Parallel Computing Based Solution for Reliability-Constrained Distribution Network Planning

TL;DR

This paper introduces a parallel computing-based algorithm to efficiently solve reliability-constrained distribution network planning problems, significantly reducing computation time for large-scale systems.

Contribution

It develops a novel parallelizable augmented Lagrangian algorithm with decomposition and acceleration strategies for RcDNP, enabling practical application to large distribution networks.

Findings

Reduces computation time significantly

Enables application of RcDNP to large-scale networks

Proven convergence and optimality under mild conditions

Abstract

The main goal of distribution network (DN) expansion planning is essentially to achieve minimal investment constrained with specified reliability requirements. The reliability-constrained distribution network planning (RcDNP) problem can be cast an instance of mixed-integer linear programming (MILP) which involves ultra-heavy computation burden especially for large scale DNs. In this paper, we propose a parallel computing-based acceleration algorithm for solve RcDNP problem. The RcDNP is decomposed into a backbone grid and several lateral grid problems with coordination. Then a parallelizable augmented Lagrangian algorithm with acceleration strategy is developed to solve the coordination planning problems. In this method, the lateral grid problems are solved in parallel through coordinating with the backbone grid planning problem. To address the presence of nonconvexity, Gauss-Seidel iteration is adopted on the convex hull of the feasible region constructed by the decomposition method. Under mild conditions, the optimality and convergence of this algorithm is proven. The numerical tests show the proposed method can significantly reduce the computation time and make the RcDNP applicable for real-world problems.