The single imbalance pricing is an emerging mechanism in European electricity markets where all positive and negative imbalances are settled at a unique price. This real-time scheme thereby stimulates market participants to deviate from their schedule to restore the power system balance. However, exploiting this market opportunity is very risky due to the extreme volatility of the real-time power system conditions. In order to address this issue, we implement a new tailored deep-learning model, named encoder-decoder, to generate improved probabilistic forecasts of the imbalance signal, by efficiently capturing its complex spatio-temporal dynamics. The predicted distributions are then used to quantify and optimize the risk associated with the real-time participation of market players, acting as price-makers, in the imbalance settlement. This leads to an integrated forecast-driven strategy, modeled as a robust bi-level optimization. Results show that our probabilistic forecaster achieves better performance than other state of the art tools, and that the subsequent risk-aware robust dispatch tool allows finding a tradeoff between conservative and risk-seeking policies, leading to improved economic benefits. Moreover, we show that the model is computationally efficient and can thus be incorporated in the very-short-term dispatch of market players with flexible resources.

中文翻译：

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风险意识参与单一价格失衡解决方案的超短期概率预测

单一不平衡定价是欧洲电力市场中的一种新兴机制，所有正负不平衡都以独特的价格结算。这种实时方案从而刺激市场参与者偏离他们的时间表以恢复电力系统平衡。然而，由于实时电力系统条件的极端波动，利用这个市场机会是非常危险的。为了解决这个问题，我们实现了一种新的定制深度学习模型，称为编码器-解码器，通过有效地捕获其复杂的时空动态来生成不平衡信号的改进概率预测。然后使用预测的分布来量化和优化与作为价格制定者的市场参与者实时参与不平衡结算相关的风险。这导致了集成的预测驱动策略，建模为强大的双层优化。结果表明，我们的概率预测器比其他最先进的工具实现了更好的性能，并且随后的风险感知稳健调度工具允许在保守策略和风险寻求策略之间找到权衡，从而提高经济效益。此外，我们表明该模型在计算上是高效的，因此可以纳入具有灵活资源的市场参与者的极短期调度中。并且随后的风险意识强大的调度工具允许在保守和寻求风险的策略之间找到权衡，从而提高经济效益。此外，我们表明该模型在计算上是高效的，因此可以纳入具有灵活资源的市场参与者的极短期调度中。并且随后的风险意识强大的调度工具允许在保守和寻求风险的策略之间找到权衡，从而提高经济效益。此外，我们表明该模型在计算上是高效的，因此可以纳入具有灵活资源的市场参与者的极短期调度中。