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

Mohammad Hossein Nejati Amiri, Mehdi Mehdinejad, Amin Mohammadpour Shotorbani () and Heidarali Shayanfar
Additional contact information
Mohammad Hossein Nejati Amiri: Center of Excellence for Power Systems Automation and Operation, School of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran
Mehdi Mehdinejad: Center of Excellence for Power Systems Automation and Operation, School of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran
Amin Mohammadpour Shotorbani: School of Engineering, University of British Columbia—Okanagan Campus, Kelowna, BC V1V 1V7, Canada
Heidarali Shayanfar: Center of Excellence for Power Systems Automation and Operation, School of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran

Energies, 2023, vol. 16, issue 3, 1-21

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.

Keywords: retail day-ahead market; online-learning; privacy preserved method; prosumer; demand response; optimal pricing (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/3/1182/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/3/1182/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:3:p:1182-:d:1043150

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().