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| Data-driven distributionally robust collaborative optimization operation strategy for a multi-microgrid based on LSTM-CGAN |
| DOI：10.19783/j.cnki.pspc.231538 |
| Key Words:multi-microgrid distributionally robust optimization cooperative benefits long short-term memory network conditional generative adversarial networks |
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| Abstract:The high uncertainty of renewable energy poses significant challenges to the reliable and secure operation of multi-microgrids. To address this, a two-stage distributed robust coordination optimization model driven by data is proposed based on long short-term memory (LSTM) networks and conditional generative adversarial networks (CGAN). First, to accurately characterize the uncertainty of renewable energy, the model generates an initial set of renewable energy scenarios for the distributionally robust optimization (DRO) set using LSTM-CGAN and the K-means++ clustering algorithm. The CGAN model uses Wasserstein distance as the discriminator loss function, with the day-ahead renewable energy forecast as the conditional variable for the generative adversarial network, and employs LSTM to construct the generator and discriminator. Secondly, a benefit allocation method based on the contribution rate of multi-node energy trading is proposed to achieve fair distribution of cooperative benefits. In addition, to protect the privacy of individual entities and improve solution efficiency, an alternating direction multiplier method (ADMM) is proposed. This couples parallel computation of columns and constraint generation (C&CG) to solve the energy trading problem. Case study results demonstrate that the proposed approach, with the scenario-driven method for generating scenario sets, can accurately and effectively describe the uncertainty of renewable energy, while considering system robustness, economic efficiency, and privacy, and achieving fair and reasonable benefit allocation for each entity. |
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