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How Green Is the Smart Grid?

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  • Hledik, Ryan

Abstract

A simulation of the U.S. power system suggests that both conservative and more technologically aggressive implementations of a smart grid would produce a significant reduction in power sector carbon emissions at the national level. A conservative approach could reduce annual CO2 emissions by 5 percent by 2030, while the more aggressive approach could lead to a reduction of nearly 16 percent by 2030.

Suggested Citation

  • Hledik, Ryan, 2009. "How Green Is the Smart Grid?," The Electricity Journal, Elsevier, vol. 22(3), pages 29-41, April.
    • Handle: RePEc:eee:jelect:v:22:y:2009:i:3:p:29-41
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    Cited by:

    1. Fatima Zahra Zahraoui & Mehdi Et-taoussi & Houssam Eddine Chakir & Hamid Ouadi & Brahim Elbhiri, 2023. "Bellman–Genetic Hybrid Algorithm Optimization in Rural Area Microgrids," Energies, MDPI, vol. 16(19), pages 1-26, September.
    2. Wichman, Casey J., 2016. "Incentives, green preferences, and private provision of impure public goods," Journal of Environmental Economics and Management, Elsevier, vol. 79(C), pages 208-220.
    3. Siddharth Sareen, 2020. "Social and technical differentiation in smart meter rollout: embedded scalar biases in automating Norwegian and Portuguese energy infrastructure," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-8, December.
    4. Hu, Zhaoguang & Tan, Xiandong & Yang, Fan & Yang, Ming & Wen, Quan & Shan, Baoguo & Han, Xinyang, 2010. "Integrated resource strategic planning: Case study of energy efficiency in the Chinese power sector," Energy Policy, Elsevier, vol. 38(11), pages 6391-6397, November.
    5. Lund, Peter D. & Lindgren, Juuso & Mikkola, Jani & Salpakari, Jyri, 2015. "Review of energy system flexibility measures to enable high levels of variable renewable electricity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 785-807.
    6. Eunice Espe & Vidyasagar Potdar & Elizabeth Chang, 2018. "Prosumer Communities and Relationships in Smart Grids: A Literature Review, Evolution and Future Directions," Energies, MDPI, vol. 11(10), pages 1-24, September.
    7. Whittle, Colin & Jones, Christopher R. & While, Aidan, 2020. "Empowering householders: Identifying predictors of intentions to use a home energy management system in the United Kingdom," Energy Policy, Elsevier, vol. 139(C).
    8. Lamnatou, Chr. & Chemisana, D. & Cristofari, C., 2022. "Smart grids and smart technologies in relation to photovoltaics, storage systems, buildings and the environment," Renewable Energy, Elsevier, vol. 185(C), pages 1376-1391.
    9. Sun, Xianming & Xiao, Shiyi & Ren, Xiaohang & Xu, Bing, 2023. "Time-varying impact of information and communication technology on carbon emissions," Energy Economics, Elsevier, vol. 118(C).
    10. Goutam Dutta & Krishnendranath Mitra, 2017. "A literature review on dynamic pricing of electricity," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(10), pages 1131-1145, October.
    11. Yue Gao & Chao Fang & Jing Zhang, 2022. "A Spatial Analysis of Smart Meter Adoptions: Empirical Evidence from the U.S. Data," Sustainability, MDPI, vol. 14(3), pages 1-14, January.
    12. Lopes, Marta A.R. & Henggeler Antunes, Carlos & Janda, Kathryn B. & Peixoto, Paulo & Martins, Nelson, 2016. "The potential of energy behaviours in a smart(er) grid: Policy implications from a Portuguese exploratory study," Energy Policy, Elsevier, vol. 90(C), pages 233-245.
    13. Blumsack, Seth & Fernandez, Alisha, 2012. "Ready or not, here comes the smart grid!," Energy, Elsevier, vol. 37(1), pages 61-68.
    14. Satchwell, Andrew & Hledik, Ryan, 2014. "Analytical frameworks to incorporate demand response in long-term resource planning," Utilities Policy, Elsevier, vol. 28(C), pages 73-81.
    15. Lin, Chen-Chun & Yang, Chia-Han & Shyua, Joseph Z., 2013. "A comparison of innovation policy in the smart grid industry across the pacific: China and the USA," Energy Policy, Elsevier, vol. 57(C), pages 119-132.
    16. Jacqueline Corbett, 2013. "Using information systems to improve energy efficiency: Do smart meters make a difference?," Information Systems Frontiers, Springer, vol. 15(5), pages 747-760, November.
    17. Kuznetsova, Elizaveta & Li, Yan-Fu & Ruiz, Carlos & Zio, Enrico, 2014. "An integrated framework of agent-based modelling and robust optimization for microgrid energy management," Applied Energy, Elsevier, vol. 129(C), pages 70-88.
    18. James Carroll & Se n Lyons & Eleanor Denny, 2013. "Reducing Electricity Demand through Smart Metering: The Role of Improved Household Knowledge," Trinity Economics Papers tep0313, Trinity College Dublin, Department of Economics.
    19. Kim, Sung Tai & Lim, Byung In & Park, Wan Kyu & Kim, Myoung Kyu & Son, Sung-Yong, 2016. "An analysis on the effectiveness of a smart grid test-bed project: The Korean case," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 868-875.
    20. Carrie Armel, K. & Gupta, Abhay & Shrimali, Gireesh & Albert, Adrian, 2013. "Is disaggregation the holy grail of energy efficiency? The case of electricity," Energy Policy, Elsevier, vol. 52(C), pages 213-234.
    21. Galo, Joaquim J.M. & Macedo, Maria N.Q. & Almeida, Luiz A.L. & Lima, Antonio C.C., 2014. "Criteria for smart grid deployment in Brazil by applying the Delphi method," Energy, Elsevier, vol. 70(C), pages 605-611.
    22. Yanshan Yu & Jin Yang & Bin Chen, 2012. "The Smart Grids in China—A Review," Energies, MDPI, vol. 5(5), pages 1-18, May.
    23. Buryk, Stephen & Mead, Doug & Mourato, Susana & Torriti, Jacopo, 2015. "Investigating preferences for dynamic electricity tariffs: The effect of environmental and system benefit disclosure," Energy Policy, Elsevier, vol. 80(C), pages 190-195.
    24. Carroll, James & Lyons, Seán & Denny, Eleanor, 2014. "Reducing household electricity demand through smart metering: The role of improved information about energy saving," Energy Economics, Elsevier, vol. 45(C), pages 234-243.
    25. Kuznetsova, Elizaveta & Li, Yan-Fu & Ruiz, Carlos & Zio, Enrico & Ault, Graham & Bell, Keith, 2013. "Reinforcement learning for microgrid energy management," Energy, Elsevier, vol. 59(C), pages 133-146.

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