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

TL;DR

This paper develops an integrated optimization and learning framework for a retailer to set optimal day-ahead dynamic prices in smart grids with diverse customer types, enhancing profit while considering customer behaviors.

Contribution

It introduces a two-level decision-making model combining genetic algorithms for dynamic pricing with customer response modeling in a mixed smart grid environment.

Findings

The proposed method effectively maximizes retailer profit.

Customer response models improve pricing accuracy.

Simulation confirms the approach's feasibility and effectiveness.

Abstract

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 lower level problem, we model the price responsiveness of different customers according to their unique characteristics. 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 confirmed via simulation results.