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.