Incentivizing system friendliness of decentralized resources in sustainable energy systems

To address the challenge of weather-dependent energy generation, synchronization tasks are shifted to the system and demand levels. The behaviour of decentralized actors, known as “system friendliness”, reduces the burden on central energy systems. This behaviour can be incentivized through financial mechanisms such as electricity prices and feed-in tariffs. However, designing pricing mechanisms specifically for system friendliness in sustainable energy systems remains unclear. In this work, we extend the methodology for assessing system friendliness to multi-node energy systems, developing indicators to capture the impact of decentralized actors on storage and grid requirements. Our approach allows for evaluating system friendliness at every node and connection using only residual load data and grid structure information. Through extensive hyper-parameter optimization of the information weightings in price signals in a future German energy system, we explore optimal incentivization mechanisms. We demonstrate the trade-off between storage and grid capacities, requiring price signals to consider both global energy balance and local grid usage. Our results produce Pareto-optimal price signals, balancing local and global information. We find that in wind-dominated regions, dynamic grid usage information is crucial for system-friendly incentives, while PV-dominated regions benefit from price signals focused on system-wide energy balance. Despite regional differences, we show that price mechanisms that incentivize system-friendly behaviour in both regions simultaneously exist.