RT Journal Article
T1 Inverse optimization with kernel regression: Application to the power forecasting and bidding of a fleet of electric vehicles
A1 Fernández-Blanco, Ricardo
A1 Morales-González, Juan Miguel
A1 Pineda-Morente, Salvador
A1 Porras, Álvaro
K1 Vehículos eléctricos
AB This paper considers an aggregator of Electric Vehicles (EVs) who aims to learn the aggregate power of his/her fleet while also participating in the electricity market. The proposed approach is based on a data-driven inverse optimization (IO) method, which is highly nonlinear. To overcome such a caveat, we use a two-step estimation procedure which requires solving two convex programs. Both programs depend on penalty parameters that can be adjusted by using grid search. In addition, we propose the use of kernel regression to account for the nonlinear relationship between the behavior of the pool of EVs and the explanatory variables, i.e., the past electricity prices and EV fleet’s driving patterns. Unlike any other forecasting method, the proposed IO framework also allows the aggregator to derive a bid/offer curve, i.e. the tuple of price-quantity to be submitted to the electricity market, according to the market rules. We show the benefits of the proposed method against the machine-learning techniques that are reported to exhibit the best forecasting performance for this application in the technical literature.
PB Elsevier
YR 2021
FD 2021-10
LK https://hdl.handle.net/10630/22439
UL https://hdl.handle.net/10630/22439
LA eng
NO This project has received funding in part by the Spanish Ministry of Economy, Industry, and Competitiveness through project ENE2017-83775-P; in part by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 755705); and in part by Fundación Iberdrola España 2018, Spain. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Malaga.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 24 ene 2026