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Spatial-temporal growth model to estimate the adoption of new end-use electric technologies encouraged by energy-efficiency programs

Author

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  • Mejia, Mario A.
  • Melo, Joel D.
  • Zambrano-Asanza, Sergio
  • Padilha-Feltrin, Antonio

Abstract

Domestic energy policies destined to foster the use of end-use electric technologies could cause rapid penetration of new residential loads and, consequently, this could cause a significant increase in the demand for electricity in urban areas. This paper presents a spatial-temporal growth model for estimating the adoption of new end-use electric technologies encouraged by energy-efficiency policies. The proposed method consists of three modules: temporal, spatial and grouping. The temporal module calculates by districts or census tracts of a city, the percentage of homes in which residents are prospective buyers of a new end-use electric technology. Then, the spatial module adjusts the calculations made by the temporal module, considering the spatial interactions among the inhabitants of the districts. Finally, the grouping module discovers the low-voltage transformer where the prospective buyers are connected. The results of the proposed model are a spatial database with information related to the percentage of homes in which residents are prospective buyers of a new end-use electric technology, as well as the number of prospective buyers connected to each low-voltage transformer. The results can visualize through thematic maps to identify the districts where the new technology will have faster adoption. The proposed method was employed to estimate the adoption of induction heating cookers in a medium-sized Ecuadorian city. The Ecuadorian government has developed a program of economic subsidies to encourage its population to use this electrical appliance. The results from this application are an important tool to estimate the spatial increase in electricity demand, decide important issues related to the planning of distributed resources, and develop demand-side management programs. Furthermore, the results can be used to evaluate and manage energy policies formulated to achieve environmental and energy goals.

Suggested Citation

  • Mejia, Mario A. & Melo, Joel D. & Zambrano-Asanza, Sergio & Padilha-Feltrin, Antonio, 2020. "Spatial-temporal growth model to estimate the adoption of new end-use electric technologies encouraged by energy-efficiency programs," Energy, Elsevier, vol. 191(C).
    • Handle: RePEc:eee:energy:v:191:y:2020:i:c:s0360544219322261
    DOI: 10.1016/j.energy.2019.116531
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    References listed on IDEAS

