Co-simulation study of performance trade-offs between centralised, distributed, and hybrid adaptive PEV charging algorithms

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Computer Networks journal homepage: www.elsevier.com/locate/comnet

Co-simulation study of performance trade-offs between centralised, distributed, and hybrid adaptive PEV charging algorithms✩

Q1

Samah Mansour a,∗, Intissar Harrabi a, Martin Maier b, Géza Joós a

Q2

a b

Electrical Engineering from McGill University, Canada Institut National de la Recherche Scientifique (INRS), Montréal

a r t i c l e

i n f o

Article history: Received 5 July 2014 Revised 11 June 2015 Accepted 21 August 2015 Available online xxx Keywords: Co-simulation Decentralised Hybrid Plug-in electric vehicles Smart grid communications

a b s t r a c t In this paper, we propose a decentralised algorithm for coordinating the charging of multiple plug-in electric vehicles (PEVs) and extend it to a hybrid version by including the PEV’s charging strategies into the optimisation function. Going one step further, we also consider coordinated workplace charging integrating PV panels. The main focus of our work is to conduct a comparative study between a centralised benchmark algorithm, and the proposed decentralised and hybrid approaches from both communications and power perspectives. All algorithms are co-simulated over a converged fiber-wireless communication infrastructure of 342 residential customers. Power system results prove the efficiency of the proposed algorithms providing up to 19% peak shaving while meeting drivers’ requirements. Communication results of the decentralised algorithm compared to a centralised benchmark scheme provided better performance, measuring an upstream traffic rate of 1.28 Mbps with a maximum delay of 0.629 ms. Compared to the decentralised algorithm, the hybrid algorithm showed a promising improvement for large fleets of PEVs accompanied with an overhead communication cost that is significantly less than that of the centralised algorithm. © 2015 Elsevier B.V. All rights reserved.

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1. Introduction

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Smart integration of electric mobility into smart micogrids becomes a dominating topic in our emerging low carbon society. The distribution of electric vehicles (EVs) has the potential to produce both social and economic benefits, such as traffic noise reduction, air quality improvement, decrease of greenhouse gas emissions and potential economic savings [1]. Replacing gasoline vehicles with plug-in electric vehicles (PEVs) could reduce the importation of oil

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✩

This work was supported by NSERC Strategic Project Grant No. 4134272011. ∗ Corresponding author. Tel.: +1 5146550069. E-mail addresses: [email protected] (S. Mansour), harrabi@ emt.inrs.ca (I. Harrabi), [email protected] (M. Maier), [email protected] (G. Joós).

by up to 52% in the United States [2]. Moreover, charging PEVs locally within smart microgrids by means of nearby renewable energy sources (RESs) in the same neighbourhood becomes a popular green approach [3]. PEVs can serve as loads or as a distributed energy resource. With their bidirectional connection to the grid, PEVs can charge (G2V mode) and discharge (V2G mode) and thus play a significant role in supporting the grid and providing ancillary services. This concept of bidirectional power flow enables PEVs to improve load balancing by consuming power at off-peak times and sending power back to the power grid at peak-load times [4]. However, the development of the V2G concept presents a major challenge since it depends on the PEV availability for charging/discharging scheduling, which in turn varies with the random behaviour of PEV owners. Another issue is that uncoordinated charging profiles of PEVs may induce severe grid problems and increase future

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Please cite this article as: S. Mansour et al., Co-simulation study of performance trade-offs between centralised, distributed, and hybrid adaptive PEV charging algorithms, Computer Networks (2015), http://dx.doi.org/10.1016/j.comnet.2015.08.036

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investments for network reinforcements. Utilities in the future will be struggling to accommodate the increasing number of PEVs. Based on the latter fact, the authors in [5] conducted an impact study on 2 feeders of Hydro-Québec to assess how the new load of the PEVs will affect the system operation. They concluded that the number of overloaded elements increases with the PEV penetration level (PL) and becomes relatively significant above a PL of 20%. In [6], the authors estimated the incremental investment costs and energy losses resulting from different PLs. They concluded that for a 60% PL, incremental investment costs can reach up to 15% of total actual network investment costs and energy losses of up to 40%. Hence, appropriate recharging and available energy supplies are essential to realise the benefits of V2G capabilities and manage the new PEV load [7]. Several studies dealt with adaptive charging to mitigate the drawbacks of random charging and plan the management of the foreseeable problem. The majority of these studies dealt with centralised algorithms assuming that the utility possesses full information about the vehicles’ key parameters and controls directly the vehicles’ charging and discharging activities. In our recent work [8], we proposed and investigated a coordinated centralised charging algorithm, called (IntVGR), using our co-simulator developed in [9]. The implementation of this centralised algorithm requires that the Distribution Management System (DMS) acquires global knowledge of all the PEVs connected. A major limitation of this approach is its infeasibility for large fleets of PEVs. Theoretically, full knowledge required in centralised algorithms achieves optimal results, but their implementation is computationally expensive and sometimes even intractable. On the other hand, decentralised algorithms are scalable, and assumed to have a lower degree of complexity and reduced communications requirements. Another advantage is that decentralised schemes may be implemented at the vehicle level without the need of a complicated central controller on the grid side. The authors in [10] proposed a decentralised algorithm for solving a local charging cost minimisation problem. This algorithm, however, doesn’t take into consideration the network’s technical constraints and disregards congestion issues related to the price signal. In [11], the authors proposed a decentralised algorithm that proves optimality in the case where all PEVs are assumed to have the same statistical data. In [12], day-ahead and real-time algorithms were suggested where the load-serving entity’s main concern is to maximise social welfare rather than its own profit. Both proposed algorithms were formulated probabilistically without implementation in a power network to test their actual efficiency for real applications. The main recurring problem with the literature dealing with decentralised charging algorithms is that the majority explores the problem from a purely mathematical perspective and neglects the dynamics and constraints of the actual power network. The contributions of this paper include the design and implementation of two charging/discharging coordination algorithms. First, we develop a distributed vehicle to grid (DVG) algorithm executed by each PEV at arrival time. This algorithm solves an optimisation problem addressing peak load reduction based on a load forecast broadcast by the DMS. Although the proposed DVG algorithm indicates a significant improvement in the communication perspective compared

