In order to reduce the negative impact of the uncertainty of load and renewable energies outputs on microgrid operation, an optimal scheduling model is proposed for isolated microgrids by using automated reinforcement learning-based multi-period forecasting of renewable power generations and loads. Firstly, a prioritized experience replay automated reinforcement learning (PER-AutoRL) is designed to simplify the deployment of deep reinforcement learning (DRL)-based forecasting model in a customized manner, the single-step multi-period forecasting method based on PER-AutoRL is proposed for the first time to address the error accumulation issue suffered by existing multi-step forecasting methods, then the prediction values obtained by the proposed forecasting method are revised via the error distribution to improve the prediction accuracy; secondly, a scheduling model considering demand response is constructed to minimize the total microgrid operating costs, where the revised forecasting values are used as the dispatch basis, and a spinning reserve chance constraint is set according to the error distribution; finally, by transforming the original scheduling model into a readily solvable mixed integer linear programming via the sequence operation theory (SOT), the transformed model is solved by using CPLEX solver. The simulation results show that compared with the traditional scheduling model without forecasting, this approach manages to significantly reduce the system operating costs by improving the prediction accuracy.

中文翻译：

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使用基于自动强化学习的多周期预测来优化孤立微电网的调度


为了减少负荷和可再生能源出力的不确定性对微电网运行的负面影响，利用基于自动强化学习的可再生能源发电量和负荷的多周期预测，提出了孤立微电网的优化调度模型。首先，设计了优先经验回放自动强化学习（PER-AutoRL），以定制的方式简化基于深度强化学习（DRL）的预测模型的部署，即基于PER-AutoRL的单步多周期预测方法首次提出针对现有多步预测方法存在的误差累积问题，然后通过误差分布修正所提出的预测方法获得的预测值，以提高预测精度；其次，构建考虑需求响应的调度模型，以最小化微电网总运行成本为目标，以修正后的预测值作为调度依据，并根据误差分布设置旋转备用机会约束。最后，利用序列运算理论（SOT）将原始调度模型转化为易于求解的混合整数线性规划，并利用CPLEX求解器对变换后的模型进行求解。仿真结果表明，与传统的无预测调度模型相比，该方法通过提高预测精度，显着降低了系统运行成本。