Control, regulation and optimization of bidirectional energy flows for electric vehicles’ charging and discharging

Sustainable Cities and Society 57 (2020) 102129

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Control, regulation and optimization of bidirectional energy flows for electric vehicles’ charging and discharging

T

Ghimar Merhya,*, Ahmed Nait-Sidi-Moha, Nazih Moubayedb a b

University of Picardie Jules Verne, LTI Laboratory, 02100, Saint Quentin, France Lebanese University, Faculty of Engineering, CRSI, LaRGES, Lebanon

ARTICLE INFO

ABSTRACT

Keywords: Electric vehicles Regulation Optimization Bidirectional Energy flows Electricity production and consumption

Vehicular electrification plays a major role in the reduction of toxic greenhouse gas emissions linked to the transportation sector. However, the differential margin between electricity supply and demand is chaotic; and only a production/consumption balance could reconcile both ends. The adaptation of energy flows is therefore necessary to confront the gigantic energy waste related to electricity storage challenges. Consequently, electric vehicle batteries recently emerged as a solution to the actual limitation of storage capability. Indeed, they are used in this study as means of storage and retrieval of energy. Thus, this paper proposes a control and regulation algorithm aimed at reaching a balanced production/ consumption system. The balance is acquired through the bidirectional control of the energy flows related to domestic residences, electric vehicles and the grid. Moreover, a multi-objective optimization of vehicular charging and discharging is assessed using the genetic algorithm to attain an optimal fulfillment of the system’s energetic needs. Once the regulation algorithm is set and the optimizations implemented, the algorithm’s simulation is performed using Matlab. Through the developed algorithm, we aim these major findings: 1. Regulate energy flows depending on supply and demand. 2. Optimize charging and discharging modes. 3. Flows merge towards the system’s equilibrium state.

1. Introduction 1.1. Background Nowadays, vehicular electrification has shown to be a consistent and efficient solution for the earth-shattering disasters related to the environmental pollution and noxious greenhouse gases emissions. Electric vehicles (EV) have become a main focus in several primary investigations. In addition to the environmental aspect, and while the electricity storage in huge amounts remains difficult and challenging, the electric vehicle batteries seem to be a good elucidation for energy storage and retrieval. Furthermore, the available research works have been continuously wavering around the focus on energy storage and vehicular charging, as well as countless optimization techniques and management strategies for the controlled-charging scheduling. In addition to the climate change and pollution reduction concerns, meeting the electricity requirements has become challenging in many countries. For example, in India, in parallel to the economic growth and increase in electricity demand, the need for sustainable strategies of development in the electricity sector has emerged. Due to this

⁎

emergence, a supply system involving several electricity sources was developed to meet the continuously growing demand of electricity. The system dynamic approach was adopted in order to align the gap between electricity supply and demand. Both entities are projected in the coming 20 years taking into account the growth rates along with electricity plans, resources availability, and energy efficiency considerations. The deficit in power supply would be compensated by a gradual increase in the renewable sources and a thermal power reduction within a sustainable strategy and designed policies (Varma & Sushil, 2019). Moreover, the demand response is assessed in relation to several renewable energy resources planning and operation scheduling (Aghaei & Alizadeh, 2013). The behavioural and structural failures of demand response programs are extensively investigated and potential solutions to the cited obstacles are proposed (Kim & Shcherbakova, 2011). 1.2. Objectives and contribution This paper proposes a control and regulation algorithm that would manipulate the energy storage and retrieval processes based on the

Corresponding author. E-mail address: [email protected] (G. Merhy).

https://doi.org/10.1016/j.scs.2020.102129 Received 3 June 2019; Received in revised form 4 March 2020; Accepted 4 March 2020 2210-6707/ © 2020 Elsevier Ltd. All rights reserved.

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energy production and consumption, particularly the electricity supply and demand. The paper also proposes a multi-objective optimization of both the charging and discharging processes defined by the regulation algorithm. In order to do so, the energy production and consumption are first investigated through defining their reversible energy flows between a domestic residence supplied by renewable sources of energy, electric vehicles and the grid. Consequently, the vehicular batteries are adopted as ways of storage and retrieval, depending on the energetic needs for a balanced system where any excess or lack of energy is avoided. In this context, the heuristic regulation algorithm would be built through an energy management strategy aiming at attaining a balanced production/consumption system, and controlling its corresponding reversible energy flows.

opportunities for services related to grid balancing and storage are increasing. That would allow EV owners to make revenues, which would compensate a part of the electric vehicles high costs that are known to be one of the prominent deterrents of consumers adopting EVs (Fitzsimmons et al., 2016). Consequently, the adoption of specific energy management strategies and scheduling would be crucial in the context of electric vehicles and energy optimization. The charging needs of vehicles have been investigated through the implementation of a predictive model that foresees the time for multiple requests of charging and the changing rate (Nait-Sidi-Moh, Ruzmetov, Bakhouya, Naitmalek, & Gaber, 2018). Furthermore, the charging process related to hybrid-electric vehicles based on electricity supply and demand has also been assessed using imperialist competitive algorithm, particle swarm optimization and teaching-learning algorithm. The analysis of all three methodologies showed that traininglearning algorithm widely outperforms the other methods in terms of load peak prevention. Yet, the imperialist competitive algorithm could considerably outshine with regards to performance costs reduction (Amirhosseini & Hosseini, 2018; Karmaker, Ahmed, Hossain, & Sikder, 2018). While the discharging mode and energy restitution have been given less importance in the literature, it would be interesting to optimize and control both energy storage and retrieval processes. The bi-directional exchange created between electric vehicles and the grid would allow for the charging of their batteries as well as the power injection back to the grid. Consequently, the power grid gets stabilized in terms of frequency and voltage regulation. Power demands would also be fulfilled; particularly during peak hours (Zgheib & Al-Haddad, 2016). Thus, the bidirectional exchange of energy flows needs to be extensively referred to in order to avoid the peak load and flatten the demand profile. Likewise, many optimization methods, particularly multi-objective ones such as the genetic algorithm optimization approach have been discussed in details in the available literature (Hamidi, Nazarpour, & Golshannavaz, 2017; McCall, 2005). For instance, the charging process of electric vehicles is highlighted through the application of the genetic algorithm as a multi-objective optimization method (Merhy, Nait-SidiMoh, & Moubayed, 2017). Briefly, based on the available literature, energy management strategies have proposed the adequate scheduling of charging electric vehicles. In addition, several optimization methods have been adopted for charging the electric vehicles based on electricity supply and demand. However, the specific contribution of this paper lays in the control of energy flows depending on electricity supply and demand. Another contribution would be the multi-objective optimization of both vehicular charging and discharging particularly using the genetic algorithm approach.

