IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v242y2019icp1697-1711.html
   My bibliography  Save this article

Development of a dairy multi-objective optimization (DAIRYMOO) method for economic and environmental optimization of dairy farms

Author

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

Abstract

The aim of this paper was to develop a Dairy Multi-Objective Optimization (DAIRYMOO) method to carry out multi-objective optimization of dairy farm equipment, management practices and electricity tariffs, optimizing based on a user specified economic and environmental weighting factor. Models of both solar thermal water heating and heat recovery systems were developed, validated, and used as part of a test case for DAIRYMOO. Optimizing dairy farm equipment, management practices and electricity tariffs both economically and environmentally is necessary because of the competing goals of increasing farm milk production and the necessity to reduce agricultural related greenhouse gas emissions. Models of solar thermal water heating and heat recovery systems were created using experimental data to evaluate the economic and environmental performance of these technologies. Multi-objective optimization was used to obtain the optimal selection of equipment, management practices and electricity tariffs which impact electricity related costs and CO2 emissions, based on a user specified economic and environmental weighting factor. A Genetic Algorithm was employed to maximize a combined objective function based on this weighting factor. For a test case with a 195 cow farm, over a ten year time horizon the optimal equipment, management and electricity tariff combination was found using 11 different weighting factor values ranging from 0 to 1. The combined objective function was gradually weighted towards the environmental criterion and away from the economic criterion. It was found that the optimal equipment combination changed incrementally relative to the change in the weighting factor. Furthermore, an analysis was carried out which performed the same multi-objective optimization on the same 195 cow farm, but with a mandatory real time pricing tariff in place. The optimal configurations using the mandatory real time pricing tariff showed that gas water heating was selected regardless of the weighting factor employed. This differed from the results with no mandatory tariff in which a day/night tariff was optimal for all weighting factor values, and electric water heating was optimal for weighting factor values of 0.9 or higher i.e. when the combined objective function was weighted heavily towards the economic criterion. For both analyses, heat recovery systems were not included in the optimal farm configuration unless the weighting factor was 0.2 or less, indicating that the economic performance of these systems was poor. Solar thermal water heating systems were not included in the optimal farm configuration regardless of the weighting factor value. The DAIRYMOO method described in this study will provide useful advice to farmers and policy makers relating to economic and environmental optimization around equipment, management and electricity tariff choices on dairy farms.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:242:y:2019:i:c:p:1697-1711
    DOI: 10.1016/j.apenergy.2019.03.059
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919304647
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.03.059?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. Allouhi, A. & Agrouaz, Y. & Benzakour Amine, Mohammed & Rehman, S. & Buker, M.S. & Kousksou, T. & Jamil, A. & Benbassou, A., 2017. "Design optimization of a multi-temperature solar thermal heating system for an industrial process," Applied Energy, Elsevier, vol. 206(C), pages 382-392.
    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. Zhang, Chuan & Zhou, Li & Chhabra, Pulkit & Garud, Sushant S. & Aditya, Kevin & Romagnoli, Alessandro & Comodi, Gabriele & Dal Magro, Fabio & Meneghetti, Antonella & Kraft, Markus, 2016. "A novel methodology for the design of waste heat recovery network in eco-industrial park using techno-economic analysis and multi-objective optimization," Applied Energy, Elsevier, vol. 184(C), pages 88-102.
    4. García-Villalobos, J. & Zamora, I. & Knezović, K. & Marinelli, M., 2016. "Multi-objective optimization control of plug-in electric vehicles in low voltage distribution networks," Applied Energy, Elsevier, vol. 180(C), pages 155-168.
    5. Bornatico, Raffaele & Pfeiffer, Michael & Witzig, Andreas & Guzzella, Lino, 2012. "Optimal sizing of a solar thermal building installation using particle swarm optimization," Energy, Elsevier, vol. 41(1), pages 31-37.
    6. 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.
