Sizing Of Energy Storage System Research Articles

Optimization of battery energy storage system size and power allocation strategy for fuel cell ship

AbstractThe fuel cell system (FCS) is commonly combined with an energy storage system (ESS) for enhancing the performance of the ship. Consequently, the battery ESS size and power allocation strategy are critical for the hybrid energy system. This paper focuses on designing a method to solve these two problems. First, a battery degradation model is employed to assess the ESS lifetime. Subsequently, the sizing problem and the optimal power allocation are integrated into a cost‐minimization problem, which is solved by a double‐loop optimization approach. The inside loop utilizes the battery degradation model to calculate ESS lifetime. In the outside loop, a power allocation strategy based on the hybrid Particle Swarm Optimization algorithm and Gray Wolf Optimization algorithm is presented. Finally, the power allocation strategy is extended to real‐time implementation by the equivalent consumption minimization strategy (ECMS) and an improved ECMS is proposed to make the FCS operates near the maximal efficiency point. Compared with ECMS, the operating cost reduces by 0.26%. The result indicates that the proposed method can optimize the ESS size efficiently, and the power allocation strategy can assure the stable operation of the fuel cell ship.

Techno-economic analysis of thermal energy storage systems

The rising energy demand can be met by increasing the share of renewable energy by overcoming the barriers of poor conversion efficiency, intermittent energy supply, and lower thermo-economic viability. Thermal energy storage technology can play a pivotal role in addressing these challenges. Thermal energy storage systems are still in the developing phase due to low energy density, higher investments, and poor storage efficiency. The present study is carried out to disseminate updated information pertaining to the technological innovations and performance analysis of different types of thermal energy storage systems. The primary focus of this study is to present a critical analysis and discussion on the current status of thermal energy storage technology that can help identify the thrust areas of future research for improved techno-economic designs. The analysis unfolds the need to reduce the size of sensible energy storage systems by enhancing the volumetric heat transfer rates and improving the thermal response of latent energy storage systems by enhancing the thermal conductance of phase change materials. The thermal response of the latent energy storage systems can be improved by the addition of extended surfaces, composites of PCM and metal foam, PCM, and metal powder. Hybrid systems are relatively new therefore more explorations are needed for ensuring the compactness and the economic feasibility of these systems.

Optimal sizing and operation of hybrid energy storage systems in co‐phase traction power supply system considering battery degradation

AbstractTo recycle regenerative braking energy (RBE) while reducing demand charge in electrified railway, a co‐phase power supply system with hybrid energy storage system (HESS) is implemented. However, the dynamical degradation characteristic of battery is necessary to be considered in optimal operation of HESS. A bi‐level model considering battery degradation is proposed to obtain optimal sizing and operation of HESS. The proposed model includes a novel real‐time energy management strategy (EMS) using average power as thresholds, to effectively reduce the demand charge and energy consumption charge. Thresholds are dynamically adjusted with the consideration of battery capacity degradation. A real measured load profile from Beijing‐Shanghai high speed railway is studied. The results demonstrate that the proposed EMS performs better than previous EMS in cost saving. The dynamically adjusted thresholds of EMS are proved to be essential under the consideration of battery degradation. With the optimal sizing of the HESS, the traction substation can achieve 8.69% annual saving of demand charge and recycle 52.33% of the RBE. The results also show that a traction substation equipped with the HESS yields higher economic benefit than the energy storage systems equipped with only a battery or a supercapacitor.

Operator-as-a-Consumer: A Novel Energy Storage Sharing Approach Under Demand Charge.

Energy storage systems (ESSs)-based demand response (DR) is an appealing way to save electricity bills for consumers under demand charge and time-of-use (TOU) price. In order to counteract the high investment cost of ESS, a novel operator-enabled ESS sharing scheme, namely, the "operator-as-a-consumer (OaaC)," is proposed and investigated in this article. In this scheme, the users and the operator form a Stackelberg game. The users send ESS orders to the operator and apply their own ESS dispatching strategies for their own purposes. Meanwhile, the operator maximizes its profit through optimal ESS sizing and scheduling, as well as pricing for the users' ESS orders. The feasibility and economic performance of OaaC are further analyzed by solving a bilevel joint optimization problem of ESS pricing, sizing, and scheduling. To make the analysis tractable, the bilevel model is first transformed into its single-level mathematical program with equilibrium constraints (MPEC) formulation and is then linearized into a mixed-integer linear programming (MILP) problem using multiple linearization methods. Case studies with actual data are utilized to demonstrate the profitability for the operator and simultaneously the ability of bill saving for the users under the proposed OaaC scheme.

