Sizing Of Energy Storage System Research Articles

An innovative approach of optimizing size and cost of hybrid energy storage system with state of charge regulation for stand-alone direct current microgrids

• This paper proposed a mathematical model for multi-objective optimization of hybrid energy storage system (HESS) in detail. • A novel penalty cost function for the HESS and a new SOC regulation concept for supercapacitor storage system have been introduced. • The proposed model was analysed under a case study. • An optimal HESS sizing was achieved to obtain the minimum total investment cost of HESS including the future replacement costs. • The best value for the energy ratio between supercapacitor storage and battery storage for the considered load profile was found as 25%. This study proposed a novel approach to optimize size and cost of hybrid energy storage systems (HESS) based on a solar photovoltaic (PV) fed stand-alone DC microgrid, while considering the state of charge (SOC) of both batteries and supercapacitors to assure the long life of batteries and well-being during the operation. The sizing strategy is combined with an optimization model and a HESS Assessment Algorithm. The strategy differs from traditional strategies since it comprises of not only the system cost, but also SOC regulation of both batteries and supercapacitors. A unique penalty cost function depending on the unutilized solar power generation and unserved demand power after HESS is fully charged or discharged has been introduced. By this penalty cost function, SOC of HESS can be kept within a safer margin to guarantee the system stability. Also, system availability can be assured by penalizing the unserved demand power. A new SOC regulation concept for supercapacitors has been introduced. SOC of supercapacitors is maintained in between a predefined focused band to ensure the supercapacitors availability to absorb or deliver sudden power fluctuations. The lifetime of HESS and the solar PV system are considered in the objective function to find the optimum sizing of HESS. The simulation was performed under Hambantota Solar Park weather conditions using MATLAB. Multi-objective genetic algorithm is used to optimize the objective functions. Comparing with a traditional sizing method, it shows that the novel optimal sizing strategy can reduce the BS system capacity by 18% by the hybridization of supercapacitors. The best value for the energy ratio between supercapacitors and batteries, for the considered load profile was found to be 25%. Results prove that the number of penalties, surplus solar power generations and unserved demand power were made minimum while obtaining an optimal sizing of HESS with a minimum system investment cost.

Power Take-Off and Energy Storage System Static Modeling and Sizing for Direct Drive Wave Energy Converter to Support Ocean Sensing Applications

This paper addresses the sizing and design problem of a permanent magnet electrical machine power take-off system for a two-body wave energy converter, which is designed to support ocean sensing applications with sustained power. The design is based upon ground truth ocean data bi-spectrums (swell and wind waves) from Martha’s Vineyard Coastal Observatory in the year 2015. According to the ground truth ocean data, the paper presents the optimal harvesting power time series of the whole year. The electrical machine and energy storage static modeling are introduced in the paper. The paper uses the ground truth ocean data in March to discuss the model integration of the buoy dynamic model, the power take-off model, and the energy storage model. Electrical machine operation constraints are applied to ensure the designed machine can fulfill the buoy control requirements. The electrical machine and energy storage systems operation status is presented as well. Furthermore, rule-based control strategies are applied to the electrical machine for fulfilling specific design demands, such as improving power generating efficiency and downsizing the electrical machine scale. The corresponding required capacities of the energy storage system are discussed. This paper relates results to the wave data sets (different combinations of significant wave heights and periods of both swell and wind waves). In this way, the power take-off system rule-based control strategy determinations can rely on current ocean wave measurements instead of a large historical ocean wave database.

