Design and Evaluation of a Micro-Grid Energy Management Scheme Focusing on the Integration of Electric Vehicles

Market penetration of electric vehicles is nowadays gaining considerable momentum and so is the move towards increasingly distributed clean and renewable electricity sources. The penetration rate varies among countries due to several factors, including the social and technical readiness of the community to adopt and use this technology. In addition, the increasing complexity of power grids, growing demand as well as environmental and energy sustainability concerns intensify the need for energy management solutions and energy demand reduction strategies. Hence, integration strategies for energy-efficiency in the building and transport sector are of increasing importance. The present study analyses key parameters leading to Electric Vehicle adoption, utilizing background data from countries where Electric Vehicles have already been introduced and adopted in everyday living, and presents a case study of an energy management scheme in Greece, where the penetration rate is still low. Based on the above, an optimization algorithm is proposed, where buildings, photovoltaic plants, storage systems, and Electric Vehicles (utilization of Vehicle to Grid technology) can efficiently meet the energy requirements and peak-hour energy demand, in both economic and sustainability terms. The study proposes a hybrid approach, based on Analytic Hierarchy Process methodology and Genetic algorithms, aiming to foster the diffusion of the Vehicle to Grid concept to support building energy demand.


INTRODUCTION
Transportation is among biggest emission-intensive sector in Europe, with greenhouse gas emissions from transportation to continue to rise, thereby challenging the overall EU climate targets [1]. Since the energy consumption for transportation heavily depends on fossil fuels, the use of Electric Vehicles (EVs) can help to decrease emission levels [2]. Moreover, with the trends encouraged by governments and political parties, strong commitments have been made on an international level to increase the penetration of Renewable Energy Sources (RES) [3]. Several papers in the literature address the possibility of using electric vehicles as a solution to fully take advantage of RES by shaping the load demand curve [4,5].
Statistically, passenger vehicles stay parked for up to 96% time of a day and their idle time is much longer than the time required to fully recharge their batteries [6]. Hence, acting as an environmentally and economically friendly choice for transportation and energy management, EVs can be used both as mobile energy storage units and as generators to support building energy demand [7]. Furthermore, with 30% of the end-use energy-related carbon emission is coming from building energy consumption, including commercial and residential ones, a reduction of the energy drawn from the grid by 39% compared to un-optimized energy management schemes as showcased in [8] is considered of great importance. As such, EVs can help increasing power supply reliability and reducing energy peaks in demand from the grid via vehicle-to-building (V2B) operation mode [9]. For this purpose, EVs need to be equipped with vehicle to grid (V2G) communication systems [10]. The management of the EV charging can be studied as an optimization problem, where the objective is to maximize the usage of energy produced by renewable sources or, alternatively, to minimize the environmental impact in terms of CO2 emission.
However, there are a number of constraints to be considered, such as battery capacity, maximum charging/discharging power, state of charge at arrival time, desired state of charge at departure, user preferences (priority charge, eco-friendly charge, and economic charge) and amount of power available from the grid or from RES.
The purposes of this work are (a) to assess the potential of EV technology adaption in countries with relatively low EV penetration level (b) to investigate the energy efficiency potential of microgrids, which include EVs with V2G capability, photovoltaic and storage systems, as well as charging stations, and (c) set the basis for the development of an integrated energy management system governing the whole spectrum of the processes. In particular, this research aims at designing and developing an algorithm that optimizes the integration of electric vehicles into a general energy management system, while focusing on calibrating relevant parameters in a way of avoiding any discomfort and annoyance caused both in peoples everyday living and the grid's operation.

