, Electric Vehicles (EVs) offer a new,for eco-friendly District making heating the networksfor areenergy commonly inOnthetheliterature as one of the most effective solutions decreasing the Wind Generators is a of very common RES, which, however, is characterized by great intermittency. Consequently, wind energy more effective means transportation. The energy stored in their batteries is used to move the EVs. Nevertheless, the penetration greenhouse gas emissions from the building sector. These systems require high investments which are returned through the heat production may varythe from zerodemand to excessive (way above the and loadwill demand), which wind power plants’ very ofsales. EVs will energy from Thermal Plants, therefore harmmakes the environment it ispenetration combined with Dueincrease to the changed climate conditions and building renovation policies, heat demand in unless the future could decrease, difficult, making the need for The energy storage essential. On use the of other hand, Electric VehiclesEVs (EVs) offer a new, eco-friendly and anprolonging increased RES integration. advantages of combined RES and EVs are evident. can augment Wind Power Plants the investment return period. more effective meanstheir of transportation. The energy stored in their batteries is usedwind to move the EVs. Nevertheless, the penetration penetration through storage capabilities and use some of their excessive energy output for environmental-friendly The main scope of this paper is to assess the feasibility of using the heat demand – outdoor temperature function for heat demand of EVs will increase the energy demand from Thermal and will therefore the environment unless combinedaswith mobility. thisdistrict paper, theAlvalade, combination of EVs, RESPlants, (Wind Generators) andasharm Thermal Plants of island itis isisexamined an forecast. InThe of located in Lisbon (Portugal), was used a case study. Theandistrict consisted of 665 an increasedfor RES integration. The advantages ofare combined use of RES and EVs are evident. EVsTaking can augment Wind Power Plants alternative the future grids. Two scenarios being studied with focus on peak shaving. into account the Medium buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district penetration through their storagewind capabilitiesand and use some theirhourly excessive wind energy output for environmental-friendly Voltage (MV) line load demand, units of output data in athe yearly basis, a special algorithm developed renovation scenarios were developedpower (shallow,thermal intermediate, deep). To estimate error, obtained heat demandisvalues were mobility. In this paper, the combination of EVs, RES (Wind Generators) andreintroducing Thermal Plants of the an Microgrid island is examined an so as to study the case of absorbing the excess energy via off peak charging and it into scheme inas peak compared with results from a dynamic heat demand model, previously developed and validated by the authors. alternative for theThe future grids. Two scenarios are being studied with focus onand peak shaving. Taking into account the Medium demand periods. studies are conducted for two cases: a) low penetration b) high penetration of EVs. All findings are The results showed that when only weather change is considered, the margin of error could be acceptable for some applications Voltage (MV) line load demand, windsection. power and thermal units output hourly data in a yearly basis, a special algorithm is developed thoroughly conclusions (the error discussed in annual in demand was lower than 20% for all weather scenarios considered). However, after introducing renovation so as to study the case of absorbing the excess energy via off peak charging and reintroducing it into the Microgrid scheme in peak scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). demand periods. The studies arebyconducted for two cases: a) low penetration and b) high penetration of EVs. All findings are ©The 2017 The Authors. Elsevier on Ltd. value of slope Published coefficient increased average within the range of 3.8% up to 8% per decade, that corresponds to the thoroughly discussed in conclusions section. Peer-review responsibility of the Euro-Mediterranean Institute for Sustainable Development (EUMISD). decrease inunder the number of heating hours of 22-139h during the heating season (depending on the combination of weather and

scenariosPublished considered). On the other ©renovation 2017 The Authors. by Elsevier Ltd. hand, function intercept increased for 7.8-12.7% per decade (depending on the © 2017 The Authors. Published by Elsevier Ltd. coupled scenarios). The valuesofsuggested could be used Institute to modify function Development parameters for(EUMISD). the scenarios considered, and Peer-review under responsibility the Euro-Mediterranean forthe Sustainable Peer-review under responsibility of the Euro-Mediterranean Institute for Sustainable Development (EUMISD). improve the accuracy of heat demand estimations. * Corresponding author.Published Tel.: +30-6977-913-262; E-mail address: [email protected] © 2017 The Authors. by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and 1876-6102 Cooling. © 2017 The Authors. Published by Elsevier Ltd. * Corresponding author. Tel.: +30-6977-913-262; E-mail address: Peer-review under responsibility of the Euro-Mediterranean Institute [email protected] for Sustainable Development (EUMISD). Keywords: Heat demand; Forecast; Climate change 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Euro-Mediterranean Institute for Sustainable Development (EUMISD).

