Two-Step Many-Objective Optimal Power Flow Based on Knee Point-Driven Evolutionary Algorithm

TL;DR

This paper introduces a novel two-step method combining knee point-driven evolutionary algorithms and decision analysis techniques to solve complex multi-objective power flow problems, improving decision support in power system operations.

Contribution

It is the first to apply knee point-driven evolutionary algorithm to many-objective optimal power flow and integrates decision analysis for better decision-making.

Findings

Effective in solving MaOPF problems on IEEE 118-bus system

Demonstrates applicability to real-world Hebei power system

Provides automatic identification of best compromise solutions

Abstract

To coordinate the economy, security and environment protection in the power system operation, a two-step many-objective optimal power flow (MaOPF) solution method is proposed. In step 1, it is the first time that knee point-driven evolutionary algorithm (KnEA) is introduced to address the MaOPF problem, and thereby the Pareto-optimal solutions can be obtained. In step 2, an integrated decision analysis technique is utilized to provide decision makers with decision supports by combining fuzzy c-means (FCM) clustering and grey relational projection (GRP) method together. In this way, the best compromise solutions (BCSs) that represent decision makers' different, even conflicting, preferences can be automatically determined from the set of Pareto-optimal solutions. The primary contribution of the proposal is the innovative application of many-objective optimization together with decision analysis for addressing MaOPF problems. Through examining the two-step method via the IEEE 118-bus system and the real-world Hebei provincial power system, it is verified that our approach is suitable for addressing the MaOPF problem of power systems.