Journal
IEEE TRANSACTIONS ON SMART GRID
Volume 11, Issue 2, Pages 1066-1076Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2019.2930299
Keywords
Microgrids; Reinforcement learning; Peak to average power ratio; Mathematical model; Energy management; Computational modeling; Deep neural network (DNN); Monte Carlo method; multi-microgrid; reinforcement learning; peak-to-average ratio (PAR)
Categories
Funding
- NSF [ECCS-1809458]
- CURENT, a NSF/DOE Engineering Research Center through NSF [EEC-1041877]
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In this paper, an intelligent multi-microgrid (MMG) energy management method is 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 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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