Optimal placement of EV charging Stations, DSTATCOM, BESS, and DGs in radial distribution systems using an enhanced Fractional-Order differential Evolution-Based optimization algorithm

Multi-objective electric vehicle charge scheduling for photovoltaic and battery energy storage based electric vehicle charging stations in distribution network

A comprehensive planning framework for electric vehicles fast charging station assisted by solar and battery based on Queueing theory and non-dominated sorting genetic algorithm-II in a co-ordinated transportation and power network

Minimizing the electric vehicle charging stations impact in the distribution networks by simultaneous allocation of DG and DSTATCOM with considering uncertainty in load

Saving energy through the minimization of power losses in a distribution system is a key activity for efficient operation. Distributed Generation (DG) is one of the most efficient approaches to minimize losses. With increase in installation of Electric Vehicle Charging Stations (EVCSs) for Electrical Vehicles (EVs) in larger scale, optimal planning of EVCSs becomes a major challenge for distribution system operator. With increased EV load penetration in the electricity system, generation-demand mismatch and power losses increases. This results in poor voltage level, and deterioration in voltage stability margin. To mitigate the adverse impacts of increasing EV load penetration on Radial Distribution Systems (RDS), it is essential to integrate EVCSs at appropriate locations. The EVs integration into smart distribution systems involves Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) in charging and discharging modes of operation respectively for exchange of power with the grid thus resulting in energy management. The inappropriate planning of EVCSs causes a negative impact on the distribution system such as voltage deviation and an increase in power losses. In order to minimize this, DG units are integrated with EVCSs. The DGs assist in keeping the voltage profile within limitations, resulting in reduced power flows and losses, thereby enhancing power quality and reliability. Therefore, the DGs should be optimally allocated and sized along with the EVCS to avoid problems such as protection, voltage rise, and reverse power flow problems. This paper showcases a method to minimize losses using optimal location and sizing of multiple DGs and EVCS operating in G2V and V2G modes. The sizing and location of different types of DG units including renewables and non-renewables along with EV charging station is proposed in this study. This methodology overall reduces the power losses and also improves voltages of the network. The implementation is done by using the Simultaneous Particle Swarm Optimization technique (PSO) for IEEE 15, 33, 69 and 85 bus systems. The results indicate that the proposed optimization technique improves efficiency and performance of the system by optimal planning and operation of both DGs and EVs.

This article presents the optimal placement of electric vehicle (EV) charging stations in an active integrated distribution grid with photovoltaic and battery energy storage systems (BESS), respectively. The increase in the population has enabled people to switch to EVs because the market price for gas-powered cars is shrinking. The fast spread of EVs depends solely on the rapid and coordinated growth of electric vehicle charging stations (EVCSs). Since EVCSs can cause power losses and voltage variations outside the permissible limits, their integration into the current distribution grid can be characterized by the growing penetration of randomly dispersed photovoltaic (PV) and battery energy storage (BESS) systems, which is complicated. This study used genetic algorithm (GA) optimization and load flow (accommodation of anticipated rise in the number of electric cars on the road) analysis with a forward and backward sweep methodology (FBSM) to locate, scale and optimize EVCSs from a distribution grid where distributed PV/BESSs are prevalent. Power optimization was demonstrated to be the objective issue, which included minimizing active and reactive power losses. To verify the proposed optimal objective solutions from the active distribution grid, an IEEE 33 bus distribution grid was considered for EVCSs’ optimization under the penetration of photovoltaic and BESS systems. MATLAB simulations for the integrated EVCS-PV-BESS system on the distribution grid for five different zones were performed using detection from zone 1 (ranging from 301.9726 kW to 203.3872 kW), reducing the power losses (accounting for 33%) in the system to a minimum level.

This paper proposes an energy management strategy (EMS) to enhance the power quality (PQ) parameters, i.e., voltage unbalance, power factor, and frequency deviation, of a smart grid station (SGS). Here, the SGS is represented as grid-connected multi-microgrids (MMGs), which are equipped with distributed generators (DGs), i.e., solar photovoltaic (PV) and wind turbines (WTs), battery energy storage systems (BESs), electric vehicle charging stations, capacitor banks, chillers, and building load power demand (LPD). Maintaining the PQ parameters of the SGS within the threshold limits is challenging due to the stochastic nature of building LPD and the dynamic behaviors of chiller operations. Furthermore, reactive power compensation with capacitor banks and robust control of DGs with BESs might not be a straightforward solution to improve the PQ parameters due to the nonlinearity of building LPD, the intermittent nature of DG power, and the limited capacity of BESs. In this context, an artificial neural network approach is used to predict the future values of building LPD, DG power, and cooling power demand. The SGS energy sources, converters, and grid connections are modeled at the system level. PQ index models are developed based on PQ parameter threshold limits. A fuzzy-based peer-to-peer (P2P) energy-sharing strategy is developed based on a unique identification index, an energy-sharing index, and DGs' energy supplying, sharing, or buying to and/or from the neighborhood building. The BESs' charging and discharging control strategy is implemented based on the available energy in BESs. Furthermore, a cooling energy demand (CED) reduction strategy is implemented based on the predicted mean voltage and building CED index. Finally, an EMS is implemented for the SGS, which consists of existing and proposed EMS. The existing EMS is the baseline strategy, which provides available DG energy to the building, and deficit energy is supplied from the grid. The proposed EMS is the PQ parameter mitigation strategy, which maintains the PQ parameters within the threshold limits through the fuzzy-based P2P energy-sharing strategy. Measured data from the SGS are used to demonstrate the effectiveness of the proposed EMS. Through simulation studies, it is shown that the proposed EMS is capable of maintaining the PQ parameters within the threshold limits and reducing CED by concurrently enabling SGS energy.

