Approximate dynamic programming with policy-based exploration for microgrid dispatch under uncertainties

Abstract

Approximate dynamic programming (ADP) is a promising approach for power system scheduling and dispatch under uncertainties. This paper presents an innovative ADP-based dispatch method for a microgrid with intermittent renewable generation, battery energy storage systems, and controllable distributed generators. The proposed ADP algorithm is based on a double-pass value iteration approach and takes advantage of the underlying properties of the microgrid dispatch problem. In the forward pass, decision variables are updated moving forward in time using an ε-greedy strategy to balance exploitation and exploration. In particular, an approximate optimization method is proposed to speed up exploitation. In addition to random exploration, a policy is designed to guide the algorithm to explore some promising solution space in a probabilistic manner. In the backward pass, the value function is updated moving backward in time using the trajectory of states, decisions, and outcomes of the sample path in the forward pass. The proposed method is evaluated through numerical experiments in both deterministic and stochastic environments. Case study results show that the proposed method demonstrates improved performance in both optimization gap and computation time in comparison to conventional methods.