A Novel Electric Vehicles Charging/Discharging ... - IEEE Xplore

IEEE ICC 2017 SAC Symposium Communications for the Smart Grid Track

A Novel Electric Vehicles Charging/Discharging Scheme with Load Management Protocol Dhaou Said, Hussein T. Mouftah School of Electrical Engineering and Computer Science (EECS), University of Ottawa, Ottawa, Canada Emails: [email protected], [email protected] Abstract—In this paper the bidirectional power flow between electric vehicle (EV) and grid; Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G), is exploited to reduce the negative impact of the huge EV penetration on the current electric networks. We make profit from the unused electric power of EVs and we present an EV load management technique based on EV charging and EV discharging coordination. We propose two algorithms: the first one is the peak load management (PLM) used to schedule EVs for charging or discharging service according to the power demand with the timing and location where each EV need to be served, the second one is the guidance algorithm (GA) used to guide each EV to the appropriate EVSE in the way to reduce its waiting time to plugin. Those algorithms are evaluated while considering mobility of vehicles in an urban scenario and time-of-use-pricing (TOUP). Simulation results show the effectiveness of the proposed approach when considering realistic EVs and charging station characteristics and constraints. Index Terms—EV, EVSE, V2G, G2V, Load management. I. INTRODUCTION The high market penetration of electric vehicles (EVs) translates into a high number of EVs needing a charging service at any time, is expected to increase smart grid solicitation and creates negative impacts on the current electric networks especially when there are no control charging patterns [1, 2]. For example, the EV chargers can inject harmonics to the grid which can disrupt its stability. Also, they can increase the losses in the grid both by potential overloads on regional transformers and the overheats of the electric cable, circuit breakers and fuses, and by causing excessive power demands on the electric distribution especially during peak periods with frequency and voltage regulation problems. This EVs impact can be intense in areas such as in cities especially when EVs start the charging process at the same time. On the other hand, if the number of electric vehicles supply equipment (EVSE) planned and deployed does not meet the demand, consumer satisfaction will be negatively impacted. Therefore, not only the smart grid (SG) needs to satisfy EVs demands, it also needs to meet EV users’ expectations in terms of availability of nearby plug-in sockets, of fast time to-plugin, and of fast charging, all while preserving grid stability.

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Besides the challenges of EV charging, EVs can act as distributed energy storage system and offer new opportunities such as storage capacity. They can provide power to the electricity system whenever connected. For example, it is shown in [3] that if 25% of cars in US are EVs then the current storage capacity will be doubled which can improve the efficiency of the electric network. As a result, and to make profit from EVs, an intelligent integration of EVs into the electric system can potentially have a positive impact on SG. Indeed, the EV, seen as a new electricity power actor similar to mobile plant, and the SG functionalities will become of special interest where the energy available will not be only limited but also fluctuating. Moreover, the efficient energy management of EVs supply will become central to achieving efficient operations of the SG. For this end, advanced scheduling algorithms, seen as a part of Vehicle-to-Grid (V2G) interaction [4], are necessary. Added to the number of advantages made recently in terms of the physical infrastructure and technologies for EVs (fast charging supply, load balancing management techniques etc.), the uncertain behavior of EVs in terms of required electric power remains, sometimes, not circumvented and may lead to grid instability. Our contributions in this paper are: 1) We present a peak load management (PLM) scheme used to schedule EVs for charging or discharging service according to the power demand with the timing and location where EV needs to be served. This scheme considers the price of the electricity according to the Time Of Use Pricing (TOUP) policy and it supposes prior EV/SG communication when EVs are on road. It takes profit from various EV profiles and the unused stored power in EV batteries to contribute to the utility meeting its peak load demand. An algorithm, called PLM, is adopted for this scheme. 2) We propose a guidance algorithm (GA) used to guide each EV to the appropriate EVSE in the way to reduce its waiting time to plugin. 3) Finally, we demonstrate that the proposed algorithms (PLM and GA) can effectively manage EVs charging and discharging demand within the defined constraints and improve the grid stability while considering realistic EV charging/discharging characteristics. The remainder of the paper is organized as follows. In Section II, we present some related works. Section III presents the system overview considered in this work. In Section IV, we formulate our proposed peak load management model. The


