Multiobjective bilevel optimization algorithm based on preference selection to solve energy hub system planning problems. (1st October 2021)

• Record Type: Journal Article
• Authors: Zou, Juan; Yang, Xu; Liu, Zhongbing; Liu, Jiangyang; Zhang, Ling; Zheng, Jinhua
• Is Part Of: Energy. Volume 232(2021)
• Publication Date: 2021-10-01
• Subjects: Energy hub -- Bilevel optimization -- Multiobjective -- Evolutionary algorithm -- Preference; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2021.120995
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Energy hub system planning is a large-scale discrete multiobjective problem and it also belongs to a Stackelberg game. It is difficult to obtain a solution to this problem in a limited time through deterministic algorithms. In order to solve the above problems, a multiobjective bilevel optimization algorithm based on preference selection is proposed, which is divided into lower-level optimization and upper-level optimization. The preference selection mechanism can solve the uncertainty of the lower-level decision-making, and the trisection search method can improve the speed of the upper-level optimization. In the energy hub system planning problem, the upper-level optimizes the best capacity of energy equipment, and the lower-level optimizes the best combination of each energy carrier. Compared with other heuristic algorithms, the proposed method saves the computational time required to solve the problem. Compared with the commercial optimizer, the proposed method makes up for the defect that the commercial optimizer cannot solve the nonlinear discrete problem. The proposed method helps to solve the planning, design and operation scheduling problems of complex energy hub systems and multi-energy flow complementary systems. This method provides a theoretical basis for further research on the optimal scheduling of the entire life cycle of the energy hub system. Highlights: Bilevel optimization is applied to the optimization problem of the energy hub system. Preference selection mechanism solves the uncertain decision-making of lower-level. Practical energy hub system planning problem is solved. The proposed method gets rid of the dependence on the commercial optimizer.