Adaptive Dynamic Programming-Based Optimal Control Scheme for Energy Storage Systems With Solar Renewable Energy

• Published in: IEEE transactions on industrial electronics (1982) Vol. 64; no. 7; pp. 5468 - 5478
• Main Authors: Wei, Qinglai, Shi, Guang, Song, Ruizhuo, Liu, Yu
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; Adaptive control; Adaptive critic designs; adaptive dynamic programming (ADP); Algorithms; Alternative energy sources; approximate dynamic programming; Batteries; Control theory; Cost analysis; Dynamic programming; Electric rates; Electricity consumption; Electricity pricing; Energy storage; energy storage system; Heuristic algorithms; Iterative algorithms; Iterative methods; Machine learning; Optimal control; Optimization; Performance analysis; Performance indices; Renewable energy; Renewable energy sources; Smart grid; Solar energy; solar renewable energy; Storage systems
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2017.2674581

In this paper, a novel optimal energy storage control scheme is investigated in smart grid environments with solar renewable energy. Based on the idea of adaptive dynamic programming (ADP), a self-learning algorithm is constructed to obtain the iterative control law sequence of the battery. Based on the data of the real-time electricity price (electricity rate in brief), the load demand (load in brief), and the solar renewable energy (solar energy in brief), the optimal performance index function, which minimizes the total electricity cost and simultaneously extends the battery's lifetime, is established. A new analysis method of the iterative ADP algorithm is developed to guarantee the convergence of the iterative value function to the optimum under iterative control law sequence for any time index in a period. Numerical results and comparisons are presented to illustrate the effectiveness of the developed algorithm.