Battery Energy Storage System Capacity Research Articles

Intelligent Control of Battery Storage for Resiliency Enhancement of Distribution System

Natural disasters and accidents have underscored the need for operative solutions that can enhance the resiliency of power distribution network (PDN). This article proposes novel methods of scheduling of battery energy storage system (BESS) and loads of microgrid to enhance the resiliency of PDN. A mixed-integer linear programming model is developed to minimize the energy mismatch between available resources and critical loads during outage period. The proposed model is aimed at minimizing the shedding of high critical loads (hcl) and maximizing the serving time of hcl during outages. In order to increase the longevity of the load serving, the fuzzy-based approach is developed to control the C_rate of the BESS. Moreover, to increase the resiliency of hcl, partitioning of BESS capacity is proposed, which provides more flexibility for charging/discharging control of the BESS. The concept of variable dispatch interval is introduced to reduce the frequent switching of loads. By considering the reactive power, an apparent power resiliency metric is proposed to quantify the resiliency of PDN. The IEEE 33-bus network is tested under various restoration periods in order to verify the performance of the proposed methodology in the MATLAB environment.

Wind Energy Resource Prediction and Optimal Storage Sizing to Guarantee Dispatchability: A Case Study in the Kenyan Power Grid

Kenya is experiencing a fast increase in grid-connected intermittent renewable energy sources (RESs) to meet its increased power demand, and at the same time be able to fulfill its Paris Agreement obligations of abating greenhouse gas emissions. For instance, Kenya has 102 MW of grid-tied solar power and 410 MW of grid-tied wind power. However, these sources are very intermittent with low predictability. Thus, after their installation and integration into the grid, they impose a new challenge for the secure, reliable, and economic operation of the system. To mitigate these and to ensure proper planning of the system operations, accurate and faster prediction of the generation output of the wind energy resources and optimal design and sizing of storage for the large-scale wind energy integration into the grid are of paramount importance. Artificial intelligence (AI) and metaheuristic techniques have proven to be efficient and robust in offering solutions to complex nonlinear prediction and optimization problems. Therefore, this study aims to utilize backpropagation neural network (BPNN) algorithm to conduct hourly prediction of the generation output of Lake Turkana Wind Power Plant (LTWPP), a 310 MW plant connected to the Kenyan power grid, and optimally size its battery energy storage system (BESS) using genetic algorithm (GA) to guarantee its dispatchability. The historical weather data, namely wind speed, ambient temperature, relative humidity, wind direction, and generation output from LTWPP, are employed in the training, testing, and validation of the neural network. LTWPP and BESS are modelled in MATLAB R2016a software. Thereafter, the developed BPNN and GA algorithms are applied to the modelled systems to predict the wind output and optimize the storage system, respectively. BESS optimization with neural prediction reduces the BESS capacity and investment costs by 59.82%, while the overall dispatchability of LTWPP is increased from 73.36% to 90.14%, hence enabling the farm to meet its allowable loss of power supply probability (LPSP) index of 0.1 while guaranteeing its dispatchability.

Placement and capacity selection of battery energy storage system in the distributed generation integrated distribution network based on improved NSGA-II optimization

The scalability of distributed generation (DG) dominated by clean energy in the distribution network is continuously increasing. Increased grid integration of DGs has aggravated the uncertainty of distribution network (DN) operation, which affects the power losses and voltage fluctuations. The battery energy storage system (BESS), as an essential part of the distribution grid, its appropriate placement and capacity selection can improve the power quality and bring economic benefits for the DGs integrated DN (DGDN). In this paper, the placement and capacity selection of BESS in the DGDN is investigated including: (1) The relationship between the placement and capacity selection of BESS with the power losses and line voltages of the DN is derived. The impact of the placement and capacity selection of BESS on the power losses and voltage fluctuations of the DGDN is analyzed. (2) In order to achieve the objectives of a minimum of power losses, voltage fluctuations and BESS capacity in the DGDN, a multi-objective model with optimized placement and capacity of the BESS is established. (3) In order to ensure the globality and uniformity of Pareto solution set, an improved algorithm based on non-dominated sorting genetic algorithm-II (NSGA-II) is proposed to optimize the process of traditional NSGA-II optimization. By adding the selection coefficient of population optimal solution and the congestion distance update function, the population dispersion is expanded and the global searching capability of the algorithm is enhanced. (4) Based on the improved NSGA-II optimization, an optimal scheme of the placement and capacity of the BESS is selected based on information entropy weight (IEW)-analytic hierarchy process (AHP). Taking an IEEE 33-node DGDN as an example, the proposed algorithm is compared with the traditional NSGA-II optimization and the multi-objective particle swarm optimization (MOPSO) algorithm to verify the effectiveness and superiority of the BESS optimal placement and capacity method based on the improved NSGA-II optimization.