1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.

1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Euro-Mediterranean Institute for Sustainable Development (EUMISD). 10.1016/j.egypro.2017.07.124

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Keywords: Electrical Vehicles; State of Charge; Microgrids; Renewable Energy Sources; Wind Generation, Thermal Units, Peak Shaving

1. Introduction Nowadays, wind energy is one of the most abundant and easily exploitable renewable energy in the generation portfolio with its installed capacity increasing rapidly, [1], [2]. However, it poses major challenges to power system operation, especially now that its penetration rises significantly. These challenges stem of the intermittent nature of wind and therefore the power that it can generate. This uncertainty makes these units unfit to be used as base load units. Additionally, full exploitation of the potential power is not always possible as the generation may exceed the load. On the contrary, it is possible to have zero generation intervals. These problems pose even greater issues to grid operation when applied to an isolated power system; like that this paper will investigate. In such cases, tight wind power penetration limits are imposed to ensure safe operation and reserve margins are particularly high. These undesirable restrictions can be alleviated greatly with storage technologies [3], as it will be possible to effectively transfer unused wind energy generation to hours that might have lower wind speeds or undesirable load peaks. Storage technologies although they are indispensable to operators, are practically scarce in power systems. The main drawback is the great cost of storage installations per MWh and the limitations many storage technologies pose. The main technologies that are used in power system operation are pumped storage, hydrogen storage, flywheels, compressed air and of course batteries. Typical applications today are big, central, pumping-hydroelectric plants sometimes combined with the exploitation of the hydroelectric potential. The aim is smoothing the load demand curve by using low cost night load for pumping purposes in order to make the operation of large thermal or nuclear units (above the technically minimum load) possible, while providing expensive hydroelectric power at peak hours and thus offering auxiliary services to the network. Application of decentralized storage systems for grids (and Microgrids) are still very limited but are expected to augment in the near future with the penetration of EVs, which is the subject of the present work. The goal is to combine wind energy and EVs in order to allow increased penetration of RES in electricity networks. A comprehensive review of storage technologies and their use in power systems is presented in [4]. However, there is an opportunity presenting itself with the rise of another automotive trend, Electric Vehicles (EVs). The number of EVs on the road is expected to increase even more in the near future, as they represent a key element for global emissions reduction. In addition, their double role of being a load and energy source makes them strategic resources for grid operation, as they are able to provide several services that can improve the operation of distribution networks. This is possible because most vehicles are typically only driven a few hours per day and are parked the rest of the time (during the night or while the owner is working) [5]. In the case of a vehicle fleet, this capability is even greater since, by grouping together a large number of vehicles, an effective and significant contribution can be obtained on the grid service provision. Theoretically, a certain number of parked EVs, managed through an aggregator, can provide several important services to the grid, such as regulation, peak power, and spinning reserve [5], [6], [7]. Especially, in grids with great Renewable Energy Sources (RES) integration, such aggregations could provide invaluable services to operators and owners as well, [8], [9]. This level of coordination however requires a significant infrastructure present for two purposes. Initially, the main coordination framework must match the charging of the EVs to high wind power generation periods and as a second step determine the periods of excessive peaks that load demand reduction is necessary, [10], [11]. All this without compromising the satisfaction of the EV owners in terms having available charge in order to meet their everyday needs. Despite the potential EVs bear, their interaction with the grid can be problematic if left uncoordinated. The key issues due to the penetration of EVs into current distribution networks are highlighted in [12] and the study showed that uncoordinated EV charging can cause local grid problems. Based on such recognition, much research effort has been made to address the challenge from different aspects and a set of technical solutions have been proposed, [12]–[15]. The mentioned level of control can be achieved by the Microgrid (MG). Microgrids are Low Voltage (LV) or Medium Voltage (MV) networks that are an aggregation of Distributed Energy Resources (DER), storage installations and loads, [16]. The characteristic of those aggregations is that they typically have one connection point with the upstream grid by which they import/export energy but if need be, they can operate autonomously. MGs have been the topic of numerous studies in terms of the financial, environmental and operational benefits, [17], [18]. Their local power generation reduces distribution costs, they promote RES integration and they bear significant investment deferral in distribution level. In the case of this paper, they allow operators to control EV charging and utilize the spare capacity of the batteries for peak shaving purposes especially in conjunction with high Wind Power penetration.



