[论文解读] Differentially Private Demand Side Management for Incentivized Dynamic Pricing in Smart Grid

Muneeb Ul Hassan, Mubashir Husain Rehmani+2 more|arXiv (Cornell University)|Feb 2, 2021

Smart Grid Energy Management参考文献 28被引用 4

一句话总结

本文提出DRDP，一种差分隐私的需量响应机制，通过动态定价激励合作的智能电表用户，同时利用噪声调节的差分隐私保护用户隐私。在不同隐私预算下，该机制在隐私保护和计费准确性方面均优于先前方法。

ABSTRACT

In order to efficiently provide demand side management (DSM) in smart grid, carrying out pricing on the basis of real-time energy usage is considered to be the most vital tool because it is directly linked with the finances associated with smart meters. Hence, every smart meter user wants to pay the minimum possible amount along with getting maximum benefits. In this context, usage based dynamic pricing strategies of DSM plays their role and provide users with specific incentives that help shaping their load curve according to the forecasted load. However, these reported real-time values can leak privacy of smart meter users, which can lead to serious consequences such as spying, etc. Moreover, most dynamic pricing algorithms charge all users equally irrespective of their contribution in causing peak factor. Therefore, in this paper, we propose a modified usage based dynamic pricing mechanism that only charges the users responsible for causing peak factor. We further integrate the concept of differential privacy to protect the privacy of real-time smart metering data. To calculate accurate billing, we also propose a noise adjustment method. Finally, we propose Demand Response enhancing Differential Pricing (DRDP) strategy that effectively enhances demand response along with providing dynamic pricing to smart meter users. We also carry out theoretical analysis for differential privacy guarantees and for cooperative state probability to analyze behavior of cooperative smart meters. The performance evaluation of DRDP strategy at various privacy parameters show that the proposed strategy outperforms previous mechanisms in terms of dynamic pricing and privacy preservation.

研究动机与目标

• 解决动态定价系统中实时智能电表数据导致的隐私泄露问题。
• 设计一种动态定价模型，仅对导致峰值负荷因素的用户进行公平计费。
• 通过噪声调节，将差分隐私与精确计费相结合。
• 通过激励合作用户行为，提升需量响应能力。
• 在不同隐私参数下评估该机制的性能表现。

提出的方法

• 提出一种改进的基于使用量的动态定价模型，仅对导致峰值负荷因素的用户收费。
• 通过向实时用电量报告中添加校准噪声，集成差分隐私机制。
• 开发一种噪声调节方法，以在引入噪声的情况下仍保持计费准确性。
• 引入偏差函数，当用户用电量接近平均值或峰值阈值时向其发出通知。
• 通过理论分析建立差分隐私保证及合作状态概率。
• 采用模拟评估方法，测试不同隐私预算（ε）下的性能表现。

实验结果

研究问题

• RQ1 如何通过仅对导致峰值负荷因素的用户收费，使动态定价更加公平？
• RQ2 如何在不损害计费准确性的前提下，有效将差分隐私集成到动态定价中？
• RQ3 不同隐私预算对计费收敛性和用户合作的影响是什么？
• RQ4 与现有方法相比，该机制如何增强需量响应能力？
• RQ5 用户合作概率与预期合作智能电表数量之间存在何种关系？

主要发现

• 即使在低隐私预算（例如 ε = 0.01）下，DRDP机制在三天结束时仍实现接近零的计费误差，表现出极强的收敛性。
• 在较高隐私预算（例如 ε = 1.0）下，计费收敛几乎立即开始，表明在极小延迟下仍具有高度准确性。
• 偏差函数能有效提醒用户其用电量接近平均值或峰值阈值，从而支持主动负荷管理。
• 随着用户合作概率（p）的提高，合作状态概率也随之上升，在 p = 0.9 时，预期合作用户数量接近最大值。
• 性能评估结果表明，DRDP在所有测试隐私参数下，均优于先前机制，在动态定价准确性和隐私保护方面表现更优。
• 理论分析证实该机制满足差分隐私保证，并为合作行为建模提供了坚实基础。