Online Convex Optimization for Coordinated Long-Term and Short-Term Isolated Microgrid Dispatch
For operators of isolated microgrids, this work provides a computationally tractable and non-anticipatory dispatch method that significantly improves cost efficiency and reliability.
This paper introduces a coordinated long-term and short-term dispatch framework for isolated microgrids with hybrid energy storages, using online convex optimization with reference tracking. The method reduces costs by 73.4% compared to state-of-the-art, eliminates load loss, and achieves an additional 2.4% cost saving via the OCO algorithm.
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.