Special issue on “Optimal design and operation of energy systems”
Bibliographic record
Abstract
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.068 | 0.027 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".