Research On Energy Management And Control Strategy Of Multi-microgrid

The structure of conventional distribution systems is changed significantly over the past decades.The introduction of distributed energy resources,along with advanced metering,communication and control technologies at the distribution level,has changed the conventional distribution systems into multi-microgrid systems which are usually faster,more controllable and reliable.In this way,the optimal management of energy generation/consumption in modern distribution systems has gained attentionin on the smart grid era.This paper presents optimized and coordinated strategies for performing and assessing energy management in multi-microgrid systems.The energy management process is formulated for multi microgrid systems that simultaneously incorporate several energy generation/consumption units,including different types of distributed generators(DGs),energy storage units,electric vehicles(EVs),and demand response.Due to the probabilistic nature of some loads and generators,their mathematical models were set up and a novel probabilistic index(EMSI)is defined to measure the success of energy management scenarios in terms of cost minimization.And through Tabu Search(TS),the best economical efficiency can be reserched.Then,the demand of multi microgrid coordinated control was analyzed,due to the demand,a distributed hybrid multi micronetwork control structure with multi agent system was proposed.The novel strategy can maintain power balance among different microgrids during interconnected operation.Morever,the feasibility of the coordinated control strategy in the multi-microgrid system is verified through simulation.Finally,the economic analysis based on the energy management scenarios was developed.Based on the cases,different economic benefits of the application of energy management scenarios for different types and different numbers of distributed energy sources were reflected.In the end,the best scenario for energy management was obtained by comparing decentralized control with cluster control.