Multi-Objective Fuzzy Logic-Based Energy Management System for Microgrids with Battery and Hydrogen Energy Storage System

This paper proposes a fuzzy logic-based energy management system (EMS) for microgrids with a combined battery and hydrogen energy storage system (ESS), which ensures the power balance according to the load demand at the time that it takes into account the improvement of the microgrid performance from a technical and economic point of view. As is known, renewable energy-based microgrids are receiving increasing interest in the research community, since they play a key role in the challenge of designing the next energy transition model. The integration of ESSs allows the absorption of the energy surplus in the microgrid to ensure power supply if the renewable resource is insufficient and the microgrid is isolated. If the microgrid can be connected to the main power grid, the freedom degrees increase and this allows, among other things, diminishment of the ESS size. Planning the operation of renewable sources-based microgrids requires both an efficient dispatching management between the available and the demanded energy and a reliable forecasting tool. The developed EMS is based on a fuzzy logic controller (FLC), which presents different advantages regarding other controllers: It is not necessary to know the model of the plant, and the linguistic rules that make up its inference engine are easily interpretable. These rules can incorporate expert knowledge, which simplifies the microgrid management, generally complex. The developed EMS has been subjected to a stress test that has demonstrated its excellent behavior. For that, a residential-type profile in an actual microgrid has been used. The developed fuzzy logic-based EMS, in addition to responding to the required load demand, can meet both technical (to prolong the devices’ lifespan) and economic (seeking the highest profitability and efficiency) established criteria, which can be introduced by the expert depending on the microgrid characteristic and profile demand to accomplish.

A modified NSGA approach for optimal sizing and allocation of distributed resources and battery energy storage system in distribution network

The integration of distributed energy resources (DERs) causes fluctuation in system voltage, thus obstructing the voltage regulation and cause efficiency issue in performance of electrical network. This obstruction can overcome by proper sizing and allocation of DERs and battery energy storage system which in returns improves the performance of electrical power network. In this work, the optimal allocation of battery energy storage system and DERs has been proposed to reduce the voltage regulation issues. For this purpose, the modified non-sorting dominated genetic algorithm (NSGA) is used to optimally allocate and size the DERs and battery energy storage system. Furthermore, the results are compared with particle swarm optimization (PSO) in terms of power losses, voltage regulation and stability. It has been found that by utilizing modified non-sorting dominated genetic algorithm, the size of DER reduces and voltage profile improves as compared to particle swarm optimization (PSO). The simulations for particular modified NSGA have been performed on MATLAB software by keeping in mind the IEEE 33 bus system.

Optimal Sizing of a Hybrid Wind-Photovoltaic-Battery Plant to Mitigate Output Fluctuations in a Grid-Connected System

A novel optimization strategy is proposed to achieve a reliable hybrid plant of wind, solar, and battery (HWSPS). This strategy’s purpose is to reduce the power losses in a wind farm and at the same time reduce the fluctuations in the output of HWSPS generation. In addition, the proposed strategy is different from previous studies in that it does not involve a load demand profile. The process of defining the HWSPS capacity is carried out in two main stages. In the first stage, an optimal wind farm is determined using the genetic algorithm subject to site dimensions and spacing between the turbines, taking Jensen’s wake effect model into consideration to eliminate the power losses due to the wind turbines’ layout. In the second stage, a numerical iterative algorithm is deployed to get the optimal combination of photovoltaic and energy storage system sizes in the search space based on the wind reference power generated by the moving average. The reliability indices and cost are the basis for obtaining the optimal combination of photovoltaic and energy storage system according to a contribution factor with 100 different configurations. A case study in Thumrait in the Sultanate of Oman is used to verify the usefulness of the proposed optimal sizing approach.

Optimal Energy Storage System Positioning and Sizing with Robust Optimization

Optimal Energy Storage System Positioning and Sizing with Robust Optimization

The influence of electric vehicle charging strategies on the sizing of electrical energy storage systems in charging hub microgrids

