Investigation of energy storage options for sustainable energy systems.
Bibliographic record
Abstract
Determination of the possible energy storage options for a specific source of energy requires a thorough analysis from the points of energy, exergy, and exergoeconomics. The main objective of this thesis is to investigate energy storage options for sustainable energy systems. A technology description and illustration of concerns regarding each system is presented. Moreover, the possibility of implementing each option into different sources of energy is investigated. Thus, integrated energy systems are developed, utilizing energy storage options with the aim of achieving more efficient systems. Energy and exergy analyses are performed for three novel, integrated renewable energy-based systems. Energy storage methods investigated here include hydrogen storage, thermal energy storage, compressed air energy storage, and battery. Solar, wind, and biomass are the energy sources considered for the integrated systems. In this research, a discussion on various energy storage systems is presented, and the potential of each storage option in the current and future energy market is studied. Each of the integrated systems is described and its operating strategy is presented. The components of the integrated systems are first modeled to obtain their operating characteristics. The energy, exergy, and exergoeconomic equations are applied to the components to calculate the rates of energy and exergy flows. The efficiencies are subsequently calculated. The results of energy and exergy analyses are combined with exergoeconomic equations to report the unit exergy cost of flows in the components. System 1 consists of a PV system, a water electrolyser and a fuel cell to generate electricity for a house. Hydrogen and thermal energy storage are considered as the storage options. The results show that the capacities of the components depend on weather data and electric power demand. In System 1, the PV electric power output exceeds demand during months with high-solar irradiance. The results of a case study based on the weather data in Toronto, Canada, and the electricity demand pattern of a Canadian house (5.74 kW maximum demand) are presented. The photovoltaic system capacity and the electrolyser nominal hydrogen production rate are 37.17 kW and 4.5 kg/day, respectively. The economic investigation of the hybrid system reports an average 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 results. The optimal exergy efficiency varies from 9.91 to 9.94%. System 2 consists of a wind park, a PV-fuel cell and a biomass-fuel cell-gas turbine system. This integrated renewable energy-based system is developed for baseload power generation and utilizes wind, solar and biomass energy resources. For a 64 bar compressed air storage system, and a 36 bar gas turbine inlet air pressure, 356 wind turbines are required. The lower the pressure difference between the compressed air in the cavern and the gas turbine inlet air pressure, the fewer the number of wind turbines required in the Wind-CAES system. The results also show that 5.4×105 PV modules (covering 0.66 Mm2 of land) are required to generate 5 MW of baseload electric power. Optimization of System 2 provides a range of optimal points at which the exergy efficiency and the total purchase cost of the system are optimum. At an optimal point, the overall exergy efficiency of the integrated system is reported as 36.85%. At this point, the optimal values of compression ratio, gas turbine expansion ratio, and CAES storage capacity are 8, 6.5, and 240 h, respectively. System 3 consists of a biomass gasifier integrated with a gas turbine cycle (biomass-GT). As another sub-part of System 3, a PV-electrolyser module is integrated with a compressed air energy storage system. The overall hybrid system supplies 10 MW baseload electric power, and 7730 MWh thermal energy. The PV is accountable for 56% of the annual exergy destruction in the hybrid system, and 38% of the annual exergy destruction occurs in the biomass-GT system. The overall energy and exergy efficiencies of System 3 are 34.8 and 34.1%, respectively. The hybrid PVbiomass system is sensitive to some parameters such as the steam-to-carbon ratio of the biomass gasifier, and the gas turbine inlet temperature and expansion ratio. A 29% increase in energy and exergy efficiencies is reported with the increase in SC from 1 to 3 mol/mol. The related specific carbon dioxide emission reduction is from 1441 to 583 g/kWh.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".