Sizing Of Battery Energy Storage Research Articles

Spider Monkey Optimization Based Optimal Sizing of Battery Energy Storage for Micro-Grid

Spider Monkey Optimization Based Optimal Sizing of Battery Energy Storage for Micro-Grid

The role of state-of-charge management in optimal techno-economic battery energy storage sizing for off-grid residential photovoltaic systems

With increased photovoltaic (PV) penetration in residential areas, an off-grid PV system is a sustainable solution to meet the zero net emissions goal by 2050. However, an off-grid PV system has a significant technical issue: low power supply reliability. Typically, battery energy storage (BES) is necessary to overcome the technical issues associated with PV systems. Conventionally, BES is operated to maintain a power balance between generation and load. However, techno-economic feasibility is a crucial aspect of BES adoption. Although BES can effectively support off-grid PV systems, it may not be available when its state-of-charge (SoC) reaches the minimum allowable limit, creating power supply failures (outages). Therefore, this study proposes, for the first time, a BES power control strategy that collaborates with SoC management to improve the reliability of an off-grid residential PV-BES system. A simulation study is performed using a residential load profile from the urban area of Bangkok, Thailand, obtained from the Metropolitan Electricity Authority (MEA). A comparative study is conducted to compare the proposed strategy’s performance with that of the conventional control strategy. Following the comparative study, the role of SoC management in BES sizing is quantitatively illustrated. Moreover, the impacts of SoC management parameter setting (i.e., SoC restoration threshold and maximum SoC restoration power) are evaluated using techno-economic performance indicators. The simulation results reveal that BES power management with SoC management could reduce BES capacity and levelized cost of energy (LCOE) by 28% and 26.28% compared with the conventional strategy.

Optimal sizing of battery energy storage in solar microgrid considering peak load shaving

The power system faces a lot of challenges due to incorporation of a large number of renewable energy sources. Here, Battery Energy Storage System (BESS) can be used effectively to deal with the grid’s rising peak demands without the necessity of upgrading the grid capacity. An energy management strategy is considered based on peak load shaving combined with transformer power limit and BESS State of Charge (SOC). Cooperation Search Algorithm (CSA) is utilised to determine the number of batteries in BESS and Photovoltaic (PV) panels with the goal of maximising economic benefits. The optimisation model of solar microgrid energy storage is established and solved for three scenarios of the grid only (Base Case), BESS without PV (Case-A) and BESS with PV (Case-B). From the result, it is verified that in Case-B, the customer will pay 31% and 34% less compared to the Base Case and Case-A, respectively, towards energy costs in a day.

Wind curtailment mitigation in presence of battery energy storage and electric vehicle: A comprehensive multi-objective decision-support framework

Concern to maintain exploitation of power system in secure mode, the wind curtailment (WR) caused by the perpetual fluctuating of wind power (WP) can menace the operational power system schedule. The mentioned phenomenon will intensify by high WP penetration into the power system. In addition, incorporating plugged-in hybrid electric vehicles (PHEVs) into the power system along with the WP intermittent will increase the vagueness rating of the problem. Due to the expansion and development of battery energy storage (BES), the possibility of power shortage compensating and accumulating additional power produced by wind farms (WFs) has been engendered. Providing a stochastic scenario-based structure with different correlation levels can increase the robustness of the model. Here, a two-stage model has been proposed to overcome such a high-dimensioned problem. The first stage determined the scheduled power output of thermal generators (TGs) as the unit commitment (UC) problem to minimize operational costs. Next, by reckoning the supplementary constraints, the scheduled WP, which is re-optimized, will be obtained. The second stage has employed to detect the BES capacity. In this stage, the intelligent parking lots (IPLs) assign price-based signal impetus to the PHEVs to participate in economic dispatch. Due to the prominence of being equipped with the supreme decision-making facility for the intelligent decision-maker operator (IDMO), the augmented epsilon-constraint method has been employed to capture the Pareto sets of some multi-objective functions. The optimum solution has been selected by the decision-making method (DMM) according to the operator's preferences. The results of proposed Multi-Objective Decision-Support (MODS) model show that the total operation cost and BES sizing have dropped by 68 and 6.1 percent, respectively. The performance of the proposed model has been testified through the IEEE-30 bus system via GAMS software.

