Experimental Validation of Approximate Dynamic Programming Based optimization and Convergence on Microgrid Applications
Avijit Das, Zhen Ni, Xiangnan Zhong, Di Xiao Wu · 2020
Stochastic optimization can better model uncertainties in power system problems. However, when state space and action space become large, many existing approaches become computationally expensive and even infeasible to solve the problem. Approximate dynamic programming (ADP) attracts researchers' attention as a powerful tool for solving power system optimization problems with reduced computational cost. In this paper, in light of the existing literature, we investigate how the ADP approach with post-decision value function approximation converges to the nearly optimal solution with improved computational speed and experimentally validate the performance of the approach for a microgrid energy optimization problem. The approximation error versus the number of iteration is studied for convergence analysis of the post-decision ADP. A flowchart is provided to illustrate the proposed ADP algorithm for a microgrid energy optimization problem. The performance of ADP and dynamic programming (DP) is compared in terms of optimization error and computational time. It has found that the post-decision ADP approach can achieve competitive optimality with improved computational speed compared to the traditional DP.