论文标题
智能网格中二元需求的最佳动态定价：公平和隐私的策略
Optimal Dynamic Pricing for Binary Demands in Smart Grids: A Fair and Privacy-Preserving Strategy
论文作者
论文摘要
由智能网格中的需求侧管理激励，提出了一个分散的受控的马尔可夫链制剂，以模拟具有二元需求的用户（即关闭或开启）的同质用户人群。在调度应用程序（例如插电式混合动力汽车）时，通常会出现二进制需求。通常，在为用户提供电力时，独立服务运营商（ISO）具有有限数量的选项。选项代表各种激励手段，发电资源和价格概况。 ISO的目的是找到最佳选项，以使需求分布接近所需的水平（通常随时间变化），通过向用户施加最低价格。这里开发了一个贝尔曼方程，以确定全球球队最佳的战略。提出的策略对所有用户都是公平的，也可以保护用户的隐私。此外，其计算复杂性与用户数量线性增加（而不是指数级）。为100个用户提供了一个数值示例，用于峰值负载管理。
Motivated by demand-side management in smart grids, a decentralized controlled Markov chain formulation is proposed to model a homogeneous population of users with binary demands (i.e., off or on). The binary demands often arise in scheduling applications such as plug-in hybrid vehicles. Normally, an independent service operator (ISO) has a finite number of options when it comes to providing the users with electricity. The options represent various incentive means, generation resources, and price profiles. The objective of the ISO is to find optimal options in order to keep the distribution of demands close to a desired level (which varies with time, in general) by imposing the minimum price on the users. A Bellman equation is developed here to identify the globally team-optimal strategy. The proposed strategy is fair for all users and also protects the privacy of users. Moreover, its computational complexity increases linearly (rather than exponentially) with the number of users. A numerical example with 100 users is presented for peak-load management.