    7. Murr, R. & Thieriot, H. & Zoughaib, A. & Clodic, D., 2011. "Multi-objective optimization of a multi water-to-water heat pump system using evolutionary algorithm," Applied Energy, Elsevier, vol. 88(11), pages 3580-3591.
    8. 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.
    9. Arteconi, A. & Hewitt, N.J. & Polonara, F., 2012. "State of the art of thermal storage for demand-side management," Applied Energy, Elsevier, vol. 93(C), pages 371-389.
    10. Gunter, Lewell & Smathers, Webb M., 1984. "Financial Analysis of Solar Energy Applications with Endogenous System Sizing," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 16(2), pages 21-30, December.
    11. Karmellos, M. & Kiprakis, A. & Mavrotas, G., 2015. "A multi-objective approach for optimal prioritization of energy efficiency measures in buildings: Model, software and case studies," Applied Energy, Elsevier, vol. 139(C), pages 131-150.
    12. Shine, P. & Scully, T. & Upton, J. & Shalloo, L. & Murphy, M.D., 2018. "Electricity & direct water consumption on Irish pasture based dairy farms: A statistical analysis," Applied Energy, Elsevier, vol. 210(C), pages 529-537.
    13. Yang, Jun & He, Lifu & Fu, Siyao, 2014. "An improved PSO-based charging strategy of electric vehicles in electrical distribution grid," Applied Energy, Elsevier, vol. 128(C), pages 82-92.
    14. Quijera, José Antonio & Alriols, María González & Labidi, Jalel, 2011. "Integration of a solar thermal system in a dairy process," Renewable Energy, Elsevier, vol. 36(6), pages 1843-1853.
    15. Hou, Jun & Sun, Jing & Hofmann, Heath, 2018. "Control development and performance evaluation for battery/flywheel hybrid energy storage solutions to mitigate load fluctuations in all-electric ship propulsion systems," Applied Energy, Elsevier, vol. 212(C), pages 919-930.
    16. Gunter, Lewell F. & Smathers, Webb M., Jr., 1984. "Financial Analysis Of Solar Energy Applications With Endogenous System Sizing," Southern Journal of Agricultural Economics, Southern Agricultural Economics Association, vol. 16(2), pages 1-9, December.
    17. 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.
    18. Murphy, M.D. & O’Mahony, M.J. & Upton, J., 2015. "Comparison of control systems for the optimisation of ice storage in a dynamic real time electricity pricing environment," Applied Energy, Elsevier, vol. 149(C), pages 392-403.
    19. 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. 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).
    2. Jorge Andres Garcia & Angelos Alamanos, 2022. "Integrated Modelling Approaches for Sustainable Agri-Economic Growth and Environmental Improvement: Examples from Greece, Canada and Ireland," Land, MDPI, vol. 11(9), pages 1-19, September.
    3. Wang, Kai & Pantaleo, Antonio M. & Herrando, María & Faccia, Michele & Pesmazoglou, Ioannis & Franchetti, Benjamin M. & Markides, Christos N., 2020. "Spectral-splitting hybrid PV-thermal (PVT) systems for combined heat and power provision to dairy farms," Renewable Energy, Elsevier, vol. 159(C), pages 1047-1065.
    4. Peters, Pedro & da Costa, Vinicius Braga Ferreira & Dias, Bruno Henriques & Bonatto, Benedito Donizeti, 2025. "A holistic analysis of environmental impacts and improvement pathways for the Brazilian electric sector based on long-term planning and life cycle assessment," Applied Energy, Elsevier, vol. 377(PB).
    5. Jorge A. Garcia & Angelos Alamanos, 2022. "Integrated modelling approaches for sustainable agri-economic growth and environmental improvement: Examples from Canada, Greece, and Ireland," Papers 2208.09087, arXiv.org.
    6. Kaab, Ali & Sharifi, Mohammad & Mobli, Hossein & Nabavi-Pelesaraei, Ashkan & Chau, Kwok-wing, 2019. "Use of optimization techniques for energy use efficiency and environmental life cycle assessment modification in sugarcane production," Energy, Elsevier, vol. 181(C), pages 1298-1320.
    7. Philip Shine & John Upton & Paria Sefeedpari & Michael D. Murphy, 2020. "Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses," Energies, MDPI, vol. 13(5), pages 1-25, March.
    8. Heidari, Mohammad Davoud & Turner, Ian & Ardestani-Jaafari, Amir & Pelletier, Nathan, 2021. "Operations research for environmental assessment of crop-livestock production systems," Agricultural Systems, Elsevier, vol. 193(C).
    9. Adam C. Castonguay & Stephen Polasky & Matthew H. Holden & Mario Herrero & Daniel Mason-D’Croz & Cecile Godde & Jinfeng Chang & James Gerber & G. Bradd Witt & Edward T. Game & Brett A. Bryan & Brendan, 2023. "Navigating sustainability trade-offs in global beef production," Nature Sustainability, Nature, vol. 6(3), pages 284-294, March.
    10. Khanali, Majid & Akram, Asadollah & Behzadi, Javad & Mostashari-Rad, Fatemeh & Saber, Zahra & Chau, Kwok-wing & Nabavi-Pelesaraei, Ashkan, 2021. "Multi-objective optimization of energy use and environmental emissions for walnut production using imperialist competitive algorithm," Applied Energy, Elsevier, vol. 284(C).

