光储充一体站和微网算法

Abstract Over the past decade, electric vehicle (EV) usage has dramatically increased. For many applications, employing vehicle-to-grid (V2G) and grid-to-vehicle (G2V) schemes can make use of EVs as temporary energy storage systems (ESS). Renewable energy resources can reduce the amount of energy consumed from the electrical grid. However, the electrical power generated from these resources fluctuates and is unpredictable. ESS can play a vital role in mitigating these negative effects, but the technology is still very expensive. Therefore, this study investigates the feasibility of using EVs as a temporary ESS according to system needs. A charging/discharging algorithm is suggested to find the number of EVs that minimizes the overall consumption of electrical energy drawn from the grid. A case study for the Microgrid (MG) system at Jordan University of Science and Technology (JUST) is used to illustrate the proposed algorithm. Results show energy saving of the suggested algorithm by comparing the amount of grid energy consumption before and after the installation of EV charging stations. In this case study, installation of charging stations at JUST reduces energy drawn from the grid, during working hours, by 90%. This reflects an increase in the percent of generated energy locally utilized, in essence reducing the energy injected into the grid for later usage, reducing grid storage fees.

… This paper investigates the energy coordination control strategy for the standalone DC microgrid integrated with PV, energy storage, and EV charging. The specific research contents …

Although electric vehicles (EVs) are experiencing a considerable upsurge, the technology associated with them is still under development. This study focused on the control and …

Abstract For micro-grid systems dominated by new energy generation, DC micro-grid has become a micro-grid technology research with its advantages. In this paper, the DC micro-grid system of photovoltaic (PV) power generation electric vehicle (EV) charging station is taken as the research object, proposes the hybrid energy storage technology, which includes flywheel energy storage and battery energy storage. Flywheel energy storage is used to stabilize high frequency power fluctuations and some low frequency power. Lithium iron phosphate (LiFePO4) battery is used for balancing the reference power to maintain the DC bus voltage balance. Composition of the DC micro-grid and various operating modes are analyzed, a hierarchical coordinated control based on the power monitoring steps of the 5-layer DC bus voltage is proposed. Finally, simulation analysis is carried out on the MATLAB/Simulink software platform. Under different working states such as PV input power change, AC and DC loads change, EV charging condition change, and battery over-discharge, the proposed control strategy can make the DC bus voltage at different voltage layers effectively switch, and keep the DC bus voltage balance, thus to achieve flexible and reliable operation of the DC micro-grid system.

The expected increase in electric vehicles necessitates an expansion in charging stations. However, this increase could introduce issues to the power grid, such as the deterioration of voltage stability and an increase in microgrid loading. To address these issues, innovative solutions are imperative. One potential solution is the implementation of charging control mechanisms. This paper analyzes the impact of a residential charging station on a low voltage microgrid from the power quality point of view using a one-year operation simulation. Thirty-seven charging station scenarios with different locations within the network were examined, including instances with no charging control and various combinations involving charging control, photovoltaic power plant, and battery energy storage system. The simulation results of the charging station without charging control show a decrease in some power quality indicators, such as exceeding voltage change thresholds after charging station connection and increasing power losses. On the contrary, the scenarios implementing charging control and local resources effectively mitigate the adverse effects of charging stations on the microgrid.

… of microgrid for photovoltaic EV charging stations with a hybrid energy storage system. This … The proposed WOA strategy is used to optimize the energy structure of the micro-grid and …

DC microgrid is supposed to be a feasible solution to reduce the negative impact of electric vehicle (EV) fast charging on the electric grid and improve the penetration of photovoltaics (PV) generation. In this paper, an improved decentralized Virtual-battery based droop control with the capability of bus voltage maintenance, load power dispatch and SOC balance of the energy storage system (ESS) is proposed to ensure the autonomous and stable operation of the DC microgrid. The reference output voltage and virtual resistance in the droop control loop are altered dynamically based on the Virtual-battery model of the ESS. The coordinated control among the PV-ESS-Grid integrated system is realized through the primary Bus-Signaling control, where the reference voltages at which the control modes of the PV array and the grid are switched are designed based on the VirtualOCV of the ESS. The effectiveness of the proposed control strategy is validated in MATLAB/Simulink environment with an equivalent bus capacitance-based model where the EV charging profile is obtained from real-world charging data of a fast charging station. The merits of the control strategy including higher PV utilization, less frequent connection of the grid and more precise voltage tracking are highlighted in comparison with the conventional droop control strategy. Finally, the sizing of the ESSs is optimized based on the total cost of the DC microgrid, including the daily electricity cost purchased from the grid and the depreciation cost of the ESSs based on the expanded capacity degradation model of Li-ion batteries.

A hybrid microgrid-powered charging station reduces transmission losses with better power flow control in the modern power system. However, the uncoordinated charging of battery electric vehicles (BEVs) with the hybrid microgrid results in ineffective utilization of the renewable energy sources connected to the charging station. Furthermore, planned development of upcoming charging stations includes a multiport charging facility, which will cause overloading of the utility grid. The paper analyzes the following technical issues: (1) the energy management strategy and converter control of multiport BEV charging from a photovoltaic (PV) source and its effective utilization; (2) maintenance of the DC bus voltage irrespective of the utility grid overloading, which is caused by either local load or the meagerness of PV power through its energy storage unit (ESU). In addition, the charge controller provides closed loop charging through constant current and voltage, and this reduces the charging time. The aim of an energy management strategy is to minimize the usage of utility grid power and store PV power when the vehicle is not connected for charging. The proposed energy management strategy (EMS) was modeled and simulated using MATLAB/Simulink, and its different modes of operation were verified. A laboratory-scale experimental prototype was also developed, and the performance of the proposed charging station was investigated.

This paper presents a capacity planning framework for a microgrid based on renewable energy sources and supported by a hybrid battery energy storage system which is composed of three different battery types, including lithium-ion (Li-ion), lead acid (LA), and second-life Li-ion batteries for supplying electric vehicle (EV) charging stations. The objective of this framework is to determine the optimal size for the wind generation systems, PV generation systems, and hybrid battery energy storage systems (HBESS) with the least cost. The framework is formulated as a mixed integer linear programming (MILP) problem, which incorporates constraints for battery ageing and the amount of unmet load for each year. The system uncertainties are managed by conducting the studies for various scenarios, generated and reduced by generative adversarial networks (GAN) and the k-means clustering algorithm for wind speed, global horizontal irradiation, and EV charging load. The studies are conducted for three levels of unmet load, and the outputs are compared for these reliability levels. The results indicate that the cost of hybrid energy storage is lower than individual battery technologies (21% compared to Li-ion, 4.6% compared to LA, and 6% compared to second-life Li-ion batteries). Additionally, by using HBESS, the capacity fade of LA batteries is decreased (for the unmet load levels of 0, 1%, 5%, 4.2%, 6.1%, and 9.7%, respectively), and the replacement of the system is deferred proportional to the degradation reduction.

A four-stage intelligent optimization and control algorithm for an electric vehicle (EV) bidirectional charging station equipped with photovoltaic generation and fixed battery energy storage and integrated with a commercial building is proposed in this paper. The proposed algorithm aims at maximally reducing the customer satisfaction-involved operational cost considering the potential uncertainties, while balancing the real-time supply and demand by adjusting the optimally scheduled charging/discharging of EV mobile/local battery storage, grid supply, and deferrable load. The chance-constrained optimization objective has been stated in stages: 1) stage I, optimization of day-ahead energy management schedules; 2) stage II, multitiered EV charging price update and optimization of discharging participation bonus; 3) stage III, optimization of hour-ahead energy management schedules; and 4) stage IV, real-time control. Such algorithm provides more resilience for unpredictable conditions, provides more incentives for EV users to participate, and better coordinates the integrated system including the building load to reliably serve the customers while lessening cost. Case studies are implemented and the comparison analysis is performed in terms of the use and benefit of each design feature of the algorithm. The results indicate that the proposed algorithm can reduce the operational cost and at the same time provide higher tolerability toward uncertainties.

Electric vehicle (EV) demand is increasing day by day raising one of the major challenges as the lack of charging infrastructure. To reduce the carbon footprint, countries are pushing for the rapid growth of the renewable energy to be used as the source of charging station. In this paper, an optimized battery energy storage system (BESS) integrated with solar PV in a charging station is designed for the overall benefit of the system. Particle swarm optimization (PSO) is used to determine the optimal cost of the battery based on the parking area capacity, PV generation capacity, the load connected to the solar PV system and the availability of the EVs. An optimum rate of charging / dis-charging of the BES will ensure a prolonged life cycle of the battery. Also, artificial neural network (ANN) is used to predict day ahead PV generation and load demand, and hence the battery power, in order to design the storage capacity of BES to satisfy the maximum demand.

In order to effectively improve the utilization rate of solar energy resources and to develop sustainable urban efficiency, an integrated system of electric vehicle charging station (EVCS), small-scale photovoltaic (PV) system, and battery energy storage system (BESS) has been proposed and implemented in many cities around the world. This paper proposes an optimization model for grid-connected photovoltaic/battery energy storage/electric vehicle charging station (PBES) to size PV, BESS, and determine the charging/discharging pattern of BESS. The multi-agent particle swarm optimization (MAPSO) algorithm solves this model is solved, which combines multi-agent system (MAS) and the mechanism of particle swarm optimization (PSO). In this model, a load simulation model is presented to simulate EV charging patterns and to calculate the EV charging demand at each time interval. Finally, a case in Shanghai, China is conducted and three scenarios are analyzed to prove the effectiveness of the proposed model. A comparative analysis is also performed to show the superiority of MAPSO algorithm.

