Dispatch optimization of energy communities for collective provision of network congestion management

Abstract

Nowadays more and more electricity consumers are transformed into prosumers by the installation of different kinds of distributed energy resources (DER) such as photovoltaic (PV) generation elements, wind turbines, electric vehicles (EV) and storage elements. The prosumers gain from increasing cost-effectiveness and the opportunity to be self-empowered. However, the constantly growing amount of renewable DER with its intermittent nature leads to the new challenges in the distribution grid; network congestion is among them. In order to face these problems, the concepts of DER scheduling such as microgrids, virtual power plants (VPP), aggregators, and energy communities (one of the arising control paradigms) should be presented. Energy communities provide the abilities for both individual and collective optimization of the energy of the prosumers, which correspond for the peak shaving and network congestion management, respectively. The main objective of the thesis is to show and analyze the difference between these two types of optimization goals.

Individual and collective types of optimization are simulated in the Python-based Pandapower environment. The results are presented as a visualization of the effectiveness of the grid indicators from which one can see that the network congestion management (collective optimization) provides more effective utilization of electric energy in the grid.