Optimization-Based Models for Estimating Residual Demand Curves for a Price-Maker Company

• Published in: IEEE transactions on power systems 2023-07, Vol.38 (4), p.3097-3106
• Main Authors: Cabana, Tiago Gomes, Baptista, Edmea Cessia, Soler, Edilaine Martins, Martins, Andre Christovao Pio, Balbo, Antonio Roberto, Nepomuceno, Leonardo
• Format: Article
• Language: English
• Subjects: Adaptation models; Analytical models; Bidding strategy; Clearing; Companies; Demand; electricity markets; Optimization; Optimization models; price-maker companies; Pricing; Quotas; residual demand curve; Stochastic processes; Uncertainty; Upper bound
• ISSN: 0885-8950; 1558-0679
• DOI: 10.1109/TPWRS.2022.3201384

The Residual Demand Curve (RDC) aims to estimate the day-ahead market clearing prices given the quotas scheduled for a price-maker generation company in the market. Traditionally, this curve has been obtained by the difference between the inverse functions of aggregated demand and aggregated supply. An important drawback of such approach is that constraints related to complex-bid markets cannot be directly represented in the RDC. In this paper we interpret the traditional RDC as an optimization model and propose Optimization-Based Residual Demand (OBRD) models which represent a series of market clearing procedures where the residual demand is progressively increased. Differently from traditional methods, the proposed approach allows for explicit representation of complex-bid markets. We show that under certain conditions the RDC obtained by the proposed OBRD model is equivalent to that provided by traditional methods. We also propose a methodology for comparing the quality of different RDCs in what regards their ability to predict market clearing prices and the company's profits. Results show that the RDCs obtained by the OBRD model are significantly more accurate than those obtained by traditional approaches for complex-bid markets.