Operational Dispatch Optimization of an Agrivoltaic System
DOI:
https://doi.org/10.52825/agripv.v3i.1348Keywords:
Agrivoltaic System, Microgrid, OptimisationAbstract
Agrivoltaic systems, which integrate solar photovoltaic (PV) arrays with agricultural land, present a promising solution to enhance both energy and food security by facilitating the simultaneous production of energy and food. However, there is a lack of comprehensive research on the operational strategies required for the efficient and profitable operation of grid-connected agrivoltaic systems. To address this gap, this paper introduces a new method for optimizing the dispatch strategy of agrivoltaic systems. This includes strategies for temporal energy arbitrage with the grid and maximizing self-consumption of excess solar PV generation. The effectiveness of the proposed method is demonstrated through numerical simulations using real-world data from an agrivoltaic system in Aotearoa New Zealand, equipped with stationary battery storage. A conceptual model of a battery-supported agrivoltaic system is used as a test case, focusing on optimizing hourly dispatch to enhance energy efficiency, demand management, and economic viability. The study employs linear programming to optimize the storage system's performance, utilizing 24-hour forecasts for electricity prices, local energy production, and demand. The goal is to charge the storage system when electricity prices are low and discharge it as needed to minimize costs. The results from the application of the method to the case study in Aotearoa New Zealand demonstrate its effectiveness, contributing to the broader goals of energy and food security by enhancing the profitability and reliability of grid-connected agrivoltaic systems.
Downloads
References
M. Kumar, D. Haillot, and S. Gibout, “Survey and evaluation of solar technologies for ag-ricultural greenhouse application,” Solar Energy, vol. 232, pp. 18–34, 2022, doi: https://doi.org/10.1016/j.solener.2021.12.033.
M. A. Zainol Abidin, M. N. Mahyuddin, and M. A. A. Mohd Zainuri, “Solar photovoltaic ar-chitecture and agronomic management in agrivoltaic system: A re-view,” Sustainability, vol. 13, no. 14, p. 7846, 2021, doi: https://doi.org/10.3390/su13147846.
Y. de Jesus et al., “Applications of solar and wind renewable energy in agriculture: A re-view,” Science Progress, vol. 102, pp. 127–140, 2019, doi: https://doi.org/10.1177/0036850419832696.
M. Nasser, T. F. Megahed, S. Ookawara, and H. Hassan, “Techno-economic assessment of clean hydrogen production and storage using hybrid renewable energy system of PV/Wind under different climatic conditions,” Sustainable Energy Technologies and As-sessments, vol. 52, p. 102195, 2022, doi: https://doi.org/10.1016/j.seta.2022.102195.
S. Mohseni, R. Khalid, and A. C. Brent, “Stochastic, resilience-oriented optimal sizing of off-grid microgrids considering EV-charging demand response: An efficiency comparison of state-of-the-art metaheuristics,” Applied Energy, vol. 341, p. 121007, 2023, doi: https://doi.org/10.1016/j.apenergy.2023.121007.
A. J. Pimm, J. Palczewski, R. Morris, T. T. Cockerill, and P. G. Taylor, “Community ener-gy storage: A case study in the UK using a linear programming method,” Energy Conver-sion and Management, vol. 205, p.112388, 2020. doi: https://doi.org/10.1016/j.enconman.2019.112388.
S. Mirjalili, “Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm,” Knowledge-Based Systems, vol. 89, pp.228–249, 2015, doi: https://doi.org/10.1016/j.knosys.2015.07.006.
S. Mohseni, R. Khalid, and A. C. Brent, “Metaheuristic-based isolated microgrid sizing and uncertainty quantification considering EVs as shiftable loads,” Energy Reports, vol. 8, pp. 11288–11308, 2022, doi: https://doi.org/10.1016/j.egyr.2022.08.254.
S. Mohseni and A. C. Brent, “Probabilistic sizing and scheduling co-optimisation of hybrid battery/super-capacitor energy storage systems in micro-grids,”. Journal of Energy Stor-age, vol. 73, p.109172, 2023, doi: https://doi.org/10.1016/j.est.2023.109172.
National Institute of Water and Atmospheric Research (NIWA). CliFlo data-base. https://cliflo.niwa.co.nz/ (date accessed: 05/05/2023).
The Electricity Market Information: The New Zealand Electricity Authority’s wholesale database. https://www.emi.ea.govt.nz/Wholesale/Reports/ (date accessed: 05/05/2024).
Exchange Rates. New Zealand Dollar (NZD) to US Dollar (USD) exchange rate history. https://www.exchangerates.org.uk/NZD-USD-exchange-rate-history.html (date accessed: 26/05/2024)
Published
How to Cite
Conference Proceedings Volume
Section
License
Copyright (c) 2025 Soheil Mohseni, Alan Brent
This work is licensed under a Creative Commons Attribution 4.0 International License.
Accepted 2025-01-27
Published 2025-04-02