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

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  • 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
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    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.
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