Abstract
In this chapter, an intelligent multi-microgrid (MMG) energy management method will be proposed based on deep neural network (DNN) and model-free reinforcement learning (RL) techniques. In the studied problem, multiple microgrids are connected to a main distribution system, and they purchase power from the distribution system to maintain local consumption. From the perspective of the distribution system operator (DSO), the target is to decrease the demand-side peak-to-average ratio (PAR) and to maximize the profit from selling energy. To protect user privacy, DSO learns the MMG response by implementing a DNN without direct access to the user’s information. Further, the DSO selects its retail pricing strategy via a Monte Carlo method from RL, which optimizes the decision based on prediction. The simulation results from the proposed data-driven deep learning method, as well as comparisons with conventional model-based methods, substantiate the effectiveness of the proposed approach in solving power system problems with partial or uncertain information.
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Li, F., Du, Y. (2024). Deep Neural Network for Microgrid Management. In: Deep Learning for Power System Applications. Power Electronics and Power Systems. Springer, Cham. https://doi.org/10.1007/978-3-031-45357-1_2
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DOI: https://doi.org/10.1007/978-3-031-45357-1_2
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