Fast Stochastic Dual Dynamic Programming for Economic Dispatch in Distribution Systems
Yu Lan, Qiaozhu Zhai, Xiaoming Liu, Xiaohong Guan
Abstract
The multistage economic dispatch solution is of paramount importance for achieving the optimal unit commitment and the optimal decentralized decision-making in smart grid operations. Through the approximations of the optimal expected cost-to-go functions, the stochastic dual dynamic programming (SDDP) has been shown effective to obtain the optimum for the multistage stochastic programming (SP) problem, while without finite termination guarantee. Thus, we propose a fast stochastic dual dynamic programming (FSDDP) method to accelerate the convergence rate of the SDDP, including updating the candidate points based on the case with the maximum difference between the upper and lower bounds of the optimal value functions, adding a backward-forward-backward inner scheme in the backward pass, and initializing the bounds with finite constant values. We can verify that the FSDDP method enjoys a nice property with a finite convergence guarantee for the multistage SP problems. Numerical tests on 4-, 33-, 34-, 69-, 118- and 123-bus distribution systems have demonstrated that FSDDP can approach the optimal economic performance at significantly improved computational efficiency.