1.3. Organization The study exposed in this paper is distributed as follows: the available literature is first discussed in the related works, Section 2. Control and regulation are performed through explaining the study’s methodology in Section 3.1 and proposing their detailed algorithm in Section 3.2, with a multi-objective optimization for charging and discharging modes exposed respectively in Sections 3.2.1 and 3.2.2. Successively, a validation and simulation of the proposed algorithm is performed in Section 4 where the data entry, results and discussion and analysis are clarified in Sections 4.1, 4.2 and 4.3. The conclusion and perspectives, appendices and references follow in Sections 5,6 and 7 respectively. 2. Related works Several energy management approaches and controlled scheduling strategies have been proposed in other studies (Qin & Zhang, 2011; Ruzmetov, Nait-Sidi-Moh, & Gaber, 2014; Kang, Duncan, & Mavris, 2013; Sundstrӧm & Binding, 2010; Ruzmetov, Nait-Sidi-Moh, Bakhouya, & Gaber, 2013; He, Venkatesh, & Guan, 2012). Due to the successful energy management systems, a big number of electric vehicles can be handled by the distribution networks. In fact, using a large number of vehicles may affect the electricity demands on the scale of the entire country. Hence, the whole sector of electric transportation might be affected worldwide as well (Kriukov & Gavrilas, 2014). In order to supervise a household’s energy consumption closely and trigger new energy saving incentives, the household’s aggregate consumption was decomposed into the individual consumption of each home appliance through a splitting approach of the convenient clusters (Aiad & Lee, 2018; Yu, Haghighat, & Fung, 2016). Moreover, the charging profiles and daily trips scheduling of a population of electric vehicles was generated using real-time vehicles’ charging data through a stochastic simulation procedure (Brady & Mahony, 2016). The energy flows from/to the electric vehicles might contribute to better efficiency, stability and reliability with regards to the grid’s performance, yet, without setting a rational scheduling, serious problems might occur due to deregulated charging or discharging, especially for a fleet of electric vehicles. Nevertheless, the power transmission over a long distance and the market price fluctuation might cause a huge energy waste referred to as battery operation convergence. Thus, it is recommended to plan a hierarchy between the EV owners and the grid. Consequently, if all EV owners start using the same realtime price and sell back at market price, the resulting synchronous activity would create new demand peaks and different price curves. The high number of electric vehicles integrated into the market might bring unstable factors to the grid, thus, in order to treat the excessive vehicular energy properly, the price trend and future electricity consumption would be predicted by the central grid which makes the decision concerning the amount of energy needed and when to charge, thus, the discharge process would occur only when needed (Guo & Zhou, 2016). As electric vehicles can supply energy storage to the power grid, their

3. Control and regulation The differential margin between energy consumption and production would contribute to a huge energy waste. Indeed, the excessive production would be discarded at times while the deficient production results in expensive compensations of the consumption at others, hence the need for energy regulation. 3.1. Methodology In order to control the processes of energy storage and retrieval, the energy production and consumption values are first assessed and analysed. Based on the excess or deficit of the supply of electricity, the charging and discharging modes of the vehicles are launched. Consequently, these two modes are optimized using the genetic algorithm multi-objective technique along with the weighted sum approach. This optimization is embodied by the needed charging or discharging of the vehicles until reaching the system’s equilibrium. During the charging process, the vehicles’ state-of-charge, propulsive energy, valley 2

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energy and losses are optimized. As for the discharging process, it involves the optimization of the vehicles’ state-of-charge, discharging time, battery life and losses. Furthermore, the energy flows regulation algorithm is created in order to fill the difference gap between production and consumption using the stored or retrieved energy. The algorithm is implemented using Matlab software and tested using a data sample including 31 fleets of vehicles with different production/consumption sets. The simulation of the algorithm and all its corresponding data is performed using Matlab as well.

for our study, a comparison between the different battery technologies available in the market has been performed, and their advantages and disadvantages have been investigated (Merhy, Moubayed, & Nait-SidiMoh, 2017). The study focuses on NiMH batteries mostly because this type of batteries has a higher storage capability than other types. However, this type has been set as an example to highlight and quantify the energy flows penetrating and leaving the vehicle. For other types of batteries, the same principles would still be applicable, yet their depth of discharge value might vary. Thus, the numerical application of the performed calculations would need to be modified depending on the battery type. The proposed algorithm has several potential users, specifically any public or private transportation electric vehicles or fleets with NiMH batteries available for energy repository. Technically, electric buses can be used in our regulation application. However, buses are supposedly circulating all day long following specific consistent schedules and would charge their batteries during the night only to be used the day after. Thus, the energy stored in bus batteries would be intended to fulfill their personal needs, and it would not be beneficiary to discharge them for the electric grid supply, especially because buses have high energetic needs and would not have much excessive energy to spare. However, applying the storage and retrieval processes on buses might be effective during the weekends when all buses are not expected to circulate all day or are dormant, in their depot, during weekends and holidays. Yet, it is particularly recommended for private vehicles whose available energy would be exceeding their personal needs, and that have the possibility to park long enough to charge/discharge their energy lack/excess. In addition, the public service vehicles intended for public works, the army vehicles as well as the fire-trucks and snowclearing vehicles also have significant downtime where they would not be circulating, thus would be perfectly fit for such storage and retrieval applications. As a first step, the regulation algorithm collects the data input related to the energy production and consumption, as well as the vehicular state-of-charge SoC and the percentage of energy to be kept in the batteries during discharge for the fleet's personal needs; and operates a comparison between the production and consumption values. The variable x embodied in the algorithm’s organizational chart represents the vehicles to be charged and/or discharged, consecutively, one by one. The energy production results from the supply of the installed renewable energy sources, and the consumption is that of the household appliances, and the vehicles’ personal needs. Typically, this algorithm evolves into three cases corresponding to the three blocks of the organizational chart (Fig. 1): If the production exceeds the consumption, the algorithm launches the vehicles’ charging process, and the excess of energy is stored within the vehicles’ batteries. Once the state-of-charge SoC of the charging vehicle reaches its allowed maximum (above which the nominal characteristics of the battery might get deteriorated or damaged and its life cycle gets shortened), the charging switches to the next vehicle of the fleet. So, as long as the vehicles are charging, a new production/consumption comparison is assessed every 5 % SoC increase in order to make a decision concerning whether to proceed in the vehicles’ charging or to switch to another cycle where the consumption would beat the production. However, once all the fleet’s vehicles are charged, and in case the production still tops the consumption, the excessive energy would then be injected into the grid for beneficial incentives, economic regulations and financial purposes. If the energy consumption surpasses its production, the discharging process starts. In fact, the supply of photovoltaic panels and the wind turbine, not being able to fulfill all the household’s needs, the lack of energy would then be covered by the energy already stored within the fleet’s batteries. The vehicles would be discharging progressively, one by one, until reaching their minimal SoC, where a certain amount of energy remains in their batteries for their personal planned trips. Moreover, during discharge, the algorithm keeps repeating the

