IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v278y2020ics0306261920310461.html
   My bibliography  Save this article

Photovoltaic systems on dairy farms: Financial and renewable multi-objective optimization (FARMOO) analysis

Author

Listed:
  • Breen, M.
  • Upton, J.
  • Murphy, M.D.

Abstract

The aim of this study was to develop a financial and renewable multi-objective optimization (FARMOO) method for dairy farms. Due to increased global milk production and European Union policies concerning renewable energy contributions, the optimization of dairy farms from financial and renewable standpoints is crucial. The FARMOO method found the optimal combination of dairy farm equipment and management practices, based on a trade-off parameter which quantified the relative importance of maximizing farm net profit and maximizing farm renewable contribution. A PV system model was developed and validated to assess the financial performance and renewable contribution of this technology in a dairy farming context. Seven PV system sizes were investigated, ranging from 2 kWp to 11 kWp. Multi-objective optimization using a Genetic Algorithm was implemented to find the optimal combination of equipment and management practices based on the aforementioned trade-off parameter. For a test case of a 195 cow spring calving dairy farm in Ireland, it was found that when the relative importance of farm net profit was high, a PV system was not included in the optimal farm configuration. When net profit and renewable contribution were of equal importance, the optimal farm configuration included an 11 kWp PV system with a scheduled water heating load at 10:00. Multi-objective optimization was carried out for the same test case with the goals of maximizing farm net profit and minimizing farm CO2 emissions. Under this scenario, the optimal farm configuration included an 11 kWp PV system when the relative importance of farm net profit was low. This study included a sensitivity analysis which investigated the use of a 40% grant aid on PV system capital costs. This sensitivity analysis did not significantly improve the financial feasibility of PV systems on dairy farms. Moreover, it was found that load shifting of a farm’s water heating enabled the majority of the PV system’s electricity output to be consumed. Hence the use of batteries with small PV systems on dairy farms may not be necessary. The method described in this study will be used to inform policy and provide decision support relating to PV systems on dairy farms.

