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.
翻译:为了在智能电网中有效地提供需求方管理,在实时能源使用的基础上进行定价被认为是最重要的工具,因为它与智能电表相关财政直接相关。因此,每个智能电算用户都希望支付最低可能的金额,同时获得最大效益。在这方面,基于需求的动态定价战略发挥作用,为用户提供具体激励,帮助根据预测的负荷量塑造其负载曲线。然而,这些报告的实时值可能会泄露智能电表用户的隐私,从而导致诸如间谍等严重后果。 此外,大多数动态定价算法对所有用户收取同等费用,而不论其在造成峰值系数方面有何贡献。因此,我们在本文件中提议修改基于使用的动态定价机制,只对造成峰值因素的用户收费。我们进一步纳入差异隐私权概念,以保护实时智能计量数据的隐私。为了计算准确的计费,我们还提议了一种噪音调整方法。我们提议需求反应加强差异计价战略,在向智能电算隐私用户提供动态定价的同时,有效地加强需求反应,同时向智能数据方位用户提供动态保值。我们提议了基于合作性保价战略。我们还进行了理论分析,以便分析。我们以合作性测测测测测测了各种隐私度。