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Decentralised optimisation of cogeneration in virtual power plants

机译:虚拟电厂热电联产的分散优化

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摘要

Within several projects we investigated grid structures and management strategies for active grids with high penetration of renewable energy resources and distributed generation (RES & DG). Those "smart grids" should be designed and managed by model based methods, which are elaborated within these projects. Cogeneration plants (CHP) can reduce the greenhouse gas emissions by locally producing heat and electricity. The integration of thermal storage devices is suitable to get more flexibility for the cogeneration operation. If several power plants are bound to centrally managed clusters, it is called "virtual power plant". To operate smart grids optimally, new optimisation and model reduction techniques are necessary to get rid with the complexity.rnThere is a great potential for the optimised management of CHPs, which is not yet used. Due to the fact that electrical and thermal demands do not occur simultaneously, a thermally driven CHP cannot supply electrical peak loads when needed. With the usage of thermal storage systems it is possible to decouple electric and thermal production. We developed an optimisation method based on mixed integer linear programming (MILP) for the management of local heat supply systems with CHPs, heating boilers and thermal storages. The algorithm allows the production of thermal and electric energy with a maximal benefit. In addition to fuel and maintenance costs it is assumed that the produced electricity of the CHP is sold at dynamic prices. This developed optimisation algorithm was used for an existing local heat system with 5 CHP units of the same type. An analysis of the potential showed that about 10% increase in benefit is possible compared to a typical thermally driven CHP system under current German boundary conditions. The quality of the optimisation result depends on an accurate prognosis of the thermal load which is realised with an empiric formula fitted with measured data by a multiple regression method.rnThe key functionality of a virtual power plant is to increase the value of the produced power by clustering different plants. The first step of the optimisation concerns the local operation of the individual power generator, the second step is to calculate the contribution to the virtual power plant. With small extensions the suggested MILP algorithm can be used for an overall EEX (European Energy Exchange) optimised management of clustered CHP systems in form of the virtual power plant. This algorithm has been used to control cogeneration plants within a distribution grid.
机译:在几个项目中,我们研究了具有高可再生能源和分布式发电渗透能力的有源电网的网格结构和管理策略(RES和DG)。这些“智能网格”应该通过基于模型的方法来设计和管理,这些方法在这些项目中都有详细阐述。热电联产厂(CHP)可以通过本地生产热量和电力来减少温室气体排放。储热装置的集成适合于为热电联产操作获得更大的灵活性。如果将多个发电厂绑定到集中管理的群集,则称为“虚拟发电厂”。为了使智能电网达到最佳运行状态,有必要采用新的优化和模型简化技术来摆脱复杂性。rn。CHP的优化管理潜力巨大,目前尚未使用。由于电气和热需求不会同时发生,因此热驱动的热电联产无法在需要时提供电峰值负载。通过使用蓄热系统,可以使电力生产与热量生产脱钩。我们开发了一种基于混合整数线性规划(MILP)的优化方法,用于管理带有热电联产,供暖锅炉和储热装置的局部供热系统。该算法可以最大程度地产生热能和电能。除燃料和维护成本外,还假设热电联产的发电量以动态价格出售。此开发的优化算法用于具有5个相同类型CHP单元的现有局部供热系统。对该潜力的分析表明,在当前的德国边界条件下,与典型的热力驱动热电联产系统相比,收益可能提高约10%。优化结果的质量取决于热负荷的准确预测,而热负荷的预测是通过经验公式通过多元回归方法拟合实测数据来实现的。rn虚拟电厂的关键功能是通过以下方法来增加发电量:聚集不同的植物。优化的第一步涉及单个发电机的本地运行,第二步是计算对虚拟电厂的贡献。通过少量扩展,建议的MILP算法可用于虚拟电厂形式的群集CHP系统的整体EEX(欧洲能源交易所)优化管理。该算法已用于控制配电网中的热电联产工厂。

著录项

  • 来源
    《Solar Energy》 |2010年第4期|p.604-611|共8页
  • 作者单位

    Fraunhofer Institute for Solar Energy Systems ISE, Heidenhofstrasse 2, 79110 Freiburg, Germany;

    Fraunhofer Institute for Solar Energy Systems ISE, Heidenhofstrasse 2, 79110 Freiburg, Germany;

    Fraunhofer Institute for Solar Energy Systems ISE, Heidenhofstrasse 2, 79110 Freiburg, Germany;

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

    cogeneration; optimisation; load prognosis; distributed generation;

    机译:热电联产优化;负荷预后分布式发电;

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