Battery Energy Storage System Capacity Research Articles

A business-oriented approach for battery energy storage placement in power systems

Battery energy storage systems (BESSs) are gaining increasing importance in the low carbon transformation of power systems. Their deployment in the power grid, however, is currently challenged by the economic viability of BESS projects. To drive the growth of the BESS industry, private, commercial, and institutional investments in large-scale BESS projects are needed. For financiers and investors, choosing an appropriate BESS installation location is a crucial task that requires important considerations. However, so far, studies targeting the BESS placement problem have mainly focused on minimizing operational losses, solving power quality issues, and improving the voltage profile of the system. Implementing such approaches only considers the operational feasibility of BESS at the installation site while ignoring its business feasibility. Therefore, in this paper, we approach the BESS placement problem from a business viewpoint by conducting a stage-level investigation of BESS projects. First, we identify the factors impacting the business feasibility of placement decisions in projects’ construction, connection, operation, and disposal stages and propose cost and time effective measures for making them business friendly. We investigate several factors such as profitability of grid services, integrability of renewable resources, affordability of connection charges, and the usability of BESS capacity. Second, we investigate the business potential of placing BESSs in different grid-levels of the power system and examine BESS connection procedures at those levels. Third, we implement the proposed approach for installing a BESS in the Danish island of Bornholm, for which, we investigate multiple locations of the Bornholm power system.

Multi-objective battery sizing optimisation for renewable energy communities with distribution-level constraints: A prosumer-driven perspective

A Renewable Energy Community (REC) is a legal entity aggregating different users sharing their own resources to reduce both electricity bills and CO2 emissions. This paper presents and analyses the impact of a bi-objective strategy to optimise the capacity of the Battery Energy Storage Systems (BESSs) of REC prosumers equipped with photovoltaic (PV) generators. The optimisation problem is solved through a custom implementation of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and has two contrasting objectives: maximising the self-sufficiency of the REC from the main grid, while minimising the BESS capacity of all REC members. A key novelty of this study is the prosumer-driven perspective, which allows to exclude the REC members who do not want to install a BESS through a linear optimisation constraint. Moreover, the proposed approach ensures that probabilities of over- or under-voltages are compliant with the limits specified by Distribution System Operators (DSOs). Such probabilities, as well as the line and BESS losses, are estimated within the optimisation loop through grid-level simulations performed in OpenDSS. Both a standard peer-to-grid (P2G) and a more REC-oriented peer-to-peer (P2P) energy sharing policy are analysed and their performance is assessed in different seasons and considering both the current energy demand and a possible future scenario, in which electrical heat pumps are widely used. The results of a case study based on a modified version of the IEEE 906-bus European Low Voltage distribution grid show that a if the total BESS capacity assigned to all REC prosumers exceeds a given threshold value, the benefits for the REC become minor. Assuming to choose the optimal BESS capacity solutions corresponding to the threshold value in the summer season (i.e., when PV and BESSs are most exploited), the overall energy losses are reduced roughly by 20%–40% for both P2G and P2P battery controls. The CO2 emissions instead, are reduced by 10% to 50% with the P2P policy having a slight edge over the P2G one. The P2P energy sharing policy spreads the economic benefits of energy savings more evenly among REC members, and the return on investment is generally higher if the electricity demand increases.

A novel algorithm based on the combination of AC-OPF and GA for the optimal sizing and location of DERs into distribution networks

This article proposes an algorithm to obtain an optimal local solution for the network planning process related to the optimal integration of different renewable energy sources (RES) and different Battery Energy Storage Systems (BESS) into a distribution network (DN). The algorithm provides strategic information related to investment and operation costs regarding the type of technology, location, and sizing. The mathematical formulation is based on an AC optimal power flow (OPF) to ensure the network’s minimal stability conditions. Besides, through the use of linearization and a modified version of a genetic algorithm (GA), the algorithm proposed breaks the 24 h wall, used until now in the literature, and extend it to 8760 h, which represents a much more realistic scenario to define the storage and power generation capacity of a DN in a planning context. The algorithm has been tested in a modified version of the IEEE 33-bus considering two cases of study: an off-grid case and grid-connected case, to measure the CapEx and OpEx variability, achieving to show that a grid-connected system reduces the installed capacity of DG and BESS in 37.4% and the CapEx 22.8%. • A novel algorithm is presented for the optimal simultaneous integration of DG and BESS into DN in terms of capacity and location. • A feasible solution for 8760 h as a time horizon, which breaks the 24-hours wall of related literature. • A much more realistic sizing for DG and BESS for distribution network planning is obtained. • The impact of an Off-grid and Grid-connected system on the planning process in terms of CapEx/OpEx.

