Purely data-driven approaches to trading of renewable energy generation.

Nicolo Mazzi, Pierre Pinson

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

In recent years so-called stochastic power producers (with portfolios including wind and solar power generation
capacities) are increasingly asked to participate in electricity markets under the same rules than for conventional generators. Stochastic power producers may act strategically in order to decrease expected penalties induced by imbalances. Many alternative offering strategies based on forecasts in various forms are available in the literature. However, they assume some form of knowledge of future market state and potential balancingprices. In contrast here, we explore whether algorithms could readily learn from market data and deduce how to offer strategically in order to maximize expected market revenues. Our analysis shows that a direct reinforcement learning algorithm can track the nominal level of the optimal quantile
forecast to trade in the day-ahead market, while yielding higher revenues than existing benchmark strategies
Original languageEnglish
Title of host publicationProceedings of European Electricity Market Conference 2016
Number of pages5
PublisherIEEE
Publication date2016
DOIs
Publication statusPublished - 2016
EventEEM16 - 13th International Conference on the European Energy market - Faculty of Engineering of University of Porto, FEUP, Porto, Portugal
Duration: 6 Jun 20169 Jun 2016
http://www.eem2016.com/

Conference

ConferenceEEM16 - 13th International Conference on the European Energy market
LocationFaculty of Engineering of University of Porto, FEUP
CountryPortugal
CityPorto
Period06/06/201609/06/2016
Internet address

Keywords

  • Renewable energy
  • Strategic offering
  • Stochastic optimization
  • Reinforcement learning
  • Direct learning

Cite this

Mazzi, N., & Pinson, P. (2016). Purely data-driven approaches to trading of renewable energy generation. In Proceedings of European Electricity Market Conference 2016 IEEE. https://doi.org/10.1109/EEM.2016.7521323