The expansion of charging infrastructure for electric vehicles (EV) in Europe is progressing steadily. However, available charging capacities are rapidly approaching their limits, particularly at highly frequented public charging points, such as those along highways. One reason for this limitation is the restriction in electrical grid capacity. A solution to this problem could be the combination of large ground-mounted photovoltaic (PV) systems and public EV charging stations. The onsite usage of the energy provided by the PV system, offers a substantial advantage, as it leads to a significant reduction in the load on the electrical grid. In order to ensure sufficient power, even during periods of darkness or bad weather, the integration of battery energy storage systems (BESS) may be an additional option. This approach not only effectively mitigates the risk of overloading the electrical grid and ensures the reliability of energy supply, it also supports demand-oriented, decentral and flexible renewable integration. The present work therefore analyses the interaction of PV-BESS systems and EV charging stations and their impact on the power grid. The study is conducted using a mathematical model in MATLAB Simulink®. The model includes a ground-mounted PV system, a BESS including battery management (BM), EV charging stations and a transformer connected to the public power grid, see Figure. The EV charging stations are additionally equipped with a variety of controls, for instance, charging management (CM) can be executed in static, dynamic or sequential modes. The function of the CM is based on the economic model predictive controller (eMPC). By varying the individual components, such as the connected load of the ground-mounted PV system, the storage capacity of the external BESS and different charging profiles of EVs, questions regarding the optimum BESS size, BESS operation or optimum CM can be answered. Based on the simulation results, statements concerning the effects on the public power grid can be made. The influence of the PV-system, the BESS and the EV charging station on the quality parameters of the public power grid, including electrical voltage and frequency, can thus be investigated.

To reduce the carbon emissions from vehicles and realize carbon neutrality, it is necessary to promote the application of battery energy storage system (BESS) and photovoltaic (PV) in electric vehicle charging stations. Meanwhile, the implementation of BESS and PV in charging stations can be a solution to help to reduce the progressively increase of the charging load in energy distribution networks. Finding the optimal size of BESS and PV is necessary for charging stations to save long-term costs. In our paper, a novel refined BESS model considering BESS dynamic characteristic is established, which takes BESS operation characteristics of dynamic capacity, dynamic lifetime and dynamic efficiency into account. Based on the established BESS model, a bi-level optimization method is proposed to find out the optimal BESS and PV capacity, and the optimal solution is found by using a genetic algorithm (GA) with elitist strategy. Then a case study of a charging station in Shanghai proves the effectiveness of the proposed method. The simulation results confirm that: 1) the BESS and PV size can be determined for optimal benefits by the proposed model; 2) the proposed refined BESS model, which considers BESS dynamic characteristics, is feasible; 3) the proposed operation strategy in typical days, could improve the benefits and the energy consumption of the PV systems for the operator of the charging stations.

Distribution systems have a lot of obstacles to deal with, like increasing load demands, environmental issues, operating limits, and infrastructure development limitations. On the other hand, the number of plug-in hybrid electric vehicles (PHEVs) has grown significantly in recent years and is likely to continue due to concerns over the environment and fossil fuel shortages. Due to the increasing use of PHEVs, distribution systems were not built to accept them, requiring planners to create parking lots that support PHEV charging. To address these issues, in this study, optimal planning of distributed generation (DG) and electric vehicle charging stations (EVCS) in radial distribution systems by the maiden application of a novel Pareto-based multi-objective artificial hummingbird optimization (MOAHO) algorithm is addressed. Three technical aspects of the distribution system are improved by optimal planning of various types of DGs and EVCSs: active power loss reduction, total voltage deviation minimization, and voltage stability improvement. The Pareto-based MOAHO is employed to generate the optimal front between the three competing objectives and later TOPSIS method is employed for selecting the most compromised solution from the optimal front. The proposed methodology is tested on IEEE-33, IEEE-69 bus radial distribution test systems. To validate the efficacy of the MOAHO algorithm, the simulation outcomes of the proposed methodology are generated using a multi-objective non-dominated sorting genetic algorithm (NSGA2), particle swarm optimization algorithm (PSO), grey wolf optimization algorithm (GWO) and compared with the outcomes of the MOAHO algorithm.

