Electric Vehicle Energy Storage Management for Renewable Energy Sources Exploitation A. Damiano, Member, IEEE, I. Marongiu, Member, IEEE, M. Porru, A. Serpi Dipartimento di Ingegneria Elettrica ed Elettronica Università degli Studi di Cagliari Cagliari, Italy
[email protected] Abstract - A novel management strategy for Electric Vehicles (EVs) storage systems is proposed in this paper. It aims to enhance the Renewable Energy Sources (RES) exploitation hour by hour, but prioritizing the EV mobility requirements. A mathematical modelling of the mobility system is firstly developed in order to estimate, hour by hour, the amount of EVs available to exchange energy with the electric grid, together with the average state of charge of their corresponding batteries. Then, a management strategy is properly introduced in order to increase the RES hourly energy production as much as possible, providing ancillary services too. The proposed management system is properly validated by means of a simulation study referred to a weakly interconnected power system, which is characterized by a strong RES penetration level too. The simulation results highlight the effectiveness of the proposed management strategy and the potentiality of the EVs storage systems in enhancing the RES exploitation.
I.
INTRODUCTION
Renewable energy sources (RES) are going to cover an important role in the future energy system. The increasing environmental awareness and the will to reduce both the dependence of fossil sources and the emission of greenhouse gases are orienting the energy policy of many governs around the world to strongly support the diffusion of RES. As a consequence, this energetic transformation is going to change the role of energy infrastructure on the base, and, in particular, of the electric power grid. Presently, the electric grid has a hierarchical structure, characterized by strong constraints, unidirectional energy flow and distinct differentiations in actions (generation, transmission, distribution and end use). The rapid diffusion of RES and the use of electric power grid as their main interface distribution system are mining, on the base, the power grid management models. In fact, the increase of generation sources, their connection to the distribution network, the variability, in time and space, of energy production and consumption, make the management models actually used no more suitable, forcing towards the adoption of new paradigms of energy management. In order to guarantee a factual improvement in the power system management under a high RES exploitation, novel electric distribution system models have been proposed, which generally resort to distributed energy storage systems (ESSs) [1]-[5]. The ESS sizing procedure mainly depends on technical and
economic constraints that involve the analysis of power quality, regulation and load following issues too [6]-[8]. Furthermore, it must take into account the different storage technologies and its performances in terms of response time, charging/discharging time at rated power, cycle efficiency, life cycle assessment, working life and the control timescale required for the power grid management [1]. In the case of the smart grid medium voltage distribution network, the use of electrochemical batteries is currently considered the most feasible and suitable solution in exploiting distributed storage systems. Particularly, at the present time, the integration of the electric mobility and the distribution network is considered one of the most interesting technical solution. Several studies that analyse the impact of plug-in electric vehicles (EVs) on the electric energy system have been presented in the technical literature. As a result, it has been shown that the benefits achievable through the increasing diffusion of EVs are superior to the critical introduced [9][11]. Different methodologies have been proposed in order to manage the complementary in the energy use between the electric and the mobility system [12],[13]. In particular, the ancillary actions at distribution level are well-suited for EVs massive diffusion, from technical and economic point of views. Among these, the most valuables ones are the extra power supply, the peak load shaving, the load shifting, the spinning reserve and the frequency regulation [14]-[19]. A review of the technical literature reveals the existence of different methods to evaluate the limit of EVs penetration on a defined medium voltage electric distribution system. Nevertheless, these studies are generally based on a standard centralized power system vision of the supply chain. In the present paper, the potentiality of EVs storage systems in enhancing the RES exploitation is investigated in details. First of all, a mathematical model of the EV mobility system is properly introduced with the aim of determining, hour by hour, the total amount of EVs available to support the electric system as storage units. Then, their charging/discharging law is properly defined hour by hour by prioritizing the EVs mobility requirements and, then, by exploiting the RES energy production as much as possible. The effectiveness of the proposed EVs management strategy is proved by a simulation study, which is carried out referring to the Sardinia Island electric power system. In fact, this last one represents a
0.07
+1) ) (k ) (k ) E (k = E (k π π + dE δπ − R
Sunday Monday - Friday Saturday
0.06
+1) (k ) (k ) E (k = E δ(k ) − dE δπ − dE δξ − T (k ) δ
E
0.05 0.04 0.03 0.02 0.01 0 0
6
12 Time (hours)
18
24
Fig. 1. Statistical time distribution of EVs on the road (in per unit).
suitable benchmark, due to its high RES penetration level and its weak interconnections with the neighbouring electric systems. MATHEMATICAL MODEL
II.
