Size Of Battery Energy Storage System Research Articles

Techno-economic simulation and sensitivity analysis of modular cogeneration with organic rankine cycle and battery energy storage system for enhanced energy performance

This paper presents a comprehensive evaluation of the efficiency and cost implications of a modular cogeneration plant integrated with a Battery Energy Storage System (BESS) and an Organic Rankine Cycle (ORC) plant. The objective is to assess the performance of the system when waste heat recovery through the ORC cycle is added to the exhaust line and compare it with the original system. Techno-economic parameters were analyzed for two different configurations: one with the maximum BESS size and another with the maximum Primary Energy Savings (PES).The results demonstrate that the incorporation of the ORC plant yields several notable outcomes. While there is a slight increase in the Simple Payback Period (SPB) and Net Present Value (NPV) in both configurations, the magnitudes of these increases are relatively low. In contrast, the introduction of the ORC cycle leads to substantial reductions in carbon dioxide (CO2) emissions and significant improvements in Primary Energy Savings (PES). For the Max PES configuration, a remarkable 25% reduction in CO2 emissions and a substantial 22% improvement in PES were observed. To further investigate the system's response to varying economic conditions, a sensitivity analysis was performed. The analysis considered the effects of changes in the cost of battery, Combined Heat and Power (CHP) unit, ORC components, electricity, and fuel on the system's Simple Payback Period (SPB) and Net Present Value (NPV). The results revealed that the sensitivity of the Max PES configuration to economic parameters was higher compared to the Max BESS system.These findings highlight the considerable advantages of integrating an ORC plant into a cogeneration system with a BESS. The significant reduction in CO2 emissions and the noteworthy improvement in Primary Energy Savings (PES) outweigh the relatively minor negative impact on the Simple Payback Period (SPB) and Net Present Value (NPV). This integrated configuration demonstrates both environmental benefits and energy efficiency improvements. The results provide valuable insights for the design, optimization, and decision-making processes involved in the implementation of cogeneration systems with energy storage, promoting sustainable and cost-effective solutions.

Assessing the economic viability of BESS in distributed PV generation on public buildings in Brazil: A 2030 outlook

This paper proposes a method to assess the financial attractiveness provided by adding a Battery Energy Storage System (BESS) in distributed photovoltaic (PV) generation on public buildings in Brazil. The method is applicable to prosumer units (PU) connected on the medium voltage grid operating under time-based electricity tariffs. The BESS primary objective was to achieve optimal reductions in electric energy expenses through effective energy arbitrage mechanisms. The procedural steps of the methodology begin with an analysis encompassing assessment of solar resource, PU consumption profile, and BESS sizing and operation. Subsequently, steps entail contracted power optimization, PU net-metering analysis, and evaluation of BESS impacts on electric energy expenses. Lastly, a regulatory and economic analysis is conducted incorporation considerations on BESS taxation, behind-the-meter regulations, and a sensitivity assessment extending to the 2030 outlook. By 2050, it is anticipated that PV generation will surpass hydropower, becoming the predominant component of Brazil's energy mix. This shift is expected to create an increased need for solutions, notably storage systems, capable of meeting flexibility requirements and maintaining grid resilience. The results suggest that the financial viability of incorporating BESS becomes favourable when the battery cost is below 365 $/kWh. In approximately 50% of the Brazilian territory, prevailing economic conditions support the adoption of BESS. Nationwide feasibility of integration is anticipated for the year 2027. It was observed that government policies to exempt BESS taxation, even if temporary, would be extremely interesting to promote the widespread adoption of this technology. The 2030 outlook of the transition to these benign renewable energy technologies is already in place, and will dominate the energy mix.

