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Multi-Period energy procurement policies for smart-grid communities with deferrable demand and supplementary uncertain power supplies

机译:针对需求递延且不确定的补充电力供应的智能电网社区的多期能源采购政策

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We analyze a multi-period energy procurement problem for an energy aggregator, which is responsible for the centralized control of energy procurement and consumption in a community with smart grid installed. The aggregator can delay time-adjustable demand in the community through the smart grid if necessary. Furthermore, the aggregator can supply renewable energy as a supplementary power source to traditional electricity markets with pre-announced day-ahead real-time prices. To determine the optimal procurement amount, the aggregator needs to make tradeoffs between two types of power supplies, namely, traditional energy with variable prices versus free renewable energy with uncertain supplies. We solve the aggregator's problem using dynamic programming and show that the optimal procurement policy is to procure traditional energy only when the price is below a threshold, which depends on the statistics of the day-ahead real-time price, wind energy distribution and the time left until the end of horizon. Through numerical studies, we compare the optimal policy with two other commonly-used policies in the wind energy setting, procurement based on the estimated supplies of wind energy and procurement up to the arrived demand. The cost-savings of our optimal policy are remarkable if the day-ahead real-time price fluctuates considerably. We also examine the robustness of our optimal policy in the scenarios of using historical wind energy data. (C) 2018 Elsevier Ltd. All rights reserved.
机译:我们分析了一个能源聚集器的多时期能源采购问题,该聚集器负责在安装了智能电网的社区中对能源采购和消耗进行集中控制。如果需要,聚合器可以通过智能电网延迟社区中时间可调的需求。此外,聚合器可以以预先宣布的日前实时价格向传统电力市场提供可再生能源,作为补充电源。为了确定最佳采购量,聚合器需要在两种类型的电源之间进行权衡,即传统的可变价格能源与不确定的自由可再生能源。我们使用动态规划解决了聚合器的问题,并表明最佳的采购策略是仅在价格低于阈值时才采购传统能源,这取决于日前实时价格,风能分布和时间的统计数据一直走到地平线尽头。通过数值研究,我们将最优策略与风能设置中的两个其他常用策略进行比较,根据风能的估计供应量进行采购,并根据达到的需求进行采购。如果日间实时价格波动很大,我们的最佳策略可以节省大量成本。在使用历史风能数据的情况下,我们还研究了最优政策的稳健性。 (C)2018 Elsevier Ltd.保留所有权利。

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