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Optimal Constrained Neuro-Dynamic Programming Based Self-learning Battery Management in Microgrids
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DOI：10.1007/978-3-319-46675-0_22.png)
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Abstract
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In this paper, a novel optimal self-learning battery sequential control scheme is investigated for smart home energy systems. Using the iterative adaptive dynamic programming (ADP) technique, the optimal battery control can be obtained iteratively. Considering the power constraints of the battery, a new non-quadratic form performance index function is established, which guarantees the value of the iterative control law not to exceed the maximum charging/discharging power of the battery to extend the service life of the battery. Simulation results are given to illustrate the performance of the presented method.
Keywords:
Adaptive dynamic programming
Approximate dynamic programming
Energy management system
Smart home
Optimal control
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