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Short-Term Scheduling of Thermal-Electric Generators Using Lagrangian Relaxation

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  • Jonathan F. Bard

    (University of Texas, Austin, Texas)

Abstract

This paper presents an expanded formulation of the unit commitment problem in which hundreds of thermal-electric generators must be scheduled on an hourly basis, for up to 7 days at a time. The underlying model incorporates the full set of costs and constraints including setup, production, ramping, and operational status, and takes the form of a mixed integer nonlinear control problem. Lagrangian relaxation is used to disaggregate the model by generator into separate subproblems which are then solved with a nested dynamic program. The strength of the methodology lies partially in its ability to construct good feasible solutions from information provided by the dual. Thus, the need for branch-and-bound is eliminated. In addition, the inclusion of the ramping constraint provides new insight into the limitations of current techniques. Computational experience with the proposed algorithm indicates that problems containing up to 100 units and 48 time periods can be readily solved in reasonable times. Duality gaps of less than 1% were achieved in all cases.

Suggested Citation

  • Jonathan F. Bard, 1988. "Short-Term Scheduling of Thermal-Electric Generators Using Lagrangian Relaxation," Operations Research, INFORMS, vol. 36(5), pages 756-766, October.
  • Handle: RePEc:inm:oropre:v:36:y:1988:i:5:p:756-766
    DOI: 10.1287/opre.36.5.756
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    Cited by:

    1. Maturana, Jorge & Riff, Maria-Cristina, 2007. "Solving the short-term electrical generation scheduling problem by an adaptive evolutionary approach," European Journal of Operational Research, Elsevier, vol. 179(3), pages 677-691, June.
    2. Jens Hönen & Johann L. Hurink & Bert Zwart, 2023. "A classification scheme for local energy trading," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 85-118, March.
    3. Dang, Chuangyin & Li, Minqiang, 2007. "A floating-point genetic algorithm for solving the unit commitment problem," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1370-1395, September.
    4. C. L. Tseng & C. A. Li & S. S. Oren, 2000. "Solving the Unit Commitment Problem by a Unit Decommitment Method," Journal of Optimization Theory and Applications, Springer, vol. 105(3), pages 707-730, June.
    5. Tadahiro Taniguchi & Koki Kawasaki & Yoshiro Fukui & Tomohiro Takata & Shiro Yano, 2015. "Automated Linear Function Submission-Based Double Auction as Bottom-up Real-Time Pricing in a Regional Prosumers’ Electricity Network," Energies, MDPI, vol. 8(7), pages 1-26, July.
    6. Löschenbrand, Markus & Wei, Wei & Liu, Feng, 2018. "Hydro-thermal power market equilibrium with price-making hydropower producers," Energy, Elsevier, vol. 164(C), pages 377-389.
    7. Adriaan Weijde & Benjamin Hobbs, 2011. "Locational-based coupling of electricity markets: benefits from coordinating unit commitment and balancing markets," Journal of Regulatory Economics, Springer, vol. 39(3), pages 223-251, June.
    8. Pascale Bendotti & Pierre Fouilhoux & Cécile Rottner, 2019. "On the complexity of the Unit Commitment Problem," Annals of Operations Research, Springer, vol. 274(1), pages 119-130, March.
    9. Jorge Valenzuela & Mainak Mazumdar, 2003. "Commitment of Electric Power Generators Under Stochastic Market Prices," Operations Research, INFORMS, vol. 51(6), pages 880-893, December.
    10. Fu Lin & Sven Leyffer & Todd Munson, 2016. "A two-level approach to large mixed-integer programs with application to cogeneration in energy-efficient buildings," Computational Optimization and Applications, Springer, vol. 65(1), pages 1-46, September.
    11. Briest, Gordon & Lauven, Lars-Peter & Kupfer, Stefan & Lukas, Elmar, 2022. "Leaving well-worn paths: Reversal of the investment-uncertainty relationship and flexible biogas plant operation," European Journal of Operational Research, Elsevier, vol. 300(3), pages 1162-1176.
    12. Vahidinasab, V. & Jadid, S., 2010. "Joint economic and emission dispatch in energy markets: A multiobjective mathematical programming approach," Energy, Elsevier, vol. 35(3), pages 1497-1504.
    13. L. A. C. Roque & D. B. M. M. Fontes & F. A. C. C. Fontes, 2014. "A hybrid biased random key genetic algorithm approach for the unit commitment problem," Journal of Combinatorial Optimization, Springer, vol. 28(1), pages 140-166, July.
    14. Ping Che & Yanyan Zhang & Jin Lang, 2019. "Emission-Intensity-Based Carbon Tax and Its Impact on Generation Self-Scheduling," Energies, MDPI, vol. 12(5), pages 1-17, February.
    15. Ludwig Kuntz & Felix Müsgens, 2007. "Modelling start-up costs of multiple technologies in electricity markets," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 66(1), pages 21-32, August.
    16. Tatiana González Grandón & Fernando de Cuadra García & Ignacio Pérez-Arriaga, 2021. "A Market-Driven Management Model for Renewable-Powered Undergrid Mini-Grids," Energies, MDPI, vol. 14(23), pages 1-29, November.
    17. de Queiroz, A.R. & Mulcahy, D. & Sankarasubramanian, A. & Deane, J.P. & Mahinthakumar, G. & Lu, N. & DeCarolis, J.F., 2019. "Repurposing an energy system optimization model for seasonal power generation planning," Energy, Elsevier, vol. 181(C), pages 1321-1330.
    18. Pérez-Iribarren, E. & González-Pino, I. & Azkorra-Larrinaga, Z. & Gómez-Arriarán, I., 2020. "Optimal design and operation of thermal energy storage systems in micro-cogeneration plants," Applied Energy, Elsevier, vol. 265(C).
    19. Zhong, Haiwang & Xia, Qing & Chen, Yuguo & Kang, Chongqing, 2015. "Energy-saving generation dispatch toward a sustainable electric power industry in China," Energy Policy, Elsevier, vol. 83(C), pages 14-25.

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