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Assessing the impact of changes in the electricity price structure on dairy farm energy costs

Author

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  • Upton, J.
  • Murphy, M.
  • Shalloo, L.
  • Groot Koerkamp, P.W.G.
  • De Boer, I.J.M.

Abstract

This study aims to provide information on the changes in electricity consumption and costs on dairy farms, through the simulation of various electricity tariffs that may exist in the future and how these tariffs interact with changes in farm management (i.e. shifting the milking operation to an earlier or later time of the day). A previously developed model capable of simulating electricity consumption and costs on dairy farms (MECD) was used to simulate five different electricity tariffs (Flat, Day&Night, Time of Use Tariff 1 (TOU1), TOU2 and Real Time Pricing (RTP)) on three representative Irish dairy farms: a small farm (SF), a medium farm (MF) and a large farm (LF). The Flat tariff consisted of one electricity price for all time periods, the Day&Night tariff consisted of two electricity prices, a high rate from 09:00 to 00:00h and a low rate thereafter. The TOU tariff structure was similar to that of the Day&Night tariff except that a peak price band was introduced between 17:00 and 19:00h. The RTP tariff varied dynamically according to the electricity demand on the national grid. The model used in these simulations was a mechanistic mathematical representation of the electricity consumption that simulated farm equipment under the following headings; milk cooling system, water heating system, milking machine system, lighting systems, water pump systems and the winter housing facilities. The effect of milking start time was simulated to determine the effect on electricity consumption and costs at farm level. The earliest AM milking start time and the latest PM milking start time resulted in the lowest energy consumption. The difference between the lowest and highest electricity consumption within a farm was 7% for SF, 5% for MF and 5% for LF. This difference was accounted for by the variation in the milk cooling system coefficient of performance. The greatest scope to reduce total annual electricity costs by adjusting milking start times was on TOU2 (39%, 34% and 33% of total annual electricity costs on the SF, MF and LF) and the least scope for reductions using this method was on the Flat tariff (7%, 5% and 7% of total annual electricity costs). The potential for reduction of annual electricity consumption and related costs per litre of milk produced by adjusting milking times was higher for the LF than the SF or MF across all electricity tariffs. It is anticipated that these results and the use of the MECD will help support the decision-making process at farm level around increasing energy efficiency and electricity cost forecasts in future electricity pricing tariff structures.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:137:y:2015:i:c:p:1-8
    DOI: 10.1016/j.apenergy.2014.09.067
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    References listed on IDEAS

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    Citations

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    3. 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.
    4. 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.
    5. Sun, Mei & Li, Juan & Gao, Cuixia & Han, Dun, 2017. "Identifying regime shifts in the US electricity market based on price fluctuations," Applied Energy, Elsevier, vol. 194(C), pages 658-666.
    6. Edwin, M. & Joseph Sekhar, S., 2018. "Techno- Economic evaluation of milk chilling unit retrofitted with hybrid renewable energy system in coastal province," Energy, Elsevier, vol. 151(C), pages 66-78.
    7. 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 1-29, November.
    8. Rutten, Martine & Achterbosch, Thom J. & de Boer, Imke J.M. & Cuaresma, Jesus Crespo & Geleijnse, Johanna M. & Havlík, Petr & Heckelei, Thomas & Ingram, John & Leip, Adrian & Marette, Stéphan & van Me, 2018. "Metrics, models and foresight for European sustainable food and nutrition security: The vision of the SUSFANS project," Agricultural Systems, Elsevier, vol. 163(C), pages 45-57.
    9. Said, Fathin Faizah & Babatunde, Kazeem Alasinrin & Md Nor, Nor Ghani & Mahmoud, Moamin A. & Begum, Rawshan Ara, 2022. "Decarbonizing the Global Electricity Sector through Demand-Side Management: A Systematic Critical Review of Policy Responses," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 56(1), pages 71-91.
    10. 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.

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