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This paper addresses a hybrid particle swarm optimization-based approach for solving a generating unit maintenance scheduling problem (GMS). We focus on the power system reliability such as reserve ratio better than cost function as the objective function of GMS problem. It is shown that particle swarm optimization-based method is more effective in obtaining feasible schedules such as GMS problem...
In this paper, a new probabilistic generation modeling method which can address the characteristics of changed electricity industry is proposed. The major contribution of this paper can be captured in the development of a probabilistic generation modeling considering generator maintenance outage and in the classification of market demand into multiple demand clusters for the applications to electricity...
The demise of the native franchise markets and emergence of competitive markets for electricity generation service is changing the way that electricity is and will be priced and is making increasingly difficult for market participants to appraise the prospects for the future electricity market. As a result, conventional generation expansion planning (GEP) problems determining the least-cost capacity...
In this presentation, the authors propose a new framework for a generation expansion planning (GEP) applicable in a competitive environment using a genetic algorithm (GA). Unlike the traditional approaches, the new GEP in a competitive market is very complex due to conflicts among generation companies (GENCOs). The objective function of each GENCO for investment decision-making is to maximize its...
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