The sizing and siting of renewable resources-based distributed generation (DG) units has been a topic of growing interest, especially during the last decade due to the increasing interest in renewable energy systems and the possible impacts of their volatility on distribution system operation. This paper goes beyond the existing literature by presenting a comprehensive optimization model for the sizing and siting of different renewable resources-based DG units, electric vehicle charging stations, and energy storage systems within the distribution system. The proposed optimization model is formulated as a second order conic programming problem, considering also the time-varying nature of DG generation and load consumption, in contrast with the majority of the relevant studies that have been based on static values.

Abstract The wide deployment of grid connected large scale photovoltaic (PV) systems and the rapid growth in the electric vehicle (EV) market opens the avenue for the PV based EV stations to participate in the energy as well as the EV arbitrage markets. This participation should comply with the utility restrictions with respect to power oscillations at the point of common coupling (PCC). Consequently, there is a need for battery energy storage system (BESS) to fulfill this task. This paper proposes a multi-objective optimization (MOO) methodology that aims at maximizing the revenues of the PV based EV (PV-EV) station while minimizing the BESS capacity fading simultaneously. The objectives are to be met under the power fluctuations restriction imposed by the hosting grid at the point of common coupling (PCC). The proposed methodology provides detailed modelling of the dynamics of the BESS as well as the EV parking garage behavior. The MOO problem is solved by the augmented e-constrained (AUGMECON2). The results showed the effectiveness of the proposed methodology in achieving the aforementioned objectives. Furthermore, sensitivity analysis was performed to show the effect of neglecting one of the objectives on the other, and the effect of the power fluctuations limit constraint on the objectives of concern in this study. Moreover, the results showed the adverse effect of neglecting the battery’s detailed modelling on the battery’s lifetime. Additionally, the effect of uncertainties in the BESS’s internal parameters coefficients of both objectives was investigated.

… In this paper, a hybrid optimization algorithm for energy storage … of the energy storage system (ESS) along with the levelized cost of PV power is used in the case of EV charging stations. …

Abstract This paper studies the capacity of electric vehicle charging station (EVCS) and energy storage, and the optimization problem and model of electric vehicle (EV) charging scheduling plan. Based on the alternative energy storage effect of EVs, it is committed to improve the renewable energy consumption capacity in micro-grid, reduce the EVCS and energy storage capacity, and improve the comprehensive benefits of micro-grid investors. In this paper, considering the factors that affect the efficiency and system security, the determination model of EVCS and energy storage capacity is established, and the traditional simulated annealing algorithm (SA) is improved to create a disturbance mechanism for the optimization of charging scheduling plan, and the joint optimization is realized. The empirical analysis results show that the EVCS and energy storage capacity are reduced by about 50%, and the benefit of micro-grid investors is increased by about 7%. Therefore, this study can provide solutions for the planning of micro-grid with distributed energy, conventional users, energy storage and adjustable load, maximize renewable energy consumption and improve the satisfaction of all participants. Keywords: Electric vehicle charging station; Alternative energy storage effect; Net annual value; Improved simulated annealing algorithm; Joint capacity optimization.

Based on the comprehensive utilization of energy storage, photovoltaic power generation, and intelligent charging piles, photovoltaic (PV)-storage charging stations can provide green energy for electric vehicles (EVs), which can significantly improve the green level of the transportation industry. However, there are many challenges in the PV-storage charging station planning process, making it theoretically and practically significant to study approaches to planning. This paper promotes a bi-level optimization planning approach for PV-storage charging stations. First, taking PV-storage charging stations and EV users as the upper- and lower-level problems, respectively, during the planning process, a bi-level optimization model for PV-storage charging stations considering user utility is established for capacity allocation and user behavior-based electricity pricing. Second, the model is converted into a single-level mixed-integer linear programming model using the piecewise linear utility function, Karush–Kuhn–Tucker (KKT) conditions, and linearization methods. Finally, to verify the validity of the proposed model and the solution algorithm, a commercial solver is used to solve the optimization model and obtain the planning scheme. The results show that the proposed bi-level optimization model can provide a more economical and reasonable planning scheme than the single-level model, and can reduce the investment cost by 8.84%, operation and maintenance cost by 13.23%, and increase net revenue by 5.11%.

Sufficient and convenient fast-charging facilities are crucial for the effective integration of electric vehicles. To construct enough fast electric vehicle-charging stations, station owners need to earn a reasonable profit. This paper proposed an optimization framework for profit maximization, which determined the combined planning and operation of the charging station considering the vehicle arrival pattern, intermittent solar photovoltaic generation, and energy storage system management. In a planning horizon, the proposed optimization framework finds an optimal configuration of a grid-connected charging station. Besides, during the operation horizon, it determines an optimal power scheduling in the charging station. We formulated an optimization framework to maximize the expected profit of the station. Four types of costs were considered during the planning period: the investment cost, operational cost, maintenance cost, and penalties. The penalties arose from vehicle customers’ dissatisfaction associated with waiting time in queues and rejection by the station. The simulation results showed the optimal investment configuration and daily power scheduling in the charging station in various environments such as the downtown, highway, and public stations. Furthermore, it was shown that the optimal configuration was different according to the environments. In addition, the effectiveness of solar photovoltaic, energy storage system, and queue management was demonstrated in terms of the optimal solution through a sensitivity analysis.

The increasing electric vehicle fleet requires an upgrade and expansion of the available charging infrastructure. The uncontrolled charging cycles greatly affect the electric grid, and for this reason, renewable energy sources and battery storage are getting incorporated into a hybrid charging station solution. Adding a renewable source and a battery to the charging station can help to “buffer” the power required from the grid, thus avoiding peaks and related grid constraints. To date, the origin of the energy coming from the battery has not been traced. In this paper, a solution of the hybrid electric vehicle charging station coupled with the small-scale photovoltaic system and battery energy storage is proposed to eliminate the adverse effects of uncontrolled electric vehicle charging, with the exact calculation of renewable energy share coming from energy stored in the battery. The methodology for the multicriteria optimization of the charging/discharging schedule of a battery and electric vehicle charging level is based on multi-attribute utility theory. The optimization criteria include the minimization of charging costs, maximization of renewable energy (from both the solar plant and the battery), and the minimization of battery degradation. The problem is solved using a genetic algorithm optimization procedure adapted to the multicriteria optimization function. The methodology is tested on an illustrative example, and it is proven that the decision-maker’s preferences greatly affects the choice of the optimal strategy and the optimal battery capacity.

Solar-powered EV charging stations offer a sustainable and reliable alternative to traditional charging infrastructure, significantly alleviating stress on legacy grid systems. However, the intermittent nature of renewable energy sources poses a challenge for energy management in power distribution networks. To address this, optimal charge/discharge scheduling of EVs becomes crucial. This paper introduces an innovative Opposition-based Competitive Swarm Optimization (OCSO) technique to minimize the total charging cost of EVs in the IEEE 33-bus distribution system. Five strategically placed solar-powered charging stations on distinct buses are evaluated under three charging modes: dumb charging, smart grid-to-vehicle (G2V) charging, and smart vehicle-to-grid (V2G) charging. Comprehensive analyses are performed on critical parameters, including bus voltage stability, EV charging load profiles, electricity cost profiles, state-of-charge (SOC) dynamics, and the thermal performance of distribution transformers. Notably, total power losses are reduced by 13.7% and 21.6% in smart G2V and smart V2G modes, respectively, compared to dumb charging. Furthermore, the cumulative ageing factor of distribution transformers under smart V2G charging is reduced by 11.86%, indicating extended transformer lifespan. These findings demonstrate that solar-powered EV charging stations, coupled with advanced energy management strategies, can effectively mitigate grid impacts, enhance operational efficiency, and contribute to reducing net carbon emissions.

The importance of electric vehicle charging stations (EVCS) is increasing as electric vehicles (EV) become more widely used. EVCS with multiple low-carbon energy sources can promote sustainable energy development. This paper presents an optimization methodology for direct energy exchange between multi-geographic dispersed EVCSs in London, UK. The charging stations (CSs) incorporate solar panels, hydrogen, battery energy storage systems, and grids to support their operations. EVs are used to allow the energy exchange of charging stations. The objective function of the solar-hydrogen-battery storage electric vehicle charging station (SHS-EVCS) includes the minimization of both capital and operation and maintenance (O&M) costs, as well as the reduction in greenhouse gas emissions. The system constraints encompass the power output limits of individual components and the need to maintain a power balance between the SHS-EVCSs and the EV charging demand. To evaluate and compare the proposed SHS-EVCSs, two multi-objective optimization algorithms, namely the Non-dominated Sorting Genetic Algorithm (NSGA-II) and the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D), are employed. The findings indicate that NSGA-II outperforms MOEA/D in terms of achieving higher-quality solutions. During the optimization process, various factors are considered, including the sizing of solar panels and hydrogen storage tanks, the capacity of electric vehicle chargers, and the volume of energy exchanged between the two stations. The application of the optimized SHS-EVCSs results in substantial cost savings, thereby emphasizing the practical benefits of the proposed approach.

