Stochastic Dispatch of Energy Storage in Microgrids: An Augmented Reinforcement Learning Approach

TL;DR

This paper introduces a novel reinforcement learning approach augmented with Monte-Carlo tree search and domain knowledge to optimize the stochastic dispatch of energy storage in microgrids, considering lifecycle costs and volatility.

Contribution

It develops an advanced RL framework with MCTS and dispatching rules to efficiently solve the complex, non-convex microgrid energy storage dispatch problem.

Findings

Outperforms baseline RL algorithms in numerical tests.

Effectively incorporates lifecycle degradation costs.

Enhances computational efficiency with MCTS.

Abstract

The dynamic dispatch (DD) of battery energy storage systems (BESSs) in microgrids integrated with volatile energy resources is essentially a multiperiod stochastic optimization problem (MSOP). Because the life span of a BESS is significantly affected by its charging and discharging behaviors, its lifecycle degradation costs should be incorporated into the DD model of BESSs, which makes it non-convex. In general, this MSOP is intractable. To solve this problem, we propose a reinforcement learning (RL) solution augmented with Monte-Carlo tree search (MCTS) and domain knowledge expressed as dispatching rules. In this solution, the Q-learning with function approximation is employed as the basic learning architecture that allows multistep bootstrapping and continuous policy learning. To improve the computation efficiency of randomized multistep simulations, we employed the MCTS to estimate the expected maximum action values. Moreover, we embedded a few dispatching rules in RL as probabilistic logics to reduce infeasible action explorations, which can improve the quality of the data-driven solution. Numerical test results show the proposed algorithm outperforms other baseline RL algorithms in all cases tested.