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Dynamic Electricity Pricing – Modeling Manufacturer Response and an Application to Cement Processing

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  • Derya Eryilmaz
  • Jeffrey Apland
  • Timothy M. Smith

Abstract

Dynamic pricing, also known as real-time pricing, provides electricity users with an economic incentive to adjust electricity use based on changing market conditions. This paper studies the economic implications of real-time pricing mechanisms in a cement manufacturing plant. Production for a representative cement manufacturing plant is modeled using stochastic mathematical programming. The results show that a cement plant can a) reduce electricity costs by shifting electricity load of certain processes to times when electricity prices are lower, and b) profitably reduce electricity use during peak prices through more efficient scheduling of production under real-time pricing compared to fixed pricing. The results suggest that building scheduling flexibility into certain industrial manufacturing processes to reschedule electricity consumption when the electricity prices at their peak may be economical. The results also suggest that shifts in the production schedule of a cement manufacturer that result from real-time pricing may also influence environmental impacts. The modelling framework modeled real-time pricing as a source of risk in this study, which is also applicable to other energy intensive industries. As such, dynamic pricing strategies that include the non-market impacts of electricity generation should be further explored.

Suggested Citation

  • Derya Eryilmaz & Jeffrey Apland & Timothy M. Smith, 2022. "Dynamic Electricity Pricing – Modeling Manufacturer Response and an Application to Cement Processing," Energy and Environment Research, Canadian Center of Science and Education, vol. 9(2), pages 1-1, December.
    • Handle: RePEc:ibn:eerjnl:v:9:y:2022:i:2:p:1
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    References listed on IDEAS

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    1. Allan N. Rae, 1971. "Stochastic Programming, Utility, and Sequential Decision Problems in Farm Management," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 53(3), pages 448-460.
    2. Peter M. Schwarz & Thomas N. Taylor & Matthew Birmingham & Shana L. Dardan, 2002. "Industrial Response to Electricity Real-Time Prices: Short Run and Long Run," Economic Inquiry, Western Economic Association International, vol. 40(4), pages 597-610, October.
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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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