STUDY OF BATTERY ENERGY MANAGEMENT FOR EV UNDER CONDITION HYBRID BESS AND V2G IN MICROGRID

Abstract  This paper discusses the installation of electric vehicles (EVs) integrated with battery energy storage systems (BESS) in the setting of transferring energy from vehicle-to-grid (V2G) to a microgrid distribution system, employing the IEEE 13 bus standard. The EVs have a power rating of 4.8 kilowatts (kW), while the BESS has a power rating of 500 kW. The overall design connects These systems to the three-phase bus, utilizing the daily load profile. Bus settings are available on 632, 633, 634, 671, 675, and 680 buses. The power flow computation in the Open Distribution System Simulation (OpenDSS) program is utilized by the simulation. Results of a research investigation comparing the implementation of EVs and BESS within microgrid systems. The study's findings were compared to both the regular system and the system that was implemented. The highest and minimum values of the per-unit system exhibit equality. The aggregate power distribution for active and reactive power exhibits a negligible disparity of less than 1 %. Regarding the power loss value in both active systems, namely free and reactive, a marginal difference of less than 1 % exists. The presence of the backup power supply does not have any impact on the primary testing system utilized in the experiment.    Keywords : Electric vehicles, Battery energy storage systems, V2G

Multi-Period Optimization of Energy Demand Control for Electric Vehicles in Unbalanced Electrical Power Systems Considering the Center Load Distance of Charging Station Areas

The rise of plug-in electric vehicles (EVs) impacts the energy demand of power systems. This study employed a multi-period power flow analysis on the IEEE 123 node test system, which was optimized for the installation of 6-position EV charging stations. Temporal load shifting was utilized to control the charging intervals of electric vehicles. Non-dominated Sorting Genetic Algorithm (NSGA-II) was applied to determine the optimal locations for installing EV charging stations, considering target functions, such as total energy loss, voltage unbalance factor (VUF), and center load distance. The results showed that the center load distance resulted in the optimal charging station location in the central area of the system, different from conventional considerations. The results showed that installing the charging station in the center of the load group (case 4) increased the total energy loss and VUF compared to installing it at the root of the load group (case 3) by about 2.1134 and 1.2287%, respectively. However, EVs reduced impacts during periods of system weakness. By controlling charging intervals during off-peak times (case 6), total energy loss and VUF were decreased by 4.7070 and 5.6896%, respectively, which effectively reduced energy demand during peak periods

Optimal Battery Energy Storage System Based on VAR Control Strategies Using Particle Swarm Optimization for Power Distribution System

We designed a battery energy storage system (BESS) based on the symmetrical concept where the required control is by the symmetrical technique known as volt/var control. The integration of BESS into the conventional distribution has significantly impacted energy consumption over the past year. Load demand probability was used to investigate optimal sizing and location of BESS in an electrical power system. The open electric power distribution system simulator (OpenDSS) was interfaced with MATLAB m-file scripts and presented by using time series analysis with load demand. The optimal BESS solution was adapted by using a genetic algorithm (GA) optimization technique and particle swarm optimization (PSO). The simulation results showed that the BESS was directly connected to the power grid with GA and PSO, and it was observed that BESS sizing also varied for these two values of 1539 kW and 1000 kW, respectively. The merit of those values is the power figure of the system, which is necessary for installation. Therefore, optimal sizing and location of the BESS are helpful to reduce the impact from the load demand to the total system loss and levelling of the energy demand from the power system network. The integration of the BESS can be applied to improve grid stability and store surplus energy very well. The grid increased the stability of the power system and reduced the impact from the large scale of BESS penetration

Optimal Placement of Distributed Photovoltaic Systems and Electric Vehicle Charging Stations Using Metaheuristic Optimization Techniques

In this study, the concept of symmetry is introduced by finding the optimal state of a power system. An electric vehicle type load is present, where the supply stores’ electrical energy causes an imbalance in the system. The optimal conditions are related by adjusting the voltage of the bus location. The key variables are the load voltage deviation (LVD), the variation of the load and the power, and the sizing of the distributed photovoltaic (DPV), which are added to the system for power stability. Here, a method to optimize the fast-charging stations (FCSs) and DPV is presented using an optimization technique comparison. The system tests the distribution line according to the bus grouping in the IEEE 33 bus system. This research presents a hypothesis to solve the problem of the voltage level in the system using metaheuristic algorithms: the cuckoo search algorithm (CSA), genetic algorithm (GA), and simulated annealing algorithm (SAA) are used to determine the optimal position for DPV deployment in the grid with the FCSs. The LVD, computation time, and total power loss for each iteration are compared. The voltage dependence power flow is applied using the backward/forward sweep method (BFS). The LVD is applied to define the objective function of the optimization techniques. The simulation results show that the SAA showed the lowest mean computation time, followed by the GA and the CSA. A possible location of the DPV is bus no. 6 for FCSs with high penetration levels, and the best FCS locations can be found with the GA, with the best percentage of best hit counter on buses no. 2, 3, 13, 14, 28, 15, and 27. Therefore, FCSs can be managed and handled in optimal conditions, and this work supports future FCS expansion