3.2. Control and regulation algorithm In order to control the flows of energy circulating between electric vehicles and either the grid or a domestic residence supplied by renewable energy sources, and aiming at attaining a balanced system, an optimization and regulation algorithm was developed. Particularly, this algorithm manipulates the vehicles’ charging and discharging processes depending on the energy production and consumption, thus the electricity demand and supply. Using the regulation algorithm for charging and discharging electric vehicles introduces several benefits to the vehicles’ owners and their environment. In fact, the regulation allows the optimization of renewable energy management and contributes to a huge environmental pollution reduction. It enables also the reduction of energy waste and acquires a responsible use of electrical energy. On the economic level, the energy savings resulting from the regulation algorithm would involve economic revenues and significant electricity costs reductions based on defined incentives imposed on the community of owners of EVs. Mostly, the system considered in this study consists of a domestic household with an average consumption of 31.1 kWh daily, which would be either partially or entirely compensated by the energy produced by a HAWT horizontal axis wind turbine of a 2.8 kW power and 33 mono-crystalline photovoltaic modules of 280 Wp of rated power under standard conditions. Another component of the studied system would be a fleet of electric vehicles (EV) used not only for its personal energetic needs, but also as energy storage and retrieval means. Each EV is equipped with nickel metal hydride on-board batteries (NiMH) of a depth of discharge of 80 % and a 75Ah capacity, thus a substantial storage capacity. The electric grid also interferes in the system to fulfil the energetic needs or recover the excess of energy when the rest of the system’s components are not enough for the balance inquiry between the energy production and consumption. Specifically, the heuristic algorithm aims to define whether there is an excess or lack of energy by comparing the production and consumption. Accordingly, the algorithm proceeds the launching of the vehicle’s charging or discharging processes so that the system’s energetic needs are fulfilled and its equilibrium is reached. Once the system gets balanced, the margin of difference between the electricity demand and its supply would then be tightened. In order to tighten the margin of difference between the supply and demand of electricity, the heuristic algorithm for control and regulation presented in Fig. 1 has been defined by comparing the energy production and consumption. This study focuses on the energy flows between the vehicles and the grid and/or the buildings, and on the vehicular batteries used for storage and retrieval. Therefore, the type of electric vehicles is not of a major importance in this context. The control and regulation process is applicable for all type of electric vehicles as long as their batteries would be available for energy repository. In fact, the proposed algorithm is applicable for several types of vehicles, including private owners’ vehicles, public vehicles, small, mid-size and large vehicles. Particularly, as mentioned in the study, the vehicle’s prototype adopted involves vehicles with a Nickel Metal Hydride (NiMH) type of batteries for their high efficiency, depth of discharge and storage capability. Consequently, the study applies for any type of electric vehicle with NiMH batteries. In fact, in order to choose the most adequate batteries 3

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Fig. 1. Control and regulation algorithm’s organizational chart.

production and consumption comparison with intervals of 5% SoC drop, in order to switch again to the first case or second case when needed. In case the stored energy would still not be enough to cover the lack of production, the insufficiency would then be insured by the electric grid. However, when the production and consumption values are equal, the system is in balance, and the algorithm does not take any action until a difference margin between both values occurs again. Particularly, once the comparison between the production and consumption values is made, in case the energy production and consumption are equivalent, the balanced system is attained. On these terms, the system is in equilibrium and it functions normally without

involving any energy storage or retrieval processes. So, the energy produced by the photovoltaic panels and wind turbine would be congruent with the house consumption of appliances and electric vehicle’s needs. The energetic model of the system would then be depicted as per the equation following equation:

NHj, PV × Pf , PV × k +

0.01328 × D 2 × v 3 = 365.25 + E0 × d

home appliances

NHj × Pf (1)

whereas NHj, PV and Pf, PV represent the daily number of hours of use of the photovoltaic panels, and their operating power, k is a correction factor of 1.3, D is the wind turbine’s rotor diameter and v is the annual 4

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average wind speed. On the other hand, NHj, Pf are the daily number of hours of use of the functional home appliances and their operating power, and E0 is the energy linked to the on-board electric outlet, and d is the distance travelled by the vehicle. It is to be mentioned that the home appliances refer to any functional household appliances such as those related to heating, ventilation, cooking, lighting, washing, drying, refrigerator, audiovisual and electronics. In the context of renewable energy, the storage and retrieval algorithm presented in this paper aims to regulate the supply and demand of electricity in order to create a balance between the produced and consumed energies. This balance optimizes the use of the available electricity while filling the needed lacks and avoiding the waste of excessive energy. It enables also to retrieve the stored energy when the consumption surpasses the production instead of buying it expensively from the grid. To do so, mathematical model-based optimization is proposed. The optimization approach adopted in this study is the multi-objective genetic algorithm embodied by the mathematical models presenting the electric vehicles’ charging and discharging processes according to the energy production and consumption. As the regulation algorithm is responsible for triggering the convenient energy storage or retrieval mode in cases 1 and 2, these cases are further brought into attention in the next section. So, the regulation algorithm sets the adequate charging and discharging processes of the fleet. For each case, we will develop the followed process and used management method to regulate the system and bring it to the equilibrium state.

