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The impact of probability of electricity price spike and outside temperature to define total expected cost for air conditioning

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  • Marwan, Marwan

Abstract

The purpose of this paper is to address the problems experienced by customers in the utilisation of electrical energy for air conditioners (ACs). According to the proposed schema in this paper, the customer will be able to evaluate the impacts of a probable electricity price spike and the outside temperature (Tout) to calculate the total expected electricity cost for an AC (TEC). The aim of this paper is to show how consumers can estimate the TEC for any temperature. In this research, a model considering two types of price spikes (PS) is developed, namely: short-duration and long-duration spikes. To evaluate and examine this model, spike durations of half hour, one hour, and one and half hour were simulated to determine TEC. This proposed schema also examines how the control system applies a pre-cooling method if there is a substantial risk of PS. The results present possible savings on the electrical energy consumption when the consumer applies this method to anticipate spike events. This model is tested considering the demand and market price curves of electricity in South Sulawesi, which were published by the Indonesian State Electricity Company (called PLN=Perusahaan Listrik Negara) and the value of Tout in the Makassar area.

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  • Marwan, Marwan, 2020. "The impact of probability of electricity price spike and outside temperature to define total expected cost for air conditioning," Energy, Elsevier, vol. 195(C).
  • Handle: RePEc:eee:energy:v:195:y:2020:i:c:s0360544220301018
    DOI: 10.1016/j.energy.2020.116994
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    1. Nelson, James & Johnson, Nathan G. & Chinimilli, Prudhvi Tej & Zhang, Wenlong, 2019. "Residential cooling using separated and coupled precooling and thermal energy storage strategies," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    2. Clements, A.E. & Herrera, R. & Hurn, A.S., 2015. "Modelling interregional links in electricity price spikes," Energy Economics, Elsevier, vol. 51(C), pages 383-393.
    3. Sandels, C. & Widén, J. & Nordström, L., 2014. "Forecasting household consumer electricity load profiles with a combined physical and behavioral approach," Applied Energy, Elsevier, vol. 131(C), pages 267-278.
    4. Tafakori, Laleh & Pourkhanali, Armin & Fard, Farzad Alavi, 2018. "Forecasting spikes in electricity return innovations," Energy, Elsevier, vol. 150(C), pages 508-526.
    5. Bigerna, Simona, 2018. "Estimating temperature effects on the Italian electricity market," Energy Policy, Elsevier, vol. 118(C), pages 257-269.
    6. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    7. repec:qut:auncer:2012_5 is not listed on IDEAS
    8. Blázquez, Leticia & Boogen, Nina & Filippini, Massimo, 2013. "Residential electricity demand in Spain: New empirical evidence using aggregate data," Energy Economics, Elsevier, vol. 36(C), pages 648-657.
    9. Burillo, Daniel & Chester, Mikhail V. & Ruddell, Benjamin & Johnson, Nathan, 2017. "Electricity demand planning forecasts should consider climate non-stationarity to maintain reserve margins during heat waves," Applied Energy, Elsevier, vol. 206(C), pages 267-277.
    10. Waite, Michael & Cohen, Elliot & Torbey, Henri & Piccirilli, Michael & Tian, Yu & Modi, Vijay, 2017. "Global trends in urban electricity demands for cooling and heating," Energy, Elsevier, vol. 127(C), pages 786-802.
    11. Kostrzewski, Maciej & Kostrzewska, Jadwiga, 2019. "Probabilistic electricity price forecasting with Bayesian stochastic volatility models," Energy Economics, Elsevier, vol. 80(C), pages 610-620.
