An Optimization Framework for Power Infrastructure Planning

TL;DR

This paper presents a novel optimization framework for planning power infrastructure routes, utilizing graph models and specialized algorithms to reduce resistance and improve flexibility in large-scale energy projects.

Contribution

It introduces a new graph-based framework with a variant of Bellman-Ford and iterative methods for efficient, low-memory, locally optimal power line routing, including diverse path outputs.

Findings

Reduces resistance by over 10% compared to previous methods.

Offers lower memory usage and faster runtime for route computation.

Provides a user-friendly open-source tool for practical deployment.

Abstract

The ubiquitous expansion and transformation of the energy supply system involves large-scale power infrastructure construction projects. In the view of investments of more than a million dollars per kilometre, planning authorities aim to minimise the resistances posed by multiple stakeholders. Mathematical optimisation research offers efficient algorithms to compute globally optimal routes based on geographic input data. We propose a framework that utilizes a graph model where vertices represent possible locations of transmission towers, and edges are placed according to the feasible distance between neighbouring towers. In order to cope with the specific challenges arising in linear infrastructure layout, we first introduce a variant of the Bellman-Ford algorithm that efficiently computes the minimal-angle shortest path. Secondly, an iterative procedure is proposed that yields a locally optimal path at considerably lower memory requirements and runtime. Third, we discuss and analyse methods to output k diverse path alternatives. Experiments on real data show that compared to previous work, our approach reduces the resistances by more than 10% in feasible time, while at the same time offering much more flexibility and functionality. Our methods are demonstrated in a simple and intuitive graphical user interface, and an open-source package (LION) is available at https://pypi.org/project/lion-sp.