Special issue on “Optimal design and operation of energy systems”
Notice bibliographique
Résumé
The growing demand for energy is driven mainly by population and income growth.As projected by the United Nations, the human population is projected to reach 9.7 billion in 2050 (vs.8.0 billion in December 2022), implying that in 2050 there will be an additional 1.7 billion energy consumers.Concurrently, under Wood Mackenzie's base-case outlook, the global economy is forecast to double in size-from US$85.6 trillion in 2022 to US$169.0 trillion-by 2050.These changes translate into an increase of nearly 40% in the world energy consumption over the next 28 years, according to the U.S. Energy Information Administration.This increasing demand for energy and the growing awareness of the severe environmental impacts of using fossil fuels have created significant interest in optimal energy generation from renewable and reusable energy resources, and in the use of more efficient energy-converting and energy-consuming systems.Great interest in improving the efficiency of energy conversion, storage, distribution, management, and consumption systems has created a plethora of optimization and optimal control problems.For example, the highly intermittent nature of direct renewable energy resources (such as sunlight and wind) requires optimal integration of these power generating systems with power storage systems and power grids, so that the integration does not lead to degradation of the quality of power that the grids deliver.In the past two decades, major advances have been made in optimal design and operation of energy conversion, storage, distribution, management, and consumption systems, and optimal integration of these systems, via applying optimization methods.This special issue includes articles that report studies on optimal design and operation of these systems and their optimal integration.These studies deal with systems such as photovoltaic (PV) systems, concentrated solar power systems, rechargeable batteries, wind turbines, hybrid systems with renewable sources (e.g., solar-wind-thermal), electrical power grids, combined heat and power systems, power plants, and commercial and residential buildings.The first article presents a multi-period integrated planning and scheduling approach for developing energy systems.The approach is applicable to energy systems with multiple resources, locations, processing pathways, and planning periods in which infrastructure decisions can be conducted.The applicability and effectiveness of the approach is shown using a case study examining the long-term development of a multi-site energy-intensive hydrogen-based energy system in Texas.The second article presents an optimal energy management for a hybrid water pumping system driven by a PV generator and a wind turbine.The proposed management system serves to guarantee the pumping system autonomy in a rural region where there is no access to the electrical network.A maximum power point tracking (MPPT) controller based on the fuzzy Takagi-Sugeno model is proposed to maximize power transfer to the pump in the presence of wind speed and insolation changes.The third article introduces a multi-objective economic-emission-dispatch optimization problem, the solution of which addresses hydro-thermal-solar-wind power scheduling arrangement in integrated PV, nonconvex thermal, and wind units.A heuristic optimization solver, named moth flame optimization, is used to solve the optimization problem.The optimization solution reduces the electric power generation cost and emission significantly.The fourth article uses linear matrix inequality techniques to find the maximum allowable time-delay bound for a proportional-integral (PI) controller used in a renewable-energy-based automatic generation control system, and it then ORCID
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,068 | 0,027 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».