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Quasi-oppositional turbulent water flow-based optimization for cascaded short term hydrothermal scheduling with valve-point effects and multiple fuels

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  • Sakthivel, V.P.
  • Thirumal, K.
  • Sathya, P.D.

Abstract

In an interconnected power system, short-term hydrothermal scheduling (SHTS) portrays a paramount important task in the economic operation of electric power system. SHTS is discerned as a formidable non-convex constrained optimization problem considering the cascade nature of hydro plants, and valve-point loading (VPL) and multi-fuel sources of thermal plants. In the present study, a novel metaheuristic approach, a quasi-oppositional turbulent water flow-based optimization (QOTWFO) is proposed to solve the SHTS problem. Turbulent water flow-based optimization (TWFO) is evolved by the whirlpool prodigy formed in a turbulent flow of water. QOTWFO is an amended version of TWFO, where the quasi-opposition-based learning is pioneered for population initialization and iteration vaulting. It generates a quasi-oppositional solution which has the highest probability to elicit the global optimum solution than a randomly generated solution. Thus, the global searching ability and computational efficiency of the algorithm is enhanced. A new heuristic constraints handling mechanism is proposed to fulfil the equality constraints notably active power balance and dynamic water flow balance. The cogency of proposed QOTWFO is examined on standard benchmark functions, and multi-reservoir cascaded hydrothermal test systems with the cogitation of VPL effects and multi-fuel sources of thermal plants. Besides, Newton Raphson load flow approach is employed to determine power losses and line flows in a 16-bus, and 35 transmission lines hydrothermal power system. To model a realistic power system model, the maximum power transfer capability of each transmission line is taken into account. Simulation outcomes substantiate that, compared with state-of-the-art heuristic techniques, QOTWFO offers better feasible solutions as well as guarantees the robustness of the algorithm.

Suggested Citation

  • Sakthivel, V.P. & Thirumal, K. & Sathya, P.D., 2022. "Quasi-oppositional turbulent water flow-based optimization for cascaded short term hydrothermal scheduling with valve-point effects and multiple fuels," Energy, Elsevier, vol. 251(C).
    • Handle: RePEc:eee:energy:v:251:y:2022:i:c:s0360544222008088
    DOI: 10.1016/j.energy.2022.123905
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    References listed on IDEAS

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    Cited by:

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    3. Saqib Akram & Muhammad Salman Fakhar & Syed Abdul Rahman Kashif & Ghulam Abbas & Nasim Ullah & Alsharef Mohammad & Mohamed Emad Farrag, 2022. "Introducing Adaptive Machine Learning Technique for Solving Short-Term Hydrothermal Scheduling with Prohibited Discharge Zones," Sustainability, MDPI, vol. 14(18), pages 1-18, September.
    4. Meng, Anbo & Xu, Xuancong & Zhang, Zhan & Zeng, Cong & Liang, Ruduo & Zhang, Zheng & Wang, Xiaolin & Yan, Baiping & Yin, Hao & Luo, Jianqiang, 2022. "Solving high-dimensional multi-area economic dispatch problem by decoupled distributed crisscross optimization algorithm with population cross generation strategy," Energy, Elsevier, vol. 258(C).

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