Energy management of a renewable-based isolated micro-grid by optimal utilization of dump loads and plug-in electric vehicles

This paper proposes a new day-ahead energy management system (EMS) for an off-grid fishing island micro-grid (MG). The MG considered in this work is well equipped with smart grid infrastructure and embedded with plug-in electric vehicles (PEVs). Moreover, it is a green and fossil fuel free MG without any conventional power plant. The MG provides its load demand by solely renewable generations, including wind farms (WF) and photovoltaic (PV) power plants. Therefore, keeping balance between generation and demand is a difficult task in day-ahead operation planning of this MG. In order to overcome this obstacle, the MG uses a mega-scale battery energy storage system (BESS). However, the capacity of the BESS is limited and it is not economically feasible to increase the BESS capacity. Therefore, the MG considers PEVs G2V/V2G operation modes planning and dump loads optimal utilization as supplementary balance options. The main dump loads in this MG are industrial fish refrigerators (IFRs) and reverse osmosis desalination system (RODS). These dump loads in addition to PEVs’ G2V/V2G modes of operation are scheduled in the EMS proposed in this paper. Numerical studies show that the proposed EMS reduces total daily wasted energy and energy not served by 96% and 30% respectively.

Implementation of Distributed Autonomous Control Based Battery Energy Storage System for Frequency Regulation

It has been mandated that 5% of the generation capacity of conventional fossil fuel power plants shall be used exclusively for frequency regulation (FR) purposes in South Korea. However, the rotational speed of generators cannot be controlled quickly, and thus the variation in the power generation for FR takes some time. Even during this short period of time, frequency fluctuations may occur, and the frequency may be out of range of its reference value. In order to overcome the limitations of the existing FR method, 374 MW (103 MWh) battery energy storage systems (BESSs) for FR have been installed and are in operation at 13 sites in South Korea. When designing the capacity of BESS for FR, three key factors, i.e., the deployment time, duration of delivery, and end of delivery, are considered. When these times can be reduced, the required capacity for BESS installation can be decreased, achieving the same operational effects with minimal investment in the facilities. However, because a BESS for FR (FR BESS) needs to be installed under a large capacity, providing a single output, a centralized control method is employed. The centralized control method has the advantage of being able to view and check the entire system at once, although in the case of FR BESS, a novel system design that can optimize the above three factors through a faster and more accurate control is required. Therefore, this paper proposes the implementation of a distributed autonomous control-based BESS for frequency regulation. For the proposed FR BESS, the central control system is responsible for the determination of external factors, e.g., power generation/demand forecasting; and the system is designed such that the optimal control method of renewable energy sources and BESS according to real-time frequency variations during practical operation is determined and operated using a distributed autonomous control method. Furthermore, this study was verified through the simulation that the proposed distributed autonomous control method conducts FR faster than an FR BESS with conventional centralized control, leading to an increase in the FR success rate, and a decrease in the deployment time required (e.g., 200 ms).

Modeling the energy-dynamic modes of a wind farm with a battery energy storage system (BESS)

The article presents the resultsof mathematical modeling of the energy-dynamic processes of a wind farm which includes a battery energy storage system (BESS). The dependence between load capacity and energy generation capabilities of the active set of a wind power plant taking into account the energy capacity of BESS has been determined. A mathematical model of the BESS has been developed. The elaborated model is compared with two other models: a black box module and a model based on equivalent circuit model. The application of the developed model provides an opportunity to optimize the energy capacity of BESS for the specified parameters and modes of operation of the wind power plant. Using the obtained results expands the possibilities of the adequate management of energy-dynamic modes of energy systems with renewable energy sources, provides mitigation of transition processes in conditions of insufficient or excessive wind speeds and consumer loads.

