Affiliations: [a] School of Artificial Intelligence, Wuchang University of Technology, Wuhan, China
| [b]
School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan, China
| [c] State Grid Beijing Electric Maintenance Company, Beijing, China
Correspondence:
[*]
Corresponding author. Ruoli Tang, School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan, China. E-mail: [email protected].
Abstract: In order to optimally control the marine hybrid power system (HPS) under increasingly complex regulation constraints or hardware constraints, an efficient power-flow scheduling model and optimization algorithm are of great importance. This work focuses on the optimal power-flow scheduling of marine HPS, especially on the efficiency improvement of the penalty functions for satisfying complex constraints. To be specific, an optimal operation model of marine HPS is discussed, and the complex model constraints are described as various penalty functions. Secondly, a novel optimization algorithm, namely adaptive multi-context cooperatively coevolving differential evolution algorithm with random topology and mutated context vector (AMCCDE - rt - mcv) is developed to optimize the aforementioned model. In order to ensure the satisfaction of the complex model constraints, the detailed forms for penalty functions are researched and the optimal parameters for penalty functions are comprehensively compared, analysed and tested by a set of numerical experiments. Finally, the developed methodologies are tested by simulation experiments. Experimental results show that the damping factor, exponent parameter and punish strength constant effect the efficiency of penalty functions a lot, and the developed penalty functions can effectively satisfy all the model constraints with fast response speed. With the integration of penalty functions, the developed methodology can obtain promising performance on the optimal scheduling of the evaluated marine HPS.
Keywords: Hybrid power system, optimal energy management, penalty function, optimization algorithm, differential evolution
