Optimal Size Of Battery Energy Storage System Research Articles

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.

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.

Best Practice in Battery Energy Storage for Photovoltaic Systems in Low Voltage Distribution Network: A Case Study of Thailand Provincial Electricity Authority Network

This research investigated the increases of the voltage profile on the Provincial Electricity Authority (PEA)’s low voltage (LV) network due to the solar photovoltaic (PV) penetration. This study proposed the solution to maintain the voltage profile within the PEA’s standard limitation by using battery energy storage system (BESS) application. The algorithm using bisection method to figure out the optimal size and location of BESS was examined and simulated in different scenarios such as summer/winter and weekend/weekday behaviors. Furthermore, the allocation of a battery in various locations was also considered. DIgSILENT power factory with DPL script and Python are the tools used to cover diverse scenario cases. The results showed that the best practice of how to implement BESS to solve the voltage rise problem was the BESS installation at the distribution transformer and the BESS installation separately at the end of each feeder near the loads. However, the optimal size of BESS installation at the distribution transformer was almost double that of installation at the end of each feeder.

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

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

A novel approach for sizing battery storage system for enhancing resilience ability of a microgrid

The deployment of battery energy storage systems (BESS) can provide numerous benefits including increased renewable energy penetration, improvements in power quality and reliability, reduction of demand peaks, and reduced greenhouse gas (GHG) emissions. During the implementation of BESS, one of the most crucial factors is to determine the optimal size of the BESS to permit the balance between the technical characteristics of the BESS and the additional overall cost including the economic operation of the microgrid (MG). Therefore, the optimal sizing of the BESS allows the storage of sufficient power during the change of dispatch from the distributed energy resources (DERs) or in critical events such as natural disasters. The proposed framework determines the optimal size of the BESS based on forecasting the natural disasters or the outage cases, to enhance the reliability and economic operation of the MG. In this study, two stages are proposed to determine the optimal size of BESS in the MG. The first stage is the forecasting stage, which predicts the location of the outage or the faults that may occur. In the second stage, obtain the optimal size of the BESS to minimize the total operation cost for an economic operation of the MG. The teaching-learning-based optimization (TLBO) combined with quadratic programming (QP) is applied to solve the proposed objective formulation proposed to minimize the total operation cost of the MG. This study has been investigated in a group of customers with a single type of MG using various fault scenarios. The obtained results are compared with the state of the art. The proposed technique yields very promising results and robust performance compared with the available methods in the literature.

Impact of demand side management on optimal sizing of residential battery energy storage system

Widespread deployment of residential solar photovoltaic (PV) systems have been encouraged with decreasing system costs. Recently, cutbacks in government incentives such as Feed-in-tariffs (FiT) and feed-in power limits enforced by grid operators have questioned economic feasibility of rooftop PV systems. Residential battery energy storage system (BESS) is not only a solution to the above issues but also helps to overcome problems related to intermittent PV power. However, high investment cost of the BESS remains the key barrier in many markets around the world for the wide implementation of the BESS. This study investigates avenues of improving the economic sense of the BESS considering the demand side management (DSM) of residential loads. Results show that the proposed method has achieved 63.71 ± 18.24% capacity reduction compared to the optimal size of BESS without considering the DSM. Further, the impacts of DSM on optimal sizing of BESS are analyzed in terms of battery degradation, individual BESS value propositions as well as return on investment (ROI). More importantly, houses which use DSM must size the BESS considering DSM which increases the ROI of the BESS by 13.59 ± 4.11% in comparison to the ROI of BESS sized without DSM.

Multi-Objective Sizing of Battery Energy Storage Systems for Stackable Grid Applications

The deployment of battery energy storage systems (BESS) is rapidly increasing as a prominent option to support future renewable-based energy systems. However, despite its benefits from a technical perspective, there are still challenges related to its economic viability. On the other hand, sizing BESS considering only their economic viability can be impractical because financial objectives could be in conflict with other aspects, such as battery degradation and grid impact. This article proposes a multi-objective approach to determine the optimal size of BESS providing stackable services, such as frequency regulation and peak shaving. The proposed optimization method comprises financial and technical aspects represented by the payback period, battery life span, and grid impact. Given a set of market rules, a cost-benefit function, a regulation signal, consumption profiles and grid data, an enumerative approach is adopted to provide a set of Pareto optimal solutions. The performance of the proposed method is validated using the regulation market structure from PJM interconnection. Furthermore, a real 240-node distribution grid is used to assess the grid impact via OpenDSS. Simulations demonstrate that the proposed approach is a flexible and practical decision-making tool that investors can exploit when designing new BESS.