    12. Hu, Maomao & Xiao, Fu & Wang, Lingshi, 2017. "Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model," Applied Energy, Elsevier, vol. 207(C), pages 324-335.
    13. Apadula, Francesco & Bassini, Alessandra & Elli, Alberto & Scapin, Simone, 2012. "Relationships between meteorological variables and monthly electricity demand," Applied Energy, Elsevier, vol. 98(C), pages 346-356.
    14. Fan, Jing-Li & Hu, Jia-Wei & Zhang, Xian, 2019. "Impacts of climate change on electricity demand in China: An empirical estimation based on panel data," Energy, Elsevier, vol. 170(C), pages 880-888.
    15. Lindström, Erik & Norén, Vicke & Madsen, Henrik, 2015. "Consumption management in the Nord Pool region: A stability analysis," Applied Energy, Elsevier, vol. 146(C), pages 239-246.
    16. Janczura, Joanna & Trück, Stefan & Weron, Rafał & Wolff, Rodney C., 2013. "Identifying spikes and seasonal components in electricity spot price data: A guide to robust modeling," Energy Economics, Elsevier, vol. 38(C), pages 96-110.
    17. Jim, C.Y., 2014. "Air-conditioning energy consumption due to green roofs with different building thermal insulation," Applied Energy, Elsevier, vol. 128(C), pages 49-59.
    18. Malik, Anam & Haghdadi, Navid & MacGill, Iain & Ravishankar, Jayashri, 2019. "Appliance level data analysis of summer demand reduction potential from residential air conditioner control," Applied Energy, Elsevier, vol. 235(C), pages 776-785.
    19. Xue, Xue & Wang, Shengwei & Sun, Yongjun & Xiao, Fu, 2014. "An interactive building power demand management strategy for facilitating smart grid optimization," Applied Energy, Elsevier, vol. 116(C), pages 297-310.
    20. Wijesuriya, Sajith & Brandt, Matthew & Tabares-Velasco, Paulo Cesar, 2018. "Parametric analysis of a residential building with phase change material (PCM)-enhanced drywall, precooling, and variable electric rates in a hot and dry climate," Applied Energy, Elsevier, vol. 222(C), pages 497-514.
    21. Hu, Maomao & Xiao, Fu & Jørgensen, John Bagterp & Wang, Shengwei, 2019. "Frequency control of air conditioners in response to real-time dynamic electricity prices in smart grids," Applied Energy, Elsevier, vol. 242(C), pages 92-106.
    22. Christensen, T.M. & Hurn, A.S. & Lindsay, K.A., 2012. "Forecasting spikes in electricity prices," International Journal of Forecasting, Elsevier, vol. 28(2), pages 400-411.
    23. Manner, Hans & Türk, Dennis & Eichler, Michael, 2016. "Modeling and forecasting multivariate electricity price spikes," Energy Economics, Elsevier, vol. 60(C), pages 255-265.
    24. Burillo, Daniel & Chester, Mikhail V. & Pincetl, Stephanie & Fournier, Eric D. & Reyna, Janet, 2019. "Forecasting peak electricity demand for Los Angeles considering higher air temperatures due to climate change," Applied Energy, Elsevier, vol. 236(C), pages 1-9.
    25. Alberini, Anna & Prettico, Giuseppe & Shen, Chang & Torriti, Jacopo, 2019. "Hot weather and residential hourly electricity demand in Italy," Energy, Elsevier, vol. 177(C), pages 44-56.
    26. Tang, Rui & Wang, Shengwei & Shan, Kui & Cheung, Howard, 2018. "Optimal control strategy of central air-conditioning systems of buildings at morning start period for enhanced energy efficiency and peak demand limiting," Energy, Elsevier, vol. 151(C), pages 771-781.
    27. Gallo Cassarino, Tiziano & Sharp, Ed & Barrett, Mark, 2018. "The impact of social and weather drivers on the historical electricity demand in Europe," Applied Energy, Elsevier, vol. 229(C), pages 176-185.
    28. Turner, W.J.N. & Walker, I.S. & Roux, J., 2015. "Peak load reductions: Electric load shifting with mechanical pre-cooling of residential buildings with low thermal mass," Energy, Elsevier, vol. 82(C), pages 1057-1067.
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