A novel Coral Reefs Optimization algorithm with substrate layers for optimal battery scheduling optimization in micro-grids

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• 作者：S. Salcedo-Sanz ; C. Camacho-Gómez ; R. Mallol-Poyato ; S. Jiménez-Fernández…
• 关键词：Micro ; grid ; Battery scheduling optimization ; Coral Reefs Optimization algorithm ; Meta ; heuristics
• 刊名：Soft Computing - A Fusion of Foundations, Methodologies and Applications
• 出版年：2016
• 出版时间：November 2016
• 年：2016
• 卷：20
• 期：11
• 页码：4287-4300
• 全文大小：1,458 KB
• 刊物类别：Engineering
• 刊物主题：Numerical and Computational Methods in Engineering
  Theory of Computation
  Computing Methodologies
  Mathematical Logic and Foundations
  Control Engineering
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1433-7479
• 卷排序：20

文摘

In this paper we propose a Coral Reefs Optimization algorithm with substrate layers (CRO-SL) to tackle the battery scheduling optimization problem in micro-grids (MGs). Specifically, we consider a MG that includes renewable generation and different loads, defined by their power profiles, and is equipped with an energy storage device (battery) to address its scheduling (charge/discharge duration and occurrence) in a real scenario of variable electricity prices. The CRO-SL is a recently proposed meta-heuristic which promotes co-evolution of different exploration models within a unique population. We fully describe the proposed CRO-SL algorithm, including its initialization and the different operators implemented in the algorithm. Experiments in a real MG scenario are carried out. To show the good battery scheduling performance of the proposed CRO-SL, we have compared the results with what we called a deterministic procedure. The deterministic charge/discharge approach is defined as a fixed way of using the energy storage device that only depends on the pattern of the loads and generation profiles considered. Hourly values of both generation and consumption profiles have been considered, and the good performance of the proposed CRO-SL is shown for four different weeks of the year (one per season), where the effect of the battery scheduling optimization obtains savings up 10 % of the total electricity cost in the MG, when compared with the deterministic procedure.