PV Self-Consumption and Self-Sufficiency for Household and Office Users: The Lockdown Effects During the COVID-19 Pandemic

The lockdown period due to the COVID-19 pandemic and the consequent adoption of homeworking had effects also on the energy sector, by shifting electrical consumption from tertiary to residential sector. This article analyzes the electric load curves in the office and at home for a group of employees, estimating the change of the Self-Consumption (SC) and the Self-Sufficiency (SS) when PV plants are installed, during the two months of the lockdown period (2020) with respect to the previous year. This investigation can help to explore the impact of homeworking, which is now usually adopted by many employees. For this purpose, the impact of different PV sizes on SC and SS rates, and the improvements due to the adoption of Battery Energy Storage Systems (BESS), are analyzed. Sizing the PV production equal to the annual consumption and the BESS capacity equal to the difference between the average PV production and the average consumption, the annual SC (=SS in this case) increases from 35% to 68% in households and from 50% to 71% in the office building. The lockdown restrictions increase and decrease these rates of about 5-7 points in households and office building respectively.

Performance Evaluation of a BESS Unit for Black Start and Seamless Islanding Operation

The main purpose of this paper is to evaluate the overall performance of a battery energy storage system (BESS) during (I) grid-connected, (II) black start, and (III) islanded operating modes. To do so, firstly, a novel three-mode controller is proposed and developed. The proportional–integral–derivative (PID) controller is implemented, including the following three components: (1) inertia emulation, (2) frequency-active power and voltage-reactive power droops, and (3) secondary frequency and voltage controllers. Secondly, to effectively evaluate the proposed controller performance under various grid operating conditions during both black start and seamless transition to islanded operation, a set of comprehensive dynamic simulations using Matlab/Simulink is carried out. To this end, the sensitivity analyses on numerous grid operating parameters, such as pre-disturbance grid power, total installed BESS capacity, battery state of charge, unbalanced three-phase load flows, implemented power-frequency controller parameters, and distribution network types with various shares of dynamic and static loads, are performed. Thirdly, to practically improve the seamless transition performance enabling the demand response participation, a fast-controlled thermostatic load scheme is implemented. Simulation results show that the BESS unit using the proposed three-mode controller has great potential to successfully control the frequency and voltage within allowable limits during both islanding and black start modes over a wide range of grid operating conditions.

A simple and effective methodology for sizing electrical energy storage (EES) systems based on energy balance

The use of the electrical energy storage (EES) plays an important role in the transition of energy generation towards renewable energy sources (RESs). An effective sizing of EES systems is very important in order to cope with the volatility of RESs and to ensure a reliable energy supply. The present paper provides a methodology which helps to determine the minimum required EES size for conceiving a fully standalone system. Its approach is based on the evaluation of the energy balance for a given design period, and it can also be applied for sizing the EES system in grid-connected applications. The methodology was validated using measurement data obtained from two different systems corresponding to: a) a near-zero energy building with local generation sources, and b) a large-scale battery energy storage system (BESS) installed in a factory and used for peak-shaving. The obtained results confirmed the effectiveness of the proposed methodology by estimating the required size of the EES system. A good correlation was found between the estimated and the installed BESS size in the considered systems. The deviation between real and estimated BESS capacities was found to be less than 5%.

Feasibility Investigation for Residential Battery Sizing Considering EV Charging Demand

Photovoltaic (PV) systems along with battery energy storage systems (BESS) are an increasing trend for residential users due to the increasing cost of energy and environmental factors. Future sustainable grids will also have electric vehicles (EVs) integrated into these residential microgrids. However, this large-scale deployment of EVs and PV systems could mean several problems in terms of power quality, hosting capacity and as well economic implications. This paper aims to provide input to more optimal design and management of domestic PV and BESS for residential users with EVs. In this work, a measurement-based data set from a low-voltage distribution network in a rural area has been used. Investigation sees different household and PV-EV penetration levels to propose the BESS capacity and use cases. An economic analysis has been performed to check the feasibility of the proposed systems. The payback period is found to be between 13 to 15 years of the proposed systems.