guidance scheme is presented in Section V. The performance evaluation of the proposed PLM with GA algorithms in term of EV satisfaction and grid stability are presented in Section VI. Finally, conclusions are drawn in Section VII. II. RELATED WORKS The literature on G2V and V2G interaction is vast and covers various aspects of load management based on scheduling of EV charging and EV discharging process. For example, a stochastic modeling for EV charging processes has been described in [5]. The model provides a characterization of grid operation conditions, voltage profiles, branch loading, grid peak power and energy losses. A Monte Carlo model for real EV commuting patterns with its system load was introduced in [6]. In [7], authors derived the parameters needed for scheduling EVs charging without taking into account factors such as EVs trips or maximum charging time for each individual EV. However, in all these works, the problem of directing EVs to charging stations with grid stability specifications consideration and EV needs in terms of reduced waiting time to plugin are not addressed. Reference [8] proposes a charging and discharging dispatching scheme for EVs based on dynamic programming algorithm while minimizing the customer’s costs. In [9], the authors propose a dynamic multi-objective heuristic EV scheduling model for EV charging and discharging process. The model is based on a multi-objective optimization scheme to maximize the aggregator’s profit and minimize of EV charging cost. Those two works suppose all EVs already plugin to the grid before running the proposed scheduling techniques. The case where EVs need to communicate with the grid before plugin phase to let SG takes profit from the unused storage power of EVs at peak periods and to improve the EVs satisfaction in terms of minimizing the waiting time to plugin are not considered. Recent work [10] attempted to maximize the energy exchange between EV and grid by considering vehicle-to-home (V2H) and V2G interaction schemes to improve the grid reliability. Nevertheless, the proposed scheme does not consider a specific charging mode (slow, rapid or fast) as in realistic situations. Also, it does ensure neither any interaction between EVs and grid before the EV plugin phase nor a regulation service procedure especially in peak loading periods. In this work, we propose an EV charging/discharging protocol with load management strategy. This protocol allows the grid to alleviate the load at peak periods. Also, it lets EVs provide the grid with some of their unused stored power (discharging process) when power demand and power prices can be twice as high as those of off-peak hours. Moreover, since some EVs may have diverse application descriptions while using the same charging service, the proposed scheme in this paper coordinates the EV charging and the EV discharging process before the plug-in phase. This coordination scheme can be exploited by the SG to take advantage, when necessary, of the power charge stored in batteries of EVs by promoting the EV discharging process.

III.

SYSTEM MODEL

We consider the system overview such as illustrated in Fig. 1, where smart grid can be linked wirelessly with EVs since they are on road side prior to plugin to plan their needs on charging or discharging service. The smart grid is also connected by some communication technologies to EVSEs for the update of their states.

Charging stations (EVSEs) Scheduling V2G & G2V /

Update of the EVSEs profiles

Communications

Management & Control Algorithm

Power Grid

Smart Grid FIG.1. SYSTEM OVERVIEW

We assume that an EV can communicate its profile to the SG via a road side unit (RSU) to know the nearest available EVSE. This EV profile is described by the current EV position (Position EV) and its trip (Trip EV) and its current state of charge (SoCEV). We do not consider the service live of each EV battery. The EVSE availability and localization are known by the SG. We consider the peak load demand period and we suppose that the SG can broadcast an advertisement to encourage EVs to sell the charge of their batteries that they do not need. This charge can be used to support SG and to reduce the peak load demand. In the following we present the details of our peak load management scheme and our guidance technique. IV.

