Techno-economic and life cycle assessment of agrivoltaic system (AVS) designs

Land use competition between agricultural activities and ground-mounted solar photovoltaic (PV) deployment has increased worldwide attention to hybrid agriculture, and PV systems known as agrivoltaic systems (AVS) in efforts to increase the efficiency of energy and food production and minimize the land use competition. However, little is known about AVS's economic feasibility and environmental tradeoffs. Here we aim to evaluate the techno-economic and environmental impacts of four AVS configurations (full density, half density, mono-axial tracking, and bi-axial tracking) and compare their performance against PV-only systems. We used the life cycle revenue generated from a hectare of land area ($/ha) as a functional unit of our analysis. We found that all AVS configurations outperformed PV-only systems in the economic feasibility assessment, where bi-axial tracking was the best-performing AVS. Further, we developed a case scenario for agricultural farmers to determine the minimum selling price of electricity required for AVS to compete with the economic performance of crop-only farms. We found that the AVS designs require additional incentives (2 cent -6 cent per kWh of electricity generation) to be as competitive as the crop-only farms. The life cycle environmental assessment demonstrated that the AVS has better environmental performance than PV-only systems, with similar to 15-55 % less environmental impacts per functional unit. On average, electricity generation accounts for-80 % of AVS environmental im-pacts, while food production and water demand account for-20 %. Additionally, a sensitivity analysis con-ducted on various uncertain parameters, such as crop yield, water demand, electricity selling price, crop selling prices, discount, and inflation rates, while varying these parameters across broader ranges, indicates that AVS designs become a more economically and environmentally sustainable alternative over PV-only systems in the majority (>66 %) of the data analyzed.