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An Integrated Resource Planning model considering customer value, emissions, and regional economic impacts

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  • Hoog, David T.
  • Hobbs, Benjamin F.

Abstract

A nonlinear programming model is developed to examine the effects of introducing demand-side measures into the utility planning process. The development of the model addresses issues previously not considered in one comprehensive model, including the cost to the utility, sulfur dioxide emissions, regional economic effects, and net value to customers (also called consumer surplus). Regional economic impacts are measured by analyzing the direct and indirect effects of changes in consumers' disposable income, demands for fuel, and shifts in utility investment between plant capacity and conservation and load management programs. The consumer surplus objective makes it possible to consider rigorously the effects of changes in electric rates on economic efficiency, which are ignored in the widely used “least cost” objective. An application of the model is used to investigate the significance of using these different objectives in Integrated Resource Planning (IRP).

Suggested Citation

  • Hoog, David T. & Hobbs, Benjamin F., 1993. "An Integrated Resource Planning model considering customer value, emissions, and regional economic impacts," Energy, Elsevier, vol. 18(11), pages 1153-1160.
  • Handle: RePEc:eee:energy:v:18:y:1993:i:11:p:1153-1160
    DOI: 10.1016/0360-5442(93)90087-T
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    Cited by:

    1. Wilkerson, Jordan & Larsen, Peter & Barbose, Galen, 2014. "Survey of Western U.S. electric utility resource plans," Energy Policy, Elsevier, vol. 66(C), pages 90-103.
    2. Carvallo, Juan Pablo & Sanstad, Alan H. & Larsen, Peter H., 2019. "Exploring the relationship between planning and procurement in western U.S. electric utilities," Energy, Elsevier, vol. 183(C), pages 4-15.
    3. De Jonghe, Cedric & Delarue, Erik & Belmans, Ronnie & D'haeseleer, William, 2011. "Determining optimal electricity technology mix with high level of wind power penetration," Applied Energy, Elsevier, vol. 88(6), pages 2231-2238, June.
    4. Mezher, Toufic & Chedid, Riad & Zahabi, Wissam, 1998. "Energy resource allocation using multi-objective goal programming: the case of Lebanon," Applied Energy, Elsevier, vol. 61(4), pages 175-192, December.
    5. Nhan T. Nguyen & Minh Ha-Duong, 2009. "The potential for mitigation of CO2 emissions in Vietnam's power sector," Working Papers 22, Development and Policies Research Center (DEPOCEN), Vietnam.
    6. De Jonghe, C. & Hobbs, B. F. & Belmans, R., 2011. "Integrating short-term demand response into long-term investment planning," Cambridge Working Papers in Economics 1132, Faculty of Economics, University of Cambridge.
    7. Mohammad Dehghani & Mohammad Mardaneh & Om P. Malik & Josep M. Guerrero & Carlos Sotelo & David Sotelo & Morteza Nazari-Heris & Kamal Al-Haddad & Ricardo A. Ramirez-Mendoza, 2020. "Genetic Algorithm for Energy Commitment in a Power System Supplied by Multiple Energy Carriers," Sustainability, MDPI, vol. 12(23), pages 1-23, December.

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