The transition to electric vehicles (EVs) has prodigious plausibility in reducing green house gas (GHG). But EVs acceptance is, however, hindered by several challenges; among them is their avidity for quicker charging at lower price. This article considers a photovoltaic (PV)-powered station equipped with an energy storage system (ESS), which is assumed to be capable of assigning variable charging rates to different EVs to fulfill their demands inside their declared deadlines at minimum price. To ensure fairness, a charging rate-dependent pricing mechanism is proposed to assure a higher price for enjoying a higher charging rate. The PV generation profile and future load request are forecasted at each time slot, to handle the respective uncertainty. An integer linear programming (ILP)-based centralized system is first proposed to minimize the charging price per EV. Due to the larger computational time, we subsequently present two game theoretic algorithms, i.e., game 1 and game 2. In game 1, players are oblivious of upcoming charging requests, whereas in game 2, players consider the future anticipated load to select their charging strategies. The games are shown to converge to a Nash equilibrium. The average unit price of game 2 is shown to be the same as the one of the optimal solution and takes considerably less computation time than the centralized method.

Electric vehicles offer many advantages ranging from easy access and abundance of electrical energy sources. The objective of this paper is to obtain the best configuration of the hybrid power systems for charging station in a rural area such as Labuan bajo, Indonesia. Thus, the best configuration obtained is then installing with three types of energy storage namely Lead Acid and UNS Lithium battery such as Lithium Ion and Lithium Ferro Phosphate (LFP) to determine the minimum cost of operation and energy cost in a year. The results showed by implementing hybrid systems from PV and DER is the best configuration for off grid charging station. The most optimal battery in off grid system achieved by installing UNS LFP batteries. As a conclusion, by utilizing hybrid power generation technology, the potential for renewable energy in rural areas can be the main key in realizing the availability of charging stations in rural areas with affordable price for supporting electric vehicles infrastructure.

The installation of ultra-fast charging stations (UFCSs) is essential to push the adoption of electric vehicles (EVs). Given the high amount of power required by this charging technology, the integration of renewable energy sources (RESs) and energy storage systems (ESSs) in the design of the station represents a valuable option to decrease its impact on the grid and the environment. Therefore, this paper proposes a multi-objective optimization problem for the optimal sizing of photovoltaic (PV) system and battery ESS (BESS) in a UFCS of EVs. The proposed multi-objective function aims to minimize, on one side, the annualized cost of the station, and on the other side, the produced pollutant emissions. The decision variables are the number of PV panels and the capacity of the ESS to be installed. The optimization problem is reduced to a single-objective problem by applying the linear scalarization method. Then the equivalent single-objective function is optimized through a genetic algorithm (GA). The proposed optimization framework is applied to a study case and the results prove that PV and ESS could lead to a significant reduction of both the annualized cost and the pollutant emissions. Finally, a sensitivity analysis is also presented to validate the effectiveness of the proposed solution.

… , using distinct profiles with two charging levels on weekdays and … energy loss with optimal allocations of the PV/WT are 45.29 % and 45.56 % for Level 1 and Level 2 of EVCS charging. …

Electric vehicle charging station have become an urgent need in many communities around the world, due to the increase of using electric vehicles over conventional vehicles. In addition, establishment of charging stations, and the grid impact of household photovoltaic power generation would reduce the feed-in tariff. These two factors are considered to propose setting up charging stations at convenience stores, which would enable the electric energy to be shared between locations. Charging stations could collect excess photovoltaic energy from homes and market it to electric vehicles. This article examines vehicle travel time, basic household energy demand, and the electricity consumption status of Okinawa city as a whole to model the operation of an electric vehicle charging station for a year. The entire program is optimized using MATLAB mixed integer linear programming (MILP) toolbox. The findings demonstrate that a profit could be achieved under the principle of ensuring the charging station’s stable service. Household photovoltaic power generation and electric vehicles are highly dependent on energy sharing between regions. The convenience store charging station service strategy suggested gives a solution to the future issues.

… fuels in integrated energy systems through hydrogen energy storage (… integrated charging stations (EHI-CSs) as a unit consisting of photovoltaic systems and HES systems for charging …

Under net-zero objectives, the development of electric vehicle (EV) charging infrastructure on a densely populated island can be achieved by repurposing existing facilities, such as rooftops of wholesale stores and parking areas, into charging stations to accelerate transport electrification. For facility owners, this transformation could enable the showcasing of carbon reduction efforts through the self-use of renewable energy while simultaneously gaining charging revenue. In this paper, we propose a dynamic energy management system (EMS) for a solar-and-energy storage-integrated charging station, taking into consideration EV charging demand, solar power generation, status of energy storage system (ESS), contract capacity, and the electricity price of EV charging in real-time to optimize economic efficiency, based on a real-world situation in Taiwan. This study confirms the benefits of ESS in contracted capacity management, peak shaving, valley filling, and price arbitrage. The result shows that the incorporation of dynamic EMS with solar-and-energy storage-integrated charging stations effectively reduces electricity costs and the required electricity contract capacity. Moreover, it leads to an augmentation in the overall operational profitability of the charging station. This increase contains not only the revenue generated from electricity sales at the charging station but also the additional income from surplus solar energy sales. From a comprehensive cost–benefit perspective, introducing this solar-and-energy storage-integrated EMS can increase facility owners’ net income by 1.25 times compared to merely installing charging infrastructure.

This research paper proposes a detailed design problem of electrical vehicle (EV) fast‐charging stations to maximize the net profit. The charging station is integrated with the renewable energy sources (RES) and battery energy storage system (BESS) to minimize the energy demand from the grid. The performance indices of the design problem, such as the number of chargers and their rating, installed RES power, energy and power of the storage units, and dynamic power contracted by the grid to the EV charging station, is estimated through the proposed algorithm. The detailed modelling of the charging process considers the EV behavioural characteristics like arrival time, state of charge, departure time, and battery capacity and is simulated through sequential Monte‐Carlo method. The hybrid Crow Search Algorithm (hCSA), along with the particle swarm optimization (hCSA‐PSO), is adopted for the first time to optimize the charging station's installation and operational cost. The effectiveness of the proposed method is compared with a hybrid genetic pattern search algorithm, CSA, and PSO. Several case studies are considered, and it is observed that hCSA‐PSO provides the best‐optimized results in profit maximization. The cost‐benefit analysis is also performed to estimate the financial feasibility of both RES and BESS.

With the fast development of the electrification of vehicles, Electric Vehicle (EV) charging stations will drastically increase in the coming years. In the meantime, the growing demand of charging power, and the intermittent nature of renewable energy are serious challenges for the charging infrastructures and the local grid. In this paper, a real time energy management for the EV charging station equipped with renewable energy generation (RE) and energy storage systems (ESS) is proposed to achieve the maximum consumption of renewable energy through the direct load control of EV charging. A convex optimization problem of energy scheduling is formulated considering the inhomogeneous EV load and real-time price market of grid electricity. The proposed energy management scheme is then evaluated through simulation case studies and results are drawn and discussed.

… As the number of electric vehicles (EVs) increases, EV charging … charging stations (GCS) to effectively manage the internal photovoltaic (PV), energy storage system (ESS), charging …

Abstract With the increase in the use of electric vehicles, charging stations may have congestion problems. The grid energy storage system can be used to satisfy the energy demand for charging electric vehicles batteries. Electric vehicles charging/discharging scheduling for vehicle-to-grid and grid-to-vehicle operations is challenging because customers have different energy requirements. Here, a charging and discharging power scheduling algorithm solved by a chance constrained programming method was applied to an electric vehicle charging station which contains maximal 500 charging piles, an 100kW/500 kWh energy storage system, and a 400 kWp photovoltaic system. Accordingly, the power dispatch can be beneficial to the charging station and electric vehicle users under the possible impact of the generation uncertainty of PV. The time-of-use adjustment method is proposed integrated with the charging/discharging priorities calculation and electricity prices, which ensures the energy usage does not exceed contract capacity. Based on the proposed algorithm, a blueprint for optimizing the contract capacity is analyzed for improving the cost of charging stations. The results indicate that the cost of electric vehicles charging can be decreased almost by 50% in a certain confidence level of photovoltaic forecasting comparing with uncoordinated method. The proposed method can assist charging station operators to evaluate suitable contract capacity and implement power dispatch strategies based on the possible scale of electric vehicles and distributed energy resources, which is conducive to the development of the power and EV industry.

This paper introduces an energy management algorithm for a hybrid solar and biogas-based electric vehicle charging station (EVCS) that considers techno-economic and environmental factors. The proposed algorithm is designed for a 20-kW EVCS and uses a fuzzy inference system in MATLAB SIMULINK to manage power generation, EV power demand, charging periods, and existing charging rates to optimize real-time charging costs and renewable energy utilization. The results show that the proposed algorithm reduces energy costs by 74.67% compared to existing flat rate tariffs and offers lower charging costs for weekdays and weekends. The integration of hybrid renewables also results in a significant reduction in greenhouse gas emissions, with payback periods for charging station owners being relatively short, making the project profitable.