A. Mobility System In order to well estimate the interaction capability of the EVs fleet with the electric grid hour by hour, an energy model of the EV mobility system is required. Hence, a classification of the EVs status is firstly introduced, basing on their mobility and plug-in electric grid conditions. Consequently, the whole EVs fleet is split in three sub-fleets: the first one (δ) represents all the EVs on the road, the second one (π) includes the EVs parked and connected to the electric grid, whereas the last one (ξ) groups all the EVs parked that are not connected to the grid. In particular, the definition of the ξ sub-fleet is mandatory to account for various phenomena, especially the possibility that a parked car cannot be connected to the grid due to the lack of charging stations. Then, in order to define the number of EVs belonging to each sub-fleet, reference is made to mobility habits, in particular to a statistical time distribution of the cars on the road, which has been properly deduced in [20] and depicted in Fig. 1. Then, the remaining number of EVs are distributed into the π and ξ groups in accordance with different factors that account for the spread of charging stations and other sociological and economic aspects. Then, denoting by ε the average sub-fleet state of charge, it is possible to compute the amount of energy stored into the EVs batteries of each sub-fleet at the k-th hour as in
E
(k) π
=n
(k ) π
⋅ε
(k) π
⋅ eb
E (k) = n δ(k ) ⋅ ε δ(k) ⋅ eb δ E
(k) ξ
=n
(k ) ξ
⋅ε
(k) ξ
(k +1) ξ
=E
(k ) ξ
+ dE
(2)
(k ) δξ
being R the energy exchanged with the grid in each hour and T the hourly consumption of the EVs due to their mobility. In particular, T is always positive, whereas a positive R value means that EVs batteries supply the electric system and a negative one means that they are recharging. Furthermore, dEδπ and dEδξ represent the stored energy variations due to the hourly EVs transition and/or turnover among the three subfleets, which are properly introduced in order to ensure the hourly replacement of the moving EVs, as depicted in Fig. 2. In particular, each EV that leaves a sub-fleet is assumed to be characterized by a battery state of charge equal to that of its native sub-fleet, except when a transition from the π to the δ sub-fleets occurs. In fact, in this case, it is assumed that each EV that is going to move is characterized by a state of charge (ε0) generally higher than the average one (επ). In addition, it is worth noting that the proposed model does not account for transition and turnover between the π and the ξ sub-fleets: in fact, it is assumed weakly probable that a car owner disconnects his car from the grid without using it for travelling. In conclusion, by employing the proposed mobility model, it is possible to determine, hour by hour, the average state of charge of each sub-fleet, an example of which is shown in Fig. 3.
Fig. 2. The EVs sub-fleets classification. 0.9 0.8 0.7 0.6 0
24
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96
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168
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48
72
96
120
144
168
24
48
72 96 Time (hours)
120
144
168
0.5 0.4
(1)
⋅ eb
0.3 0 0.5 0.4
being n the number of EVs belonging to the corresponding sub-fleet, whereas eb is the EV rated capacity. The energy stored in each group generally changes hour by hour in accordance with the following relationship:
0.3 0
Fig. 3. Time evolutions of the average state of charge of each sub-fleet: επ (green), εδ (yellow) and εξ (red).
