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Stochastic demand charge management for commercial and industrial buildings

机译:商业和工业建筑物的随机需求收费管理

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In this paper, we propose a strategy for battery operation to manage monthly peak electricity demand of commercial & industrial buildings and reduce the associated Demand Charges (DC) in their utility bills. In specific, we present a stochastic optimization model that calculates the monthly Demand Charge Thresholds (DCT) at month-ahead. During the month, the battery charges and discharges according to DCT and measured demand at each time step. We apply techniques that convert the rule-based control algorithm into linear constraints and incorporate them in the model. Case studies suggest that, compared with other methods, the stochastic method generates DCTs closer to their expected value and achieves higher Demand Charge (DC) savings.
机译:在本文中,我们提出了一种电池操作策略,以管理商业和工业建筑的每月峰值用电需求,并减少其公用事业账单中的相关需求收费(DC)。具体而言,我们提出了一种随机优化模型,该模型可以计算未来一个月的每月需求收费阈值(DCT)。在这个月中,电池会根据DCT和每个时间步长上测得的需求进行充电和放电。我们应用了将基于规则的控制算法转换为线性约束并将其纳入模型的技术。案例研究表明,与其他方法相比,随机方法生成的DCT接近其预期值,并实现了更高的需求费用(DC)节省。

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