Research On Multi-objective Optimal Scheduling Of Microgrid Considering Demand Response

In the context of the global energy crisis and worsening environmental problems,renewable energy(Renewable Energy Resource,RES)power generation technology has been extensively developed.Microgrid,as an effective carrier of renewable energy power generation,has become a research hotspot in recent years.The research on optimal scheduling of microgrid system has very important theoretical significance and engineering value.First of all,aiming at the micro-grid economic dispatching problem of unit output and landscape absorption,the operation cost model of the generator set,the environmental penalty cost model caused by greenhouse gas and pollutant emissions and the price demand response model are built.With the goal of minimizing the operating cost and power fluctuation of microgrid,and comprehensively considering the constraints such as power balance and tie line transmission power,a multi-objective economic dispatching model of microgrid considering demand response is constructed.Secondly,in view of the problem that the diversity and convergence of non-dominant solutions in multi-objective evolutionary algorithm are difficult to balance,an efficient multi-objective optimization algorithm based on level swarm optimizer is proposed to obtain the Pareto frontier of multi-objective function,then the weight is determined based on the improved entropy weight method,and the grey correlation analysis method are used to select the optimal scheme.The simulation results indicate that the implementation of demand response can improve the economy of the microgrid and reduce the volatility,and verify the superior performance of the EMOSO algorithm.Finally,considering the uncertain factors that affect the fan output and photovoltaic output,and the interval number is used to describe the uncertainty of fan and photovoltaic output,and the interval multi-objective optimization model of microgrid based on demand response is established.The interval uncertainty model of the objective function is transformed into a deterministic model by using the interval order relation,and then the uncertainty constraint is transformed into a deterministic constraint by using the interval possibility degree.Then an efficient multi-objective optimization algorithm based on level swarm optimizer is used to solve the model,then the improved entropy weight method and the grey correlation analysis method are used to determine the final compromise scheme.