Economic, ecologic, and social benefits support the rapid diffusion of grid-connected microgrids (MG). Economic feasibility still stands out as the primary goal of commercial MGs. A stationary electrical energy storage system (ESS) is often a central component of MGs, facilitating islanding and cost-effective management of main grid use. Therefore, previous research has focused on the sizing of stationary ESS. The advent of large-scale electric vehicle (EV) charging hub MGs (CHMGs) such as the one along the freeway A8 near Augsburg, Germany, profoundly changes the economically optimal capacity of stationary ESS. While it is well conceived that EVs can be aggregated and then compensated for stationary ESS, research still lacks quantifiable evidence and methodological guidance on how the charging strategy (immediate, controlled, bidirectional) influences the economically optimal capacity of the stationary ESS. To address this gap, this paper proposes a method that includes a mixed-integer linear programming model for scheduling decisions under various conceivable ESS capacities and provides scenario analyses on the EV charging strategies as well as on ESS cost. Thereby, the method thus identifies the economically optimal capacity of the ESS. The results show that in the considered CHMG near Augsburg, the stationary ESS sizing decision is relevant in all but extreme scenarios. In particular, the economically optimal stationary ESS capacity soars if more than 65% of the EVs begin to charge immediately and the storage costs falls below 150 EUR/kWh. In contrast, smaller portions of controlled charging EVs can already drastically reduce stationary ESS. Remarkably, this paper also gives quantitative evidence that investments in bidirectional charging do not to pay off in the CHMG near Augsburg.

Reliability Improvement of Radial Distribution System by Optimal Placement and Sizing of Energy Storage System using TLBO

Abstract This paper proposed an algorithm employing the Teacher Learning based Optimization (TLBO) method for optimal placement and sizing of energy storage systems (ESSs) to enhance the reliability of a radial distribution system. The formulation of the problem includes the cost of energy not supplied (CENS), an additional cost such as investment cost and operational cost of ESSs (Life cycle cost), and power loss due to line flows in the distribution system. The proposed algorithm utilizes the TLBO method to minimize the objective function. A various case study has been carried out using a 30 bus radial distribution system. A comparative performance analysis is also presented in the paper based on the results obtained by using TLBO, Particle Swarm Optimization (PSO), Differential Evolution (DE), and Genetic Algorithm (GA). Furthermore, rigorous analysis is performed by imposing a limit on the number of applied ESSs and by varying the maximum capacity of participating ESSs. Lastly, the application of the proposed method for placement of ESSs in the IEEE 69 bus distribution system provides a 17% reduction in the total cost of operation.

Allocation and sizing of battery energy storage system for primary frequency control based on bio-inspired methods: A case study

This paper presents a new procedure for optimal allocation and optimal sizing of a battery energy storage system (BESS) for primary frequency support in an isolated power system. For the BESS allocation, a transmission bus system with larger frequency decline is recognized and the BESS sizing is performed by a constrained optimization strategy based on a new modified metaheuristic, called Developed Harris Hawks Optimization (DHHO). The simulation results of the suggested DHHO are compared with Bat Optimization Algorithm (BOA) and Genetic Algorithm (GA) from the literature to show the method efficiency. The final results showed higher precision with lower required iterations for the suggested DHHO method. Also, the proposed DHHO gives lower investment costs for BESS with lower power and energy requirement toward the other compared methods.

Two-Layer Optimization Model for the Siting and Sizing of Energy Storage Systems in Distribution Networks

One of the most important issues that must be taken into consideration during the planning of energy storage systems (ESSs) is improving distribution network economy, reliability, and stability. This paper presents a two-layer optimization model to determine the optimal siting and sizing of ESSs in the distribution network and their best compromise between the real power loss, voltage stability margin, and the application cost of ESSs. Thereinto, an improved bat algorithm based on non-dominated sorting (NSIBA), as an outer layer optimization model, is employed to obtain the Pareto optimal solution set to offer a group of feasible plans for an internal optimization model. According to these feasible plans, the method of fuzzy entropy weight of vague set, as an internal optimization model, is applied to obtain the synthetic priority of Pareto solutions for planning the optimal siting and sizing of ESSs. By this means, the adopted fuzzy entropy weight method is used to obtain the objective function’s weights and vague set method to choose the solution of planning ESSs’ optimal siting and sizing. The proposed method is tested on a real 26-bus distribution system, and the results prove that the proposed method exhibits higher capability and efficiency in finding optimum solutions.