Planning Flexibility With Non-Deferrable Loads Considering Distribution Grid Limitations

Non-deferrable loads equipped with distributed energy resources, such as solar photovoltaic generation and energy storage, can offer a wide range of benefits to the power system, including load leveling, hedging against forecast uncertainty, and ancillary services. This paper presents a techno-economic approach to optimally equip a flexible energy resource, namely a battery energy storage system (BESS), with non-deferrable loads for capacity enhancement of a distribution grid. The proposed approach is generic in applicability, and includes two different optimization mathematical models. The first model quantifies the needed flexibility with non-deferrable loads, based on a consideration of distribution grid limitations, while the second model determines the optimal flexibility amount associated with non-deferrable loads considering investor profitability. Thereafter, a distribution operations model is developed to evaluate the impact of non-deferrable loads, equipped with an optimal size of BESS, on the distribution system loading capacity. An example of non-deferrable loads considered in this paper is that of the rapid charging loads of plug-in electric vehicles (PEVs) that cannot be controlled or shifted due to the short stay of PEVs at a charging facility. Hence, a probabilistic model for PEV rapid charging loads is first developed. Case studies and simulation findings, considering a 32-bus distribution system, demonstrate that the proposed approach helps reduces the impact of non-deferrable loads on a distribution system and enhances the system power margin, which defers the need for system upgrades.

Cycle-Life-Aware Optimal Sizing of Grid-Side Battery Energy Storage

Grid-side electrochemical battery energy storage systems (BESS) have been increasingly deployed as a fast and flexible solution to promoting renewable energy resources penetration. However, high investment cost and revenue risk greatly restrict its grid-scale applications. As one of the key factors that affect investment cost, the cycle life of battery heavily depends on its charging/discharging actions during the operation, particularly in the presence of uncertain renewable generation. In this context, it is necessary to consider the operation-dependent cycle life of batteries in optimal BESS sizing, which imposes great challenges to the modeling and solving of the planning problems. In this paper, we propose a novel two-level optimal sizing model for grid-scale BESS, considering its operation under uncertainties induced by volatile wind generation. In the lower level, a long-term chronological operation simulation of BESS is processed with an accurate cycle life model of batteries; in the upper level, marginal economic utility analysis and BESS size reforming are conducted to approach the optimal size of BESS. An iterative algorithm is designed to solve the model effectively. The proposed method is verified on a modified IEEE RTS-24 system and a real provincial power grid of China.

Optimal Sizing of Energy Storage System to Reduce Impacts of Transportation Electrification on Power Distribution Transformers Integrated With Photovoltaic

Transportation systems are one of the leading sectors that contribute to greenhouse gas emissions that lead to enhance global warming. The electrification of vehicles is a promising solution to this widespread problem; however, integrating electric vehicles (EVs) into existing grid systems on a large scale creates several problems, both for consumers and for utilities. Accelerated aging of expensive grid assets, such as power transformers, is one of the primary issues that these utilities are facing. This problem can be addressed with battery energy storage systems (BESS), which acts as buffer between demand and supply. Accordingly, this paper proposes a novel strategy for optimal sizing of BESS based on thermal loading of transformers. This paper also investigates issues associated with high penetration levels of rooftop photovoltaics (PVs), determining the synergy between EV charging load and BESS. The proposed solution is treated as an optimization problem, in which a new time of use (ToU) tariff is utilized as a demand response signal to reduce the accelerated aging of transformers. Extensive simulation results show that the size of BESS can be considerably reduced based on the proposed methodology, thereby avoiding accelerated aging of transformers without the need to augment existing grids.

Optimal sizing of battery energy storage system in smart microgrid considering virtual energy storage system and high photovoltaic penetration

In the smart microgrid system, the optimal sizing of battery energy storage system (BESS) considering virtual energy storage system (VESS) can minimize system cost and keep system stable operation. This paper proposes a two-layer BESS optimal sizing strategy considering dispatch of VESS in a smart microgrid with high photovoltaic (PV) penetration. In the first layer, VESS modelling and aggregation are established, and the initial size of BESS is determined by considering VESS participation in demand response program. In the second layer, the optimal sizing of BESS is studied and the optimal energy resources dispatching strategy is formulated via considering various constraints in the system. The mean-variance Markowitz theory is applied to assess the risk of system cost variability due to the presence of PV and load uncertainties. With the ratio of load varies from 70% to 130%, and PV generation ratio from 40% to 100%, sensitivity analysis reveals the optimal size of BESS is less impacted by PV generation change. Also with VaR(95%) the risk of system cost variability can be further reduced through VESS participation.

