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295. Large-Scale Binary Matrix Optimization for Multi-Microgrids Network Structure Design

Invited abstract in session WC-19: OR in Energy II, stream OR in Energy.

Wednesday, 12:30-14:00
Room: 44 (building: 116)

Authors (first author is the speaker)

1. Rui Wang
College of System Engineering, National University of Defense Technology
2. Gang Zhou
College of Systems Engineering, National University of Defense Technology
3. Bo Jiang
College of Systems Engineering, National University of Defense Technology

Abstract

The Multi-Microgrid Network Structure Design Problem (MNSDP) is a binary matrix optimization problem that aims to minimize the cumulative length of preset power lines in a multi-microgrid system, while satisfying specific constraints. This problem is of great significance for improving the utilization rate of renewable energy sources and the stability and resilience of power systems in remote areas from the central grid. Due to the large-scale, sparse and nonlinear characteristics of this problem, traditional optimization methods are difficult to obtain satisfactory solutions within a reasonable time. This paper proposes a mathematical model of the MNSDP, incorporating three types of nodes with different reliability requirements. The paper also introduces a benchmark test suite, MNSDP-LIB, based on real-world scenarios, to evaluate the performance of algorithms. To solve the MNSDP, the paper develops a new matrix-based constrained differential evolution algorithm, LBMDE, which employs a binary-matrix-centric DE operator and an enhanced feasibility rule-based environmental selection strategy. The paper conducts extensive experiments to compare LBMDE with several state-of-the-art evolutionary algorithms and a commercial solver on MNSDP-LIB and other binary matrix optimization problems. The results demonstrate the superiority of LBMDE in terms of solution quality and computational efficiency, as well as its contribution to advancing renewable energy and microgrid technologies.

Keywords

Status: accepted

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