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Optimal Constrained Self- learning Battery Sequential Management in Microgrid Via Adaptive Dynamic Programming
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DOI：10.1109/JAS.2016.7510262.png)
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Abstract
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This paper concerns a novel optimal self-learning battery sequential control scheme for smart home energy systems. The main idea is to use the adaptive dynamic programming (ADP) technique to obtain the optimal battery sequential control iteratively. First, the battery energy management system model is established, where the power efficiency of the battery is considered. Next, considering the power constraints of the battery, a new non-quadratic form performance index function is established, which guarantees that the value of the iterative control law cannot exceed the maximum charging/discharging power of the battery to extend the service life of the battery. Then, the convergence properties of the iterative ADP algorithm are analyzed, which guarantees that the iterative value function and the iterative control law both reach the optimums. Finally, simulation and comparison results are given to illustrate the performance of the presented method.
Keywords:
Adaptive critic designs
adaptive dynamic programming (ADP)
approximate dynamic programming
battery management
energy management system
neuro-dynamic programming
optimal control
smart home
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