Scheduled Operation of Wind Farm with Battery System Using Deep Reinforcement Learning

With increasing amounts of wind power generation installed, the steep fluctuation of wind power generation output, called ramp events, causes serious problems for power system operation. Controlling fluctuations is an important issue for increasing the amount of wind power generation as a wind farm (WF) in the future. The authors reported the scheduled operation method of WF using a battery energy storage system (BESS) and forecast data of wind power generation output. In this paper, the authors propose a new scheduled operation method of WF. In particular, we propose the application of deep reinforcement learning to decide the output schedule of WF. Moreover, we compare the conventional method, the reinforcement learning method, and the deep reinforcement learning method in terms of the number of ramp events. In addition, we calculate the reducing effect of the storage capacity of BESS. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Application of Battery Energy Storage Systems for Primary Frequency Control in Power Systems with High Renewable Energy Penetration

In power systems, high renewable energy penetration generally results in conventional synchronous generators being displaced. Hence, the power system inertia reduces, thus causing a larger frequency deviation when an imbalance between load and generation occurs, and thus potential system instability. The problem associated with this increase in the system’s dynamic response can be addressed by various means, for example, flywheels, supercapacitors, and battery energy storage systems (BESSs). This paper investigates the application of BESSs for primary frequency control in power systems with very high penetration of renewable energy, and consequently, low levels of synchronous generation. By re-creating a major Australian power system separation event and then subsequently simulating the event under low inertia conditions but with BESSs providing frequency support, it has been demonstrated that a droop-controlled BESS can greatly improve frequency response, producing both faster reaction and smaller frequency deviation. Furthermore, it is shown via detailed investigation how factors such as available battery capacity and droop coefficient impact the system frequency response characteristics, providing guidance on how best to mitigate the impact of future synchronous generator retirements. It is intended that this analysis could be beneficial in determining the optimal BESS capacity and droop value to manage the potential frequency stability risks for a future power system with high renewable energy penetrations.

A Stochastic Planning Model for Battery Energy Storage Systems Coupled with Utility-Scale Solar Photovoltaics

With recent technology advances and price drop, battery energy storage systems (BESSs) are considered as a promising storage technology in power systems. In this paper, a stochastic BESS planning model is introduced, which determines optimal capacity and durations of BESSs to co-locate utility-scale solar photovoltaic (PV) systems in a high-voltage power system under the uncertainties of renewable resources and electric load. The optimization model minimizing total costs aims to obtain at least 20% electric energy from renewable sources, while satisfying all the physical constraints. Furthermore, two-stage stochastic programming is applied to formulate mathematical optimization problem to find out optimal durations and capacity of BESSs. In scheduling BESSs, chronology needs to be considered to represent temporal changes of BESS states; therefore, a scenario generation method to generate random sample paths with 1-h time step is adopted to explicitly represent uncertainty and temporal changes. The proposed mathematical model is applied to a modified IEEE 300-bus system that comprises 300 electric buses and 411 transmission lines. Optimal BESS durations and capacity are compared when different numbers of scenarios are employed to see the sensitivity to the number of scenarios in the model, and “value of stochastic solution” (VSS) is calculated to verify the impacts of inclusion of stochastic parameters. The results show that the building costs and capacity of BESSs increase when the number of scenarios increases from 10 to 30. By inspecting VSSs, it is observed that an explicit representation of stochastic parameters affects the optimal value, and the impacts become larger when the larger number of scenarios are applied.

Loss-Voltage Sensitivity Analysis Based Battery Energy Storage Systems Allocation and Distributed Generation Capacity Upgrade

Distributed Generation (DG) has become a key component in modern power industry due its significant advantages over the traditional power generation methods. Nevertheless, best expected outcomes can only be achieved with optimal allocation of DG resources where inappropriate allocation may impose problems in power system stability, protection and quality. This paper presents analytical approaches for optimizing the DG size, BESS (Battery Energy Storage Systems) capacities and power dispatch in Medium Voltage (MV) networks. Since most of the existing analytical approaches related to optimizing DG sizes for minimizing network losses and voltage deviations have considered individual objective function separately, both parameters may not be minimized simultaneously. Thus, in this paper, an analytical methodology was formulated based on an objective function built on new parameter Loss-Voltage Sensitivity Index (LVSI) that evaluates both minimum impact of network loss and voltage variations for optimizing the DG size. The results obtained from this approach were compared with a conventional Genetic Algorithm (GA) formulated by the authors. The BESS capacities are determined considering the effects of Load Proportionality Factor (LPF), State of Charge limits (SOC) of battery storages, number of load areas and the portion of daily off-peak solar generation period energy consumption expected to be served by each BESS unit. The significance of this BESS capacity determination methodology is highlighted as the BESS capacities can be numerically calculated whereas in existing work, they are heavily relying on conventional optimization techniques which do not give an idea about the internal behavior of parameters which determine the capacities of BESS units. Moreover, an optimal BESS dispatch algorithm is also presented in this paper for minimizing the energy losses and voltage deviations. The applicability of the proposed methodologies are verified using the standard IEEE-33 and IEEE-69 test bus systems. Simulations carried out in MATLAB is used to illustrate the accuracy and the appropriateness of the proposed approaches.