Suggested Citation

  • Breen, M. & Upton, J. & Murphy, M.D., 2020. "Photovoltaic systems on dairy farms: Financial and renewable multi-objective optimization (FARMOO) analysis," Applied Energy, Elsevier, vol. 278(C).
    • Handle: RePEc:eee:appene:v:278:y:2020:i:c:s0306261920310461
    DOI: 10.1016/j.apenergy.2020.115534
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920310461
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.115534?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Di Somma, M. & Yan, B. & Bianco, N. & Graditi, G. & Luh, P.B. & Mongibello, L. & Naso, V., 2017. "Multi-objective design optimization of distributed energy systems through cost and exergy assessments," Applied Energy, Elsevier, vol. 204(C), pages 1299-1316.
    2. Machairas, Vasileios & Tsangrassoulis, Aris & Axarli, Kleo, 2014. "Algorithms for optimization of building design: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 101-112.
    3. Breen, M. & Murphy, M.D. & Upton, J., 2019. "Development of a dairy multi-objective optimization (DAIRYMOO) method for economic and environmental optimization of dairy farms," Applied Energy, Elsevier, vol. 242(C), pages 1697-1711.
    4. Hernández, J.C. & Ruiz-Rodriguez, F.J. & Jurado, F., 2017. "Modelling and assessment of the combined technical impact of electric vehicles and photovoltaic generation in radial distribution systems," Energy, Elsevier, vol. 141(C), pages 316-332.
    5. Nacer, T. & Hamidat, A. & Nadjemi, O. & Bey, M., 2016. "Feasibility study of grid connected photovoltaic system in family farms for electricity generation in rural areas," Renewable Energy, Elsevier, vol. 96(PA), pages 305-318.
    6. Gomez-Gonzalez, M. & Hernandez, J.C. & Vera, D. & Jurado, F., 2020. "Optimal sizing and power schedule in PV household-prosumers for improving PV self-consumption and providing frequency containment reserve," Energy, Elsevier, vol. 191(C).
    7. Ishaque, Kashif & Salam, Zainal & Mekhilef, Saad & Shamsudin, Amir, 2012. "Parameter extraction of solar photovoltaic modules using penalty-based differential evolution," Applied Energy, Elsevier, vol. 99(C), pages 297-308.
    8. Bey, M. & Hamidat, A. & Benyoucef, B. & Nacer, T., 2016. "Viability study of the use of grid connected photovoltaic system in agriculture: Case of Algerian dairy farms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 333-345.
    9. Lukuyu, June M. & Blanchard, Richard E. & Rowley, Paul N., 2019. "A risk-adjusted techno-economic analysis for renewable-based milk cooling in remote dairy farming communities in East Africa," Renewable Energy, Elsevier, vol. 130(C), pages 700-713.
    10. Hernández, J.C. & Sanchez-Sutil, F. & Muñoz-Rodríguez, F.J., 2019. "Design criteria for the optimal sizing of a hybrid energy storage system in PV household-prosumers to maximize self-consumption and self-sufficiency," Energy, Elsevier, vol. 186(C).
    11. Upton, J. & Murphy, M. & Shalloo, L. & Groot Koerkamp, P.W.G. & De Boer, I.J.M., 2015. "Assessing the impact of changes in the electricity price structure on dairy farm energy costs," Applied Energy, Elsevier, vol. 137(C), pages 1-8.
    12. Jubril, A.M. & Olaniyan, O.A. & Komolafe, O.A. & Ogunbona, P.O., 2014. "Economic-emission dispatch problem: A semi-definite programming approach," Applied Energy, Elsevier, vol. 134(C), pages 446-455.
    13. Nadjemi, O. & Nacer, T. & Hamidat, A. & Salhi, H., 2017. "Optimal hybrid PV/wind energy system sizing: Application of cuckoo search algorithm for Algerian dairy farms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1352-1365.
    14. Fan, Yuling & Xia, Xiaohua, 2017. "A multi-objective optimization model for energy-efficiency building envelope retrofitting plan with rooftop PV system installation and maintenance," Applied Energy, Elsevier, vol. 189(C), pages 327-335.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tomé Sicuaio & Pengxiang Zhao & Petter Pilesjö & Andrey Shindyapin & Ali Mansourian, 2024. "A Multi-Objective Optimization Approach for Solar Farm Site Selection: Case Study in Maputo, Mozambique," Sustainability, MDPI, vol. 16(17), pages 1-20, August.
    2. Shi, Hao & Xu, Huining & Tan, Yiqiu & Li, Qiang & Yi, Wei, 2022. "Multi-objective optimization of operation strategy in snow melting system for airfield runway using genetic algorithm: A case study in Beijing Daxing International Airport," Renewable Energy, Elsevier, vol. 201(P2), pages 100-116.
    3. Vaziri Rad, Mohammad Amin & Forootan Fard, Habib & Khazanedari, Kian & Toopshekan, Ashkan & Ourang, Shiva & Khanali, Majid & Gorjian, Shiva & Fereidooni, Leila & Kasaeian, Alibakhsh, 2024. "A global framework for maximizing sustainable development indexes in agri-photovoltaic-based renewable systems: Integrating DEMATEL, ANP, and MCDM methods," Applied Energy, Elsevier, vol. 360(C).
    4. Mohammed Chakib Sekkal & Zakarya Ziani & Moustafa Yassine Mahdad & Sidi Mohammed Meliani & Mohammed Haris Baghli & Mohammed Zakaria Bessenouci, 2024. "Assessing the Wind Power Potential in Naama, Algeria to Complement Solar Energy through Integrated Modeling of the Wind Resource and Turbine Wind Performance," Energies, MDPI, vol. 17(4), pages 1-34, February.
    5. Song, Chenchen & Guo, Zhiling & Liu, Zhengguang & Hongyun, Zhang & Liu, Ran & Zhang, Haoran, 2024. "Application of photovoltaics on different types of land in China: Opportunities, status and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    6. Zvonimir Šimić & Danijel Topić & Ilija Crnogorac & Goran Knežević, 2021. "Method for Sizing of a PV System for Family Home Using Economic Indicators," Energies, MDPI, vol. 14(15), pages 1-18, July.
