Research On Electric Cloud Energy Storage And Its Capacity Configuration

With the proposal of the carbon peaking and carbon neutrality goals,distributed clean energy power generation technology,which uses wind power and photovoltaic as the primary energy,has been promoted vigorously.However,as the installed capacity continues to rise,the issue of insufficient absorption of clean energy power generation has become increasingly prominent due to the uncertainty of wind power output.Utilizing energy storage facilities is one of the effective measures to solve the above problems,but the high cost of energy storage equipment has restricted its wide popularization.Cloud Energy Storage(CES),which is grid-based energy storage,can effectively reduce the cost of energy storage while improving the consumption of distributed clean energy.This thesis adopts a business model of providing CES services by third-party service providers to meet the energy storage needs of power users.To optimize the configuration of virtual energy storage capacity for CES users and physical energy storage capacity for CES servicer,the following research has been done:(1)Above all,the concept of CES was introduced and the architecture and service process of CES were analyzed.The bi-level energy storage capacity optimization scheme,including CES users and CES servicer,was designed to support the configuration of the user’s virtual energy storage capacity and the servicer’s physical energy storage capacity.(2)Research was conducted on optimizing the configuration of virtual energy storage capacity and was carried out to handle the issue of high overall cost of using energy storage for users.The factors affecting the optimization of virtual energy storage capacity for users were considered,and a model for optimizing virtual energy storage capacity for users was established with the objective function of minimizing the comprehensive cost of users.Considering the source and load uncertainty,the demand for virtual energy storage capacity for users was taken into account,and the above model was improved using fuzzy chance-constrained programming.Case studies were conducted on the virtual energy storage capacity demand of CES users under source and load certainty and uncertainty conditions.The results show that the virtual energy storage capacity optimization model established in this article can effectively improve energy storage economics and reduce the overall costs of distributed users.Considering the uncertainty of source and load,the virtual energy storage capacity requirement of users has increased,leading to an increase in energy storage costs.However,with the rise in the photovoltaic consumption rate,the overall costs of users have decreased.(3)In order to address the issues of economic inefficiency and power instability in CES systems among servicer,a study was conducted to optimize the physical energy storage capacity based on the virtual energy storage demand of users.The study took into account various factors that influence the optimization of physical energy storage capacity configuration,and developed a servicer physical energy storage capacity optimization configuration model with objective functions aimed at minimizing the annual comprehensive cost of the servicer and the mean square deviation of the net load of the CES system.To solve the multi-objective optimization model at the servicer level,the non-dominated sorting genetic algorithm II(NSGA-II)was utilized.Additionally,the study examined the effects of energy storage charging and discharging strategies on energy storage lifespan and conducted case studies to examine the configuration of physical energy storage capacity for CES servicer throughout the entire lifecycle.The results demonstrate that the servicer physical energy storage capacity optimization configuration model established in this study can balance the economic efficiency of the servicer and the effect of smoothing power fluctuations in the CES system.CES servicer can recover costs and gain more benefits within a relatively short period of time.