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Control of such entities can be performed in various ways and it is dependent on the size of the network, relation of participants with the Distribution System Operator (DSO) and between themselves, presence of adequate communication infrastructure and of course privacy issues [19]. In smaller networks, centralized approaches are more appropriate as they can provide optimal utilization of the grid’s resources without requiring extensive communication capabilities. However, as networks increase in size, having a central coordination point connected to every resource of the MG is expensive and sometimes impractical. In addition, many participants may not be comfortable to disclose details or let the operator have full control over their assets and DER. In such cases, decentralized approaches are more appropriate for MG operation. In this paper, an isolated power system of Ikaria’s island incorporating thermal power plants and wind parks is examined. In the network exist EVs that are to be utilized by the operator for peak shaving purposes. The purpose is to inspect how the unused capacity of the EVs batteries can decrease wind power curtailment and aid the operator in load management. Using yearly data for load and wind generation, an algorithm is developed in order to utilize excess energy via the EVs and inject it back into the grid during peak hours. Two illustrative cases were chosen to depict low and high penetration of EVs in the system. All data are taken from Hellenic Distribution/Transmission System Operators and Public Power Corporation [20], [21], [22]. The simulation environments are developed based on MATLAB version R2010b. 2. Wind Generation Wind energy is exploited through Wind Turbines (WT). A WT operates by extracting kinetic energy from the wind passing through its rotor. The power developed by a WT is given by the equation (1).

 Pw (v)

1  C p Av3 , vcut in  v  vno min al 2

Pw (v)  0, v  vcut in & v  vcut out

(1)

 Pw (v) Pnom , vno min al  v  vcut out where:

Pw (v) Pnom 

Cp

Wind turbine’s power output for wind speed equal to Wind turbine’s nominal power Wind density (equal to 1,25 kg/m3) Aerodynamic coefficient of the wind turbine

A

Surface of blades (m2) Wind speed (m/s) Cut-in wind speed (m/s)

vnomin al

Nominal wind speed (m/s)

v vcut in

vcut out

v

Cut-out wind speed (m/s)

A 1,5MW WT have a rotor of some 60m mounted on a 60-90m high tower. Similar WT is used for the purpose of this paper. Figure 1a is the power curve of a WT which indicates its output at various wind speeds. At wind speeds below cut-in (3.5m/s) no significant power is developed. The output power then increases rapidly with wind speed until it reaches its rated value (14m/s) and is then limited by some control action of the turbine. This part of the characteristic follows an approximately cubic relationship between wind speed and output power, although this is

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modified by changes in Cp. Then at the shut-down wind speed (in this case is 25m/s) the rotor is parked for safety.

WT production(p.u.)

Annual WT Output Παραγωγη αιολικων

hours Fig. 1. (a) Typical power curve of a WT; (b) Annual WT production;

Figure 1b shows the annual WT production. Wind power production estimation is derived from the island’s wind speed data and the study of wind turbines’ power curve. It is known that the forecasted generation of wind power generator Pfwind is determined by forecasted wind speed uf and turbine parameters, i.e. cut-in wind speed uci, cut-out wind speed uco, nominal wind speed ur and nominal power of wind power generator Pr, which has been well investigated in the literature. In this work, we adopt the existing model of wind power generator from [23], [ 24] as given in (2).