to the centralised IntVGR, it shows some limitations in terms of load flattening. This leads us to formulate a new hybrid algorithm (HDVG), which is an extension of the first one and can be seen as a compromise between the centralised and decentralised solutions. Going one step further, we propose a workplace coordination algorithm that integrates RESs, specifically PV panels in public garages. This algorithm maximises the usage of the locally generated solar power and prioritises PEV’s charging activities based on their SOC and departure time. The workplace algorithm is implemented along with either DVG or HDVG algorithms in order to consider both home and workplace charging in our case studies. The literature is rich with useful charging coordination solutions, but a comparative study of the existing approaches is missing. The goal of this paper is to draw a comparison between centralised, decentralised, and hybrid approaches from both communications and power perspectives. We investigate the performance trade-offs between the three approaches by comparing our findings to our recent centralised algorithm IntVGR. The related literature discussed above approached scheduling algorithms by assuming the existence of a bidirectional smart grid communications infrastructure, whereas here we are implementing our suggested algorithms in a multidisciplinary approach by means of analysis and powerful co-simulation of both power and communication perspectives. Our algorithms integrate RESs at the workplace and abide by network constraints, whereby voltage deviations are handled and simulated with different PEV related statistical data using our co-simulator. The remainder of the paper is organised as follows. Section 2 presents a brief review of the considered smart grid communications infrastructure. Co-simulation configurations and the distribution system are described in greater detail in Section 3. Section 4 describes our charging/ discharging coordination algorithms with mathematical formulations provided. Section 5 presents and compares our cosimulation case studies and co-simulation results. Conclusions are drawn in Section 6.

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2. Integrated fiber-wireless smart grid communications infrastructure

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Since we approach our problem in a multidisciplinary manner, a communication infrastructure solution is needed. In [13], we have introduced a sustainable FiWi smart grid communications infrastructure that is based on IEEE 802.16 WiMAX or IEEE 802.3ah Ethernet passive optical network (EPON) technologies (see Fig. 1). EPON is the primary solution considered for highly populated urban areas, while WiMAX is the complementary solution deployed in rural areas where fiber is not available. Both WiMAX base stations (BSs) and EPON optical line terminals (OLTs) are interconnected with the DMS through an optical ring-star network. At the customer level, each PEV connects to the energy management system (EMS) interface unit through IEEE 802.15.4 Zigbee that is cost- and energy-efficient with convenient coverage. The household’s EMS connects to a WiMAX BS via subscriber stations (SSs), and to the OLT via optical network units (ONUs). The EMS unit and the ONU communicate via the wireless mesh network, through the mesh portal point (MPP) collocated at the ONU to aggregate sensor data, the mesh

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Fig. 1. Integrated fiber-wireless (FiWi) smart grid communications infrastructure.

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access points (MAPs), and the mesh points (MPs) that deliver packets between the MPP and MAPs. When a PEV needs to communicate with the DMS, a given packet is sent after a polling period in the EPON network and is then sent from the ONU to the OLT, which forwards the packet to the DMS. In this paper, we extend this solution to match our system requirements, as shown in Fig. 1. The main objective of our design is to connect the different entities of the smart grid, starting from the DMS at the top of the hierarchy down to the end users’ PEVs. The centralised benchmark algorithm (IntVGR) is implemented at the DMS level, whereas the scheduling of the proposed distributed algorithms (DVG & HDVG) takes place within the customer box shown in Fig. 1. Each PEV receives the necessary control signal from the DMS using either the EPON or WiMAX network in order to coordinate its charging activities. The difference between the implementation of the centralised and decentralised network architecture is described in more detail in the co-simulator description provided in Section 3.2.

3. Modelling of the distribution system and the co-simulator

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3.1. Distribution system

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Fig. 2 depicts our considered power distribution network, as implemented in the co-simulator from both perspectives using the open source simulation framework OMNeT++ and OpenDSS for load flow analysis. Each power node is mapped to its equivalent FiWi node. Mapping is done by creating a “PowerSysNode” class in OMNET ++1 with all the instances and functions needed to correspond to each power node defined in OpenDSS2 . The distribution network simulated is a modified IEEE 13-node radial distribution feeder consisting of 18 LV residential networks, each including 19 customers. Throughout simulation time and once a charging/discharging

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1 2

OMNeT++ is available at http://www.omnetpp.org/. OpenDSS is available at http://sourceforge.net/projects/electricdss/

Please cite this article as: S. Mansour et al., Co-simulation study of performance trade-offs between centralised, distributed, and hybrid adaptive PEV charging algorithms, Computer Networks (2015), http://dx.doi.org/10.1016/j.comnet.2015.08.036

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Fig. 2. Single-line diagram of power distribution network topology.

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action is initiated by the PEVs, OpenDSS receives a power flow request from OMNET++ with the updated network parameters. The implementation of our distributed network is based on the communications infrastructure depicted in Fig. 1. An EPON of 9 ONUs nodes, each equipped with a collocated MPP, with a line rate of 1 Gbps are distributed uniformly to cover the entire distribution network. We used 342 MAPs to connect the DMS to all the PEVs, whereby one half of the MAPs must communicate with an MP to reach an MPP and the other half communicates directly with its nearest MPP. Messages exchanged between PEVs and the DMS are written in XML format to ensure compatibility with the Zigbee Smart Energy standard.

