RT Generic
T1 Optimal day-ahead offering strategy for large producers based on market price response learning
A1 Alcántara Mata, Antonio
A1 Ruiz Mora, Carlos
A2 Universidad Carlos III de Madrid. Departamento de Estadística, 
AB 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 marginalprice 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.
SN 2387-0303
YR 2022
FD 2022-04-22
LK https://hdl.handle.net/10016/34605
UL https://hdl.handle.net/10016/34605
LA eng
DS e-Archivo
RD 20 may. 2024