A Coordination Control Strategy Of Battery And Virtual Energy Storage To Smooth The Micro-grid Tie-line Power Fluctuations

The intermittency of distributed renewable generation poses significant challenges for micro grids(MG),battery is usually used to smooth the tie-line power flow fluctuations of MG.However,the initial investment and operating cost of battery are relatively high,which is the main reason for the low economy.Therefore,we should reduce the use of battery while maintaining the economical operation of MG.Demand response(DR)has received more attention in recent years,it can serve for the operation of MG and realize the control tie-line power flow fluctuations.The DR can be considered as a kind of virtual demand response in MG,so it can be used to coordinate with the batteries.Based on this idea,a coordination control strategy of battery and virtual energy storage(say DR)to smooth the tie-line power flow fluctuations of MG is proposed in this paper.1.An index-priority-list(IPL)is presented to construct the DR model with considering the group characteristics of the thermostatically-controlled loads.Similar to the state of charge(SOC)of battery,a state indexTC was defined for the DR system to describe its regulation capability.2.A coordination control strategy to smooth the MG power flow fluctuations based on Butterworth filter with varying time constants is proposed.Two filters were introduced for DR and battery system respectively.Their time constants can be automatically coordinated and adjusted so that DR is used to smooth the high-frequency fluctuations while battery system is used to balance low-frequency fluctuations.With the coordination strategy,MG tie-line power flow fluctuations can be effectively smoothed.At the same time,the comfort of the customers participating in the DR can be maintained.The charging times of battery can be reduced and the overcharging/discharging of battery can be avoided,so the battery life can be prolonged.3.An influence index system to the proposed control strategy is established.Numerical examples show: with the increasing of the ratio of the incorrect status information,the final tie-line power flow has more difficulty following the control target;with the increasing of the numbers of the batteries or the charging rates of the batteries,the actual response can fit the battery power target much better.