BACKGROUND
According to European Parliament official briefing [11], the European Union has set new targets for renewable energy use, which are expected to be partially met not only by solely energy consumers, but also by consumers who also produce energy (introduction of the term "prosumers"). Since 2015, the European Commission has set a vision for an Energy Union with citizens lying at the center of it, taking advantage of new technologies to reduce their bills [12].
Prosumers investing in renewable energy technologies could help the EU finance the transition to the energy union, increase the share of renewable energy (RES) and, under certain conditions, reduce energy costs, dramatically changing the energy system. At the same time, by generating their own electricity, prosumers could reduce their demand from the grid and so their bills, benefit from a sense of empowerment, and help introduce elements of "energy democracy" [13]. This reduced demand and the ability to sell surplus electricity from energy storage devices, creates added value in the market, including balance enhancement of the electricity system, which customers could be compensated for.
It is this decision-making capability of selling their surplus energy, that puts prosumers at the center of the research interest. Some other projects propose ways to use electric vehicle batteries solely as energy storage. Vehicles could be charged at lower demand (and energy pricing) periods with the energy being sold back to the grid at higher demand (and energy pricing) periods. This vehicle-grid (V2G) idea is already operating in a small scale in the United Kingdom, as will be presented below [14].
Other research-works have proposed and integrated EV charging stations with PV generation, as a means to help lower the cost as well as to reduce the carbon footprint. To deal with the inelasticity of demand and performance variability of the renewable energy sources during the day, many optimization or control algorithms have been proposed, including ordinal optimization [15], genetic algorithm [16] and model predictive control approaches [17]. To give an example, EV charging cost, as a convex function of load demand, is minimized in Tang W. (2017) [18]. Although a lot of work aimed at reducing the complexity of the optimization algorithms for EV charging process, e.g., by sacrificing minimum performance gap, substantial time is still required to compute and obtain the optimal results.
The increasing complexity of power grids, as a result of the introduction of renewable energy sources, the flourishing energy demand due to the introduction of EVs and the greater distribution of power elements, justify the need for a shift from conventional to smart grids. The main objectives of smart grids are to provide (a) operational efficiency with distributed energy generation, network optimization and improved assets utilization, (b) energy efficiency, (c) customer satisfaction and (d) CO2 emission reduction. The grid, as a supply network, can be considered as a complex system of systems, which aims to ensure a reliable power supply to all its customers. Nowadays, the dependency on the centralized power system is changing, being progressively replaced by smaller and more distributed generation located closer to load to meet their requirements effectively [19].
Microgrids are small scale supply networks designed to provide electrical and heat load in a small community, such as a housing estate, a suburban locality, an academic or a public community, an industrial site, or a commercial area. For the purposes of analysing the energy scheme proposed in this work, a theoretical framework has been established and applied in a microgrid layout case scenario.

MATERIALS AND METHODS
Energy management at a microgrid level appears to be subjected to a great level of complexity due to the integration of several systems i.e., PVs and other renewable energy sources, EVs, and varying needs and requirements of the users. The proposed method considers these interrelationships and aims at maximizing energy efficiency and microgrid sustainability. More specifically, it integrates optimization algorithms (genetic algorithms) with multicriteria decision-making techniques (Analytic Hierarchy Process-AHP) for developing a robust optimization tool needed to unravel the multiple and conflicting objectives.
The proposed process consists of four stages: (1) Microgrid operational assessment, (2) AHP decision criteria weighting, (3) Genetic Algorithm optimization, and (4) Energy management scheme selection. Figure 1 provides a schematic representation of the proposed approach. In the first stage, an overview of the microgrid characteristics (energy demand, demand fluctuation during the day) and assets (ESS, PVs, EVs) are analysed and alternative microgrid operation schemes are being presented. Following, the AHP parameters/criteria are determined, and a decision-making hierarchy is developed. In particular, factors of "relative importance" are assigned to the decision-making parameters (based on pair-wise comparisons) and weights are calculated for the decision parameters to make up the objective function. A set of constraints are identified to supplement the optimization model. The next step includes the iterative processes proposed by the genetic algorithms' methodology. In this stage the initial population is being selected, the problem limitations are being set and the genetic operators are being quantified. In the last step, the selected energy scheme is being checked and approved for adoption by each user. Each user is being given the ability to approve the energy management scheme proposed to him/her, based on his own schedule and regardless the choice of other users. If approved, the system will be automatically authorized to manage the microgrid while if the proposed scheme is rejected by any of the users, then the system returns to stage 3 of the procedure and recalculates the scheme for the users that didn't approve it and run the process again through the last stage for approval.
The proposed approach focuses on the integration of EVs as a viable alternative energy resource in a microgrid which can support other existing resources, such as PV installations and batteries installed in situ, in order to provide sustainable and efficient energy management within the microgrid.