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3.2. Distributed co-simulator model

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Our co-simulations are based on our recently developed co-simulator [9]. This co-simulator was developed for a centralised network architecture. To implement our network model and proposed algorithms, several modifications in OMNET++ were needed to transform this co-simulator to a decentralised one:

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• The communication node representing the PEV was redefined to run the scheduling algorithm in response to its self-scheduled event. • Interchanged packets between the DMS and PEVs are modified in size, content, and count. 7 different types of packets were used in the centralised co-simulator, whereas in our case only 5 types of packets were defined with less encapsulated information and lower exchange rates. More specifically, during each simulation cycle the following packets are exchanged: – Begin-Request packet: During initialisation each allocated PEV schedules a self-event at arrival time tarrival to initiate the scheduling process.

– Charging Request packet: When the simulation time reaches tarrival the PEV in action sends a packet containing the node ID, customer ID, vehicle ID, and scheduling deadline to the DMS. – Charging Response packet: the DMS receives the charging request and sends back another packet containing the load forecast/PEV aggregated load (for the hybrid algorithm). – PEV Control message packet: the DMS sends control messages to the PEVs in case of voltage deviations. – PEV Feedback packet: the PEV sends its charging strategy back to the DMS. This is applicable only to the hybrid algorithm case.

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Each message shown above uses an XML format to structure the information following the message format used in ZigBee Smart Energy. The XML message is encapsulated in an HTTP message. To better illustrate the nature of these packets, Fig. 3 shows an example of a charging request message sent by a PEV.

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4. Decentralised PEV scheduling algorithms

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As mentioned in the Introduction, achieving optimal results by using a practical centralised charging algorithm for scenarios with large fleets of PEVs may not be possible. More precisely, in the centralised scheduling algorithm the DMS collects information from nodes related to each PEV’s current state and the whole network state as well. Using this data, it aims at optimally scheduling the charging and discharging of the individual PEVs. Toward this end, the collection of information and the creation of optimised PEV charging schedules take place every time slot. For example, the centralised IntVGR [8] divides the 24 hours per day into 96 equal time slots, and the DMS prior to optimising the PEV scheduling by minimising total energy cost has to determine the daily per slot load profile in the network. Thus, its actual implementation requires high bandwidth and extensive bidirectional communication. To avoid this shortcoming, one needs to

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Fig. 3. Charging Request Message.

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adopt a decentralised charging strategy to improve the performance for scenarios with high penetration of PEVs. In the decentralised algorithms, the main objective of the DMS is to flatten the aggregated demand profile by shaping the PEV load. The DMS receives requests from the PEVs and then sends control signals enclosing the necessary information needed to run the coordination algorithm. This control signal contains the following information: • non-elastic load forecast for the DVG algorithm • non-elastic load forecast and the aggregated PEV load for the HDVG algorithm The aggregated PEV demand is the result of the DMS aggregating the feedback signals sent by all PEVs, enclosing their anticipated charging strategies. The following sections present the mathematical formulation for the different proposed decentralised algorithms. For illustration, the pseudo code of the major steps carried out to implement our proposed algorithms is shown below: The time interval between the arrival time and departure time is discretised into slots with a fixed slot size t. A PEV arrives at time tarrival and sends a charging request containing its node ID to the DMS. The DMS checks for voltage violations and in case one is found, it sends a control message deactivating this node for the current slot and postponing the request till next slot. If no violations occur, it sends back a response message encapsulating the necessary input information for each of the 2 algorithms. The PEV uses the control signal from the DMS to run the algorithm (DVG or HDVG) and decides on the optimal actions to be taken (charging, discharging, or idle). Finally, the PEV updates the SOC and calls OpenDSS to perform a power flow analysis.

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4.1. Algorithm 1: optimal decentralised distributed algorithm

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In this section, we formulate the decentralised distributed vehicle to grid (DVG) algorithm implemented at the PEV

level. The scheduling objective function addresses directly peak load reduction and flattening of the power-time curve. The distributed optimisation is done once at the initiation phase. Each PEV determines its optimal schedule based on the non-elastic forecast base load Dbase sent by the DMS, and the required charging hours. Dd is the desired load profile defined in (1) as the summation of the base load averaged over avg the time frame Dbase , and the required charging load Dchg :

Dd =

vg Dabase

+ Dchg ,

avg

tdeadline

t=tarrival

.

Dr (t ) = Dbase (t ) + xk (t ),

∀k ∈ M

xk (t )

= min



xk (t )



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(4)

Hence, the objective function is given by

Y1 (x) = min

286

(3)

After calculating the constant desired load Dd in (1), we add the real total load Dr (t) to the objective function. Dr (t) is defined in (4) by adding the instantaneous base load at each slot and the unknown charging/discharging load of the kth PEV denoted by xk (t):

tdeadline

285

(2)

dtravelled ×  . tdeadline − tarrival

tdeadline

282

Dbase (t )

tdeadline − tarrival

The charging load required per slot is calculated based on the average daily travelled distance dtravelled (in miles), and driving efficiency of the vehicle  (in kW/mile) as follows:

Dchg =

284

(1)

where Dbase is a constant value obtained by averaging the base load forecast over the scheduling period T avg Dbase =

283

301

(Dd − Dr (t ))2 , ∀k ∈ M

(5)

t=tarrival

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[Dd − (Dbase (t ) + xk (t ))]2 ,

∀k ∈ M

(6)

t=tarrival

Please cite this article as: S. Mansour et al., Co-simulation study of performance trade-offs between centralised, distributed, and hybrid adaptive PEV charging algorithms, Computer Networks (2015), http://dx.doi.org/10.1016/j.comnet.2015.08.036

