Integrated Optimal Design and Operation of Compressed Air Energy Storage for Decentralized Applications
Bibliographic record
Abstract
This thesis aims to investigate the integration of compressed air energy storage (CAES) technology into decentralized energy systems, addressing associated technological and integration challenges within the dynamic energy system environment. A multi-layer simulation-optimization framework is developed to comprehensively evaluate the feasibility of integrating decentralized CAES into local hybrid energy systems (HES) through optimal sizing and operation. In the first layer, an improved energy management operation strategy (I-EMOS) is designed to enhance the integration of adiabatic-CAES (A-CAES) systems into decentralized applications. In doing so, the interaction and limitations of A-CAES subsystems, including power conversion units, air storage tank, and thermal energy storage, are considered to evaluate the long-term performance and dynamic behavior of A-CAES systems, especially when connected to intermittent renewable energy sources and end-user load demand. Subsequently, the second layer develops a holistic sizing-planning framework, including a generic A-CAES model and various alternative power dispatch strategies (PDS), based on the application potentials of A-CAES. This module aims to enhance A-CAES contribution while minimizing the levelized cost of energy and achieving the optimal configuration for the corresponding applications. Eventually, the final layer focuses on improving the resilience of the energy system, incorporating A-CAES technology, within scenarios involving limited energy sources and hybrid energy storage solutions. Therefore, an operational unit-commitment optimization model is developed, considering the A-CAES system's response and charging-discharging transition times. This model is integrated into the sizing-planning module to co-optimize the economic performance and system resilience through two-stage optimization, involving long-term planning and short-term scheduling. The methodology is applied to Concordia University buildings in Montreal, Canada. Validation against data from an existing A-CAES pilot plant shows a 42.5% improvement using I-EMOS compared to traditional EMOS. \nOptimal configurations under various PDSs demonstrate energy cost savings between $0.015 and $0.021 per kWh, with significant improvements in electrical load management (52%) and carbon emission reduction (65%) for the system in which A-CAES is planned for both solar energy integration and seasonal load shifting. Furthermore, under the worst-case scenario (zero selling back), the HES achieves a PV self-consumption rate of around 92% and a payback time of 15.5 years. In scenarios of limited grid dependency, a substantial annual resiliency improvement of approximately 41.1% is achieved by integrating the energy storage system. Additionally, despite the superior cost performance of the PV/A-CAES system, the PV-based HES featuring hybrid A-CAES, and battery storage achieves a 47.3% electrical load management ratio and a 96% self-consumption rate, improving by about 6% over HES with only A-CAES system. Furthermore, findings indicate that under optimal operational conditions, even with the highest PV power availability during grid interruptions, the HES could meet 94% of load demand using individual A-CAES, increasing to 100% by integrating fast-response batteries. In conclusion, the proposed framework offers a reliable approach for integrating and customizing decentralized A-CAES systems, considering specific service requirements and constraints. It identifies critical times of loss of power probability, enhances understanding of local energy system design, and facilitates better integration with renewable energy sources and storage systems. The findings provide valuable insights for decision-makers, helping select suitable systems and scenarios based on key performance indicators. The framework also is adaptable to various scale scenarios, accommodating both local and regional generation considerations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".