capacity, the charging power, and the valley power (Kisacikoglu, Erden, & Erdogan, 2018). iii.) Optimization of the vehicles’ needs by controlling their autonomy in numerous trip circumstances such as the functional on-board accessories and the type of roads: The optimization of the vehicular needs involves the modeling of their propulsive energy Ep represented by the equation:

Ep =

iv.) Fulfillment of the infrastructural energetic needs: As the network’s reinforcement would vary depending on the length of the transmission lines, and the difficulty of vehicular grid integration, the fulfillment of the infrastructural energetic needs is directly linked to the minimization of energy losses expressed by:

l= 100 ×

ch

Pch Eb

SoC (t )) × Pvalley Pch

(5)

Pch

i

f1 = SoC (t ) = SoC (t f2 = E valley =

Q × (1

Pch Eb SoC (t )) × Pvalley

1) +

ch

Pch A × (Pp + Paux ) f3 = Ep = V Pch (Pp + Paux ) f4 = l = 100 × Pch

(6)

Likewise, the constraints of this optimization would involve the maximal or minimal boundaries defined by the vehicles’ charge or discharge, as well as the charging power that must surpass the summedup auxiliary and propulsive powers in order for the vehicle to circulate. These constraints are modeled as per Eq. (3):

(2)

g1 g2 g3

SoCmin SoC SoCmax Pch Pchmax Epmin E p Epmax

g4 g5

l

lmin Pch > Pp + Paux

(7)

Once the multi-objective optimization model has been set, the heuristic genetic algorithm approach has been adopted to assess the optimal values related to each objective function. The application of the genetic algorithm is based on Haupt’s method for chromosomes mating and offspring generation (Haupt & Haupt, 2004). In this method parent chromosomes are crossed to generate offspring chromosomes. Consequently, the optimal solution set obtained for the studied objective functions is exposed in Table 1. Based on the genetic algorithm’s optimization calculations, as some of the objective functions defined depend from the same parameters, the obtained optimal solutions would fix conflicting values that would result in one optimal objective’s solution at the expense of another objective. So, the concluded optimal solution of the four objective functions cannot be reached all at the same time. Thus, several case

ii.) Control of the vehicles’ charging by switching the charging loads towards off-peak hours whenever the vehicles are connected to the electric grid, so that the demand curve gets as flattened as possible: Whenever the electric vehicles would be grid-connected, the avoidance of voltage fluctuations and energy losses requires a relatively flat demand curve achieved through a charging loads’ shift towards off-peak hours. Consequently, the valley energy E valley , depicted by the following equation should be maximized:

Q × (1

(Pp + Paux)

• f (for i=1, 2, 3, 4) are objective functions:

whereas ch , Pch and Eb consecutively represent the vehicles’ charging efficiency, their charging power, and batteries’ capacity. However, this maximization is complemented with a SoC constraint limit that would not fall short of a minimal value SoCmin or surpass a maximal value SoCmax , in order to avoid any batteries’ damage: SoCmin < SoC < SoCmax (Guzzella & Sciaretta, 2007).

E valley =

Pch

So, the multi-objective optimization’s modeling is summed up by a linear system highlighting the objectives’ mathematical models:

i.) Maximization of the longevity of vehicular batteries and the optimization of their life cycles through the maximization of their stateof-charge: The vehicles’ state-of-charge SoC during the charging process to be maximized is formally presented by the following equation:

1) +

(4)

whereas A, V, Pp and Paux respectively denote the vehicles’ autonomy, their relative speed, their propulsive power and the auxiliary power linked to the functional on-board electrical accessories. It is to be noted that the vehicular autonomy fluctuates depending on traffic and the types of roads being travelled (Joshi & Deshmukh, 2006;Gaudin, Krotova, & Guerlais, 2011).

3.2.1. Energy storage – Multi-objective optimization for charging Provided that the production tops the consumption, the energy storage gets launched by the algorithm in order to recover the excess of energy, and redirect it towards a beneficial usage of the energy waste. The vehicular charging is then launched with a multi-objective optimization of the energy flows where several objective functions are defined, and their corresponding solutions are calculated for an optimal charging of the vehicles using genetic algorithm method. In order to attain the inquired optimality, the objectives that the optimization aims to satisfy are first enumerated and modeled:

SoC (t ) = SoC (t

A × (Pp + Paux ) V

(3)

whereas Q , Pch and Pvalley consecutively stand for the rated batteries’ 5

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3.2.2. Energy retrieval – Multi-objective optimization for discharging At the phase when the energy production falls short of its consumption, it would be interesting then to optimize the energy flows related to this retrieval. The optimization is performed using the genetic algorithm approach where each function’s Pareto-front is indicated. For this energy retrieval process, we follow the same steps as in the storage process. So, first, the discharging optimization involves defining and modeling the exact objectives sought. Accordingly, the main objectives at this stage are:

Table 1 Optimal solution for the charging mode of vehicles. f1 = SoC(t)

f2 = E valley

f3 = E p

f4 = l

80.13 %

77.4 kW h

232.108 kWh

2.34 %

studies were identified, and the optimal solutions were modified in order to fit the decision maker’s priorities and preferences. Accordingly, the obtained optimal solutions converge towards the calculated theoretical optimums based on the preference set for each one of the objective functions. As the objective functions’ optimality has been brought to a preference-based context, the optimization results have been combined into a single objective function through the use of weighted sum approach. So, the objective functions have been normalized and the weighted sum approach has been implemented by assigning a precise weight to every function, so the multi-objective problem gets condensed into the scalar single objective function:

i.) Minimization of the vehicles’ SoC in order to harness the largest possible amount of the batteries’ energy, considering the possibility to retain some energy (intended for the vehicles’ personal trips) from restitution. The discharging batteries’ SoC is presented by the following model:

f1 = SoC (t ) = SoC (t

min F (X ) = w1 × |f1 (x )| + w2 × |f2 (x )| + w3 × |f3 (x )| + w4 × |f 4 (x )| whereas the preference-based chosen weights wi have a total sum of 1, and f 'i represent the normalized objective functions. The |f 'i (x)| equations referred to in the weighted sum approach application are expressed as follows:

