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.
Highlights
Optimal power flow (OPF) plays a major part role in guaranteeing the safe and economical operation of power systems [1,2], and it has been receiving the wide-spread attention of professionals and researchers from academia and industry [3,4], especially in the case of large-scale integrations of renewable energy resources [5,6]
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 many-objective optimal power flow (MaOPF) problem of power systems
In [36], six different evolutionary algorithms (EAs) are tested, and the results prove that multi-objective evolutionary algorithms (MOEAs) exhibit their own capabilities in dealing with different many-objective optimization problems (MaOPs)
Summary
Optimal power flow (OPF) plays a major part role in guaranteeing the safe and economical operation of power systems [1,2], and it has been receiving the wide-spread attention of professionals and researchers from academia and industry [3,4], especially in the case of large-scale integrations of renewable energy resources [5,6]. At the same time, the power flow characteristics of a modern power system are becoming increasingly complex due to the growing penetration of distributed generations [14,15,16,17] and the deployments of novel power electronic loads [18,19,20,21,22] In this context, multi-objective OPF (MOPF) has received the extensive attention of researchers in the field of OPF [23,24,25,26,27,28], since it can coordinate different-weight or even conflicting multiple objectives. Motivated by the recent work in literature [23], a new powerful MOEA, called knee point-driven evolutionary algorithm (KnEA), is applied for solving this problem in the paper, which is helpful to better adapt the increasingly diversified operating requirements for the construction of the modern power systems.
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