Optimal sizing of a utility-scale energy storage system in transmission networks to improve frequency response

The frequency response of a large power system is affected by the penetration of renewable energy sources (RESs), where a utility-scale energy storage system (ESS) can alleviate the problem. This paper presents a strategy for sizing an ESS to improve frequency response of transmission networks. The location of the ESS in the transmission network is determined through a sensitivity analysis targeting minimum line loading around a bus. The ESS sizing strategy considers the minimization of frequency deviation as well as rate of change of frequency (ROCOF) after generator or load tripping events. The tuning of PQ controller parameters of the ESS (active power part) is also performed for frequency response improvement. The proposed approach is tested in a modified IEEE-39 bus power system considering a variety of scenarios where RESs are integrated as four different schemes for peak and off-peak load conditions. DIgSILENT PowerFactory is used for developing, testing, and analyzing the system models. A fitness-scaled chaotic artificial bee colony (FSCABC) optimization algorithm (a hybrid meta-heuristic approach) is used for optimization through a Python script automating simulation events in PowerFactory. The results obtained from the FSCABC approach are verified through the application of a particle swarm optimization algorithm. The simulation results suggest that the proposed ESS sizing technique including ESS controller tuning can successfully improve the frequency response of a transmission network.

Theoretical energy storage system sizing method and performance analysis for wind power forecast uncertainty management

Forecasting uncertainties limit the widespread adoption of wind power generation. Energy storage systems (ESSs) are essential for managing uncertainty, and ESS sizing determines the availability of uncertainty management. However, most ESS sizing studies utilize heuristic approaches. Therefore, research on the determination of ESS sizing related to uncertainty management performance is needed. This paper proposes a theoretical ESS sizing method that considers the stochastic properties of the uncertainty. In the proposed method, the power subsystem (PS) and energy subsystem (ES) capacities, which are related to the instantaneous and accumulated uncertainty characteristics of the ESS, respectively, are determined in terms of the confidence interval of the uncertainty statistic. They are presented as simple formulas by applying the extreme value theory. Furthermore, to demonstrate the uncertainty management performance of ESS sizing, the mean absolute error (MAE) is analyzed, as the variance and absolute errors of the uncertainty determine the MAE of the PS and ES, respectively. A numerical study using real wind power generation and its forecasting data verifies that the proposed method suitably reflects the characteristics of the uncertainty, with an analysis gap between the analyzed MAE and the actual measured value of less than 1%. This study can act as a reference for the expected performance when using ESS and can be extended to the theoretical economic evaluation of ESS usage.

A Dual-Stage Modeling and Optimization Framework for Wayside Energy Storage in Electric Rail Transit Systems

In this paper, a dual-stage modeling and optimization framework has been developed to obtain an optimal combination and size of wayside energy storage systems (WESSs) for application in DC rail transportation. Energy storage technologies may consist of a standalone battery, a standalone supercapacitor, a standalone flywheel, or a combination of these. Results from the dual-stage modeling and optimization process have been utilized for deducing an application-specific composition of type and size of the WESSs. These applications consist of different percentages of energy saving due to regenerative braking, voltage regulation, peak demand reduction, estimated payback period, and system resiliency. In the first stage, sizes of the ESSs have been estimated using developed detailed mathematical models, and optimized using the Genetic Algorithm (GA). In the second stage, the respective sizes of ESSs are simulated by developing an all-inclusive model of the transit system, ESS and ESS management system (EMS) in MATLAB/Simulink. The mathematical modeling provides initial recommendations for the sizes from a large search space. However, the dynamic simulation contributes to the optimization by highlighting the transit system constraints and practical limitations of ESSs, which impose bounds on the maximum energy that can be captured from decelerating trains.

