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Mathematics > Optimization and Control

arXiv:1812.01588 (math)
[Submitted on 4 Dec 2018]

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

Authors:Yahui Li, Yang Li
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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.
Comments: Accepted by Journal Processes
Subjects: Optimization and Control (math.OC)
Cite as: arXiv:1812.01588 [math.OC]
  (or arXiv:1812.01588v1 [math.OC] for this version)
  https://doi.org/10.48550/arXiv.1812.01588
Journal reference: Processes 6 (2018) 250
Related DOI: https://doi.org/10.3390/pr6120250

Submission history

From: Yang Li [view email]
[v1] Tue, 4 Dec 2018 18:46:23 UTC (825 KB)
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