Energy community with shared photovoltaic and storage systems: influence of power demand in cost optimization

• Published in: Applied stochastic models in business and industry 2024-11, Vol.40 (6), p.1612-1634
• Main Authors: De Blasis, Riccardo, Pacelli, Graziella, Vergine, Salvatore
• Format: Article
• Language: English
• Subjects: Alternative energy sources; Categories; Cost benefit analysis; Cost control; cost optimization; Demand analysis; Electric power demand; energy community; Energy consumption; Energy costs; Energy management; Energy resources; Energy storage; energy storage system; Monte Carlo simulation; Operating costs; Optimization; power demand; Renewable energy sources; Renewable resources
• ISSN: 1524-1904; 1526-4025
• DOI: 10.1002/asmb.2860

Energy management of distributed energy resources has gradually become a complex problem because of the intermittent nature of renewable energy sources, such as photovoltaic power, and the large use of energy storage systems. A way to deal with these issues is to operate within an energy community. However, the efficient management of the community in terms of costs is particularly relevant. Specifically, the minimization of the energy community costs, which consists of properly utilizing shared energy storage and renewable energy sources, becomes an important objective. In this context, a fundamental role is played by demand power characteristics which strongly influence the benefits brought by this energy management scheme. This work investigates the influence of the variability of power demand on the minimization of the operating cost problem of an energy community while determining the optimal capacity of the energy storage system that increases the self‐consumption potential of the photovoltaic source. Two main scenarios are implemented where the effects of considering the community photovoltaic capacity as a variable or a parameter on costs and energy storage system size are investigated. This analysis consists of a multi‐objective optimization coupled with a Monte Carlo framework. The community management is conducted by considering random power demand profiles of each unit belonging to the same community, and different sizes, categories of users and users' aggregations. A comparison is led among different users' categories in terms of costs, photovoltaic unit and energy storage system size. The results provide an overview of how each category benefits from taking part in an energy community both in terms of cost and energy storage and photovoltaic sizes and show how these aspects change within a multi‐category aggregation where each category makes a different contribution to the community. In particular, we find evidence of the “synergy effect” brought by multi‐category aggregations capable of exploiting differences in consumption profiles. Each building category, with its numerosity, has a different effect on the energy community, resulting in a different impact on total costs and cost savings. We also investigate how the energy storage system capacity is affected by both the available photovoltaic capacity and the consumption profiles of the categories within the energy community.