LAPSE:2023.22082
Published Article
LAPSE:2023.22082
Conditional-Robust-Profit-Based Optimization Model for Electricity Retailers with Shiftable Demand
March 23, 2023
Abstract
This paper investigates the problem of how to deploy customers’ shiftable load (SL) for electricity retailers’ risk management under uncertainty of the day-ahead (DA) wholesale market price. The robust profit (RP) and the conditional robust profit (CRP) are introduced for a risk-averse retailer’s risk-reward trade-off analysis in its decision-making of electricity procurement from various options. A CRP-based bi-level optimization model is proposed for the risk-averse retailer to determine its electricity procurement strategy taking into consideration customers’ shiftable load. In the upper problem, the retailer decides its electricity procurement from various options and the SL incentive prices to maximize its CRP under a given confidence level, and in the lower problem, the customers shift their load according to the SL incentive prices to minimize their comprehensive costs including the discomfort cost caused by rescheduling electricity consumption. Finally, a case study is used to verify the effectiveness of this model. It is shown that the retailer can achieve larger profit and less risk by utilizing customers’ SL and the retailer’s risk-aversion level has an important impact on its electricity procurement and SL incentive strategies.
Keywords
conditional robust profit, electricity procurement, risk management, risk-averse retailer, shiftable load
Suggested Citation
Zhang Q, Zhang S, Wang X, Li X, Wu L. Conditional-Robust-Profit-Based Optimization Model for Electricity Retailers with Shiftable Demand. (2023). LAPSE:2023.22082
Author Affiliations
Zhang Q: Department of Automation, Shanghai University, Shanghai 200444, China [ORCID]
Zhang S: Department of Automation, Shanghai University, Shanghai 200444, China [ORCID]
Wang X: Department of Automation, Shanghai University, Shanghai 200444, China
Li X: Department of Automation, Shanghai University, Shanghai 200444, China [ORCID]
Wu L: Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA [ORCID]
Journal Name
Energies
Volume
13
Issue
6
Article Number
E1308
Year
2020
Publication Date
2020-03-11
ISSN
1996-1073
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Original Submission
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PII: en13061308, Publication Type: Journal Article
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LAPSE:2023.22082
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https://doi.org/10.3390/en13061308
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