B. Storage System
C. Electric System
Referring to a storage system made up of the EVs batteries, it can be defined by both its rated capacity Eb and power Pb. In particular, Eb is the maximum energy storable, being Pb the maximum power exchangeable with the grid. These values can be computed by multiplying the rated charging/discharging power and capacity values of each EV (pb and eb respectively) by the total number of EVs belonging to the π sub-fleet previously mentioned (nπ). Hence, since nπ generally varies hour by hour due to mobility requirements, the storage system is characterized by variable rated power and capacity, denoted by Pb(k) and Eb(k) respectively. As a result, referring to the generic k-th hour, the power exchangeable with the grid S(k) is bounded in accordance with Q(k) ≤ S(k) ≤ P(k)
(3)
being Q(k) and P(k) the maximum power absorbable by the grid and deliverable to the grid respectively. In particular, assuming η as the storage efficiency, Q(k) and P(k) can be defined as reported in (4): E ( k ) − E (0k +1) Q(k ) = max −Pb( k ) , − max ≤0 η P
(k )
{
(
(k)
( k +1)
= min η ⋅ Pb , η ⋅ E 0
(k)
− E min
)} ≥ 0
(4)
being Emax(k) and Emin(k) the maximum and minimum storage energy level respectively, here assumed as follow: E (max) = 0.8 ⋅ n (π ) ⋅ e b k
k
(5)
k) E (min = 0.2 ⋅ n (πk ) ⋅ e b
Furthermore, E0(k+1) is the estimated energy storage level at hour k+1 in the case of no energy exchange with the electric grid in the previous hour. Therefore, basing upon the mobility model introduced in (2), E0(k+1) can be computed as E (0
k +1)
+1) = E (k π
R
(k)
(6)
=0
In conclusion, once defined the energy exchange with the electric grid in the k-th hour, the energy storage level at the hour k+1 can be computed as +1) E (k = E (0 π
k +1)
− R (k )
(7)
Considering an electric system equipped with a storage unit, its power balance at the generic k-th hour can be expressed by the following equation: (k) P0( k ) + PRES + Q 0( k ) + S ( k ) = 0
(10)
being P0 the power delivered by programmable power sources, such as thermoelectric and hydro power plant, PRES the power delivered by time-variable RES and Q0 the estimated load demand. In addition, in order to preserve the electric grid stability, it is assumed that time-variable power production is upper bounded as in (k ) PRES + (1 − μ ( k ) ) Ps( k ) + (1 − ν ( k ) ) Q s( k ) ≤
≤ − (1 − σ ) ⋅ ( Q (0k ) + ν (k ) ⋅ Q s,k ) + PΓ( k )
(11)
being σ the grid stabilization coefficient, which represents the load share that must be supplied by programmable sources in order to ensure the grid stability [21], whereas μ and ν are appropriate dimensionless positive coefficients, smaller than one. In particular, μ represents the programmability level of the storage power delivered to the electric grid, which takes into account the forecasting errors due to the inherent reliance of the EVs mobility model on statistical data. Similarly, the overall load demand is made up of Q0 and a fraction ν of the storage demand Qs, accounting for the reinstatement of the EVs mobility consumption. Furthermore, PΓ is the RES extra power deliverable to the electric grid thanks to the reserve service (Γ) offered by the storage system, which is upper bounded as follow: P (k) (k ) (k ) ⋅ ν Qs (12) Q(k) Furthermore, the introduction of a dimensionless reserve allocation coefficient (ρ) leads to PΓ definition by (13): Γ (k) ≤ P (k) − Ps(k) −
PΓ(k) = ρ ⋅ Γ(k)
(13)
As a result, equation (11) lower bounds the reserve service offered by the storage system for a given RES power production as follow: Γ (k) ≥
(k ) (k) (k) (k) (k) 1 PRES + (1 − μ ) Ps + (1 − σ ⋅ ν ) Q s + (k) ρ 1 Q + − σ ( ) 0
(14)
in which: P( k ) k R (k ) = s + η ⋅ Q(s ) η
(8)
being: 1 (k ) S − S(k ) 2 1 = S(k ) + S(k ) 2
Q s( ) =
(
)
Ps(
(
)
k
k)
(9) Fig. 4. ESS and electric grid constraints representations on the (S,PRES) plane.
k P(k) Q(k) S(k-1)
ESS
Pˆ
(k) RES
(k)
k+1
Q0 (k) PRES
S(k)
δ (k) Fig. 5. Equivalent block control scheme of the proposed storage management strategy
constraints introduced in the previous section, i.e. both equations (3) and (15). Hence, referring to Fig. 5, it is firstly necessary to determine the maximum storage power exchangeable with the electric grid by means of (4). Then, knowing the estimation of the load demand Q0, PRES can be maximized in accordance with
{
(k) (k) (k ) PRES = min Pˆ RES , PRES,max
Referring to the (S, PRES) plane, both (3) and (15) combine to identify the plane region highlighted in Fig. 4, whose points satisfy all the storage and electric grid constraints previously introduced. As a consequence, it can be employed in choosing, hour by hour, the most suitable storage system operation for a given RES power production level. In particular, such operating region is bounded by the straight lines rp and rq, which correspond to the upper and lower boundaries imposed by (3), and by the straight lines sμ and sν, both introduced by (15). Hence, since their slopes depend on the μ and ν values respectively, sμ and sν may rotate, hour by hour, around the point A that, on the contrary, does not depend on these coefficients values, as pointed out in Fig. 4 too. III.