A new methodology for optimal location and sizing of battery energy storage system in distribution networks for loss reduction

Abstract In this study, a new methodology has been proposed for optimal allocation and optimal sizing of a lithium-ion battery energy storage system (BESS). The main purpose is to minimize the total loss reduction in the distribution system. The optimization process is applied using a newly developed type of Cayote Optimization Algorithm (COA). The proposed technique includes two different approaches. In the first approach, the optimization for allocation and the sizing are performed one by one and in the second approach, the optimization has been done simultaneously. To analyze the proposed system, four different scenarios have been analyzed which include different conditions without/with PVs and also using single/two BESS. The results showed that using two BESS can reduce the total error of the distribution system. the results also showed that using PVs can also decrease the total losses. Finally, the proposed approach based on ICOA is compared with Firefly Algorithm (FA), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO) to show the proposed method's prominence efficiency.

New hybrid probabilistic optimisation algorithm for optimal allocation of energy storage systems considering correlated wind farms

Abstract Wind power integration with high penetration in a power system is indispensable. However, wind power integration, especially with high level, raises the power system instability problems due to its natural variability and unpredictability, which increases system uncertainties. Thus, uncertainties and correlations amongst wind farms should be considered in a power system operation and planning. One of the best solutions for facilitating the wind power integration is the installation of an energy storage system (ESS). However, the location and sizing of ESSs should be optimally planned to achieve maximum benefits such as minimising total cost, time shifting, reliability and power quality enhancement, minimising power loss, improving the power factor and providing environmental support. In this paper, a new probabilistic discretising method is derived and developed to discretise the continuous joint power distribution of correlated wind farms. Combining the new probabilistic discretising method with a multi-objective hybrid particle swarm optimisation (MOPSO) and non-dominated sorting genetic algorithm (NSGAII), a new hybrid probabilistic optimisation algorithm is proposed. The proposed hybrid algorithm aims to search for the best location and size of energy storage system (ESSs) and considers the power uncertainties of multi-correlated wind farms. The objective functions to be minimised include a system's total expected cost restricted by investment budget, total expected voltage deviation and total expected carbon emission. IEEE 30-bus and IEEE 57-bus systems are adopted to perform the case studies using the proposed hybrid probabilistic optimisation algorithm. The simulation results demonstrate the effectiveness of the proposed hybrid method in solving the optimal allocation problem of ESSs and considering the uncertainties of wind farms’ output power and the correlation amongst them.

Optimal Sizing of Battery Energy Storage System in a Fast EV Charging Station Considering Power Outages

To determine the optimal size of an energy storage system (ESS) in a fast electric vehicle (EV) charging station, minimization of ESS cost, enhancement of EVs' resilience, and reduction of peak load have been considered in this article. Especially, the resilience aspect of the EVs is focused due to its significance for EVs during power outages. First, the stochastic load of the fast-charging station (FCS) and the resilience load of the EVs are estimated using probability distribution functions. This information is utilized to maintain the energy level in the ESS to ensure the resilience of EVs during power outages. Then, the annualized cost of the ESS is determined using the annual interest rate and lifetime of ESS components. Finally, the optimal ESS size is determined using the annualized ESS cost, penalty cost for buying power during peak hours, and penalty cost for resilience violations. Simulations along with sensitivity analysis of uncertainties (market price, arrival time of EVs, and the residual energy level of EVs), number of EVs in the FCS, and converter ratings are conducted. Simulation results have shown that increasing the penalty cost for peak intervals is a viable solution to decrease the peak load while controlling the cost of the FCS.

Optimal Sizing and Control of Battery Energy Storage Systems for Hybrid Turboelectric Aircraft

<div class="section abstract"><div class="htmlview paragraph">Hybrid-electric gas turbine generators are considered a promising technology for more efficient and sustainable air transportation. The Ohio State University is leading the NASA University Leadership Initiative (ULI) <i>Electric Propulsion: Challenges and Opportunities</i>, focused on the design and demonstration of advanced components and systems to enable high-efficiency hybrid turboelectric powertrains in regional aircraft to be deployed in 2030. Within this large effort, the team is optimizing the design of the battery energy storage system (ESS) and, concurrently, developing a supervisory energy management strategy for the hybrid system to reduce fuel burn while mitigating the impact on the ESS life. In this paper, an energy-based model was developed to predict the performance of a battery-hybrid turboelectric distributed-propulsion (BHTeDP) regional jet. A study was conducted to elucidate the effects of ESS sizing and cell selection on the optimal power split between the turbogenerators (TGs) and ESS. To this extent, the supervisory energy management strategy is formulated into a discrete time optimal control problem and solved via dynamic programming. The performance of BHTeDP was compared to a turboelectric distributed-propulsion (TeDP) next-gen aircraft that assumes improvements in weight, drag, and engine efficiency consistent with regional jet entering operation in 2035.</div></div>

