RSM-Based Approximate Dynamic Programming for Stochastic Energy Management of Power Systems
Yelin Zhuo, Jianquan Zhu, Jiajun Chen, Zeshuang Wang, Hanfang Ye, Haixin Liu, Mingbo Liu
Abstract
The stochastic energy management (SEM) of power systems is computationally intractable due to its randomness, nonconvexity, and nonlinearity. To solve this problem, a response surface method (RSM)-based approximate dynamic programming (ADP) algorithm is proposed in this paper. Since the value function can be directly obtained by RSM, the proposed algorithm does not need to iteratively approach them as existing ADP algorithms, which facilitates reducing the computing time. In addition, an improved generalized polynomial chaos (IgPC) method (i.e., an extension of RSM) is proposed to calculate the expectation of the value function of ADP in the stochastic environment. Compared with the Monte Carlo method, which is commonly used in existing ADP algorithms, IgPC requires fewer sampling scenarios while providing similar results. Simulation results with two modified IEEE test systems and a real 2778-bus system demonstrate the effectiveness of the proposed algorithm in terms of accuracy and computation efficiency.