A robust convex optimization framework for autonomous network planning under load uncertainty
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.
Cite
@article{arxiv.1703.06795,
title = {A robust convex optimization framework for autonomous network planning under load uncertainty},
author = {Benoît Martin and François Glineur and Emmanuel De Jaeger},
journal= {arXiv preprint arXiv:1703.06795},
year = {2017}
}