| Author: | Lin, Zeyang |
| Title: | Risk constrained distributionally robust EV scheduling under high penetration of renewables |
| Advisors: | Bu, Siqi (EEE) |
| Degree: | M.Sc. |
| Year: | 2024 |
| Department: | Department of Electrical and Electronic Engineering |
| Pages: | 1 volume (various pagings) : color illustrations |
| Language: | English |
| Abstract: | In the context of a power grid with a high penetration of renewable energy, the scheduling of electric vehicles (EVs) faces a series of complex challenges. The inherent randomness of renewable energy generation—characterized by unstable supply and demand—complicates the integration of electric vehicles into the grid. As the number of electric vehicles increases, their uncoordinated influx into charging stations may lead to severe grid congestion. This congestion poses risks to the operational safety of the grid and undermines its economic efficiency. To address these challenges, the primary objective of this study is to establish a distributionally robust optimization framework that seeks to determine the minimum cost in the worst-case scenario. This framework will incorporate various potential uncertainties in data distributions, allowing for a comprehensive analysis of risk. By focusing on the worst-case scenarios, the framework aims to ensure that the solutions derived are not only optimal under typical conditions but also resilient against unexpected variations. Ultimately, this approach will provide decision-makers with a robust tool to minimize costs while effectively managing risks associated with uncertainty in real-world applications. The distributionally robust optimization framework consists of two parts. The first area involves the development of a Worst-Case Conditional Value at Risk (WCVaR) model for risk assessment. This model evaluates potential risks associated with fluctuations in renewable energy production and their corresponding impacts on electric vehicle charging plans. By quantifying risk and uncertainty, the WCVaR model provides a systematic decision-making approach that prioritizes the reliability and resilience of grid operations under the most adverse conditions. The model can be used to identify the highest-risk scenarios for wind farms. The second focus area is the construction of a bi-layer optimal scheduling framework for electric vehicles. This framework is designed to optimize EV scheduling across two interconnected levels: the upper level considers the perspective of the grid, aiming to minimize overall costs, while the lower layer focuses on minimizing the usage costs for electric vehicle owners. This hierarchical approach simultaneously takes into account the maximization of multiple stakeholders' interests while fully leveraging the peak shaving and valley filling capabilities of electric vehicle scheduling. The effectiveness of the framework is subsequently verified through simulation cases. The report concludes by summarizing the content of the article, highlighting its limitations, and suggesting areas for future improvement. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 8491.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 1.76 MB | Adobe PDF | View/Open |
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