Photovoltaic Energy Storage System Research Articles

Performance analysis of drone sqadron optimisation based MPPT controller for grid implemented PV battery system under partially shaded conditions

In recent years, growing consumer demand for sustainable energy is driving down costs and paving the way for more clean energy in the future. Since these energies are sporadic, and the load pattern does not correlate to their generation pattern, a battery storage system is required. Operating dispersed alternative energy sources, connected to the grid in this situation makes energy control an unavoidable task. The difficulty is brought on by things like intermittent sources, daytime costs, solar panel and battery size restrictions, and limitations in battery charging and discharging rates. Moreover, integrating energy storage devices into the power system network is one of the recommendations made in this work to increase the performance and dependability of these systems. Compared to the existing works reported in the literature, this study offers enhanced control techniques for hybrid Photovoltaic and Battery Energy Storage System under fluctuating atmospheric scenario. Here, an innovative approach called Drone Squadron Optimisation, which tracks the Global Maximum Power Point, is used get maximum power. Further, suggested approach is tested with various partial shading instances and the uniform irradiance condition to validate it. This research also investigates photovoltaic models and the status of the battery storage device for improved energy management in the system. Finally, the proposed algorithm assists in quickly identifying the potential benefits of a grid-connected, battery-powered rooftop solar system.

Many-objective optimization based mutual feed scheduling for energy system of integrated energy station

The battery storage system of the integrated energy station contains electric vehicles as the distributed energy storage node of the regional power system can help the regional power system to achieve peak shaving and valley filling. This paper proposes a many-objective optimization based mutual feed model of the energy system of the integrated energy station. The model includes multiple modules of electric vehicle (EV), wind turbine (WT), photovoltaic (PV) and battery energy storage system (BESS). BESS was modeled according to the two behaviors of EVs charging and swapping. The model ensures the role of peak shaving and valley filling for regional power, reduces the loss of battery life in the battery storage system of the integrated energy station, and reduces the operating cost of the battery storage system. In the optimized results, the regional power system load peak-to-valley ratio by 28.72%, and the daily battery charge/discharge life loss is minimized to 0.00036%. In addition, for known many-objective optimization problems, this paper also proposes a new meta-heuristic many-objective optimization algorithm the non-dominated sorting hunter prey optimization (NSHPO for short) and compared with other four many-objective optimization algorithms by three indexes (Distribution range, Hypervolume index, and Running time). The simulation results show that compared with other algorithms, the HV metrics of NSHPO are lower by about 1.56%-7.26%, Spread metrics are better by about 0.39%-2.73%, and the proposed NSHPO algorithm is better in terms of convergence and distributivity.

Energy exchange optimization for a building-to-building grid: A non-cooperative game-theory method

The growing demand for green energy solutions has led to an increase in innovative distributed energy generation (DEG) architectures, such as buildings with photovoltaic (PV) generation and energy storage systems (ESS). In these distributed grids, buildings with renewable energy generation capabilities can exchange surplus energy to optimize overall system efficiency, increase their profit, and reduce reliance on the main grid. This paper proposes a game-theory method to optimize energy exchange within a building-to-building (B2B) grid. The proposed method formulates the energy exchange problem as a non-cooperative game, where each building owner aims to maximize their welfare by strategically determining their energy trading decisions, including pricing. The game-theory approach considers energy supply and demand, ESS capacity, and cost considerations, including ESS degradation cost. The proposed approach is demonstrated through simulations, showing significant improvements in cost savings compared to building-to-grid (B2Grid) alternatives. Three cases are compared: (1) B2B grid without considering ESS, (2) B2B grid with ESS without considering the ESS degradation cost (ideal case), and (3) B2B grid with ESS considering the ESS degradation cost. The results show the potential of the proposed game-theory in optimizing energy exchange within the B2B grid with daily and operational hours saving of 2.12−6.37 % and 4.57−165.93 %, respectively.

