A Distributed Online Pricing Strategy for Demand Response Programs
A Distributed Online Pricing Strategy for Demand Response Programs
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DOI：
10.1109/tsg.2017.2739021
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发表时间：
2017-02
影响因子：
9.6
通讯作者：
Pan Li;Hao Wang;Baosen Zhang
中科院分区：
文献类型：
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作者：
Pan Li;Hao Wang;Baosen Zhang
本文从电力公司（也称为运营商）的角度出发，在现实环境中研究了电力公司面临不确定性和有限沟通的需求响应问题。具体来说，公用事业公司不知道消费者的成本函数，无法与消费者进行多轮信息交换。考虑时变的DR目标和消费者的响应，提出了电力公司运行成本最小化的优化问题。我们开发了一个联合在线学习和定价算法。在每个时隙，公用事业公司向所有消费者发出价格信号，并根据消费者的噪声响应估计消费者的成本函数。我们使用后悔分析来衡量我们的算法的性能，并表明我们的在线算法在操作视界方面实现了对数后悔。此外，我们的算法采用线性回归来估计消费者的总响应，使其易于在实践中实现。仿真实验验证了理论结果，表明算法与离线最优性之间的性能差距衰减很快。
We study a demand response (DR) problem from utility (also referred to as operator)’s perspective with realistic settings, in which the utility faces uncertainty and limited communication. Specifically, the utility does not know the cost function of consumers and cannot have multiple rounds of information exchange with consumers. We formulate an optimization problem for the utility to minimize its operational cost considering time-varying DR targets and responses of consumers. We develop a joint online learning and pricing algorithm. In each time slot, the utility sends out a price signal to all consumers and estimates the cost functions of consumers based on their noisy responses. We measure the performance of our algorithm using regret analysis and show that our online algorithm achieves logarithmic regret with respect to the operating horizon. In addition, our algorithm employs linear regression to estimate the aggregate response of consumers, making it easy to implement in practice. Simulation experiments validate the theoretic results and show that the performance gap between our algorithm and the offline optimality decays quickly.