PEAK LOAD MANAGEMENT MODEL

We suppose all vehicles are traveling in a city with a speed that cannot exceed a certain value (e.g., 50 km/h). We suppose also EVs, SG and EVSEs can communicate with each other while EVs are on the road. Based on the EVs profiles (Position EV, SoCEV and TripEV) parameters, the SG calculates for each EV the required power if the EV has a trip (TripEV in terms of SoC) and starts the scheduling phase for the charging or discharging process according to EV needs. We suppose that the full battery is sufficient to complete the trip of


maximum distance on a single charge (D_Max) which is 172km for Nissan Leaf, and 270km for Tesla. The SoC needed to fulfil a trip of distance (D) is given by the following relation: SoC EV (1) SoC D   trip

D _ Max

where  is the driving efficiency factor which depends on driving efficiency, driving environment (city or highway), inverter efficiency (AC to DC) and charging or discharging efficiency. We propose a new EV-SG-EVSE interaction scheme for the peak load period considering EVs’ preferences. The SG uses our PLM model to decide which EV will participate in the charging or discharging process based on the preferences and profiles of the EVs. The charging and discharging process will be given by the following equations: SoC EV ( Ch )  SoC EV  I (2)  K Ch * D Ch * T EV

charging until the power level is sufficient. In case that the EV has no planned trips, the proposed PLM schedules the EV for the discharging process until the EV battery depletion threshold. Our PLM technique updates the EVs profile in the network and repeats the same process every T second. This period T can be used to give the chance to EV to change or update its charging or discharging plan during the time before reaching any EVSE. The SG returns by the end, the address of the EVSE suitable for the considered EV. Fig. 2 shows a flowchart of the proposed EV- SG-EVSE interaction. We adopt for our scheme the following PLM algorithm given by Algorithm. I. We take into account the EV preferences given the profile and the price politics doted in the peak load period. Additionally, the proposed PLM algorithm can give the EV the opportunity to change its travel schedule at any time before plug-in phase. This decision is considered at each time slot T. The algorithm takes also into account the lifecycle of the EV battery and it also supports the grid on peak time which can minimize the power demand.

_ schedule

SoC EV ( Disch )  SoC EV  I  K Disch * D Disch * T EV _ schedule

(3)

@ EVSE

Where  SoC EV  I is the initial EV state of charge, 

KCh and K Disch are respectively the charging and the



discharging efficiency. These two parameters describe the battery efficiency and performance of the EV. They can be affected by several factors such as: the frequency of charge/discharge, the discharge rate (voltage, current), the operations conditions (temperature) etc. The D Ch and D Disch are the charging and discharging rate.



TEV _ schedule is the timing dedicated to EV charging or EV discharging process. This parameter can be deducted from eq.(1) or eq.(2) by replacing SoC EV ( Ch ) or SoC

EV

( Disch ) by the final state of charge of EV.

We can calculate the total expected power which can be given by EVs to support the SG as: (4) PTot ( EV SG)   (SoCEV  SoCTrip * f trip  SoCBDT ) EV

where

f trip

0  1

if if

no trip trip

where SoCBDT is battery depletion threshold (BDT) and ftrip is a Boolean function used to indicate if EV has a trip or not. The main concept of our PLM proposal is explained as follows; we suggest our scheme for peak time, where the electricity prices and the power demand is higher than other periods. However, we consider the EV’s travel plans communicated to SG at the beginning; we propose to consider the EV’s trip schedule every time slot T. This time slot is used to update the EV profile considering the mobility speed of the EV on road. Our PLM scheme considers the EV trip; it calculates instantaneously the battery power needed for the trip and checks if the SoCEV in the battery is sufficient. If the SoC is not sufficient the PLM model schedules the EV for the

Smart Grid

EV profile

Peak Load Management Algorithm EV profile

EVSE Status

Guidance Algorithm (GA) EVSE Status

@ EVSE

FIG. 2. OUR PROPOSED EV-SG-EVSE INTERACTION

Algorithm I. PLM algorithm Input: EV profiles, price profile, T Output: EV scheduled for charging or discharging 1. Every T time do 2. SG updates all EV profiles received 3. If peak or mid-peak period 4. 5. 6. 7.

For each EV profile do If Trip EV > 0 Calculate SoCTrip according to Eq.(1) Check SoCEV If SoCEV > SoCTrip

EV scheduled for discharging process until 8. 9. 10.

