An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants. (15th December 2021)

• Record Type: Journal Article
• Authors: Marcelino, C.G.; Leite, G.M.C.; Delgado, C.A.D.M.; de Oliveira, L.B.; Wanner, E.F.; Jiménez-Fernández, S.; Salcedo-Sanz, S.
• Is Part Of: Expert systems with applications. Volume 185(2021)
• Publication Date: 2021-12-15
• Subjects: Cascading hydro-power plant modeling -- Multi-objective optimization -- Swarm intelligence -- MESH -- Energy production; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2021.115638
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system — a cascade-based operation scenario. For this, we propose a new mathematical modeling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412, 500 per month in a projection analysis carried out. Highlights: An efficient Multi-objective Evolutionary Swarm Hybrid algorithm is proposed. Development of a nonlinear model to operational control of Hydro-power plants. Hydro-power plant data regression obtains the maximum efficiency of the power units. Efficiency energy goals achieved an increasing the profit in the energy production.