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Stochastic dispatch of energy storage in microgrids: An augmented reinforcement learning approach
ارسال تصادفی ذخیره انرژی در میکرو شبکه ها: یک رویکرد یادگیری تقویتی تقویت شده-2020 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. Keywords: Microgrid | Energy storage | Volatile energy resource | Dynamic dispatch | Reinforcement learning |
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