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

Benoît Martin; François Glineur; Emmanuel De Jaeger

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arXiv · 1703.06795

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

Benoît Martin·François Glineur·Emmanuel De Jaeger

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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.

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Benoît Martin, François Glineur, Emmanuel De Jaeger. 2017-03-20. A robust convex optimization framework for autonomous network planning under load uncertainty. https://arxiv.org/abs/1703.06795

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