Multi-Objective Optimal Dispatch of Hydro-Wind-Solar Systems Using Hyper-Dominance Evolutionary Algorithm

In response to the challenge of multi-objective optimal scheduling and efficient solution of hydropower stations under large-scale renewable energy integration, this study develops a multi-objective optimization model with the dual goals of maximizing total power generation and minimizing the varian...

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Veröffentlicht in:Water (Basel) Jg. 17; H. 14; S. 2127
Hauptverfasser: Xie, Mengfei, Liu, Bin, Peng, Ying, Wu, Dianning, Qian, Ruifeng, Yang, Fan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 17.07.2025
Schlagworte:
Alternative energy sources
Buildings and facilities
Carbon
Clean technology
Climate change
Comparative analysis
Decision making
Dry season
Electricity distribution
Energy consumption
Energy resources
Energy transition
Evolutionary algorithms
Genetic algorithms
Hydroelectric power
Hydroelectric power plants
Management
Mathematical optimization
Optimization algorithms
Renewable resources
Scheduling
Solar energy
Solar power plants
Technology application
Trends
Wind power
China
Yunnan China
ISSN:2073-4441, 2073-4441
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Zusammenfassung:In response to the challenge of multi-objective optimal scheduling and efficient solution of hydropower stations under large-scale renewable energy integration, this study develops a multi-objective optimization model with the dual goals of maximizing total power generation and minimizing the variance of residual load. Four complementarity evaluation indicators are used to analyze the wind–solar complementarity characteristics. Building upon this foundation, Hyper-dominance Evolutionary Algorithm (HEA)—capable of efficiently solving high-dimensional problems—is introduced for the first time in the context of wind–solar–hydropower integrated scheduling. The case study results show that the HEA performs better than the benchmark algorithms, with the best mean Hypervolume and Inverted Generational Distance Plus across nine Walking Fish Group (WFG) series test functions. For the hydro-wind-solar scheduling problem, HEA obtains Pareto frontier solutions with both maximum power generation and minimal residual load variance, thus effectively solving the multi-objective scheduling problem of the hydropower system. This work provides a valuable reference for modeling and efficiently solving the multi-objective scheduling problem of hydropower in the context of emerging power systems. This work provides a valuable reference for the modeling and efficient solution of hydropower multi-objective scheduling problems in the context of emerging power systems.
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ISSN:2073-4441
2073-4441
DOI:10.3390/w17142127