Modeling,Simulation,and Risk Analysis of Battery Energy Storage Systems in New Energy Grid Integration Scenarios

Energy storage batteries can smooth the volatility of renewable energy sources.The operating conditions during power grid integration of renewable energy can affect the performance and failure risk of battery energy storage system(BESS).However,the current modeling of grid-connected BESS is overly simplistic,typically only considering state of charge(SOC)and power constraints.Detailed lithium(Li)-ion battery cell models are computationally intensive and impractical for real-time applications and may not be suitable for power grid operating conditions.Additionally,there is a lack of real-time batteries risk assessment frameworks.To address these issues,in this study,we establish a thermal-electric-performance(TEP)coupling model based on a multitime scale BESS model,incorporating the electrical and thermal characteristics of Li-ion batteries along with their performance degradation to achieve detailed simulation of grid-connected BESS.Additionally,considering the operating characteristics of energy storage batteries and electrical and thermal abuse factors,we developed a battery pack operational riskmodel,which takes into account SOCand charge-discharge rate(Cr),using amodified failure rate to represent the BESS risk.By integrating detailed simulation of energy storage with predictive failure risk analysis,we obtained a detailed model for BESS risk analysis.This model offers a multi-time scale integrated simulation that spans month-level energy storage simulation times,day-level performance degradation,minutescale failure rate,and second-level BESS characteristics.It offers a critical tool for the study of BESS.Finally,the performance and risk of energy storage batteries under three scenarios—microgrid energy storage,wind power smoothing,and power grid failure response—are simulated,achieving a real-time state-dependent operational risk analysis of the BESS.

A distributed VSG control method for a battery energy storage system with a cascaded H-bridge in a grid-connected mode

With the high penetration of renewable energy,new challenges,such as power fluctuation suppression and inertial support capability,have arisen in the power sector.Battery energy storage systems play an essential role in renewable energy integration.In this paper,a distributed virtual synchronous generator(VSG)control method for a battery energy storage system(BESS)with a cascaded H-bridge converter in a grid-connected mode is proposed.The VSG is developed without communication dependence,and state-of-charge(SOC)balancing control is achieved using the distributed average algorithm.Owing to the low varying speed of SOC,the bandwidth of the distributed communication networks is extremely slow,which decreases the cost.Therefore,the proposed method can simultaneously provide inertial support and accurate SOC balancing.The stability is also proved using root locus analysis.Finally,simulations under different conditions are carried out to verify the effectiveness of the proposed method.

A novel coordinated control strategy considering power smoothing for a hybrid photovoltaic/battery energy storage system

This work presents a novel coordinated control strategy of a hybrid photovoltaic/battery energy storage(PV/BES) system. Different controller operation modes are simulated considering normal, high fluctuation and emergency conditions. When the system is grid-connected, BES regulates the fluctuated power output which ensures smooth net injected power from the PV/BES system. In islanded operation, BES system is transferred to single master operation during which the frequency and voltage of the islanded microgrid are regulated at the desired level. PSCAD/EMTDC simulation validates the proposed method and obtained favorable results on power set-point tracking strategies with very small deviations of net output power compared to the power set-point. The state-of-charge regulation scheme also very effective with SOC has been regulated between 32% and 79% range.

Multifunction Battery Energy Storage System for Distribution Networks

Battery Energy Storage System(BESS)is one of the potential solutions to increase energy system flexibility,as BESS is well suited to solve many challenges in transmission and distribution networks.Examples of distribution network’s challenges,which affect network performance,are:(i)Load disconnection or technical constraints violation,which may happen during reconfiguration after fault,(ii)Unpredictable power generation change due to Photovoltaic(PV)penetration,(iii)Undesirable PV reverse power,and(iv)Low Load Factor(LF)which may affect electricity price.In this paper,the BESS is used to support distribution networks in reconfiguration after a fault,increasing Photovoltaic(PV)penetration,cutting peak load,and loading valley filling.The paper presents a methodology for BESS optimal locations and sizing considering technical constraints during reconfiguration after a fault and PV power generation changes.For determining themaximumpower generation change due to PV,actual power registration of connected PV plants in South Cairo Electricity Distribution Company(SCEDC)was considered for a year.In addition,the paper provides a procedure for distribution network operator to employ the proposed BESS to perform multi functions such as:the ability to absorb PV power surplus,cut peak load and fill load valley for improving network’s performances.The methodology is applied to a modified IEEE 37-node and a real network part consisting of 158 nodes in SCEDC zone.The simulation studies are performed using the DIgSILENT PowerFactory software andDPL programming language.The Mixed Integer Linear Programming optimization technique(MILP)in MATLAB is employed to choose the best locations and sizing of BESS.

