Modelling Solar PV Adoption in Irish Dairy Farms using Agent-Based Modelling
Iias Faiud, Michael Schukat, Karl Mason
TL;DR
The study tackles forecasting PV adoption among Ireland's dairy farms in the context of rising energy costs by using an agent-based simulation to capture individual decision making. The approach defines economic utility $EU = NPV - IIC + Subsidies$, with $NPV = \sum_{t=0}^{n} \frac{R_t}{(1 + i)^t}$, and adopts a probabilistic rule $P = \frac{\beta}{1 + \exp(-\alpha \cdot (EU / total\_farmers))}$ to determine adoption and $PV_adoption = P \times total\_farmers$; yearly updates of energy price and subsidies drive year-over-year dynamics. The ABM is validated against 2022 PV adoption data, achieving a small deviation (0.45%), indicating the method's ability to reproduce observed uptake and provide forecasts. The work lays groundwork for extending the framework to other renewables and for including policymakers as agents to inform energy policy in agriculture.
Abstract
The agricultural sector is facing mounting demands to enhance energy efficiency within farm enterprises, concurrent with a steady escalation in electricity costs. This paper focuses on modelling the adoption rate of photovoltaic (PV) energy within the dairy sector in Ireland. An agent-based modelling approach is introduced to estimate the adoption rate. The model considers grid energy prices, revenue, costs, and maintenance expenses to calculate the probability of PV adoption. The ABM outputs estimate that by year 2022, 2.45% of dairy farmers have installed PV. This is a 0.45% difference to the actual PV adoption rate in year 2022. This validates the proposed ABM. The paper demonstrates the increasing interest in PV systems as evidenced by the rate of adoption, shedding light on the potential advantages of PV energy adoption in agriculture. This study possesses the potential to forecast future rates of PV energy adoption among dairy farmers. It establishes a groundwork for further research on predicting and understanding the factors influencing the adoption of renewable energy.