Journal
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 62, Issue 4, Pages 2509-2518Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2014.2361485
Keywords
Adaptive critic designs; adaptive dynamic programming (ADP); approximate dynamic programming; neural networks; optimal control; Q-learning; smart grid
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Funding
- National Natural Science Foundation of China [61034002, 61374105, 61233001, 61273140]
- Beijing Natural Science Foundation [4132078]
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In this paper, a novel iterative Q-learning method called dual iterative Q-learning algorithm is developed to solve the optimal battery management and control problem in smart residential environments. In the developed algorithm, two iterations are introduced, which are internal and external iterations, where internal iteration minimizes the total cost of power loads in each period, and the external iteration makes the iterative Q-function converge to the optimum. Based on the dual iterative Q-learning algorithm, the convergence property of the iterative Q-learning method for the optimal battery management and control problem is proven for the first time, which guarantees that both the iterative Q-function and the iterative control law reach the optimum. Implementing the algorithm by neural networks, numerical results and comparisons are given to illustrate the performance of the developed algorithm.
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