Optimal setting of time-and-level-of-use prices for an electricity supplier

• Published in: Energy (Oxford) Vol. 225; p. 120517
• Main Authors: Anjos, Miguel F., Brotcorne, Luce, Gomez-Herrera, Juan A.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 15.06.2021; Elsevier BV; Elsevier
• Subjects: Bilevel optimization; Computer applications; Computer Science; Consumption; Demand side management; Demand-response; Electric power demand; Electrical loads; Electricity; Energy management; Load shifting; Operations Research; Optimization; Price-setting; Prices; Residential energy; Smart buildings; Smart grid; TLOU; Price-setting; TLOU; Bilevel optimization; Smart buildings; Smart grid; Demand-response
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2021.120517

This paper presents a novel price setting optimization problem for an electricity supplier in the smart grid. In this framework the supplier provides electricity to a residential load aggregator using Time-and-Level-of-Use prices (TLOU). TLOU is an energy pricing structure recently introduced in the literature, where the prices vary depending on the time and the level of consumption. This problem is formulated as a bilevel optimization problem, in which the supplier sets the prices that maximize the profit in a demand response context, anticipating the reaction of a residential load aggregator that minimizes total cost. These decisions are made in a competitive environment, while explicitly considering the aggregator’s load shifting preferences and the level of consumption, and ensuring a user-friendly price structure. The optimization problem is reformulated as a single-level problem to be solved using off-the-shelf solvers. We present computational experiments to validate the performance of TLOU, and provide insights on the relationship between the user’s demand flexibility, the capacity profile and the resulting structure of prices. We show that the supplier’s economical benefit is increased up to 10% through the implementation of this type of demand response program, while providing savings of up to 6% for the consumers. •We present a framework to determine TLOU energy prices for an electricity retailer.•We formulate the problem as a bilevel optimization problem to set optimal prices.•We explicitly consider user preferences in the in the price setting problem.•Our model can be reformulated and solve by of-the-shelf solvers.•We provide evidence of the effect of TLOU on retailer’s and end-users’ performance.