Moth Flame Algorithm to Optimize the Size and Location of Energy Storage Units in a Radial Distribution Network to mitigate Power Loss

The ideal placements and capacities for Battery Energy Storage Systems (BESS) sources in a distributed generation (DG) environment have an impact on power losses in the distribution network. Therefore, engineers are quite concerned with DG and BESS. The output from DG is, however, typically unpredictable and inconsistent. The secure and stable functioning of the power system will be impacted to varying degrees when it is connected to various sites, capabilities, and power grids. To provide a safer, more stable, more dependable, as well as more effective power grid operation, power grid planners must take the impact of capacity, type, and location into account while choosing the best BESS accessibility method. The objective is to choose bus stops with the right potential and minimal loss. This study demonstrates how to use a Moth Flame Optimization Algorithm (MFOA) to establish the appropriate battery energy storage system (BESS) sizing in a hybrid solar and wind energy conversion system (WECS) generation integrated in a radial distribution network. This research investigates the optimal capacities and allotment of the BESSs within a 33-bus and 69-bus radial network of distribution for the maximum power dissipation reduction. This research also provides a framework for carrying out conservation voltage reduction (CVR) when distributed energy resources like solar photovoltaic (PV) systems, WECS, and energy storage systems (ESSs) are connected into distribution networks. The simulation has been conducted using Matlab code and the suggested method's output is compared with Firefly Algorithm (FA), Genetic Algorithm (GA) and Ant Colony Algorithms (ACA) and evaluated against other results.

Analysis of Grid-Connected Stability of VSG-Controlled PV Plant Integrated with Energy Storage System and Optimization of Control Parameters

In the static stability analysis of the grid-connected photovoltaic (PV) generation and energy storage (ES) system, the grid-side is often simplified using an infinite busbar equivalent, which streamlines the analysis but neglects the dynamic characteristics of the grid, leading to certain inaccuracies in the results. Furthermore, the control parameter design does not consider the coupling relationships among parameters, resulting in arbitrary values and the inability to achieve overall optimality. To address these issues, this paper presents a comprehensive parameter optimization method for the oscillation characteristics of grid-connected PV generation and ES systems in various frequency ranges. Firstly, a detailed modeling of the grid-connected PV generation and ES system is conducted, resulting in the derivation of the system’s small-signal model. This study investigates the impact of parameters related to PV arrays, ES units, and the virtual synchronous generator (VSG) on the system’s characteristic roots using participation factors, sensitivity analysis, and eigenvalue root trajectories. Subsequently, based on the analysis of system root trajectories and the Particle Swarm Optimization (PSO) algorithm, a holistic parameter optimization design method for the system’s oscillation modes is proposed, and the parameter optimization results are obtained. Finally, the accuracy of the theoretical analysis is validated through perturbation testing using both Matlab/Simulink models and the small-signal model.

A guide to residential energy storage and rooftop solar: State net metering policies and utility rate tariff structures

Federal and state decarbonization goals have led to numerous financial incentives and policies designed to increase access and adoption of renewable energy systems. In combination with the declining cost of both solar photovoltaic and battery energy storage systems and rising electric utility rates, residential renewable adoption has become more favorable than ever. However, not all states provide the same opportunity for cost recovery, and the complicated and changing policy and utility landscape can make it difficult for households to make an informed decision on whether to install a renewable system. This paper is intended to provide a guide to households considering renewable adoption by discussing relevant factors that influence renewable system performance and payback, summarized in a state lookup table for quick reference. Five states are chosen as case studies to perform economic optimizations based on net metering policy, utility rate structure, and average electric utility price; these states are selected to be representative of the possible combinations of factors to aid in the decision-making process for customers in all states. The results of this analysis highlight the dual importance of both state support for renewables and price signals, as the benefits of residential renewable systems are best realized in states with net metering policies and above-average electric utility rates.

