Predictive day-ahead offering for renewable generators in uncertain spot and balancing markets

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 applied in many markets due to their increasing contribution to the energy mix. We consider a RES generator participating in both a day-ahead market and a balancing market. We seek the RES generator’s optimal day-ahead offerings in terms of hourly energy volumes, considering the impact of the subsequent 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 and market prices, assessing the potential to enhance RES’s profitability. Additionally, we evaluate the impact of deploying storage technologies to mitigate the intermittency associated with RES available 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. Numerical results indicate that the optimal day-ahead offering strategy, assisted by the proposed ML techniques, leads to increasing and stable profits for the RES generator. When working with a single predictive scenario, prevalent strategic behaviors such as “zero or max” and “null individual imbalance” are observed to facilitate arbitrage or to hedge possible imbalance penalties under single and dual pricing mechanisms. However, in the multiscenario extension, the RES generator trades optimal day-ahead commitments for higher profits or penalty compromises by combining scenario-dependent information for system signs and market prices. Under the single pricing mechanism, storage devices are employed in the balancing market for arbitrating and as an energy backup, while for the dual pricing mechanism, they influence the coordinated day-ahead and balancing trading strategies.