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Optimal Re-Dispatching of Cascaded Hydropower Plants Using Quadratic Programming and Chance-Constrained Programming

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  • Krešimir Fekete

    (Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia)

  • Srete Nikolovski

    (Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia)

  • Zvonimir Klaić

    (Faculty of Electrical Engineering, Computer Science and Information Technology Osijek, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia)

  • Ana Androjić

    (HEP Proizvodnja d.o.o., 10000 Zagreb, Croatia)

Abstract

Stochastic production from wind power plants imposes additional uncertainty in power system operation. It can cause problems in load and generation balancing in the power system and can also cause congestion in the transmission network. This paper deals with the problems of congestion in the transmission network, which are caused by the production of wind power plants. An optimization model for corrective congestion management is developed. Congestions are relieved by re-dispatching several cascaded hydropower plants. Optimization methodology covers the optimization period of one day divided into the 24 segments for each hour. The developed optimization methodology consists of two optimization stages. The objective of the first optimization stage is to obtain an optimal day-ahead dispatch plan of the hydropower plants that maximizes profit from selling energy to the day-ahead electricity market. If such a dispatch plan, together with the wind power plant production, causes congestion in the transmission network, the second optimization stage is started. The objective of the second optimization stage is the minimization of the re-dispatching of cascaded hydropower plants in order to avoid possible congestion. The concept of chance-constrained programming is used in order to consider uncertain wind power production. The first optimization stage is defined as a mixed-integer linear programming problem and the second optimization stage is defined as a quadratic programming (QP) problem, in combination with chance-constrained programming. The developed optimization model is tested and verified using the model of a real-life power system.

Suggested Citation

  • Krešimir Fekete & Srete Nikolovski & Zvonimir Klaić & Ana Androjić, 2019. "Optimal Re-Dispatching of Cascaded Hydropower Plants Using Quadratic Programming and Chance-Constrained Programming," Energies, MDPI, vol. 12(9), pages 1-25, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:9:p:1604-:d:226443
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    References listed on IDEAS

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    1. Séguin, Sara & Fleten, Stein-Erik & Côté, Pascal & Pichler, Alois & Audet, Charles, 2017. "Stochastic short-term hydropower planning with inflow scenario trees," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1156-1168.
    2. Nazari-Heris, M. & Mohammadi-Ivatloo, B. & B. Gharehpetian, G., 2017. "Short-term scheduling of hydro-based power plants considering application of heuristic algorithms: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 116-129.
    3. Perica Ilak & Ivan Rajšl & Josip Đaković & Marko Delimar, 2018. "Duality Based Risk Mitigation Method for Construction of Joint Hydro-Wind Coordination Short-Run Marginal Cost Curves," Energies, MDPI, vol. 11(5), pages 1-12, May.
    4. Belanger, Camille & Gagnon, Luc, 2002. "Adding wind energy to hydropower," Energy Policy, Elsevier, vol. 30(14), pages 1279-1284, November.
    5. Suresh K. Damodaran & T. K. Sunil Kumar, 2018. "Hydro-Thermal-Wind Generation Scheduling Considering Economic and Environmental Factors Using Heuristic Algorithms," Energies, MDPI, vol. 11(2), pages 1-19, February.
    6. Martin N. Hjelmeland & Arild Helseth & Magnus Korpås, 2019. "Medium-Term Hydropower Scheduling with Variable Head under Inflow, Energy and Reserve Capacity Price Uncertainty," Energies, MDPI, vol. 12(1), pages 1-15, January.
    7. Ivan Rajšl & Perica Ilak & Marko Delimar & Slavko Krajcar, 2012. "Dispatch Method for Independently Owned Hydropower Plants in the Same River Flow," Energies, MDPI, vol. 5(9), pages 1-17, September.
    8. Omid Hoseynpour & Behnam Mohammadi-ivatloo & Morteza Nazari-Heris & Somayeh Asadi, 2017. "Application of Dynamic Non-Linear Programming Technique to Non-Convex Short-Term Hydrothermal Scheduling Problem," Energies, MDPI, vol. 10(9), pages 1-17, September.
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    Cited by:

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