Development of a Dynamic Operational Scheduling Algorithm for an Independent Micro-Grid with Renewable Energy

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A micro-grid with the capacity for sustainable energy is expected to be a distributed energy system that exhibits quite a small environmental impact. In an independent micro-grid, “green energy,” which is typically thought of as unstable, can be utilized effectively by introducing a battery. In the past study, the production-of-electricity prediction algorithm (PAS) of the solar cell was developed. In PAS, a layered neural network is made to learn based on past weather data and the operation plan of the compound system of a solar cell and other energy systems was examined using this prediction algorithm. In this paper, a dynamic operational scheduling algorithm is developed using a neural network (PAS) and a genetic algorithm (GA) to provide predictions for solar cell power output. We also do a case study analysis in which we use this algorithm to plan the operation of a system that connects nine houses in Sapporo to a micro-grid composed of power equipment and a polycrystalline silicon solar cell. In this work, the relationship between the accuracy of output prediction of the solar cell and the operation plan of the micro-grid was clarified. Moreover, we found that operating the micro-grid according to the plan derived with PAS was far superior, in terms of equipment hours of operation, to that using past average weather data.

収録刊行物

  • Journal of Thermal Science and Technology

    Journal of Thermal Science and Technology 3(3), 474-485, 2008

    一般社団法人日本機械学会・社団法人日本伝熱学会

各種コード

  • NII論文ID(NAID)
    130000098814
  • 本文言語コード
    ENG
  • 資料種別
    Journal Article
  • データ提供元
    IR  J-STAGE 
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