௙

ܲ௪௜௡ௗ

Ͳǡ‫ݑ‬௖௜ ‫ݑݎ݋‬௙ ൐ ‫ݑ‬௖଴ ‫ ۓ‬య య ۗ ௨೑ ି௨೎೔ ൌ ή ܲ ‫ݒ‬ ൑ ‫ݑ‬ ൏ ‫ݑ‬ ௥ǡ ௖௜ ௙ ௥ య య ‫ ۔‬௨ೝ ି௨೎೔ ۘ ‫ܲ ە‬௥ ǡ‫ݒ‬௥ ൑ ‫ݑ‬௙ ൑ ‫ݑ‬௖௢ ۙ

(2)

3. Operational Algorithm

The steps towards the solution of the problem are the following: 1. Data for load, wind’s speed, power curve of wind parks and operational boundaries, number of EVs, charging and discharging boundaries, size of storage units, initial conditions, peak cut percentage, power absorption from thermal units percentage as for load must be inserted in the system. 2. From every wind park’s power curve and from wind speed time series the maximum production of every wind park per hour is calculated. 3. The entire year is split in 24-hour intervals, in which the peaks of the load are calculated as below:  The maximum load for one day is determined  All loads that are equal or greater than maximum load multiplied with a specific percentage are found. In this case, the percentage is set to 90%. So: PeakLoad  { x : x  0.9 * max_ load } 4. The maximum EV production per hour is calculated as below: Pmax  max{Ppeak , Ch arg ingRate * NumEVs } where Ch arg ingRate * NumEVs is the nominal EVs’ output power and

Ppeak 5.

is defined as the power of the peak which is to be cut-off.

Since charging case is selected, charging energy and the hours in which it will take place are to be calculated.  Batteries’ energy reserves and their output capability (while not violating the lower State Of ChargeSOC limit) are calculated in the beginning of each day.  Wind park’s energy output capability during the day is estimated until Peak Shaving and transportations are in operation. Particular emphasis is given to the fact that the EV should be able to absorb this power, namely to be inside the charging limits. This energy along with the previous one defines the available energy.



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The energy needed for peak shaving and EVs' transportation is calculated. If the energy needed is more than the available, energy is absorbed from the thermal units before the operation of peak shaving, when the load is lower. The energy absorbed by thermal units per hour is the minimum of the following:  A percentage of the current load, defined as simulation step  The maximum energy that can be absorbed by the batteries in an hour  The difference between the demanded and available power If the available energy is more than or equal to the demanded, then the process halts. Otherwise, the previous step is repeated until the two quantities are equal. Then, the maximum possible EV output is calculated as below:  If EVs have to function as production units, their charging process stops (so they absorb neither from wind parks nor from thermal plants) and they inject to the system power equal to the minimum of the following: o The power that can be injected because of the battery’s size o The maximum power as defined in a previous step  If the discharging process does not function (either there was no energy reserve or the available energy quantity was not sufficient to operate above the battery’s technical minima) then the charging process will operate. The energy that the batteries are going to absorb is the minimum of the following: o The sum of: wind park’s power, the power absorbed by thermal units and the initial condition. o Charging nominal power o The power that will cause the filling of the battery Afterwards and until the end of the examination day, EVs are charged with any power excess from wind parks which is available during these hours. The charging process follows the same power and energy boundaries used in every charging procedure. The data are processed and the necessary graphs are presented  

6. 7.

8.

9.