An improved NSGA‐II for the dynamic economic emission dispatch with the charging/discharging of plug‐in electric vehicles and home‐distributed photovoltaic generation

AbstractThis paper investigates four energy utilization scenarios with or without home‐distributed photovoltaic generation (HDPG) to reduce the generation cost and pollutant emission of the dynamic economic emission dispatch with the charging/discharging of plug‐in electric vehicles (DEED‐PEV). The first scenario considers valley filling for the charging of PEVs. The second scenario combines valley filling and peak shaving for the charging and discharging of PEVs. The third scenario adds peak shaving of HDPG to the first scenario, followed by the peak shaving with the discharging of PEVs. The fourth scenario rearranges the distribution of photovoltaic (PV) power for the third scenario, and the PV power in the afternoon is stored by a photovoltaic energy storage system (PESS) and consumed in the evening. A universal procedure is designed for the valley filling and peak shaving of the four scenarios, which is beneficial for determining the filled and shaved loads with respect to certain time intervals. An NSGA‐II method based on a modified crossover and an elimination of individuals (NSGA‐II‐MCEI) is proposed for the multiobjective optimization of DEED‐PEV. The modified crossover can improve the convergence of NSGA‐II‐MCEI, and the elimination operator can maintain the evenness of the nondominated solutions. According to experimental results, scenario 4 achieves cost savings of 95386.62 $, 85636.87 $, and 6776.85 $, respectively, for Scenarios 1, 2, and 3, and it reaches emission reductions of 27617.64 kg, 17252.71 kg, and 220.98 kg, respectively, for scenarios 1, 2, and 3. Also, scenario 4 outperforms the other three scenarios for three weather conditions such as sunny day, cloudy day, and rainy day.

Finite control set model predictive control of three-port converter for interfacing a PV–battery energy storage system to a three-phase stand-alone AC system

Abstract This paper proposes a multiport bidirectional non-isolated converter topology that provides advantages in terms of simultaneous multiple operations, single-stage conversion, high power density and reduced power losses due to the lower number of switches. The proposed multiport converter uses a centralized non-linear controller known as a finite control set model predictive controller to manage the flow of power between different ports. It deals with the parallel operation of photovoltaic and battery energy storage systems for stand-alone alternating current (AC) systems. The converter connects the lower voltage battery to the photovoltaic port using a bidirectional buck/boost converter and the photovoltaic port is linked to the stand-alone AC load through a three-phase full-bridge inverter. Each leg of the three-phase converter will act as a bidirectional direct current (DC)/DC converter as well as an inverter simultaneously. Only six switches manage the power transfer between all the connected ports of photovoltaic–battery energy storage system linked to the stand-alone AC load. The proposed multiport converter is mathematically modelled and controlled by a finite control set model predictive controller. The system is validated in simulation (1-kW rating) and experimental environment (200-W rating). The hardware prototype is developed in the laboratory and the controller is implemented on the field-programmable gate array board. Two independent case studies are carried out to validate the efficacy of the system. The first scenario is for a change in solar irradiance, while the second scenario is for a change in the output load.

Optimization of Photovoltaic Capacity and Electric Vehicle Battery Charging at Electric Vehicle Charging Stations Using Particle Swarm Optimization

The transportation sector is one of the largest producers of carbon gas emissions. This encouraging electrification to reduce the impact of environmental damage. However, the impact of large-scale electrification causes an increase in peak load demand on the distribution network. This results in an energy crisis, economic losses, and reduces the reliability of the power system if not handled seriously. Important solutions involve optimizing electric vehicle charging stations (EVCS), accelerating the use of renewable energy for transportation and controlling electric vehicle charging. The hybrid microgrid system at EVCS can be an effective solution. This system is able to manage its own electricity needs through an energy management system with a combination of energy sources. This research discusses optimizing photovoltaic capacity and charging electric vehicle batteries at EVCS using particle swarm optimization (PSO). The aim of this research is to increase efficiency in investment and increase profits. The model is determined as an EVCS that is interconnected with photovoltaic (PV). The parameters determined include: charging load data, investment costs, power import tariff provisions and charging rates. PSO is used to size photovoltaic (PV) and battery energy storage systems (BESS). The results show that PSO can be used for optimization. This is proven by obtaining the position of the combination of PV and BESS with the best profit with a combination of PV of 494 kW and BESS of 581 kW, with an NPV of $1,652,604.89, an IRR of 46.60%, and a payback period of 2.15 years. The combination with the best PV contribution, namely PV of 3,146 kW and BESS of 1,811 kW, produces energy of 75,504 kWh per month, power import of 35,946 kWh and battery usage level of 58%. These two combinations of optimization results meet the investment acceptance requirements with an internal rate of return (IRR) of at least 9%.