Frequency Stability Constrained BESS Sizing Model for Microgrids

The grid integration of renewable energy sources necessitates using energy storage systems (ESSs) to provide more flexibility and controllability. This paper proposes a frequency stability-constrained battery energy storage system (BESS) sizing model for microgrids formulated as a mixed-integer linear programming (MILP) problem and decomposed using Benders decomposition. The optimal size of BESS is determined as a trade-off between minimizing the operating costs or maximizing the benefits and the high investment costs of BESS. Both the grid-connected and stand-alone operating modes are modeled for the microgrid along with the corresponding generation contingencies. The microgrid scheduling optimization model is built for cost-benefit analysis considering unit commitment and frequency stability (FS) constraints. The transient frequency dynamics are considered using an accurate time-domain model based on the discretized swing equation. The proposed method ensures frequency stability criteria, such as frequency nadir/overshoot, rate of change of frequency (RoCoF), and steady-state frequency, are within their allowable ranges following generation contingencies. Simulation results based on a medium-voltage microgrid test case with historical data of load, PV generation, and market price verify the effectiveness of the proposed method.

On sizing of battery energy storage systems for PV plants power smoothing

The high variability of solar irradiance causes fluctuations in the generation of photovoltaic (PV) power plants. This characteristic affects power system operation, thus, strategies are necessary to mitigate the intermittent characteristics of PV power plants. Several countries adopt grid codes incorporating ramp rate (RR) limitations for PV power injection into the grid, also known as power smoothing control. In this sense, battery energy storage systems (BESS) with coordinated RR control algorithms are commonly applied to mitigate current fluctuations from the PV system to the grid. Therefore, studies on the effect of local solar irradiance on BESS requirements for PV power smoothing applications are very important. It is also worth highlighting that in the literature there is a gap in the evaluation of aspects of battery lifetime, BESS volume, price and BESS oversizing for selecting an ideal battery model, among a catalog of batteries, constituting an industrial concern. Therefore, this work presents a detailed BESS sizing methodology, evaluating aspects of the energy requirements, power, and discharging rate (C-rate), based on the mission profile (featuring a customized solution for each project). In addition, the effect of the battery temperature control on the BESS sizing is evaluated. The case study is based on a 1 MW PV system, assessed using a one-year mission profile of solar irradiance and ambient temperature from Goiânia-Brazil. The PV plant is connected to a 440 V grid associated with a BESS that mitigates oscillations of 10%/min. The BESS sizing is evaluated in terms of BESS volume, lifetime, and oversizing factor for power and energy. The proposed methodology reduces the BESS volume by 57.14% in relation to the traditional BESS design of literature.

Battery Energy Storage System Planning Based on Super-Resolution Source-Load Uncertainty Reconstruction

Low-time resolution electricity data have been used to drive battery energy storage system (BESS) planning due to data barriers. However, the coarse-resolution time series cannot reflect real power variation, and the planning results may be inappropriate due to the unrealistic representation of source-load uncertainties. To this end, this paper proposes a BESS planning method based on super-resolution (SR) source-load uncertainty reconstruction. The Gramian angular field (GAF) is employed to transform the 1D time series into 2D images to enrich the time dependency of signals. With advanced computer vision (CV), the entropy decrease generative adversarial network (EDGAN) based on information-growth attention is designed to recover the source-load profile from low resolution to high resolution. To depict source-load uncertainties in BESS sizing, a two-stage multi-distributionally robust optimization (2S-MDRO) is established under SR ambiguity sets. Case studies validated the accuracy of the proposed SR method and proved the effectiveness of the proposed sizing model. The recommended time resolution for BESS planning is presented in the comparative results.

Large-Scale BESS for Damping Frequency Oscillations of Power Systems with High Wind Power Penetration

With the high penetration of renewable energy into power grids, frequency stability and oscillation have become big concerns due to the reduced system inertia. The application of the Battery Energy Storage System (BESS) is considered one of the options to deal with frequency stability and oscillation. This paper presents a strategy to size, locate, and operate the BESS within the power grid and, therefore, investigate how sizing capacity is related to renewable energy penetration levels. This paper proposes an identification method to determine the best location of the BESS using the Prony method based on system oscillation analysis, which is easy to implement based on measurements while actual physical system models are not required. The proposed methods for BESS size and location are applied using MATLAB/Simulink simulation software (version: R2023a) on the Kundur 2-area 11-bus test system with different renewable energy penetration levels, and the effectiveness of the applied method in enhancing frequency stability is illustrated in the study cases. The case studies showed a significant improvement in steady-state frequency deviation, frequency nadir, and Rate of Change of Frequency (ROCOF) after implementing BESS at the selected bus. The integration of BESS can help to avoid Under-frequency Load Shedding (UFLS) by proper selections of size, location, and operating strategy of the BESS within the power grid.