Optimal operation and stochastic scheduling of renewable energy of a microgrid with optimal sizing of battery energy storage considering cost reduction

In order to alleviate worldwide worries about environmental issues, power system operators and planning entities are looking for new energy sources that will produce fewer emissions than conventional fossil-fuel power plants. When it comes to powering their systems, utilities are increasingly choosing renewable energy sources (RESs). Here, microgrids (MG) would furnish the ideal conditions for incorporating RESs. Thus, this study presents an seasonal optimization framework for the short-term operation of an MG, including energy storage and solar photovoltaic (PV) systems, while thoroughly exploring the effects of varying climatic factors on the optimal scheduling of resources. The day-ahead MG scheduling is addressed in this model, taking into account the impact of varying irradiances across a year. The resulting single-objective optimization problem is then tackled with the help of the converged barnacles mating optimizer (CBMO), as a robust and effective optimization method. The simulation findings show that the total operating cost of the grid-integrated microgrid is alleviated when a PV model is used in a real-world setting to raise the accuracy of the energy management system. In case 1, the CBMO reduces the operating cost of the MG by 262.784 €ct/day, which is less than the results obtained by some other algorithms. Using the hybrid CBMO, the reported costs for Case 2 is 298.8513 €ct/day. The CBMO approach yielded the best results of 352.1964 €ct on a warm sunny day, 285.2851 €ct on a cold sunny day, 257.7912 €ct on a warm cloudy day, and 252.135 €ct on a cold cloudy day. After running the simulations, it has been found out that the suggested CBMO has the shortest mean simulation time by 114.217 s in Case 3, while other algorithms need longer simulation time such that the GA needs 120.364 s, the PSO needs 118.487 s, the DE needs 118.039 s and the ICA needs 116.287 s. Therefore, the CBMO takes significantly less time than other methods.

Renewable Energy Communities in Islands: A Maltese Case Study

Renewable energy communities are considered as key elements for transforming the present fossil fuel-based energy systems of islands into renewable-based energy systems. This study shows how renewable energy communities can be deployed in the Maltese context to achieve higher penetration of residential-scale photovoltaic systems. Case studies for five renewable energy communities in the Maltese LV distribution network have been analyzed in detail. A novel community battery energy storage sizing strategy was proposed to determine the optimal storage capacity at each energy community. The main objective of the community battery storage in each REC is to minimize the reverse power injection in the grid (minimize the total reverse energy and reverse peak power values), as well as to reduce the peak evening electricity demand. The optimal sizes for communal BESSs were determined to be of 57 kWh (EC 1), 55 kWh (EC 2), 31 kWh (EC 3), 37 kWh (EC 4) and 10 kWh (EC 5), respectively. The community storage systems were observed to reduce the overall impact of all five energy communities on the grid infrastructure. Power system simulations were performed for a typical spring day to evaluate the impact of communal BESS placement on the node voltages for all five energy communities. The results showed that the community storage was more effective at reducing the node rms voltage magnitudes when deployed at the end of the respective energy communities, rather than at the beginning of the community. During peak generation hours, reductions of up to 0.48% in the node rms voltage magnitudes were observed. This contrasts with reductions of only 0.19% when the community storage was deployed at the beginning of the energy communities.

Multi-Objective Optimal Operation of Building Energy Management Systems with Thermal and Battery Energy Storage in the Presence of Load Uncertainty

This paper presents the optimal operation of a building energy management system (BEMS), with combined heat and power (CHP) generation, thermal energy storage (TES), and battery energy storage (BES), subject to load demand uncertainty. The main objective is to reduce the total operating cost (TOC) and total CO2 emission (TCOE). First, we develop two models of load demand forecasting, one for weekday and the other for weekend, using artificial neural networks, long short-term memory, and convolutional neural networks. Then, we incorporate the predicted load demand and load demand uncertainty for planning the energy dispatch of the BEMS. TES aims to store the thermal energy waste from the power generation of CHP and discharge the thermal energy to the absorption chiller to supply the cooling load. BES and spinning reserve (SR) play an important role in handling the uncertainty of the load demand. The operation of BEMS, subject to the load demand uncertainty, is formulated as a linear program. We can efficiently solve the linear program and provide an optimal solution that satisfies the dispatch constraints. Thereafter, we determine the optimal size of BES, based on economics and environmental optimal operation. The proposed BEMS is compared to the previous BEMS, without BES and SR. Furthermore, we propose the multi-objective optimal operation, where the normalization for TOC and TCOE is introduced, and the multi-objective function is defined as a linear combination of normalized TOC and TCOE. The numerical results reveal the trade-off relationship between TOC and TCOE. In particular, when TCOE is minimum, TOC becomes maximum. On the other hand, when TOC is minimum, TCOE becomes maximum. The relationship provides a method to select the operating point, as well as analyze the power flow for the multi-objective optimal operation.