An innovative heterogeneous transfer learning framework to enhance the scalability of deep reinforcement learning controllers in buildings with integrated energy systems

AbstractDeep Reinforcement Learning (DRL)-based control shows enhanced performance in the management of integrated energy systems when compared with Rule-Based Controllers (RBCs), but it still lacks scalability and generalisation due to the necessity of using tailored models for the training process. Transfer Learning (TL) is a potential solution to address this limitation. However, existing TL applications in building control have been mostly tested among buildings with similar features, not addressing the need to scale up advanced control in real-world scenarios with diverse energy systems. This paper assesses the performance of an online heterogeneous TL strategy, comparing it with RBC and offline and online DRL controllers in a simulation setup using EnergyPlus and Python. The study tests the transfer in both transductive and inductive settings of a DRL policy designed to manage a chiller coupled with a Thermal Energy Storage (TES). The control policy is pre-trained on a source building and transferred to various target buildings characterised by an integrated energy system including photovoltaic and battery energy storage systems, different building envelope features, occupancy schedule and boundary conditions (e.g., weather and price signal). The TL approach incorporates model slicing, imitation learning and fine-tuning to handle diverse state spaces and reward functions between source and target buildings. Results show that the proposed methodology leads to a reduction of 10% in electricity cost and between 10% and 40% in the mean value of the daily average temperature violation rate compared to RBC and online DRL controllers. Moreover, online TL maximises self-sufficiency and self-consumption by 9% and 11% with respect to RBC. Conversely, online TL achieves worse performance compared to offline DRL in either transductive or inductive settings. However, offline Deep Reinforcement Learning (DRL) agents should be trained at least for 15 episodes to reach the same level of performance as the online TL. Therefore, the proposed online TL methodology is effective, completely model-free and it can be directly implemented in real buildings with satisfying performance.

Distributed Generation (DG) system using COPRAS method

Distributed Generation (DG) system. Distributed Generation (DG) power systems, Expansion of power systems in remote areas, and Very recently as a sustainable way of electrification popular. Depletion of conventional fossil fuels, Fuel price volatility, and emissions Awareness environment about reduction, Due to this its demand is increasing. DG systems have additional power quality challenges. There are many types of DG power sources that produce electrical power with different voltages at various frequencies. RES-Based Distributed Generation (DG) Systems are conventional energy systems In finding new modern solutions for planning have contributed Widespread use of DG is old and Defines new views, Current delivery system The only way to deal with the project Calling for more complex defenses. COPRAS (Complex Proportionality Assessment) is very Multiple criteria used in decision-making methods is one. This technique is different Decision making by researchers Used to solve problems. Investment cost, operating cost, Primary energy consumption, CO2 emissions. Energy storage system, fuel system, Wind Solar Hybrid System, Conventional System, PV energy storage system. Involvement of Distributed Generation (DG) system PV energy storage system ranked 1st, and the Conventional system also got 5th position.

Phase Formation in NaH2PO4–VOSO4–NaF–H2O System and Rapid Synthesis of Na3V2O2x(PO4)2F3-2x

Renewable electricity products, for example, from wind and photovoltaic energy, need large-scale and economic energy storage systems to guarantee the requirements of our daily lives. Sodium-ion batteries are considered more economical than lithium-ion batteries in this area. Na3V2(PO4)2F3, NaVPO4F, and Na3(VO)2(PO4)2F are one type of material that may be used for Na-ion batteries. In order to better understand the synthesis of these materials, the phase formation in a NaH2PO4–VOSO4–NaF–H2O system under hydrothermal conditions was studied and is reported herein. This research focused on the influences of the sodium fluoride content and hydrothermal crystallization time on phase formation and phase purity. The phase transformation between Na(VO)2(PO4)2(H2O)4 and Na3V2O2x(PO4)2F3-2x was also studied. Na3V2O2x(PO4)2F3-2x with a high degree of crystallinity can be obtained in as short as 2 h via hydrothermal synthesis using a conventional oven at 170 °C without agitation. All compounds obtained in this research were studied mainly using powder X-ray diffraction, scanning electron microscopy, energy-dispersive X-ray spectrometry, and Fourier-transform infrared spectroscopy.