PROPOSED STORAGE MANAGEMENT STRATEGY
The storage management strategy proposed in this paper is summed up in Fig. 5. In particular, it aims to define, hour by hour, the storage operation that is required in order to fully exploit a given RES energy production profile P̂ RES. Obviously, it is necessary to satisfy all the storage and grid
(16)
being: (k ) PRES,max = − (1 − σ ) Q (0k ) +
P(k ) + ρ
P (k) +max − (1 − σν ( k ) ) Q ( k ) − ν ( k ) , 0 ρ
As a consequence, by properly combining (12) with (14), the upper bound of the RES energy production can be achieved: 1 ν(k) P(k ) (k ) − σν ( k ) Q s( k ) + PRES ≤ − 1 + − μ ( k ) Ps( k ) − 1 + (k ) ρ ρ Q (15) P (k ) + − (1 − σ ) Q (0k ) ρ
}
(17)
In particular, assuming PRES equal to P̂ RES, it means that all the RES power production can be delivered to the electric grid. In such cases, as shown in Fig. 6, it is possible to select the most suitable value for S in order to properly manage the EVs batteries state of charge in accordance with the relation: k) k) S(min ≤ S( k ) ≤ S(max
(18)
being Smin and Smax the minimum and maximum power exchangeable by the storage system with the electric grid, which respectively correspond to the points P1 and P2 shown in Fig. 6. Therefore, S is chosen as close as possible to S*, which represents the power exchange desired by the storage system in order to reinstate the mobility consumption. Finally, after choosing both the PRES and S values, the strictly required reserve service Γ can be easily computed by means of Γ
(k)
(k ) (k ) (k ) (k ) 1 PRES + (1 − μ ) Ps + (1 − σ ) Q0 (19) = min 0, (k ) (k ) ρ + − σν 1 Q ( ) s
Then, in order to well-evaluate the performance of the proposed management strategy, reference is made to both the actual and forecasted RES hourly productions, denoted by PRES and P̂ RES respectively, which can be expressed as: (k) (k) (k) (k ) PRES = PRES,d + PRES, Γ + PRES,q (k) (k ) (k) = PRES + PRES,w Pˆ RES
(20)
in which PRES,d represents the RES power production level directly deliverable to the electric grid, PRES,Γ is its increase due to the storage reserve service and PRES,q is the RES power production absorbed by the ESS. Finally, PRES,w represents the RES power production that cannot be exploited due to electric grid and/or storage constraints, and, consequently, wasted. In addition, PRES,q can be also expressed as follow: Fig. 6. Graphical representation of the proposed storage management strategy.
T (k) T (k) (k) PRES,q = − ⋅ PRES,q + 1 + ⋅ PRES,q Q Q
(21)
being T the overall EVs consumption over a given period of time, whereas Q represents the total energy absorbed by the EVs batteries in the same period as reported in (22). T = T(k ) k
(22)
Q = Q(sk ) k
In particular, the first term of (21) accounts for the EVs energy reinstatement coming from RES, which is due to the mobility consumption only, whereas the second one represents the employment of the EVs batteries as a RES energy buffer. As a result, it is possible to define an appropriate performance index ψ as follow:
ψ=
P ( )
k RES,d
k
(k) + PRES, Γ −
T (k) T (k) ⋅ PRES,q + η2 ⋅ 1 + ⋅ PRES,q Q Q (23) (k ) ˆ P RES
k
In particular, it consists in the ratio between the overall actual non-programmable RES energy production and the corresponding maximum one, in which the last terms of the numerator is properly weighted by the overall energy buffer efficiency. As a consequence, higher ψ values entail less RES curtailment and, hence, their better exploitation. In conclusion, the overall EV batteries employment can be take into account by introducing the index φ as follow: ϕ=
η⋅ Q n EV ⋅ e b
(24)
which represents the average number of charge/discharge cycles performed by a single EV battery over a given period of time. IV.