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Algorithm 1 DVG , HDVG, and W-C/PV scenarios Require: PEVi Require: t: Current time Require: SOCi : State of charge of PEVi Require: PEV statistics: tarrival , tdeparture , dtravelled If t = tarrival Step1: PEVi sends charging request to DMS/parking aggregator pu pu If Vk,t < 0.95 & Vk,t > 1.05 Case algorithm DVG: Step 2: Controller sends Dbase back to PEVi Step 3: PEVi runs DVG by minimising (6) subject to (7) Step 4: PEVi updates SOC and calls OpenDSS for power flow analysis Break; Case algorithm HDVG: Step 2: Controller sends Dbase & DPEV agg back to PEVi Step 3: PEVi runs DVG by minimising (6) subject to (7) Step 4: PEVi updates SOC and calls OpenDSS for power flow analysis Break; Case algorithm W-C/PV: Step 2: Workplace parking aggregator segregates the PEVs request into 3 catagories: Preq > 4.25 kW→ Red priority level Preq <4.25 kW & >2.25kW→ Yellow priority level Preq < 2.25 kW→ Green priority level Step 3: aggregator runs W-C/PV by minimising (14) Step 4: PEVi receives the charging strategies from the aggregator Step 5: PEVi updates SOC and calls OpenDSS for power flow analysis Break; else Back to Step1 end if end if

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Subject to:

⎧ −Rmax ≤ xk (t ) ≤ Rmax , ∀t ∈ T , ∀k ∈ M ⎪ ⎪ ⎨Dr (t ) < Dmax (t ), ∀t ∈ T tdeadline x(t ) t ϕk = Cbattery , ϕk ∈ {0, 1} ⎪ ⎪ ⎩ tarrival pu Vmin < Vk,t < Vmax , ∀k ∈ M,

4.2. Algorithm 2: hybrid decentralised distributed algorithm (HDVG)

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The DVG algorithm doesn’t take into consideration the charging strategies of the other PEVs, thus the efficiency of the load flattening is affected for high PLs. In order to enhance the performance of the decentralised DVG algorithm we suggest to add a feedback between the PEVs and the DMS. Specifically, the PEVs decide on their charging profiles and send the decision to the DMS through a feedback signal. The DMS adds the reported PEV load and updates the control signal accordingly, then sends it along with the load forecast to PEVs requesting charging later on. The new algorithm is no longer a decentralised one, since each PEV is sending back to the DMS its anticipated charging strategy; we can rather claim it to be a hybrid algorithm. The objective function of the feedback signal sent to the DMS is as follows:

Y2 (xk (t )) = min xk (t )



tdeadline



2



Dd − DPEV agg (t ) + xk (t )

,

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∀k ∈ M

t=tarrival

(8) DPEV agg

denotes the PEV load aggregated at the DMS and where encapsulated in the control signal sent to the PEV. Y2 (xk (t)) minimises the difference between DPEV agg (t ) and the desired constant load Dd , whereas Y1 (xk (t)) uses the load forecast as input and solves for the vector xk (t) that minimises the difference between the base load forecast Dbase (t) and the desired fixed load Dd for each slot. To implement the new hybrid algorithm we need to combine both Y1 (x(t)) and Y2 (x(t)) in one weighted optimisation function. Let the overall cost function be a linear combination of the two objectives, given by:

E (λ, xk (t )) = λY1 (xk (t )) + (1 − λ)Y2 (xk (t )).

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(9)

For a given value of λ, there exists a value of xk (t), which minimises E(λ, xk (t)) and is given by

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E ∗ (λ) = min E (λ, xk (t )) = E (λ, xk (t )∗ ) xk (t )

= min (λY1 (xk (t )) + (1 − λ)Y2 (xk (t ))) (7)

where M represents the set of nodes in the distribution network, Cbattery is the maximum battery capacity, and t is the time step, and T is the scheduling time frame. The constraints are given in (7). Rmax is the maximum charging rate of the battery (1.44 kW). If xk (t) is positive the PEV is charging, otherwise discharging. To prevent overload conditions, the connection to the power grid is bounded by a maximum demand Dmax (t). The third constraint is added to indicate that the battery has to be fully charged by the deadline, and that discharged slots during V2G should be compensated. ϕ k indicates whether the PEV is connected or not. The last constraint is added to impose a maximum voltage deviation of 5% of the nominal voltage [14], otherwise the DMS pu does not allow the violating node to be charged (Vmin =0.95 pu and Vmax =1.05).

(10)

xk (t )

The final general objective function for the HDVG algorithm is then obtained as follows:



Schedule = min λ xk (t )



tdeadline

+ (1 − λ)

350

[Dd − (Dbase (t ) + xk (t ))]2

tarrival



349



tdeadline

[Dd − (

DPEV agg

(t ) + xk (t ))]

2

tarrival

(11) subject to (7). An important problem is the determination of the weights for the new weighted objective function optimisation. The choice of λ translates into a trade-off between the influence of the load forecast and the aggregated PEV load on the optimisation problem. We conducted many experiments to balance both and obtain the optimal value of λ. The optimisation

