Personalized retail pricing design for smart metering consumers in electricity market
Author(s)
Qiu, Dawei
Wang, Yi
Wang, Junkai
Jiang, Chuanwen
Strbac, Goran
Type
Journal Article
Abstract
In the current deregulated electricity market, flexible consumers are more active in participating in market activities via the representation of electricity retailers. However, without an effective communication infrastructure, the connection between retailers and the consumers they serve is incomplete. Nowadays, smart meters are being rolled out worldwide to enhance the connection and data exchanges between retailers and consumers. Specifically, smart meters enable retailers to provide customers with detailed information about retail tariffs and their energy usage at different times of the day, which in turn enables customers to manage their energy use more proactively. This paper drops this assumption and makes use of data acquired from smart meters to design a personalized retail pricing scheme for different types of consumers. To formulate this problem, a bi-level optimization model is proposed, with the upper-level problem representing the pricing decision made by the retailer and two lower-level problems representing the demand response of consumers and the wholesale market clearing process, respectively. Afterward, we convert this bi-level optimization model into a single-level mathematical program with equilibrium constraints by using its Karush Kuhn Tucker optimality conditions and complementary conditions. The scope of the examined case studies is fourfold. First, consumers are classified based on their daily load profiles using the advanced clustering method. Second, the physical benefit of fully exploring the consumer’s demand flexibility as well as the economic benefits of increasing retailers’ profitability and reducing consumers’ energy bills are evaluated with respect to the traditional uniform retail pricing scheme. Third, the impacts of consumers’ demand flexibility on electricity market outcomes and business cases are investigated. Finally, the proposed personalized retail pricing scheme is verified to relieve the strategic retailer’s market power reduction caused by the flexibility of demand, which is beneficial to the retailer’s profitability.
Date Issued
2023-10-15
Date Acceptance
2023-07-01
Citation
Applied Energy, 2023, 348
ISSN
0306-2619
Publisher
Elsevier
Journal / Book Title
Applied Energy
Volume
348
Copyright Statement
© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:001039358500001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
Subjects
Bi-level optimization
Clustering model
Demand response
DEMAND RESPONSE
Electricity retailer
Energy & Fuels
ENERGY-CONSUMPTION
Engineering
Engineering, Chemical
FLEXIBILITY
FRAMEWORK
MODEL
RESILIENCE
Science & Technology
Smart meter
Technology
Publication Status
Published
Article Number
121545
Date Publish Online
2023-07-16