A Multi-Agent Based Optimization Model for Microgrid Operation with Hybrid Method Using Game Theory Strategy

Owing to the increases of energy loads and penetration of renewable energy with variability, it is essential to determine the optimum capacity of the battery energy storage system (BESS) and demand response (DR) within the microgrid (MG). To accomplish the foregoing, this paper proposes an optimal MG operation approach with a hybrid method considering the game theory for a multi-agent system. The hybrid method operation includes both BESS and DR methods. The former is presented to reduce the sum of the MG operation and BESS costs using the game theory, resulting in the optimal capacity of BESS. Similarly, the DR method determines the optimal DR capacity based on the trade-off between the incentive value and capacity. To improve optimization operation, multi-agent guiding particle swarm optimization (MAG-PSO) is implemented by adjusting the best global position and position vector. The results demonstrate that the proposed approach not only affords the most economical decision among agents but also reduces the utilization cost by approximately 8.5%, compared with the base method. Furthermore, it has been revealed that the proposed MAG-PSO algorithm has superiority in terms of solution quality and computational time with respect to other algorithms. Therefore, the optimal hybrid method operation obtains a superior solution with the game theory strategy.

Reduction of Power Imbalances Using Battery Energy Storage System in a Bulk Power System with Extremely Large Photovoltaics Interactions

Power imbalances such as power shortfalls and photovoltaic (PV) curtailments have become a major problem in conventional power systems due to the introduction of renewable energy sources. There can be large power shortfalls and PV curtailments because of PV forecasting errors. These imbalances might increase when installed PV capacity increases. This study proposes a new scheduling method to reduce power shortfalls and PV curtailments in a PV integrated large power system with a battery energy storage system (BESS). The model of the Kanto area, which is about 30% of Japan’s power usage with 60 GW grid capacity, is used in simulations. The effect of large PV power integration of 50 GW and 100 GW together with large BESS capacity of 100 GWh and 200 GWh has been studied. Mixed integer linear programming technique is used to calculate generator unit commitment and BESS charging and discharging schedules. The simulation results are shown for two months with high and low solar irradiance, which include days with large PV over forecast and under forecast errors. The results reveal that the proposed method eliminates power shortfalls by 100% with the BESS and reduce the PV curtailments by 69.5% and 95.2% for the months with high and low solar irradiance, respectively, when 200 GWh BESS and 100 GW PV power generation are installed.

Multiobjective nested optimization framework for simultaneous integration of multiple photovoltaic and battery energy storage systems in distribution networks

The rapid growth of renewables in modern distribution networks results in the spilling of energy due to the limited hosting capacity of these networks, violation of system constraints, reduced network efficiency, and improper utilization of resources. Battery energy storage system (BESS), in spite of its high cost, a shorter life and complex control, offers a flexible solution for the problem. In this paper, a multiobjective nested optimization framework is developed for the simultaneous optimal allocation of multiple solar photovoltaics (SPVs) and BESSs in the distribution networks. The framework involves a two-layered structure; the outer layer provides tentative planning solutions to the inner layer that optimizes the desired objectives of network operations and then returns the functional values back to the outer layer. The purpose of the inner-layer is to satisfy the operational constraints of the networks and ensure the optimal utilization of BESS capacities, suggested by the outer layer, at the time of planning itself. A new BESS operating strategy is proposed for optimum utilization of BESS. The nested multiobjective optimization problem is handled by suggesting a new weighted sum approach in conjunction with a recently developed swarm intelligence-based algorithm, i.e. moth search optimization. Overall, the proposed deterministic model essentially ensures the high penetration of SPVs and the optimal utilization of BESSs to justify their installation. The optimization model is investigated on a benchmark 33-bus test distribution network. The application results highlight enhanced energy efficiency, peak load shaving, high renewable penetration, voltage profile improvement, and mitigation of reverse power flow while effectively absorbing the excess renewable power generation during light load hours.

