Multiobjective optimization of multisource heating system based on improving diversification and implementation

The multiobjective optimization of the system usually focuses on the optimization of the objective functions while ignoring the influence of decision variables on the implementation of the solution. This paper proposes a new improved optimization method by embedding the decision variable diversification mechanism in the optimization process, adopting the discretization mechanism in the multisource complementary heating model, and improving the search space. The new improved optimization method and the original method have similar performance in obtaining the Pareto front, and the hypervolume of the two algorithms differs by only 1.37% on average. The standard deviations of the decision variables in the optimal solutions obtained by the improved algorithm are increased by 70%, and it has a higher diversity of solutions in the decision space. The equipment capacity obtained by the improved algorithm is discretized, and avoids equipment with lower capacity which is beneficial to construction. In this paper, the optimal implementation solution is obtained through the selection of the objective functions by the overall planners and the construction preference of the solution implementers. In this way, the overall planners' requirements for energy conservation, emission reduction and economy, as well as the solution implementers' choice of implementation solutions can be comprehensively considered. In addition, this paper also obtains another optimal solution for adopting the carbon pricing method.