The role of energy communities in electricity grid balancing: A flexible tool for smart grid power distribution optimization

Abstract

The unpredictability of renewable energy systems can affect the stability of the electricity grid, causing voltage and frequency imbalances. In this work, a suitable methodology based on the peer-to-peer scheme applied to energy communities is developed and implemented in a simulation tool useful for investigating energy management strategies for decision-making aims. The developed model discretizes the energy community and its users into multiple control volumes, taking into account various technologies. It incorporates energy balances for individual users as well as the entire energy community, considering prosumers, consumers, energy storage systems, and electric vehicles. Moreover, the model enables the exploration of different solutions for grid frequency regulation and optimization of distributed energy resources. Additionally, the tool can predict electricity demand one day ahead, facilitating the organization of renewable energy availability and storage systems to minimize grid interactions and flatten electricity demand. The model incorporates different objective functions, including self-consumption, self-sufficiency, and grid-balancing factors, to evaluate the performance of energy communities. To show the capability of the developed model, it will be adopted to optimize the performance of an investigated community. As a result, an increase in renewable energy self-consumption from 59.4 to 83.9 MW h/year is achieved. Furthermore, the objective of grid balancing was achieved by guaranteeing a non-fluctuating load and providing 1.46 and 7.71 MW h/year for upward and downward grid frequency regulation. These findings illustrate the positive impact of energy dispatching management on the integration of renewable energy sources and the importance of further studying this topic to ensure grid stability.