Typical Application Scenarios and Economic Benefit Evaluation Methods of Battery Energy Storage System

Energy storage system is an important means to improve the flexibility and safety of traditional power system,but it has the problem of high cost and unclear value recovery path.In this paper,the typical application scenarios of energy storage system are summarized and analyzed from the perspectives of user side,power grid side and power generation side.Based on the typical application scenarios,the economic benefit assessment framework of energy storage system including value,time and efficiency indicators is proposed.Typical battery energy storage projects are selected for economic benefit calculation according to different scenarios,and key factors are selected for sensitivity analysis.Finally,the key factors affecting economic benefit of the energy storage system are analyzed.

Multi-Scale Fusion Model Based on Gated Recurrent Unit for Enhancing Prediction Accuracy of State-of-Charge in Battery Energy Storage Systems

Accurate prediction of the state-of-charge(SOC)of battery energy storage system(BESS)is critical for its safety and lifespan in electric vehicles.To overcome the imbalance of existing methods between multi-scale feature fusion and global feature extraction,this paper introduces a novel multi-scale fusion(MSF)model based on gated recurrent unit(GRU),which is specifically designed for complex multi-step SOC prediction in practical BESSs.Pearson correlation analysis is first employed to identify SOC-related parameters.These parameters are then input into a multi-layer GRU for point-wise feature extraction.Concurrently,the parameters undergo patching before entering a dual-stage multi-layer GRU,thus enabling the model to capture nuanced information across varying time intervals.Ultimately,by means of adaptive weight fusion and a fully connected network,multi-step SOC predictions are rendered.Following extensive validation over multiple days,it is illustrated that the proposed model achieves an absolute error of less than 1.5%in real-time SOC prediction.

Improved Energy Management Strategy for Prosumer Buildings with Renewable Energy Sources and Battery Energy Storage Systems

The concept of utilizing microgrids(MGs)to convert buildings into prosumers is gaining massive popularity because of its economic and environmental benefits.These pro-sumer buildings consist of renewable energy sources and usually install battery energy storage systems(BESSs)to deal with the uncertain nature of renewable energy sources.However,because of the high capital investment of BESS and the limitation of available energy,there is a need for an effective energy management strategy for prosumer buildings that maximizes the profit of building owner and increases the operating life span of BESS.In this regard,this paper proposes an improved energy management strategy(IEMS)for the prosumer building to minimize the operating cost of MG and degradation factor of BESS.Moreover,to estimate the practical operating life span of BESS,this paper utilizes a non-linear battery degradation model.In addition,a flexible load shifting(FLS)scheme is also developed and integrated into the proposed strategy to further improve its performance.The proposed strategy is tested for the real-time annual data of a grid-tied solar photovoltaic(PV)and BESS-powered AC-DC hybrid MG installed at a commercial building.Moreover,the scenario reduction technique is used to handle the uncertainty associated with generation and load demand.To validate the performance of the proposed strategy,the results of IEMS are compared with the well-established energy management strategies.The simulation results verify that the proposed strategy substantially increases the profit of the building owner and operating life span of BESS.Moreover,FLS enhances the performance of IEMS by further improving the financial profit of MG owner and the life span of BESS,thus making the operation of prosumer building more economical and efficient.

Virtual Transmission Solution Based on Battery Energy Storage Systems to Boost Transmission Capacity

The increasing penetration of variable renewable energy(VRE)generation along with the decommissioning of conventional power plants in Chile,has raised several operational challenges in the Chilean National Power Grid(NPG),including transmission congestion and VRE curtailment.To mitigate these limitations,an innovative virtual transmission solution based on battery energy storage systems(BESSs),known as grid booster(GB),has been proposed to increase the capacity of the main 500 kV corridor of the NPG.This paper analyzes the dynamic performance of the GB using a wide-area electromagnetic transient(EMT)model of the NPG.The GB project,composed of two 500 MVA BESS units at each extreme of the 500 kV corridor,allows increasing the transmission capacity for 15 min during N-1 contingencies,overcoming transmission limitations under normal operation conditions while maintaining system stability during faults.The dynamic behavior of the GB is also analyzed to control power flow as well as voltage stability.The results show that the GB is an effective solution to allow greater penetration of VRE generation while maintaining system stability in the NPG.

