Optimal sizing and control of a grid-connected battery in a stacked revenue model including an energy community

Recent years have seen rapid increases in intermittent renewable generation, requiring novel battery energy storage systems (BESS) solutions. One recent trend is the emergence of large grid-connected batteries that can be controlled to provide multiple storage and flexibility services, using a stacked revenue model. Another emerging development is renewable energy communities (REC), in which prosumers invest in their own renewable generation capacity, but also require battery storage for flexibility. In this paper, we study settings in which energy communities rent battery capacity from a battery operator through a battery-as-a-service (BaaS) model. We present a methodology for determining the sizing and pricing of battery capacity that can be rented, such that it provides economic benefits to both the community and the battery operator that participates in the energy market. We examine how sizes and prices vary across a number of different scenarios for different types of tariffs (flat, dynamic) and competing energy market uses. Second, we conduct a systematic study of linear optimization models for battery control when deployed to provide flexibility to energy communities. We show that existing approaches for battery control with daily time windows have a number of important limitations in practical deployments, and we propose a number of regularization functions in the optimization to address them. Finally, we investigate the proposed method using real generation, demand, tariffs, and battery data, based on a practical case study from a large smart battery operator in the Netherlands. For the settings used in our case study, we find that a community of 200 houses equipped with a 330 kW wind turbine can save up to €12,874 per year by renting just 280 kWh of battery capacity (after subtracting the battery rental costs), and the methodology is applicable for a wide variety of other settings and tariff types. • New stacked revenue model for providing battery storage as a service for energy communities. • Methodology for determining rental prices to benefit both operators and communities. • Systematic comparison of linear optimization approaches for battery control. • Regularization approaches that increase profits and reduce battery degradation. • Validation using a real case study and data from a large battery operator in the Netherlands.