Demand-side management (DSM) is a process by which the user demand patterns are modified to meet certain desired objectives. Traditionally, DSM was utility-driven, but with an increase in the integration of renewable sources and privacy-conscious consumers, it also becomes a “consumer-driven” process. Promising theoretical studies have shown that privacy can be achieved by shaping the user demand using an energy storage system (ESS). In this paper, we present a framework for utility-driven DSM while considering the user privacy and the ESS operational cost due to its energy losses and capacity degradation. We propose an ESS model using a circuit-based and data-driven approach that can be used to capture the ESS characteristics in control strategy designs. We measure privacy leakage using the Bayesian risk of a hypothesis testing adversary and present a novel recursive algorithm to compute the optimal privacy control strategy. Further, we design an energy-flow control strategy that achieves the Pareto-optimal trade-off between privacy leakage, deviation of demand from a DSM target profile, and the ESS cost. With numerical experiments using real household data and an emulated lithium-ion battery, we show that the desired level of privacy and demand shaping performance can be achieved while reducing the ESS degradation.

中文翻译：

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具有现实储能模型的隐私感知需求侧管理设计框架

需求方管理 (DSM) 是修改用户需求模式以满足某些预期目标的过程。传统上，帝斯曼是由公用事业驱动的，但随着可再生能源和注重隐私的消费者的整合增加，它也变成了一个“消费者驱动”的过程。有希望的理论研究表明，可以通过使用储能系统 (ESS) 塑造用户需求来实现隐私。在本文中，我们提出了一个公用事业驱动的 DSM 框架，同时考虑了用户隐私和 ESS 由于其能量损失和容量退化而产生的运营成本。我们提出了一种使用基于电路和数据驱动的方法的 ESS 模型，该方法可用于在控制策略设计中捕获 ESS 特性。我们使用假设检验对手的贝叶斯风险来衡量隐私泄漏，并提出了一种新颖的递归算法来计算最佳隐私控制策略。此外，我们设计了一种能量流控制策略，以实现隐私泄漏、需求与 DSM 目标配置文件的偏差以及 ESS 成本之间的帕累托最优权衡。通过使用真实家庭数据和模拟锂离子电池的数值实验，我们表明可以在减少 ESS 退化的同时实现所需的隐私和需求塑造性能水平。