A Multi-Objective Planning Framework for Coordinated Generation From Offshore Wind Farm and Battery Energy Storage System

This paper proposes a novel multi- objective planning framework to determine optimal capacity of battery energy storage system (BESS) for coordinated operation of large scale offshore wind farm (OWF) and BESS. Multiple objectives such as battery cost and lifetime, availability of wind turbines (WTs), expected energy not supplied (EENS), loss of load hour (LOLH) and curtailment of wind energy have been considered for determining optimal capacity of BESS for OWF. Wake effect loss in OWF and uncertainty of wind speed have been also incorporated the optimal BESS framework for determining aforementioned objectives. Moreover, as WTs are susceptible to random failure, availability of WTs is an important issue which has been also considered in the planning stage for determining optimal size of BESS in this study. One of the main features of the proposed approach is that it provides flexibility to decision makers in selecting best available alternative for BESS (among different types) to minimize mismatch between forecasted generation schedule and actual generation of OWF, while taking into account economical and operational reliability issues. The effectivene ss of the proposed planning strategy has been validated through different case studies conducted on Horns Rev- 1 OWF situated in Denmark.

Enhancing the reliability of a microgrid through optimal size of battery ESS

Reliability is one of the most important factors to judge a power system. So, assessing reliability is an important task in power system planning. Many techniques have been developed to enhance the reliability of a power system. Some of those techniques depend on integrating new components and other techniques depend on changing some characteristics of existing components. One of the techniques depending on integrating new components is to integrate an energy storage system (ESS) with a power system. If the ESS is optimally sized to minimise the investment and operating cost, it enhances the reliability of the power system with a notable difference. This paper illustrates how to optimally size an ESS to be integrated with a grid-connected microgrid using the mixed-integer linear programming method. Moreover, load uncertainty is considered and the optimisation problem is solved using two different approaches to optimally size the ESS. Reliability indices of the microgrid are calculated before integrating it with the ESS and after integrating it to illustrate the enhancement of the reliability indices. Simulation results depict the effectiveness of the proposed approach.

A Fuzzy Optimization Model for Distribution System Asset Planning With Energy Storage

The distribution system asset (DSA) planning process for a new or existing subdivision involves a comparison of several feasible plans and selection of the least costly plan. The costs comprise the cost of new assets and their replacements, operation and maintenance costs, as well as reliability costs. DSA planning tools have been used for distribution utilities for decades, so that mature methods are available for traditional distribution systems. However, most of the DSA planning tools fail to include battery energy storage systems (BESS) as an asset solution for systems with high penetration of renewable energy sources. In addition, minimizing the investment cost and maximizing the reliability performance, which can be measured by the system average interruption duration index (SAIDI), are contradicting objectives. With a large investment, the system will have a small SAIDI, and vice versa. In this paper, a multi-objective algorithm that uses fuzzy optimization technique to handle these contradicting objectives is proposed. The algorithm includes traditional types of assets as well as BESS. One of the main advantages of the proposed method is that the customer interruption costs are not explicitly necessary, as these values cannot be precisely determined for all loads. In the tool, the annual investment cost and SAIDI are minimized, and the optimal size of BESS is obtained. Its performance is demonstrated on a 33-bus distribution system and the results are compared with other methods available in the literature.

Optimal Integration of Battery Energy Storage Systems and Control of Active Power Curtailment for Distribution Generation

Power flows exceeding the thermal capacity on power system lines and severe rise on bus voltages are a potential problem in the future distribution grids where development of photovoltaic (PV) installations is expected. This problem could cause damage on components such as transformers and distribution lines provoking triggering the system protections that would eventually lead to system failures. The economic and environmental implications related to reinforcing of the existing network, make this option not the immediate solution for implementation. Installation of battery energy storage systems (BESS) and control of active power curtailment (APC) would be another option to phase the upcoming problems. In this paper a forecast for the year 2035 in a real residential area in Switzerland was investigated, where large amount of PV panels are expected to be installed. This project investigates different solutions based on BESS and APC to deal with the inevitable increase of power flow in the network. The solutions presented here, prevent the overload in a distribution system transformer and avoid excess power flows on the lines. For a given set of demand and PV production curves, simulations in Matlab were performed and computations of optimal power flows were calculated. The solutions explored on this work consider economical aspects such as discounted cash flow method that finds the optimal size of BESS installed. Results also show that combining BESS with curtailment is the most viable solution.