Optimal sizing of battery energy storage systems and reliability analysis under diverse regulatory frameworks in microgrids

The integration of battery energy storage systems (BESS) with microgrids (MG) is crucial to improve the reliability and flexibility of renewable energy sources (RES) integration. However, the reliability and regulatory policies are critical factors that affect the optimal operation of MGs in the market. This study aims to enhance the reliability of MGs integrated with RES and BESS by evaluating their performance under different regulatory frameworks, namely feed-in tariff (FiT), net metering (NM), and energy storage incentive (ESI). Also, a dynamic FiT (D-FiT) framework is utilized to improve the reliability of the MG. An artificial bee colony optimization algorithm is utilized to optimize the size of BESS for each regulatory policy to minimize the total cost of the MG. Each policy is formulated based on its specific constraints in the problem. Subsequently, the reliability indices of Loss of Load Expectation (LOLE) and Expected Energy not Supplied (EENS) are calculated for each optimized solution. Moreover, we have integrated the dynamic thermal rating (DTR) system into our proposed model, focusing on the safe augmentation of system component ratings. The study finds that the D-FiT and standard FiT frameworks provide the best reliability level, whereas the reliability improvement under the ESI policy is not significant, as most of the MG's demand is supplied by the main grid. Furthermore, the study shows that the improvements in EENS are higher than LOLE, indicating that installing BESS reduces the loss of energy rather than the number of interruption hours. D-FiT framework has a significant positive impact on both reliability indices, unlike the other frameworks that have a greater effect on EENS. Furthermore, we have noticed a substantial improvement in reliability indices when the DTR system is taken into account, as compared to the static thermal rating (STR) system.

Optimal sizing and scheduling of battery energy storage system with solar and wind DG under seasonal load variations considering uncertainties

Battery energy storage systems (BESS) are integrated with renewable distribution generators (DG) within the distribution network (DN) to mitigate active power loss and improve the bus voltage profile through optimal placement and sizing. This work identifies the optimal location for BESS and DGs placement by deriving a loss sensitivity factor by considering the power flow through the branches and bus losses. With optimal location, a novel approach for selecting an optimal charging/discharging time schedule is proposed. This method aims to determine the optimal size and scheduling of BESS through the minimization of the voltage deviation and real power loss in the DN. Following the installation of BESS, the wind and solar DG optimal sizes are determined at maximum power generating hours. To address the uncertainties present in DN, Hong’s (2m+1) point estimation method is utilized to optimize the network operation incorporated with BESS and DGs. Furthermore, a techno-economic analysis is conducted to examine the energy savings resulting from integrating DGs and BESS in the DN. The power losses are further reduced by implementing network reconfiguration to reduce the energy dependency on the grid. The effectiveness of the proposed approach is tested on a 33-bus, 69-bus, and 118-bus DN under seasonal load variations. Incorporating the renewable DG with BESS into the DN reduces the grid energy supply by 30%–40%. The results of this study pave the way for more sustainable and efficient power systems by contributing to the development of techniques for integrating renewable energy sources into DNs.

A novel virtual inertia-based damping stabilizer for frequency control enhancement for islanded microgrid

The Inverter-based generator lacks natural inertia, which leads to instability problems in Microgrid frequency. Virtual inertia is a promising control concept that rapidly injects/absorbs active power to/from the power system to ensure the frequency stability of low-inertia systems. This paper proposes an innovative virtual damping stabilizer (VDS) integrated into battery energy storage system (BESS) based virtual inertia and damping control loops. The proposed VDS adds supplementary damping and stabilizing capability to the BESS by properly correcting and controlling its inertia pumped/drawn power. The VDS aims to enhance frequency stability, decrease the necessary BESS large capacity, and lower the initial expense. Furthermore, the proposed VDS helps operate the BESS efficiently by smoothing the output power profile during transient disturbances, consequently elongating the lifetime. The Particle Swarm Optimization (PSO) method is utilized to get the optimal parameters of the proposed VDS. A frequency control model of an isolated microgrid with the proposed controller is presented, considering various operating conditions. Time-domain simulations and experimental validation using OPAL-RT verify the efficacy of the proposed VDS scheme. Sensitivity and eigenvalues analyses assess the VDS's dynamic stability enhancement and robustness against inherent uncertainties in Microgrid parameters. The results demonstrate a superior frequency response and a reduction in the BESS size compared to PSO and model predictive controller (MPC) based virtual inertia and damping strategies.

