This paper proposes a robust cost-optimal scheduling of a battery energy storage system (BESS) integrated with a photovoltaic power plant (PV). A power grid with an incentive policy is considered. Power transactions between the grid and its energy resources are normally charged according to the hourly price. Additional hourly incentive is provided if the day-ahead submitted schedule is maintained. Accurate forecasting and robust scheduling are essential for PV-BESS owners to maximize both revenues. The PV power forecast model, which is based on an RNN, uses a CNN discriminator to decrease the gap between its open-loop training and closed-loop test dynamics. The application of a GAN concept to the model training process ensures a stable day-ahead hourly forecast performance. The robust BESS scheduling model handles the remaining forecast error as a box uncertainty set to consider the cost-optimality and cost-robustness of the resulting control schedule. The scheduling model is formulated as a concise mixed-integer linear programming form to enable fast online optimization with the consideration for both transaction and incentive revenues. The introduction of adversarial learning to the forecast model increased the incentive revenue by 7.33%. Moreover, the online BESS scheduling with the incentive consideration enhanced the overall revenue by 3.73%.

中文翻译：

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具有预测精度激励的电网的鲁棒 PV-BESS 调度

本文提出了与光伏电站 (PV) 集成的电池储能系统 (BESS) 的稳健成本优化调度。考虑具有激励政策的电网。电网与其能源之间的电力交易通常按小时收费。如果保持提前一天提交的时间表，则提供额外的每小时奖励。准确的预测和稳健的调度对于 PV-BESS 所有者最大化这两项收入至关重要。基于 RNN 的 PV 功率预测模型使用 CNN 鉴别器来减少其开环训练和闭环测试动态之间的差距。GAN 概念在模型训练过程中的应用确保了稳定的日前每小时预测性能。鲁棒 BESS 调度模型将剩余的预测误差处理为一个框不确定性集，以考虑最终控制调度的成本最优性和成本稳健性。调度模型被制定为简洁的混合整数线性规划形式，以实现快速在线优化，同时考虑交易和激励收入。在预测模型中引入对抗性学习使激励收入增加了 7.33%。此外，基于激励考虑的在线BESS调度使整体收入提高了3.73%。调度模型被制定为简洁的混合整数线性规划形式，以实现快速在线优化，同时考虑交易和激励收入。在预测模型中引入对抗性学习使激励收入增加了 7.33%。此外，基于激励考虑的在线BESS调度使整体收入提高了3.73%。调度模型被制定为简洁的混合整数线性规划形式，以实现快速在线优化，同时考虑交易和激励收入。在预测模型中引入对抗性学习使激励收入增加了 7.33%。此外，基于激励考虑的在线BESS调度使整体收入提高了3.73%。