Clean Energy-farming Model under Uncertainty Using Stochastic Optimization and Life Cycle Cost Analysis

This paper proposes an optimization model for clean energy planning in farming that takes uncertain future projections into account. A stochastic optimization model is applied to determine optimal energy and farming equipment combinations, considering scenarios of uncertainty under multiple farming area sizes. The energy mixes comprise solar photovoltaic panels and battery storage to provide electricity to farm-related equipment, which is categorized into two types by their load usage pattern, including daily-load and periodic-load. A mixed-integer scenario-based stochastic optimization model is developed to minimize the life cycle costs of the optimal solutions through the expected economic life. A case study of a remote cacao-growing site in northern Thailand is presented. The empirical results proved the economies of scale, demonstrating that the larger the farm, the more energy-efficient it becomes; however, the correlation is not linear. The life cycle cost analysis findings from the objective function indicated that, given the listed uncertainties, the average levelized cost of electricity could be reduced to 0.217 USD/kWh. The proposed model could contribute to an emissions reduction in the agricultural sector of up to $1,417.86 \mathrm{~kg} / \mathrm{CO}_{2}$-eq annually per hectare of farmland. The empirical findings in this study offer quantitative support for decision-makers seeking off-grid clean-energy planning for remote horticulture sites while efficiently accounting for uncertainty.