Techno-Economic Optimization of a Solar-Battery Agricultural Energy Hub for Irrigation in Banyumas Village, Sampang: A HOMER Pro Simulation Study

Abstract

Agricultural productivity in Banyumas Village, Sampang Regency, Madura, faces significant constraints due to fluctuating water availability and high dependence on diesel-powered irrigation pumps, which impose economic burdens and generate substantial greenhouse gas emissions. This study designs and optimizes a Solar-Battery Agricultural Energy Hub integrating photovoltaic generation and battery storage to supply irrigation and auxiliary agricultural loads. The proposed system comprises a 750 W irrigation pump operating eight hours daily, two 10 W LED lamps operating six hours daily, and a 45 W electric fan operating six hours daily, resulting in a total installed load of 815 W and a daily energy demand of 6.39 kWh. The methodology employs HOMER Pro simulation to evaluate multiple PV and battery configurations based on technical indicators, including annual energy production, renewable fraction, unmet electrical load, excess electricity, and capacity shortage, as well as economic indicators comprising Net Present Cost, Cost of Energy, and payback period. The optimization identified an optimal off-grid configuration consisting of a 3 kW photovoltaic array, a 20 kWh lead-acid battery bank, and a 2 kW converter, achieving a renewable fraction of 100% with annual PV production of 4,658 kWh against an annual electrical load of 3,123 kWh. The system records minimal unmet electrical load of 1.04 kWh/year and capacity shortage of 1.88 kWh/year, demonstrating reliable supply capability. Economically, the configuration yields a Net Present Cost of USD 21,861.93 and a Cost of Energy of USD 0.8915/kWh, with the battery bank representing the largest lifecycle cost. These findings indicate that while the proposed energy hub is technically feasible and environmentally sustainable, its economic competitiveness relative to conventional diesel-based irrigation remains challenging. The study contributes primary load profile data and context-specific optimization results for the Madura region.