Microgrid Operational Assessment
Based on the overview of the microgrid assets and architecture, the available or the desirable resources it may have, the specifications concerning Electric Mobility and Vehicle-to-Grid (V2G) technology, and the assessment of the field research analysis results, three alternative energy schemes (Alternative Energy Scheme -AES) are developed: Vehicles (V2G) These alternatives represent the current state (AES-1), a future state with the installation of RES systems (AES-2) and a future state based on the integration of both RES systems and EVs (AES-3).
The main aim of the analysis is the assessment of the energy scheme that results in resource exploitation in a sustainably and economically viable way. In this direction, four parameters (criteria) are considered to contribute towards to these objectives, namely grid energy cost, utilization of renewable energy sources, use of energy storage elements and balanced energy demand. The decision-making process is performed based on a hierarchy structure consisting of four levels, as presented in Figure 2, while the interconnections between the different elements of each layer are depicted. The overall goal of the proposed approach lies in the top level. The decision criteria and sub-criteria are shown in the second and third level respectively. The level of sub-criteria can be recalibrated and include the corresponding data of each case study. The alternative energy management schemes lie on the fourth level of hierarchy.

Analytic hierarchy process criteria weighting
The former hierarchy provides AHP with input for assigning weights towards shaping the objective-function, that will govern the selection of the Energy scheme in the following stages. The AHP is developed with the use of a pairwise comparison matrix. The results from the criteria comparison based on scale of importance and the values geometric means are presented in the table below (Table 1).  The weights resulted by AHP calculation are presented in Table 2, along with the calculations related to the consistency ration, which is 0.065 < 0.1, leading to safely conclude that the weights assigned are consistent and can be approved and proceed to the next stages of the process. Based on the assessment, the balanced energy demand and the utilization of renewable sources appear to be the most important criteria, a fact that complies with the logic supporting the approach and increases the likelihood of achieving the overall goal.

Miscellaneous Parameters
To make the system economically viable and promote sustainability, a supplementary set of constrains should be introduced in the objective function. Because of the complexity and multilevel nature of this problem, the objective function is of a polynomial form and consists of a set of variables considering the different EVs operational scenarios, indicating the state and rate of charge/discharge (-1 for minimum to -10 for maximum rate discharging, to 1 for minimum to 10 for maximum charging rate). Although the system is dynamic -so that it could work on a continuous operation, the algorithm configuration process charging/discharging time is set at a steady time frame. This time frame is selected to ensure the orderly operation of the bidirectional energy transfer process between the vehicle and the building installation network. Therefore, the optimization algorithm uses a 30-minute time frame of reference for configuring the power management program (tperiod = 30 min).

Optimization
The optimization procedure starts with setting up the decision-making parameters (design) and developing the objective function based on these parameters. Following, the problem constraints are formed, which represent functional relationships among the design variables and other parameters describing physics and resource-based limitations [20]. The proposed optimization tool focuses primarily on sustainability performance. In this respect, it is not only the economic parameters, such as the energy cost per KWh, that solely determine the optimization output, but also the level of demand fluctuation during the day, expressed in periods (which show about the same demand profile) and the energy produced by RES. Although, as a common measurement unit is required, all parameters are converted into costs (Euro), based on information gathered from the current energy market.

