Research on Multi-Objective Coordinated Planning of Distribution Networks Based on Improved Generative Adversarial Networks
This paper addresses the significant challenges that the uncertainty of renewable energy (RE) outputs, such as wind and solar power, bring to distribution network planning and operation by proposing a multi-objective bi-level distribution network planning model based on an improved generative advers...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11096593/ |
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| Summary: | This paper addresses the significant challenges that the uncertainty of renewable energy (RE) outputs, such as wind and solar power, bring to distribution network planning and operation by proposing a multi-objective bi-level distribution network planning model based on an improved generative adversarial network and carbon footprint analysis. Firstly, a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) is employed to simulate numerous wind and solar output scenarios, which are then reduced using the K-medoids clustering algorithm. Secondly, carbon footprint coefficients for each generation unit are determined through a life cycle assessment method. Next, a bi-level distribution network planning model considering carbon footprints is established: the upper level minimizes the annual comprehensive cost by optimizing the planning schemes of distributed generation (DG), energy storage systems (ESS), and capacitor banks (CB); the lower level minimizes operating costs, voltage deviations, and carbon emissions by formulating operation strategies under typical scenarios, considering on-load tap changers (OLTC), controllable loads, capacitor banks, energy storage, and distributed generation. Then, the upper and lower levels of the model are coupled and unified into a single-level model. The normalized normal constraint (NNC) method is used to solve the single-level multi-objective model. Finally, simulation analyses are conducted on the IEEE 33-node distribution system to verify the model’s effectiveness. |
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| ISSN: | 2169-3536 |