Dynamic modeling and control of a solar-powered Brayton cycle using supercritical CO2 and optimization of its thermal energy storage

Recompression Brayton cycles using supercritical CO2 as the working fluid appear as a prominent alternative for thermo-solar power applications. Also, solar energy's natural variability and intermittence make it difficult for solar plants to operate consistently and predictably. Thus, two of the most explored mitigating alternatives are thermal energy storage and auxiliary heating systems. Hence, this paper used actual meteorological data and transient numerical simulations to investigate the power output dynamics of a 10 MW plant. The modeling of an active control system of the working fluid mass inventory allowed the plant to operate in a stable manner while accounting for the significant variations in the fluid's thermophysical properties. Also, the study investigated the effect of the sizes of the thermal energy storage system and solar collectors field on the dynamics of the system. Finally, statistical analyses with actual meteorological data from Florianopolis/Brazil for nine days between 2017 and 2018 supported determining the optimal thermal energy storage system size. Hence, depending on the daily conditions, the results showed the operating settings that minimize the use of auxiliary heating with reductions of fuel consumption larger than 10%.

Composite Demand-Based Energy Storage Sizing for an Isolated Microgrid System

This paper presents a comprehensive model for optimal energy storage system (ESS) design for an isolated microgrid. The model presented is a mixed integer linear program (MILP) that considers seasonal varying generation (VG) demand, more specifically seasonal solar cell generator (SCG) demand, SCG maintenance (failure and restoration) rates, and practical operation of conventional generation (CG) while satisfying the required demand and reserve. The model is based on unit commitment (UC) to simulate real operations and physical constraints of CG units, the power balance, and reserve requirements. The objective function aims at minimizing the associated cost of CG, namely, production (fuel), costs of startup and shutdown procedures, and the investment cost of power and energy. The proposed model is assessed on a case study system consisting of multiple SCGs in addition to CG to meet a specific demand. The proposed model showed that the ESS sizing when considering Li-Ion technology and a SCG penetration of 25% was on average approximately 3 MWh and 1.70 MW. Meeting the demand and reserve requirements were the two major constraints when determining the optimal ESS sizing. Moreover, introducing the ESS substantially reduced the operating cost of the system.

Optimal placement and capacity sizing of energy storage systems via NSGA-II in active distribution network

In recent years, with the rapid development of renewable energy, the penetration rate of renewable energy generation in the active distribution network (ADN) has increased. Because of the instability of renewable energy generation, the operation stability of ADN has decreased. Due to the ability to cut peak load and fill valley load, battery energy storage systems (BESSs) can enhance the stability of the electric system. However, the placement and capacity of BESSs connected to ADN are extremely significant, otherwise, it will lead to a further decline in the stability of ADN. To ensure the effectiveness of the BESSs connected to the grid, this work uses the fuzzy kernel C-means (FKCM) method for scene clustering. Meanwhile, a multi-objective optimization model of BESS configuration is established with the objective of BESS configuration cost, voltage fluctuation, and load fluctuation, and solved by non-dominated sorting genetic algorithm-II (NSGA-II). In this work, the grey target decision method based on the entropy weight method (EWM) is used to obtain the optimal compromise solution from the Pareto non-dominated set. Moreover, the proposed method is tested and verified in the extended IEEE-33 node system and the extended IEEE-69 node system. The results show that the BESSs configuration scheme obtained by NSGA-II can effectively reduce the fluctuation of voltage and load, and improve the stability of ADN operation.