Objective -Function
As mentioned above, the criteria used for AHP calculations represent the objective function decision-making parameters. In each reporting period, calculations are made on energy requirements related to the cost per KWh provided by the Time-of-Use strategy. All parameters of the objective function are being expressed in cost units (based on energy market costs values), in order to facilitate the following stages of the process. Another reason for choosing cost unit as an overall measurement unit is the provided ability to follow the dynamic changes taking place in the energy market, which are expressed by the cost price of a kilowatt-hour. Based on the AHP calculations, the objective-function of the system is described by the following equation: Where: Cgrid is the grid energy cost (€), BRes is the Price Benefit from renewable sources utilization (€), Bbat is the benefit from managing energy stored in batteries in and out at low and high energy pricing levels from the grid (€), Cbat is the cost of the energy attributed by the batteries where also: Where DNgroups stands as the difference in the number of energy demand groups (groups of periods with common energy demand profile) between the initial state and the state proposed by the energy management scheme. Each one of the respected criteria and sub-criteria presented above is further analysed and correlated with energy unit cost.
Grid Energy cost is a straightforward matching of Energy cost, based on ToU, and the energy demand according to the following formula: Where Et(i) is the energy cost at period t(i) based on time of use (ToU) (€) and Ct(i) is energy cost at period t(i) based on time of use (ToU) (€). Utilization of renewable sources applies for the energy surplus created by the use the photovoltaic installation. This parameter is calculated according to the following formula: where EPVt(i) is the energy production from PV installation at period t(i) (KWh) and CRES: price per KWh for energy production (considered equal to the lowest energy cost price CRES =0.08 €/KWh, due to lack of dynamic connection with the Energy Market). In real life case, this value will be linked with the corresponding value, provided by the Energy regulating authority, based on the period of the day and the type of RES.
Use of storage elements includes energy from either the storage units of an office-building (BatESS), or the batteries installed in houses (ΣHBat, corresponding to the house units included for the purposes of this application) and the EV batteries (ΣEVBat). This criterion is subdivided in two -equally weighted-sub-criteria and so a weight of 0.500 is being assigned to each one of them. The equations describing the above are the following: Where cdstate(i) is the state of charge or discharge of each battery installed in the scheme and Ct(i) is the energy cost at period t(i) based on time of use (ToU) (€).
The second sub-criterion is the cost of the energy stored in the storage elements at the end of the optimization (Ebat (t=48) -end of the day as indicated based on the 48 periods of reference and EMS selection period). This amount of energy is considered as a cost in the objective-function as this energy will be available for use beyond the period of reference examined, and therefore the microgrid will be not benefited by the unused resources. This cost responds to the following equation: where Ct(i) is the energy cost at period t(i) based on time of use (ToU) (€) Balanced energy demand and minimized demand fluctuations are important aspects for a sustainable microgrid. Based on the idea of the standard deviation in each period of reference, the spread of the Grid Energy Cost is calculated and grouped into respective periods with energy demand deviating up to 15% from the average value of the respective consumption. The goal is to have less groups than the original state and a stable demand profile related to the grid. This parameter is expected to be of great value in the context of a fully integrated and dynamic energy market in the near future. For the purposes of the current research, a trade-off equal to 1/10 of the mean energy cost is proposed as the benefit for each reduction of the groups calculated by the proposed procedure.
After the selection of the proposed energy management scheme, the next and final stage of user approval follows. Each user, in a real-life scenario will have to approve the proposed scheme in order to be adopted by the microgrid. In case there is not a unanimous approval of the proposed scheme, then the procedure must run again. If the users don't approve the proposed scheme unanimously after the 2nd effort, then the system continuous with the charging/discharging schedules for the users who have approved it and rerun the procedure for the ones that have not, in order to meet their needs in the best possible way -if yet no approval is being provided, then these users are excluded from the procedure.

CASE STUDY
A simulation paradigm has been designed, in order to test the proposed method at a first level. Nowadays, there are many countries in Europe, where Electric Vehicles already occupy a significant proportion of vehicles on the road, while each country is developing the necessary infrastructure related to the vehicle charging process. Great Britain is one of the model-countries in terms of Electric vehicle integration, as well as the development, support, and adoption of Vehicle-to-Grid (V2G) technology. In fact, many projects have been designed and implemented in the United Kingdom (V2G-Britain, Sciurus, EV-elocity, etc.), in order to test V2G's efficiency. Among the most important challenges the scientific community faces in this field, is the availability of data on the costs and the potential of utilizing and adopting this technology. Based on research conducted by Cenex, an independent non-governmental organization in UK [21], it was concluded, that the ECO professionals are among the first people to join a new market, such as the EV market and adopt new technologies as V2G. Eco professionals are considered as highly motivated by environmental benefits and likely have PV panels installed on their homes. This archetype is most likely to be met among people of high education, working in academia or technology companies. Therefore, in order to make the simulation more realistic, the simulation has been selected to run for a reference territory which will include the University of Patras in Greece where offices/workplaces of reference are placed, and the city of Patras for the houses of reference of this simulation (each house was considered to have both a vehicle for daily usage as a second car, a 10 kW photovoltaic system and a tesla battery as an energy storage system for providing power to the EVs when recharging). The allocation of a specific area of reference helps in providing the simulation with realistic data regarding the daily home-work-home driving distances, preferences of the drivers and microgrid layout.
The University of Patras reference microgrid in this work consists of the following components: a 10-office building with a 10-kW photovoltaic installation, an energy storage system (ESS), 4 electric vehicle charging stations as well as users' electric vehicles and the houses, equipped with the appropriate equipment for bidirectional energy transfer and EV charging. Figure 4 displays the microgrid of reference and its components. The specific characteristics used for simulation purposes will be presented along with the limitations and special preferences that are proposed to be applied in the framework.