= min[

5.2

82.55

SoC 10 ] 80

f2 = t d =

Ep

184 52

l

0.1 ] 29.1

(9)

iv.) Minimization of the vehicles’ losses involving the instantaneous power entering the vehicle and departing out of it. The vehicles’ losses can be calculated using the formula:

f 4 = l= 100 ×

|(Pp + Paux)

Pd | (13)

Pp + Paux

with Pp and Paux identifying the vehicles’ propulsive power and the power linked to the auxiliary functional on-board electric accessories (Apostolaki-Iosifidou, Codani, & Kempton, 2017; Guo & Zhou, 2016; Xu & Chung, 2015; Merhy, Moubayed et al., 2017). Consequently, the optimization system’s model can be summarized as follows:

Table 2 Optimal Solution - initial populations of 12 and 200 chromosomes.

• f (for i=1, 2, 3, 4) are objective functions:

Optimal Solution

f1 = SoC(t) (%) f2 = Evalley (kWh) f3 = Ep (kWh) f4 = l (%) x1 = t (s) x2 = Pval (kW) x3 = Pch (kW) x4 = Pp (kW) x5 = Paux (kW)

(11)

with n c , and DoD being respectively the number of cycles of the batteries and their depth of discharge (Serrao, Chehab, Guezennec, & Rizzoni, 2005).

Computation and verification Subsequently, the charging multi-objective optimization has been computed and implemented in gamultiobj solver of Matlab software. The Pareto-front has been generated as a unique solution starting first with a population of 12 chromosomes in order to verify the calculation results. Then the computation was expanded to start with an initial population of 200 chromosomes. The generated optimal solutions for both computations are shown in Table 2: Consecutively, as big as the starting population would be, the optimal solutions would further converge towards the identified objective functions’ reference values.

Initial population of 12 chromosomes 90 −87.75 −186.134 0.1 0.963051 94.78714 93.08849 90.39052 2.676681

Eb Id

iii.) Maximization of the vehicles’ battery life and battery cycles’ control. The vehicles’ battery life Lb is presented in the following formula: f3 = Lb = n c × DoD× Eb (12)

]

|f 4 (x )| = min (f 4 (x )) = min (l) = min [

(10)

whereas Id is the discharging current of the battery (Vulturescu, Butterbach, Forgez, Coquery, & Friedrich, 2010).

]

|f3 (x )| = min ( f3 (x )) = min ( Ep) = min [

Pd Eb

ii.) Minimization of the vehicles’ discharging time, so that the available energy gets restituted as soon as the consumption exceeds the production. The vehicles’ discharging time model t d is modeled as follows:

|f 2 (x )| = min ( f 2 (x )) = min( E valley ) E valley

d

whereas Eb , d and Pd consecutively represent the batteries’ nominal capacity, discharging efficiency and discharging power (Hamidi et al., 2017); (Clement-Nyns, Haesen, & Driesen, 2010); (Deilami, Masoum, Moses, & Masoum, 2011).

(8)

|f1 (x )| = min ( f1 (x )) = min ( SoC (t ) ) = min [

1)

i

Initial population of 200 chromosomes 88.6625 −87.75 −236 0.1 1.968175 103.9565 118.0117 110 8

f1 = SoC (t ) = SoC (t

1)

d

Pd Eb

Eb Id f3 = Lb = nc × DoD × Eb f2 = td =

f4 = l = 100 ×

|(Pp + Paux ) Pp + Paux

Pd | (14)

The constraint related to the discharging optimization particularly 6

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Table 3 Optimal solution for the discharging mode of vehicles. f2 = td

f1 = SoC (t )

y

0 Km

13.99 %

y

Table 4 Optimal Solution - initial populations of 12 and 200 chromosomes. f3 = Lb

Optimal Solution

f4 = l

20 Km

36.99 %

0.675 h =40 min 30 sec

115632 Ah

f1 = SoC(t) (%) f2 = Evalley (kWh) f3 = Ep (kWh) f4 = l (%) x1 = t (sec) x2 = Pd (kW) x3 = Id (A) x4 = nc (cycles) x5 = DoD (%) x6 = Pp (kW) x7 = Paux (kW)

0.4 %

involves the SoC’s lower limit SoC min where the SoC should get minimized considering both the vehicle’s own trips’ usage and its specifications boundaries beyond which the battery would get damaged (Joshi and Deshmukh, 2006). This constraint is depicted as per Eq. (15):

{g1

SoC

SoCmin + y

(15)

whereas SoCmin and y representing the minimal allowable value for SoC that would not deteriorate the batteries’ specifications, and the amount of energy related to the vehicles’ personal trips respectively. Following the same procedure adopted for the charging mode, the optimization of each one of the predefined objectives was made using the genetic algorithm approach (Hamidi et al., 2017); (McCall, 2005). Hence, the optimal solution found for the studied objective functions is exposed in Table 3 noting that it includes the optimal SoC equivalent for respective distances of 0 Km (immobilized vehicle) then 20 Km of round trips (vehicle used for owner personal needs), as well as the optimal discharging time td , battery life Lb , and losses. Similarly to the charging optimization, the conflicting optimal solutions of the objectives cannot all be obtained at one time. Thus, the decision maker’s preference has been taken into consideration through prioritizing some objectives over the others, and the multi-objective problem is converted into a single objective one using the weighted sum approach and assigning random preference-based weights to each objective function. The resulting single objective scalar equation of the discharging system would then be:

Initial population of 200 chromosomes 10 3.50676957642366

−120000 0.100000000000000 72.0824896780670 111.335337884925 124.999999999782 707.350012327776 30.7533658615563 103.644711807971 7.60221201125857

