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A variant of the dynamic programming algorithm for unit commitment of combined heat and power systems

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  • Rong, Aiying
  • Hakonen, Henri
  • Lahdelma, Risto

Abstract

The paper addresses the unit commitment in multi-period combined heat and power (CHP) production planning under the deregulated power market. In CHP plants (units), generation of heat and power follows joint characteristics, which means that production planning must be done in coordination. We introduce in this paper the DP-RSC1 algorithm, which is a variant of the dynamic programming (DP) algorithm based on linear relaxation of the ON/OFF states of the units and sequential commitment of units one by one. The time complexity of DP-RSC1 is proportional to the number of generating units in the system, the number of periods over the planning horizon and the time for solving a single-period economic dispatch problem. We have compared the DP-RSC1 algorithm with realistic power plants against the unit decommitment algorithm and the traditional priority listing method. The results show that the DP-RSC1 algorithm gives somewhat more accurate results (0.08-0.5% on average, maximum 10% for the individual sub-case) and executes 3-5 times faster on average than the unit decommitment algorithm. It is not surprising that the solution quality of the DP-RSC1 algorithm is much better than that of the priority listing method.

Suggested Citation

  • Rong, Aiying & Hakonen, Henri & Lahdelma, Risto, 2008. "A variant of the dynamic programming algorithm for unit commitment of combined heat and power systems," European Journal of Operational Research, Elsevier, vol. 190(3), pages 741-755, November.
    • Handle: RePEc:eee:ejores:v:190:y:2008:i:3:p:741-755
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    References listed on IDEAS

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    1. Makkonen, Simo & Lahdelma, Risto, 2006. "Non-convex power plant modelling in energy optimisation," European Journal of Operational Research, Elsevier, vol. 171(3), pages 1113-1126, June.
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    6. Rong, Aiying & Lahdelma, Risto, 2007. "An efficient envelope-based Branch and Bound algorithm for non-convex combined heat and power production planning," European Journal of Operational Research, Elsevier, vol. 183(1), pages 412-431, November.
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    Cited by:

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    3. Rong, Aiying & Lahdelma, Risto, 2017. "An efficient model and algorithm for the transmission-constrained multi-site combined heat and power system," European Journal of Operational Research, Elsevier, vol. 258(3), pages 1106-1117.
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    5. Fan, Lei & Pan, Kai & Guan, Yongpei, 2019. "A strengthened mixed-integer linear programming formulation for combined-cycle units," European Journal of Operational Research, Elsevier, vol. 275(3), pages 865-881.
    6. Jochem, Patrick & Schönfelder, Martin & Fichtner, Wolf, 2015. "An efficient two-stage algorithm for decentralized scheduling of micro-CHP units," European Journal of Operational Research, Elsevier, vol. 245(3), pages 862-874.
    7. Kumbartzky, Nadine & Schacht, Matthias & Schulz, Katrin & Werners, Brigitte, 2017. "Optimal operation of a CHP plant participating in the German electricity balancing and day-ahead spot market," European Journal of Operational Research, Elsevier, vol. 261(1), pages 390-404.
    8. Marshman, D.J. & Chmelyk, T. & Sidhu, M.S. & Gopaluni, R.B. & Dumont, G.A., 2010. "Energy optimization in a pulp and paper mill cogeneration facility," Applied Energy, Elsevier, vol. 87(11), pages 3514-3525, November.
    9. Alipour, Manijeh & Zare, Kazem & Mohammadi-Ivatloo, Behnam, 2016. "Optimal risk-constrained participation of industrial cogeneration systems in the day-ahead energy markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 421-432.
    10. Abujarad, Saleh Y. & Mustafa, M.W. & Jamian, J.J., 2017. "Recent approaches of unit commitment in the presence of intermittent renewable energy resources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 215-223.
    11. Hu, Mengqi & Weir, Jeffery D. & Wu, Teresa, 2012. "Decentralized operation strategies for an integrated building energy system using a memetic algorithm," European Journal of Operational Research, Elsevier, vol. 217(1), pages 185-197.
    12. 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.
    13. Jiang, Ruiwei & Zhang, Muhong & Li, Guang & Guan, Yongpei, 2014. "Two-stage network constrained robust unit commitment problem," European Journal of Operational Research, Elsevier, vol. 234(3), pages 751-762.
    14. Hamdi Abdi, 2023. "A Survey of Combined Heat and Power-Based Unit Commitment Problem: Optimization Algorithms, Case Studies, Challenges, and Future Directions," Mathematics, MDPI, vol. 11(19), pages 1-36, October.
    15. Kia, Mohsen & Setayesh Nazar, Mehrdad & Sepasian, Mohammad Sadegh & Heidari, Alireza & Sharaf, Adel M., 2017. "Coordination of heat and power scheduling in micro-grid considering inter-zonal power exchanges," Energy, Elsevier, vol. 141(C), pages 519-536.

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