Stage 1 -Microgrid Operational Assessment
Following the methodological approach presented above, the first stage considers the citizens' opinion in regards with the development of the energy management scheme by analysing their responses on the research field conducted for the area of reference (overview of the demographic characteristics is presented on Table 3 -a questionnaire-based survey was organized and conducted by the research team, targeting to 104 citizens of the City of Patras in Greece, during year 2020). Figure 5 presents the most important reasons for users to buy an EV. Based on the responses, it is observed that environmental concerns, energy price and cost, along with the utilization of EV batteries lie among the most important motives. More specifically, it is observed, that there is an alignment between the responses mentioned above and the architecture and operation of the proposed system, as the above mentioned most

Stage 2 -Optimization Process
The user (EV owner) profile should be created into the system. This profile includes information regarding the average route distance (home to work commute) and other parameters, such as expected daily travelling time and EV systems' energy consumption needs (e.g., AC and media player). The system will then estimate the energy required for each EV based on this dataset. The energy amount available to be supplied to the microgrid can be then calculated over any reference period by using the following equation: Where is the total energy required for a trip by an EV (KWh), DS is the round-trip distance (km) and the energy required for a trip accounting for the desired level of comfort and considering the meteorological conditions (KWh) and ni is a safety factor introduced equal to 1.3 towards safeguarding that in all cases there will be sufficient amount of energy available for the users to complete their everyday commute, regardless of unplanned needs that might require additional energy.
An important goal within the energy management system is to ensure that the EV owner comfort level is not disrupted even in cases that the microgrid requires to absorb the maximum of the available energy provided by the EV. The available EV energy for exploitation by the microgrid is: Where is the available battery capacity at the start of the scheduling (KWh). Nowadays, EVs have higher battery capacity than previous generations and faster or even super-charging capability. The increasing EV mass production and the competition among the world biggest car manufactures have helped in decreasing the purchase cost in levels close to that of conventional vehicles. These are some important factors that have led to increasing EV penetration to the market with further increase to be anticipated in the future. In the case study, a 40% EV penetration rate has been set, based on current trends.
The microgrid consists of the following components: the 10-office building, the photovoltaic plant, the energy storage system (ESS), the electric vehicle charging station and the electric vehicles connected to the charging station. The microgrid is designed with 4 charging sites (one at each building), taking into consideration the suggested penetration rate. Apart from the number of available EVs which are at any time connected to the system, there are also some constrains regarding the EV batteries. The battery is one of the most expensive EV components and among those that determine EV usability and viability as a product. The battery life cycle and performance are of the main parameters considered in the proposed method.
EV batteries nowadays provide a capacity higher than what daily usage would demand. A battery of a 75 KWh capacity is a standard for the vehicle manufacturers while statistically the average daily demand is not more than 10 KWh. The available excess energy can be used for alleviating building energy demand peaks during the day.
EV batteries life span and charging cycles are also important parameters. Tesla batteries present about 5,000 to 6,000 cycles before starting to lose their capacity. There are studies arguing that if the battery is used till 80% of its capacity, the lifespan loss is minimized [22]. Based on this consideration, formula 10 is formed: Where is EV battery energy that may be available for use by the system. Based on the average EV life charging cycles, a soft constraint is set in the optimization process to allow no more than four cycles of EV charge/discharge on a daily basis. The number stated above is set according to the results of a survey reporting an 89% EV owner sample that would follow this constraint: Where is acceptable EV battery charging and discharging cycles on a daily basis. The user preferences and willingness to participate in the Energy Management Scheme is also a matter of study. This is included in the system by introducing a value showing the degree of desired participation in a scale from 1 to 10 (with 1 indicating the lowest and 10 the highest potential participation). This input, along with the number of available cycles of the EV Battery, corresponds to two important indications of availability, which have an impact on the energy that the system can use for the energy management scheme. The energy of EV battery which can be available for use by the system is assumed and shown in Table 4.
The last constraint regarding the EV battery is the level of charging and discharging provided by the charging station. There are three levels of charging, starting from level 1, which is provided by the national grid simple outlet, till level 3 provided by supercharges. In this work, the intermediate level 2 chargers are considered which are 5.6% more efficient than level 1 and way less expensive than superchargers. Level 2 chargers come typically ranging from 16 amps to 40 amps and may be referred to as 3.3 kW and 7.2 kW respectively. Having superchargers installed in a specific microgrid solution would not affect the national grid much; having more systems utilized though (including more EVs and batteries that need to be charged simultaneously) would affect the national grid as they are 480V, require special equipment to be installed and the possibility of common use in homes would create risks of more frequent demand peaks.
The optimization process is designed and executed using the methodology of genetic algorithms and the use of the computer package Evolver of the Palisade company. This software provides the ability to work with the excel software of the Microsoft Office package, where all the parameters and the problem have been formed in order to proceed with the process of evaluating the algorithm effectiveness. The software enables the change of the three basic parameters of the genetic algorithm methodology, which are the initial population size, the crossover rate, and the mutation rate.

