Improved Triangle Splitting Algorithm Based Community Integrated Energy Systems Bi-objective Optimal Scheduling

Community integrated energy system(CIES)on the user side has received extensive attention in recent years with the integration of renewable distributed generators,energy storage devices and flexible loads.According to coordinating and managing electricity,cold,heat and other forms energy resources in CIES,the cascade utilization of energy resources is realized.However,in the operation of CIES,environmental concerns,reliability and other indicators are required to take into account simultaneously for comprehensive consideration and unified optimization.Therefore,this thesis focuses on the bi-objective optimal scheduling of CIES.A bi-objective optimal scheduling model of CIES is set up and an improved triangle splitting algorithm for Pareto front is proposed.In addition,a bi-objective optimal scheduling framework of CIES with uncertainties is proposed.Finally,a bi-objective rolling optimal scheduling method accelerated by the surrogate model is used to realize the efficient and accurate operation of CIES.The main contributions are summarized as follows:1)A bi-objective optimal scheduling model of CIES is established.Considering the basic structure,equipment composition and operation mode of a typical CIES,a biobjective optimal scheduling model of CIES in summer is established,which includes the economic and environmental objective expressed as minimizing the daily operation cost and maximizing the renewable energy utilization,respectively.The constraints include the upper and lower limits of the cooling power of ground source heat pumps,cold water storage tanks,ice storage tanks,water-cooled chillers and dual-duty chillers,ON/OFF statuses of devices,energy storage capacity constraints,equipment power consumption constraints,and the cold and electricity power balance constraints of the system.2)A bi-objective optimal scheduling method of CIES considering uncertainties is proposed.Considering the preference of decision-makers and the threshold,a biobjective optimal scheduling algorithm named improved triangle splitting algorithm is proposed to realize the efficient optimization.Then,a bi-objective optimal scheduling framework considering uncertainties of renewable energy generators,such as wind generators and photovoltaic,and load forecast error is proposed.The uncertain scenarios are generated by Monte Carlo simulation and the Pareto front of each sampled scenario is solved by improved triangle splitting algorithm.The robust operation strategy is determined by the solution probability function.3)The surrogate model accelerated bi-objective rolling optimal scheduling method of CIES is proposed.The Adaboost-NN surrogate model classifier is used to accelerate the improved triangle splitting algorithm.Before searching the ideal knee area,the Adaboost-NN classifier is used to predict the result.Continue the searching process if the classifier predicts that there exists a solution in the searching area,otherwise skip to the next area if there exists no solution.In this way,the invalid searching time in the unsolvable area can be saved.