Knee Point-Guided Multiobjective Optimization Algorithm for Microgrid Dynamic Energy Management

Author:

Li Wenhua1ORCID,Zhang Guo1,Zhang Tao12,Huang Shengjun12ORCID

Affiliation:

1. College of Systems Engineering, National University of Defense Technology, Changsha 410073, China

2. Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology, Changsha 410073, China

Abstract

Model predictive control (MPC) technology can effectively reduce the bad effect caused by inaccurate data prediction in microgrid energy management problem. However, the use of MPC technology needs to dynamically select an optimal solution from the Pareto solution set to implement, which needs the participant of the decision-makers frequently. In order to reduce the burden on decision-makers, we designed a knee point-based evolutionary multiobjective optimization algorithm, termed KBEMO. Knee point is the solution on Pareto front with the maximum marginal utility, which is considered as the preferred solution if there is no other preference. This algorithm focuses on obtaining the knee region and automatically outputs knee points after the optimization. By combining this algorithm with MPC technology, it can effectively reduce the amount of computational consumption and obtain better convergence. Experimental results show that this method is more competitive than the traditional single-objective MPC method.

Publisher

Hindawi Limited

Subject

Multidisciplinary,General Computer Science

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