Computing Necessary Conditions for Near-Optimality in Capacity Expansion Planning Problems

TL;DR

This paper introduces a framework to identify necessary conditions within near-optimal solutions for large-scale power system capacity expansion problems, aiding decision-makers in understanding feasible investment features.

Contribution

It develops a generic method based on epsilon-optimal feasible space analysis and applies it to capacity expansion planning in the European electricity network.

Findings

Identifies minimal investment conditions for transmission, storage, and generation.

Provides a practical approach for decision-makers to interpret near-optimal solutions.

Demonstrates the framework's effectiveness through a European electricity network case study.

Abstract

In power systems, large-scale optimisation problems are extensively used to plan for capacity expansion at the supra-national level. However, their cost-optimal solutions are often not exploitable by decision-makers who are preferably looking for features of solutions that can accommodate their different requirements. This paper proposes a generic framework for addressing this problem. It is based on the concept of the epsilon-optimal feasible space of a given optimisation problem and the identification of necessary conditions over this space. This framework has been developed in a generic case, and an approach for solving this problem is subsequently described for a specific case where conditions are constrained sums of variables. The approach is tested on a case study about capacity expansion planning of the European electricity network to determine necessary conditions on the minimal investments in transmission, storage and generation capacity.