Research on optimal scheduling of integrated energy system based on improved multi-objective artificial hummingbird algorithm
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| Publicado en: | PLoS One vol. 20, no. 6 (Jun 2025), p. e0325310 |
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Public Library of Science
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 022 | |a 1932-6203 | ||
| 024 | 7 | |a 10.1371/journal.pone.0325310 |2 doi | |
| 035 | |a 3215949370 | ||
| 045 | 2 | |b d20250601 |b d20250630 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Wei, Liming | |
| 245 | 1 | |a Research on optimal scheduling of integrated energy system based on improved multi-objective artificial hummingbird algorithm | |
| 260 | |b Public Library of Science |c Jun 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a To accelerate energy efficiency improvement and green transition in industrial parks while addressing energy utilization and carbon reduction requirements, this study proposes a low-carbon economic dispatch model for integrated energy systems (IES) based on an enhanced multi-objective artificial hummingbird algorithm (MOAHA). The main contributions are threefold: First, we establish an optimized dispatch model incorporating combined cooling, heating and power (CCHP) systems, a refined two-stage power-to-gas (P2G) conversion process, and carbon capture technologies. Second, a stepwise carbon trading mechanism is introduced to further reduce carbon emissions from the IES. Third, a multi-strategy enhanced MOAHA is developed through three key improvements: 1) Logistic-sine fused chaotic mapping for population initialization to enhance distribution uniformity and solution quality; 2) Elite opposition-based learning and adaptive spiral migration foraging mechanisms to optimize individual positions and population diversity; 3) Simplex method integration to strengthen local search capabilities and optimization precision. Comprehensive case studies demonstrate the model’s effectiveness, achieving an 82.9% reduction in carbon emissions and 17.3% decrease in operational costs compared to conventional approaches. The proposed framework provides a technically viable solution for sustainable energy management in industrial parks, effectively balancing economic and environmental objectives. | |
| 653 | |a Energy management | ||
| 653 | |a Carbon emissions | ||
| 653 | |a Algorithms | ||
| 653 | |a Emissions trading | ||
| 653 | |a Sustainable energy | ||
| 653 | |a Energy efficiency | ||
| 653 | |a Clean energy | ||
| 653 | |a Multiple objective analysis | ||
| 653 | |a Objectives | ||
| 653 | |a Industrial plants | ||
| 653 | |a Energy utilization | ||
| 653 | |a Pareto optimum | ||
| 653 | |a Energy consumption | ||
| 653 | |a Carbon sequestration | ||
| 653 | |a Efficiency | ||
| 653 | |a Simplex method | ||
| 653 | |a Environmental objective | ||
| 653 | |a Economics | ||
| 653 | |a Industrial parks | ||
| 653 | |a Monte Carlo simulation | ||
| 653 | |a Emissions | ||
| 653 | |a Carbon | ||
| 653 | |a Industrial areas | ||
| 653 | |a Cooling | ||
| 653 | |a Sustainable development | ||
| 653 | |a Costs | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Integrated energy systems | ||
| 653 | |a Renewable resources | ||
| 653 | |a Optimization | ||
| 653 | |a Alternative energy sources | ||
| 653 | |a Demand side management | ||
| 653 | |a Optimization algorithms | ||
| 653 | |a Power dispatch | ||
| 653 | |a Parks & recreation areas | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Zhang, Fengyang | |
| 773 | 0 | |t PLoS One |g vol. 20, no. 6 (Jun 2025), p. e0325310 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3215949370/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3215949370/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3215949370/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |