Search

Community energy projects have gained popularity in recent years, and encouraging citizens to form local energy communities (LEC) is considered an effective tool for raising awareness about renewable energy. Since no single universal method exists for operating LECs, this study investigated the impact that different business models and asset dispatch methods have on LECs' economic and energy-related indicators. We carried out a case study, which included the development, modelling, and simulation of seven scenarios using mixed-integer linear programming (MILP). To measure and compare the prospective performance of the LECs in each scenario, six key metrics were evaluated and assessed. The authors find that simple, rule-based control systems might be well suited for LECs with a limited number of controllable assets that aim to provide increased levels of self-consumption of up to 3%. We also conclude that when the LEC utilises an energy cooperative business model, the selected asset dispatch method provides only minor differences in LEC performance, while for prosumer communities, the importance of selecting a suitable asset dispatch method is higher. We also conclude that LECs have the potential to significantly increase their economic performance by more than 10% by acting as aggregators and providing grid services directly to system operators. [ABSTRACT FROM AUTHOR]

Renewable and local energy communities are viewed as a key component to the success of the energy transition. In this paper, we estimate wind power potential for such communities. Acquiring the most accurate weather data is important to support decision-making. We identify the most reliable publicly available wind speed data and demonstrate a case study for typical energy community scenarios such as a single commercial turbine at coastal and inland locations in Ireland. We describe our assessment methodology to evaluate the quality of the wind source data by comparing it with meteorological observations. We make recommendations on which publicly available wind data sources, such as reanalysis data sources (MERRA-2, ERA-5), PVGIS, and NEWA are best suited to support Renewable Energy Communities interested in exploring the possibilities of renewable wind energy. ERA5 is deemed to be the most suitable wind data source for these locations, while an anomaly is noted in the NEWA data. [ABSTRACT FROM AUTHOR]