An Enhanced MILP Model for Multistage Reliability-Constrained Distribution Network Expansion Planning

Reliability is an essential factor in distribution networkt expansion planning. However, standard distribution reliability assessment techniques rely on quantifying the impact of a pre-specified set of events on service continuity through the simulation of component outages, one at a time. Due to such a simulation-based nature, the incorporation of reliability into distribution network expansion planning has customarily required the application of heuristic and metaheuristic approaches. Recently, alternative mixed-integer linear programming (MILP) models have been proposed for distribution network expansion planning considering reliability. Nonetheless, such models suffer from either low computational efficiency or over-simplification. To overcome these shortcomings, this paper proposes an enhanced MILP model for multistage reliability-constrained distribution network expansion planning. Leveraging an efficient, yet accurate reliability evaluation model, proposing a customized technique for effectively imposing radial operation, as well as utilizing pragmatic measures to model reliability-related costs are the salient features of this work. In this respect, practical reliability-related costs are considered based on reliability incentive schemes and the revenue lost due to undelivered energy during customer outages. The proposed planning approach is tested on four networks with 24, 54, 86, and 138 nodes to illustrate its efficiency and applicability.

Automated summary

This paper proposes an enhanced mixed-integer linear programming (MILP) model for multistage reliability-constrained distribution network expansion planning. The model incorporates an efficient and accurate reliability evaluation model, a customized technique for radial operation, and pragmatic measures to model reliability-related costs. The proposed planning approach considers practical reliability-related costs based on reliability incentive schemes and revenue lost during outages. The efficiency and applicability of the approach are illustrated through testing on four networks of different sizes.