Research On Optimal Scheduling Of Combined Heat And Power Microgrid Considering Demand Response

With people's awareness of the energy crisis and environmental crisis,the development of clean and sustainable energy represented by wind and solar has become the main direction of energy development.Combined heat and power(CHP)microgrid has become a hot topic in power system research because it can realize the large-scale integration of renewable energy into the network and meet the diverse energy needs of users.Demand response(DR)is an important measure to achieve energy saving and emission reduction in the electric power industry.Given the development goal of "double carbon" and the growing power demand of the society,it is more important to improve the absorption rate of renewable energy through demand response measures while increasing the capacity of wind and solar power units.Therefore,how to implement the demand response strategy in the CHP microgrid with wind and solar units to make the microgrid system run more economically and stably has become a key issue.Optimal scheduling of combined heat and power microgrid considering demand response is studied in this dissertation.Firstly,the research background and significance of CHP microgrid considering demand response,the development status of demand response and CHP microgrid,and the research status of CHP microgrid optimal scheduling are introduced.The mathematical models of common equipments in CHP microgrid are introduced,and the price-based demand response and incentive-based demand response are summarized.Secondly,a two-level multi-objective optimization scheduling model of CHP microgrid based on price-based demand response is constructed.In order to show that electric vehicles(EVs)are huge response resources on the demand side,the system electric loads are divided into conventional electric loads and EVs charging loads.The upper model takes the dynamic time-of-use electricity price as the decision variable,and guides the conventional loads of users to transfer on the premise of satisfying the minimum satisfaction of users,so as to achieve the goal of minimizing the peak-valley difference of the system.The lower model adopts price compensation measures to guide EVs to participate in the evening peak discharge scheduling to further reduce the system peak value,and a scheduling model is established for minimizing operation cost and pollutant emission of CHP microgrid.The proposed scheduling model is solved by multi-objective particle swarm optimization based on Pareto theory,and it is verified that the proposed two-level multi-objective optimal scheduling model can effectively reduce the system operation cost and pollutant emission.Finally,considering the integrated demand response of power and heat in CHP microgrid,the system loads are divided into rigid loads which cannot be scheduled and DR resources which can be scheduled,and the day-ahead and intra-day scheduling model of CHP microgrid based on incentive-based demand response is established.Taking into account the multi-time scale scheduling characteristics of DR resources,the ability of shiftable loads,transferable loads and reducible loads to optimize the loads curve is considered in day-ahead scheduling,and the minimum comprehensive cost and unit output plans are solved.Then the residual capacity of reducible loads and temperature control loads are used to optimize the loads curve after the intra-day fluctuation.On the premise of meeting the intra-day supply and demand balance,a rolling revision model aiming at minimizing the adjustment cost in the forecast time domain is established to correct the day-ahead scheduling plan.The improved empire competition algorithm is used to solve the simulation example,and the effectiveness of day-ahead and intra-day scheduling model,which takes into account the comprehensive demand response of power and heat,to eliminate system uncertainty and reduce the comprehensive operation cost is verified.