Research On Optimal Scheduling Of Combined Cooling Heating And Power Microgrid Based On Swarm Intelligence Optimization Algorithm

In today’s society,energy shortage,environmental pollution,and climate change are significant factors confining the endurable advancement of the world’s economy and society,so energy conservation and environmental protection are the top priority of China’s growth.The combined cooling,heating and power microgrid can realize the cascade employment of energy and considerably improve energy efficiency.It has the characteristics of adjusting the energy structure,alleviating the shortage of power supply,and reducing pollutant emissions.The traditional combined cooling,heating and power microgrid uses natural gas as the primary fuel during operation.However,China’s natural gas resources are scarce,and it is pressing to seek clean and renewable energy resources with abundant reserves as a substitute.Biomass energy is the only renewable resource that can straight produce clean energy,such as gas and liquid,on a large scale.It has the characteristics of wide resource distribution,low environmental pollution,and a sufficient supply of raw materials.In addition,there are abundant reserves of biomass resources in rural areas,but it has yet to be reasonably and efficiently utilized.For this reason,this paper have designed a combined cooling,heating,and power microgrid system considering biomass pyrolysis and gasification.The biomass gasification device can fully use biomass resources and absorb wind energy.Therefore,it is of great theoretical and practical significance to conduct modeling and research on optimal scheduling of combined cooling,heating and power microgrid considering biomass gasification.This paper comprehensively considers the influence and significance of combined cooling,heating and power microgrids on economic and environmental protection problems.The economic and ecological optimization scheduling model of combined cooling,heating,and power microgrids is established.Each micropower supply’s minimum operating and environmental pollution costs are taken as the optimization objectives,and the sparrow optimization algorithm is used to optimize the solution.This paper improves it by aiming at the problems of uneven population distribution,poor global search ability,and easily falling into local optimum in the traditional sparrow algorithm.It forms a random walk sparrow algorithm for optimizing the scheduling model.Firstly,the Sinusoidal chaotic map generates the initial sparrow population with uniform distribution in space.Secondly,to strengthen the information exchange between individuals in the field and improve the global search ability,this paper adds a sharing factor in the optimization process of the discoverer.Finally,a random walk strategy is used to form a new individual to enhance the algorithm’s local explore capability in the optimal position.Taking the grid-connected combined cooling,heating and power microgrid as an example,it is concluded that compared with the optimization results of particle swarm optimization,original sparrow algorithm,and chaotic sparrow algorithm,the improved sparrow algorithm has the best total cost in typical summer and winter days,which verifies the rationality and effectiveness of the random walk sparrow algorithm in solving the economic and environmental optimization dispatch problem of the combined cooling,heating and power microgrid.Considering the influence of net load variance on the stable operation of the microgrid,an optimum scheduling pattern of the combined cooling,heating and power microgrid with operation expense,environmental cost,and netload variance as multi-objective functions are established.A multi-objective sparrow algorithm founded on Pareto theory and niche technology is proposed to optimize the solution.Firstly,Gauss chaos is used to initialize the population position and calculate the initial fitness value.Then,the initial fitness value is non-dominated sorted,and the external file is established.Secondly,niche technology is used to divide the population,and the adaptive spiral search strategy is used to update the location of the discoverer.Finally,the center of gravity reverse mutation strategy is used to update the optimal sparrow,and the obtained non-inferior solution set is added to the external file.The external file is quickly non-dominated sorted to retain the non-dominated solution.Taking the grid-connected combined cooling,heating and power microgrid as an example,the multi-objective sparrow algorithm is used for simulation analysis and compared with the simulation results obtained by the multi-objective particle swarm optimization algorithm and multi-objective bee colony algorithm.The simulation findings prove the productiveness of the advised model and show the superiority of using the bettered multi-objective sparrow algorithm to settle the multi-objective combined cooling,heating and power microgrid optimal scheduling model.