… energy management strategy of the SSTbased PVCS. The main contributions are as follows. 1) The SST is introduced to the design of the PVCS. By integration … charging station. 2) Rule-…

… the comprehensive operation efficiency of charging station in the … , and obtains the optimal energy management strategy by … management and operation of multifunctional charging …

The energy management of the energy storage system in PV-integrated EV charging station is a typical multi-objective optimization problem. This paper mainly studies the energy management optimization method of the energy storage system. Firstly, the system structure of the PV-integrated EV charging station is introduced. Based on Monte Carlo method, the load data of the charging station and the day-ahead measured PV output data, the operation strategy of the energy storage system is analyzed. Then, in order to realize the economy of the charging station and the reliability of the power grid operation, the energy storage management model is established by taking the purchase cost of the charging station from the distribution network and the load power variance of the distribution network as the target. Finally, NSGA-II algorithm is used to solve the energy storage management model to get Pareto optimal solution set, and TOPSIS method is used to compromise the Pareto optimal solution set and calculate the daily energy storage capacity demand of charging station. The feasibility of the energy management method of the energy storage system is verified by an example analysis.

… The energy management of the charging station can be controlled with … of management. The key areas that have been discussed in the current era are the integration of multiple energy …

… -based charging primarily based on fossil fuels for most regions. This paper presents a costeffective energy management system (EMS) at a fast-charging station (FCS) for charging EVs …

Abstract The optimal management of charging stations has become a critical issue in recent years. In this paper, the energy management of a hybrid charging station composed of an electrolyzer, fuel cell and hydrogen storage is analyzed that is integrated with a photovoltaic system. As well, the station is connected to the local power market to increase flexibility and it is assumed that the manager of the charging station is an intelligent decision-maker who tries to minimize the cost of vehicle. Due to the existence of uncertainties, generation of photovoltaic, market price and load demand are considered as uncertain parameters and two-stage stochastic programming is applied to model them. To achieve optimal management, a robust optimization approach is proposed for the uncertainty of day-ahead market price where the decision-maker adjusts the conservatism level. The presented method is linear risk-constrained programming that the results for risk-neutral and risk-averse strategies are compared. To validate the accuracy and robustness of the approach, interval-based stochastic programming is also implemented. According to the robust optimization, day-ahead market price uncertainty increases the total expected cost by about 8.9%. In return, the risk of scheduling is reduced significantly with the risk-averse strategy.

… of an integrated Photovoltaic-Storage Charging Station (PS-… the optimal compromise of energy management strategy by … suitable for real-time energy management. The efficiency of the …

… and discharge controls within a charging station to dispatch power … energy conservation can be significantly accelerated by integrating a smart, energy-efficient EV energy management …

At the present context, Plug-in electric vehicles (PEVs) are gaining popularity in the automotive industry due to their low CO2 emissions, simple maintenance, and low operating costs. As the number of PEVs on the road increases, the charging demand of PEVs affects distribution network features, such as power loss, voltage profile, and harmonic distortion. Furthermore, one more problem arises due to the high peak power demand from the grid to charge the PEVs at the charging station (CS). In addition, the location of CS also affects the behavior of EV users and CS investors. Hence, this paper applies CS investor, PEV user, and distribution network operator who could approach to CS's optimal location and capacity. Integrating renewable energy sources (RESs) at the charging station is suggested to lower the energy stress on the grid. Moreover, to keep down the peak power demand from the grid and utilize renewable energy more efficiently, energy management strategies (EMS) have been applied through the control of charging and discharging of the battery storage system (BSS). In addition, vehicle to grid (V2G) strategy is also applied to discharge the EV battery at charging station. Furthermore, the uncertainties related to PEV charging demand and PV power generation are addressed by the Monte Carlo Simulation (MCS) method.

… energy management strategies for DC fast charging stations. First, the energy management goals for DC fast charging stations … Then, the energy management strategies are presented. …

… Dispatch and Scheduling (HADS) framework for grid-connected electric vehicle (EV) charging stations integrated with photovoltaic (PV… EV charging loads with solar availability and ESS …

… photovoltaic (PV) power, real-time balancing pressure of energy storage systems, and spatiotemporal uncertainty of EV … prominent spatiotemporal mismatches in integrated PV-storage-…

With the energy transition, the cyber-physical system (CPS) is expanded to the cyber-physical-social system (CPSS), and connected by the communication network, which is introduced in the form of a highly reliable cellular star network in this paper. Due to the uncertainties of wind turbine/photovoltaic power outputs, an improved rolling ultra-short-term forecast method with high reliability is proposed, which is based on long short-term memory networks, and the superiority is proved by comparing with multi-layer perception neural networks, linear regression, support vector regression and random forest. Then, two stochastic optimization models are constructed, one is based on CPS, the other is based on CPSS. As for social factors, electric vehicle aggregator is stimulated by cost to actively participate in the dispatch operation. Moreover, in order to ensure the stable operation of the smart energy system, the frequency deviation of automatic generation control system is studied as one security constraint. The optimal results display that social factors can not only make the system more economic and flexible, but also further increase the consumption of RE. Moreover, accurate forecast results can improve the performance of optimization dispatch operations.

Microgrids are an effective solution to decentralize electrical grids and improve usage of distributed energy resources (DERs). Within a microgrid there are multiple active players and it can be computationally expensive to consider all their interactions. An optimal scheduler ensures that the needs within the microgrid are met without wasting electricity. With higher requirements for electric vehicle charging stations (EVCSs), schedulers are essential to ensure EV charging demands are met while being profitable and flattening peak load on the main power grid (MPG). This paper introduces two novel microgrid models, combining energy generated by a DER, the possibility of storage with an energy storage system (ESS), a load entity in the form of an EVCS and electricity trading with the MPG. The model incorporates all important environment parameters created by these players in an intelligent way that keeps the action space relatively small and thus avoiding the problems associated with a high computational complexity. These models are proven to successfully shift the load from the MPG, while still providing high customer satisfaction and throughput, in a profitable way, despite costs incurred by the DER. Instead of relying on models, real data is used, ensuring that the model is robust. Additional real world stress tests are carried out with respect to electricity costs, wind energy generation, and charging rates. Reinforcement learning is implemented to find the optimal scheduler by maximizing overall profits. In all cases considered a self-sustaining system is established, that is a more profitable and reliable EVCS.

… in microgrid technologies as well as the integration of electric … flow problem from rooftop photovoltaic (PV) elements to the … In our microgrid system, we assume that some homes have …

… for a microgrid that incorporates EV, ESS, curtailable load, and … optimal charging of EV and ESS in a building with PV and wind … -based method used for optimal dispatch of EV and ESS. …

… adaptive PV–battery energy storage system (BESS) dispatch strategy that integrates short-term PV … The primary novelty of this work lies in the tight integration of a seasonally adaptive, …

This paper focuses on the core value of the PV-ESS-EV Microgrid under the “dual carbon” goals, systematically analyzing the system integration architecture of photovoltaic power generation, multi-type energy storage, and intelligent charging infrastructure. It delves into key technological breakthroughs in energy management and multi-energy complementarity, summarizes application models and economic feasibility in scenarios such as industrial and commercial parks, transportation hubs, and remote areas, and reveals current technical bottlenecks and future evolution paths. The study aims to provide theoretical support for industrial implementation.

The world is on a course toward total electrification of vehicles. In the near future, most vehicles will run on electric power. One of the main reasons for users' dissatisfaction with electric vehicles is the lack of public direct current charging stations. Since electric vehicles charging can cause an additional increase in peak load on the grid, the optimal solution is direct current charging stations with photovoltaic generation with a micro grid architecture. If the charging station has a connection to the public grid, then, provided that the solar energy and storage system are optimally utilized, the station aggregator's profit can be increased by selling excess energy to the grid. This paper analyzes the charging habits of customers at direct current charging stations. It was found that the peak demand for charging is observed around 9:00 and 14:00-17:00, the same time as the general peak load on the grid. Thus, the peak charging demand coincides with the peak grid load and increases the net peak of the system. However, this excess demand on the system in the form of charging load can be met by the installed solar photovoltaic system, as the output power of the photovoltaic system is sufficient to meet the charging demand during the peak hours of solar radiation. Thus, for the considered direct current charging station, the optimization problem of dynamic economic dispatch was formulated, since the generation and load schedules change over time. The goal of optimization is to minimize the cost of primary energy. This problem, formalized as a mixed integer linear programming problem, was solved using the interior-point solver of the GEKKO library in Python. Four scenarios for the operation of the station were worked out, in summer and winter, with a fixed and dynamic electricity tariff. According to the results of the study, it was found that in the conditions of a fixed tariff in the summer, the cost of primary energy can be reduced 2.5 times, in fact, increase profits, thanks to the sale of electricity to the public grid. In winter, the use of the optimization algorithm of the station will provide an insignificant cost savings due to low photovoltaic generation. Under the conditions of a dynamic tariff that corresponds to the prices on the day-ahead market, using the optimization algorithm, it was found that for this experimental variant of the station's operation, the maximum profit in summer will be 207.60 UAH, while in winter the cost of primary energy will be 177.47 UAH. The results obtained indicate that the operation of a charging station under dynamic tariffs in the day-ahead market in Ukraine is a promising direction for the development of charging infrastructure in the country and proves the possibility of efficient use of renewable energy sources. Thus, this paper analyzes the global experience of developing charging stations based on micro grids, the integration of renewable energy sources into them, and approaches to building electricity dispatch schedules. The financial feasibility of the station's operation in the context of the electricity market in Ukraine was also investigated

Navigating the complex terrain of microgrid energy management is challenging due to the uncertainties linked with abundant renewable resources, fluctuating demand, and a wide range of devices including batteries, distributed energy sources, electric vehicles, and compensatory devices. This paper presents an advanced two-stage robust day-ahead optimization model designed specifically for MG operations. The model primarily addresses challenges arising from the integration of power electronics-based generation units, the unpredictable nature of demand in microgrids, and the integration of small-scale renewable energy sources. The proposed model includes detailed formulations for MG energy management, covering optimal battery usage, efficient EV energy management, compensator usage, and strategic dispatching of DG resources. The multi-objective function aims to minimize various costs related to energy losses, power purchases, load curtailment, DG operation, and battery/EV expenses over a 24-hour period. To efficiently solve this optimization problem, the C&CG algorithm is utilized. Numerical simulations on a test system validate the effectiveness of the proposed model and solution algorithm, showing a significant reduction in the operating costs of the microgrid. This approach offers a robust framework to enhance the resilience and efficiency of microgrid energy management. The results conclusively demonstrate that the proposed approach surpasses comparable methods by at least 5%, highlighting its effectiveness in improving key indicators within the microgrid system.

