OPF solution for a real Czech urban meshed distribution network using a genetic algorithm

Abstract

Electrical distribution networks are facing an energy transition which entails an increase of decentralised renewable energy sources and electric vehicles. The resulting temporal and spatial uncertainty in the generation/load patterns challenges the operations of an infrastructure not designed for such a transition. In this situation, Optimal Power Flow methods can play a key role in identifying system weak points and supporting efficient management of the electrical networks, including the distribution level. In this work, to support distribution system operators' decision-making process, we aim at attaining a quasi-optimal solution in the shortest time possible in an electrical network experiencing a large growth of distributed energy sources. We propose an optimisation method based on a modified version of a genetic algorithm and the Python pandapower package. The method is tested on a model of a real urban meshed network of a large Czech city. The optimisation method minimises the total operating costs of the distribution network by controlling selected network components and parameters, namely the transformer tap changers and the active power demand at consumption nodes. The results of our method are compared with the exact solution showing that a close-to-optimal solution of the observed problem can be reached in a relatively short time.