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This paper develops a many-objective optimization model, which contains objectives representing the interests of the electricity and gas networks, as well as the distributed district heating and cooling units, to coordinate the benefits of all parties participated in the integrated energy system (IES). In order to solve the many-objective optimization model efficiently, an improved objective reduction (IOR) approach is proposed, aiming at acquiring the smallest set of objectives. The IOR approach utilizes the Spearman's rank correlation coefficient to measure the relationship between objectives based on the Pareto-optimal front captured by the multi-objective group search optimizer with adaptive covariance and Lévy flights algorithm, and adopts various strategies to reduce the number of objectives gradually. Simulation studies are conducted on an IES consisting of a modified IEEE 30-bus electricity network and a 15-node gas network. The results show that the many-objective optimization problem is transformed into a bi-objective formulation by the IOR. Furthermore, our approach improves the overall quality of dispatch solutions and alleviates the decision making burden.

This paper presents a tri-level many-objective optimization (TLMaO) approach to provide a final solution for many-objective optimization problems (MaOPs). In this approach, the proposed objectives' number reduction (ONR) method is utilized as the first level to select the most conflicting objectives for the second level to optimize. The second level outputs a set of Pareto-optimal solutions using the multiobjective optimization algorithm, however, a unique solution must be selected for real world problems. Therefore, we propose an improved entropy weight (IEW) method for decision making as the third level to determine the final solution. The effectiveness of the ONR and IEW method is first demonstrated on test problems. Then, the features and efficacy of the proposed TLMaO approach are investigated on a real world problem, the many-objective optimization of power flow (MaOPF). The simulation results verify that when compared with a general method used for MaOPs, our TLMaO approach can offer more competitive and robust solutions.

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