The proposed work presents a scenario analysis framework for analyzing the impacts of local energy communities on the distribution systems. End-consumers in modern distribution systems are increasingly equipped with flexible resources such as rooftop PV, electric vehicles, heat pumps, and home batteries through both private investment and public support schemes. These distributed resources alter power flows and may increase grid-side variability, creating new requirements and opportunities for flexibility provision through local energy communities. Local energy communities operate collectively to optimize the community resources to maximize their profits. They pose the potential to reduce feeder peak demand and line congestion from a distribution system management perspective. However, the performance and benefits of LECs are largely shaped by their control strategies, feeder composition, and available assets. Most studies consider only a narrow set of community compositions and do not systematically explore variations in prosumer demand types, technology configurations, and community sizes. To address this gap, a scenario analysis framework is developed to evaluate the impacts of local energy communities on the performance of distribution networks and economic implications. The proposed study can be helpful for the distribution system operators in designing their pricing mechanisms in optimal management of the available flexibility in the distribution systems.

Scenarios are constructed using three prosumer categories, namely residential, commercial, and industrial, defined by their electricity demand profiles. Using these prosumer types, all possible community compositions are generated and benchmarked against a reference case in which prosumers purchase electricity under a self-consumption strategy. The pricing model for prosumer electricity is obtained from Goteborg Energi. The scenario analysis framework is implemented in two stages. First, operating schedules are optimized to minimize total energy procurement cost. Second, optimized schedules are evaluated using linearized distribution power flow equations to assess network performance. Each scenario is evaluated using network KPIs, including maximum feeder loading and minimum nodal voltage, and economic KPIs, including energy cost, peak cost, and overall cost. The results are then evaluated for a homogeneous mixed-feeder demand composition of residential, commercial and industrial demand.

It was observed that local energy communities primarily achieve overall cost minimization through reductions in peak-related costs. Across all configurations, 13 % cases exhibit a higher maximum line loading than in the reference case. The cases wherein communities consisting of a single prosumer profile contributed to the above said cases while communities consisting of mixed profiles were found to be more likely to reduce feeder peak demand and the maximum line loading. Further, with an increase in community size, it was found that communities consisting of single demand profiles were more likely to lead in higher maximum line loading while with an increase in community size, communities with mixed demand profiles more likely reduced the maximum line loading in the feeder.