Size Of Battery Energy Storage System Research Articles

Optimal Sizing of PV and BESS for a Smart Household Considering Different Price Mechanisms

The study on the sizing of renewable energy generation systems and energy storage systems together in a household considering different price mechanisms can further promote the development of the home energy management system (HEMS). In this paper, a HEMS expressed as a bi-level model is provided to investigated capacity allocation strategy of the photovoltaic (PV) and battery energy storage system (BESS) in a smart household considering: 1) the impact of electricity price mechanisms which include the time-of-use pricing (TOU), the real-time pricing (RTP), and the stepwise power tariff (SPT); 2) the effect of subsidies of PV; and 3) the uncertainty in the PV output and seasonal load profiles. Then, the hybrid approach which combines the cataclysmic genetic algorithm and the DICOPT solver in GAMS is employed to find an optimal solution. Finally, six cases with different price mechanisms and approaches, as well as the sensitivity analysis of optimal solution to subsidies are presented. Results indicate that, with the subsidies, only the PV system needs to be equipped in a household under the SPT, while the PV system and BESS need to be equipped together under the RTP and TOU. Only when the subsidies of PV reach a certain level will the installation of PV be considered.

Optimized Energy Management System to Reduce Fuel Consumption in Remote Military Microgrids

This paper presents an optimized energy management system (OEMS) to control the microgrid of a remote temporary military base (FOB) featuring diesel generators, a battery energy storage system (BESS), and photovoltaic (PV) panels. The information of the expected electric demand is suitably used to improve the sizing and management of the BESS, according to the days of operation. The OEMS includes power electronics to charge the batteries from either the PV source or the diesel generators, and it can function as a current source when it is supplementing the power from one of the generators or as a voltage source when it is the sole source of power for the loads. The new contribution of this paper includes the optimization of a FOB's microgrid, where critical loads must be serviced at all times. The proposed optimization, which uses Special Order Sets for the semicontinuous function handling, also integrates economic evaluations by properly taking into account how the size of BESS affects its charge/discharge cycle; thus, the FOBs’ battery lifetime, in addition to its fuel consumption. Results from optimization are employed by the OEMS to coordinate the energy sources, and match the critical and noncritical loads with the available supply. Fuel savings of $\approx {\text{30}}\%$ (and $\approx {\text{50}}\%$ adding the PV source) can be achieved with respect to the already improved, but not optimal, solution of a previous work.

A new strategy based on hybrid battery–wind power system for wind power dispatching

Battery energy storage system (BESS) is one of the best solutions to compensate the wind power fluctuations and forecasting errors for participation in the power markets by contribution in the energy production several hours ahead. Based on coordination of BESS and wind power, this study aims to present a new strategy to optimise the BESS size and increase the battery lifetime. In the proposed strategy, the average wind power is considered as the dispatch power to minimise the battery capacity and two back up battery sets are utilised to avoid shallow charge-discharge cycles for saving the battery efficiency and lifetime. In addition, the short-term operation criterion is chosen to deal with the prediction error effects. The proposed method is applied to a 2 MW wind turbine as a case study with 2 years wind data. Simulation results show the effectiveness of the proposed strategy.

FEASIBILITY ANALYSIS OF A PHOTOVOLTAIC/BATTERY ENERGY STORAGE HYBRID SYSTEM: AN HOURLY ESTIMATION BASED APPROACH AND A REAL LIFE CASE STUDY