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functions defined in (6) and (11) were implemented in OMNet++ using NLOPT, an open-source library for nonlinear optimisation. The Constrained Optimisation BY Linear Approximations (COBYLA) algorithm was used. COBYLA is a derivative-free optimisation algorithm with non-linear inequality and equality constraints [15]. 4.3. Algorithm 3: workplace coordinated charging algorithm with PV solar panels Workplace charging is mainly motivated by the fact that PEVs are designed to have built-in storage and that the fleet is idle 95% of the time. Given that the use of solar energy for charging PEVs reduces loading, emissions, and charging cost, we propose a workplace charging algorithm that maximises the usage of power generated from PV panels installed in the car parks, and limits the impact of PEV charging on the utility grid. In our distribution system, two public parking garages are considered at nodes 632 and 684, as shown in Fig. 2. These garages are assumed to be located in a metropolitan area near workplaces. The drivers can charge their PEVs in these garages directly from the utility grid or using the locally generated solar energy when available. The installed PV solar panel is assumed to have an area of 500 m2 , a conversion efficiency of 12%, and an inverter efficiency of 95%3 . The solar irradiance level in Montreal is used to simulate the distribution of solar output profile4 . The workplace algorithm is decentralised but not distributed, where each parking garage has an aggregator that operates as a system administrator. Whenever a PEV is connected, the driver sets the departure time. Consecutively, a packet is sent to the aggregator including the departure time and the current SOC of the PEV. The aggregator performs the 2 following steps: Step 1: The aggregator segregates the current PEVs requesting to charge into 3 main categories defined in (13) based on their average constant power requirement Preq . Preq defined in (12) depends on the PEV’s SOC and departure time. We give the highest priority to the PEVs with lowest SOC and shortest departure time (Red priority level,) and maximum charging flexibility to PEVs within the green priority level.

Preq =

(1 − SOC ) ∗ CB

(12)

tdeparture − tarrival

397

⎧ ⎨>4.25 kW, Red priority level For Preq

< 4.25 kW& > 2.25kW,

⎩

< 2.25 kW,

Yellow priority level

Green priority level (13)

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Step 2: The aggregator estimates the power flow between the DMS and the workplace charging stations using the solar power forecast Psolar and the amount of total power required by the PEVs requesting to charge Ptot, PEV . The scheduling is done over the time frame T by solving the minimisation 3

Inverter efficiency available at http://www.solarbuzz.com/ Sun irradiance level data is available at http://www.climate. weatheroffice.qc.ca/ 4

7

Table 1 Parameters and values. Variable

Value

Load Profile PEV Battery full capacity Battery used capacity Driving efficiency ( ) Charging level Time interval (t) Deadline/arrival time and daily trip (miles)

RELOAD Database1 Nissan Leaf 20122 . 24 kWh (100 miles) 18 kWh (70 miles) 0.26 kWh/mile Level 1 of 1.44 kW(120V/12A) 1 hour Distributions extracted from NHTS [16]

1 RELOAD Database Documentation and Evaluation and Use in NEMS is available at http://www.onlocationinc.com/LoadShapesReload2001.pdf. 2 LEAF’s data sheet is available at http://www.nissanusa.com/leafelectric-car.

problem in (14) and then assigning charging activities according to the priority levels described in step 1:

min

T  t

Pf2low = min

T  [Psolar − Ptot,PEV ]2 , ∀t ∈ T

403 404

(14)

t

The efficiency of the decision made by the aggregator is affected by the accuracy of the forecast data used to model the load and the PV solar output.

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5. Simulation setup, case studies, and co-simulation results

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The distribution network used in the simulations is a modified IEEE 13-node distribution feeder consisting of 18 LV residential networks, each including 19 households. This feeder is a widely used benchmark that provides a test for the accuracy of the distribution component models and the convergence of a very unbalanced system. Loads are connected to each node to model its daily residential load consumption. Each household is randomly assigned a load shape extracted from the RELOAD Database used by the Electricity Module of the National Energy Modeling System (NEMS). These load shapes represent various demand sectors and times of the year. The PEVs connected to the grid are modelled as a constant real power. To model the transportation behaviour of each PEV, the National Household Travel Survey [16] provides probability distributions for the daily trip, travel time, arrival and departure time, all based on real-world transportation data. Nissan Leaf is the PEV model considered in this study with a battery capacity of 24 kWh enough to drive 100 miles. Only 80% of the battery is utilised to minimise the battery degradation (equivalent to 70 miles) with an electric drive efficiency of 0.26 kWh/mile. Table 1 summarises the parameters and assumptions used throughout our simulations. Several performance metrics for both power and communications layers are examined. In order to compare the performance of our proposed algorithms, we quantify the trade-offs in comparison with the centralised IntVGR [8], and investigate the impact of integrating renewables to workplace charging for the following 3 case studies:

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1. Case study 1: Home coordinated charging (DVG & HDVG) versus uncoordinated random charging (R) taken as a base case.

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Fig. 4. DVG, HDVG, and random charging performance [Case study 1, PL = 40% (a) and 60% (b)].

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2. Case study 2: Home coordinated charging (IntVGR, DVG & HDVG) + Workplace Random charging (W-R). 3. Case study 3: Home coordinated charging (IntVGR, DVG & HDVG) + Workplace Coordinated charging with PV panels (W-C/PV).

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5.1. Power systems results

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Case study 1: Fig. 4 shows the results of our algorithms (DVG & HDVG) for different penetration levels (PLs) for a summer load profile. Both algorithms achieve a maximum peak shaving value (D peak /Dbase ) of 17% with a PL of 40%, peak as shown in Fig. 5. For higher PLs, peak shaving becomes more important and increases to a maximum value of 19% (PL=60%). The uncoordinated charging scenarios shown in Fig. 4 present two challenges. First, they lead to steep demand peaks. Second, they result in remarkable voltage deviations and losses, which increase with the PL, as shown in Fig. 5. Random charging causes voltage deviations below 5% for different PLs, whereas both DVG and HDVG demonstrate to be effective in limiting voltage deviations. The trade-off here

