Title :
A Novel Dual Iterative
-Learning Method for Optimal Battery Management in Smart Residential Environments
Author :
Qinglai Wei ; Derong Liu ; Guang Shi
Author_Institution :
State Key Lab. of Manage. & Control for Complex Syst., Inst. of Autom., Beijing, China
Abstract :
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.
Keywords :
battery management systems; dynamic programming; iterative methods; learning (artificial intelligence); optimal control; smart power grids; dual iterative Q-learning method; external iterations; internal iterations; iterative control law; optimal battery management; power loads; smart residential environments; $Q$-learning; Adaptive critic designs; Q-learning; adaptive dynamic programming; adaptive dynamic programming (ADP); approximate dynamic programming; neural networks; optimal control; smart grid;
Journal_Title :
Industrial Electronics, IEEE Transactions on
DOI :
10.1109/TIE.2014.2361485