Optimization of PV and Battery Energy Storage Size in Grid-Connected Microgrid

This paper proposes a new method to determine the optimal size of a photovoltaic (PV) and battery energy storage system (BESS) in a grid-connected microgrid (MG). Energy cost minimization is selected as an objective function. Optimum BESS and PV size are determined via a novel energy management method and particle swarm optimization (PSO) algorithm to obtain minimum total cost. The MG was designed to use its own energy as much as possible, which is produced from renewable energy resources. Since it is a grid-connected system, it can demand energy from the grid within the determined limit with penalty. It differs from the studies in the literature in terms of optimizing both parameters such as PV and BESS size, being a grid-connected self-contained MG structure and controlling the grid energy by an energy management algorithm and optimizing the parameter via PSO with an energy management system (EMS). Results are compared for different PV and BESS. Moreover, effectiveness of the novel energy management method with PSO is compared with the genetic algorithm, which is the one of the well-known optimization algorithms. The results show that the proposed algorithm can achieve optimum PV and BESS size with minimum cost by using the new energy management method with the PSO algorithm.

Impact of Electric Vehicle Load Demand and Energy Storage Device in Integrated Renewable Energy Sources

ABSTRACT The intermittent nature of renewable-based generation may cause the dip or rise generation and load imbalances. Besides, the higher penetration of Electric Vehicles can affect the voltage profile and imbalances. The main contribution of the proposed work is to determine (i) the realistic load model of electric vehicle (EV) charging station (ii) the size of battery energy storage (BES) considering the EV load demand with the most realistic ZIP load. (iii) the cost-benefit analysis and the probabilistic modeling of EV load demand are considered for BES sizing. (iv) minimization of the daily energy loss (v), maximizing the total benefit of the system, (vi) the nature of the electric vehicle load demand has been modeled for size determination of energy storage devices. The fuel cost of the thermal generator has been minimized, and the operating and maintenance cost of the Solar Photovoltaic (PV), and Wind Turbine (WT) have been considered in the objective function. The daily energy loss has been reduced to 30.085%. The voltage deviation has been reduced to 1.165%. The analysis has been carried out for the IEEE-33 bus test system. The iterative Monte Carlo Simulation and Nonlinear Programming have been used to solve the multi-objective problem.

Integrated expansion planning of electric energy generation, transmission, and storage for handling high shares of wind and solar power generation

In this paper, an integrated multi-period model for long term expansion planning of electric energy transmission grid, power generation technologies, and energy storage devices is introduced. The proposed method gives the type, size and location of generation, transmission and storage devices to supply the electric load demand over the planning horizon. The sitting and sizing of Battery Energy Storage (BES) devices as flexible options is addressed to cover the intermittency of Renewable Energy Sources (RESs), mitigate lines congestion, and postpone the need for new transmission lines and power plants installation. For efficient handling of RESs uncertainties, and operational flexibility, the upward and downward Flexible Ramp Spinning Reserve (FRSR) are modeled. Besides, the Low-Carbon Policy (LCP) is considered in the objective function of the proposed Transmission, Generation, and Storage Expansion Planning (TGSEP) model. A hierarchical clustering method that can preserve the chronology of input time series throughout the planning horizon periods is developed to capture the short-term uncertainties of load demand and RESs. The short-term operational flexibility requirements make the joint long-term transmission and generation planning a high computational problem. Therefore, the Mixed-Integer Linear Programming (MILP) formulation of the model is solved using an accelerated Benders Dual Decomposition (BDD) method. The IEEE RTS test system is utilized to validate the effectiveness of the proposed joint expansion planning model.

Impact of Advanced Load-Frequency Control on Optimal Size of Battery Energy Storage in Islanded Microgrid System

The application of battery energy storage (BES) in microgrid systems has attracted much attention in recent years. It is because the BES is able to store excess power and discharge its power when needed. In islanded microgrid systems, BES is starting to be considered as a unit that can regulate the system frequency. The control used in the BES to display frequency regulation performance is called load-frequency control (LFC). However, this participation resulted in the large size of the battery and high expansion planning cost. In this paper, an advanced LFC control that has frequency limitation compared to traditional LFC is proposed. The proposed control implies droop control as the base and has frequency limitations. Compared to the traditional LFC, the proposed control can reduce the system expansion planning costs. A performance simulation was done to validate battery performance. The results of the numerical simulation showed that the proposed control participated in reducing the operation cost. It directly led to a reduction in the expansion planning cost. A study of battery selection was conducted to draw the practicality of the BES sizing solutions.