Optimal participation of price-maker battery energy storage systems in energy and ancillary services markets considering degradation cost

This paper proposes a bi-level optimization framework to investigate the optimal market operation strategies of price-maker battery energy storage systems (BESSs) in real-time energy, spinning reserve, and pay as performance regulation markets, with a special focus on understanding BESS’s excessive regulation market participation observed by several system operators and the impact of battery degradation cost on BESS market activities. An accurate battery degradation cost function is integrated into the BESS’s strategic bidding model. An automatic generation control (AGC) signal dispatch model is proposed to deploy AGC signals in the bi-level framework. This enables thorough studies for BESS’s operating characteristics in the frequency regulation market, when both battery degradation and detailed AGC signal following activities are considered. Case studies on a synthetic system with real-world data are performed to study interactions between BESS profit maximization and wholesale market operations, considering system annual load and ancillary service requirements variations. The impacts of BESS capacity and replacement cost on its revenue from energy and ancillary services markets are also investigated.

A predictive and adaptive control strategy to optimize the management of integrated energy systems in buildings

The management of integrated energy systems in buildings is a challenging task that classical control approaches usually fail to address. The present paper analyzes the effect of the implementation of a reinforcement learning-based control strategy in an office building characterized by integrated energy systems with on-site electricity generation and storage technologies. The objective of the proposed controller is to minimize the operational cost to meet the cooling demand exploiting thermal energy storage and battery system considering a time-of-use electricity price schedule and local PV production. Two control solutions, a Soft-Actor-Critic agent coupled with a rule-based controller, and a fully rule-based control strategy, used as a baseline, are tested and compared considering various configurations of battery energy storage system capacities, and thermal energy storage sizes. Results show that the proposed control strategy leads to a reduction of operational energy costs respect to the fully rule-based control ranging from 39.5% and 84.3% among different configurations. Moreover the advanced control strategy improves the on-site PV utilization leading to an average increasing of self-sufficiency and self-consumption of 40% among different scenarios. The baseline control strategy results more sensitive to the size of storage whereas the proposed control achieves high savings also when smaller capacities of battery energy storage systems and sizes of thermal energy storage are implemented. The outcomes of the work prove the impact of implementation of advanced control as a way to optimize energy costs with a comprehensive view of the whole integrated energy system considering both thermal and electrical energy storage operation.

Reliability Assessment of Renewable Power Systems Considering Thermally-Induced Incidents of Large-Scale Battery Energy Storage

The battery energy storage system (BESS) has been envisaged as an effective solution for renewable energy accommodation in power systems. However, the residual capacity and maximum power of large-scale BESS are highly affected by thermally-induced incidents such as battery degradation and Thermal Runaway (TR) propagation. In the prior-art studies, the impacts of thermally-induced incidents on the BESS service performance have not been well modeled, resulting in relatively over-optimistic reliability estimation of power systems. In this paper, the reliability of large-scale grid-connected BESSs as well as its impacts on the overall reliability of power systems are investigated considering the battery degradation and TR propagation. To quantify the time-varying performance of the BESS, a multi-state model is constructed. The proposed model describes the aging process of batteries inside the BESS, incorporating the combined effects of sequential TR and the performance degradation of the surrounding batteries due to heat absorption. Based on the Monte Carlo method, scenarios that reflect the uncertainties of the intermittent wind generation and fluctuating loads are simulated. An optimal scheduling model is deployed, and a solution algorithm is proposed to calculate the scheduling results of the BESS in the real-time performance range subject to its thermal conditions. Case studies are conducted to validate the effectiveness of the proposed model and technique.