Power Management Strategy for an Electric Vehicle Driven by Hybrid Energy Storage System

The selection, sizing, and management of energy storage system for an electric vehicle (EV) are critical while considering later's large-scale deployment. The complementary nature of battery and ultracapacitor (UC) makes the combination a suitable hybrid energy storage system (HESS) for EV since the peak to average power demand ratio is very high especially for an urban drive commonly characterized by frequent acceleration and deceleration. A suitably sized UC combined with the battery pack provides necessary high power density as well as high energy density. The HESS makes the EV more effective with improved dynamic response, higher kilometers per charge, and longer battery life. This paper presents a fuzzy logic control based power management strategy to employ UC to relieve the battery of peak discharge currents as well as rapid charging currents. The power conditioning unit limits the battery current as well as its rate of change by maintaining the UC state of charge in the desired limits. The paper presents some of the key simulation and experimental results for a prototype EV system tested for various modes of operations.

Power Output Smoothing for Renewable Energy System: Planning, Algorithms, and Analysis

The growing penetration of renewable energy sources in electricity generation will bring challenges to the power grid operations due to the intermittency and fluctuation of renewables. In this article, we employ an energy storage system (ESS) in a grid-connected renewable energy system (RES) to serve the electrical load from the power grid. We study the control algorithms of the ESS to smooth the renewable generation and analyze the tradeoff between the ESS size and the system performance, i.e., renewable utilization and operation cost. We provide performance guarantees of the proposed algorithms and propose an optimization framework to configure the ESS. To analyze the reliability of the RES, an analytical framework using the Markov modulated fluid queue is devised. It is shown in our simulation studies that the devised power output smoothing algorithm outperforms other existing algorithms in terms of operation cost under different sizes of ESSs. We also find that, to satisfy the renewable utilization requirement, the required ESS size blows up with both the amplitude of fluctuation and the intermittency level of the profile gap between the renewable generation and the load.

Improved gray wolf optimization algorithm for solving placement and sizing of electrical energy storage system in micro-grids

Micro-grids consist of distributed power generation systems (DGs), distributed energy storage devices (DSs), and loads. Micro-grids are small-scale networks at low voltage levels that are use to provide thermal and electrical loads of small locations where there is no access to the main electrical grid. Given the environmental and economic issues for these areas, micro-grids can be a good solution for energy production. In this paper, determining the size and location of optimal electrical energy storage systems is presented. In other side, a new method based on the cost benefit analysis for optimal sizing of an energy storage system in a microgrid (MG) is proposed. The uncertainties associated with renewable energy sources and the occurrence of defects in the grid connection network and the effect of the contribution of load responses in a micro-grid are taken into account. The combined system consists of wind turbines and fuel cells. Basically, wind power is not definitively available. The new proposed method is based on two-stage randomization design (TSRD) for modeling the effect of wind power uncertainty so that the predicted wind energy error is considered as the main random parameter in the model. A standard probability distribution function is used to represent the error variations. Given the continuity of the mentioned function, the probability error function is extracted using the new discrete method and a certain number of scenarios with a certain probability. Finally, the problem has been transformed into an optimization problem, and a gray wolf optimization (GWO) algorithm has been used to solve it. In the proposed developed model based on local and global search, the algorithm tries to reach the final result in the shortest possible time and with the most precision. The results of the simulation show the efficiency of the proposed method in solving the micro-grid problem.