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Heuristic Optimization for the Discrete Virtual Power Plant Dispatch Problem

机译：离散虚拟电厂调度问题的启发式优化

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摘要

We consider a virtual power plant, which is given the task of dispatching a fluctuating power supply to a portfolio of flexible consumers. The flexible consumers are modeled as discrete batch processes, and the associated optimization problem is denoted the discrete virtual power plant dispatch problem (DVPPDP). First, the nondeterministic polynomial time (NP)-completeness of the discrete virtual power plant dispatch problem is proved formally. We then proceed to develop tailored versions of the meta-heuristic algorithms hill climber and greedy randomized adaptive search procedure (GRASP). The algorithms are tuned and tested on portfolios of varying sizes. We find that all the tailored algorithms perform satisfactorily in the sense that they are able to find sub-optimal, but usable, solutions to very large problems (on the order of units) at computation times on the scale of just 10 s, which is far beyond the capabilities of the optimal algorithms we have tested. In particular, GRASP sorted shows with the most promising performance, as it is able to find solutions that are both agile (sorted) and well balanced, and consistently yields the best numerical performance among the developed algorithms.
机译：我们考虑一个虚拟发电厂，其任务是将波动的电源分配给灵活的用户群。柔性用户被建模为离散的批处理过程，而相关的优化问题称为离散的虚拟电厂调度问题（DVPPDP）。首先，正式证明了离散虚拟电厂调度问题的不确定多项式时间（NP）-完备性。然后，我们继续开发元启发式算法“爬山者”和贪婪随机自适应搜索过程（GRASP）的定制版本。这些算法已在各种规模的产品组合上进行了调整和测试。我们发现，所有量身定制的算法在令人满意的意义上都能够在10 s的计算时间内找到次优但可用的非常大问题（按单位数量级）的解决方案，这是合理的。远远超出了我们测试过的最佳算法的能力。特别是，GRASP排序的显示具有最有前途的性能，因为它能够找到既灵活（排序）又平衡良好的解决方案，并始终在已开发算法中产生最佳数值性能。

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