Optimal Design Model for a Residential PV Storage System an Application to the Spanish Case

Self-consumption of photovoltaic energy is being promoted as an effective way for energy consumption in residential households. The European Directive 944/2019 promotes the use of green energy and battery energy storage systems (BESS) for self-consumption and, in Spain, the 244/2019 Royal Decree of the Spanish electrical regulatory framework allows the self-consumption of energy with a photovoltaic (PV) facility for residential use, as well as the injection of the surplus energy into the grid for which compensation will be received. At the same time, new developments in PV and BESS technologies reduce the costs of facilities, a fact that can increase the profitability of self-consumption through PV energy. This study evaluates the profitability of a household PV facility with BESS using a model based on real market prices, hourly data from user smart meters, and their own location; especially, the model gives the best configuration of PV panels power and BESS capacity. The financial indicators taken as reference for the results and conclusions are the Net Present Value (NPV), Internal Rate of Return (IRR), and Investment Return (IR). Our method examines also the effect of the BESS and PV panel costs on the profitability of the facility. Unlike other studies, our model is based on actual (not simulated) demand and price data, and it can be easily extended to other locations and market prices.

State-of-Charge Balancing Control for Optimal Cell Utilisation of a Grid-Scale Three-Phase Battery Energy Storage System Using Hybrid Modular Multilevel Converter Topology Without Redundant Cells

Cell state-of-charge (SoC) balancing within each branch of a three-phase battery energy storage system (BESS) and among three branches is crucial to overcome the inability to fully utilise the available capacity of a three-phase BESS. The proposed topology is constructed with one branch instead of three branches to take advantage of its idle cells/modules (Ms) (one-third of the total cells/ Ms ) and to eliminate the need of SoC balancing among the branches. Contrary to conventional topologies, idle cells/ Ms can serve as redundant cells/ Ms or can be dropped out of a BESS, thereby leading to a reduction in the cost, control complexity, size, and losses of a BESS. A novel SoC balancing strategy for the proposed topology of a three-phase BESS is introduced in this paper. Moreover, the cell/ M activation algorithm is implemented to minimise the duration needed to activate the cells/ Ms required to generate voltage for the phases, thereby leading to an improvement of battery operational efficiency. Based on the simulation results, SoC balancing among 3996 cells, 2664 cells, 333/222 Ms , and 12 cells in M with the lowest and the highest average SoC is achieved in 53 min, 48 min, 38 min, 18 min and 53 min, respectively.

Research on Improving Transient Stability of Power System with Distributed BESS and its Coordinated Control Method

The feasibility of battery energy storage system (BESS) in ameliorating power system transient stability has been proved. However, the required BESS capacity is large, which leads to high price. Meanwhile, the improving effect is significantly affected by fault location and BESS location. To address the problem, a distributed configuration of BESS, with a coordinated control method, is proposed in this paper. Firstly, an independent double-closed loop decoupling controller is designed for each BESS, so that the BESS unit can realize decentralized control according to its local correlation information. Secondly, particle swarm optimization is applied to optimize the control parameters of the distributed controllers to achieve coordinated control. Simulation results show that the distributed BESS controllers optimized by particle swarm optimization can better coordinate with each other, and the transient stability of the system is significantly improved.

Moving Regression Filtering With Battery State of Charge Feedback Control for Solar PV Firming and Ramp Rate Curtailment

