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537. ML-Driven Day-ahead Offering for RES Generators in Uncertain Balancing Markets
Invited abstract in session MB-35: Stochastic Optimization for Energy Transition, stream Stochastic, Robust and Distributionally Robust Optimization.
Monday, 10:30-12:00Room: 44 (building: 303A)
Authors (first author is the speaker)
| 1. | Wenxiu Feng
|
| Department of Statistics, Universidad Carlos III de Madrid | |
| 2. | Carlos Ruiz
|
| Universidad Carlos III de Madrid |
Abstract
We examine a renewable energy source (RES) generator engaged in short-term electricity market trading. Traditionally, variable RES generation has been exempted from balancing responsibilities; however, this exemption is no longer prevalent in many markets due to increasing penetration. We consider a generator participating in both a day-ahead market and a subsequent balancing market. We seek the RES generator's optimal day-ahead offerings in terms of energy volumes, considering the impact of balancing market settlements. To this end, we implement and analyze the accuracy of state-of-the-art Machine Learning (ML) models in forecasting imbalance signs, prices, and other market outcomes, assessing the potential to enhance RES's profitability. Additionally, we evaluate the impact of employing storage techniques to mitigate the intermittency associated with RES capacity. We develop a two-stage stochastic optimization model. The first stage involves day-ahead decisions that anticipate scenario-dependent balancing settlements and real-time battery operation in the second stage. The model uses improved forecasting scenarios generated by an ensemble of ML models, considering contextual and past historical market outcomes. We conduct an extensive set of out-of-sample simulations using real data from the Spanish market, and under the two main financial settlement mechanisms available for balancing markets: single and dual pricing, each with distinct implications for the RES generator.
Keywords
- OR in Energy
- Stochastic Optimization
- Machine Learning
Status: accepted
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