Optimal day-ahead offering strategy for large producers based on market price response learning

TL;DR

This paper develops a data-driven, stochastic optimization approach for large electricity producers to determine optimal day-ahead market offering strategies, considering their influence on market prices and profit maximization.

Contribution

It introduces a novel methodology combining supply curve summarization, Bayesian regression modeling, and constraint learning for strategic bidding in electricity markets.

Findings

Optimal strategies significantly increase profits and influence market prices.

The approach effectively predicts market responses and enhances bidding decisions.

Out-of-sample tests confirm real-world applicability of the strategy.

Abstract

In day-ahead electricity markets based on uniform marginal pricing, small variations in the offering and bidding curves may substantially modify the resulting market outcomes. In this work, we deal with the problem of finding the optimal offering curve for a risk-averse profit-maximizing generating company (GENCO) in a data-driven context. In particular, a large GENCO's market share may imply that her offering strategy can alter the marginal price formation, which can be used to increase profit. We tackle this problem from a novel perspective. First, we propose a optimization-based methodology to summarize each GENCO's step-wise supply curves into a subset of representative price-energy blocks. Then, the relationship between the market price and the resulting energy block offering prices is modeled through a Bayesian linear regression approach, which also allows us to generate stochastic scenarios for the sensibility of the market towards the GENCO strategy, represented by the regression coefficient probabilistic distributions. Finally, this predictive model is embedded in the stochastic optimization model by employing a constraint learning approach. Results show how allowing the GENCO to deviate from her true marginal costs renders significant changes in her profits and the market marginal price. Furthermore, these results have also been tested in an out-of-sample validation setting, showing how this optimal offering strategy is also effective in a real-world market contest.