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Two-stage stochastic model for the price-based domestic energy management problem

机译:基于价格的家庭能源管理问题的两阶段随机模型

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Residential buildings have become an active market participant in future power grid transactions due to the development of smart grid technologies, particularly smart meters. Keeping this in mind, this paper proposes a two-stage stochastic model including day-ahead and real-time local energy markets with the aim of domestic equipment scheduling, which reflects the uncertain mobility pattern of Electric Vehicle (EV) as well as the variability of micro wind turbine generation. The contribution of EV and battery in providing additional flexibility through bi-directional energy trading has been investigated considering deterministic and stochastic EV mobility patterns. Moreover, the smart home is modeled as a price-taker agent in the local market. Hence, different price-based Demand Response (DR) programs can affect its decisions. On this basis, a comprehensive analysis on the participation of a smart home in various price-based DR strategies is carried out with the aim of determining the most effective DR program from smart home owner point of view. The obtained results reveal that the participation of the smart home in Time-of-Use (ToU) pricing scheme not only reduces the operation cost, but also leads to smart home profitability.
机译:由于智能电网技术(尤其是智能电表)的发展,住宅建筑已成为未来电网交易中活跃的市场参与者。考虑到这一点,本文提出了一个两阶段的随机模型,该模型包括日前和实时本地能源市场,旨在实现国内设备调度,该模型反映了电动汽车(EV)的不确定的机动性模式和可变性微型风力发电机组。考虑到确定性和随机性的电动汽车出行方式,已经研究了电动汽车和电池在通过双向能源交易提供额外灵活性方面的贡献。此外,智能家居被建模为本地市场上的价格代理。因此,不同的基于价格的需求响应(DR)程序可能会影响其决策。在此基础上,对智能家居参与各种基于价格的灾难恢复策略进行了全面分析,目的是从智能家居所有者的角度确定最有效的灾难恢复计划。获得的结果表明,智能家居参与使用时间(ToU)定价方案不仅降低了运营成本,而且还提高了智能家居的盈利能力。

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