4. Case Study Network – Data – Scenarios The application that is being studied is the 15kV MV rural distribution line of Ikaria where wind energy penetration is so high that wind production often exceeds demand. Initially, the study focuses on examining the possibility of absorbing the excess energy in order to inject it back into the network in periods of high load demand (peak periods). This MV power line (R22 in Figure 2a) is one of the three MV power lines of Ikaria’s network. It is chosen because of its great length and because it covers a high wind potential region while its load is characterized by seasonality. Peak load demand is about 2.39MW and minimum load demand is about 0.46MW. The total demand per year is about 9.220 MWh. The annual load demand distribution curve for the whole MV power line is given in Figure 2b. Storage unit EVs are assumed to be connected to the grid, thus providing storage capabilities. The EVs batteries size is considered as constant for all units and equal to 29kWh. More specifically, charging and discharging processes are carries out utilizing residential power outlets, so the maximum power absorption or output is about 4kW. Moreover, the hypothesis of battery adequacy for car mobility must also be satisfied. Every transportation is assumed to last for 20km and the demand for two transportations daily is kept constant. Transportations are limited in the range of two hours in the morning and two hours in the afternoon, since 70% of the fleet is assumed to be absent every time due to the stochasticity of the values. There are also technical limitations regarding battery’s condition since complete discharging is forbidden. The lower SOC limit is 10%. The losses during storage and output of energy into the grid must also be taken into consideration. Losses’ percentage is assumed to be equal to 10%.

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R22 line

Fig. 2. (a) R22. The 15kV MV rural distribution power line of Ikaria island; (b) Annual load demand distribution of R22 MV power line

Study Scenarios For Ikaria’s rural line system, the options presented in the Table I below were examined. Table 1. Study Scenarios Scenarios Low Penetration of EVs High Penetration of EVs

Number of EVs (N) 30 100

Wind Parks’ Power Output (MW) 1.2 1.6

The selection of the above terms was done on the basis of the existing network’s data. A number of vehicles that could meet the peak demand and be served by the current network is chosen. Larger number of vehicles does not significantly contribute in the conclusions of this study and is likely to cause grid stability problems. Something similar applies also to wind data, since the installation of a smaller quantity leads to complete absorption of the generated power from the grid and, consequently, there is no excess. Moreover, the installation of a larger quantity than necessary exceeds the grid’s stability limits and, to make matters worse, the excess wind energy is untapped. 5. Simulation Results The results from every simulation and the corresponding comments are presented below, in order to conclude to a comparative study. The necessary energy to be cut throughout the year during the hours of high demand per day is presented below, Figure 3a.



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Fig. 3. (a) Energy to be cut throughout the year; (b) Wind energy parks’ excess output

Fig. 4. (a) Wind energy absorption; (b) Distribution production from thermal units throughout the year;

The total energy to be cut throughout the year is 123.37MWh. During the summer months, the necessary energy to be cut is greater, which shows the smoothening of the peak load, and high power values appear for more hours. Initially, the nominal output of the wind park is assumed at 1.2MW. The chosen capacity does not cause operational issue to the grid. In order for the load demand to be satisfied, great amounts of energy are absorbed by the power network with the exception of the days depicted in Figure 3b. The total available energy from Wind Power Parks is 26.43MWh. This quantity is very low since most of it is absorbed by the base load demand. Nevertheless, EVs’ charging process is adjusted in such a way that wind power output excess is also tapped by EVs. Since wind power output is so limited, the main charge load will be covered by thermal units. As mentioned before, load coverage depends on the number of EVs, given the wind turbines capacity and the load demand. 5.1. Scenario 1 – Low Penetration of EVs (N=30) Initially, it can be noted that penetration of few EVs does not aid significantly in reducing peak loading. Due to charge limitations of the vehicles, wind generation has to be curtailed still and thus remains unexploited. Additionally, during mobility hours EVs cannot be charged as it is expected and thus more potential wind energy is not absorbed by the system. The results clearly indicate that the main problem appears in peak days. Whilst the maximum cut off energy is about 1 MWh/day, its coverage capacity is limited at 0,4 MWh/day due to the maximum storage capacity constraint. Consequently, the total energy to be cut throughout the year is 101,9 MWh which corresponds to a 82,5% coverage percentage (compared to 123,37 MWh). The rest 21,93 MWh correspond to the energy that is not covered in periods where the load demand is at its peak, namely at summer or winter months. One part of the energy available from wind parks is available from EVs. The limitation of power charge again does not allow the absorption, at times when the excess energy is above technical limits. Moreover, any excess energy at times of vehicle mobility or at peak times cannot be exploited (charging or only discharging operation).

