In a community energy project, batteries are the asset with the shortest lifespan and are therefore key contributors to cost. Understanding the influence of the battery state of health model on a control algorithm designed for redistribution of benefits in terms of financial gains in a community energy project can help elongate battery lifetime and reduce need for replacement hence minimising costs and reaping environmental benefits. Battery depreciation is predominantly stimulated by cyclic degradation and thus incurred costs are compared by simulating degradation curves for different battery storage systems in terms of chemistry and capacity. Costs are calculated by applying battery models to the control algorithm proposed by Norbu et al. (2021), which factors in cyclic degradation using the rainflow counting algorithm. The experiment explores the influences on cost of different battery chemistry types and capacities. Results demonstrate that lithium-ion batteries, which are the current norm in utility-scale applications, incur the lowest costs. Specifically, lithium manganeseoxide batteries appear to be most effective. Additionally, costs tend to decrease with increasing capacity until a minima corresponding to the optimal battery capacity.

Energy communities are not yet fully self sufficient, mostly due to financial factors. Efforts are made to reduce these factors. Communities invest in community-owned assets, which provide more savings compared to individually-owned assets. Prosumers share their loads for better energy distribution, this can provide a significant impact. A good predictor for identifying the financial benefit for a community is the diversity of the consumption behaviors of the prosumers. However, an open question is how the diversity exactly affects the community costs. In this paper, we introduce Two-level K-means, an improvement on K-means, and use it on real consumption data to find energy profiles. We use the energy profiles to model communities, varying in diversity. Finally, we provide an analysis of the affects of diversity on costs. Results from the analysis show that an increased diversity factor can provide financial benefit. This is a result of residual demand being compensated by excess energy generated. However, the added financial benefit depends on the composition of the community.