A Data-Driven Optimisation Model for Designing Islanded Microgrids


In practice, electrification of remote and islanded communities with no connection to the main grid is entangled with many techno-economic issues. These technical and more importantly economical challenges often justify the use of Micro-Grids (MGs) as self-sufficient electrical networks with a group of controllable/non-controllable consumers and producers in remote and islanded areas. However, the optimal design of sustainable MGs, even in small communities, is a complex optimisation problem due to the uncertain nature of load consumption and renewable production as well as the non-convex characteristics of network constraints. In this paper, we propose a model to design sustainable MGs using the notion of Distributionally Robust Optimisation (DRO) to handle the uncertainties arising from forecast data wherein the non-convex AC power flow equations are reformulated into convex constraints. Furthermore, a three-step approach is introduced to recast the tri-level DRO-based model into a tractable single-stage Mixed-Integer Linear Programming (MILP) problem. The proposed approach is tested on a modified Europrean CIGRE 18-bus test network and its performance is compared with the stochastic optimisation approach.

2022 17th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

Roy Billington best student paper award

Petros Aristidou
Petros Aristidou
Assistant Professor