A rolling-horizon optimization algorithm for the long term operational scheduling of cogeneration systems

A rolling-horizon optimization algorithm for the long term operational scheduling of cogeneration systems
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热电联产系统长期运行调度的滚动优化算法

DOI:
10.1016/j.energy.2017.12.022
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发表时间:
2017
期刊:
影响因子:
9
通讯作者:
E. Macchi
E. Macchi
中科院分区:
工程技术1区
文献类型:
--
作者:
A. Bischi;L. Taccari;E. Martelli;E. Amaldi;G. Manzolini;Paolo Silva;S. Campanari;E. Macchi

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在考虑时变负荷、电价和环境条件以及年度财政激励的情况下,提出了一种滚动地平线算法来优化给定热电联产能源系统的运行计划。该算法基于作者提出的混合整数线性规划(MILP)模型,用于热电联产系统和热电厂网络的日调度优化。首先,将MILP模型扩展到优化周运行计划,以更好地管理冷热系统。然而,为了考虑到欧洲高效热电联产的资格框架,以及各国具体的激励政策,有必要考虑平均每年的节能指数,因此需要解决全年的问题。由于MILP模型从1天扩展到7天已经大大增加了计算量,因此将相同的优化方法简单地应用到一整年实际上是不可行的;因此,本研究提出了一种滚动地平线算法,该算法求解了一系列每周的MILP子模型,同时考虑了基于历史数据需求概况的生产和消费估计。报告并讨论了实际测试用例的结果。
A rolling-horizon algorithm is proposed for optimizing the operating schedule of a given cogeneration energy system while taking into account time-variable loads, tariffs and ambient conditions, as well as yearly fiscal incentives. The presented algorithm is based on the Mixed Integer Linear Programming (MILP) model developed by the authors for optimizing the daily schedule of cogeneration systems and networks of heat and power plants.First the MILP model is extended to optimize the weekly operation schedule to better manage the heat-cold storage systems. However, in order to account for the European qualification framework for high efficiency cogeneration, as well as for country-specific incentive policies, it is necessary to consider average yearly-basis energy saving indexes, thus requiring to tackle the problem for the whole year. Since the extension of the MILP model from one day to seven days already increases remarkably the computational requirements, a simple application of the same optimization approach to a whole year would be practically unfeasible; therefore, this work proposes a rolling-horizon algorithm in which a sequence of weekly MILP submodels is solved, while considering production and consumption estimates based on demand profiles from historical data. The results obtained for a real-world test case are reported and discussed.