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An interactive operation management of a micro-grid with multiple distributed generations using multi-objective uniform water cycle algorithm

机译:多目标均匀水循环算法的多分布式发电微电网交互式运行管理

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Accommodation of DGs (distributed generations) close to loads has led to the concept of MG (micro-grid) for better reliability and quality of energy supply. MG as a clump of consumers and DGs can operate in stand-alone and grid-connected modes, and often needs ESS (energy storage system) to handle generation surplus/shortage. Variations of renewable sources and consumptions along with economical and environmental issues necessitate an efficient OM (operation management) of MG for short-term scheduling of energy outputs of DGs, ESS and exchange route to upstream macro-grid. This paper presents MOUWCA (multi-objective uniform water cycle algorithm) for optimal OM of MG considering operation cost and emission as objectives. The problem is casted to find 24 POFs (pareto-optimal fronts) corresponding to 24 h of the day (unlike previous studies giving one POF per day) to provide more flexibility for selecting hourly compromise solutions. Through an interactive process, charging/discharging of ESS is balanced over a day based on the desired order of hours for discharging ESS. MOUWCA is examined on some benchmark problems and compared with NSGA-II (non-dominated GA-II), MOPSO (multi-objective particle swarm optimization) and NCA (normal constraint algorithm) to verify its effectiveness. MOUWCA is then applied to a typical MG where its superiority is confirmed in comparison to other previously used algorithms. (C) 2016 Elsevier Ltd. All rights reserved.
机译:接近负荷的分布式发电设备(DG)的适应性导致了MG(微电网)的概念,以提高能源供应的可靠性和质量。 MG作为一类消费者和DG,可以以独立和并网模式运行,并且通常需要ESS(能源存储系统)来处理发电剩余/短缺。可再生资源和消费的变化以及经济和环境问题,使得MG必须有一个有效的OM(运行管理),用于DG的能源输出,ESS和到上游宏观电网的交换路线的短期调度。本文提出了以运行成本和排放为目标的MG最佳OM的MOUWCA(多目标均匀水循环算法)。该问题被迫寻找对应于一天中24小时的24个POF(最优最优阵线)(与先前的研究每天提供一个POF不同),从而为选择每小时折衷解决方案提供了更大的灵活性。通过交互过程,ESS的充电/放电在一天内会根据放电ESS所需的小时顺序进行平衡。对MOUWCA进行了一些基准测试,并与NSGA-II(非主导GA-II),MOPSO(多目标粒子群优化)和NCA(法向约束算法)进行了比较,以验证其有效性。然后,将MOUWCA应用于典型的MG,与以前使用的其他算法相比,它的优越性得到了证实。 (C)2016 Elsevier Ltd.保留所有权利。

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