… In microgrids, EVs act as distributed energy resources, … introduces a smart grid energy dispatch strategy using an EPSO … the data for the EV, ESS, and PV to support the evaluation. …

The integration of renewable energy sources (RES) and electric vehicles (EVs) into microgrids (MGs) has significant potential for enhancing energy resilience, addressing environmental concerns, and promoting decentralized energy systems as a global shift towards sustainable energy solutions. Therefore, this survey paper provides a comprehensive discussion on improving MG operation through EV integration. This study evaluates the status of EV integration into MGs, focusing on technological advancements, and emerging trends, while pinpointing key technical challenges and opportunities. Furthermore, this paper examines the pivotal role of EVs in participating in vehicle-to-grid (V2G) services, providing ancillary support to improve MG performance. The importance of a reliable communication infrastructure for information exchange between EVs, EV charging stations (EVCSs), and MGs has been emphasized for the effective implementation of V2G services. This discussion extends to the contributions of EVs to primary, secondary, and tertiary MG controls. The paper also analyzes the integration of EVs into AC and DC MGs and further proposes configurations for both MG cases. Finally, the paper concludes by providing recommendations for future research to unlock the full potential of EV contributions to MG performance, thereby contributing to the ongoing advancement of sustainable and resilient energy systems. The key findings of this work include solutions for MG voltage and frequency regulation implemented through EV bidirectional converter power flow control, EV charger configurations for integration into AC and DC MGs, EV contributions in improving the MG’s operational resilience and adaptability, and the noteworthy challenges arising from V2G implementation in such systems.

This paper focuses on a park microgrid with electric vehicles, establishing a comprehensive economic cost dispatch model that includes both operational costs and environmental management costs. A Simulated Annealing-Particle Swarm Optimization algorithm is proposed to address this model. Through case study simulations, the integrated energy optimization and dispatch strategies of microgrids with electric vehicle clusters under different scenarios are analyzed. The results indicate that the participation of electric vehicle clusters in microgrid dispatch can significantly reduce economic costs, with strategies for the orderly charging of electric vehicles further optimizing the microgrid's scheduling efficiency. Compared to traditional particle swarm optimization, the SA-PSO algorithm demonstrates superior solution quality and convergence.

A wide diffusion of fast and ultra-fast stations could affect power quality and the safe operation of distribution networks. Therefore, proper strategies for the optimal management of vehicles, along with the combination of storage systems and renewable sources, is required in order to avoid grid problems. In this context, DC microgrids combining renewables sources, storage systems and electric vehicle stations are demonstrated to foster the integration of these facilities, also allowing optimal exploitation of their functionalities. This study aims at inspecting the effect of electric vehicle fast-charging integration on the optimal operation of a DC-based supply infrastructure and its interaction with the distribution grid. The proposed methodology employs a mixed-integer linear optimization considering economic and technical targets and taking into account charging station cable losses by means of linearization technique. The impact of stochastic optimization and objective combination is further discussed. The procedure is implemented into a DC microgrid integrating a fast-charging station based on realistic facility data, and several scenarios are simulated and compared by means of cost and power loss evaluation.

Energy management in grid-connected systems Micro-grids (MG) has evolved rapidly in recent years as a result of environmental concerns, rising energy consumption, and the market liberalization of electricity merchandising. The Energy Management System (EMS) optimizes the utilization of MG's energy resources and power storage facilities in the supply-demand prices at the lowest cost possible. The purpose is to develop a Particle Swarm Optimization (PSO) EMS in MG, which assists in minimizing the dependence on the grid and decreasing the cost of Electric Vehicle (EV) charging by utilizing Renewable Energy Resources (RES) which comprises power production and power purchase from the electricity utility. An MG model for all over the distribution network contains photovoltaic (PV), wind turbine (WT), battery energy storage system (BESS), and various EVs, the load of which also varies. This paper analyzes the following operating conditions: the EV demand, the load and production variations, and the grid power costs to determine the effectiveness of an EMS that coordinates power from multiple sources. The study also supports the efficacy of the suggested EMS, as it proves that these systems contribute to the efficient EMS transactions with the grid, under the variety of pricing models and the integration of RES. In addition, the findings show that the integrated solution of the proposed PSO-based optimization decreases the costs by 8%. This recommends a scheduling strategy that lowers EV energy use and has a BESS charging /discharging management system during peak and off-peak hours. Also, the provision of EV charging is an effective way of cutting costs while at the same time preserving energy.

The integration of photovoltaic (PV) systems, electric vehicles (EVs), and charging stations (CSs) faces critical challenges, including PV intermittency, uncertain EV charging demand, and inefficient energy management. Existing strategies often overlook the forecasting precision of PV generation, the economic risks of electricity trading, and the diverse demands of EVs, leading to suboptimal performance. To address these drawbacks, we propose a two-tier management framework for PV–CS–EV systems, optimizing the CS operations by balancing profit maximization and risk minimization. The first tier employs accurate PV forecasting and power trading strategies between CS, PV farms, and the grid to mitigate economic risks from PV intermittency and market fluctuations. The second tier provides diverse charging strategies to maximize user satisfaction and profit. A key challenge lies in the complex interdependencies between the two tiers, requiring simultaneous optimization of power trading and user-specific charging scheduling under uncertainties. To tackle this, we introduce a hierarchical multiobjective reinforcement learning (MORL) algorithm, which efficiently coordinates decision tasks of both tiers through partial environment information interaction. Experimental results demonstrate the effectiveness of the proposed framework in enhancing the economic performance of PV–CS–EV systems.

In this work, a novel optimal scheduling approach is proposed for isolated microgrids (MGs) with renewable generations by incorporating demand response of electric vehicles (EVs). First, a bi-level programming-based MG scheduling model is proposed under real-time pricing environments, where the upper- and lower- levels seek to minimize the MG net operating cost and the EV charging cost. Second, a hybrid solution algorithm called JAYA-interior point method is put forward to solve the model. And finally, the simulation results demonstrate that incorporating demand response of electric vehicles is able to guide EV users to actively participate in MG scheduling and achieve the peak load shaving, which offers a fundamental way to balance the interests between MG and EV users.

… microgrid economic system that incorporates transferable load (TL), electric vehicles, and other distributed generations (DG) such as photovoltaic … the economic dispatch optimization …

… emerging bus charging scenario where photovoltaic-storage-charging (PSC… photovoltaic (PV) panels to absorb solar power and a battery set to store electricity, which can either charge …

… charging demand on the power grid. This study focuses on a novel battery electric bus (BEB) charging scheduling problem involving solar photovoltaic (PV) and battery energy storage …

… scheduling optimization model for PV/BESS integrated EV charging stations, which combines hybrid modeling for PV power prediction and optimal scheduling method for charging piles…

The economic and environmental benefits brought by electric vehicles (EVs) cannot be fully delivered unless these vehicles are fully or partially charged by renewable energy sources (RES) such as photovoltaic system (PVS). Nevertheless, the EV charging management problem of a parking station integrated with RES is challenging due to the uncertain nature of local RES generation. This paper aims to address these difficulties by deploying an energy storage system (ESS) in parking stations and exploiting the charging and discharging scheduling of EVs to achieve better utilization of intermittent PVS for EV charging. A real-time charging optimization scheme is also formulated, using mixed-integer linear programming (MILP) to coordinate the charging or discharging power of EVs along with the power dispatches of power grid and ESS based on the vehicles’ charging or discharging priorities and electricity price preferences. Extensive simulations show that the proposed approach not only maximizes the satisfaction of EV owners in terms of fulfilling all charging and discharging requests, but also minimizes the overall operational cost of the parking station by prioritizing the utilization of energy from PVS, ESS, and scheduling of every EV’s charging and discharging.

The problem of electric vehicle (EV) charging scheduling in commercial parking lots has become a meaningful study in recent years, especially for the parking lots near the workplace that serve fixed users. This paper focuses on the optimization of the EV charging in the parking lot integrating energy storage system (ESS) and photovoltaic (PV) system. A smart charging management system is first established. The charging optimization problem is formulated as a cost minimization problem. Then, grey wolf optimizer (GWO) is introduced as a method to find the optimal solution. Considering the constraint conditions in the optimization problem, an improved binary grey wolf optimizer (IBGWO) is proposed, which can improve the convergence speed and optimization accuracy. Finally, a real-time EV charging scheduling strategy based on short-term PV power prediction and IBGWO is proposed. Several cases are simulated to analyze the performance of the proposed strategy. The experimental results show that the proposed IBGWO is superior in solving the proposed charging scheduling problem compared with other meta-heuristic algorithms. Moreover, the proposed strategy can effectively improve the utilization rate of the PV power and reduce the electricity cost of operators.

