Customized Multi-Energy Pricing in Smart Grids: A Bilevel and Evolutionary Computation Approach

Qiuyi Hong, Fanlin Meng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper proposes a customized energy pricing scheme for energy retailers in multi-energy (i.e., electricity and natural gas) retail markets. Microgrids with distributed energy resources (DERs) and demand response (DR) programs are considered on the demand side. We adopt a bilevel single-leader multi-follower model to analyze the customized multi-energy pricing decisions where the retailer’s profit maximization problem is formulated at the upper level, and the microgrids’ operation costs minimization problems are considered at the lower level. A particle swarm optimization (PSO) based evolutionary solution approach is developed to solve the proposed bilevel decision-making problem. Through a numerical case study, we demonstrate the feasibility and effectiveness of the proposed bilevel model and the solution algorithm. We reveal that the proposed customized pricing scheme could offer differentiated optimal pricing decisions to various microgrids characterized by their energy conversion efficiencies.
Original languageEnglish
Title of host publicationThe 21st UK Workshop on Computational Intelligence
Publication statusAccepted/In press - 20 Jul 2022

Fingerprint

Dive into the research topics of 'Customized Multi-Energy Pricing in Smart Grids: A Bilevel and Evolutionary Computation Approach'. Together they form a unique fingerprint.

Cite this