    as
    1. Lund, P.D., 2010. "Exploring past energy changes and their implications for the pace of penetration of new energy technologies," Energy, Elsevier, vol. 35(2), pages 647-656.
    2. Marcello Graziano & Kenneth Gillingham, 2015. "Spatial patterns of solar photovoltaic system adoption: The influence of neighbors and the built environment," Journal of Economic Geography, Oxford University Press, vol. 15(4), pages 815-839.
    3. García-Álvarez, María Teresa & Cabeza-García, Laura & Soares, Isabel, 2018. "Assessment of energy policies to promote photovoltaic generation in the European Union," Energy, Elsevier, vol. 151(C), pages 864-874.
    4. Radpour, Saeidreza & Hossain Mondal, Md Alam & Kumar, Amit, 2017. "Market penetration modeling of high energy efficiency appliances in the residential sector," Energy, Elsevier, vol. 134(C), pages 951-961.
    5. Chen, T. Donna & Wang, Yiyi & Kockelman, Kara M., 2015. "Where are the electric vehicles? A spatial model for vehicle-choice count data," Journal of Transport Geography, Elsevier, vol. 43(C), pages 181-188.
    6. Troncoso, Karin & Soares da Silva, Agnes, 2017. "LPG fuel subsidies in Latin America and the use of solid fuels to cook," Energy Policy, Elsevier, vol. 107(C), pages 188-196.
    7. Noori, Mehdi & Tatari, Omer, 2016. "Development of an agent-based model for regional market penetration projections of electric vehicles in the United States," Energy, Elsevier, vol. 96(C), pages 215-230.
    8. Kim, Moon-Koo & Oh, Jeesun & Park, Jong-Hyun & Joo, Changlim, 2018. "Perceived value and adoption intention for electric vehicles in Korea: Moderating effects of environmental traits and government supports," Energy, Elsevier, vol. 159(C), pages 799-809.
    9. De Groote, Olivier & Pepermans, Guido & Verboven, Frank, 2016. "Heterogeneity in the adoption of photovoltaic systems in Flanders," Energy Economics, Elsevier, vol. 59(C), pages 45-57.
    10. Athukorala, P.P.A Wasantha & Wilson, Clevo, 2010. "Estimating short and long-term residential demand for electricity: New evidence from Sri Lanka," Energy Economics, Elsevier, vol. 32(Supplemen), pages 34-40, September.
    11. Athukorala, P.P.A Wasantha & Wilson, Clevo, 2010. "Estimating short and long-term residential demand for electricity: New evidence from Sri Lanka," Energy Economics, Elsevier, vol. 32(Supplemen), pages 34-40, September.
    12. Aydin, Erdal & Brounen, Dirk, 2019. "The impact of policy on residential energy consumption," Energy, Elsevier, vol. 169(C), pages 115-129.
    13. Allan, Grant J. & McIntyre, Stuart G., 2017. "Green in the heart or greens in the wallet? The spatial uptake of small-scale renewable technologies," Energy Policy, Elsevier, vol. 102(C), pages 108-115.
    14. Navarro-Espinosa, Alejandro & Mancarella, Pierluigi, 2014. "Probabilistic modeling and assessment of the impact of electric heat pumps on low voltage distribution networks," Applied Energy, Elsevier, vol. 127(C), pages 249-266.
    15. Rodrigues, João L. & Bolognesi, Hugo M. & Melo, Joel D. & Heymann, Fabian & Soares, F.J., 2019. "Spatiotemporal model for estimating electric vehicles adopters," Energy, Elsevier, vol. 183(C), pages 788-802.
    16. Nam, SeungBeom & Hur, Jin, 2019. "A hybrid spatio-temporal forecasting of solar generating resources for grid integration," Energy, Elsevier, vol. 177(C), pages 503-510.
    17. Zhang, Jie & Cui, Mingjian & Hodge, Bri-Mathias & Florita, Anthony & Freedman, Jeffrey, 2017. "Ramp forecasting performance from improved short-term wind power forecasting over multiple spatial and temporal scales," Energy, Elsevier, vol. 122(C), pages 528-541.
    18. Baeten, Brecht & Rogiers, Frederik & Helsen, Lieve, 2017. "Reduction of heat pump induced peak electricity use and required generation capacity through thermal energy storage and demand response," Applied Energy, Elsevier, vol. 195(C), pages 184-195.
    19. Saarenpää, Jukka & Kolehmainen, Mikko & Niska, Harri, 2013. "Geodemographic analysis and estimation of early plug-in hybrid electric vehicle adoption," Applied Energy, Elsevier, vol. 107(C), pages 456-464.
    20. Bridge, Gavin & Bouzarovski, Stefan & Bradshaw, Michael & Eyre, Nick, 2013. "Geographies of energy transition: Space, place and the low-carbon economy," Energy Policy, Elsevier, vol. 53(C), pages 331-340.
    21. Balta-Ozkan, Nazmiye & Watson, Tom & Mocca, Elisabetta, 2015. "Spatially uneven development and low carbon transitions: Insights from urban and regional planning," Energy Policy, Elsevier, vol. 85(C), pages 500-510.
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    Cited by:

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    2. Granderson, Jessica & Fernandes, Samuel & Touzani, Samir & Lee, Chih-Cheng & Crowe, Eliot & Sheridan, Margaret, 2020. "Spatio-temporal impacts of a utility’s efficiency portfolio on the distribution grid," Energy, Elsevier, vol. 212(C).
    3. Nazir Ullah & Waleed S. Alnumay & Waleed Mugahed Al-Rahmi & Ahmed Ibrahim Alzahrani & Hosam Al-Samarraie, 2020. "Modeling Cost Saving and Innovativeness for Blockchain Technology Adoption by Energy Management," Energies, MDPI, vol. 13(18), pages 1-22, September.
    4. Mrówczyńska, Maria & Skiba, Marta & Bazan-Krzywoszańska, Anna & Sztubecka, Małgorzata, 2020. "Household standards and socio-economic aspects as a factor determining energy consumption in the city," Applied Energy, Elsevier, vol. 264(C).
    5. Jabeen, Gul & Ahmad, Munir & Zhang, Qingyu, 2021. "Perceived critical factors affecting consumers’ intention to purchase renewable generation technologies: Rural-urban heterogeneity," Energy, Elsevier, vol. 218(C).

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