Energy Storage Modeling of Inverter Air Conditioning for Output Optimizing of Wind Generation in the Electricity Market

In order to achieve the compatibility of the air conditioning(AC)loads with the current dispatch models,this pa-per utilizes demand response(DR)technology as energy storage resources to optimize the aggregator’s behaviors in the real-time market for less economic loss caused by the fluctuations of wind power.The inverter AC,as a typical demand response resource,is constructed as a power type battery model(PTBM)and a capacity type battery model(CTBM)according to the different control methods,which are expressed through a circuit model and mathematical model to describe the energy storage characteristics of ACs.Moreover,the comparisons between the PTBM and CTBM are given analytically by their response speed,power&energy capacity and the cost of control,which will be helpful to guide the associated operators to choose the appropriate models to take part in demand response.Considering that the wind generation fluctuates frequently and greatly,the PTBM is chosen to take part of the demand response for output optimizing of the wind generation.The simulation results demonstrate that PTBMs can work in the way of conventional batteries(CBs)to optimize wind generation in the real-time market.

Combined hybrid energy storage system and transmission grid model for peak shaving based on time series operation simulation

This study proposes a combined hybrid energy storage system(HESS) and transmission grid(TG) model, and a corresponding time series operation simulation(TSOS) model is established to relieve the peak-shaving pressure of power systems under the integration of renewable energy. First, a linear model for the optimal operation of the HESS is established, which considers the different power-efficiency characteristics of the pumped storage system, electrochemical storage system, and a new type of liquid compressed air energy storage. Second, a TSOS simulation model for peak shaving is built to maximize the power entering the grid from the wind farms and HESS. Based on the proposed model, this study considers the transmission capacity of a TG. By adding the power-flow constraints of the TG, a TSOS-based HESS and TG combination model for peak shaving is established. Finally, the improved IEEE-39 and IEEE-118 bus systems were considered as examples to verify the effectiveness and feasibility of the proposed model.

Resilience-oriented Valuation for Energy Storage Amidst Extreme Events

In power grids,the frequency is increasing of extreme accidents which have a low probability but high risk such as natural disasters and deliberate attacks.This has sparked discussions on the resilience of power grids.Energy-storage systems(ESSs)are critical for enhancing the resilience of power grids.ESSs,with their mechanism of flexible charging and discharging,adjust energy usage as needed during disasters,thereby mitigating the impact on the grid and enhancing security and resilience.This,in turn,ensures the power system’s stable operation.Currently,there is limited systematic research quantifying the economic value of energy storage in resilience scenarios.Therefore,a model and methodology were proposed to quantify the value of energy storage systems for enhancing grid resilience during extreme events.A two-stage stochastic optimization mathematical model was developed.The first stage involves pre-deployment based on day-ahead expectations,and the second stage involves simulating potential failure scenarios through real-time scheduling.Considering the temporal dimension,the energy storage systems with flexible regulation capabilities was used as emergency power sources to reduce occurrences of load-shedding.Here,a novel index was proposed that quantifies the resilience value of energy storage as the economic value of energy storage per unit of capacity,as reflected in the emergency dispatch model.This index helps determine the balance between the energy storage investment cost and resilience value.Finally,an IEEE-30 node transmission system was used to verify the feasibility and effectiveness of the proposed method.The findings revealed a significant improvement in the resilience value,with a 23.49%increase observed when energy storage systems were implemented compared to the scenario without energy storage systems.The optimal capacity configurations for the flywheel,lithium-ion batteries,and pumped hydro storage were 10 MW,11 MW,and 12 MW,respectively,highlight their potential to maximize value in experimental system.

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.