The surge in energy demand needs to be met with environmentally pleasant resources to reduce the production of greenhouse gases. Solar Photovoltaic (PV) power is a widespread choice as it is accessible in plenty and is comparatively inexpensive. However, the large-scale penetration of intermittent PV power causes multiple variabilities in the grid such as frequency issues and voltage deviations. To counteract these instabilities, Battery Energy Storage System (BESS) is integrated into the grid as it reduces the PV fluctuations and promotes optimal operation. Nevertheless, storage systems are expensive, and thus smoothing filters are coupled with the BESS for cost reduction and power smoothing. Formerly, traditional filters such as Low Pass Filters (LPF), Moving Average (MA), and Moving Median (MM) filters have been proposed for power smoothing. However, these filters have inadequate power tracking capabilities particularly with longer window sizes (W.S) and time constants, which subsequently depreciates the storage system performance. To compensate for the delayed power tracking, larger energy storage systems are required which in turn adds to the overall operational costs. This paper proposes the Moving Regression (MR) filter combined with state of charge (SoC) feedback control for solar PV variability reduction, reduced time delay, decreased battery charging/discharging power, and ramp rate. Simulation results attest that the MR filter achieves better solar power smoothing without increasing the BESS capacity. Also, the performance of the MR filter is less affected with the increase in window sizes. Additionally, the execution of the introduced smoothing filter was found to be superior when assessed against the LPF, MA, MM, Savitsky-Golay (SG), and the Gaussian filter. In comparison to the SG (W.S = 53), the MR (W.S = 45) filter reduces the battery charging/discharging power by approximately 30.48% and as opposed to the LPF (T.C = 48), the peak SoC is reduced by 19.1%.

Analysis on Operation Modes of Residential BESS with Balcony-PV for Apartment Houses in Korea

The integration of battery energy storage systems (BESS) with renewable energy is a potential solution to address the disadvantages of renewable energy systems, which is irregular and intermittent power. In particular, residential BESS is advancing in numerous countries. The residential BESS connected to the photovoltaic system (PV) can store the PV power in the battery through charging, and supply the PV power, which was stored in the battery, to the load through discharging when there is no PV power. Therefore, the utilization of residential BESS with PV reduces the daily electric power consumption and the electricity bills that households have to charge. However, it is understood that there is no case of installing and using residential BESS in Korea yet. Most residential houses in Korea are apartment houses, and thus residential BESS can be used with balcony PV. This paper presents operation modes of residential BESS with balcony PV for apartment houses. The BESS capacity was estimated by considering the balcony PV capacity, which can be installed in households, and power consumption. The applicability of the residential BESS was analyzed through performance and economics evaluation under current and various conditions. The operation modes of BESS were divided into four types according to PV power supply priority and battery charging source, and a test took place in a demonstration house. The risk of fully discharging the battery has been discovered when PV power is first charged to the battery or when only PV power is charged with the battery. As a result, preferential charging of the battery with PV power and then with PV and grid power was found to be the most optimal operation mode. In addition, additional functions were proposed for residential BESS in apartment households. The results will contribute to effective application of residential BESS with balcony PV in the near future.

Determination of optimal battery energy storage system capacity for commercial electricity consumers with photovoltaic systems by integrating battery aging and PV forecast uncertainty

A battery energy storage system (BESS) is a promising technology to augment the benefits provided by photovoltaic (PV) power generation. This study proposes a method to evaluate the optimal capacity of a BESS for commercial electricity consumers with PV systems. This method considers the aging effect of a BESS and the PV output uncertainty through linear programming and a real‐time control method. The benefit of BESS installation is assessed through cost–benefit analysis by using the net present value (NPV) in the determination of the optimal BESS capacity. Subsequently, the method is applied to the numerical simulation of a commercial load. In the numerical simulation, an optimal capacity is demonstrated at which the benefits of the BESS are maximum, especially the NPV. In addition, we highlight the impact of PV output uncertainty on the benefits of the BESS and verify the effectiveness of the proposed real‐time control method in mitigating this impact. The importance of considering the degradation of a BESS in the evaluation of the optimal capacity is also discussed. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Power system stability improvement using Battery Energy Storage System (BESS) in isolated grid

Rural areas and islands distant from main power grids mostly rely on diesel fuel for electricity generations, which assembles an isolated network. In eastern Indonesia, these isolated grids began to hybridize with solar photovoltaic (PV) systems to reduce the generation costs. However, the penetration of solar PV systems has risen one crucial problem related to the power system stability due to its output intermittency and variability. The Battery Energy Storage System (BESS) can be utilized as a frequency regulator so that the system stability can be maintained well. Besides, BESS can also function as an energy arbitrage so that the target of generation costs saving can also be achieved. This study analyzes the effectiveness of BESS utilization to reduce the stability issues of an isolated grid consisting of diesel generators and PV systems in eastern Indonesia by using PowerFactory software. The scenario of the daily operating pattern of each component is simulated as well by considering the optimal solar PV system and BESS capacity.