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考虑综合需求响应的不确定可再生能源发电的社区综合能源系统的数据驱动分布式稳健调度 - ScienceDirect
Source: sciencedirect.com
Article summary:
1. A two-stage distributed robust optimization (DRO) model is proposed to coordinate the response of renewable energy generation (RGs) to integrated demand and uncertainty.
2. A Wasserstein distance generative adversarial network (WGAN-GP) is used to generate RG scenarios in order to address the uncertainty of renewables.
3. The fuzzy nature of human thermal comfort and building thermal inertia are taken into account in order to promote the consumption of renewable energy.
Article analysis:
The article “Data-Driven Distributed Robust Scheduling for Community Integrated Energy Systems Considering Integrated Demand Response with Uncertain Renewable Generation” provides a comprehensive overview of a two-stage distributed robust optimization (DRO) model that can be used to coordinate the response of renewable energy generation (RGs) to integrated demand and uncertainty. The article is well written, providing clear explanations and examples throughout, as well as detailed descriptions of the methods used. The authors also provide a thorough analysis of their results, which adds credibility to their claims.
However, there are some potential biases in the article that should be noted. For example, while the authors do discuss some potential risks associated with their proposed model, they do not explore any counterarguments or alternative solutions that could be used instead. Additionally, while they do provide evidence for their claims, it is limited and does not cover all aspects of their argument. Furthermore, there is no mention of any promotional content or partiality in the article, which could lead readers to believe that all information presented is unbiased and objective when this may not necessarily be true.
In conclusion, while this article provides an interesting perspective on data-driven distributed robust scheduling for community integrated energy systems considering integrated demand response with uncertain renewable generation, it should be read critically in order to identify any potential biases or unsupported claims made by the authors.