−120000 0.323708305908542 55.037783588448356 103.261495942000 21.3872050516898 873.529873478065 54.3758630601104 100.134468559499 3.46237898265463

4. Simulation and validation of the control algorithm 4.1. Data entry In order to confirm the relevancy of the proposed regulation algorithm, and consequently tighten the margin of difference between the energy production and consumption, the algorithm was implemented and verified through a simulation over Matlab software. Therefore, average data inputs of production, consumption and fleets’ SoC were set for a sample period of 31 days. Referring to the technical specifications of the system’s components, the energy production and consumption have been calculated in order to define precise input values. These calculations were based on realistic energy values defined by the photovoltaic panels and wind turbine, and exact household appliance consumption. For instance, the first week has been set to be very sunny, yet very windy. Therefore, as the number of sun exposure hours has been set between 9 and 12 h, and the annual average wind speed ranging between 24 and 31 miles per hour, the corresponding production of photovoltaic panels and wind turbine were summed up to calculate the energy production during this week. Similarly, having fixed the exact functional household appliances and the electric vehicle’s consumption each day, the total energy consumption was calculated. Successively, the second week (including days 8 through 14), was set as very sunny, and barely windy. As for the third week (days 15–21), it is windy but barely sunny. The last 9 days (day 22–31) were barely sunny or windy. The fleet’s states-of-charge were randomly defined with a daily sample set of 3–6 vehicles per fleet. So, the defined data input are summarized in the below table, where SoC, P, and C respectively represent the fleet’s state-of-charge, the energy production and its consumption. As for ΔP and ΔC, they represent the consecutive daily variations of production and consumption. Once the simulation has been performed and the regulation has been applied for the set input data, the output values of the corresponding production and consumption, as well as the charged and discharged energy quantities were assessed as shown in Table 5.

minF(X) = w1 × |f 1'(x)| + w2 × |f '2 (x)| + w3 × |f '3 (x)| + w4 × |f '4 (x)| (16) Whereas

SoC 10 ) 80 t 0.6 |f 2' (x )| = min (f2' (x )) = min d 749.4 Lb |f3' (x )| = min( f3' (x )) = min( ) 120000 l 0.1 |f 4' (x )| = min (f 4' (x )) = min ( ) 99.8

Initial population of 12 chromosomes 10 0.600000000001047

|f1' (x )| = min (f1' (x )) = min (

(17)

Similarly to the storage process, and based on the set preference for specific objectives, the optimization results allow a precise convergence of the prioritized objective functions towards the calculated values of their Pareto-front. Computation and verification In order to verify the validity of the discharging mode’s calculations, the optimization has been computed using the gamultiobj software of Matlab, and the Pareto-front has been initially generated with a population of 12 genetic chromosomes, then with a wider population of 200 as per the obtained solutions of Table 4. Referring to the obtained Pareto-fronts of both populations, the wider initial population would generate a closer solution to the theoretical reference values. Once both charging and discharging optimizations were performed, the regulation of both processes still needed to be verified by simulation.

4.2. Obtained results Having run the control and regulation algorithm, the following table (Table 5) gathers the input and output data before and after the vehicular charging and discharging. Furthermore, the plotted curves of the production and consumption inputs and outputs as well as the vehicles’ SoC are displayed in the graphs and diagrams of Figs. 2 and 3. As can be noted in Fig. 3, the color coded bars vary on each day depending on the number of vehicles in the fleet intended for charging

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Table 5 Control and regulation algorithm's input and output data. Input Data Day 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

SoC of a set of EV (%) [11 28 46 53] [19 23 27 74 33] [26 12 12 14 12] [26 56 42 29] [16 26 16] [43 39 64] [26 33 10 19 31 25] [35 43 29 72 88] [26 41 18 29 33 12] [22 28 65 42 31 20] [39 28 74 54] [81 69 42] [17 76 83] [70 64 38 29] [17 32 43 14 82] [73 52 21 38] [47 32 16 75] [74 83 29] [66 74 59 21] [63 87 32 90] [19 36 45 66 81] [77 66 86 30] [88 43 25 12 68 23] [19 90 90 31 22] [71 41 21 61] [82 36 61] [25 36 42 48] [26 43 90] [90 69 31 25] [65 89 86 78 49] [28 60 43]

P (kWh) 145.75 145.48 130 122.39 122.72 134.39 136.69 84.64 64.77 61.37 82.22 71.78 85.03 77.8 8.12 24.16 10.6 14.77 20.79 5.78 14.72 1.87 18.56 4.69 11.54 14.04 21.5 8.59 5.23 24.93 13.72

Output Data ΔP (kWh) 0 −0.27 −15.48 −7.61 0.33 11.67 2.3 −52.05 −19.87 −3.4 20.85 −10.44 13.25 −7.23 −69.68 16.04 −13.56 4.17 6.02 −15.01 8.94 −12.85 16.69 −13.87 6.85 2.5 7.46 −12.91 −3.36 19.7 −11.21

C (kWh) 40.36 38.7 34.2 9.8 1.9 30.51 40.1 35.3 32.1 26.36 17.9 5.2 3.17 14.3 38.7 34.2 27.3 14.7 11.5 7.3 4.9 36.5 33.27 27.4 14.6 14 12.1 8.9 5.2 4.8 13.72

ΔC(kWh) 0 3.95 −4.5 −24.4 −7.9 28.61 9.59 −4.8 −3.2 −5.74 −8.46 −12.7 −2.03 11.13 24.4 −4.5 −6.9 −12.6 −3.2 −4.2 −2.4 31.6 −3.23 −5.87 −12.8 −0.6 −1.9 −3.2 −3.7 −0.4 8.92

and/or discharging. For instance, days 1 and 25 consist of fleets of 4 vehicles represented each by 4 bars, three of which are different shades of blue and the fourth is green, days 10 and 23 consist of fleets of 6 vehicles represented by bars in shades of blue, green, orange and yellow. Of particular note is the fact that the number of bars drawn for each day matches the number of vehicles in the input fleet of that day. Therefore, as presented in Fig. 3, some days include only 3 bars of 3 shades of blue, while others vary up to 6 bars of 3 shades each of blue, green, orange and yellow.