Optimal Allocation of Battery Energy Storage Systems to Enhance System Performance and Reliability in Unbalanced Distribution Networks

The continuously increasing renewable distributed generation (DG) penetration rate significantly reduces environmental pollution and power generation cost and satisfies society’s rapid growth in electricity demand. Nevertheless, high penetration of renewable DGs, such as wind power and photovoltaics (PV), might deteriorate the system’s efficiency and reliability due to its intermittent and stochastic natures. Introducing battery energy storage systems (BESSs) to the distribution system provides a practical method to compensate for the above deficiency since it can deliver and absorb power when needed. Hence, it is important to determine the optimal allocation of BESS to achieve maximum assistance in the grid. This study proposes an optimal BESS allocation methodology to improve reliability and economics in unbalanced distribution systems. The optimal BESS allocation problem is solved by simultaneously minimizing the cost of energy interruption, expected energy not supplied, power loss, line loading, voltage deviation, and BESS cost. The proposed technique is implemented and analyzed on a high renewable DG penetrated unbalanced IEEE-33 bus network using DIgSILENT PowerFactory software (version 2020 SP2A). An enhanced grey wolf optimization (EGWO) algorithm is developed to optimize BESS location and size according to the selected objective function. The simulation results show that the proposed optimal BESS optimization technique significantly improves the economics and reliability in unbalanced distribution systems and the EGWO outperforms the gray wolf optimization (GWO) and particle swarm optimization (PSO) algorithms.

Optimal BESS Sizing for Industrial Facilities Participating in RTP DR

The predictability of their manufacturing lines allows industrial facilities to optimize their production scheduling and to participate in demand response (DR), in day-ahead, real-time pricing (RTP) electricity markets. Battery energy storage systems (BESSs) make the electrical demand of industrial facilities more flexible and increase their potential to benefit from DR. The BESS sizing problem, for industrial facilities participating in RTP DR, is complex due to the discreteness of their manufacturing lines and the stochastic nature of electricity pricing. In this paper, an approach to BESS sizing is proposed. Scenario extraction using k-means clustering is used to reduce the problem complexity, and the extracted scenarios are preprocessed to reduce the search space for the optimal size of the BESS. The steps involved in the proposed approach are demonstrated, in detail, through a case study that uses a generic model of an industrial unit. The results of the case study show the effectiveness and validity of the problem reduction techniques used and highlight the role of electricity storage in maximizing the profits of the industrial unit. Finally, a sensitivity analysis is carried out to illustrate the impact of the BESS installation cost on the results.

Management of electric vehicle charging stations in low-voltage distribution networks integrated with wind turbine–battery energy storage systems using metaheuristic optimization

This study investigates the IEEE 69-bus distribution network with three wind turbines (WTs) connected at the same buses of three battery energy storage systems (BESSs), with three 20- or 30-outlet electric vehicle charging stations (EVCSs) for charging electric vehicles (EVs). The honey badger algorithm (HBA) is adopted to minimize daily energy loss. The HBA determines the best size and position for three WT-BESS buses and three EVCS buses. The HBA calculates BESS size and operation mode to minimize daily energy loss. The demand of EVCSs varies throughout the day depending on the random choice of the number and state of charge of EVs entering the station. This results in the active and reactive energy losses and utility input energy decreasing by 63.5%, 60.6% and 59.6%, respectively, and the minimum voltage increasing from 0.9256 to 0.9839 pu. The network voltage profile and stability are also improved.

