When a constraint violation occurs due to adding a new load to distribution systems, it can be resolved by reconfiguring the distribution systems by changing the states of switches and/or by minimum necessary investment. However, it is difficult to reconfigure the distribution system while satisfying the constraints made more severe by the new load installation. To find distribution system candidates that satisfy the severe constraints, this paper proposes a constrained evolutionary multi-objective optimization algorithm (CMOEA). The proposed CMOEA utilizes a constraint-guided dominance-based and archive-based individual preservation strategy, and it efficiently finds distribution system candidates that satisfy the severe constraints. Furthermore, the proposed CMOEA achieves equalization of the number of sections, minimization of the number of remote/manual switch replacements, and suppression of the upgraded length of distribution lines. The effectiveness of the proposed CMOEA is verified by case studies using a large-scale distribution system model equipped with many manual and remote switches.
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