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A comprehensive study of economic unit commitment of power systems integrating various renewable generations and plug-in electric vehicles

机译：集成了各种可再生能源发电和插电式电动汽车的电力系统经济单位承诺的综合研究

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摘要

Significant penetration of renewable generations (RGs) and mass roll-out of plug-in electric vehicles (PEVs) will pay a vital role in delivering the low carbon energy future and low emissions of greenhouse gas (GHG) that are responsible for the global climate change. However, it is of considerable difficulties to precisely forecast the undispatchable and intermittent wind and solar power generations. The uncoordinated charging of PEVs imposes further challenges on the unit commitment in modern grid operations. In this paper, all these factors are comprehensively investigated for the first time within a novel hybrid unit commitment framework, namely UCsRP, which considers a wide range of scenarios in renewable generations and demand side management of dispatchable PEVs load. UCsRP is however an extremely challenging optimisation problem not only due to the large scale, mixed integer and nonlinearity, but also due to the double uncertainties relating to the renewable generations and PEV charging and discharging. In this paper, a meta-heuristic solving tool is introduced for solving the UCsRP problem. A key to improve the reliability of the unit commitment is to generate a range of scenarios based on multiple distributions of renewable generations under different prediction errors and extreme predicted value conditions. This is achieved by introducing a novel multi-zone sampling method. A comprehensive study considering four different cases of unit commitment problems with various weather and season scenarios using real power system data are conducted and solved, and smart management of charging and discharging of PEVs are incorporated into the problem. Test results confirm the efficacy of the proposed framework and new solving tool for UCsRP problem. The economic effects of various scenarios are comprehensively evaluated and compared based on the average economic cost index, and several important findings are revealed. (C) 2016 Elsevier Ltd. All rights reserved.
机译：可再生能源（RGs）的大量普及和插电式电动汽车（PEV）的大规模推广将在实现低碳能源的未来和温室气体（GHG）的低排放方面发挥至关重要的作用，而低碳能源和温室气体（GHG）构成了全球气候更改。然而，精确地预测不可调度和间歇性的风能和太阳能发电量是很大的困难。 PEV的不协调充电给现代电网运行中的机组承诺带来了进一步的挑战。在本文中，所有这些因素都是在一个新颖的混合动力单位承诺框架UCsRP中首次进行全面研究的，该框架考虑了可再生能源发电和可调度PEV负荷需求侧管理的各种情况。然而，UCsRP不仅是大规模，混合整数和非线性的问题，而且由于与可再生能源发电和PEV充放电有关的双重不确定性，是一个极富挑战性的优化问题。本文介绍了一种用于解决UCsRP问题的元启发式求解工具。提高机组承诺可靠性的关键是，在不同的预测误差和极端的预测值条件下，基于可再生能源发电的多种分布来产生一系列情景。这是通过引入一种新颖的多区域采样方法来实现的。进行并解决了使用实际电力系统数据考虑了四种不同情况下的机组承诺问题的情况，这些案例涉及各种天气和季节情景，并且将智能电动汽车的充电和放电管理纳入了该问题。测试结果证实了所提出的框架和针对UCsRP问题的新解决工具的有效性。根据平均经济成本指数对各种情景的经济影响进行了综合评估和比较，并揭示了一些重要发现。 （C）2016 Elsevier Ltd.保留所有权利。

著录项

  • 来源
    《Energy Conversion & Management》 |2017年第1期|460-481|共22页
  • 作者

    Yang Zhile; Li Kang; Niu Qun; Xue Yusheng;

  • 作者单位

    Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast BT9 5AH, Antrim, North Ireland;

    Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast BT9 5AH, Antrim, North Ireland;

    Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai Key Lab Power Stn Automat Technol, Shanghai 200072, Peoples R China;

    State Grid Elect Power Res Inst, Wuhan 210003, Jiangsu, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Unit commitment; Multi-zone sampling; Uncertainties; Wind power; Solar power; Plug-in electric vehicles;

    机译：单位承诺;多区域采样;不确定性;风力;太阳能;插电式电动汽车;

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