Optimal constrained self-learning battery sequential management in microgrid via adaptive dynamic programming

• Published in: IEEE/CAA journal of automatica sinica Vol. 4; no. 2; pp. 168 - 176
• Main Authors: Wei, Qinglai, Liu, Derong, Liu, Yu, Song, Ruizhuo
• Format: Journal Article
• Language: English
• Published: Piscataway Chinese Association of Automation (CAA) 01.04.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive systems; Algorithms; Batteries; Computer simulation; Constraints; Control theory; Distributed generation; Dynamic programming; Energy conversion efficiency; Energy management; Iterative algorithms; Iterative methods; Learning; Optimal control; Performance analysis; Performance indices; Power efficiency; Quadratic forms; Residential energy; Sequential control; Service life; Smart buildings; Smart homes
• ISSN: 2329-9266; 2329-9274
• DOI: 10.1109/JAS.2016.7510262

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 U+0028 ADP U+0029 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.