SoCTrip Else

EV scheduled for charging process until

SoCTrip EndIf Else (TripEV = 0)

EV scheduled for discharging process until SoCBDT Battery Depletion Threshold (BDT) 11.

EndIf

12. EndFor 13. EndIf


V.

GUIDANCE MODEL

Now, we focus on the interaction between SG and EVSEs. We consider the case where historical and real time data (EVSEs positions, EVSEs states, peak period, power price, forecasting of power that will be available etc.) will be provided to and from SG. Thus, SG can guide EVs to the best available EVSE in the way to enhance the grid stability during the day and ensure the EV satisfaction in term of power requested with reduced waiting time. Indeed, according to the EV profile description and its charging or discharging needs, the SG can select from available EVSEs the nearest one with the reduced waiting time. We suppose that the time needed by any EV to reach any EVSE can be calculated by some navigation tools which can easily calculate time to destination given a current position, and traffic conditions. For an EVSE ranked k, (k is from 1 to K), the expected waiting time (EW) for a new comer EV can be given by: (5) EW EVSE ( k )   TEV _ schedule (i )



Notation

TABLE I SIMULATION PARAMETERS. Value

T

 ( Nissan Leaf)

30 second 86% [14]

D ch, DDisch Charging time

60 kW DC [11,12,13] 20-30 min [14, 15]

NEV Number of EVSE, K SoC

1000 EV 20 Uniform distribution between 20% and 100%

EV Battery capacity

24 kWh [2, 12]

We use the TOUP as the electricity price strategy for all the study. Fig. 6 shows the price variation with three level; 0.08$/kWh during the off-peak hours and double this value i.e., 0.16$/kWh during peak hour and 0.122$/kWh for the mid-peak period [1].



EV ( i )EVSE ( k )

where K is the total number of EVSEs and the expression ( EV ( i )  EVSE ( k ) ) means a set of EVs which are assigned or plugin to kth EVSE. For each EV, the expected waiting time is the cumulative time needed by EVs coming before and assigned to the same kth EVSE. We present in the following our guidance algorithm (GA) used by SG to select the suitable EVSE for each EV. Algorithm II. Guidance Algorithm (GA) Input: Peak period, output of PLM algorithm, K Output: @ EVSE /* selected station */ 1. For each new comer EV do 2. For k=1 to K do 3. Calculate EW EVSE (k ) according to eq.(5), 4. 5. 6. 7. 8.

EndFor Select the nearest EVSE with minimum EW EVSE SG sends @ of the suitable EVSE to EV EVSE send its state (queue length, occupancy). EndFor VI.

P ERFORMANCE EVALUATION

In this section, we present the simulation results and discussions about the performance of our proposed EV charging/discharging protocol described by the PLM and the GA algorithms. We consider a scenario where 1000 EVs and 20 EVSEs are sharing a road infrastructure area. We suppose that the EVSEs positions distribution is random. All vehicles are travelling at a maximum speed of 50 Km/h. We used MATLAB to perform the simulations. We assume that all EVSEs are equipped with a level 3 plug-in which is the most rapid kind of EV charger. This kind of charger is the one expected to be adopted soon for public EVSEs. The parameters for our study are presented in Table I.

F IG. 6. PRICE VARIATION

To prove the performance of our proposed scheme, we study the peak power demand reduction level of our (PLM+GA) algorithms. For this issue, we compare the average EV (dis)charging power among three variants: (PLM+GA), without (PLM) only and with Nearest Station Selection Algorithm (NSSA) only mode. In NSSA, an EV is assigned to the nearest EVSE without taking into account of the cumulative EV charging or discharging time needed by all EVs in each queue. Fig. 7 and 8 compare (PLM + GA) (represented by blue curve) and our GA only (highlighted by green curve) and the NSSA only (highlighted by black dash curve). Table II, Table III and Table IV summarize the observation results obtained from Fig. 7 and Fig. 8. As shown in Table II, it is clear that our (PLM+GA) algorithms reduce the peak power demand with a saving rate of more than 27% and 24 %, respectively, for the peak period ([5:00 10:00]) and mid-peak period ([16:00 19:00]). This result proves the effectiveness of our proposed (PLM + GA) algorithms in term of reducing the grid stress. The Table III compares the performance of our scheme with and without our PLM algorithm. The result is showing that our PLM algorithm reduces the peak power demand with a saving rate more than 22% and 19 %, respectively, for the peak ([5:00 10:00]) and mid-peak period ([16:00 19:00]). The Table IV presents the performance of our GA algorithm compared to the NSSA one. It is clear that our GA performs the peak power


