Keeping the balance between electricity generation and consumption is becoming increasingly challenging and costly, mainly due to the rising share of renewables, electric vehicles and heat pumps and electrification of industrial processes. Accurate imbalance forecasts, along with reliable uncertainty estimations, enable transmission system operators (TSOs) to dispatch appropriate reserve volumes, reducing balancing costs. Further, market parties can use these probabilistic forecasts to design strategies that exploit asset flexibility to help balance the grid, generating revenue with known risks. Despite its importance, literature regarding system imbalance (SI) forecasting is limited. Further, existing methods do not focus on situations with high imbalance magnitude, which are crucial to forecast accurately for both TSOs and market parties. Hence, we propose an ensemble of C-VSNs, which are our adaptation of variable selection networks (VSNs). Each minute, our model predicts the imbalance of the current and upcoming two quarter-hours, along with uncertainty estimations on these forecasts. We evaluate our approach by forecasting the imbalance of Belgium, where high imbalance magnitude is defined as $|$SI$| > 500\,$MW (occurs 1.3% of the time in Belgium). For high imbalance magnitude situations, our model outperforms the state-of-the-art by 23.4% (in terms of continuous ranked probability score (CRPS), which evaluates probabilistic forecasts), while also attaining a 6.5% improvement in overall CRPS. Similar improvements are achieved in terms of root-mean-squared error. Additionally, we developed a fine-tuning methodology to effectively include new inputs with limited history in our model. This work was performed in collaboration with Elia (the Belgian TSO) to further improve their imbalance forecasts, demonstrating the relevance of our work.

翻译：保持发电与用电平衡正变得越来越具有挑战性和成本高昂，这主要源于可再生能源、电动汽车、热泵以及工业过程电气化占比的持续上升。准确的电力系统不平衡预测及其可靠的不确定性估计，能使输电系统运营商（TSO）合理调度备用容量，从而降低平衡成本。此外，市场参与者可利用这些概率预测制定策略，通过释放资产灵活性来帮助电网平衡，并在已知风险下创造收益。尽管该问题至关重要，但关于系统不平衡（SI）预测的文献仍然有限。现有方法也未聚焦于高不平衡量级的情景，而这种情景对TSO和市场参与者的精准预测而言至关重要。因此，我们提出一种基于C-VSN的集成模型，该模型是我们对变量选择网络（VSN）的改进。该模型每分钟预测当前及未来两个刻钟内的系统不平衡值，并提供这些预测的不确定性估计。我们通过对比利时电网的不平衡预测验证该方法，其中高不平衡量级定义为$|$SI$| > 500\,$MW（在比利时发生概率为1.3%）。针对高不平衡量级情景，我们的模型在连续分级概率评分（CRPS）上较现有最优方法提升23.4%，同时总体CRPS也提升6.5%。在均方根误差指标上也取得类似改进。此外，我们还开发了一种微调方法，可将历史数据有限的新输入有效融入模型。本研究与比利时TSO Elia合作开展，旨在进一步优化其不平衡预测，充分证明了我们工作的实际应用价值。