Investigation of energy storage options for sustainable energy systems.
Bibliographic record
Abstract
Determination of the possible energy storage options for a specific source of\nenergy requires a thorough analysis from the points of energy, exergy, and\nexergoeconomics. The main objective of this thesis is to investigate energy storage\noptions for sustainable energy systems. A technology description and illustration of\nconcerns regarding each system is presented. Moreover, the possibility of\nimplementing each option into different sources of energy is investigated. Thus,\nintegrated energy systems are developed, utilizing energy storage options with the aim\nof achieving more efficient systems.\nEnergy and exergy analyses are performed for three novel, integrated renewable\nenergy-based systems. Energy storage methods investigated here include hydrogen\nstorage, thermal energy storage, compressed air energy storage, and battery. Solar,\nwind, and biomass are the energy sources considered for the integrated systems. In\nthis research, a discussion on various energy storage systems is presented, and the\npotential of each storage option in the current and future energy market is studied.\nEach of the integrated systems is described and its operating strategy is presented.\nThe components of the integrated systems are first modeled to obtain their operating\ncharacteristics. The energy, exergy, and exergoeconomic equations are applied to the\ncomponents to calculate the rates of energy and exergy flows. The efficiencies are\nsubsequently calculated. The results of energy and exergy analyses are combined with\nexergoeconomic equations to report the unit exergy cost of flows in the components.\nSystem 1 consists of a PV system, a water electrolyser and a fuel cell to generate\nelectricity for a house. Hydrogen and thermal energy storage are considered as the\nstorage options. The results show that the capacities of the components depend on\nweather data and electric power demand. In System 1, the PV electric power output\nexceeds demand during months with high-solar irradiance. The results of a case study\nbased on the weather data in Toronto, Canada, and the electricity demand pattern of a\nCanadian house (5.74 kW maximum demand) are presented. The photovoltaic system\ncapacity and the electrolyser nominal hydrogen production rate are 37.17 kW and 4.5\nkg/day, respectively. The economic investigation of the hybrid system reports an\naverage cost of electricity of 0.84 $/kWh based on 25 years of operation. The optimal nominal capacity of the fuel cell is found to be 1.5 kW, according to the optimization\nresults. The optimal exergy efficiency varies from 9.91 to 9.94%.\nSystem 2 consists of a wind park, a PV-fuel cell and a biomass-fuel cell-gas\nturbine system. This integrated renewable energy-based system is developed for\nbaseload power generation and utilizes wind, solar and biomass energy resources. For\na 64 bar compressed air storage system, and a 36 bar gas turbine inlet air pressure,\n356 wind turbines are required. The lower the pressure difference between the\ncompressed air in the cavern and the gas turbine inlet air pressure, the fewer the\nnumber of wind turbines required in the Wind-CAES system. The results also show\nthat 5.4??105 PV modules (covering 0.66 Mm2 of land) are required to generate 5 MW\nof baseload electric power. Optimization of System 2 provides a range of optimal\npoints at which the exergy efficiency and the total purchase cost of the system are\noptimum. At an optimal point, the overall exergy efficiency of the integrated system is\nreported as 36.85%. At this point, the optimal values of compression ratio, gas turbine\nexpansion ratio, and CAES storage capacity are 8, 6.5, and 240 h, respectively.\nSystem 3 consists of a biomass gasifier integrated with a gas turbine cycle\n(biomass-GT). As another sub-part of System 3, a PV-electrolyser module is\nintegrated with a compressed air energy storage system. The overall hybrid system\nsupplies 10 MW baseload electric power, and 7730 MWh thermal energy. The PV is\naccountable for 56% of the annual exergy destruction in the hybrid system, and 38%\nof the annual exergy destruction occurs in the biomass-GT system. The overall energy\nand exergy efficiencies of System 3 are 34.8 and 34.1%, respectively. The hybrid PVbiomass\nsystem is sensitive to some parameters such as the steam-to-carbon ratio of\nthe biomass gasifier, and the gas turbine inlet temperature and expansion ratio. A 29%\nincrease in energy and exergy efficiencies is reported with the increase in SC from 1\nto 3 mol/mol. The related specific carbon dioxide emission reduction is from 1441 to\n583 g/kWh.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".