An Analytical Method for Delineating Feasible Region for PV Integration Capacities in Net-zero Distribution Systems Considering Battery Energy Storage System Flexibility

To provide guidance for photovoltaic(PV)system integration in net-zero distribution systems(DSs),this paper proposes an analytical method for delineating the feasible region for PV integration capacities(PVICs),where the impact of battery energy storage system(BESS)flexibility is considered.First,we introduce distributionally robust chance constraints on network security and energy/carbon net-zero requirements,which form the upper and lower bounds of the feasible region.Then,the formulation and solution of the feasible region is proposed.The resulting analytical expression is a set of linear inequalities,illustrating that the feasible region is a polyhedron in a high-dimensional space.A procedure is designed to verify and adjust the feasible region,ensuring that it satisfies network loss constraints under alternating current(AC)power flow.Case studies on the 4-bus system,the IEEE 33-bus system,and the IEEE 123-bus system verify the effectiveness of the proposed method.It is demonstrated that the proposed method fully captures the spatio-temporal coupling relationship among PVs,loads,and BESSs,while also quantifying the impact of this relationship on the boundaries of the feasible region.

Smart optimization in battery energy storage systems:An overview

The increasing drive towards eco-friendly environment motivates the generation of energy from renewable energy sources (RESs). The rising share of RESs in power generation poses potential challenges, including uncertainties in generation output, frequency fluctuations, and insufficient voltage regulation capabilities. As a solution to these challenges, energy storage systems (ESSs) play a crucial role in storing and releasing power as needed. Battery energy storage systems (BESSs) provide significant potential to maximize the energy efficiency of a distribution network and the benefits of different stakeholders. This can be achieved through optimizing placement, sizing, charge/discharge scheduling, and control, all of which contribute to enhancing the overall performance of the network. In this paper, we provide a comprehensive overview of BESS operation, optimization, and modeling in different applications, and how mathematical and artificial intelligence (AI)-based optimization techniques contribute to BESS charging and discharging scheduling. We also discuss some potential future opportunities and challenges of the BESS operation, AI in BESSs, and how emerging technologies, such as internet of things, AI, and big data impact the development of BESSs.

Deep reinforcement learning for online scheduling of photovoltaic systems with battery energy storage systems

A new online scheduling algorithm is proposed for photovoltaic(PV)systems with battery-assisted energy storage systems(BESS).The stochastic nature of renewable energy sources necessitates the employment of BESS to balance energy supplies and demands under uncertain weather conditions.The proposed online scheduling algorithm aims at minimizing the overall energy cost by performing actions such as load shifting and peak shaving through carefully scheduled BESS charging/discharging activities.The scheduling algorithm is developed by using deep deterministic policy gradient(DDPG),a deep reinforcement learning(DRL)algorithm that can deal with continuous state and action spaces.One of the main contributions of this work is a new DDPG reward function,which is designed based on the unique behaviors of energy systems.The new reward function can guide the scheduler to learn the appropriate behaviors of load shifting and peak shaving through a balanced process of exploration and exploitation.The new scheduling algorithm is tested through case studies using real world data,and the results indicate that it outperforms existing algorithms such as Deep Q-learning.The online algorithm can efficiently learn the behaviors of optimum non-casual off-line algorithms.

Optimal control and management of large-scale battery energy storage system to mitigate fluctuation and intermittence of renewable generations

Battery energy storage system(BESS)is one of the effective technologies to deal with power fluctuation and intermittence resulting from grid integration of large renewable generations.In this paper,the system configuration of a China’s national renewable generation demonstration project combining a large-scale BESS with wind farm and photovoltaic(PV)power station,all coupled to a power transmission system,is introduced,and the key technologies including optimal control and management as well as operational status of this BESS are presented.Additionally,the technical benefits of such a large-scale BESS in dealing with power fluctuation and intermittence issues resulting from grid connection of large-scale renewable generation,and for improvement of operation characteristics of transmission grid,are discussed with relevant case studies.