    7. Pannee Suanpang & Pattanaphong Pothipassa & Kittisak Jermsittiparsert & Titiya Netwong, 2022. "Integration of Kouprey-Inspired Optimization Algorithms with Smart Energy Nodes for Sustainable Energy Management of Agricultural Orchards," Energies, MDPI, vol. 15(8), pages 1-18, April.
    8. Sheha, Moataz & Mohammadi, Kasra & Powell, Kody, 2021. "Techno-economic analysis of the impact of dynamic electricity prices on solar penetration in a smart grid environment with distributed energy storage," Applied Energy, Elsevier, vol. 282(PA).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Breen, M. & Murphy, M.D. & Upton, J., 2019. "Development of a dairy multi-objective optimization (DAIRYMOO) method for economic and environmental optimization of dairy farms," Applied Energy, Elsevier, vol. 242(C), pages 1697-1711.
    2. Ching-Ming Lai & Jiashen Teh & Yuan-Chih Lin & Yitao Liu, 2020. "Study of a Bidirectional Power Converter Integrated with Battery/Ultracapacitor Dual-Energy Storage," Energies, MDPI, vol. 13(5), pages 1-23, March.
    3. Gong, Yu & Liu, Pan & Liu, Yini & Huang, Kangdi, 2021. "Robust operation interval of a large-scale hydro-photovoltaic power system to cope with emergencies," Applied Energy, Elsevier, vol. 290(C).
    4. Maammeur, H. & Hamidat, A. & Loukarfi, L. & Missoum, M. & Abdeladim, K. & Nacer, T., 2017. "Performance investigation of grid-connected PV systems for family farms: case study of North-West of Algeria," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 1208-1220.
    5. Glotić, Arnel & Zamuda, Aleš, 2015. "Short-term combined economic and emission hydrothermal optimization by surrogate differential evolution," Applied Energy, Elsevier, vol. 141(C), pages 42-56.
    6. Yi, Tao & Cheng, Xiaobin & Chen, Yaxuan & Liu, Jinpeng, 2020. "Joint optimization of charging station and energy storage economic capacity based on the effect of alternative energy storage of electric vehicle," Energy, Elsevier, vol. 208(C).
    7. Mohamed Hamdy & Gerardo Maria Mauro, 2017. "Multi-Objective Optimization of Building Energy Design to Reconcile Collective and Private Perspectives: CO 2 -eq vs. Discounted Payback Time," Energies, MDPI, vol. 10(7), pages 1-26, July.
    8. Bey, M. & Hamidat, A. & Nacer, T., 2021. "Eco-energetic feasibility study of using grid-connected photovoltaic system in wastewater treatment plant," Energy, Elsevier, vol. 216(C).
    9. Antonio José Steidle Neto & Daniela de Carvalho Lopes, 2021. "Technical analysis of photovoltaic energy generation for supplying the electricity demand in Brazilian dairy farms," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(2), pages 1355-1370, February.
    10. Jiang, Jianhua & Ming, Bo & Huang, Qiang & Guo, Yi & Shang, Jia’nan & Jurasz, Jakub & Liu, Pan, 2023. "A holistic techno-economic evaluation framework for sizing renewable power plant in a hydro-based hybrid generation system," Applied Energy, Elsevier, vol. 348(C).
    11. Gomez-Gonzalez, M. & Hernandez, J.C. & Vera, D. & Jurado, F., 2020. "Optimal sizing and power schedule in PV household-prosumers for improving PV self-consumption and providing frequency containment reserve," Energy, Elsevier, vol. 191(C).
    12. Zhai, Yingni & Wang, Yi & Huang, Yanqiu & Meng, Xiaojing, 2019. "A multi-objective optimization methodology for window design considering energy consumption, thermal environment and visual performance," Renewable Energy, Elsevier, vol. 134(C), pages 1190-1199.
    13. Wang, Zhuo & Gladwin, Daniel T. & Smith, Matthew J. & Haass, Stefan, 2021. "Practical state estimation using Kalman filter methods for large-scale battery systems," Applied Energy, Elsevier, vol. 294(C).
    14. Maeder, Mattia & Weiss, Olga & Boulouchos, Konstantinos, 2021. "Assessing the need for flexibility technologies in decarbonized power systems: A new model applied to Central Europe," Applied Energy, Elsevier, vol. 282(PA).
    15. Shadram, Farshid & Bhattacharjee, Shimantika & Lidelöw, Sofia & Mukkavaara, Jani & Olofsson, Thomas, 2020. "Exploring the trade-off in life cycle energy of building retrofit through optimization," Applied Energy, Elsevier, vol. 269(C).
    16. Attia, Ahmed M. & Al Hanbali, Ahmad & Saleh, Haitham H. & Alsawafy, Omar G. & Ghaithan, Ahmed M. & Mohammed, Awsan, 2021. "A multi-objective optimization model for sizing decisions of a grid-connected photovoltaic system," Energy, Elsevier, vol. 229(C).
    17. Kavian, Soheil & Aghanajafi, Cyrus & Jafari Mosleh, Hassan & Nazari, Arash & Nazari, Ashkan, 2020. "Exergy, economic and environmental evaluation of an optimized hybrid photovoltaic-geothermal heat pump system," Applied Energy, Elsevier, vol. 276(C).
    18. Zou, Dexuan & Li, Steven & Wang, Gai-Ge & Li, Zongyan & Ouyang, Haibin, 2016. "An improved differential evolution algorithm for the economic load dispatch problems with or without valve-point effects," Applied Energy, Elsevier, vol. 181(C), pages 375-390.
    19. Marcin Bukowski & Janusz Majewski & Agnieszka Sobolewska, 2021. "Macroeconomic Efficiency of Photovoltaic Energy Production in Polish Farms," Energies, MDPI, vol. 14(18), pages 1-19, September.
    20. Zhang, Haifeng & Tian, Ming & Zhang, Cong & Wang, Bin & Wang, Dai, 2021. "A systematic solution to quantify economic values of vehicle grid integration," Energy, Elsevier, vol. 232(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:278:y:2020:i:c:s0306261920310461. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.