Optimal Operation Approach With Combined BESS Sizing and PV Generation in Microgrid

Photovoltaic (PV) energy generation in microgrids (MGs) is increasing. Battery energy storage systems (BESSs) reduce the fluctuations in PV outputs caused by the intermittent availability of solar energy. Although BESSs are advantageous for stable MG operation, they are still relatively expensive. By remaining within the operational limits during normal and contingency operation, optimal sizing of BESS is required to maintain security considering cost of MG system. This paper proposes a BESS sizing optimization approach for MGs by solving the security constrained optimal power flow (SCOPF), considering the stochastic errors in forecasting the PV outputs. The degree of compensation for the solar energy forecasting error is firstly configured. To address these errors, the combined PV and BESS operation system is modeled by applying a control strategy to smooth PV fluctuations and minimize battery life degradation. BESS sizing optimization, under a certain degree of compensation, minimizes the PV penalty cost and BESS operation cost. The optimal BESS capacity and schedule are then obtained for the MG. To enhance the convergence and computational efficiency, decomposed-probabilistic security constrained optimal power flow (D-PSCOPF) is proposed. It efficiently solves the problem by dividing it into a master problem and a slave problem. The base case solution is computed in the slave problem, which induces the partial optimal size of BESS. By adding the feasibility cut through the violation of the slave problem, the master problem derives the optimal BESS capacity. Different case studies were analyzed, confirming the superiority and computational efficiency of the proposed approach.

Hybrid Interval-Robust Adaptive Battery Energy Storage System Dispatch With SoC Interval Management for Unbalanced Microgrids

Battery energy storage systems (BESSs) have been widely deployed in microgrids to deal with uncertain output power of renewable distributed generation (DG) and improve renewable energy utilization efficiency. However, due to the short-term dispatch mode and BESS capacity limitation, current BESS dispatch decisions may not be efficient from a whole-day perspective, leading to an inadequate/excessive state of charge (SoC) for subsequent dispatch. To address this issue, an adaptive BESS dispatch method with SoC interval management is proposed for unbalanced three-phase microgrids, aiming to minimize the operating cost. In a day-ahead stage, an SoC interval is optimized considering the uncertainties of renewable DG, loads and market prices. During the day, the BESS is dispatched inside the SoC interval with a rolling horizon procedure to track uncertainty realization. Accordingly, a hybrid interval-robust optimization method is developed to solve the proposed scheduling problem under the uncertainties and procure the optimal SoC interval. Particularly, a decomposing and alternating solution algorithm is developed to identify the pessimistic case based on robust optimization while the SoC interval is determined on the basis of interval optimization. Simulation results verify high efficiency and solution robustness of the proposed hybrid interval-robust adaptive BESS scheduling method.

Energy storage capacity allocation for distribution grid applications considering the influence of ambient temperature

Modern distribution networks have an urgent need to increase the accommodation level of renewable energies facilitated by configuring battery energy storage systems (BESSs). In view of the contradictions of BESS capacity, cost, life, and operation environment, an optimal capacity allocation algorithm of BESS in modern distribution networks considering the ambient temperature has been proposed from the perspective of the life cycle cost-benefit optimisation. First, a refined BESS model under the influence of the ambient temperature has been established to accurately track life degradation and charging-discharging efficiencies. Subsequently, an electric vehicle (EV) charging load forecasting model that considers the influence of travel characteristics, traffic congestion and environmental factors has been established. Finally, a bi-level BESS optimisation algorithm has been proposed to maximise the system benefits on the EV charging station side and the grid side, and the marginal ageing cost has been considered while optimising the charging and discharging strategies of BESS, to achieve the best cost-benefit of BESS throughout the life cycle. The simulation results verify the effectiveness and economy of the proposed configuration schemes and operation strategies under different seasons.

Оптимальне завантаження генеруючих потужностей енергосистеми за умови експлуатації сонячних електростанцій із системами акумулювання