Cost-Effective Size of HESS Incorporated in VSG With Enhanced Performance of Battery Providing Implicit Inertia

Virtual synchronous generator (VSG) incorporates hybrid energy storage system (HESS) comprising supercapacitor (SC) and battery energy storage system (BESS) for frequency support services (FSS). In this work, new aspects namely implicit contribution of BESS in inertia and subjection of higher magnitude stress factors (SFs) on BESS during FSS are observed, while HESS is sized by existing methodology. Least square estimation and novel performance evaluation model are used to quantify the BESS implicit contribution in inertia and decrease in its desirable performance due to higher SFs, respectively. The resulting computations are utilized in reducing the SC size and marginally increasing the BESS size that leads to reduction in SFs on it. The estimated HESS size also demonstrates lower investment cost (INVC). Further, the implicit contribution by BESS in inertia and reduction in SFs with its estimated size is validated at Power Hardware-In-Loop (PHIL) level.

Use of Physics-Based Models for Different Applications of Electrochemical Energy Storage and Conversion Devices

This talk will present the application of physics-based models applied to two different electrochemical storage and conversion systems, viz. Li-ion batteries and Proton Exchange Membrane Fuel Cells (PEMFCs). Battery energy storage system (BESS) is becoming a crucial part of standalone renewable hybrid power systems due to the intermittent nature of power generation and for aiding in various operations like frequency regulation, voltage support, and peak shaving. The lithium-ion battery is a promising choice for BESS due to its high energy density, power density, operating voltage, negligible self-discharge, suitable operating temperature range, modularity, and reliability. A standalone renewable hybrid power system framework with lithium-ion battery energy storage is developed to investigate its performance using physics-based battery models. Power management and control strategies are developed for standalone renewable hybrid power systems. These control strategies can track the MPP of the solar cells and wind turbines while avoiding overcharging the battery and guaranteeing 0% dumping power under different ambient and working conditions. The battery is exposed to harsh ambient conditions in the real world, resulting in the dynamic and continuous capacity fade. A thermal management and control strategy is developed to analyze the effect of temperature on lithium-ion batteries' performance and degradation without using external cooling systems. Dynamic battery degradation analysis and life prediction are essential for better techno-economic estimation of renewable hybrid power systems. The impact of BESS size variation on degradation and the cost of energy generation is analyzed. The developed renewable hybrid power system framework is independent of location and system size and can be extended to incorporate other renewable energy generation sources and energy storage systems.The second part of this talk will focus on using physics-based models to understand the water dynamics and degradation mechanisms inside PEMFC, which is necessary for water management, long-life operation, and cost reduction. PEMFCs have emerged as an alternative green energy technology in various applications such as automobiles, portable and stationary applications, and auxiliary power supplies for space exploration missions. The cost and durability targets remain unmet largely, which restrains the commercialization of the PEMFCs. The major difficulty in modeling lies in the wide scale of dimensions from the millimeter level (flow channel) to the micron level (catalyst layer (CL) thickness). The multi-scale physics-based model enables the life cycle analysis of PEMFC under various operating conditions. The mass transport limitation is the major hurdle in performance enhancement for higher current density operations. It arises from the liquid water generated during the electrochemical reactions, which hinders the reaction sites of CL and oxygen transport reaction sites in the gas diffusion layer, resulting in performance deterioration. Many experimental studies reported the two-phase flow visualization techniques in PEMFCs, viz. neutron imaging, optical visualization, and X-ray radiography. However, the difficulties in conducting experiments for flow visualization and related parameters such as liquid volume fraction have encouraged researchers to adopt numerical simulations that are less expensive and computationally efficient. Understanding liquid water movement inside the cell at the micro-scale help to improve the PEMFC performance at the stack level. Here, we develop a comprehensive 3-D, multiphase, non-isothermal, steady-state physics-based model of the PEMFC that can closely approximate the liquid water flooding and membrane drying. The findings from this work can be used to design an efficient flow configuration and to study membrane deformation due to the shrinking-swelling under humidification and thermal cycling. The ultimate goal of the multi-scale model is to enhance the PEMFC stack output with less degradation.

