Abstract
Energy costs can represent a large portion of the total production costs, and therefore, any changes in electricity tariffs can have a significant impact on profitability. This paper analyses how different types of electricity tariffs can affect the scheduling of a case study model, in particular, how time-of-use tariffs and real-time-pricing tariffs affect the single-machine scheduling problem of a production process with the introduction of the energy vector in the optimization cost. The influence of tariffs is examined, and their impact on optimal production scheduling is evaluated from an approach in demand response price-based programs, for ensuring cost-effectiveness while looking at the carbon footprint of the industrial process. The results indicate that the cost improvement of one tariff over the other is not consistent across all time periods. Meanwhile, the carbon footprint is reduced with a real-time-pricing tariff, since the real-time-pricing mechanism and the generation mix of fossil fuel technologies are positively correlated.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Albadi, M.H., El-Saadany, E.F.: Demand response in electricity markets: an overview. In: 2007 IEEE Power Engineering Society General Meeting, pp. 1–5 (2007). https://doi.org/10.1109/PES.2007.385728
Jordehi, A.R.: Optimisation of demand response in electric power systems, a review. Renew. Sustain. Energy Rev. 103, 308–319 (2019). https://doi.org/10.1016/j.rser.2018.12.054
Leal, P., Castro, R., Lopes, F.: Influence of increasing renewable power penetration on the long-term Iberian electricity market prices. Energies 16(3), 1054 (2023). https://doi.org/10.3390/en16031054
Nandy, P., Botts, A., Wenning, T.: Demand Response in Industrial Facilities: Peak Electric Demand. ORNL/SPR-2021/2299, Oak Ridge National Laboratory, Oak Ridge, TN (2022). https://doi.org/10.2172/1842610
Paschalidis, I., Li, B., Caramanis, M.C.: Demand-side management for regulation service provisioning through internal pricing. IEEE Trans. Power Syst. 27, 1531–1539 (2012). https://doi.org/10.1109/TPWRS.2012.2183007
Siano, P.: Demand response and smart grids—a survey. Renew. Sustain. Energy Rev. 30, 461–478 (2014). https://doi.org/10.1016/j.rser.2013.10.022
Torriti, J.: Price-based demand side management: assessing the impacts of time-of-use tariffs on residential electricity demand and peak shifting in Northern Italy. Energy 44(1), 576–583 (2012). https://doi.org/10.1016/j.energy.2012.05.043
World Bank: State and Trends of Carbon Pricing 2021. Washington, DC: World Bank (2021). https://openknowledge.worldbank.org/handle/10986/35620. Accessed 13 Mar 2023
Yeardley, A.S., Roberts, D., Milton, R., Brown, S.F.: An efficient hybridization of Gaussian processes and clustering for electricity price forecasting. Comput. Aided Chem. Eng. 48, 343–348 (2020). https://doi.org/10.1016/B978-0-12-823377-1.50058-6
Acknowledgement
This work has been supported by the FLEX4FACT project, funded by the European Union under the Horizon Europe research and innovation programme, under the grant agreement number 101058657. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. Eduard Bullich-Massagué is lecturer of the Serra Húnter programme.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Pau, FC., Eduard, BM., Bruno, D., Marc, J., Rafael, P., Matteo, R. (2024). The Impact of Electricity Tariffs on Optimal Production Scheduling. In: Bautista-Valhondo, J., Mateo-Doll, M., Lusa, A., Pastor-Moreno, R. (eds) Proceedings of the 17th International Conference on Industrial Engineering and Industrial Management (ICIEIM) – XXVII Congreso de Ingeniería de Organización (CIO2023). CIO 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 206. Springer, Cham. https://doi.org/10.1007/978-3-031-57996-7_48
Download citation
DOI: https://doi.org/10.1007/978-3-031-57996-7_48
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-57995-0
Online ISBN: 978-3-031-57996-7
eBook Packages: EngineeringEngineering (R0)