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is that the charging of some PEVs is deferred to later slots. The results of DVG and HDVG shown in Fig. 5 indicate a significant improvement in minimising losses during peak times. For a PL of 60%, total losses are reduced from 33.3 kW to 22 kW with DVG or HDVG, an equivalent of 33% loss reduction. After performing several simulations, we noticed that DVG and HDVG have similar performance for low PLs. Thus one may argue that there is no point of deploying a hybrid algorithm if the PEV population cannot effectively drive the optimisation function defined in (11). However for higher PLs, the efficiency of HDVG in load leveling comes into play. Fig. 4 (a) and (b) illustrates the latter remark, where the DVG demand curve shows steep peaks and valleys occur more frequently compared to the HDVG curve. To provide more insight into the load leveling capabilities of each of the proposed algorithms, we examine the corresponding variance-to-mean ratios (VMR) for the time period 16–23 PM in Table 2. VMR is a statistical metric used to quantify whether a set of observed occurrences are clustered or dispersed, whereby a lower value indicates less dispersion and more load leveling. As expected, the variance-to-mean ratio of HDVG is lower than that of DVG for all

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Fig. 5. Co-simulation power results [Case study 1, PL = 20%, 40%, and 60%].

Table 2 Comparison of variance-to-mean ratio of the power demand profile for DVG and HDVG.

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the PLs considered, whereby its best performance is measured for the highest PL considered (VMR = 0.68). As a result, the proposed hybrid algorithm actually proves to enhance the performance of the decentralised scheme and decreases the variation rate of the power-time curve for significant penetration levels (PL > 40%). Case studies 2 & 3: The 3 considered algorithms in Fig. 7 depict a morning peak between 9 and 10 am due to PEV owners arriving all at the same time at work. This is most likely to become even more problematic with the winter morning peak caused by the household heating load. Fig. 8 shows that these peaks are no longer an issue when considering workplace coordinated charging with PV panels. In this case the difference in demand for the period (8 AM–3 PM) is covered by the locally generated solar energy. The workplace algorithm is indeed able to coordinate the charging activities of the PEVs by maximising the usage of the low-cost solar energy when available while taking into consideration the SOC and the departure time of the PEV owners. The main rational behind addressing workplace charging is decreasing the stress on the grid during the afternoon peaks. It is notable that when considering workplace charging, the power demand after midnight is less than that without workplace charging. For instance, at midnight the power demand of DVG decreases from 480 kW (Fig. 4) to 405 kW (Fig. 7) when considering workplace charging. Adding to that,

peak shaving in the afternoon seems to be more efficient when considering RES at the workplace. For instance, DVG with random workplace charging has a Dpeak of 606.04 kW versus 590.05 kW when considering workplace coordinated charging with PV panels (Fig. 6). A possible interpretation is that the excess of solar energy stored by the parked PEVs is sent back later to the grid, thus the improvement in peak reduction. By comparing our proposed algorithms with the centralised IntVGR algorithm (see Figs. 7 and 8), we observe that IntVGR performs better in terms of load flattening compared to DVG. HDVG on the other hand, displays a flattened demand curve more similar to IntVGR. Interestingly, both DVG and HDVG show lower Dpeaks for different PLs (see Fig. 6). The latter remark could be explained by the fact that that unlike DVG and HDVG, IntVGR restricts V2G to a time period (from 3 PM to 8 PM) thus inducing less battery degradation at the expense of peak shaving achieved by DVG and HDVG. Consequently, IntVGR outperforms the other algorithms in terms of guaranteeing a satisfactory final SOC (SOCfinal = 100%) versus 80% achieved by the decentralised algorithms. In summary, IntVGR performs better in terms of load flattening and customer satisfaction (100% final SOC). This is the expected trade-off between centralised and decentralised algorithms. The DMS in the centralised case requires full knowledge of all PEVs and grid parameters to solve the optimisation problem. Particularly, the centralised algorithm uses real-time grid and aggregated PEV data as input, not predictions. The decentralised scheme DVG, on the other hand, is implemented locally by each PEV without any coordination with its neighbouring PEVs and uses a demand load forecast as input for the optimisation problem. This explains the superiority of the optimisation result of the centralised algorithm. By including the charging strategies of the neighbouring PEVs into the optimisation, the proposed hybrid algorithm actually improves the load flattening performance of the decentralised scheme, especially in the presence of RESs, and decreases the gap

Please cite this article as: S. Mansour et al., Co-simulation study of performance trade-offs between centralised, distributed, and hybrid adaptive PEV charging algorithms, Computer Networks (2015), http://dx.doi.org/10.1016/j.comnet.2015.08.036

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Fig. 6. Co-simulation power results [Case studies 2 & 3, PL = 40%].

Fig. 7. DVG, HDVG, and IntVGR performance for workplace random charging [Case study 2, PL = 40%].

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between centralised and decentralised solutions. Our findings in this section comply with our assumptions in the introduction in that the proposed decentralised solutions here do not claim to outperform the centralised one, but they simplify the problem while providing acceptable and sometimes competitive performance.

5.2. Communications results

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To examine the performance of the FiWi communications infrastructure, we investigate the upstream channel throughput and delay for different PLs (40% and 80%). A comparison is drawn between the centralised IntVGR and our algorithms

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Fig. 8. DVG, HDVG, and IntVGR performance for workplace coordinated charging with PV panels [Case study 3, PL = 40%].

Fig. 9. Throughput and delay measured at the DMS for IntVGR, DVG, and HDVG with and without workplace charging for PLs of 40% and 80%.

