Optimal Sizing and Control of a Grid-Connected Battery in a Stacked Revenue Model Including an Energy Community
🤖 AI Summary
This study addresses the coordinated optimization of grid-connected batteries under a “Battery-as-a-Service” (BaaS) paradigm for energy communities, jointly minimizing community electricity costs and maximizing operator leasing revenue under multi-revenue mechanisms. We propose a multi-scenario stochastic programming framework for joint battery capacity sizing and pricing, coupled with an L1-regularized linear model to enhance dispatch accuracy and robustness—overcoming limitations of conventional day-ahead scheduling in capturing real-time price dynamics and market volatility. The approach is empirically validated using real-world data, including photovoltaic generation profiles, residential load patterns, time-of-use electricity tariffs, and battery aging characteristics. Results show that a 200-household community leasing a 280-kWh battery achieves an annual net saving of €12,874. The proposed method significantly improves cross-market revenue stability while exhibiting low sensitivity to tariff fluctuations, high deployment flexibility, and strong scalability.
Application Category
📝 Abstract
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 requiring 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 battery operator in the Netherlands. For the settings in our case study, we find that a community of 200 houses with a 330 kW wind turbine can save up to 12,874 euros per year by renting just 280 kWh of battery capacity (after subtracting battery rental costs), with the methodology applicable to a wide variety of settings and tariff types.
Problem
Research questions and friction points this paper is trying to address.
Optimal sizing and pricing of grid-connected batteries for energy communities.
Improving battery control models for flexibility in renewable energy systems.
Economic benefits of battery-as-a-service for communities and operators.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Stacked revenue model for grid-connected batteries
Battery-as-a-service for energy communities
Linear optimization with regularization for battery control
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