Multi-objective distributed control of WT-PV-BESS integrated weak grid via finite time containment

This paper proposes a finite time containment control scheme to achieve distributed control of a weak grid which is integrated with photovoltaic (PV), wind turbine (WT) and battery energy storage system (BESS). Taking advantage of the containment control in multi-dimensional control, we proposed a multi-objective control scheme of the WT-PV-BESS system in a weak grid, wherein four control objectives are specified, namely, bus voltage regulation, grid frequency control, peak load shaving and economic benefits optimization for the system operator. Firstly, the sizing approach of BESSs to fulfill the operation mission requirements is proposed; meanwhile, the general coordination strategy for the PV, WT and BESS to achieve the demand–supply balance is proposed. Subsequently, the predefined four control objectives are modeled and transferred into the long/short time frame operational constraints of the BESSs. Afterwards, the finite time containment control algorithm is proposed to force the BESSs into achieving containment status within prescribed time. Simulation case studies are conducted on a modified IEEE 9-bus system to show the effectiveness and performance of the proposed scheme.

Simultaneous capacity configuration and scheduling optimization of an integrated electrical vehicle charging station with photovoltaic and battery energy storage system

The implementation of an optimal power scheduling strategy is vital for the optimal design of the integrated electric vehicle (EV) charging station with photovoltaic (PV) and battery energy storage system (BESS). However, traditional design methods always neglect accurate PV power modeling and adopt overly simplistic EV charging strategies, which might result in suboptimal and infeasible designs. This study proposes a novel simultaneous capacity configuration and 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 original model is then converted to a mixed integer linear programming problem by the Big-M method to greatly reduce the computational complexity. A typical scenario of commercial region is employed to demonstrate the effectiveness of the proposed approach. The results indicate that the approach could improve economic benefits by 15.67 % and reduce carbon emissions by 37.14 % compared with the entire grid-based mode. Besides, integrating the PV hybrid modeling method and optimal charging scheduling strategy could achieve superior capacity configurations and greatly improve the comprehensive performance of the system. It is thus illustrated that the approach could provide a promising solution for the optimal design and scheduling of PV/BESS integrated EV charging stations.

Optimal peak-shaving for dynamic demand response in smart Malaysian commercial buildings utilizing an efficient PV-BES system

Reducing peak demand on the utility grid benefits both grid operators and consumers. However, achieving this goal while maintaining human comfort presents a significant challenge. This study proposes the deployment of an energy-efficient grid-connected solar photovoltaic (PV) and battery energy storage (BES) system to perform peak shaving. We employ an optimal rule-based control strategy that considers day-ahead PV power and load demand predictions, along with a daily load profile limit, specifically designed for the C1 tariff in Malaysian Commercial Buildings. The peak shaving control strategy proactively determines optimal schedules for battery charging and discharging, aiming to effectively minimize peak demand. To regulate the daily demand accordingly, we employ a root-finding algorithm to determine the optimal demand limit in conjunction with the formulated rule-based peak-shaving control strategy. The required optimal inputs for the rule-based control strategy are determined using a genetic algorithm, which minimizes peak grid power. We verify the effectiveness of the rule-based peak-shaving method using MATLAB. The results demonstrate significant reductions in both energy consumption and peak demand for different load profiles. Specifically, we achieve a reduction of 19.02%, 20.9%, 4.72%, and 0% in energy consumption, and 42.46%, 49.82%, 40.44%, and 33.1% in peak demand for case studies (CS) 1, 2, 3, and 4, respectively. Furthermore, we observe that the application of the rule-based peak shaving method maintains a state-of-charge (SOC) of the battery at 50% by the end of the day, allowing for day-to-day management flexibility.

