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Cost-Effective and Privacy-Preserving Energy Management for Smart Meters

机译：智能电表的经济高效且保护隐私的能源管理

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摘要

Smart meters, designed for information collection and system monitoring in smart grid, report fine-grained power consumption to utility providers. With these highly accurate profiles of energy usage, however, it is possible to identify consumers’ specific activities or behavior patterns, thereby giving rise to serious privacy concerns. This paper addresses these concerns by designing a cost-effective and privacy-preserving energy management technique that uses a rechargeable battery. From a holistic perspective, a dynamic programming framework is designed for consumers to strike a tradeoff between smart meter data privacy and the cost of electricity. In general, a major challenge in solving dynamic programming problems lies in the need for the knowledge of future electricity consumption events. By exploring the underlying structure of the original problem, an equivalent problem is derived, which can be solved by using only the current observations. An online control algorithm is then developed to solve the equivalent problem based on the Lyapunov optimization technique. It is shown that without the knowledge of the statistics of the time-varying load requirements and the electricity price processes, the proposed online control algorithm, parametrized by a positive value , is within (1/) of the optimal solution to the original problem, where the maximum value of is limited by the battery capacity. The efficacy of the proposed algorithm is demonstrated through extensive numerical analysis using real data.
机译：智能电表设计用于智能电网中的信息收集和系统监控，可向公用事业提供商报告细粒度的功耗。但是，通过这些高度精确的能源使用情况，可以识别消费者的特定活动或行为方式，从而引起严重的隐私问题。本文通过设计使用可充电电池的具有成本效益和隐私保护的能源管理技术来解决这些问题。从整体的角度来看，动态编程框架是为消费者设计的，可以在智能电表数据隐私和电费之间进行权衡。通常，解决动态编程问题的主要挑战在于需要了解未来的用电事件。通过探索原始问题的潜在结构，可以得出等效问题，仅使用当前的观察结果即可解决。然后，基于Lyapunov优化技术，开发了一种在线控制算法来解决等效问题。结果表明，在不了解随时间变化的负荷需求和电价过程的统计信息的情况下，所提出的在线控制算法（以正值为参数）在原始问题的最优解的（1 /）范围内，其中的最大值受电池容量限制。通过使用实际数据进行的大量数值分析证明了该算法的有效性。

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