Stochastic multi-objective optimal sizing of battery energy storage system for a residential home

The deployment of renewable local generation sources at home can help reduce emission contributions within residential homes. Furthermore, the adoption of the residential PV system is increasing as the economics of installation continues to decrease. An optimally sized battery energy storage system can help maximise the benefits of the power generated from the PV systems while being economical. In this paper, a stochastic optimisation problem is formulated to determine the optimal size of the energy storage system and investigate the benefits of uncertainty consideration in the formulation of the sizing optimisation problem. A multi-objective problem is formulated consisting of two objectives: minimise the cost of purchasing the battery energy storage system, and minimise the amount of energy imported from the grid within the period. The stochastic problem is formulated as a two-stage scenario stochastic problem. The optimal sizing and operation problem was formulated as a mixed-integer linear programming problem and solved using the CPLEX solver. This work also utilises a Monte Carlo approach to deal with the uncertainty in load forecasting. Simulation results show that the proposed approach can estimate an optimal battery energy storage system at the current cost of BESS and clearly indicate the benefit of a stochastic approach.

Voltage regulation and power loss mitigation by optimal allocation of energy storage systems in distribution systems considering wind power uncertainty

Due to the ever increasing in the energy demand and the growth of the pollution problem, further interest in renewable energy becomes prominent, especially wind energy. However, the intermittent nature of the wind speed results in the system's insecurity problems. Integrating the energy storage system (ESS) optimally is the best so far solution. In this paper, a probabilistic discretizing method is derived and generalized to consider the uncertainties of the wind generators. Moreover, a mixed integer non-linear optimisation problem is constructed comprises three objective functions to be minimized simultaneously. The objectives comprising the total operation and planning cost of ESSs, the average voltage deviation and average power losses. The solving method is a hybrid algorithm incorporates the non-dominated sorting genetic algorithm (NSGAII), multi-objective particle swarm optimisation (MOPSO) and a decision-making algorithm. The aim is to determine the optimal location and sizing of ESSs considering their reactive power capability. 69 radial bus distribution system is adopted to demonstrate the efficacy of the proposed hybrid algorithm and performing some case studies. The simulation results verified the capability of ESSs to enhance the voltage profile and reduce the power losses, notably when reactive power capability is examined.

Optimal sizing method with sensitivity analysis for hybrid energy storage system in electric vehicle using hybrid technique

This manuscript proposes a hybrid method for optimum sizing and energy management (EM) of hybrid energy storage systems (HESSs) in Electric vehicle (EV). The proposed hybrid method is combined performance of Honey Badger Algorithm (HBA) and recalling-enhanced recurrent neural network (RERNN), commonly called HBA-RERNN method. The major objective of proposed system is reducing the vehicle life time cost. The HESSs are incorporated with battery and super capacitor (SC). The proposed method is utilized to solve combined energy management and optimization size. Based on the variables, such as size of battery pack and super capacitor pack, HESS size is reflected. Depend on various sensitivity factors, optimum hybrid energy storage systems size and financial costs are analyzed. At last, the performance of proposed system is implemented on MATLAB site and compared with several existing systems. From this simulation outcome, it concludes that the proposed system diminishes the overall cost and battery degradation cost as 66625 USD than the existing systems. The efficiency of the proposed system achieves 94.8763%.

Review on Ancillary Services and Optimal Sizing of Energy Storage System in Microgrid

Review on Ancillary Services and Optimal Sizing of Energy Storage System in Microgrid

Optimum configuration of a dispatchable hybrid renewable energy plant using artificial neural networks: Case study of Ras Ghareb, Egypt

