Speaker
Description
Farming is becoming increasingly energy-demanding due to the widespread adoption of Agriculture 4.0 technologies and decarbonization policies. To address this challenge, the transition toward sustainable energy and widespread deployment of on-farm renewable energy systems (RES) is needed.
Modelling tools enable farmers to evaluate the performance of energy technologies and support informed decision-making. However, most existing tools for the integration of RES on farms are either commercial or require complex technical expertise, limiting their practical use. This article addresses both challenges by developing models for the multi-vector RES integration on farms and by creating a user-friendly web-based tool that enables practical decision support for farmers.
The tool incorporates a range of RES technologies, including photovoltaic, wind turbines, hydropower systems, biogas units, wood-chip boilers, and energy storage systems. Each technology is represented by a dedicated model that estimates its energy production. The proposed framework enables the operation of multi-vector RES technologies as a coordinated system rather than individual assets. To ensure the optimal operation while satisfying operational constraints of the integrated energy system, a centralised Economic Model Predictive Control and optimisation function have been incorporated into the model. The Python model was embedded in a user-friendly open-source platform.
The model and tool were tested using real operational data from a farm in Norway within the HarvRESt Horizon project. The model is validated via the farm's data. In addition, an integrated multi-vector configuration capable of meeting the farm’s annual energy demand is identified from multiple modelling-based alternatives.
| Topic Area | Smart Energy Storage, Integration and Utilization |
|---|