… PV and BESS, and (3) minimizing emission from upstream grid. Moreover, the EVs are also scheduled optimally at each charging … Furthermore, the smart EV charge scheduling reduces …

The transition from internal combustion engine vehicles to electric vehicles (EVs) is gaining momentum due to their significant environmental and economic benefits. This study addresses the challenges of integrating renewable energy sources, particularly solar power, into EV charging infrastructures by using deep learning models to predict photovoltaic (PV) power generation and EV charging demand. The study determines the optimal battery energy storage capacity and charging schedule based on the prediction result and actual data. A dataset of a 15 kWp rooftop PV system and simulated EV charging data are used. The results show that simple RNNs are most effective at predicting PV power due to their adept handling of simple patterns, while bidirectional LSTMs excel at predicting EV charging demand by capturing complex dynamics. The study also identifies an optimal battery storage capacity that will balance the use of the grid and surplus solar power through strategic charging scheduling, thereby improving the sustainability and efficiency of solar energy in EV charging infrastructures. This research highlights the potential for integrating renewable energy sources with advanced energy storage solutions to support the growing electric vehicle infrastructure.

… and electric vehicle charging load … energy storage system of the photovoltaic-storage charging station based on intelligent reinforcement learning is proposed. Firstly, the energy storage …

… , yet large-scale charging strains grid stability and … PV-ESS scheduling system integrating photovoltaic (PV) and energy storage systems (ESS) to optimize electric bus (EB) charging. A …

This paper proposes the integration of photovoltaic-energy storage charging stations with mobile charging services (MCD)to form a photovoltaic-energy storage mobile charging station (PV-ES-MCS). The PV-ES-MCS establishes a charging service framework that simultaneously achieves low-carbon environmental benefits and operational flexibility. Furthermore, an energy management strategy is developed that considers both operational economics and mobile charging scheduling requirements. Through detailed analysis of the PV-ES-MCS system architecture, comprehensive modeling of operational costs and revenues is conducted. By incorporating the energy replenishment demands of mobile charging scheduling and operational cost reduction, an energy management objective function with corresponding constraints is formulated and solved using the Grey Wolf Optimizer algorithm. Case study results demonstrate that the proposed strategy enables coordinated multi-element regulation within the PV-ES-MCS station, reducing operational costs by 6.7% while effectively ensuring the energy replenishment efficiency of mobile charging scheduling with 100% MCD battery swap success.

The growing demand for electric vehicles (EV) in the last decade and the most recent European Commission regulation to only allow EV on the road from 2035 involved the necessity to design a cost-effective and sustainable EV charging station (CS). A crucial challenge for charging stations arises from matching fluctuating power supplies and meeting peak load demand. The overall objective of this paper is to optimize the charging scheduling of a hybrid energy storage system (HESS) for EV charging stations while maximizing PV power usage and reducing grid energy costs. This goal is achieved by forecasting the PV power and the load demand using different deep learning (DL) algorithms such as the recurrent neural network (RNN) and long short-term memory (LSTM). Then, the predicted data are adopted to design a scheduling algorithm that determines the optimal charging time slots for the HESS. The findings demonstrate the efficiency of the proposed approach, showcasing a root-mean-square error (RMSE) of 5.78% for real-time PV power forecasting and 9.70% for real-time load demand forecasting. Moreover, the proposed scheduling algorithm reduces the total grid energy cost by 12.13%.

… the case of charging demand, … scheduling of multi-mode photovoltaic-assisted charging stations. The developed model uses interval formulation to model uncertainties from photovoltaic …

In line with the strategic plan for emerging industries in China, renewable energy sources like wind power and photovoltaic power are experiencing vigorous growth, and the number of electric vehicles in use is on a continuous upward trend. Alongside the optimization of the distribution network structure and the extensive application of energy storage technology, the active distribution network has evolved into a more flexible and interactive “source–grid–load–storage” diversified structure. When electric vehicles are plugged into charging piles for charging and discharging, it inevitably exerts a significant impact on the control and operation of the power grid. Therefore, in the context of the extensive integration of electric vehicles, delving into the charging and discharging behaviors of electric vehicle clusters and integrating them into the optimization of the active distribution network holds great significance for ensuring the safe and economic operation of the power grid. This paper adopts the two-stage “constant-current and constant-voltage” charging mode, which has the least impact on battery life, and classifies the electric vehicle cluster into basic EV load and controllable EV load. The controllable EV load is regarded as a special “energy storage” resource, and a corresponding model is established to enable its participation in the coordinated control of the active distribution network. Based on the optimization and control of the output behaviors of gas turbines, flexible loads, energy storage, and electric vehicle clusters, this paper proposes a two-layer coordinated control model for the scheduling layer and network layer of the active distribution network and employs the improved multi-target beetle antennae search optimization algorithm (MTTA) in conjunction with the Cplex solver for solution. Through case analysis, the results demonstrate that the “source–grid–load–storage” coordinated control of the active distribution network can fully tap the potential of resources such as flexible loads on the “load” side, traditional energy storage, and controllable EV clusters; realize the economic operation of the active distribution network; reduce load and voltage fluctuations; and enhance power quality.

… charging loads of large… EV charging station, the charging cost of EV users and power loss, a multi-objective optimal scheduling model of EV charging, power grid, pumped hydro-storage …

… energy and stochastic electric vehicle (EV) loads in buildings and power … storage sharing with centralized storage scheduling decreased annual energy costs for both buildings and EV …

The rapid growth in the number of electric vehicles (EVs), driven by the ‘double‐carbon’ target, and the impact of uncontrolled charging and discharging behaviour and discharged battery losses severely limit electric vehicles’ low carbon characteristics. Existing research on systemic low‐carbon emissions and electric vehicle charging and discharging issues is usually determined by considering only carbon trading markets or charging and discharging management on the source side. In this regard, a coordinated and optimized operation model that considers the participation of electric vehicle clusters in deep peaking and the source network load and storage adjustable resources is proposed. The upper layer establishes a real‐time price‐based demand response mechanism for the load side with the minimum net load fluctuation as the objective function; the middle layer establishes a comprehensive operation mechanism for the source and storage side that includes an orderly charging and discharging peaking compensation mechanism for electric vehicles, and a deep peaking mechanism that takes into account clean emission, and constructs an optimal operation model with the minimum comprehensive operating cost as the objective function; the lower layer establishes a distribution network loss minimization model for the network side that takes into account the orderly charging and discharging of electric vehicle as the objective function. The optimal load model with the objective function of minimizing the distribution network loss is established at the lower level. Finally, the original problem is transformed into a mixed integer linear programming problem, and the model's effectiveness is verified by setting different scenarios. The model reduces the total cost by 22.22%, improves the wind power consumption rate by 19.55%, reduces the actual carbon emission by 16.66%, and reduces the distribution network loss by 13.91% compared to the basic model.

… supply the EV charging load in addition to utilizing local renewable generation and battery … charging station equipped with PV, battery storage, and hosting 100 EVs. The battery storage …

… spatial characteristics of EV load. For example, more than 2 MW the EV load can be brought … At the same time, the growth of EV charging load(the penetration rate of EV can reach up to …

… But uncoordinated system with inconsistency in DGs forecasts may lead to unscheduled charge of the buffer storage, further EV charging load consumes more power without the PV …

Abstract Driven by the booming of electric vehicle (EV) market, the cost of lithium ion battery observes a remarkable decline which could significantly improve the capability of EVs in coordinating with the power generation from distributed renewable energy (DRE). This paper realizes that there are different EV-DRE coordination strategies while the costs and the associated infrastructure of these strategies significantly differ. An economic evaluation that compares these coordination strategies is therefore important. In this study, an economic evaluation is conducted among four EV-DRE coordination strategies. It finds that the cost of power supply from demand side PV plus storage systems could be lower than that of power grid supply before 2025. Besides, although the smart charging is a cost-efficient EV-DRE coordination strategy in the short term, V2G could be more economically attractive in the long run due to its capacity to fully realize the potential of on-board EV batteries. This paper also identifies the key barriers that EVs and distributed storage are facing in participating in the current electricity wholesale market in China and provides policy recommendations in terms of electricity time of use (TOU) tariffs, market thresholds and metering issues.

… real-time coordination among EV charging stations, hydrogen production and storage units, … such as cost reduction, charging efficiency, and load balancing, while a cooperative game-…

In the complex environment of microgrid deployments targeted at geographic regions, the seamless integration of renewable energy sources meets a variety of essential challenges. These include the unpredictable nature of renewable energy, characterized by intermittent energy generation, as well as ongoing fluctuations in load demand, the vulnerabilities present in distribution network failures, and the unpredictability that results from unfavorable weather conditions. These unexpected events work together to disturb the delicate balance between energy supply and demand, raising the alarming threat of system instability and, in the worst cases, the sudden advent of damaging blackouts. To address this issue, a fuzzy logic-based energy management system has been developed to monitor, manage, and optimize energy consumption in microgrids. This study focuses on the control of diesel generators and utility grids in a grid-connected microgrid which manages and evaluates numerous energy consumption and distribution features within a specified system, e.g., building or a microgrid. An energy management system is suggested based on fuzzy logic as a swift fix for complications with effective and competent resource management, and its presentation is compared with both the grid-connected and off-grid modes of the microgrid. In the end, the results exhibit that the proposed controller outclasses the predictable controllers in dropping sudden variations that arise during the addition of sources of renewable energy, supporting the refurbishment of the constant system.

