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Optimal operation of microgrid under a stochastic environment.

机译:随机环境下微电网的最佳运行。

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摘要

With its technological and regulatory innovation of scale and structure, microgrids have been developed all over the world as a mean to address the high penetration level of renewable generation, reduce the greenhouse gas emission, and provide economical solutions for the currently non-electrified area. The operation of microgrid requires resource planning for those fossil-fuel based generators, energy storage systems, and demand resources if demand side management is implemented. Due to the stochastic nature of renewable energy resources, load behaviors and market prices, enormous uncertainties are involved in the microgrid operation and scheduling problems for both short-term and longer term. These uncertainties may result in a non-optimal operation or even jeopardizing the reliability of the microgrid if they are not fully considered in the scheduling stage.;This dissertation applies stochastic modeling and optimization techniques to address the challenges brought by uncertainties in the microgrid operation through. The microgrid day-ahead scheduling problem, demand side management scheduling problem, and medium-term operation scheduling problem are modelled via stochastic approaches to achieve the optimal operation decisions under an environment with high degree of uncertainties. Meanwhile, a microgrid carbon emission co-optimized scheduling algorithm is also proposed to address the carbon emission in the microgrid operation. Correspondingly, the uncertainty models and solving methods for those formulations are also proposed by this dissertation and numerical results are presented for verification and illustration purpose.
机译:凭借其规模和结构的技术和法规创新,微电网已在世界范围内开发,以解决可再生能源发电的高渗透水平,减少温室气体排放并为当前非电气化地区提供经济的解决方案。微电网的运行要求对那些基于化石燃料的发电机,能量存储系统进行资源规划,如果实施了需求侧管理,则需要需求资源。由于可再生能源的随机性,负荷行为和市场价格,无论是短期还是长期,微电网的运行和调度问题都涉及巨大的不确定性。如果在调度阶段没有充分考虑这些不确定性,可能会导致非最佳运行,甚至可能危及微电网的可靠性。;本文采用随机建模和优化技术,通过以下方法解决微电网运行不确定性带来的挑战: 。通过随机方法对微电网提前调度问题,需求侧管理调度问题和中期运营调度问题进行建模,以在不确定性较高的环境下实现最优运营决策。同时,提出了微电网碳排放协同优化调度算法,以解决微电网运行中的碳排放问题。相应地,本文还提出了这些公式的不确定性模型和求解方法,并给出了数值结果以供验证和说明。

著录项

  • 作者

    Ding, Zhaohao.;

  • 作者单位

    The University of Texas at Arlington.;

  • 授予单位 The University of Texas at Arlington.;
  • 学科 Electrical engineering.;Alternative Energy.
  • 学位 Ph.D.
  • 年度 2015
  • 页码 125 p.
  • 总页数 125
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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