Optimal configuration of 5G base station energy storage considering sleep mechanism

The high-energy consumption and high construction density of 5G base stations have greatly increased the demand for backup energy storage batteries.To maximize overall benefits for the investors and operators of base station energy storage,we proposed a bi-level optimization model for the operation of the energy storage,and the planning of 5G base stations considering the sleep mechanism.A multi-base station cooperative system composed of 5G acer stations was considered as the research object,and the outer goal was to maximize the net profit over the complete life cycle of the energy storage.Furthermore,the power and capacity of the energy storage configuration were optimized.The inner goal included the sleep mechanism of the base station,and the optimization of the energy storage charging and discharging strategy,for minimizing the daily electricity expenditure of the 5G base station system.Additionally,genetic algorithm and mixed integer programming were used to solve the bi-level optimization model,analyze the numerical example test comparison of the three types of batteries and the net income of the configuration,and finally verify the validity of the model.Furthermore,the sleep mechanism,the charging and discharging strategy for energy consumption,and the economic benefits for the operators were investigated to provide reference for the 5G base station energy storage configuration.

Research on energy storage capacity configuration for PV power plants using uncertainty analysis and its applications

Compensating for photovoltaic(PV)power forecast errors is an important function of energy storage systems.As PV power outputs have strong random fluctuations and uncertainty,it is difficult to satisfy the grid-connection requirements using fixed energy storage capacity configuration methods.In this paper,a method of configuring energy storage capacity is proposed based on the uncertainty of PV power generation.A k-means clustering algorithm is used to classify weather types based on differences in solar irradiance.The power forecast errors in different weather types are analyzed,and an energy storage system is used to compensate for the errors.The kernel density estimation is used to fit the distributions of the daily maximum power and maximum capacity requirements of the energy storage system;the power and capacity of the energy storage unit are calculated at different confidence levels.The optimized energy storage configuration of a PV plant is presented according to the calculated degrees of power and capacity satisfaction.The proposed method was validated using actual operating data from a PV power station.The results indicated that the required energy storage can be significantly reduced while compensating for power forecast errors.

A Storage and Transmission Joint Planning Method for Centralized Wind Power Transmission

Centralized delivery has become the main operation mode under the scaled development of wind power.Transmission channels are usually the guarantee of out-delivered wind power for large-scale wind base.The configuration of transmission capacity,which has the features of low utilization and poor economy,is hardly matching correctly due to the volatility and low energy density of wind.The usage of energy storage can mitigate wind power fluctuations and reduce the requirement of out-delivery transmission capacity,but facing the issue of energy storage cost recovery.Therefore,it is necessary to optimize the allocation of energy storage while considering the problem of wind power transmission.This paper studies the joint optimization of large-scale wind power transmission capacity and energy storage,reveals the mechanism of energy storage in order to reduce the power fluctuation of wind power base and slow down the demand of transmission.Then,analyze the multi-functional cost-sharing mode of energy storage,improve the efficiency of energy storage cost recovery.Constructs the coordination optimization configuration model to deal with the problem of large-scale wind power transmission capacity and energy storage,and realizes the transmission capacity optimization coordination and optimization with energy storage.The proposed method is verified by a wind base located in Northeast China.