Data-Driven Resource Planning for Virtual Power Plant Integrating Demand Response Customer Selection and Storage

Battery energy storage (BES) and demand response (DR) are two important resources to increase the operational flexibility of a virtual power plant (VPP) and thus reduce the economic risks that VPP faces in the short-term electricity market. This article develops a data-driven approach for VPP resource planning (VRP), in which BES sizing and DR customer selection are optimized synergistically to maximize VPP's profit in the electricity market. Heterogeneity in DR potential across individual customers is considered in the planning framework by utilizing the knowledge learnt from smart meter data. The overall VRP problem is formulated by a risk-managed, multistage stochastic programming framework to address the uncertainties from the intermittent renewable energy sources, load demands, market prices, and DR resources. Case studies compare the VRP results under two market imbalance settlement settings, namely, penalty-charged and penalty-free markets. The results demonstrate that jointly optimizing BES and DR customer selection leveraging the smart meter data can improve the VPP's expected profit under both market settings.

Optimal sizing of battery energy storage in a microgrid considering capacity degradation and replacement year

Nowadays, microgrids (MGs) have received significant attention. In a cost-effective MG, battery energy storage (BES) plays an important role. One of the most important challenges in the MGs is the optimal sizing of the BES that can lead to the MG better performance, more flexible, effective, and efficient than traditional power systems. This paper proposes a novel set of formulations to determine the optimal BES size, technology, depth of discharge (DOD), and replacement year considering its technical characteristics, service life, and capacity degradation to minimize the MG scheduling total cost and improve the precision and economic feasibility of the BES sizing method. Moreover, the modeling of BES considering capacity degradation is presented. The proposed model of the planning problem is formulated using mixed-integer linear programming (MILP) to ensure the convergence of the optimization problem. The effectiveness of the proposed approach is confirmed by numerical simulation based on historical data of a real MG.

Optimal Siting and Sizing of Battery Energy Storage: Case Study Seventh Feeder at Nakhon Phanom Substation in Thailand

The optimal siting and sizing of battery energy storage system (BESS) is proposed in this study to improve the performance of the seventh feeder at Nakhon Phanom substation, which is a distribution network with the connected photovoltaic (PV) in Thailand. The considered objective function aims to improve the distribution network performance by minimizing costs incurred in the distribution network within a day, comprising of voltage regulation cost, real power loss cost, and peak demand cost. Particle swarm optimization (PSO) is applied to solve the optimization problem. It is found that the optimal siting and sizing of the BESS installation could improve the performance of the distribution network in terms of cost minimization, voltage profile, real power loss, and peak demand. The results are investigated from three cases where case 1 is without PV and BESS installation, case 2 is with only PV installation, and case 3 is with PV and BESS installations. The comparison results show that case 3 provided the best costs, voltage deviation, real power loss, and peak demand compared to those of cases 1 and 2; system costs provided by cases 1, 2 and 3 are USD 4598, USD 5418, and USD 1467, respectively.

Optimal Charging Strategy of Electric Vehicles with Consideration of Battery Storage

The high penetration of electric vehicles (EVs) will increase burden of a power grid. However, the expansion of capacity of distribution facilities is not always possible, especially in some old residential community. This paper proposes to use an optimal charging strategy of EVs with additional battery energy storage (BES) to improve the charging capabilities in a residential community. By modeling the EV charging behavior, the required charging capacity is evaluated using Monte Carlo method and the BES size is determined as the difference between the required capacity and the distribution capacity. An optimal charging strategy is then proposed to reduce the charging cost and ensure the safe running of distribution network.

Optimal energy management strategy for a renewable‐based microgrid considering sizing of battery energy storage with control policies

SummaryMicrogrids (MGs) are known as suitable options to accommodatethe high penetration of renewable energies, like solar and wind. MGs have provided the requirements of controlling and adjusting these sources. In addition, batteries are becoming indispensable components of MGs because of their capabilities in addressing the renewable energies' power output intermittency. In MGs, the problem of smart energymanagement along with battery sizing has been introduced as the necessity to ensure the efficient use of renewable sources and decrease traditional fossil‐fuel‐based generation technology penetration level in power systems. Accordingly, a novel method is presented in this paper to effectively address the above‐mentioned requirements, utilizing the modified shuffled frog leaping algorithm (MSFLA), applied to different case studies. The results, obtained from the numerical simulation, are compared to several well‐established optimization approaches to verify the performance of MSFLA. In terms of computational efficiency and quality of the obtained solution, the MSFLA has demonstrated promising outcomes, together with superior performance in comparison with other algorithms. The results show that the presented framework, including the battery sizing, would be very beneficial to minimize the operating cost of MGs.

