Abstract

Appropriate planning and optimization strategies for day-ahead power management play important roles in efficient operation of Microgrids (MGs). Due to the uncertainties in electricity demand and renewable generations, and the multi-objective (MO) nature of MG power management, conventional optimization techniques have not been as effective in giving satisfactory results. This paper aims at solving the day-ahead power management problem as a MO optimization problem, with a focus on increasing the system's resiliency using an agent-based Dynamic Programming (DP) approach named Value Iteration (VI) and a model-free Q-learning (QL) algorithm. The two objectives of the MO problem are: maximizing load serviceability and minimizing operational cost. Both the approaches are data-driven, and the behavior of the agent of each component of a MG is formulated as a finite-horizon Markov Decision Process (MDP). VI guarantees an optimal solution to the MO problem given the MDP model, and QL has the ability to work under uncertainty and incomplete information. The effectiveness of the two algorithms have been evaluated using a benchmark MG test system.

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