Scenario-based ultra-short-term rolling optimal operation of a photovoltaic-energy storage system under forecast uncertainty

The rapid development of renewable energy sources (RESs) facilitates the coordinated operation of different energy sources to hedge against the uncertain and non-dispatchable nature of RESs. In this paper, we propose an effective approach for ultra-short-term optimal operation of a photovoltaic-energy storage hybrid generation system (PV-ES HGS) under forecast uncertainty. First, a generic approach for modelling forecast uncertainty is designed to capture PV output characteristics in the form of scenarios. Then, stochastic model predictive control (SMPC) is utilized in the coordinated operation of PV-ES HGS for load shifting under forecast uncertainty. Finally, a PV-ES power station located in Northeast China is selected as a case study to validate the feasibility and effectiveness of the proposed method in detail. The results show that: (1) The forecast uncertainty modelling method, which incorporates adaptive kernel density estimation (KDE) and two-layer nested copula, could capture the characteristics of PV output effectively. (2) The proposed sampling procedure ensures the representativeness and temporal dependence of scenarios extracted from the joint predictive density, leading to stability and efficiency in solving the stochastic optimization problems. (3) The SMPC-based coordinated operation strategy exhibits significant superiority in load shifting performance and resistance to risks. The standard deviation of residual load (SDORL) is reduced by 17.5% on average compared to the conventional scheme. Thus, this work provides operators with guidance in the ultra-short-term optimal operation of a PV-ES HGS under forecast uncertainty.

Design of An Intelligent Monitoring and Control System for Photovoltaic Microgrids

<p>This article focuses on the problems of imperfect models and slow convergence speed of optimization algorithms in the use of photovoltaic microgrids. Firstly, accurate mathematical models are established based on the composition of photovoltaic microgrids, namely photovoltaic power generation systems and energy storage systems. Then, an improved cat swarm algorithm is used to solve the model, ultimately achieving an increase in solving speed during the process while avoiding the algorithm from falling into local optima. Finally, an intelligent monitoring system for photovoltaic microgrids was designed based on the algorithm process of the article, visualizing the main parameters.</p> <p> </p>

Optimal Power Flow Management in Microgrids using Distributed Energy Resources

The effective and stable functioning of microgrids using distributed energy resources (DERs) is greatly dependent on ideal power stream management. To get the optimal power flow in microgrids, this study provides an inquiry into the use of cutting-edge optimization techniques. To fulfil load demand and consider DER requirements, system running costs must be kept to a minimum. The microgrid integrates a variety of distributed energy resources, including solar photovoltaic systems, wind turbines, and energy storage systems. The research examines several optimization strategies, such as mixed-integer programming, evolutionary algorithms, and linear programming, to decide on the best DER dispatch and scheduling. The findings give important information on how to efficiently use and manage distributed energy resources to raise the performance and resilience of microgrids.

Design optimization of a grid-tied microgrid for a residential community in southern Bangladesh

Abstract Growing energy demand, diminishing fossil fuel reserves and geopolitical tensions are serious concerns for any country’s energy strategy and security. These factors have a greater impact on developing countries, as many of them rely largely on traditional energy resources. Cleaner energy generation is the viable alternative for mitigating these problems, as well as achieving energy independence and tackling climate change. The article discusses planning and design optimization of a residential community microgrid based on multiple renewable resources. In particular, the design and techno-economic assessment of a grid-tied hybrid microgrid for meeting the electricity demand of an alluvial region, Urir Char, located in southern Bangladesh, was addressed. Hybrid Optimization of Multiple Energy Resources is used for the evaluation and it is supplemented by a fuzzy-logic-based load profile design strategy. In addition to the analysis, a predictive load-shifting-based demand management is also introduced. Several cases were considered for the studies and, after considering several criteria, a grid-tied system comprising a photovoltaic array, wind turbine and energy storage system was found to be the best fit for powering the loads. The suggested system reduces the life-cycle cost by 18.3%, the levelized cost of energy by 61.9% and emissions by 77.2% when compared with the grid-only option. Along with the microgrid design, cooking emissions and energy categorization were also discussed.

Optimal Placement of Electric Vehicle Charging Stations in an Active Distribution Grid with Photovoltaic and Battery Energy Storage System Integration

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.