Multi-objective optimization of smart community integrated energy considering the utility of decision makers based on the L′evy flight improved chicken swarm algorithm

A community integrated energy system can play a key role in alleviating urban energy shortages and environmental degradation, but the presence of multiple participants and links makes the operation of such a system more complicated. How to choose the optimal energy use strategy among different decision makers is a problem that urgently needs to be solved today. Therefore, first, this paper proposes a comprehensive energy multiobjective scheduling model based on the established smart community energy management framework, which considers the utility of decision makers. From the perspective of risk, the model divides decision makers into adventurous, intermediate and conservative types in order to study the impacts of different decision makers on energy use strategies. Second, to prevent the phenomenon of the solution of the intelligent algorithm falling into the local optimum, this paper innovatively uses the Le ' vy flight to optimize the learning step length of the chicken swarm algorithm to quickly solve the proposed mixed integer nonlinear programming model. Finally, the model is tested through multi-scenario simulation. The results show that decision makers have an important influence on energy use strategies, and different decision makers have different energy use strategies when utility is maximized. In addition, compared with the chicken swarm algorithm, the improved algorithm increases the utility of adventurous, intermediate and conservative decision makers in type-I cases by 0.54 %, 1.2 %, and 1.5 %, respectively; and in type-II cases by 4.5 %, 4.8 %, and 3.6 %, respectively.