Energy Management and Operational Control Methods for Grid Battery Energy Storage Systems

Energy storage is one of the key means for improving the flexibility,economy and security of power system.It is also important in promoting new energy consumption and the energy Internet.Therefore,energy storage is expected to support distributed power and the micro-grid,promote open sharing and flexible trading of energy production and consumption,and realize multi-functional coordination.In recent years,with the rapid development of the battery energy storage industry,its technology has shown the characteristics and trends for large-scale integration and distributed applications with multi-objective collaboration.As a grid-level application,energy management systems(EMS)of a battery energy storage system(BESS)were deployed in real time at utility control centers as an important component of power grid management.Based on the analysis of the development status of a BESS,this paper introduced application scenarios,such as reduction of power output fluctuations,agreement to the output plan at the renewable energy generation side,power grid frequency adjustment,power flow optimization at the power transmission side,and a distributed and niohile energy storage system at the power distribution side.The studies and application status of a BESS in recent years were reviewed.The energy management,operation control methods,and application scenes of large-scale BESSs were also examined in the study.

A Fuzzy Hierarchical Strategy for Improving Frequency Regulation of Battery Energy Storage System

Battery energy storage systems(BESSs)can provide instantaneous support for frequency regulation(FR)because of their fast response characteristics.However,purely pursuing a better FR effect calls for continually rapid cycles of BESSs,which shortens their lifetime and deteriorates the operational economy.To coordinate the lifespan savings and the FR effect,this paper presents a control strategy for the FR of BESSs based on fuzzy logic and hierarchical controllers.The fuzzy logic controller improves the effect of FR by adjusting the charging/discharging power of the BESS with a higher response speed and precision based on the area control error(ACE)signal and the change rate of ACE in a non-linear way.Hierarchical controllers effectively reduce the life loss by optimizing the depth of discharge,which ensures that the state of charge(SOC)of BESS is always in the optimal operating range,and the total FR cost is the lowest at this time.The proposed method can achieve the optimal balance between ACE reduction and operational economy of BESS.The effectiveness of the proposed strategy is verified in a two-area power system.

Aggregator Service for PV and Battery Energy Storage Systems of Residential Building

Distributed energy resources(DERs),including photovoltaic(PV)systems,small wind turbines,and energy storage systems(ESSs)are being increasingly installed in many residential units and the industry sector at large.DER installations in apartment buildings,however,pose a more complex issue particularly in the context of property ownership and the distribution of DR benefits.In this paper,a novel aggregator service is proposed to provide centralized management services for residents and DER asset owners in apartment buildings.The proposed service consists of a business model for billing and benefits distribution,and a model predictive control(MPC)control algorithm for managing and optimizing DER operations.Both physical and communication structures are proposed to ensure the implementation of such aggregator services for buildings.Three billing tariffs,i.e.,flat rate,time-of-use(TOU),and real time pricing(RTP)are compared by way of case studies.The results indicate that the proposed aggregator service is compatible with the business model.It is shown to offer good performance in load shifting,bill savings,and energy trading of DERs.Overall,the aggregator service is expected to provide benefits in reducing the pay back periods of the investment.

Stochastic coordinated operation of wind and battery energy storage system considering battery degradation

Grid-scale battery energy storage systems(BESSs)are promising to solve multiple problems for future power systems.Due to the limited lifespan and high cost of BESS,there is a cost-benefit trade-off between battery effort and operational performance.Thus,we develop a battery degradation model to accurately represent the battery degradation and related cost during battery operation and cycling.A linearization method is proposed to transform the developed battery degradation model into the mixed integer linear programming(MILP)optimization problems.The battery degradation model is incorporated with a hybrid deterministic/stochastic look-ahead rolling optimization model of windBESS bidding and operation in the real-time electricity market.Simulation results show that the developed battery degradation model is able to effectively help to extend the battery cycle life and make more profits for wind-BESS.Moreover,the proposed rolling look-ahead operational optimization strategy can utilize the updated wind power forecast,thereby also increase the wind-BESS profit.