Multi-objective sizing and real-time scheduling of battery energy storage in energy-sharing community based on reinforcement learning

Reducing peak demand and enhancing self-sufficiency have made the battery energy storage system (BESS) essential in microgrids when combined with photovoltaic (PV) systems. Previous studies have shown that BESS usage remains expensive, and it is more practical to share it within a community rather than installing individual systems. Therefore, this study aimed to propose a framework for BESS sizing and scheduling in an energy-sharing community based on reinforcement learning. To validate the proposed framework, a case study was conducted based on the BESS ownership scenarios (i.e., individual-owned BESS (IOB) and community-shared BESS (CSB)) considering the following purposes of potential stakeholders: (i) enhancing SSR; (ii) reducing peak demand; and (iii) increasing economic profit. The maximum SSR of CSB was 12.4% higher than IOB, while the peak load of CSB was 0.07% lower than IOB. As the BESS size increased, the total cost of BESS increased, and the total profits on electricity decreased. The economic profit increased by about 38% when CSB was used compared to IOBs. The proposed framework offers more promise for a broader range of building loads and PV generation within the energy-sharing community. Ultimately, this study would allow prosumers, community practitioners, and policymakers to better understand the sharing mechanisms in the community and provide decision guidelines for ownership types, operational strategies, and sizing of BESS and PV systems.

Optimal planning and forecasting of active distribution networks using a multi-stage deep learning based technique

This paper presents a comprehensive methodology for long-term planning in distribution networks to address the challenges associated with integrating renewable energy resources (RERs) and battery energy storage systems (BESSs). The methodology incorporates active network management (ANM) schemes, coordinated voltage control (CVC), and demand side management (DSM) to optimize the sizing and siting of RERs and BESS. Deep learning-based long-term forecasting techniques are utilized to capture the hourly variations in load demand, solar irradiance, and wind speed throughout the year. The objective is to minimize overall network costs, considering investment and operational costs, while planning over a 5-year horizon with a 3% annual load growth. The model includes tie-lines as a decision variable for network expansion investments, ensuring efficient network flexibility. The linearized mathematical model is developed as a mixed integer linear programming (MILP) problem to enhance scalability. A case study is conducted on the UKGDS 16-bus system to verify the optimization model, and further validation is performed on the IEEE 69-bus system to assess scalability. The results demonstrate the effectiveness of the proposed method in achieving accurate forecasting and efficient sizing and siting decisions for RERs and BESSs, including tie-line investments, providing valuable insights for real-world distribution network scenarios.

Energy storage systems for commercial buildings in dense urban regions: NYC case study

In response to the mounting interest in Battery Energy Storage Systems (BESS) from a wide range of entities—commercial, private, and governmental—this paper analyzes the decision-making criteria for BESS implementation. The structure of electricity bills are analyzed to understand the current cost structure of building utility tariffs. In addition, the potential revenue streams provided by various incentive programs, including demand response contracts, when implementing BESS were investigated. Highlighting the need for a pragmatic and efficient ranking system, we propose a model that leverages load profile data to determine the suitability of BESS implementation within a portfolio of buildings. This approach offers property owners a streamlined method to prioritize which buildings to equip with BESS. To validate the efficacy of our load profile-based model, we compare it against established financial models, specifically employing the System Advisory Model (SAM) from the National Renewable Energy Lab (NREL). Our findings indicate that a load profile-centric ranking system can effectively determine the most appropriate buildings for ESS implementation. This paper provides a substantial contribution to the field of BESS sizing, offering a simplified yet robust methodology for ranking that aligns well with more complex financial models. By focusing on the practical utility of load profile data, we enable a simplified and informed process for property managers to make sound decisions in the deployment of energy storage systems.

Operation Simulation and Economic Analysis of Household Hybrid PV and BESS Systems in the Improved TOU Mode