… control strategy to address the challenge of locally balancing the power supply and demand in a grid-connected microgrid … was integrated into the control system of a microgrid situated …

Abstract The purpose of this paper is to propose an efficient model and a robust control that ensures good power quality for the AC microgrid (MG) connected to the utility grid with the integration of an electric vehicle (EV). The MG consists of two renewable energy sources: a photovoltaic system (PVS) and a wind turbine system (WTS) based on a permanent magnet synchronous generator (PMSG), with the integration of an EV. These sources are used to supply active and reactive power to the AC bus and the utility grid. Maximum power point tracking (MPPT) based on the perturb-and-observe (PO) method is used to increase the efficiency of the photovoltaic modules and improve overall performance. The MG system includes a 2-MW WTS, a 100-kW PVS and 12 kW provided by the EV. To validate the performance of the proposed system, a series of simulations were conducted using the MATLAB®/Simulink® environment. The results demonstrate that the proposed system ensures high performance in terms of power quality, system stability, power tracking and safe integration of the EV.

… charging /discharging model of plug in hybrid electric vehicles (PHEVs) in an office microgrid … In this study a grid connected office microgrid is considered as a test system [8] consists of …

This study proposes a grid-connected inverter for photovoltaic (PV)-powered electric vehicle (EV) charging stations. The significant function of the proposed inverter is to enhance the stability of a microgrid. The proposed inverter can stabilize its grid voltage and frequency by supplying or absorbing active or reactive power to or from a microgrid using EVs and PV generation. Moreover, the proposed inverter can automatically detect an abnormal condition of the grid, such as a blackout, and operate in the islanding mode, which can provide continuous power to local loads using EV vehicle-to-grid service and PV generation. These inverter functions can satisfy the requirements of the grid codes, such as IEEE Standard 1547–2018 and UL 1741 SA. In addition, the proposed inverter can not only enhance the microgrid stability but also charge EVs in an appropriate mode according to the condition of the PV array and EVs. The proposed inverter was verified through experimental results with four scenarios in a lab-scale testbed. These four scenarios include grid normal conditions, grid voltage fluctuations, grid frequency fluctuations, and a power blackout. The experimental results demonstrated that the proposed inverter could enhance the microgrid stability against grid abnormal conditions, fluctuations of grid frequency and voltage, and charge EVs in an appropriate mode.

… management of two microgrids (MGs) in both off-grid and interconnected modes, incorporating a 200 kW DC fast charging (DCFC) station with four charging … simultaneous charging or …

… , including power regulation of each dispatchable DG unit, charging and discharging of ESS… comprehensive aspects of energy management, such as difference between grid-connected …

This study presents a comprehensive bibliometric analysis of grid-connected microgrid (MG) scheduling controller techniques. An extensive search was done in the Scopus database using preset parameters to extract articles relating to the MG scheduling controller. The selection of the most cited paper involved careful keyword filtering on grid-connected MG scheduling controllers over the period from 2013 to 2024. Within the timeframe, a total of 115 top-cited articles were extracted, focusing on the scheduling controller algorithms applied to the grid-connected MG system. These highly cited articles originated from a diverse source, encompassing 49 distinct journals, spanning 28 different regions, and representing the publications of 7 distinct publishers. This paper seeks to identify and analyze the highly referenced published articles in the relevant area to yield an in-depth analysis of advanced controllers and optimization strategies in MG energy management systems. The key challenges such as power electronic interface, quality, controller, safety and optimization were also highlighted to provide the clearest insight on the recent MG development. Valuable recommendations for future research directions are also provided, aimed at promoting the sustainable growth of MGs. A substantial total of 63.56% articles were published based on simulation while 18.6%, 13.95% and 3.87% of total articles were published on the experimental setup, critical analysis and review-based study. Therefore, it can be inferred that ongoing research and development efforts continually seek to improve the sustainability of MG systems within the electric power sector. The bibliometric analysis was employed to identify pivotal research publications concerning grid-connected MG scheduling controller technique. This analysis aimed to delineate the multidisciplinary nature, illustrate trends, and outline areas warranting further research in the field. Thus, to ensure an effective, economical, reliable, and sustainable power supply, this analysis will broaden the scope and offer context for the development of MG scheduling controller integrated grid systems.

… ing and control of the DGs integrated to the DCMG, this paper also proposes an algorithm for balancing the power under varying loads in islanded and grid connected … its charging and …

… performance of this microgrid in grid-connected and islanding … show that the proposed microgrid design with SVC has the … A microgrid is a special form of grid which is integrated with a …

As an environmentally friendly method of distributing energy production, the integration of photovoltaic systems into micro grids has drawn in significant focus on. Our goal in doing is to examine features regarding micro grid that is linked to the power grid, with a focus on photovoltaic energy management in particular. Finding the optimal micro grid capacity for the solar system seeks to increase energy efficiency, decrease dependence on main grid, and promote an utilization of green power. The outlined optimization approach evaluates the micro grid’s dynamic interactions using state-of-the-art modelling and simulation tools. These components include photovoltaic panels, energy storage systems, alongside the main grid. The refinement method takes into account crucial factors including patterns of load demand, costs of the grid electricity, and variations in solar irradiation. Finding a happy medium between increasing the amount of power generated by renewable sources and decreasing overall energy costs is the objective. That study takes a multi-scenario approach to determining how various micro grid sizes affect overall system efficiency. Using scenario-based simulations and techno-economic criteria, the appropriate size of the photovoltaic system was determined. Factors like payback time, ROI, and system reliability are taken into account here. The study’s findings provide light on grid-connected micro grids, particularly in regards to photovoltaic energy management, which is crucial for their planning and implementation. In order to make educated decisions towards more robust and ecologically friendly power systems, stakeholders, lawmakers, and decision-makers can use the optimal micro grid size as a benchmark for future renewable power projects. This paper reviews the relevant literature and proposes a division and performance strategy based on its findings. By classifying energy management into three groups according to grid connection, configuration, and control method, this article provides a description of the performance, application, advantages, and disadvantages of algorithms that may be used as a reference for selecting an appropriate algorithm. Also included is a comparison table for the control strategies that were used to regulate a micro grid system that is connected to the grid.

This study introduces an advanced control strategy for a photovoltaic (PV)-based energy management system, designed for integration with an Electric Vehicle Charging Station (EVCS) in microgrid in both Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) modes. The proposed system utilizes a combination of Phase-Locked Loop (PLL) for grid synchronization, Proportional-Integral (PI) controllers for current regulation, and voltage reference generation to ensure smooth and efficient operation. The system optimizes power flow management by seamlessly switching between PV energy supply, V2G, and G2V modes based on the vehicle's state of charge (SOC) and available solar power. Simulation results validate the effectiveness of the control strategy, demonstrating successful power flow transitions, grid synchronization, and efficient reactive power management under varying operational conditions. The system proves its robustness in managing dynamic loads, ensuring high energy transfer efficiency in both grid-connected and stand-alone operations. This study paves the way for smart grid applications, improving the reliability and performance of renewable energy systems in EV charging infrastructure.

The vision of smart cities are being realized gradually by converting each building into a smart building. These are cyber-physical systems meant to provide best comfort to its habitant at most economical and environmentally sustainable way. There can be multiple loads for heating, cooling, lighting purposes. Renewables such as rooftop solar photovoltaic and small-scale wind turbines are also integrated for generating electricity that reduces the grid-dependency. This forms a Building Integrated Microgrid (BIMG) system consisting of its own sources, storage, and loads. Handling intermittent generation, variable load conditions, and external grid dynamics are the three essential factors to increase buildings’ smartness. In this work, an adaptive Distributed Model Predictive Control (DMPC) principle for energy management of renewables based grid-tied BIMG is presented. All possible arbitrages, viz., generation intermittency of renewables, time-variable load profiles, storage unit's charging and discharging cycles, interruption of non-critical loads, and dynamic pricing of grid energy are considered. The feasibility of the given strategy is validated by realizing extensive test cases over 24 hrs duration using variable profiles of solar irradiance, continuous dynamic load variation at small and large scale, interruption of non-critical loads, two-tier tariff, and battery longevity by accounting for smooth charging/discharging cycles on the MATLAB software and Real-Time Digital Simulator (RTDS) based Hardware-in-Loop (HIL) setup.

In the near future, residential homes are expected to have a grid connected microgrid structure with photovoltaic (PV) based autonomous power generation and electric vehicles (EVs). The capacity utilization of grid connected converter in a microgrid is minimum when PV power is less, especially during night time. On the other hand, the current practice of charging electric vehicles with a dedicated EV charger increase the expenditure. In this reference, this paper presents a power management and control of a grid connected microgrid with inbuilt EV charging feature. With the proposed system the utilization of grid connected converter can be increased and expenditure on EV charger can be saved. The proposed system is simulated in OPAL-RT-OP4510, a real time digital simulator and validated through results.

