Universität zu Köln

Naseri, Nastaran ORCID: 0000-0002-5239-1280, Talari, Saber ORCID: 0000-0003-1368-781X, Ketter, Wolfgang and Collins, John (2022). Dynamic retail market tariff design for an electricity aggregator using reinforcement learning. Electr. Power Syst. Res., 212. LAUSANNE: ELSEVIER SCIENCE SA. ISSN 1873-2046

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Abstract

The role of retailers, as energy providers for end-users, in restructured retail electricity markets becomes substantial. The increasing share of distributed energy resources and electrification in different sectors bring several challenges to retailers. Among these challenges, procuring electricity and maintaining system reliability during peak times induce high costs to retailers. Therefore, they need to accurately predict customers' demand to participate in the wholesale market and develop proper tariff mechanisms considering other retailers' behavior to maximize their profit. This paper develops the design of an autonomous retailer in which a Sequence-to-Sequence (Seq2Seq) algorithm is employed to predict customers' net demand. Furthermore, using Reinforcement Learning (RL), the proposed retailer designs tariff mechanisms based on other retailers' behavior and customers' load profiles. The proposed design of the retailer is evaluated on a retail market simulation platform called Power TAC, in which autonomous retailers compete in retail, wholesale, and balancing markets to maximize their profits. The results show the accuracy of the proposed load prediction method compared with other methods and successful profit growth with a drop in fixed costs and balancing costs.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Naseri, Nastaran UNSPECIFIED orcid.org/0000-0002-5239-1280 UNSPECIFIED Talari, Saber UNSPECIFIED orcid.org/0000-0003-1368-781X UNSPECIFIED Ketter, Wolfgang UNSPECIFIED UNSPECIFIED UNSPECIFIED Collins, John UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-670506
DOI:	10.1016/j.epsr.2022.108560
Journal or Publication Title:	Electr. Power Syst. Res.
Volume:	212
Date:	2022
Publisher:	ELSEVIER SCIENCE SA
Place of Publication:	LAUSANNE
ISSN:	1873-2046
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language BILEVEL MODEL; BIG DATA; STRATEGIES; MANAGEMENT; SEGMENTATION; SIMULATION Multiple languages Engineering, Electrical & Electronic Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/67050