<abstract> <p>The present paper examines the potential hybridization for a dispatchable hybrid renewable energy system (HRES). The plant has been examined for existence in the city of Ras Ghareb, Egypt and follows the load profile of Egypt. The proposed plant configuration contains a wind plant, a solar photovoltaic plant, vanadium redox flow batteries (VRFBs) and a hydrogen system consisting of an electrolyzer, hydrogen tanks and fuel cells (FCs), the latter of which are for both daily and seasonal storage. Professional software tools have been used to model the wind and solar resources. Simulations for both the battery and hydrogen generation and electrolyzer operation are also considered. The output of these simulations is used to configure the HRES using MATLAB. The optimization objective function of the HRES is based on the least levelized cost of energy (LCOE) with constraints for a zero loss of power supply probability (LPSP) and curtailed energy. The optimization has been achieved by using artificial neural networks and a MATLAB program. The results show that the optimal system can handle 91.2% of the load directly from the renewable energy sources (wind and solar), while the rest of the demand comes from the storage system (FCs and VRFBs). The LCOE of the optimal system configuration is (USD) 9.3 %/kWh, with both the LPSP and curtailed energy at zero values. This cost can be reduced by 14.5% if the constraint of zero curtailed energy is relaxed by 10%. Despite the load being maximum in summer, the energy storage requirement is predicted to be maximum in winter due to the low wind profile and solar radiation in winter months. Energy storage system size is dependent on both seasonal and daily variations in wind and solar profiles. In addition, energy storage size is the main factor that determines the LCOE of the system.</p> </abstract>

Provision of Ramp-rate Limitation as Ancillary Service from Distribution to Transmission System: Definitions and Methodologies for Control and Sizing of Central Battery Energy Storage System

Provision of Ramp-rate Limitation as Ancillary Service from Distribution to Transmission System: Definitions and Methodologies for Control and Sizing of Central Battery Energy Storage System

Enhanced Microgrid Reliability Through Optimal Battery Energy Storage System Type and Sizing

Enhanced Microgrid Reliability Through Optimal Battery Energy Storage System Type and Sizing

Optimal Placement of Multiple DG Units With Energy Storage in Radial Distribution System by Hybrid Techniques

In recent years, distributed generations (DGs) are extremely fast in detecting their location, which helps to satisfy the ever-increasing power demands. The placement of energy storage systems (ESSs) could be a substantial opportunity to enhance the presentation of radial distribution system (RDS). The major part of DG units in RDS deals with the detection of ideal placement and size of the DGs, which efficiently balance the power loss and voltage stability. The ideal location and size of ESSs are examined in standard IEEE-33 and 69 bus systems, which is important to reduce power losses. Nowadays, several algorithms or techniques are modified for the development of hybrid algorithms to improve the quality of DG allocation. In this research, a hybrid shuffled frog leap algorithm (SFLA) with ant lion optimizer (SFLA-ALO) is proposed for the optimal placement and size of the DG and ESS in the RDS to reduce power losses and maintain the stability of voltage. The performance of the proposed SFLA-ALO technique is compared with the implemented BPSO-SFLA technique.

Hybrid Model-Based BESS Sizing and Control for Wind Energy Ramp Rate Control

This paper presents a hybrid model constituting dynamic smoothing technique and particle swarm optimization techniques to optimally size and control battery energy storage systems for wind energy ramp rate control and power system frequency performance enhancement. In today’s modern power system, a high-proportion renewable energy grid is inevitable. This high-proportion renewable energy grid is a power system with abundant integration of renewable energy resources under the presence of energy storage tools. Energy storage tools are integrated into such power systems to balance the fluctuation and intermittence of renewable energy sources. One of the requirements in a high-proportion renewable energy grid is the fractional power balance between generation and load. One of the requirements set by power system regulators is the generation variation between two time points. A power producer is mandated to satisfy the ramp rate requirement set by the grid owner. This paper proposes dynamic smoothing techniques for initial size determination and particle swarm optimization based on optimal sizing and control of battery energy storage systems for ramp rate control and frequency regulation performance of a power system integrated with a large percentage of wind energy systems. Wind energy data taken from Zhangjiakou wind farm in China are used. The results indicate that the battery energy storage system improves the ramp rate characteristics of the wind farm. In addition, the virtual inertia capability of the battery energy storage system enabled the transient and steady-state frequency response of the test power system to improve significantly.