Optimization Results
The purpose of the presented application is to efficiently manage the microgrid energy resources and plan the charging and discharging cycles of the electric vehicles. Based on the results it appears that by using the proposed Energy Management Scheme -including photovoltaic system installations and using the energy available from electric vehicle batteries, the scheme has a positive performance. For the purposes of this application a total of 3 EV users has been specified, with the same car and specifications, although with different travel requirements and energy availability. The EVs preferences are presented in the Table 5. In Figure 6, the proposed schedule for charging and discharging of the Electric vehicles is presented. All constrains set above have been taken into account. The system has shown good response to the expectations, assisting the microgrid to achieve the environmental and economic goals. This is being achieved by scheduling the charge of the EV when the price of energy is low and discharge it when it is high, as provided by the ToU model ( Figure 3). Another indication of efficient functionality is the concentration of scheduled discharges within the period between 12:00 to 14:00, when a peak in demand and a high cost of energy is expected. Figure 7 shows an overview related to the energy consumption, the use of the EV batteries and PV installation along with the financial savings that result from the use of the proposed Energy Management scheme. More specifically, the demand during the day of reference is shown, where the initial state (original state) is compared with the one proposed by the energy management scheme. In the demand profile proposed when EMS is enabled, there is a reduction from 17 to 15 groups of demand.
There is a distinct reduction in demand during the peak hours indicated by the ToU, which is also combined with a maximization of energy storage utilization, towards reducing peak demand on the grid. What is more, the scheme follows the preferences of the users in regards with the energy availability and the number of charging/discharging cycles of the EV batteries scheduled for the day of reference. These observations indicate the scheme's proper operation  There are some metrics based on the set criteria set that supplementary confirms this conclusion. The energy grid cost for the initial state was 55.19 Euro (for the day the scheme was proposed), and has been reduced to 42.63 Euro, presenting economic efficiency equal to 22,75 %. From this reduction, a part is attributed to the utilization of renewable sources, which provided a 9.22% reduction of cost, equal to 5.09 Euro. It becomes evident that the use of storage elements, such as the batteries installed in the office building and the houses, along with the EV batteries, are responsible for the major part of the cost savings achieved by the use of the proposed Energy management Scheme.

CONCLUSIONS
The modern lifestyle combined with the increased energy requirements for the service of human activities are among the main factors that negatively affect the environment and are associated with phenomena such as the increase in average temperature and the greenhouse effect. In addition, the provision for the installation of the required infrastructure to ensure a smooth transition to electrification, as well as the encouragement of the use of electric vehicles by consumers can have a number of positive effects on the environment and everyday life in the cities.
It has been shown that the development of charging infrastructure combined with the implementation of appropriate strategies to reverse the behaviour of citizens (users of electric vehicles) in the workplace can reduce the burden on the electricity system during peak hours. Part of the daily energy demand of electric vehicles, i.e., consuming the battery to drive from home to work, can be recovered in the morning at work, when system demand is still relatively low, or can be treated by charging against at night, with energy produced by photovoltaic systems and stored in batteries.
In this paper, a hybrid approach combining AHP methodology for parameter weight definition is used along with genetic algorithms optimization techniques, towards integrating both RES and electric vehicles in a smart microgrid. The proposed model combined with dynamic pricing policy (Time of Use) provides the necessary incentives to attract the user's interest and delivers better results against uncontrolled charging.
The proposed solution was examined in a simulation context by using case scenarios in specified area in Greece. After the parameterization of the problem, a model was formed that serves both energy management and optimization in its use. The proposed scheme is configured in a way, that with small modifications could be implemented in real pilot applications in the future.