Grid-Aware Real-Time Dispatch of Microgrid with Generalized Energy Storage: A Prediction-Free Online Optimization Approach
Kaidi Huang, Lin Cheng, Ning Qi, David Wenzhong Gao, Asad Mujeeb, Qinglai Guo
TL;DR
This work tackles real-time microgrid dispatch with generalized energy storage under uncertainty without relying on forecasts. It builds a prediction-free two-stage framework that offline generates hindsight SoC trajectories and online learns references for SoC and opportunity costs via kernel regression, then tracks them with an adaptive OCO algorithm that enforces nonanticipativity and time-coupling constraints. The authors prove sublinear dynamic regret and time-varying hard constraint violation bounds and validate the approach on AEMO data, showing 5-6% reductions in operational cost and notable decreases in voltage violations, with strong robustness and scalability. The method offers a practical, theory-backed, grid-aware dispatch tool suitable for microgrids with diverse energy storage assets and real-time operation needs.
Abstract
This paper proposes a novel prediction-free two-stage coordinated dispatch framework for the real-time dispatch of grid-connected microgrid with generalized energy storages (GES). The proposed framework explicitly addresses grid awareness, non-anticipativity constraints, and the time-coupling characteristics of GES, providing microgrid operators with a near-optimal, reliable, and adaptable dispatch tool. In the offline stage, we generate the hindsight state-of-charge (SoC) trajectories of GES by solving the multi-period economic dispatch with historical scenarios. Subsequently, leveraging this historical information (SoC trajectories, net loads, and electricity prices), we synthesize and dynamically update online references for both SoC and opportunity cost through kernel regression. We propose an adaptive Lagrange multiplier-based online convex optimization algorithm, which innovatively incorporates reference tracking for global vision and expert-tracking for step-size updates. We provide theoretical proof to show that the proposed OCO algorithm achieves a sublinear bound of both dynamic regret and time-varying hard constraint violation. Numerical studies using ground-truth data from the Australian Energy Market Operator demonstrate that the proposed method outperforms state-of-the-art methods, reducing operational costs by 5.0-6.2% and voltage violations by 0.8-9.1%. These improvements mainly result from mitigating myopia by reference tracking and the adaptive capability provided by dynamically updated references and adaptive Lagrange multipliers. Sensitivity analysis demonstrates the robustness, computational efficiency, and scalability of the proposed method.