Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Electrical and Electronic Engineering | en_US |
| dc.contributor.advisor | Bu, Siqi (EEE) | en_US |
| dc.creator | Shan, Wenxi | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/14127 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Prediction and optimal operation of urban multi-energy systems | en_US |
| dcterms.abstract | There is a significant improvement of penetration rate of renewable energy. Therefore, how to efficiently use these energy sources has become a key issue. IESs (IESs) have also received increasing attention as a reliable solution to improve the efficiency of new energy use. These systems encompass multiple forms of energy and multiple loads, with urban multi-energy systems being the largest application scenario. The optimization of UMES operation is crucial for promoting the widespread use of distributed generators, reducing CO2 emissions, and enhancing overall efficiency. Therefore, considering the instability of renewable energy sources, how to optimize the operation to maximize the value of urban multi-energy sources has become an urgent problem to be solved. | en_US |
| dcterms.abstract | To solve this problem, first, this paper constructs a grid-connected urban energy system to facilitate the synergistic interaction of electric, heating, and cooling energy. The system considers the complementary nature of biomass biogas, solar, and wind energy and uses biomass cogeneration and energy form conversion equipment to achieve a multi-energy supply. The energy production sectors were modeled using physical modeling or a combination of physical and data-driven modeling, resulting in more realistic simulation results. Second, the demand side of the urban multi-energy system is modeled, and a combination of deep learning and multi-task learning is used to predict the demand-side energy-using activities. This approach improves the accuracy compared to the prediction of a single load, effectively simulating the demand side and providing a benchmark for subsequent optimal control. Third, the optimization objectives of multiple urban multi-energy systems are established, and the models developed for both supply and demand sides are transformed into physical constraints and simulated based on the ieee30 node system. The problem is then solved using the GA and NSGA-II algorithm, and the results are compared and analyzed. Comparison with the non-optimized case highlights the effectiveness of the two approaches in reducing operating costs and CO2 emissions in urban multi-energy systems. In addition, the two components of operating costs and environmental protection are not conflicting objectives when the new energy generation component is insufficient, whereas the opposite is true when the new energy generation component is sufficient and can satisfy the demand side. This study is essential for the subsequent improvement of the performance of urban multi-energy systems. | en_US |
| dcterms.extent | xi, 96 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2024 | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 8591.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 3.8 MB | Adobe PDF | View/Open |
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