A Distributed Algorithm for Multi-scale Multi-stage Stochastic Programs with Application to Electricity Capacity Expansion

TL;DR

This paper introduces a scalable distributed algorithm for solving complex multi-scale multi-stage stochastic programs, demonstrated on electricity capacity expansion, outperforming existing methods in efficiency and scalability.

Contribution

The paper proposes a novel hybrid decomposition method combined with an N-block PCPM algorithm for improved scalability in large-scale stochastic optimization problems.

Findings

The proposed algorithm outperforms ADMM and PHA in scalability.

Hybrid scenario-node-realization decomposition effectively handles uncertainties.

Orthogonal projection simplifies iterations and reduces communication overhead.

Abstract

This paper applies the N-block PCPM algorithm to solve multi-scale multi-stage stochastic programs, with the application to electricity capacity expansion models. Numerical results show that the proposed simplified N-block PCPM algorithm, along with the hybrid decomposition method, exhibits much better scalability for solving the resulting deterministic, large-scale block-separable optimization problem when compared with the ADMM algorithm and the PHA algorithm. The superiority of the algorithm's scalability is attributed to the two key features of the algorithm design: first, the proposed hybrid scenario-node-realization decomposition method with extended nonanticipativity constraints can decompose the original problem under various uncertainties of different temporal scales; second, when applying the N-block PCPM algorithm to solve the resulting deterministic, large-scale N-block convex optimization problem, the technique of orthogonal projection we exploit greatly simplifies the iteration steps and reduce the communication overhead among all computing units, which also contributes to the efficiency of the algorithm.