Hybrid solar thermal/photovoltaic‐battery energy storage system in a commercial greenhouse: Performance and economic analysis

AbstractPerformance and economic analyses of a hybrid solar thermal/photovoltaic‐battery energy storage (ST/PV‐BES) system for a commercial greenhouse were developed. One of the objectives of the study is to evaluate the best configuration of photovoltaic (PV) and solar thermal (ST) modules, and battery energy storage size to have the minimum levelized cost of energy. The system was economically optimized. Moreover, to assess the influence of economic parameters such as ST system cost, PV system cost, natural gas price, electricity price, and large renewable procurement (LRP) rate, a series of sensitivity analyses were undertaken. The results showed that the ST system cost and natural gas price were the most effective parameters on both levelized cost of energy and payback period. The role of future evolution of electricity, fuel cost, and financial incentives (LRP rate and carbon tax) in the economic performance of the system were also analyzed.

A Battery Energy Storage Sizing Method for Parking Lot Equipped With EV Chargers

Parking lots (PLs) equipped with electric vehicle (EV) chargers will be the most convenient places for EV users to charge their cars. However, there is a high degree of unpredictability around how car parks are used, especially shopping center parking lots (SCPLs). Moreover, the cluster charging of EVs in PLs may destabilise the grid. The application of a battery energy storage system (BESS) in PLs is a potential way to reduce the impact of EV charging on the grid. This article proposes an approach for estimating the size of a stationary BESS for PLs in a constrained grid. At first, the intermittent charging demand of EVs is determined by considering the travel patterns, charging needs, and driver behavior of EVs. Then, a region reduction method combined with nonlinear optimization is proposed to estimate the optimal size of the BESS such that the capital cost and operational cost of the SCPL are minimized. The proposed sizing method 1) ensures the maximum utilization of the stationary BESS; 2) avoids over/undersizing; 3) reduces operational costs; and 4) maintains; the reliability of the system. The applicability of the proposed method is shown by simulating different cases, characterized by real household travel survey data and shopping center car park occupancy data.

Sizing of Energy Storage with Network Constraints Economic Dispatch of Fuel Cell Micro Turbine and Renewable Energy Sources

Micro-Grid is the appropriate solution to various problems in the power system. Different types of energy sources, likewise Fuel cell (FC), Micro-Turbine (MT) and renewable energy sources can be integrated with micro-grids (MG). The battery energy storage has played a crucial role to support the power mismatch of on-grid or off-grid MG. Therefore the optimal size of battery energy storage along with the optimal cost-based calculation has become an essential part for the micro-grid operator. The piecewise linear cost method is used for the cost based analysis. The main contribution of this paper is: (i) the optimal size of battery energy storage has been determined with a Fuel cell (FC) and Micro-Turbine (MT) based distribution generation (DG). (ii) The impact of battery storage with DG and renewable energy sources (RES) has been considered. (iii) The total benefit and market benefit has been maximised. (iv) The unit-commitment cost of FC and MT with spinning reserve, piecewise linear cost function, ramp rate, minimum up and downtime constraints has been considered for the sizing of battery storage. (v) The network constrained has been found to obtain minimum daily energy loss for the optimal size of battery storage. (vi) The state of charge (SOC) of battery, the power output of DG’s and RES, power loss, battery cost per day, operating cost of generation, etc. have been determined. The optimal sizing of battery energy storage determination is helpful for the both Microgrid operators as well as designers. The IEEE-33 bus test system with ZIP load has been carried out for analysis and result validation. The general algebraic modeling system (GAMS) is used to solve the deterministic optimisation problem.

Battery energy storage sizing optimisation for different ownership structures in a peer-to-peer energy sharing community

Existing studies have shown the benefits of battery energy storage systems (BESS) inclusion, but do not consider optimal BESS sizing and operation in a peer-to-peer (P2P) energy sharing network under different BESS ownership structures. Under the P2P framework, two different BESS ownership structures, namely the ESP owned structure and the user owned structure are investigated in this study, which are compared to the traditional user owned BESS under the peer-to-grid (P2G) framework. It is found that in campus buildings with a P2P energy sharing network, the user owned BESS exhibits the highest NPV comparing to the other two BESS ownership structures. The ESP owned structure is economically less beneficial, but provided the opportunity for the prosumers to engage in P2P energy sharing and reduce their energy costs without a BESS investment cost.