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Optimized dispatch in a first-principles concentrating solar power production model

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  • Wagner, Michael J.
  • Newman, Alexandra M.
  • Hamilton, William T.
  • Braun, Robert J.

Abstract

Concentrating solar power towers, which include a steam-Rankine cycle with molten salt thermal energy storage, is an emerging technology whose maximum effectiveness relies on an optimal operational and dispatch policy. Given parameters such as start-up and shut-down penalties, expected electricity price profiles, solar availability, and system interoperability requirements, this paper seeks a profit-maximizing solution that determines start-up and shut-down times for the power cycle and solar receiver, and the times at which to dispatch stored and instantaneous quantities of energy over a 48-h horizon at hourly fidelity. The mixed-integer linear program (MIP) is subject to constraints including: (i) minimum and maximum rates of start-up and shut-down, (ii) energy balance, including energetic state of the system as a whole and its components, (iii) logical rules governing the operational modes of the power cycle and solar receiver, and (iv) operational consistency between time periods.

Suggested Citation

  • Wagner, Michael J. & Newman, Alexandra M. & Hamilton, William T. & Braun, Robert J., 2017. "Optimized dispatch in a first-principles concentrating solar power production model," Applied Energy, Elsevier, vol. 203(C), pages 959-971.
    • Handle: RePEc:eee:appene:v:203:y:2017:i:c:p:959-971
    DOI: 10.1016/j.apenergy.2017.06.072
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    10. Vasallo, Manuel Jesús & Cojocaru, Emilian Gelu & Gegúndez, Manuel Emilio & Marín, Diego, 2021. "Application of data-based solar field models to optimal generation scheduling in concentrating solar power plants," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 1130-1149.
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    12. Gabriel J. Soto & Ben Lindley & Ty Neises & Cory Stansbury & Michael J. Wagner, 2022. "Dispatch Optimization, System Design and Cost Benefit Analysis of a Nuclear Reactor with Molten Salt Thermal Storage," Energies, MDPI, vol. 15(10), pages 1-23, May.
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    14. Norambuena-Guzmán, Valentina & Palma-Behnke, Rodrigo & Hernández-Moris, Catalina & Cerda, Maria Teresa & Flores-Quiroz, Ángela, 2024. "Towards CSP technology modeling in power system expansion planning," Applied Energy, Elsevier, vol. 364(C).
    15. Li, Xiaoen & Wang, Ningling & Wang, Ligang & Yang, Yongping & Maréchal, François, 2018. "Identification of optimal operating strategy of direct air-cooling condenser for Rankine cycle based power plants," Applied Energy, Elsevier, vol. 209(C), pages 153-166.
    16. Calderón, Alejandro & Palacios, Anabel & Barreneche, Camila & Segarra, Mercè & Prieto, Cristina & Rodriguez-Sanchez, Alfonso & Fernández, A. Inés, 2018. "High temperature systems using solid particles as TES and HTF material: A review," Applied Energy, Elsevier, vol. 213(C), pages 100-111.
    17. McPherson, Madeleine & Mehos, Mark & Denholm, Paul, 2020. "Leveraging concentrating solar power plant dispatchability: A review of the impacts of global market structures and policy," Energy Policy, Elsevier, vol. 139(C).
    18. Jesse G. Wales & Alexander J. Zolan & William T. Hamilton & Alexandra M. Newman & Michael J. Wagner, 2023. "Combining simulation and optimization to derive operating policies for a concentrating solar power plant," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 119-150, March.
    19. Fernández-Torrijos, M. & Albrecht, K.J. & Ho, C.K., 2018. "Dynamic modeling of a particle/supercritical CO2 heat exchanger for transient analysis and control," Applied Energy, Elsevier, vol. 226(C), pages 595-606.
    20. Cruz, N.C. & Salhi, S. & Redondo, J.L. & Álvarez, J.D. & Berenguel, M. & Ortigosa, P.M., 2018. "Hector, a new methodology for continuous and pattern-free heliostat field optimization," Applied Energy, Elsevier, vol. 225(C), pages 1123-1131.
    21. Ana Carolina do Amaral Burghi & Tobias Hirsch & Robert Pitz-Paal, 2020. "Artificial Learning Dispatch Planning for Flexible Renewable-Energy Systems," Energies, MDPI, vol. 13(6), pages 1-21, March.
    22. Ana Carolina do Amaral Burghi & Tobias Hirsch & Robert Pitz-Paal, 2020. "Artificial Learning Dispatch Planning with Probabilistic Forecasts: Using Uncertainties as an Asset," Energies, MDPI, vol. 13(3), pages 1-25, February.
    23. Kahvecioğlu, Gökçe & Morton, David P. & Wagner, Michael J., 2022. "Dispatch optimization of a concentrating solar power system under uncertain solar irradiance and energy prices," Applied Energy, Elsevier, vol. 326(C).
    24. Kong, Yanqiang & Wang, Weijia & Yang, Lijun & Du, Xiaoze, 2020. "Energy efficient strategies for anti-freezing of air-cooled heat exchanger," Applied Energy, Elsevier, vol. 261(C).
    25. Hu, Bangjie & Cai, Fulin & Tai, Nengling & Wang, Pei, 2024. "Dual-time scale optimal dispatch of the CSP-PV hybrid power plant considering dynamic operation," Energy, Elsevier, vol. 306(C).

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