This study has been undertaken to develop a consumer-oriented feasibility method for a hybrid photovoltaic (PV)-battery energy storage (BES) system by analyzing a real life house in Istanbul, Turkey, as a case study. The hourly electricity demand of the house was estimated by carrying out a detailed survey of the life style and daily habits of the household. No algorithm of any kind was used for the estimation of the energy demand with the exception of relating the lighting requirement to the daylight hours and the heating and cooling requirements to the seasonal weather changes. The developed method estimates the annual demand with an overall error of 8.68%. The net grid dependency and the feasibility of the PV-BES system was calculated for different combinations of PV and BES system sizes. It was found that when the maximum available roof area is used for PV installation and when the BES system size is increased, it is possible to achieve almost zero net grid dependency, and it is estimated that houses that are in regions with more abundant solar radiation and/or with lower annual electricity consumption, can reach zero net grid dependency. However, the feasibility indicator, which is the payback period, turned out to be no less than 25 years in any of the scenarios. The reasons for the infeasibility are the high prices of PV and BES systems as well as the current restriction in the regulations in Turkey, which prevents BES system owners from participating in unlicensed energy generation schemes and selling excess electricity back to the grid. In order to overcome this situation, regulations should be updated to allow BES system owners to benefit from feed-in-tariff schemes, thereby increasing the popularity of both PV and BES usage in Turkey.

Slide mode control of microgrid using small hydro driven single‐phase SEIG integrated with solar PV array

This study presents a susceptance theory-based sliding mode control (STSMC) algorithm for an improved power quality voltage and frequency control of a single-phase microgrid. The proposed microgrid includes a governor-free small hydro turbine-driven single-phase two winding self-excited induction generator (SEIG), a solar PV array and a battery energy storage system (BESS). The non-linear relationship among magnetising reactance, frequency and speed of SEIG along with random fluctuation in active power output of the solar PV array create the major challenge in frequency and voltage control of such renewable energy-based microgrid. The STSMC algorithm is found suitable to control frequency and voltage of such non-linear and complex system. In this proposed control, the system frequency control in dynamics and steady-state conditions and balance of power among various energy sources and BESS are achieved using the sliding mode control. The STSMC eliminates all possibilities of overshoot and undershoot problem in DC-link voltage of the VSC, which in turn reduces the required size of DC-link capacitor and BESS.

Stochastic optimal battery storage sizing and scheduling in home energy management systems equipped with solar photovoltaic panels

This paper presents an efficient home energy management system (HEMS) by optimal utilizing battery energy storage system (BESS) and photovoltaic (PV) systems. In the proposed HEMS, charging-discharging regime, capacity, and power of BESS are considered as design variables and optimally determined. Three operating conditions are considered for the home including: (i) home can receive energy from the network during off-peak low-cost hours, (ii) home can send energy to the main grid during on-peak high-cost hours for making the profit, and (iii) home can work on net-zero energy (NZE) model or standalone mode. The BESS is utilized to store energy during off-peak low-cost hours and discharge energy during on-peak high-cost hours. The proposed planning for determining the optimal operation strategy and sizing of BESS is expressed as a stochastic mixed integer nonlinear programming (MINLP). As well, output power produced by photovoltaic (PV) system is regarded as uncertain parameter and modeled by probability distribution function (PDF). Monte-Carlo Simulation (MCS) is applied to cope with uncertainties. The proposed stochastic MINLP is solved by Meta-heuristic optimization techniques. Simulation results demonstrate that the proposed HEMS can significantly reduce annual electricity bill. As well, NZE model can also be achieved by installing BESS and PV system at the same time.

Coordinated Control Strategy of a Battery Energy Storage System to Support a Wind Power Plant Providing Multi-Timescale Frequency Ancillary Services

With increasing penetrations of wind generation on electric grids, wind power plants (WPPs) are encouraged to provide frequency ancillary services (FAS); however, it is a challenge to ensure that variable wind generation can reliably provide these ancillary services. This paper proposes using a battery energy storage system (BESS) to ensure the WPPs’ commitment to FAS. This method also focuses on reducing the BESS's size and extending its lifetime. In this paper, a state-machine-based coordinated control strategy is developed to utilize a BESS to support the obliged FAS of a WPP (including both primary and secondary frequency control). This method takes into account the operational constraints of the WPP (e.g., real-time reserve) and the BESS (e.g., state of charge [SOC], charge and discharge rate) to provide reliable FAS. Meanwhile, an adaptive SOC-feedback control is designed to maintain SOC at the optimal value as much as possible, and, thus, reduce the size and extend the lifetime of the BESS. The effectiveness of the control strategy is validated with an innovative multi-area interconnected power system simulation platform that can mimic realistic power systems operation and control by simulating real-time economic dispatch, regulating reserve scheduling, multi-area automatic generation control, and generators’ dynamic response.

