Optimal scheduling of micro-energy grid with integrated demand response based on chance-constrained programming

Abstract

The micro-energy grid can meet various load demands and realize the complementary advantages of different energy sources, which provides a new way to solve the problems of energy utilization efficiency and environmental pollution. However, how coordinating multiple energy sources and improving the flexibility of the micro-energy grid is an urgent problem to be solved. This paper proposes an optimal scheduling model based on chance-constrained programming (CCP), which considers electric vehicle (EV) charging characteristics, integrated electricity-heat demand response, and ladder-type carbon trading in the background of various renewable uncertainty. Firstly, this paper uses power to gas (P2G) technology and combined heat and power (CHP) technology to improve the flexibility of the system and realize the coupling between different energy sources. Secondly, integrated demand response (IDR) is used to explore potential interaction capabilities between electric-heat flexible load and micro-energy grid. Then, the ladder-type carbon trading mechanism is introduced in the optimization scheduling model to reduce the carbon emissions of the system. Finally, sequence operation theory (SOT) transforms the original CCP model into a conveniently solvable mixed-integer linear programming (MILP) model. The simulation results show that all subsystems are closely coupled due to the participation of P2G and CHP, which reduces the operation cost of the system by 3.9 %. The results also indicate that the IDR mechanism improves energy efficiency and reduces operating costs by 7.8 %. Finally, the results substantiate that the ladder-type carbon trading mechanism reduces the carbon emissions of the system by 18.1 % and improves the environmental benefits of the system.