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An optimization study on a typical renewable microgrid energy system with energy storage

机译:典型可再生微电网能量系统的优化研究

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摘要

In isolated microgrids and remote regions, the challenge of developing reliable and self-sufficient renewable energy systems is amplified due to the lack of grid flexibility options. One of the leading solutions to increase renewable energy usage in isolated systems is the commission of energy storage. The current study proposes a novel optimization model that sizes the most cost-efficient renewable power capacity mix of an autonomous microgrid supported by storage technologies. The proposed algorithm considers operational, technical and land-use constraints. The problem is formulated using linear programming, is tested and scrutinized with sets of historical weather, load demand and installation prices data, and is modelled hour-by-hour. The method is applied to Corvo, an island in the Azores archipelago, Portugal. The results obtained exhibit that the proposed approach provides the optimal configuration of the renewable-based microgrid with an LCOE (Levelized Cost of electricity) of 0.21 V/kWh, a value lower than a diesel-based alternative, and while ensuring minimum land area occupation. Furthermore, sensitivity analysis is also presented to examine the effect of variables on the LCOE and PC (present cost) of the system. The present study shows that the developed optimal sizing model can improve electricity planning and facilitate energy transition in distributed power systems. (c) 2021 Elsevier Ltd. All rights reserved.
机译:在孤立的微电网和偏远地区,由于缺乏网格灵活性选项,显影可靠和自充足的可再生能源系统的挑战。增加孤立系统中可再生能源使用的领先解决方案之一是储存委员会。目前的研究提出了一种新颖的优化模型,其尺寸大小由存储技术支持的自主微电网的最具成本效益的可再生能力组合。该算法认为运营,技术和土地使用约束。使用线性编程配制了该问题,通过历史天气,负载需求和安装价格数据进行了测试和仔细检查,并为单小时进行建模。该方法应用于Corvo,葡萄牙亚速尔群岛群岛的岛屿。所获得的结果表明,所提出的方法提供了可再生基微电网的最佳配置,其中LCOE(电力调整为电力成本)为0.21 v / kWh,该值低于基于柴油的替代品,而确保最小土地面积占用。此外,还提出了灵敏度分析以检查变量对系统的LCOE和PC(目前成本)的影响。本研究表明,开发的最佳施胶模型可以改善分布式电力系统中的电力规划并促进能量转换。 (c)2021 elestvier有限公司保留所有权利。

著录项

  • 来源
    《Energy》 |2021年第1期|121210.1-121210.15|共15页
  • 作者单位

    Shanghai Jiao Tong Univ China UK Low Carbon Coll Shanghai 200240 Peoples R China|Sino Portuguese Ctr New Energy Technol Shanghai C Shanghai 200335 Peoples R China;

    Shanghai Jiao Tong Univ China UK Low Carbon Coll Shanghai 200240 Peoples R China;

    Shanghai Jiao Tong Univ China UK Low Carbon Coll Shanghai 200240 Peoples R China;

    Shanghai Jiao Tong Univ China UK Low Carbon Coll Shanghai 200240 Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Renewable microgrid; Optimization; Energy storage; Distributed generation; Bayesian artificial neural network;

    机译:可再生微电网;优化;能量存储;分布生成;贝叶斯人工神经网络;

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