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Grid integration and smart grid implementation of emerging technologies in electric power systems through approximate dynamic programming

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  • Xiao, Jingjie

Abstract

A key hurdle for implementing real-time pricing of electricity is a lack of con-sumers’ responses. Solutions to overcome the hurdle include the energy management system that automatically optimizes household appliance usage such as plug-in hybrid electric vehicle charging (and discharging with vehicle-to-grid) via a two-way com-munication with the grid. Real-time pricing, combined with household automation devices, has a potential to accommodate an increasing penetration of plug-in hybrid electric vehicles. In addition, the intelligent energy controller on the consumer-side can help increase the utilization rate of the intermittent renewable resource, as the demand can be managed to match the output profile of renewables, thus making the intermittent resource such as wind and solar more economically competitive in the long run. One of the main goals of this dissertation is to present how real-time retail pricing, aided by control automation devices, can be integrated into the wholesale electricity market under various uncertainties through approximate dynamic programming. What distinguishes this study from the existing work in the literature is that whole-sale electricity prices are endogenously determined as we solve a system operator’s economic dispatch problem on an hourly basis over the entire optimization horizon. This modeling and algorithm framework will allow a feedback loop between electricity prices and electricity consumption to be fully captured. While we are interested in a near-optimal solution using approximate dynamic programming; deterministic linear programming benchmarks are use to demonstrate the quality of our solutions.The other goal of the dissertation is to use this framework to provide numerical ev-idence to the debate on whether real-time pricing is superior than the current flat rate structure in terms of both economic and environmental impacts. For this pur-pose, the modeling and algorithm framework is tested on a large-scale test case with hundreds of power plants based on data available for California, making our findings useful for policy makers, system operators and utility companies to gain a concrete understanding on the scale of the impact with real-time pricing.

Suggested Citation

  • Xiao, Jingjie, 2013. "Grid integration and smart grid implementation of emerging technologies in electric power systems through approximate dynamic programming," MPRA Paper 58696, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:58696
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    References listed on IDEAS

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    1. Hugo P. Simão & Jeff Day & Abraham P. George & Ted Gifford & John Nienow & Warren B. Powell, 2009. "An Approximate Dynamic Programming Algorithm for Large-Scale Fleet Management: A Case Application," Transportation Science, INFORMS, vol. 43(2), pages 178-197, May.
    2. Kevin A. Hassett & Aparna Mathur & Gilbert E. Metcalf, 2009. "The Incidence of a U.S. Carbon Tax: A Lifetime and Regional Analysis," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 155-178.
    3. Hadley, Stanton W. & Tsvetkova, Alexandra A., 2009. "Potential Impacts of Plug-in Hybrid Electric Vehicles on Regional Power Generation," The Electricity Journal, Elsevier, vol. 22(10), pages 56-68, December.
    4. Cedric De Jonghe & Benjamin F. Hobbs & Ronnie Belmans, 2011. "Integrating Short-term Demand Response Into Long-Term Investment Planning," Working Papers EPRG 1113, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    5. Huang, Shisheng & Safiullah, Hameed & Xiao, Jingjie & Hodge, Bri-Mathias S. & Hoffman, Ray & Soller, Joan & Jones, Doug & Dininger, Dennis & Tyner, Wallace E. & Liu, Andrew & Pekny, Joseph F., 2012. "The effects of electric vehicles on residential households in the city of Indianapolis," Energy Policy, Elsevier, vol. 49(C), pages 442-455.
    6. Dan Zhang & Daniel Adelman, 2009. "An Approximate Dynamic Programming Approach to Network Revenue Management with Customer Choice," Transportation Science, INFORMS, vol. 43(3), pages 381-394, August.
    7. Severin Borenstein, 2005. "The Long-Run Efficiency of Real-Time Electricity Pricing," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 93-116.
    8. Steven A. Gabriel & Andy S. Kydes & Peter Whitman, 2001. "The National Energy Modeling System: A Large-Scale Energy-Economic Equilibrium Model," Operations Research, INFORMS, vol. 49(1), pages 14-25, February.
    9. Adam Rose & Gbadebo Oladosu & Shu‐Yi Liao, 2007. "Business Interruption Impacts of a Terrorist Attack on the Electric Power System of Los Angeles: Customer Resilience to a Total Blackout," Risk Analysis, John Wiley & Sons, vol. 27(3), pages 513-531, June.
    10. Blanco, María Isabel, 2009. "The economics of wind energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1372-1382, August.
    11. O'Neill, Richard P. & Sotkiewicz, Paul M. & Hobbs, Benjamin F. & Rothkopf, Michael H. & Stewart, William R., 2005. "Efficient market-clearing prices in markets with nonconvexities," European Journal of Operational Research, Elsevier, vol. 164(1), pages 269-285, July.
    12. Axsen, Jonn & Kurani, Kenneth S. & McCarthy, Ryan & Yang, Christopher, 2011. "Plug-in hybrid vehicle GHG impacts in California: Integrating consumer-informed recharge profiles with an electricity-dispatch model," Energy Policy, Elsevier, vol. 39(3), pages 1617-1629, March.
    13. Torriti, Jacopo & Hassan, Mohamed G. & Leach, Matthew, 2010. "Demand response experience in Europe: Policies, programmes and implementation," Energy, Elsevier, vol. 35(4), pages 1575-1583.
    14. Warren B. Powell & Abraham George & Hugo Simão & Warren Scott & Alan Lamont & Jeffrey Stewart, 2012. "SMART: A Stochastic Multiscale Model for the Analysis of Energy Resources, Technology, and Policy," INFORMS Journal on Computing, INFORMS, vol. 24(4), pages 665-682, November.
    15. Allcott, Hunt, 2011. "Rethinking real-time electricity pricing," Resource and Energy Economics, Elsevier, vol. 33(4), pages 820-842.
    16. Cappers, Peter & Goldman, Charles & Kathan, David, 2010. "Demand response in U.S. electricity markets: Empirical evidence," Energy, Elsevier, vol. 35(4), pages 1526-1535.
    17. 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.
    18. Bushnell, James & Hobbs, Benjamin F. & Wolak, Frank A., 2009. "When It Comes to Demand Response, Is FERC Its Own Worst Enemy?," The Electricity Journal, Elsevier, vol. 22(8), pages 9-18, October.
    19. Richard Bellman, 1956. "Dynamic Programming and the Smoothing Problem," Management Science, INFORMS, vol. 3(1), pages 111-113, October.
    20. Aalami, H.A. & Moghaddam, M. Parsa & Yousefi, G.R., 2010. "Demand response modeling considering Interruptible/Curtailable loads and capacity market programs," Applied Energy, Elsevier, vol. 87(1), pages 243-250, January.
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    More about this item

    Keywords

    Energy Economics; Electricity Markets; Plug in hybrid vehicles; electric vehicles; mathematical modeling;
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • Q2 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy

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