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.

Market-Driven Energy Storage Planning for Microgrids with Renewable Energy Systems Using Stochastic Programming

Battery Energy Storage Systems (BESS) can mitigate effects of intermittent energy production from renewable energy sources and play a critical role in peak shaving and demand charge management. To optimally size the BESS from an economic perspective, the tradeoff between BESS investment costs, lifetime, and revenue from utility bill savings along with microgrid ancillary services must be taken into account. The optimal size of a BESS is solved via a stochastic optimization problem considering wholesale market pricing. A stochastic model is used to schedule arbitrage services for energy storage based on the forecasted energy market pricing while accounting for BESS cost trends, the variability of renewable energy resources, and demand prediction. The uniqueness of the approach proposed in this paper lies in the convex optimization programming framework that computes a globally optimal solution to the financial trade-off solution. The approach is illustrated by application to various realistic case studies based on pricing and demand data from the California Independent System Operator (CAISO). The case study results give insight in optimal BESS sizing from a cost perspective, based on both yearly scheduling and daily BESS operation.

Impact Analysis of Demand Response Intensity and Energy Storage Size on Operation of Networked Microgrids

Integration of demand response (DR) programs and battery energy storage system (BESS) in microgrids are beneficial for both microgrid owners and consumers. The intensity of DR programs and BESS size can alter the operation of microgrids. Meanwhile, the optimal size for BESS units is linked with the uncertainties associated with renewable energy sources and load variations. Similarly, the participation of enrolled customers in DR programs is also uncertain and, among various other factors, uncertainty in market prices is a major cause. Therefore, in this paper, the impact of DR program intensity and BESS size on the operation of networked microgrids is analyzed while considering the prevailing uncertainties. The uncertainties associated with forecast load values, output of renewable generators, and market price are realized via the robust optimization method. Robust optimization has the capability to provide immunity against the worst-case scenario, provided the uncertainties lie within the specified bounds. The worst-case scenario of the prevailing uncertainties is considered for evaluating the feasibility of the proposed method. The two representative categories of DR programs, i.e., price-based and incentive-based DR programs are considered. The impact of change in DR intensity and BESS size on operation cost of the microgrid network, external power trading, internal power transfer, load profile of the network, and state-of-charge (SOC) of battery energy storage system (BESS) units is analyzed. Simulation results are analyzed to determine the integration of favorable DR program and/or BESS units for different microgrid networks with diverse objectives.

Enhancement of a Small Power System Performance Using Multi-Objective Optimization

Optimal multi-objective design of proportional-integral-derivative controller parameters for a small power system using epsilon multi-objective genetic algorithm ( $\varepsilon $ -MOGA) has been presented in this paper. The small power system includes a wind turbine generator (WTG), a diesel generator, a battery energy storage system (BESS), and a load. The proposed scheme is applied for controlling the pitch angle system of the WTG to minimize the wind turbine mechanical blades stress, reduce the wind output power deviation, control the system frequency, and decrease the size of BESS by regulating its charging level. The deviations of input wind power are considered in a frequency domain. The low-frequency component is reduced by the pitch angle control system of WTG, while the high-frequency component is mitigated by the charge/discharge of the BESS, respectively. The input of the pitch angle control system of WTG is determined according to the low-frequency component of the input wind power deviation and the BESS state of charge. The output power of BESS is determined according to its state of charge, the high-frequency component of the input wind power deviation, and the frequency deviations. The effectiveness of the proposed controller is confirmed by numerical simulations.

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.