P (kWh) 103.57 94.89 82.1 83.06 82.44 110.83 88.39 59.97 48.44 43.87 50.87 56.90 67.17 47.59 23.7 34.2 25.61 14.73 16.15 7.3 9.81 32.08 33.27 27.4 14.6 14.02 16.8 8.9 5.21 14.87 13.72

C (kWh) 82.54 94.89 82.1 49.13 42.18 54.07 88.39 59.97 48.44 43.87 49.25 20.02 21.03 44.51 38.7 34.2 27.3 14.73 16.15 7.3 9.81 36.5 33.27 27.4 14.6 14.02 16.8 8.9 5.21 14.87 13.72

Charging units(kWh) 42.18 50.59 47.9 39.33 40.28 23.56 48.3 24.67 16.34 17.5 31.35 14.82 17.86 30.21 0 0 0 0.04 4.64 0 4.91 0 0 0 0 0.02 4.7 0 0.01 10.07 0

Discharging units (kWh) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 15.58 10.04 15.01 0 0 1.52 0 30.21 14.71 22.71 3.06 0 0 0.31 0 0 0

4.3. Discussion and analysis The implementation of the regulation algorithm does not affect the size of the grid and whether it is adopted in a big city or a small community. So, the algorithm was conceived in a way to include an unlimited number of vehicles so it could adapt to any size of network. Yet, even though the regulation algorithm proposed in this study is widely applicable for all types of networks, cities and communities, it would have a higher and obvious impact in the following cases:

• When

integrated into environments that are conducive of the

Fig. 2. Input and output production and consumption curves. 8

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Fig. 3. SoC of vehicles before and after charging/discharging.

production of renewable energies (solar energy, wind power, etc…)

• In small-sized communities, villages, cities, etc…

•

The multi-objective optimizations performed for the charging and discharging of EVs contribute to the convergence of objectives’ Paretofronts towards the theoretical reference values. Using the heuristic genetic algorithm optimization method, as the starting population is bigger; the Pareto-front would congregate further towards the theoretical solution. However, the optimal solutions of the defined objective functions cannot all be obtained at once. Thus, the weighted sum approach would intervene as a compromise where a preference for specific objectives over the others is set. Besides, based on the plotted curves of the input production and consumption compared with the output curves after regulation (Fig. 2), it is clearly shown that the margin between both curves is tighter, and in some cases, the regulation allows for a balanced system where production is equal to consumption. Besides, the state-of-charge of the EVs is affected by the regulation process, and referring to the excess or lack of energy, the vehicles get charged or discharged. Particularly, in the regulation example set, the vehicular depth of discharge is of 80 %, so with respect to the batteries, the maximal value of SoC is of 90 %, and its minimal value is of 10 %. However, taking into consideration a percentage of 15 % of the SoC to be kept in the vehicles for their personal needs, the discharging process does not allow a SoC drop beyond 25 %. Thus, as shown on the presented bar graphs of Fig. 3, some of the fleets get partially or fully charged, and, if available, the excess of energy would then be used for grid supply. And some other fleets get fully or partially discharged. Consequently, based on the available input and output values for the production and consumption of the 31 days example set, the regulation’s percentage of equilibrium was calculated. So, this percentage has been found to be 80.26 % for the first week, consecutively 82.89 % and 91.54 for weeks 2 and 3, and 96.67 % for the last 10 days. Hence, these calculations have led to a total average equilibrium percentage of 88.43 % for the 31 days example set.

•

Consequently, the production and consumption regulation proposed by this study and the assessed optimization of both the vehicular charging and discharging modes seem to be an extremely effective solution for the energy waste caused by the misuse of the excess and lack of energy. However, it is to be noted that the study applies to vehicles whose batteries involve an 80 % depth of discharge only. Even though the study does not define any limitations in the number of vehicles to be charged/discharged, the storage and retrieval processes are directly linked to the lack or excess of energy defined by the production and consumption. Thus, it would be useless to hold the availability of vehicles that would not be used for storage/retrieval when the production and consumption difference range is not wide enough. As the study allows an endless number of vehicles to be integrated as a storage system, an optimization of the number of vehicle to charge/discharge would allow a convenient energy transfer and a reduced consumption of batteries. For future studies and implications, it would be interesting to optimize the number of vehicles and order of charging/discharging based on their available SoC as well as their battery lives. It would also be intriguing to generate a realistic prototype that would exemplify the theoretical studies. Moreover, even though it is always recommended to flatten the demand profile and avoid charging electric vehicles during peak hours (for which the electricity tariffs become more substantial), an economic study of such a problem remains to be addressed in future research projects.

5. Conclusion and perspectives

Funding

In this paper, a control and regulation algorithm for the energy flowing between a domestic household, electric vehicles and the grid has been assessed. The major findings and conclusions are:

• The

electricity supply and demand, as well as seeking a balanced production/consumption system. Once triggered by the regulation algorithm, vehicular charging and discharging modes are optimized through the multi-objective genetic algorithm method. The regulation algorithm was simulated through a sample input set of 31 days and the output results showed a total convergence of 88.43 % towards the equilibrium state.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

energy flows regulation is mostly performed based on the 9

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Declaration of Competing Interest

interests or personal relationships that could have appeared to influence the work reported in this paper.