    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. & 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).
    2. Shine, P. & Scully, T. & Upton, J. & Shalloo, L. & Murphy, M.D., 2018. "Electricity & direct water consumption on Irish pasture based dairy farms: A statistical analysis," Applied Energy, Elsevier, vol. 210(C), pages 529-537.
    3. 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).
    4. Ascione, Fabrizio & Bianco, Nicola & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2019. "A new comprehensive framework for the multi-objective optimization of building energy design: Harlequin," Applied Energy, Elsevier, vol. 241(C), pages 331-361.
    5. 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, July.
    6. O'Grady, Małgorzata & Lechowska, Agnieszka A. & Harte, Annette M., 2017. "Quantification of heat losses through building envelope thermal bridges influenced by wind velocity using the outdoor infrared thermography technique," Applied Energy, Elsevier, vol. 208(C), pages 1038-1052.
    7. Kangji Li & Lei Pan & Wenping Xue & Hui Jiang & Hanping Mao, 2017. "Multi-Objective Optimization for Energy Performance Improvement of Residential Buildings: A Comparative Study," Energies, MDPI, vol. 10(2), pages 1, February.
    8. Moon, Sang-Keun & Kim, Jin-O, 2017. "Balanced charging strategies for electric vehicles on power systems," Applied Energy, Elsevier, vol. 189(C), pages 44-54.
    9. Masera, Kemal & Tannous, Hadi & Stojceska, Valentina & Tassou, Savvas, 2023. "An investigation of the recent advances of the integration of solar thermal energy systems to the dairy processes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
    10. Delgarm, N. & Sajadi, B. & Kowsary, F. & Delgarm, S., 2016. "Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO)," Applied Energy, Elsevier, vol. 170(C), pages 293-303.
    11. Martínez-Rodríguez, Guillermo & Fuentes-Silva, Amanda L. & Velázquez-Torres, Daniel & Picón-Núñez, Martín, 2022. "Comprehensive solar thermal integration for industrial processes," Energy, Elsevier, vol. 239(PD).
    12. Ascione, Fabrizio & Bianco, Nicola & Mauro, Gerardo Maria & Napolitano, Davide Ferdinando, 2019. "Retrofit of villas on Mediterranean coastlines: Pareto optimization with a view to energy-efficiency and cost-effectiveness," Applied Energy, Elsevier, vol. 254(C).
    13. Shine, P. & Scully, T. & Upton, J. & Murphy, M.D., 2019. "Annual electricity consumption prediction and future expansion analysis on dairy farms using a support vector machine," Applied Energy, Elsevier, vol. 250(C), pages 1110-1119.
    14. Philip Shine & John Upton & Paria Sefeedpari & Michael D. Murphy, 2020. "Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses," Energies, MDPI, vol. 13(5), pages 1-25, March.
    15. Damilola A. Asaleye & Michael Breen & Michael D. Murphy, 2017. "A Decision Support Tool for Building Integrated Renewable Energy Microgrids Connected to a Smart Grid," Energies, MDPI, vol. 10(11), pages 1, November.
    16. Chinese, D. & Orrù, P.F. & Meneghetti, A. & Cortella, G. & Giordano, L. & Benedetti, M., 2022. "Symbiotic and optimized energy supply for decarbonizing cheese production: An Italian case study," Energy, Elsevier, vol. 257(C).
    17. Meyers, Steven & Schmitt, Bastian & Vajen, Klaus, 2018. "Renewable process heat from solar thermal and photovoltaics: The development and application of a universal methodology to determine the more economical technology," Applied Energy, Elsevier, vol. 212(C), pages 1537-1552.
    18. Juan R Lizárraga-Morazán & Guillermo Martínez-Rodríguez & Amanda L Fuentes-Silva & Martín Picón-Núñez, 2021. "Selection of solar collector network design for industrial applications subject to economic and operation criteria," Energy & Environment, , vol. 32(8), pages 1504-1523, December.
    19. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2016. "Multi-stage and multi-objective optimization for energy retrofitting a developed hospital reference building: A new approach to assess cost-optimality," Applied Energy, Elsevier, vol. 174(C), pages 37-68.
    20. Saleh Aghajan-Eshkevari & Sasan Azad & Morteza Nazari-Heris & Mohammad Taghi Ameli & Somayeh Asadi, 2022. "Charging and Discharging of Electric Vehicles in Power Systems: An Updated and Detailed Review of Methods, Control Structures, Objectives, and Optimization Methodologies," Sustainability, MDPI, vol. 14(4), pages 1-31, February.

    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:242:y:2019:i:c:p:1697-1711. 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.