Reliability Studies of Distribution Systems Integrated with Energy Storage
Bibliographic record
Abstract
The integration of distributed generations (DGs) - renewable DGs, in particular- into distribution networks is gradually increasing, driven by environmental concerns and technological advancements. However, the intermittency and the variability of these resources adversely affect the optimal operation and reliability of the power distribution system. Energy storage systems (ESSs) are perceived as potential solutions to address system reliability issues and to enhance renewable energy utilization. The reliability contribution of the ESS depends on the ownership of these resources, market structure, and the regulatory framework. This along with the technical characteristics and the component unavailability of ESS significantly affect the reliability value of ESS to an active distribution system. It is, therefore, necessary to develop methodologies to conduct the reliability assessment of ESS integrated modern distribution systems incorporating above-mentioned factors. This thesis presents a novel reliability model of ESS that incorporates different scenarios of ownership, market/regulatory structures, and the ESS technical and failure characteristics. A new methodology to integrate the developed ESS reliability model with the intermittent DGs and the time-dependent loads is also presented. The reliability value of ESS in distribution grid capacity enhancement, effective utilization of renewable energy, mitigations of outages, and managing the financial risk of utilities under quality regulations are quantified. The methodologies introduced in this thesis will be useful to assess the market mechanism, policy and regulatory implications regarding ESS in future distribution system planning and operation. Another important aspect of a modern distribution system is the increased reliability needs of customers, especially with the growing use of sensitive process/equipment. The financial losses of customers due to industrial process disruption or malfunction of these equipment because of short duration (voltage sag and momentary interruption) and long duration (sustained interruption) reliability events could be substantial. It is, therefore, necessary to consider these short duration reliability events in the reliability studies. This thesis introduces a novel approach for the integrated modeling of the short and long duration reliability events caused by the random failures. Furthermore, the active management of distribution systems with ESS, DG, and microgrid has the potential to mitigate different reliability events. Appropriate models are needed to explore their contribution and to assist the utilities and system planners in reliability based system upgrades. New probabilistic models are developed in this thesis to assess the role of ESS together with DG and microgrid in mitigating the adverse impact of different reliability events. The developed methodologies can easily incorporate the complex protection settings, alternate supplies configurations, and the presence of distributed energy resources/microgrids in the context of modern distribution systems. The ongoing changes in modern distribution systems are creating an enormous paradigm shift in infrastructure planning, grid operations, utility business models, and regulatory policies. In this context, the proposed methodologies and the research findings presented in this thesis should be useful to devise the appropriate market mechanisms and regulatory policies and to carry out the system upgrades considering the reliability needs of customers in modern distribution systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".