… integrated energy system (IES) with mobile charging stations (… , flexible, and efficient energy management system. To achieve … supplying energy, absorbing surplus photovoltaic energy, …

Aiming at the problem of source-load uncertainty caused by the increasing penetration of renewable energy and the large-scale integration of electric vehicles (EVs) into modern power system, a robust optimal operation scheduling algorithm for regional integrated energy systems (RIESs) with such uncertain situations is urgently needed. Based on this background, aiming at the problem of the irregular charging demand of EV, this paper first proposes an EV charging demand model based on the trip chain theory. Secondly, a multi-RIES optimization operation model including a shared energy storage station (SESS) and integrated demand response (IDR) is established. Aiming at the uncertainty problem of renewable energy, this paper transforms this kind of problem into a dynamic robust optimization with time-varying parameters and proposes an improved robust optimization over time (ROOT) algorithm based on the scenario method and establishes an optimal scheduling mode with the minimum daily operation cost of a multi-regional integrated energy system. Finally, the proposed uncertainty analysis method is verified by an example of multi-RIES. The simulation results show that in the case of the improved ROOT proposed in this paper to solve the robust solution of renewable energy, compared with the traditional charging load demand that regards the EVs as a whole, the EV charging load demand based on the trip chain can reduce the cost of EV charging by 3.5% and the operating cost of the multi-RIES by 11.7%. With the increasing number of EVs, the choice of the starting point of the future EV trip chain is more variable, and the choice of charging methods is more abundant. Therefore, modeling the charging demand of EVs under more complex trip chains is the work that needs to be studied in the future.

In this paper, the implementation and control of solar photovoltaic (PV) array, and wind energy conversion system (WECS) based charging station is proposed for charging the electric vehicle (EV) and to power the domestic loads uninterruptedly. Grid connected mode (GCM) and islanded mode (IM) of operations are taken for the proposed control. In IM, the EV directly takes power from the DC link using bidirectional DC-DC converter. However to supply the household loads, it generates a sinusoidal voltage at common point of coupling (CPC). In GCM, the charging system exchanges power with the grid at unity power factor (UPF), while maintaining the grid current total harmonic distortion (THD) less than 5% even though the current drawn by the home loads, are non-sinusoidal. Moreover, the control of the charging station also includes the voltage synchronizing strategy that ensures the smooth transition between the GCM and IM of operation. The multimode operation and power management under different mode of operation of the charging station is validated through the laboratory scale prototype.

… for electric vehicle charging stations (EVCSs) … integration strategy effectively supports the growing electric vehicles (EVs) infrastructure while contributing to a more sustainable energy …

… sorts of charging stations are deemed regarding with power supply … 1 charging stations utilize the power grid, renewable energy sources and vehicle-to-grid technology, type 2 charging …

… of different information sources on it. This paper investigates a charging demand prediction … First, an information interaction framework of integrated road network, vehicles and HEVCS …

This paper studies the problem of stochastic dynamic pricing and energy management policy for electric vehicle (EV) charging service providers. In the presence of renewable energy integration and energy storage system, EV charging service providers must deal with multiple uncertainties—charging demand volatility, inherent intermittency of renewable energy generation, and wholesale electricity price fluctuation. The motivation behind our work is to offer guidelines for charging service providers to determine proper charging prices and manage electricity to balance the competing objectives of improving profitability, enhancing customer satisfaction, and reducing impact on power grid in spite of these uncertainties. We propose a new metric to assess the impact on power grid without solving complete power flow equations. To protect service providers from severe financial losses, a safeguard of profit is incorporated in the model. Two algorithms—stochastic dynamic programming (SDP) algorithm and greedy algorithm (benchmark algorithm)—are applied to derive the pricing and electricity procurement policy. A Pareto front of the multi-objective optimization is derived. Simulation results show that using SDP algorithm can achieve up to 7% profit gain over using greedy algorithm. Additionally, we observe that the charging service provider is able to reshape spatial-temporal charging demands to reduce the impact on power grid via pricing signals.

… grid) and the renewable power source (RPS), by optimising the charging process of a large … Renewable energy is efficiently integrated to centralised EV battery charging, without having …

In this paper, the stochastic energy management of electric bus charging stations (EBCSs) is investigated, where the photovoltaic (PV) with integrated battery energy storage systems (BESS) and bus-to-grid (B2G) capabilities of electric buses (EBs) are included for cost-effective charging of EBs. Also, the day-ahead dynamic prices are derived to mitigate charging impacts on power distribution systems. This problem is formulated as a distributionally robust Markov decision process (DRMDP) with uncertain transition probabilities and costs to address the impacts of random bus loads with inaccurate probability density function estimation. An event-based ambiguity set with combined statistical distance and moment information is developed to achieve minimax-regret criteria for less-conservative and robust solutions. To facilitate practical applications with reduced computational complexity, a heuristic regret function is proposed, based on which the dynamic prices are derived. Case studies based on EB data from St. Albert Transit and IEEE test feeders indicate that the proposed method can minimize EB charging cost with mitigated impacts on power distribution systems.

Renewable energy technologies have had a significant impact on the global electricity market by providing uninterrupted power supply. Using renewable energy to meet the load demands of a tourist complex with electric vehicle charging stations is an important area of study in developing countries. This research examines the technical and economic viability of grid and renewable energy systems for an environmentally-friendly tourist complex. The system is designed to enable power exchange between the grid and various energy components. The location is selected with favorable wind and solar potential in mind. This study evaluates various scenarios with different configurations to minimize greenhouse gas (GHG) emissions, cost of energy (COE), and net present costs (NPC). The assessment considers factors like capital, replacement, decommissioning, operation, and maintenance costs. It conducts optimal power planning and a techno-economic-environmental analysis for a grid-connected hybrid renewable energy system comprising elements such as wind turbines (WT), photovoltaic (PV) systems, diesel generators (DG), boilers, converters, thermal load controllers (TLC), and battery energy storage systems (BESS). This approach is tested on a small-scale system in Manjil, Iran, using HOMER and GAMS software, focusing on its effectiveness in meeting sustainable electrical and thermal demands with renewable energy.

Abstract The commercialization and wide application of electric vehicles (EVs) rely on the development of EV charging infrastructure and its coordinated operation in the electric power grid with renewable energy sources. As the EV aggregator is introduced as intermediate control entity to form a multi-layer control framework, the optimal configuration of EV aggregator and the charging power regulation are closely related to each other. This paper proposes an integrated optimization to solve the mixed configuration and operation problem of EV aggregator. A hybrid optimization algorithm based on partial swarm optimization (PSO) and sequential quadratic programming (SQP) is formulated to find the best solution for the location and size of EV aggregator in the distribution network and the charging scheduling of each individual EV. The master problem in the coupled optimization algorithm is to calculate the optimal configuration of the EV aggregator based on the estimation of the EV charging load, and based on the setting of EV aggregator the subproblem is to obtain the charging plan for individual EV under the day-ahead optimal dispatching of charging demand in the distribution grid. Finally, 123-bus distribution system is adopted to analyze the characteristics of the proposed model for concurrent optimization of EV aggregator and its operation in the test distribution network. The simulation results validate the hybrid optimization algorithm in integrated configuration and operation of EV charging infrastructure.

Abstract A hybrid bi-level programming approach is presented in this paper to enhance the system reliability by optimally integrating the Vehicle Charging Stations (VCS) of Plug-in Hybrid Electric Vehicle (PHEV) and the Renewable Distributed Generation (RDG) simultaneously. A wide spectrum of requirement exists among the customers to have a continuity of supply in the presence of the fluctuating nature of RDS. Thus, a non-linear objective function is formulated to minimize the Energy Not Supplied (ENS) to the customers based on the various contingency analysis. Two notable contributions distinguish this work with existing endeavours. Firstly, Simultaneous selection of optimal place for both RDG and charging station are recognized. Succeeded by the consideration of simultaneous integration of VCS and RDG, a Hybrid Nelder-Mead Cuckoo Search (HNM-CS) algorithm based method is put into operation to minimize the ENS, which seamlessly diminishes the power loss and enhances the voltage magnitude of the system. The distribution systems of standard IEEE 33-bus and real time TamilNadu (TN) 84 bus are considered with different RDGs such as photovoltaic and fuel cell systems. Further, the operational cost of PHEVs scheduling in the VCS is analysed for a 24-hour scenario. From the results obtained, the proposed method provides maximum advantage to the vehicle holder by placing and utilizing more RDGs and meanwhile it satisfies their preferences also.

“Electric vehicles (EVs) are one of the most promising technologies to green the transportation systems. However, high penetration of EVs brings heavy electricity demand to the power grid.” Due to the intermittent character of renewable energy sources (RESs), it turns out to be very challenging to manage EV charging with other renewable generation and grid load. This paper aims to introduce a dispatch strategy assisted with the optimization concept for enhancing the economy of the microgrid system. The major objective is to minimize the cost of system operation and environmental control when meeting system load requirements. The output constraints related to the distributed power supply, such as power limits, are subjected to optimization. To solve this optimization issue, a new “Fitness Sorted Moth Search algorithm (FS‐MSA)” is introduced. Finally, the proposed work is compared and validated with other existing works with respect to various measures. The enhanced outcomes prove the efficacy of the implemented FS‐MSA model. This work concentrates on EV adoption integrated with RES for sustainable mobility. On observing the result, it can be noticed that the adopted scheme was 81.97%, 82.03%, 82%, and 81.97%, better than existing genetic algorithm, particle swarm optimization (PSO), moth search algorithm, and lagrange multiplier optimization (LMO) models for mean case scenario.