demand reduction by a saving rate more than 5 % for the peak and 7 % for mid-peak period. All those results prove that with our proposed scheme, the grid can manage more efficiently the EV charging and discharging process especially in peak and mid-peak period. Those results can be explained by the fact that our scheme coordinates charging and discharging processes and allows grid to take profit from the unused stored energy in EV batteries especially in peak and mid-peak period. We study the performance of our scheme (PLM+GA) in term of the number of EVs served with the charging or the discharging process. Fig. 9 shows the number of EV served during the day with and without our proposed scheme considering the TOUP model. In addition to the results described in Fig. 7 and 8, we observe from Fig. 9 that our proposed scheme improves significantly the total number of served EVs even more during the peak and the mid-peak period.

TABLE II PEAK POWER DEMAND PERFORMANCE OF OUR PROPOSED SCHEME (PLM+GA) Our proposed scheme (PLM+GA)

Without (PLM+GA), only NSSA

Peak demand in: [5:00 10:00]

370

510

27.4%

Peak demand in: [16:00 19:00]

210

280

25%

Saving rate

TABLE III PEAK POWER DEMAND PERFORMANCE OF OUR PLM ALGORITHM Our proposed scheme (PLM+GA)

Peak demand in: [5:00 10:00] Peak demand in: [16:00 19:00]

Without PLM, only GA

Saving rate

370

480

22.9%

210

260

19.2%

TABLE IV PEAK POWER DEMAND PERFORMANCE OF OUR GA ALGORITHM Without PLM, only GA

Peak demand in: [5:00 10:00] Peak demand in: [16:00 19:00]

Without(PLM+GA), only NSSA

Saving rate

480

510

5.8%

260

280

7.1%

FIG. 7. G RID POWER P EAK REDUCTION USING THE PLM AND GA ALGORITHMS FOR THE PEAK PERIOD [05:00 10:00]. F IG. 9. THE PLM AND GA ALGORITHMS PERFORMANCE : IMPROVEMENT OF THE NUMBER OF CHARGED EV S.

VII. CONCLUSION

FIG. 8. G RID POWER PEAK REDUCTION USING THE PLM AND GA ALGORITHMS FOR THE MID -PEAK PERIOD [16:00 19:00].

In this paper, we propose an Electric Vehicles Charging/Discharging protocol based on V2G technology, which aims to use the PEV battery as energy storage. Two algorithms are proposed; first one is the PLM which is used to schedule EVs for charging or discharging service according to the power demand with the timing and location where EV needs to be served. This algorithm uses efficiently unused stored energy in PEV battery to supply the grid to fulfil power demand especially in peak times during the day. The second one is the GA which is used to guide EVs to the supply station for charging or discharging process. This algorithm minimises the waiting time for each EV before plugin phase. Those two algorithms are tested through simulations considering realistic EVs and EVSE constraints. Simulations show that this proposed scheme manages the EV charging and the EV discharging process in an efficient way.


In the future, we plan to extend our proposed scheme onto a global guidance system for EV which will be an enabler of SG. EV can be also seen as a principal actor for the electricity markets especially with multi charging service providers and possible incentives for EV users to allow discharging their batteries at peak time periods, when the price and demand are both high, to promote grid stability. Moreover, future EV will be an enabler of the smart cities concept; by exploiting the benefits of Internet of Things (IoT) technology in EV context such as Internet of EV (IoEV), we can consider the autonomous connected electric vehicle (ACEV) to manage smartly the human and goods traffic mobility (mobility service provider, business to business (B2B) model) considering the smart cooperative transportation with smart security. REFERENCES [1] [2]

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