The global surge in electric vehicle (EV) adoption has driven significant research into electric vehicle charging stations (EVCS) due to their environmentally friendly attributes, including low CO₂ emissions. However, integrating EVCS into existing distribution grids presents challenges such as power losses and voltage instability, especially with the increasing incorporation of renewable distributed generation (RDG) sources and battery energy storage systems (BESS). This study introduces a novel honey badger optimization algorithm (HBOA), designed to enhance solution convergence and optimize multi-objective criteria efficiently. HBOA strategically places EVCS while considering vehicle-to-grid (V2G) capabilities and user driving behaviors over a full 24-hour cycle, effectively addressing uncertainties and dynamic conditions. Simulations on modified IEEE 69-bus and Indian 28-bus radial distribution systems (RDS) demonstrate significant results: in the IEEE 69-bus system, power loss is reduced by 62.0%, the voltage stability index (VSI) increases from 0.7139 to 0.8311, and CO₂ emissions decrease by 66.0%. In the Indian 28-bus system, power loss decreases by 55.5%, with VSI improving from 0.7394 to 0.9964, leading to a 50.0% reduction in CO₂ emissions. The proposed smart microgrid (MG) structure incorporates interconnected MGs for residential, commercial, and industrial sectors, emphasizing the efficacy of RDGs in mitigating the impact of EVCS on RDS. The advantages of the HBOA method lie in its superior optimization capabilities, which enhance system performance, reduce operational costs, and promote sustainability, highlighting the proposed methodology’s potential for future integration of EVCS in distribution networks.

In order to improve the utilization efficiency of photovoltaic system (PV system) and battery energy storage system (BESS) in photovoltaic and battery energy storage integrated electric vehicle (EV) charging stations, the capacity of PV system and BESS need to be allocated reasonably. In this paper, the maximization of the net annual financial value is used as the objective function of photovoltaic and battery energy storage integrated EV charging station in its whole life cycle. The control strategy of BESS is proposed based on time-of-use pricing. And then the optimization configuration model of PV system and BESS capacity is built. The economic efficiency of EV charging stations is compared and analyzed with actual test system when PV system and BESS configurating. The results indicate that the income and investment payback period of charging stations can be significantly improved through the reasonable configuration of PV system and BESS.

This article presents implementation of a solar energy-based DG (Distribution Generation) system, which is optimally designed by taking combined effect of solar irradiation and temperature into consideration to minimize the power loss in the Radial Distribution Systems (RDS). A power gap of 35 W/panel is reduced when the solar panel is considered under ambient temperature effect. Novelty of this work lies in the implementation of the obstructed solar astronomical model to improve the accuracy in the prediction of radiation level. In this work, the considered radiation level is determined to be 14.86% of the global radiation data. Consideration of self-shadowing losses for maximum efficiency from PV panels is another significant aspect of this work. Two different test systems are designed by integrating the proposed DG system to IEEE-15 and IEEE-28 RDS. Load Impedance Matrix (LIM) method is adopted to perform load flow in above mentioned test systems due to its single step analysis with faster results unlike traditional Backward Forward Sweep (BFS) method. Grey Wolf Optimization (GWO) technique is used to obtain optimal location of DG for both the test systems for its simple evaluation, good flexibility, avoidance of local optimal point and derivation free tool, which makes this technique popular among the other heuristic methods. Following DG allocation in IEEE-15 bus RDS, it is observed that active power loss is reduced by 39.69%. Similarly, for IEEE-28 bus RDS, reduction in active power loss is found to be 41.20% after integration of a DG unit. There is also a remarkable improvement in voltage profile in both systems after integration of DG units.

The rapid development of electric vehicles (EV) has increased the load on charging stations (CS), placing significant pressure on distribution power systems, particularly with adverse effects on voltage stability, peak loads, and high power losses.In this study, we propose a solution combining the deployment of distributed generation (DG) and battery energy storage systems (BESS) to support electric vehicle charging stations (EVCS) in distribution networks.The objective is to minimize power losses and maintain stable operating voltage.An improved algorithm, the Chaotic Wild Horse Optimizer (CWHO), derived from the original Wild Horse Optimizer (WHO), is proposed to achieve better solutions for the problem model.The standard IEEE 33-bus distribution network is used for testing and simulating solutions with Matlab R2022a under two scenarios: integrating DG and BESS over a 24-hour framework and increasing the EVCS intensity factor.The results are compared with previous studies to demonstrate the effectiveness and superiority of the improved CWHO algorithm for the EVCS optimization problem.

AI-based optimal allocation of BESS, EV charging station and DG in distribution network for losses reduction and peak load shaving

Smart control of BESS in PV integrated EV charging station for reducing transformer overloading and providing battery-to-grid service