The authors declare that they have no known competing financial Appendix A

of plug-in electric vehicle charging in smart grids to minimize power losses and improve voltage profile. IEEE Transactions on Smart Grid, 2(3), 456–467. Fitzsimmons, J., Kritzer, S., Muthiah, V., Parmer, J., Rykal, T., Stone, M., ... Lambert, J. (2016). Simulation of an electric vehicle fleet to forecast availability of grid balancing resources. IEEE Systems and Information Engineering Design Conference (SIEDS). Gaudin, C., Krotova, M., & Guerlais, L. (2011). Distribution network applications and recomendations for 2020 EV infrastructure charge development in France. 21st International Conference on Electricity Distribution (CIRED). Guo, D., & Zhou, C. (2016). Potential performance analysis and future trend prediction of electric vehicle with V2G/V2H/V2B capability. AIMS Energy, 4(2), 331–346. Guzzella, L., & Sciaretta, A. (2007). Vehicle propulsion systems: Introduction to modeling and optimization. Berlin Heidelberg: Springer-Verlag 1. Hamidi, A., Nazarpour, D., & Golshannavaz, S. (2017). Multi-objective scheduling of microgrids to harvest higher photovoltaic energy. IEEE Transactions on Industrial Informatics, 14(1), 47–57. Haupt, R. L., & Haupt, S. E. (2004). Practical genetic algorithms. New Jersey: John Willy & Sons, Inc. He, Y., Venkatesh, B., & Guan, L. (2012). Optimal scheduling for charging and discharging of electric vehicles. IEEE Transactions on Smart Grid, 3, 1095–1105. Joshi, R., & Deshmukh, A. (2006). Hybrid electric vehicles: The next generation automobile revolution. IEEE Electric and Hybrid Vehicles, 1–6. Kang, J., Duncan, S. J., & Mavris, D. N. (2013). Real-time scheduling techniques for

References Aghaei, J., & Alizadeh, M. I. (2013). Demand response in smart electricity grids equipped with renewable energy sources: A review. Renewable and Sustainable Energy Reviews, 18, 64–72. Aiad, M., & Lee, P. H. (2018). Energy disaggregation of overlapping home appliances consumptions using a cluster splitting approach. Sustainable Cities and Society, 43, 487–494. Amirhosseini, B., & Hosseini, S. M. (2018). Scheduling charging of hybrid-electric vehicles according to supply and demand based on a particle swarm optimization, imperialist competitive and teaching-learning algorithms. Sustainable Cities and Society, 43, 339–349. Apostolaki-Iosifidou, E., Codani, P., & Kempton, W. (2017). Measurement of power loss during electric vehicle charging and discharging. Energy, 127, 730–742. Brady, J., & Mahony, M. O. (2016). Modelling charging profiles of electric vehicles based on real-world electric vehicle charging data. Sustainables Cities and Society, 26, 203–216. Clement-Nyns, K., Haesen, E., & Driesen, J. (2010). The impact of charging plug-in hybrid electric vehicles on a residential distribution grid. IEEE Transactions on Power Systems, 25(1), 371–380. Deilami, S., Masoum, A. S., Moses, P. S., & Masoum, M. A. (2011). Real-time coordination

10

Sustainable Cities and Society 57 (2020) 102129

G. Merhy, et al. electric vehicle charging in support of frequency regulation. Procedia Computer Science, 767–775 Elsevier. Karmaker, K., Ahmed, R., Hossain, A., & Sikder, M. (2018). Feasibility assessment & design of hybrid renewable energy based electric vehicle charging station in Bangladesh. Sustainable Cities and Society, 189–202. Kim, J., & Shcherbakova, A. (2011). Common failures of demand response. Energy, 36, 873–880. Kisacikoglu, M., Erden, F., & Erdogan, N. (2018). Distributed control of PEV charging based on energy demand forecast. IEEE Transactions on Industrial Informatics, 14(1), 332–341. Kriukov, A., & Gavrilas, M. (2014). ). Smart energy management in distribution networks with increasing number of electric vehicles. International Conference and Exposition on Electrical and Power Engineering (EPE 2014). McCall, J. (2005). Genetic algorithms for modelling and optimization. Journal of Computational and Applied Mathematics, 184(1), 205–222. Merhy, G., Moubayed, N., & Nait-Sidi-Moh, A. (2017). Energy Flows Management: Notes on implementation of the charging and discharging of Electric Vehicles technologies. International Journal of E-Learning and Educational Technologies in the Digital Media (IJEETDM), 4(2), 53–60. Merhy, G., Nait-Sidi-Moh, A., & Moubayed, N. (2017). Control of electric vehicles energy flows through a multi-objective and multi-criteria optimization algorithm. 12th International Conference on Multiple Objective Programming and Goal Programming (MOPGP). Nait-Sidi-Moh, A., Ruzmetov, A., Bakhouya, M., Naitmalek, Y., & Gaber, J. (2018). A prediction model of electric vehicle charging requests. The 9th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN) (pp. 127– 134). Qin, H., & Zhang, W. (2011). Charging scheduling with minimal waiting in a network of

electric vehicles and charging stations. Proceedings of the Eighth ACM International Workshop on Vehicular Inter-Networking (pp. 51–60). Ruzmetov, A., Nait-Sidi-Moh, A., & Gaber, J. (2014). A (max - plus)-based approach for charging management of electric vehicles. The 2nd World Conference on Complex Systems (WCCS14). Ruzmetov, A., Nait-Sidi-Moh, A., Bakhouya, M., & Gaber, J. (2013). Towards an optimal assignment and scheduling for charging electric vehicles. IEEE International Renewable and Sustainable Energy Conference (IRSEC). Serrao, L., Chehab, Z., Guezennec, Y., & Rizzoni, G. (2005). An aging model of Ni-MH models for hybrid electric vehicles. IEEE Vehicle Power and Propulsion Conference (pp. 78–85). Sundstrӧm, O., & Binding, C. (2010). Planning electric-drive vehicle charging under constrained grid conditions. International Conference on Power System Technology (pp. 1–6). Varma, R., & Sushil (2019). Bridging the electricity demand and supply gap using dynamic modeling in the Indian context. Energy Policy, 132, 515–535. Vulturescu, B., Butterbach, S., Forgez, C., Coquery, G., & Friedrich, G. (2010). Ageing study of a supercapacitor-battery storage system. XIX International Conference on Electrical Machines (pp. 1364–1369). Xu, N., & Chung, C. (2015). Reliability evaluation of distribution systems including vehicle-to-home and vehicle-to-grid. IEEE Transactions on Power Systems, 31(1), 759–768. Yu, Z., Haghighat, F., & Fung, B. (2016). Advances and challenges in building engineering and data mining applications for energy-efficient communities. Sustainable Cities and Society, 25, 33–38. Zgheib, R., & Al-Haddad, K. (2016). V2G, G2V and active filter operation of a bidirectional battery charger for electric vehicles. IEEE International Conference on Industrial Technology (ICIT) (pp. 1260–1265).

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