Coordinated expansion planning of transmission and distribution systems integrated with smart grid technologies

Integration of smart grid technologies in distribution systems, particularly behind-the-meter initiatives, has a direct impact on transmission network planning. This paper develops a coordinated expansion planning of transmission and active distribution systems via a stochastic multistage mathematical programming model. In the transmission level, in addition to lines, sitting and sizing of utility-scale battery energy storage systems and wind power plants under renewable portfolio standard policy are planned. Switchable feeders and distributed generations are decision variables in the distribution level while the impact of demand response programs as a sort of behind-the-meter technologies is accommodated as well. Expansion of electric vehicle taxi charging stations is included as a feasible option in both transmission and distribution levels. In order to deal with short-term uncertainty of load demand, renewable energy sources output power, and the charging pattern of electric vehicle taxis in each station, a chronological time-period clustering algorithm along with Monte Carlo simulation is utilized. The proposed model is tackled by means of Benders Dual Decomposition (BDD) method. The IEEE RTS test system (as the transmission system) along with four IEEE 33-node test feeders (as distribution test systems) are examined to validate effectiveness of the proposed model.

Modernized Planning of Smart Grid Based on Distributed Power Generations and Energy Storage Systems Using Soft Computing Methods

The modest objective is to check the integrated effect of energy storage systems (ESSs) and distributed generations (DGs) and compare the optimization of the size and location of ESS and DG to explore its challenges for smart grids (SGs) modernization. The research enlisted different algorithms for cost-effectiveness, security, voltage control, and less power losses. From this perspective, optimization of the distribution network’s energy storage and capacity are being performed using a variety of methods, including the particle swarm, ant-lion optimization, genetic, and flower pollination algorithms. The experimental outcomes demonstrate the effectiveness of these techniques in lowering distribution network operating costs and controlling system load fluctuations. The efficiency and dependability of the distribution network (DN) are both maximized by these strategies.

Bilevel optimization model for sizing of battery energy storage systems in a microgrid considering their economical operation

Battery energy storage systems (BESSs) play extremely important roles in managing power supply and demand in a microgrid. The BESSs, on the other hand, have problems of high initial investment costs and limited lifetime. In this paper, a problem formulation and its solution are presented for sizing an aggregated BESS in a microgrid that minimizes the sum of initial costs of the aggregated BESS and operational costs of the microgrid. The target problem is originally categorized into a bilevel optimization because it comprises two optimization problems: the sizing of aggregated BESS and the operational scheduling of microgrid’s components. The authors, however, apply the Karush–Kuhn–Tucker approach and reformulate the target problem as a single-level optimization problem. This reformulation enables us to treat the problem as if it was a type of operation scheduling problems. That is, the solution techniques developed to obtain the optimal operation scheduling are applicable by the problem reformulation. As the basis for the solution method, an algorithm combining binary particle swarm optimization and quadratic programming is applied in this paper. To verify the validity of the proposal, numerical simulations are performed, and their results are discussed.

Technical Energy Assessment and Sizing of a Second Life Battery Energy Storage System for a Residential Building Equipped with EV Charging Station

This study investigates the design and sizing of the second life battery energy storage system applied to a residential building with an EV charging station. Lithium-ion batteries have an approximate remaining capacity of 75–80% when disposed from Electric Vehicles (EV). Given the increasing demand of EVs, aligned with global net zero targets, and their associated environmental impacts, the service life of these batteries, could be prolonged with their adoption in less demanding second life applications. In this study, a technical assessment of an electric storage system based on second life batteries from electric vehicles (EVs) is conducted for a residential building in the UK, including an EV charging station. The technical and energy performance of the system is evaluated, considering different scenarios and assuming that the EV charging load demand is added to the off-grid photovoltaic (PV) system equipped with energy storage. Furthermore, the Nissan Leaf second life batteries are used as the energy storage system in this study. The proposed off-grid solar driven energy system is modelled and simulated using MATLAB Simulink. The system is simulated on a mid-winter day with minimum solar irradiance and maximum energy demand, as the worst case scenario. A switch for the PV system has been introduced to control the overcharging of the second life battery pack. The results demonstrate that adding the EV charging load to the off-grid system increased the instability of the system. This, however, could be rectified by connecting additional battery packs (with a capacity of 5.850 kWh for each pack) to the system, assuming that increasing the PV installation area is not possible due to physical limitations on site.