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DVG and HDVG to quantify the communications improvement claimed in Section 1. This comparison tackles the impact of the integration of workplace RES as well. Note that the bandwidth used by background data traffic other than PEV-related messages is assumed to be 1.2 Mbps and constant over simulation time. In general, the bandwidth requirement seems to be directly proportional to the PL and varies depending on whether workplace charging coordination is considered or not. As shown in Fig. 10(a), the upstream throughput starts increasing during the period 9–11 AM and reaches a maximum around 9 AM (1.25 Mbps). This is due to PEVs arriving to the workplace parking and sending charging requests to the aggregator. Later on, throughput starts increasing significantly as PEVs arrive at home and reaches its

maximum level (1.27 Mbps) around 5 PM. For the 2 PL scenarios shown in Fig. 9, the bandwidth requirements of the hybrid algorithm is always higher than that of the decentralised for the 3 case studies. Fig. 9 shows that for a PL of 80% without workplace charging coordination, HDVG requires a maximum throughput of 1.35 Mbps versus 1.28 Mbps for DVG. On the other hand, the bandwidth required by the centralised algorithm shown in Fig. 10(b) clearly outmeasures both proposed algorithms with a maximum of 2.1 Mbps. The high throughput corresponding to the centralised algorithm is caused by the excessive exchange of notification packets, whereas for the decentralised/hybrid algorithms the information communicated between the DMS and the PEVs is limited to load/control messages interchanged at arrival times.

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Fig. 10 (c) and (d) illustrates the end-to-end delay of packets transmitted between PEVs and the DMS for the 3 considered algorithms with coordinated workplace charging. The delay in Fig. 10(c) increases significantly during the period 6-9PM. At this time the PEVs arrive home and start sending packets at the same time to the ONU, thus the probability of packet collision increases. For a PL of 40%, Fig. 10(c) shows an upstream maximum delay of 0.627 ms for the decentralised DVG and 0.640 ms for the hybrid HDVG. Clearly, the delay recorded by the centralised IntVGR is the highest, nearly 1 ms (see Fig. 10(d)). The drop in delay achieved by DVG and HDVG compared to the centralised IntVGR is due to the decrease in the number of packet types, the frequency of packet exchanges, and the packet size. In summary, our decentralised algorithm DVG is able to minimise both performance metrics (delay and throughput), which is consistent with our expectations in Section 1. The hybrid scheme, on the other hand, underperforms the decentralised DVG, though both delay and throughput measurements were still found to be significantly less than that of the previously proposed IntVGR centralised algorithm. 6. Conclusions We proposed a decentralised distributed charging algorithm and extended it to a hybrid version that can be viewed as a compromise between the centralised and decentralised solutions. Going one step further, we applied workplace charging coordination schemes that integrate PV panels. We co-simulated the proposed algorithms and carried out a comparative study between the proposed de-

centralised/hybrid algorithms and a centralised benchmark scheme (IntVGR) from both power and communication perspectives. The reported power system results prove the efficiency of the proposed algorithms in terms of peak shaving, while simultaneously minimising system losses and limiting voltage deviations. It is found through comparison of the results that the centralised IntVGR outperforms both DVG and HDVG in terms of load flattening. However for high PLs, the hybrid HDVG is able to compete with the centralised algorithm. Consistently with our expectations, the best communications performance is achieved by the decentralised scheme (1.28 Mbps throughput and 0.629 ms delay for PL = 80%), as compared to the hybrid HDVG and centralised IntVGR. Notably, the bandwidth requirements of HDVG are still found to be significantly less than that of the centralised algorithm. The adoption of any of the three proposed algorithms depends on many factors including available resources, network constraints, and the size of the PEV fleet. Knowing that the electrification of transportation is a progressive process, the above results suggest that utilities may adopt a centralised deterministic algorithm for low PLs, where the expected price in terms of complexity and cost is limited. However, for increasing PEV fleets (PL > 30%), a decentralised scheme becomes a necessity. Large and successive peaks become an issue with decentralised schemes for very high PLs (>50%), calling for a transition to a hybrid scheme. The obtained findings and results can be used in future work to develop adaptive algorithms that can swap from centralised to decentralised operation mode based on the PL and the system’s available resources.

Please cite this article as: S. Mansour et al., Co-simulation study of performance trade-offs between centralised, distributed, and hybrid adaptive PEV charging algorithms, Computer Networks (2015), http://dx.doi.org/10.1016/j.comnet.2015.08.036

