Research on optimal scheduling of integrated energy system based on improved multi-objective artificial hummingbird algorithm

Guardado en:
Detalles Bibliográficos
Publicado en:PLoS One vol. 20, no. 6 (Jun 2025), p. e0325310
Autor principal: Wei, Liming
Otros Autores: Zhang, Fengyang
Publicado:
Public Library of Science
Materias:
Acceso en línea:Citation/Abstract
Full Text
Full Text - PDF
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!

MARC

LEADER 00000nab a2200000uu 4500
001 3215949370
003 UK-CbPIL
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