SIMULATIONS
A. Simulation set-up
In order to validate the proposed EVs storage management procedure, a simulation study is performed by means of the Matlab software package. The Sardinia island is assumed as a benchmark because it represents a weakly interconnected power system, characterized by a strong RES penetration level
too. Two scenarios have been analysed: the first one refers to the 2009 Electrical Energy Sardinia database, which is characterized by a yearly energy consumption of about 12 TWh, a strong non-programmable RES installed power (600 MW of wind power, 40 MW of photovoltaic power and 44 MW of river hydro power) and a potential RES energy production of 750 GWh. The second one refers to an hypothetical 2020 Sardinia Energy target that is assumed in accordance with the European Strategic Energy Plan, which foresees the same yearly energy consumption, but a higher RES installed power. In the present paper, an increasing of 1400 MW of wind power and a 360 MW of photovoltaic power has been supposed in Sardinia in 2020 with respect to 2009, reaching about 2000 MW of wind power and 400 MW of photovoltaic one. Thus, the procedure described in the previous section is recursively employed for different sizes of the EVs fleet in terms of number of vehicles (nEV) and their corresponding rated power (pb). On the contrary, eb is assumed equal to 30 kWh in accordance with the average commercial values of EVs batteries capacity. As a consequence, a variable number of EVs between 103 and 2·104 is considered, which correspond to 0.1 and 2 per cent of the overall Sardinian car fleet respectively. In addition, pb varies between 1 kW and 20 kW, in accordance to commercial charging station rated powers. Hence, assuming an appropriate average daily distance covered by each EV, it is possible to determine the corresponding energy consumption, which is properly distributed over each hour in accordance with the statistical time distribution of the vehicles on the road previously mentioned. Furthermore, the average state of charge of each EV coming from the π sub-fleet (ε0) is set to 0.8 in order to well-guarantee its mobility. Then, the stabilization and the reserve allocation coefficients (σ and ρ) are set constant to 0.9 and 0.1 respectively. Contrariwise, the μ and ν coefficients properly vary hour by hour in accordance with the average state of charge of the π sub-fleet. B. Simulation results
Referring to the first energy scenario (@2009), the proposed management strategy has been firstly implemented
0.8
0.7
0.6
0
Fig. 7. The Pres time evolution at 2009 with nEV = 5000, pb = 2 kW.
24
48
72 96 Time (hours)
120
144
Fig. 8. The εδ time evolution at 2009 with nEV = 5000, pb = 2 kW.
168
Fig. 9. The Pres time evolution (MW) at 2009 (nEV = 5000, pb = 4 kW).
Fig. 11. The Pres time evolution (MW) at 2009 (nEV = 10000, pb = 2 kW).
0.8
0.8
0.7
0.7
0.6
0.6
0
24
48
72 96 Time (hours)
120
144
168
0
24
48
72 96 Time (hours)
120
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168
Fig. 10. The εδ time evolution at 2009 (nEV = 5000, pb = 4 kW).
Fig. 12. The εδ time evolution at 2009 (nEV = 10000, pb = 2 kW).
by employing 5000 EVs and considering a corresponding rated charging/discharging power pb of 2 kW. The hourly simulation results of the RES electric power delivered to the power grid over a week is reported in Fig. 7. In particular, it is possible to clearly distinguish the different contributions to the overall RES power production: the RES power production directly deliverable to the electric grid (PRES,d), its increase due to ESS reserve service (PRES,Γ) and that absorbed by the ESS (PRES,q). In addition, the wasted RES power production (PRES,w) is depicted too, thus the envelope of upper trace shows the potential RES power production (P̂ RES). It can be seen that PRES,Γ is much more relevant than PRES,q: it means that providing ancillary service by means of EVs leads to a stronger RES power production exploitation than that achievable resorting to energy buffering, preserving the EVs batteries employment at the same time. Nevertheless, it is worth noting that PRES,w is still quite high, denoting that the ESS is still undersized to fully exploit a such RES power production level. The corresponding time evolution of the average state of charge of the π sub-fleet is reported in Fig. 8. It can be seen that it stands firmly around 0.75, being not subjected to great fluctuations. This confirms that the connection to the grid of EVs batteries to provide ancillary services does not impair EVs mobility requirements, although it produces a frequency increase in the εδ ripple. Considering now the same number of EVs, the increase of
pb from 2 kW to 4 kW produces a sensible effects on the RES power exploitation, as shown in Fig. 9. This is mainly due to the greater availability of the reserve action due to the ESS power increase, which leads to higher PRES,Γ contributions. The corresponding εδ time evolution is reported in Fig. 10. Its comparison with Fig. 8 highlights an increase in amplitude of the εδ ripple due to the pb increase, as expected. Very similar results in terms of RES exploitation can be achieved by employing 10000 EVs with a rated charging/discharging power pb of 2 kW, as shown in Fig. 11 and Fig. 12. However, in this case, the εδ ripple is smoother due to both the increase number of EVs and the lower pb value, as expected. The overall simulation results achieved on the 2009 scenario can be well synthesized by the indexes ψ and φ previously introduced. Hence, referring to the (nEV,pb) plane, the ψ loci are depicted in Fig. 13, whereas the corresponding φ loci are shown in Fig. 14. In particular, different colour of traces represent different value of the indexes in accordance with the corresponding colorbars. The hyperbolic nature of the ψ loci determines that high pb values entail a relatively small number of EVs in order to achieve a given RES exploitation. The same result can be also obtained resorting to lower pb values and corresponding larger numbers of EVs. As a result, an unplanned increase of pb and/or nEV does not always assure better RES exploitation. In addition, referring to the φ loci depicted in Fig. 14, it can be seen that the EVs batteries
20
0.98
20
18
0.96
18
16
0.94
14
0.9
16 0.8
14 0.92
12
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10 8 6 4
12
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8
0.86
6
0.84
2
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10
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4 2
0.2
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0.6
0.8
1 nEV
1.2
1.4
1.6
1.8
2
x 10
0.82
0.2
4
0.4
Fig. 13. The yearly ψ loci at 2009.
0.6
0.8
1 nEV
1.2
1.4
1.6
1.8
2
x 10
0.4
4
Fig. 15. The yearly ψ loci at 2020
20
170
20
18
160
18
16
150
16
14
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14
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10
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6
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4
90
2
400 350 300
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4 2
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0.8
1 nEV
1.2
1.4
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2
x 10
80
0.2
4
0.4
0.6
0.8
1 nEV
1.2
1.4
1.6
1.8
2
x 10
100
4
Fig. 14. The yearly φ loci at 2009.
Fig. 16. The yearly φ loci at 2020.
employment due to the RES exploitation decreases as nEV increases. Similar remarks can be made with respect to the hypothetic Sardinia Energy Scenario at 2020, whose corresponding simulation results are depicted in Fig. 15 and Fig. 16. The comparison between the two scenarios can be better performed referring to both Table I and Table II, which highlight the ψ and φ values in correspondence of some (nEV,pb) pairs of values for both the Sardinia scenarios. It highlights that the proposed management strategy allows to handle even strong RES penetration levels by means of
reasonable amounts of EVs, characterized by household values of rated charging/discharging power too. Furthermore, such results can be achieved without strongly increasing the charge/recharge cycles of the EVs batteries.
In this paper, a novel management strategy of EVs batteries has been proposed. It consists in employing the batteries of the parked EVs as an ESS devoted to enhance the RES exploitation as much as possible, but prioritizing the EVs mobility requirements. In particular, a mathematical modelling
TABLE I THE PERFORMANCE INDEX ψ AT 2009 (2020)
TABLE II THE EVS BATTERIES EXPLOITATION INDEX φ AT 2009 (2020)
pb
2 kW
4 kW
8 kW
12 kW
20 kW
1000
0.800 (0.395)
0.836 (0.425)
0.890 (0.478)
0.906 (0.496)
0.906 (0.496)
5000
0.915 (0.506)
0.981 (0.612)
0.988 (0.757)
0.988 (0.794)
10000
0.980 (0.612)
0.988 (0.764)
0.988 (0.918)
15000
0.988 (0.696)
0.988 (0.861)
20000
0.988 (0.763)
0.988 (0.923)
nEV
V.
pb
CONCLUSIONS
2 kW
4 kW
8 kW
12 kW
20 kW
1000
74 (138)
116 (219)
159 (361)
175 (424)
175 (426)
0.989 (0.795)
5000
73 (130)
99 (191)
105 (278)
105 (305)
105 (306)
0.988 (0.939)
0.989 (0.937)
10000
72 (124)
83 (170)
83 (223)
83 (233)
83 (233)
0.989 (0.968)
0.989 (0.968)
0.989 (0.968)
15000
71 (118)
76 (155)
76 (188)
76 (189)
76 (189)
0.989 (0.970)
0.989 (0.970)
0.989 (0.970)
20000
70 (114)
72 (143)
72 (161)
72 (162)
72 (162)
nEV
of the mobility system has been firstly introduced in order to estimate, hour by hour, the amount of EVs available to exchange energy with the electric grid. Then, a novel management strategy is developed with the aim of increasing the RES hourly energy production. Its effectiveness has been properly validated by means of a simulation study, which refers to a weakly interconnected power system, characterized by a strong RES penetration level too. The simulation results highlight that providing ancillary services by means of EVs batteries allows a better RES exploitation than that achievable by means of energy buffering only, preserving the EVs batteries employment at the same time. REFERENCES [1]
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