Due to the increase in the share of electricity production at solar photovoltaic power plants (PV-plants), with a decrease in consumption and low flexibility of the power system, forced dispatch restrictions of generating capacities are already occurring. The problem of the emergence of a power surplus with an increase in the capacity of PV-plant can be solved by introducing a battery energy storage system (BESS) into its structure. The purpose of this study is to develop a mathematical model for the joint operation of a PV-plant and BESS for the formation of characteristic hourly daily power profiles for the supply of electrical energy to the power system. With the subsequent assessment of the impact of such hybrid PV-plants on changing the loading modes of traditional generating capacities in the Integrated power system of Ukraine. The proposed mathematical model for the joint operation of a PV-plant and BESS is, in fact, an algorithm for controlling such a hybrid PV-plant. The main principle of which is that during the period of maximum solar irradiation, the batteries are charged to the maximum possible level, which corresponds to the available capacity, and if the volume of BESS produced at the PV-plant is less than the available capacity, then all the generated electrical energy is accumulated for further discharge. Thus, the main modes of joint operation of a PV-plant and BESS are distinguished: mode No. 1 – when all PV generated electrical energy is charged with subsequent discharge; mode No. 2 – only a part of PV generated electrical energy is transferred in time, and the other part is supplied to the power system directly. Based on the developed mathematical model, daily profiles were formed for the joint operation of a PV-plant and BESS for a whole year. The analysis of the obtained daily profiles for the whole year showed, that for a day with maximum solar irradiation, in order to transfer 20% of PV-plants generated electrical energy, the discharge power of the BESS should be about 37% of the installed capacity of the inverters of the PV-plants. Thus, for 2040, with the installed capacity of PV-plants at the level of 11 GW, the total capacity of BESS should be 4 GW, and their charging capacity – 16 GWh. Using a mathematical programming model, determining the optimal structure and loading of power units of the power system when covering the daily schedule of electrical loads for each day of the year, it was determined that the introduction of BESS in PV-plants affects the performance of the power system as a whole. The transfer of 20% of the peak generation capacity of PV-plants with an installed capacity of 11 GW at the level of 2040 leads to: an increase in the production of electricity from nuclear power plants by 8% with an increase in the number of power units with an installed capacity of 1000 MW; the volume of electricity production at coal-fired thermal power plants is reduced by 20%; the generation volumes of pumped storage power plants are reduced by 4.5% and the discharge volumes of the system-scale storage systems are reduced by 57%. Reducing coal consumption by 19% leads to a 15‒19% reduction in emissions of carbon dioxide, sulfur oxides, nitrogen oxides and dust, which are important results for achieving environmental goals of Ukraine. Keywords: structure of generating capacities, power system, PV-plant, battery energy storage system, mathematical model

A robust game-theoretic optimization model for battery energy storage in multi-microgrids by considering of renewable based DGs uncertainty

With the development of electricity market, in the future, various stakeholders such as battery energy storage system (BESS), multi-microgrid (MMG), photovoltaic (PV), and wind turbine (WT) clusters, can trade with the distribution network or each other to meet their power balance needs and to maximizing their profits. In order to predict the charging and discharging power of the battery, a Markov Decision Process (MDP) model of random variables is established. Then, a robust game-theoretic optimization (GO) model is established for MMG's economical operation by considering the renewable based DGs uncertainty. And suggestions for battery operation modes under different compensation coefficients are given. In addition, BESS capacity and demand response (DR) for each microgrid are considered in this proposed model, and an uncertainty adjustment parameter is introduced to adjust the level of conservatism of the robust solution against the modeled uncertainty. In order to search the robust Nash equilibrium solution, this paper proposes a two-level game model based on game theory to study the operation strategy of BESS in the distribution network by considering of renewable based DGs uncertainty. This model is decomposed into a master problem (1st level) and a sub-problem (2nd level), which are solved based on the column and constraint generation algorithm , particle swarm optimization (PSO) and MDP model for random variables of BESS. Simulation result shows that the proposed approach has a well performance in tackling the uncertainty of renewable based distributed energy generation.

The Impact of Aging-Preventive Algorithms on BESS Sizing under AGC Performance Standards

It is normally accepted that Battery Energy Storage Systems improve frequency regulation by providing fast response to the Automatic Generation Control. However, currently available control strategies may lead to early Energy Storage Systems aging given that Automatic Generation Control requirements are increasing due to zero carbon power generation integration. In this sense, it is important to analyze the aging phenomena in order to assess the technical–economical usefulness of Battery Energy Storage Systems towards zero carbon power systems. In order to avoid early aging, various proposals on aging-reducing algorithms can be found; however, it is unclear if those aging-reducing algorithms affect the performance of Battery Energy Storage Systems. It is also unclear whether those effects must be internalized to properly dimension the capacity of Battery Energy Storage Systems to both comply with performance standards and to prevent early aging. Thus, this paper estimates the storage capacity of a Battery Energy Storage Systems to comply with Automatic Generation Control performance standard under aging-reducing operating algorithms by dynamics simulations of a reduced-order, empirically-validated model of the Electric Reliability Council of Texas. The results show the relationship between the required performance of Automatic Generation Control and Battery Energy Storage System capacity, considering a 1-year simulation of Automatic Generation Control dynamics. It can be concluded that the compliance with performance standards is strongly related to the storage capacity, regardless of how fast the device can inject or withdraw power from the grid. Previous results in the state-of-the-art overlook the quantification of this relationship between compliance with performance standards and storage capacity.

