Sketch-Based Optimisation for Distribution Grid Expansion Planning: User-driven research to accelerate distribution grid expansion planning at Alliander

Distribution Network Operators (DNOs) are confronted with a significant challenge to expand the capacity of the electricity distribution grid to facilitate the energy transition. Grid expansion planning for the distribution grid is a complex problem with many constraints and objectives. Earlier research has focused on metaheuristics such as genetic algorithms to optimise grid expansion designs in terms of resolved congestions, redundancy and costs. However, this algorithm is not used in practice by its intended users; grid architects. This research first identifies why the algorithm is not yet used and proposes several ideas to improve user adoption. Then a technical implementation of sketch-based optimisation using a novel shape similarity measure is presented. We investigate its behaviour with a realistic case study. Furthermore, a qualitative user study is done to evaluate the potential impact of sketch-based optimisation on distribution grid expansion planning. We conclude that the novel shape similarity measure enables sketch-based optimisation of distribution grid expansion planning and that it has potential to accelerate electricity grid design processes. Alliander, the largest DNO of the Netherlands, will implement sketch-based optimisation in their distribution grid design application later this year, making it available to 20+ grid architects to accelerate their design process. / Computer Science