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Fig. 5. (a) Batteries’ initial SOC; (b) Wind energy parks’ excess output (N=100, P=1.6MW)

Fig. 6. (a) Wind energy absorption; (b) Batteries’ initial SOC;

The overall wind energy absorption is 14.08MWh as seen from the above Figure 4a. The non-exploitation of wind power is high in those days where the excess energy is very high and because of the EVs’ limited capacity the rest is not utilized. The total value is equal to 12.35MWh. The rest of the energy necessary for the energy equilibrium during peak hours, and the coverage of the required energy for mobility is given by thermal power units at low load demand (night hours). This amount of energy is equal to 141.32MWh for the whole year and its distribution throughout the year is presented in Figure 4b. The initial EV battery’s condition is presented in the Figure 5a. For most hours, this value corresponds to the lower SOC limit (10% SOC) except from the days with excessive WTs production. These days, charging is carried out on the basis of maximum discharge and consequently batteries charge as much as they can depend on the available, spare, WTs production. Briefly, the exploited wind energy is equal to 53.27% of the available energy from Wind Power Parks. Of course, grid’s energy absorption from wind parks is very high, so the exploitation margins are limited. 5.2. Scenario 2 – High Penetration of EVs (N=100) Since 1.2 MW of wind power capacity has proven insufficient to provide enough energy for effective peak shaving that is why the case of augmented wind power penetration is examined, since it does not cause technical problems on the network because of EVs’ presence. The wind park's nominal output is now increased to 1.6MW. In this case, the wind parks’ excess energy is significantly increased and is equal to 154.5MWh for the whole year. Its dispersion through the year shows that the maximum excess is found on low demand-load periods, namely during spring or autumn, Figure 5b. The overall wind energy absorption is 90.22MWh as seen from the below Figure 6a. The coverage percentage is 58.4%, augmented from the case of low penetration by 9.6%. Nevertheless, the non-absorbed wind energy is significant since 64.28MWh remains unexploited. Even if EVs’ penetration increases too much, an important part of wind energy will not be absorbed since it concerns very high excess energy in very few days. Of course, the rest of the energy necessary for the energy equilibrium during peak hours, and the coverage of the required energy for mobility is given by thermal power units at low load demand (night hours). This amount of energy is now limited to 211.68MWh for the whole year. It both covers the mobility and the peak shaving needs.



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The initial EV battery’s condition is presented in the Figure 6b. For most hours, this value corresponds to the lower SOC limit (10% SOC) except from the days with excessive WTs production. 6. Conclusion In this paper, the isolated power system of Ikaria Island with high wind penetration and EVs was examined, in the context of manipulating charging patterns to reduce wind curtailment and effectively smooth peak loading utilizing primarily WT power generation. In the first scenario, where 30 EVs were present, a 53.27% utilization of unused wind power was achieved. The percentage is relatively low due to charging limitations, the vehicles not being plugged in during hours of excess wind generation and of course due to the limited battery capacity. Despite the large untapped wind energy potential, RES utilization was greatly augmented in this case proving that especially in isolated systems, V2G has a significant role to play in wind integration and load management. Subsequently, a second scenario with significantly larger EV presence was studied. 100 EVs in conjunction with additional 0.4MW of wind park capacity were coordinated. A 58.4% utilization of unused wind power was achieved, augmented from the case of low penetration by 9.6%. All in all it is clear that while EVs number increases, so does the coverage percentage of the peak energy to be cut. At the same time, the EVs’ energy needs which will be covered from further thermal unit output also increase. EVs combine the benefits of both static and mobile storage units. They are also flexible loads for the grid. This double role makes the V2G prototype very important. This scenario seems technically immature, but its implementation on isolated grids must not be excluded from a future implementation References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20]

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