Multi-objective Optimization of Production Scheduling Using Particle Swarm Optimization Algorithm for Hybrid Renewable Power Plants with Battery Energy Storage System

Considering the increasing integration of renewable energies into the power grid,batteries are expected to play a key role in the challenge of compensating the stochastic and intermittent nature of these energy sources.Besides,the deployment of batteries can increase the benefits of a renewable power plant.One way to increase the profits with batteries studied in this paper is performing energy arbitrage.This strategy is based on storing energy at low electricity price moments and selling it when electricity price is high.In this paper,a hybrid renewable energy system consisting of wind and solar power with batteries is studied,and an optimization process is conducted in order to maximize the benefits regarding the dayahead production scheduling of the plant.A multi-objective cost function is proposed,which,on the one hand,maximizes the obtained profit,and,on the other hand,reduces the loss of value of the battery.A particle swarm optimization algorithm is developed and fitted in order to solve this non-linear multi-objective function.With the aim of analyzing the importance of considering both the energy efficiency of the battery and its loss of value,two more simplified cost functions are proposed.Results show the importance of including the energy efficiency in the cost function to optimize.Besides,it is proven that the battery lifetime increases substantially by using the multi-objective cost function,whereas the profitability is similar to the one obtained in case the loss of value is not considered.Finally,due to the small difference in price among hours in the analyzed Iberian electricity market,it is observed that low profits can be provided to the plant by using batteries just for arbitrage purposes in the day-ahead market.

Reverse-blocking modular multilevel converter for battery energy storage systems

Energy storage systems with multilevel converters play an important role in modern electric power systems with large-scale renewable energy integration.This paper proposes a reverse-blocking modular multilevel converter for a battery energy storage system(RB-MMCBESS). Besides integrating distributed low-voltage batteries to medium or high voltage grids, with the inherited advantages of traditional MMCs, the RB-MMC-BESS also provides improved DC fault handling capability. This paper analyzes such a new converter configuration and its operating principles. Control algorithms are developed for AC side power control and the balancing of battery state of charge. The blocking mechanism to manage a DC pole-topole fault analyzed in depth. Comprehensive simulation results validate both the feasibility of the RB-MMC-BESS topology and the effectiveness of the control and fault handling strategies.

Charging-rate-based Battery Energy Storage System in Wind Farm and Battery Storage Cooperation Bidding Problem

Wind power has been proven to have the ability to participate in the frequency modulation(FM)market.Using batteries to improve wind power stability can better aid wind farms participating in the FM market.Battery energy storage system(BESS)has a promising future in applying regulation and load management in the power grid.For regulation services,normally,the regulation power prediction is estimated based on the required maximum regulation capacity;the power needed for the specific regulation service is unknown to the BESS owner.However,this information is needed in the regulation model when formulating the linearised BESS model with a constraint on the state of charge(SoC).This compromises the accuracy of the model greatly when it is applied for regulation service.Moreover,different control strategies can be employed by BESS.However,the current depth of discharge(DoD)based models have difficulties in being used in a linearization problem.Due to the consideration of the control strategy,the model becomes highly nonlinear and cannot be solved.In this paper,a charging rate(C-rate)based model is introduced,which can consider different control strategies of a BESS for cooperation with wind farms to participate in wind farm estimation error compensation,load management,energy bid,and regulation bid.First,the limitation of conventional BESS models are listed,and a new C-rate-based model is introduced.Then the C-rate-based BESS model is adopted in a wind farm and BESS cooperation scheme.Finally,experimental studies are carried out,and the DoD model and C-rate model optimization results are compared to prove the rationality of the C-rate model.

Dual Control Strategy for Grid-tied Battery Energy Storage Systems to Comply with Emerging Grid Codes and Fault Ride Through Requirements

Battery energy storage systems(BESSs)need to comply with grid code and fault ride through(FRT)requirements during disturbances whether they are in charging or discharging mode.Previous literature has shown that constant charging current control of BESSs in charging mode can prevent BESSs from complying with emerging grid codes such as the German grid code under stringent unbalanced fault conditions.To address this challenge,this paper proposes a new FRTactivated dual control strategy that consists of switching from constant battery current control to constant DC-link voltage control through a positive droop structure.The results show that the strategy ensures proper DC-link voltage and current management as well as adequate control of the positive-and negative-sequence active and reactive currents according to the grid code priority.It is also shown that the proposed FRT control strategy is tolerant to initial operating conditions of BESS plant,grid code requirements,and fault severity.