An Integrated Optimization + Learning Approach to Optimal Dynamic Pricing for the Retailer with Multi-type Customers in Smart Grids

In this paper, we consider a realistic and meaningful scenario in the context of smart grids where an electricity retailer serves three different types of customers, i.e., customers with an optimal home energy management system embedded in their smart meters (C-HEMS), customers with only smart meters (C-SM), and customers without smart meters (C-NONE). The main objective of this paper is to support the retailer to make optimal day-ahead dynamic pricing decisions in such a mixed customer pool. To this end, we propose a two-level decision-making framework where the retailer acting as upper-level agent firstly announces its electricity prices of next 24 hours and customers acting as lower-level agents subsequently schedule their energy usages accordingly. For the upper level problem, we optimize the dynamic prices for the retailer to maximize its profit subject to realistic market constraints. The above two-level model is tackled by genetic algorithms (GA) based distributed optimization methods while its feasibility and effectiveness are con-2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. Please cite this accepted article as: Fanlin Meng, Xiao-Jun Zeng, Yan Zhang, Chris J. Dent, Dunwei Gong, An Integrated Optimization Learning Approach to Optimal Dynamic Pricing for the Retailer with Multi-type Customers in Smart Grids, Information Sciences (2018), doi: 10.1016/j.ins.2018.03.039 Preprint submitted to Information Sciences March 22, 2018 firmed via simulation results. Keywords: Bilevel Modelling, Genetic Algorithms, Machine Learning, Dynamic Pricing, Demand-side Management, Demand Response, Smart Grids 1. Introduction With the large-scale deployment of smart meters and two-way communication infrastructures, dynamic pricing based demand response and demand-side management programs [37] [12] have attracted enormous attentions from both academia and industry and are expected to bring great benefits to the whole power system. Real-time pricing (RTP), timeof-use pricing (ToU) and critical-peak pricing (CPP) are commonly used dynamic pricing strategies [20].