Configuration method of BESS in the wind farm and photovoltaic plant considering active and reactive power coordinated optimization.

To promote the coordinated development between renewable energy and the distribution network, a capacity allocation model of battery energy storage systems (BESS) is proposed to achieve the coordinated optimization for active and reactive power flow, which can reduce the voltage deviation and improve the absorptive capacity for renewable energy. In addition, BESS with four-quadrant operation characteristics, on-load tap changer, and capacitor banks are treated as flexible devices to improve the adaptability for renewable energy fluctuations. In view of the uncertainties of renewable energy caused by the inaccuracy of historical sample data, a set of extreme scenarios with the characteristics of temporal and spatial correlation are considered to obtain a robust BESS configuration decision. The big-M approach and the second-order conic relaxation technique are utilized to convert the BESS capacity allocation model into a mixed-integer linear programming problem. Finally, the IEEE 33-node distribution system is taken as an example to verify the effectiveness of the proposed method.

Optimal Configuration with Capacity Analysis of PV-Plus-BESS for Behind-the-Meter Application

As the cost of photovoltaic (PV) systems and battery energy storage systems (BESS) decreases, PV-plus-BESS applied to behind-the-meter (BTM) market has grown rapidly in recent years. With user time of use rates (TOU) for charging and discharging schedule, it can effectively reduce the electricity expense of users. This research uses the contract capacity of an actual industrial user of 7.5 MW as a research case, and simulates a PV/BESS techno-economic scheme through the HOMER Grid software. Under the condition that the electricity demand is met and the PV power generation is fully used, the aim is to find the most economical PV/BESS capacity allocation and optimal contract capacity scheme. According to the load demand and the electricity price, the analysis shows that the PV system capacity is 8.25 MWp, the BESS capacity is 1.25 MW/3.195 MWh, and the contract capacity can be reduced to 6 MW. The benefits for the economical solution are compared as follows: 20-year project benefit, levelized cost of energy (LCOE), the net present cost (NPC), the internal rate of return (IRR), the return on investment (ROI), discounted payback, total electricity savings, renewable fraction (RF), and the excess electricity fraction. Finally, the sensitivity analysis of the global horizontal irradiation, electricity price, key component cost, and real interest rate will be carried out with the most economical solution by analyzing the impacts and evaluating the economic evaluation indicators. The analysis method of this research can be applied to other utility users to program the economic benefit evaluation of PV/BESS, especially an example for Taiwan’s electricity prices at low levels in the world.

Comprehensive Design of DC Shipboard Power Systems for Pure Electric Propulsion Ship Based on Battery Energy Storage System

With the strengthening of international environmental regulations, many studies on the integrated electric propulsion systems applicable to eco-friendly ship are being conducted. However, few studies have been performed to establish a guide line for the overall pure electric propulsion ship design. Therefore, this paper introduces the comprehensive design of DC shipboard power system for pure electric propulsion ship based on battery energy storage system (BESS). To design and configure the pure electric propulsion ship, 2 MW propulsion car ferry was assumed and adopted to be the target vessel in this paper. In order to design the overall system, a series of design processes, such as the decision of the ship operation profile, BESS capacity selection, configuration of the power conversion systems for propulsion, battery charging/discharging procedures, classification of system operation modes, and analysis of the efficiency, were considered. The proposed efficient design and analysis of the pure electric propulsion ship was qualitatively and quantitatively validated by MATLAB Simulink tool. The methodology presented in this paper can help design real ships before the system commissioning.

태양광 발전의 출력 예측 불확실성 및 간헐성 보상 방법에 관한 연구

Variable Renewable Energy(VRE) is impossible to maintain stable output due to uncertainty and intermittency. In this paper, Photovoltaic(PV) output is predicted through deep learning prediction technology and compared with the actual PV output. The data used as the input of the deep learning prediction model was partially selected through correlation analysis to reduce overfitting and to have high prediction performance. A Hyperparameter optimization model was used in several deep learning prediction models and the Recurrent Neural Network(RNN) prediction model was selected after comparing the performances. The difference between Predicted PV output and actual PV is compensated using Battery Energy Storage System(BESS). Moreover, the BESS capacity is calculated and the BESS State of Charge (SoC) range profile is observed.