Solution for short-term generation scheduling of cascaded hydrothermal system with turbulent water flow optimization

• Published in: Expert systems with applications 2023-03, Vol.213, p.118967, Article 118967
• Main Authors: Thirumal, K., Sakthivel, V.P., Sathya, P.D.
• Format: Article
• Language: English
• Subjects: Constraints handling; Hydrothermal scheduling; Multi-fuel sources; Turbulent water flow optimization; Valve point effects
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2022.118967

•New turbulent water flow optimization for scheduling hydrothermal power is bestowed.•Chaotic logistic map is employed to enrich the solution quality of the algorithm.•Examined on three hybrid power systems under different traits.•Proposed approach is competitive in terms of solution accuracy and convergence speed. For economic operation of hybrid power systems, short-term hydrothermal planning is a significant operation which is focused to reduce the fuel costs of thermal power plants while fulfilling the different constraints of electrical and hydraulic networks. This paper addresses a newly developed algorithm, turbulent water flow optimization (TWFO) to solve the hydrothermal scheduling problem. TWFO is based on the whirlpool phenomenon which is formed through turbulent flow of water in seas or oceans. To enrich the solution quality of TWFO and to avoid trapping into local optimum, a chaotic logistic map is employed in the algorithm. The proposed TWFO is used to offer an optimal hourly generation schedule of hydrothermal power system considering the spillage effects of hydro plants, and the valve point loading effects, transmission losses and multi-fuel sources of thermal power plants. Besides, a repair mechanism is applied to fulfil all the equality and inequality constraints and to govern the solutions in the direction of feasible search space. The superiority of the proposed TWFO is examined on three hybrid power systems and the results of TWFO are compared with the performance of recent heuristic approaches including monarch butterfly optimization, Harris hawks optimization, red fox optimizer, remora optimization algorithm, student psychology-based optimization, and other erstwhile approaches reported in the literature. Numerical results demonstrate that the TWFO shows a superior performance in comparison with other approaches.