This paper presents a multi-objective optimal power flow technique using particle swarm optimization. Two conflicting objectives, generation cost, and environmental pollution are minimized simultaneously. A multiobjective particle swarm optimization method is used to solve this highly nonlinear and
A study of an interactive fuzzy multi-objective optimal power flow
β Scribed by Junji Kubokawa; Yoshito Okubo; Hiroshi Sasaki; Ryuichi Yokoyama
- Publisher
- John Wiley and Sons
- Year
- 2000
- Tongue
- English
- Weight
- 276 KB
- Volume
- 130
- Category
- Article
- ISSN
- 0424-7760
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β¦ Synopsis
As the necessity of high-quality electric power supply increases, it has been required to develop sophisticated power system operations in which several requirements stemming from economy, security, and environmental aspects are simultaneously satisfied. In general, a problem of satisfying several noncommensurable criteria is called a multiobjective optimization problem. Although the most powerful means to obtain desirable system operations is optimal power flow (OPF), a straightforward application of the conventional OPF optimizes only one objective and the remaining objectives must be treated as constraints. However, since such objectives are in trade-off relationships with each other, it is necessary to develop an efficient multiobjective OPF.
In this paper, we propose a solution method of multiobjective OPF by means of fuzzy coordination. In the first step, the degree of Decision Maker (DM) satisfaction on each objective would be maximized with a prespecified membership function. The membership function would be updated in accordance with the DMs preference information and target value of the objective function. The degree of satisfaction will be improved step by step by updating the membership functions. Finally, the satisficing solution for the DM would be obtained. The proposed method has been applied to the IEEE 57 and 118 test systems, producing successful results.
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