Generation-scheduling-coupled battery sizing of stand-alone hybrid power systems

Properly sizing the battery energy storage system (BESS) of a stand-alone hybrid power system is an important step to guarantee its reliability and low cost. This study applies the technique of storage-integrated generation scheduling using metaheuristics to the BESS sizing, which helps to achieve the optimal scheduling scheme for each sizing plan, as its advantage over the rule-based sizing method. Such technique incorporates the storage dispatch with the scheduling of the dispatchable generators, and is formulated and solved as an optimisation with metaheuristics. Compared with existing approaches of storage-integrated generation scheduling, the metaheuristics-enabled approach proposed here relieves the modelling complexity of the optimisation, by using fewer decisions variables. Different degrees of solar and wind, as the renewable energy, are penetrated into the system, together with traditional diesel generators. The mixed-coded non-dominated sorting genetic algorithm II (NSGA-II) is employed as the main numeric tool, which shows the advantage of mixed-coded modelling over the real-coded modelling for the generation scheduling problem. The numeric evaluation of the system planning adopts the levelised cost of electricity (LCOE) as the economic indicator, to guide the real system planning and operation.

Optimum battery energy storage system using PSO considering dynamic demand response for microgrids

This article proposes a novel optimum sizing of battery energy storage system (BESS) using particle swarm optimization (PSO) incorporating dynamic demand response (DR) to improve a fast, smooth and secure system stability and performance, avoiding a microgrid from instability and system collapse during an emergency situation. An optimum size of BESS integrating DR can play an important role in frequency control of the microgrid in order to rapidly improve the system stability, restore the power equilibrium and prevent system collapse in the microgrid. The optimum size of BESS is evaluated by PSO incorporating DR based on frequency control of the microgrid. The results show that the optimum size of BESS-based PSO with DR can improve a fast, smooth and safe system performance and dynamic stability compared with the optimum size of BESS-based simulated annealing (SA) with DR and the conventional size. Nevertheless, the proposed sizing methods also determined the impact of BESS specified costs between modern and conventional BESS technologies. The capital cost, operating and maintenance cost of BESS were then investigated and compared in terms of economical performance for microgrid operations.

Optimal placement and sizing of the storage supporting transmission and distribution networks

Developments of renewable energy resources imposes many uncertainties and variabilities in power grids. One of the best approaches to mitigate these stochastic disturbances is thought the use of Battery Energy Storage System (BESS). Besides application of the BESS such as decreasing the disturbances in distribution system, the grid frequency can be controlled in contingencies using the appropriate storage in transmission network to compensate the power shortage. Thus, the optimal siting and sizing of the BESS is important to have the minimum costs and losses. This paper describes a heuristic method to find the optimal location(s) and capacity of a multi-purpose BESS including transmission and distribution parts. In the transmission storage part, a sensitive analysis is performed using Complex-Valued Neural Networks (CVNN) and Time Domain Power Flow (TDPF) in order to detect the optimal BESS location(s). Additionally, running TDPF and Economic Dispatch (ED) leads to the optimal BESS size. In the distribution storage part, the optimal BESS size is calculated to perform distribution grid services such as peak load shaving and load curve smoothing. The proposed method has been applied to a real model (Maui island in Hawai'i -United States) to calculate the optimal results for both transmission and distribution sides.

Intelligent Determination of a Battery Energy Storage System Size and Location Based on RBF Neural Networks for Microgrids

As an installation of Battery Energy Storage Systems (BESS) at a non-optimum size and location in a microgrid can cause stability issues, increase in cost, system losses and larger BESS size. Thus, the optimum size and location of BESS is a main challenge for combining BESS into a microgrid. For this reason, this paper proposes a novel method to evaluate an optimal size and location of the BESS using the Radial Basis Function Neural Networks (RBFNN). The proposed method consists of a two-stage based on an optimum size process and optimum location process. In the first stage, the optimal size of the BESS is evaluated using the RBFNN based on frequency and voltage control so that frequency and voltage of the microgrid can return to nominal values under the sudden changes in the microgrid. In the second stage, the optimal location of the BESS is determined using the RBFNN based on minimizing power losses in the microgrid. With a suitable RBFNN training, the results indicate that the proposed RBFNN method can achieve the superb performances in predicting the optimum size and location of the BESS with a minor change compared to the measured data based on the simulation. Therefore, it is obvious that if BESS is located at the optimum location and has the optimum size, it can save an enormous amount of power in a microgrid and can avoid the microgrid from instability and system collapse.

