A Markov Decision Process and Adapted Particle Swarm Optimization-Based Approach for the Hydropower Dispatch Problem—Jirau Hydropower Plant Case Study

Mateus Santos (), Marcelo Fonseca, José Bernardes, Lenio Prado, Thiago Abreu, Edson Bortoni and Guilherme Bastos
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Mateus Santos: Systems Engineering and Information Technology Institute, Itajubá Federal University, Itajubá 37500-903, Brazil
Marcelo Fonseca: JIRAU ENERGIA, Distrito de Jaci-Paraná, Porto Velho 76840-000, Brazil
José Bernardes: Electric and Energy Systems Institute, Itajubá Federal University, Itajubá 37500-903, Brazil
Lenio Prado: Systems Engineering and Information Technology Institute, Itajubá Federal University, Itajubá 37500-903, Brazil
Thiago Abreu: Electric and Energy Systems Institute, Itajubá Federal University, Itajubá 37500-903, Brazil
Edson Bortoni: Electric and Energy Systems Institute, Itajubá Federal University, Itajubá 37500-903, Brazil
Guilherme Bastos: Systems Engineering and Information Technology Institute, Itajubá Federal University, Itajubá 37500-903, Brazil

Energies, 2025, vol. 18, issue 18, 1-34

Abstract: This work focuses on optimizing energy dispatch in a hydroelectric power plant (HPP) with a large number of generating units (GUs) and uncertainties caused by sediment accumulation in the water intakes. The study was realized at Jirau HPP, and integrates Markov Decision Processes (MDPs) and Particle Swarm Optimization (PSO) to minimize losses and enhance the performance of the plant’s GUs. Given the complexity of managing the huge number of units (50) and mitigating load losses from sediment accumulation, this approach enables real-time decision-making and optimizes energy dispatch. The methodology involves modeling the operational characteristics of the GUs, developing an objective function to minimize water consumption and maximize energy efficiency, and utilizing MDPs and PSO to find globally optimal solutions. Our results show that this methodology improves efficiency, reducing the turbinated flow by 0.9 % while increasing energy generation by 0.34 % and overall yield by 0.33 % compared to the HPP traditional method of dispatch over the analyzed period. This strategy could be adapted to varying operational conditions, and could provide a reliable framework for hydropower dispatch optimization.

Keywords: hydroelectric power plants; energy dispatch optimization; Markov decision processes (MDPs); particle swarm optimization (PSO); operational efficiency (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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