A Scheduling Optimization For Integrated Community Energy System Based On Model Predictive Control

The development of renewable energy and the improvement of comprehensive energy utilization efficiency are inevitable choices to solve the contradiction between energy demand growth and energy shortage,energy utilization and environmental protection with the increasing concerns on energy consumption and environmental protection.Integrated community energy system(ICES)is an efficient and environmentally friendly energy supply technology,which has become an important development trend of future energy technologies.Focus on the economic optimal scheduling of the ICES,the main research work and results obtained in this paper are as follows:1)A detailed analysis of the structure and operation mechanism of key equipment for a typical ICES was conducted based on the energy demand of the users,and a quasi-steady-state model of critical devices in the system was established.2)A robust optimization engineering game model was adopted at the source side to deal with renewable energy resources power uncertainty in allusion to the large prediction error of renewable energy,relatively high accuracy of load prediction,and a stochastic optimization method was used at the load side to deal with load uncertainty in the typical ICES.3)For ICES integrated with valve control units,the day-ahead scheduling model is a large-scale mixed integer nonlinear programming(MINLP)problem solved through a linearization method proposed in this study.The real-time correction optimization model is a nonlinear programming problem solved by the quantum genetic algorithm(QGA),whose performance was compared with particle swarm algorithm(PSO).The influence of the integration of valve control units on the economy of the system was discussed.4)A two-stage optimal scheduling model is proposed considering that renewable energy output and user load tend to have large fluctuations and randomness.The model predictive control(MPC)framework was used to implement real-time optimal scheduling,which effectively solved the short-sighted effect during real-time optimization process.A two-stage optimal scheduling model based on MPC was established for the ICES proposed in this paper.A case study has been carried out,and results show that the method proposed in this paper can improve energy utilization efficiency,adjust the peak-valley difference of load effectively and enhance economical system operation.