Modeling Scenarios for PV-Powered Agricultural Energy Systems using Seasonally Varying Demand Profiles at Hourly Resolution

Abstract

The agricultural sector holds the potential to integrate a resilient and renewable energy system due to high penetration of renewable energy sources. This study addresses the obstacles towards this transition. The first obstacle is the Dutch electricity grid. As of 2024, the greatest bottleneck in the Dutch energy transition is the lagging grid capacity. Instead of relying on grid power, matching local generation and demand of energy provides an elegant alternative. The second obstacle is that on-farm activity is powered mainly by diesel, since the majority of the demand is from tractor activities. To these two ends, this report investigates two primary aims: first, to provide an overview of the hourly and seasonal energy demand for dairy and open-field farms; second, to evaluate the feasibility of decarbonizing on-farm energy systems given the current grid capacity. The first aim was achieved by identifying the main energy-consuming activities in both dairy and open-field farming, revealing that dairy farming has consistent day profiles throughout the year, whereas open-field farming shows significant variability depending on crop needs and practices. The activities constituting most of the electricity demand on dairy farms have been found to be 1) milking, 2) milk cooling, 3) water pumping and 4) lighting. Feeding and grassland management (fertilizing and mowing) have been identified as the main diesel-consuming activities. For open-field farming, electricity is consumed largely for crop storage in the form of cooling or ventilation, or a combination. This contribution is small compared to the diesel consumption for field operations (mainly for tillage).
The second aim involved accurate modeling of various photovoltaic (PV) system architectures with TNO’s BIGEYE software and linear programming (LP) optimisation using Open Energy Modeling Framework (OEMOF) to assess their impact on the feasibility of decarbonization. The findings suggest that while the choice of PV systems can dampen the mismatch peaks by several percentages, the type of farm and its specific practices play the most significant role. For open-field farms, minimal crop storage requirements and sowing/harvesting periods, such as broad beans and wheat, that align with PV yield profiles are more suitable for achieving self-sufficiency. Short-term forms of energy storage, shifting loads and clipping maximum PV power are measures that directly enhance self-sufficiency and decarbonization without additional grid reinforcement. The highest self-sufficiency rates reach to 82%. In the discussion, the potential contribution of long-term storage needed to decarbonize the farm types with high mismatch levels is discussed. The report concludes with an outlook for further studies to enhance the reliability of energy demand data and suggests actionable and effective alternatives to grid reinforcement for the successful electrification and decarbonization of the agricultural sector. The most straightforward and pressing step in further research is broadening the scope to matching mismatch profiles with other prosuming systems, such as other farmers, towns and businesses, forming resilient energy communities