标题： 在线凸优化用于协调长期和短期孤岛微电网调度 显示英文标题

标题： Online Convex Optimization for Coordinated Long-Term and Short-Term Isolated Microgrid Dispatch

摘要： 本文提出了一种新颖的非前瞻性长短期协调调度框架，用于具有混合短长期储能（LDES）的孤立微电网。 我们引入了一个凸包近似模型来模拟非凸LDES电化学动态，从而提高计算的可行性和准确性。 为了解决SoC动态和长期合同中的时间耦合问题，我们生成了LDES和净负荷的回顾性最优荷电状态（SoC）轨迹，用于离线训练。 在在线阶段，我们采用核回归动态更新SoC参考值，并提出一种带有SoC参考跟踪和专家跟踪的自适应在线凸优化（OCO）算法，以减轻短视问题并实现自适应步长优化。 我们严格证明了长期和短期策略在时间上都实现了次线性遗憾界，随着更多回归场景、更强的跟踪惩罚和更精细的凸近似而改善。 仿真结果表明，所提出的方法优于最先进的方法，成本降低了73.4%，通过参考跟踪消除了负荷损失，并通过OCO算法实现了额外的2.4%成本节约。 这些优势随着LDES持续时间的延长而扩大，该方法在较差的预测和意外系统故障中表现出韧性。 显示英文摘要

摘要： This paper proposes a novel non-anticipatory long-short-term coordinated dispatch framework for isolated microgrid with hybrid short-long-duration energy storages (LDES). We introduce a convex hull approximation model for nonconvex LDES electrochemical dynamics, facilitating computational tractability and accuracy. To address temporal coupling in SoC dynamics and long-term contracts, we generate hindsight-optimal state-of-charge (SoC) trajectories of LDES and netloads for offline training. In the online stage, we employ kernel regression to dynamically update the SoC reference and propose an adaptive online convex optimization (OCO) algorithm with SoC reference tracking and expert tracking to mitigate myopia and enable adaptive step-size optimization. We rigorously prove that both long-term and short-term policies achieve sublinear regret bounds over time, which improves with more regression scenarios, stronger tracking penalties, and finer convex approximations. Simulation results show that the proposed method outperforms state-of-the-art methods, reducing costs by 73.4%, eliminating load loss via reference tracking, and achieving an additional 2.4% cost saving via the OCO algorithm. These benefits scale up with longer LDES durations, and the method demonstrates resilience to poor forecasts and unexpected system faults.