Optimization of a battery energy storage system using particle swarm optimization for stand-alone microgrids

The main challenge in integrating a Battery Energy Storage System (BESS) into a microgrid is to evaluate an optimum size of BESS to prevent the microgrid from instability and system collapse. The installation of BESS at a random size or non-optimum size can increase in cost, system losses and larger BESS capacity. Thus, this paper proposes the new method to evaluate an optimum size of BESS at minimal total BESS cost by using particle swarm optimization (PSO)-based frequency control of the stand-alone microgrid. The research target is to propose an optimum size of BESS by using the PSO method-based frequency control in order to prevent the microgrid from instability and system collapse after the loss of the utility grid (e.g., blackout or disasters) and minimize the total cost of BESS for 15years installation in the microgrid. Then, the economical performance of BESS with modern different storage technologies is investigated and compared in the typical microgrid. Results show that the optimum size of BESS-based PSO method can achieve higher dynamic performance of the system than the optimum size of BESS-based analytic method and the conventional size of BESS. In terms of BESS economical performance with modern storage technologies, the installation of the polysulfide–bromine BESS is likely to be more cost-effective than the installation of the vanadium redox BESS for 15years installation in the typical microgrid. It is concluded that the proposed PSO method-based frequency control can improve significantly power system stability, grid security, and planning flexibility for the microgrid system. At the same time, it can fulfill the frequency control requirements with a high economic profitability.

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.

Predictive control and sizing of energy storage to mitigate wind power intermittency

AbstractThis paper studies how the control algorithm impacts the required capacity of battery energy storage system (BESS) to mitigate wind intermittency. We study a futuristic scenario in which wind generation is traded in the energy market on an hourly basis, and the wind power producer has to procure reserves to handle wind fluctuations. The wind power producer can avoid paying for conventional reserves by charging/discharging BESS to compensate for wind surplus and deficit. We develop control algorithms using the model predictive control (MPC) methodology, which incorporates wind forecasts for the next few hours when determining wind scheduling. The MPC algorithm is developed by solving a non‐linear optimization problem to minimize operation costs to the wind power producer. In addition to operation costs, the MPC algorithm considers two practical aspects: the efficiency loss of BESS and the smoothness in wind power scheduling. BESS sizing is studied by parametric analyses. Simulations show that BESS is more effective in reducing operation costs and reducing wind curtailment than conventional reserves. In addition, MPC is a horizon‐based control algorithm and can preview future information in its control action. Simulations also show that MPC consistently outperforms an instantaneous heuristic algorithm that does not use future information. Therefore, we confirm that MPC can reduce the BESS capacity required to cover wind uncertainties. Copyright © 2015 John Wiley & Sons, Ltd.

A robust [formula omitted] controller based frequency control approach using the wind-battery coordination strategy in a small power system

In this paper, a coordinated control method for a wind turbine generator (WTG) and a battery energy storage system (BESS) of a small power system have been presented. The coordinated control approach applies to control the system frequency to reduce the size of BESS and to control the pitch angle system to mitigate the wind turbine blades stress. To achieve these objectives simultaneously, the robust H∞ control method is applied in this paper. The pitch angle system of the WTG and the output power command system of the BESS are controlled by the H∞ controllers. The output power command of the WTG is determined from the wind velocities and it is controlled by the pitch angle control system. Concurrently, the output power command of the BESS is calculated according to the state of charge and the frequency deviations in the small power system. The small power system includes a WTG, a BESS and a diesel generator. Numerical simulations are conducted by the MATLAB/SIMULINK® environment to validate the proposed method.