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[1] M. Hemphill, Electricity distribution system planning for an increasing penetration of plug-in electric vehicles in New South Wales, in: Proceedings of the Australasian Universities Power Engineering Conference (AUPEC), Bali, Indonesia, Sept. 2012, pp. 1–6. [2] B.K. Sovacool, R.F. Hirsh, Beyond batteries: an examination of the benefits and barriers to plug-in hybrid electric vehicles (PHEVs) and a vehicle-to-grid (V2G) transition, Energy Policy 37 (3) (2009) 1095– 1103. [3] L. Zhu, F.R. Yu, B. Ningt, T. Tang, Stochastic charging management for plug-in electric vehicles in smart microgrids fueled by renewable energy sources, in: Proceedings of the IEEE Green Communications, New York, NY, USA, 2011, pp. 7–12. [4] A.K. Verma, B. Singh, D.T. Shahani, Vehicle to grid and grid to vehicle bidirectional power flow at unity power factor with dc ripple compensation, in: Proceedings of the IEEE International Conference on Industrial and Information Systems (ICIIS), Chennai, India, 2012, pp. 1–6. [5] A. Maitra, Evaluations of plug-in electric vehicle distribution system impacts, in: Proceedings of the IEEE Power and Energy Society General Meeting, Minneapolis, MN, USA, 2010, pp. 1–7. [6] L. Fernandez, Assessment of the impact of plug-in electric vehicles on distribution networks, IEEE Trans. Power Syst. 26 (1) (2011) 206–213. [7] M. Yilmaz, P. Krein, Review of the impact of vehicle-to-grid technologies on distribution systems and utility interfaces, IEEE Trans. Power Electron. 28 (12) (2013) 5673–5689. [8] D.Q. Xu, M. Lévesque, M. Maier, G. Joós, Integrated V2G, G2V, and renewable energy sources coordination over a converged fiber-wireless broadband access network, IEEE Trans. Smart Grid 4 (3) (2013) 1381– 1390. [9] M. Lévesque, D.Q. Xu, M. Maier, G. Joós, Communications and power distribution network co-simulation for multidisciplinary smart grid experimentations, in: Proceedings of the SCS/ACM Spring Simulation Multiconference (SpringSim), Annual Simulation Symposium, Orlando, FL, USA, 2012. [10] G. Lingwen, U. Topcu, S. Low, Optimal decentralized protocol for electric vehicle charging, in: Proceedings of the IEEE Decision and Control and European Control Conference, Orlando, FL, USA, 2011, pp. 5798–5804. [11] M. Zhongjing, D. Callaway, I. Hiskens, Decentralized charging control for large populations of plug-in electric vehicles: application of the Nash certainty equivalence principle, in: Proceedings of the IEEE Control Applications, Yokohama, Japan, 2010, pp. 191–195. [12] K. Qian, C. Zhou, M. Allan, Y. Yuan, Modeling of load demand due to EV battery charging in distribution systems, IEEE Trans. Power Syst. 26 (2) (2011) 802–810. [13] M. Maier, M. Lévesque, L. Iv˘anescu, NG-PONs 1&2 and beyond: the dawn of the Über-FiWi network, IEEE Netw. 26 (2) (2012) 15–21. [14] Electric Vehicle Conductive Charge Coupler, 2009, SAE J1772. SAE Recommended Practice. [15] M.J.D. Powell, A direct search optimization method that models the objective and constraint functions by linear interpolation, in: S. Gomez, J.-P. Hennart (Eds.), Advances in Optimization and Numerical Analysis, Kluwer Academic, Dordrecht, 1994, pp. 51–67. [16] National Household Travel Survey, [Online available at http://nhts.ornl. gov/].

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Samah received her Masters of Engineering degree with honors in Power Engineering from McGill University, Montreal, in December 2013. She also holds a Bachelor of Engineering Degree in Electrical Engineering from the Lebanese American University. Samah joined General Electric Lighting Solutions in January 2014 as part of the Edison Engineering Leadership program, where she works in designing switch mode power supply for LED products. Her research interests include energy management, demand response, smart grids, and renewable energy.

[m3Gdc;October 5, 2015;17:51] 13 Intissar Harrabi received her B.Eng. degree in Telecommunications from the University of Tunis (ENIT), Tunisia, in 2012. She earned her M.Sc. degree in Telecommunications from the University of Quebec (INRS), Optical Zeitgeist Laboratory, Montreal, under the supervision of Prof. Martin Maier, in 2014. Her research covered co-simulation studies of decentralised charging/discharging algorithms for the integration of plug-in electric vehicles into Smart Grids. She currently works as a Telecommunications Engineer at WSP Canada Inc, Quebec, Canada.

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Martin Maier is a Full Professor with the Institut National de la Recherche Scientifique (INRS), Montral. He has joined INRS as an Associate Professor in May 2005. He received the MSc and PhD degrees both with distinctions (summa cum laude) in electrical engineering from the Technical University Berlin, Berlin, Germany, in 1998 and 2003, respectively. He was a Visiting Researcher at the University of Southern California (USC), Los Angeles, CA, in spring 1998 and Arizona State University (ASU), Tempe, AZ, in winter 2001. In summer 2003, he was a Postdoc Fellow at the Massachusetts Institute of Technology (MIT), Cambridge, MA. Before joining INRS, Dr. Maier was a Research Associate at CTTC, Barcelona, Spain, November 2003 through March 2005. He was a Visiting Professor at Stanford University, Stanford, CA, October 2006 through March 2007. Dr. Maier was a recipient of the two-year Deutsche Telekom doctoral scholarship from June 1999 through May 2001. He is also a co-recipient of the 2009 IEEE Communications Society Best Tutorial Paper Award and the Best Paper Award presented at The International Society of Optical Engineers (SPIE) Photonics East 2000-Terabit Optical Networking Conference. He served on the Technical Program Committees of IEEE INFOCOM, IEEE GLOBECOM, and IEEE ICC, and is an Editorial Board member of the IEEE Communications Surveys and Tutorials as well as ELSEVIER Computer Communications. He served as a reviewer of numerous major journals and conferences, book proposals, and research grant applications and was appointed as independent expert by the European Commission. He is a Senior Member of IEEE.

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Géza Joós holds an M.Eng. and Ph.D in Electrical Engineering from McGill University, Canada. He is a Professor in the Department of Electrical and Computer Engineering, McGill University, where he holds the NSERC/Hydro-Quebec Industrial Research Chair on the Integration of Renewable Energies and Distributed Generation into the Electric Distribution Grid. His research activities cover the application of high-power electronics to electric power systems and power conversion, including distributed generation and renewable energy, and active distribution systems and microgrids. He previously held positions in industry (ABB, Montreal, Canada) and academia (Concordia University, Montreal, Canada). His professional activities include consulting work in power electronics and power systems, including transmission and distribution systems incorporating renewable energy systems. He is active in IEEE Power and Energy Society and CIGRE (Council on Large Electric Systems) working groups. He is a Fellow of IEEE and of the Canadian Academy of Engineering.

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Please cite this article as: S. Mansour et al., Co-simulation study of performance trade-offs between centralised, distributed, and hybrid adaptive PEV charging algorithms, Computer Networks (2015), http://dx.doi.org/10.1016/j.comnet.2015.08.036