# A robust convex optimization framework for autonomous network planning under load uncertainty

## TL;DR

This paper introduces a robust convex optimization framework for autonomous microgrid planning that accounts for load uncertainty, balancing computational complexity with more reliable investment and operational cost estimates.

## Contribution

It extends previous deterministic SOC relaxation methods by incorporating load uncertainty through robust optimization, improving planning reliability under uncertain conditions.

## Key findings

- Higher investment costs due to load uncertainty
- Increased operational costs when accounting for uncertainty
- Robust optimization enhances planning reliability

## Abstract

Autonomous microgrid planning is a Mixed-Integer Non Convex decision problem that requires to consider investments in both distribution and generation capacity and represents significant computation challenges. We proposed in a previous publication a deterministic Second-Order Cone (SOC) relaxation of this problem that made it computationally tractable for realsize cases. However, this problem is subject to considerable uncertainty emanating from load consumption, RES-based generation and contingencies. In this paper, we thus present a robust optimization approach that extends our previous work by including load related uncertainty at the cost of a substantial increase of the computational burden. The results show that significantly higher investment and operational costs are incurred to account for the load related uncertainty.