Maximum PV Penetration Under Voltage Constraints Considering Optimal Sizing of BESS on Brazilian Secondary Distribution Network

The Distributed Generation (DG) has substantially increased around the world and it is expected to play an important role in the future of power systems. Most of DG systems are composed by renewable energy sources, such as Photovoltaic (PV), which has an intermittent behavior, resulting in negative impacts on the distribution network, such as voltage fluctuation. In order to mitigate the voltage rise problem, this paper presents an optimal sizing of a Battery Energy Storage System (BESS) with an efficient charge and discharge strategy, as a way to increase PV penetration, without exceed voltage limits. MATLAB / Simulink is used to simulate a typical Brazilian residential secondary distribution system using actual photovoltaic data and residential load profile from Southeastern Brazil. The results show that the photovoltaic system penetration could reach 40% for a transformer operating at nominal load, without BESS. However, if a BESS is used, the PV penetration could be increased up to 80%. Moreover, it is showed that the BESS can provide an efficient peak shaving in the original load curve.

Optimal Battery Energy Storage Size Using Particle Swarm Optimization for Microgrid System

This paper presents the method to evaluate the optimal size of Battery Energy Storage System (BESS) for the microgrid based on load frequency control. The novel optimization and control algorithm considering load shedding as dynamic demand response has been proposed with the combination of the optimal size of BESS in order to control the system frequency of the microgrid. The feasibility of using optimal BESS with load shedding is proposed when the microgrid is disconnected from the main utility grid. Particle Swarm Optimization (PSO) is developed and presented to determine the optimal size, and to achieve the lowest total cost of BESS in the microgrid. Results show that the optimal size of BESS-based PSO with load shedding can achieve higher performance of dynamic stability compared to the optimal size of BESS-based analytic algorithm with load shedding. At the same time, the sizing methods also evaluated the impact of BESS specified costs with different storage technologies for the microgrid system. The total costs of BESS were demonstrated and compared.

Optimal Sizing of Battery Storage Systems for Industrial Applications when Uncertainties Exist

Demand response (DR) can be very useful for an industrial facility, since it allows noticeable reductions in the electricity bill due to the significant value of energy demand. Although most industrial processes have stringent constraints in terms of hourly active power, DR only becomes attractive when performed with the contemporaneous use of battery energy storage systems (BESSs). When this option is used, an optimal sizing of BESSs is desirable, because the investment costs can be significant. This paper deals with the optimal sizing of a BESS installed in an industrial facility to reduce electricity costs. A four-step procedure, based on Decision Theory, was used to obtain a good solution for the sizing problem, even when facing uncertainties; in fact, we think that the sizing procedure must properly take into account the unavoidable uncertainties introduced by the cost of electricity and the load demands of industrial facilities. Three approaches provided by Decision Theory were applied, and they were based on: (1) the minimization of expected cost; (2) the regret felt by the sizing engineer; and (3) a mix of (1) and (2). The numerical applications performed on an actual industrial facility provided evidence of the effectiveness of the proposed procedure.

Optimal Sizing of BESS for Customer Demand Management

AbstractThe paper proposes an optimal sizing method of a customer's Battery Energy Storage System(BESS) which aims at managing the electricity demand of the customer to minimize electricity cost under the time of use(TOU) pricing. Peak load limit of the customer and charging and discharging schedules of the BESS are optimized on annual basis to minimize annual electricity cost, which consists of peak load related basic cost and actual usage cost(energy cost). The optimal scheduling is used to assess the maximum cost savings for all sets of candidate capacities of BESS. An optimal size of BESS is determined from the cost reduction rate(hereinafter reduction rate) curves via capacity of BESS. Case study uses real data from industry customer and shows how the proposed method can be employed to optimally design the size of BESS for customer demand management.

Optimal sizing of battery energy storage system in microgrid system considering load shedding scheme

In the past, Battery Energy Storage System (BESS) is used for primary frequency regulation. As developments in batteries progress, advancement in applications of BESS including the implementation in high power penetration is expected. Load shedding is one of frequency control methods during stand-alone operation, and the performance of frequency control improves in combination with BESS. However, without optimal size of BESS, it can cause the oscillations to the system. Thus, this article proposes the feasibility of using optimal BESS with load shedding scheme when the microgrid is disconnected from a main source. In this paper, the analytic algorithm-based DPL (DIgSILENT Programming Language) script is developed and presented to determine the optimal size of BESS with load shedding scheme. Results show that the optimal size of BESS-based analytic algorithm with load shedding scheme can achieve higher performance of frequency control compared to the BESS without optimal size. The optimal sizing method of BESS with load shedding scheme is achieved by using a tool for analysis of industrial, utility and commercial electrical power system called DPL script.