Heuristic Retailer’s Day-Ahead Pricing Based on Online-Learning of Prosumer’s Optimal Energy Management Model

Abstract

Smart grids have introduced several key concepts, including demand response, prosumers—active consumers capable of producing, consuming, and storing both electrical and thermal energies—retail market, and local energy markets. Preserving data privacy in this emerging environment has raised concerns and challenges. The use of novel methods such as online learning is recommended to address these challenges through prediction of the behavior of market stakeholders. In particular, the challenge of predicting prosumers’ behavior in an interaction with retailers requires creating a dynamic environment for retailers to set their optimal pricing. An innovative model of retailer–prosumer interactions in a day-ahead market is presented in this paper. By forecasting the behavior of prosumers by using an online learning method, the retailer implements an optimal pricing scheme to maximize profits. Prosumers, however, seek to reduce energy costs to the greatest extent possible. It is possible for prosumers to participate in a price-based demand response program voluntarily and without the retailer’s interference, ensuring their privacy. A heuristic distributed approach is applied to solve the proposed problem in a fully distributed framework with minimum information exchange between retailers and prosumers. The case studies demonstrate that the proposed model effectively fulfills its objectives for both retailer and prosumer sides by adopting the distributed approach.