With the popularization of electric vehicles and electric boilers, household electricity consumption will increase significantly. Household hybrid photovoltaic (PV) systems and battery energy storage systems (BESSs) can supply increasing household electricity consumption without expanding the existing distribution network. This paper validates the technical feasibility of connecting a large number of household power users that contain BESSs and PVs in a distribution line by a simulation in Matlab. In addition to technical feasibility, this article improves the time-of-use (TOU) form to achieve economic feasibility (covering equipment costs). In the past, the TOU was set from the perspective of the load demand of the grid, but the actual user participation would affect this effect. In this paper, based on a social science survey, a new three-level rate TOU is introduced, which has little impact on residents’ lifestyle, to effectively increase the response frequency effectively. Combined with the improved TOU and the state of PVs, the BESS control mode is set for simulation. To compare the three-tier rate TOU with the normal TOU tariff and select the best household BESS size, a MATLAB simulation is used to simulate the common household BESS capacity. The results indicate that the combination of the three-tier rate TOU with a 4 kWh household BESS can afford the investment of household PVs and BESSs. The high cost issue that previously primarily limited the true use of BESSs is expected to be resolved.

Sizing of community centralized battery energy storage system and aggregated residential solar PV system as virtual power plant to support electrical distribution network reliability improvement

Virtual Power Plants (VPP) have been seen as one of the techniques to integrate more decentralized and distributed variable renewable energy systems into the grid. They will thus facilitate the greater democratization of the energy systems. VPP is an aggregation of renewable energy technologies, such as solar PV. It is usually operated with battery energy storage system (BESS) facilities to solve the variability issue of solar PV. However, BESS used as part of VPP has to be appropriately sized to work in the electrical network. While VPP operated with BESS may provide significant technical and financial advantage, most of the BESS sized to be used as VPP in the literature are large scale, operated with large scale VRE’s in the grid. Some of the literatures also provide the use of BESS sizing with Solar PV but for shared residential community use only. The context of BESS sizing for VPP from a residential community is not prevalent in the literature. In this paper, the context of a sizing BESS for VPP from a residential neighbourhood is studied. It is aimed that by optimally sizing BESS for a residential community with rooftop solar PV can supply power to the community and any excess energy may be used for VPP application for the grid. This paper provides a practical process for evaluating the proper size of a centralized BESS in a community with rooftop Solar PV by considering the energy consumption and peak demand profiles of the residential units in three types of communities with rooftop Solar PV, namely low-cost, medium-cost, and high-end community, by considering the mean and maximum energy consumption and peak demand profiles of each residential unit. Such values are used to allocate for the Solar PV system per residential unit and the centralized BESS at mean, 75% of maximum, maximum, and 125% of maximum energy consumption, to be used for the community and for any excess energy to support the electrical networks reliability improvement. The result shows that using the maximum and 125% of maximum energy consumption can provide more than enough energy capacity for VPP application, particularly electrical networks reliability improvement by supplying for the Energy not Supplied (EnS), EN. The result provides that for a High-end community, the stored energy in the BESS can reach up to 24.578 MW h using 125% of maximum sizing at either mean or maximum energy consumption. While for Medium-cost and Low-cost communities, the stored energy can reach up to 17.549 MW h and 14.527 MW h using 125% of maximum sizing at either mean or maximum energy consumption, respectively. Moreover, the result also shows that to alleviate Energy not Supplied (EnS) and contribute to reliability improvement, more residential units are needed if sizing is based on maximum energy consumption, while a lesser number of residential units if the size is based on 125% of the maximum energy consumption.

Operational cost minimization of a microgrid with optimum battery energy storage system and plug-in-hybrid electric vehicle charging impact using slime mould algorithm

Microgrid (MG) with battery energy storage system (BESS) is the best for distribution system automation and hosting renewable energies. The proliferation of plug-in hybrid electric vehicles (PHEV) in distribution networks without energy management (EM) puts additional pressure on the utility and creates challenges for MG. This research article proposes a stochastic expert method to minimize the total operational cost through proper EM of a grid-connected low-voltage MG by considering the charging impact of PHEVs with the optimal size of BESS. Three strategies are used to control the PHEV charging demand. Economically improved performance of MG is obtained as compared to previous research without considering the daily cost of the BESS (fBESS) and operation and maintenance cost of different distributed generation sources (OMcost). Then, the study is extended by incorporating these two parameters into the objective function of operational cost. Finally, this article analyzes to what extent the fBESS and OMcost factors raise the microgrid's operational cost. Due to non-linear optimization issues, the slime mould algorithm (SMA) is proposed, which performed better EM with a lower operational cost of MG than other methods.