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108 results on '"Sun, Yingyun"'

14. Short-Term Wind Power Scenario Generation Based on Conditional Latent Diffusion Models

Author

Dong, Xiaochong, Mao, Zhihang, Sun, Yingyun, and Xu, Xinzhi

Abstract

Quantifying short-term uncertainty in wind power plays a crucial role in power system decision-making. In recent years, the scenario generation community has conducted numerous studies employing generative models. Among these generative models, diffusion models have shown remarkable capabilities with excellent posterior representation. However, diffusion models are seldom used to quantify renewable energy uncertainty. To fill this research gap, this manuscript proposes a novel conditional latent diffusion model (CLDM) adapted for short-term scenario generation. CLDM decomposes the wind power scenario generation task into deterministic forecasting and forecast error scenario generation. The embedding network is used to regress deterministic forecasts, which reduces the denoising complexity of diffusion models. The denoising network generates forecast error scenarios in a latent space. Subsequently, the wind power scenarios are reconstructed by combining deterministic forecasts and forecast error scenarios. The case study compares with existing state-of-the-art methods, CLDM demonstrates superior evaluation metrics and enhances the denoising efficiency.

Published
2024
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15. Scenario Reduction Network Based on Wasserstein Distance With Regularization

Author

Dong, Xiaochong, Sun, Yingyun, Malik, Sarmad Majeed, Pu, Tianjiao, Li, Ye, and Wang, Xinying

Abstract

Power systems with high penetration of renewable energy contain various uncertainties. Scenario-based optimization problems need a large number of discrete scenarios to obtain a reliable approximation for the probabilistic model. It is important to choose typical scenarios and ease the computational burden. This paper presents a scenario reduction network model based on Wasserstein distance. Entropy regularization is used to transform the scenario reduction problem into an unconstrained problem. Through an explicit neural network structure design, the output of the scenario reduction network corresponds to the Sinkhorn distance function. The scenario reduction network can generate the typical scenario set through unsupervised learning. An efficient algorithm is proposed for continuous/discrete scenario reduction. The superiority of the scenario reduction network model is verified through case studies. The numerical results highlight the high accuracy and computational efficiency of the proposed model over state-of-the-art models, and it makes an ideal candidate for large-scale scenario reduction problems.

Published
2024
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34. Investigate Networked Microgrids to Enhance Distribution Network System Resilience

Author

Sanjarbek Rakhimov, Sun Yingyun, Ai Xin, Nilufar Makhamadjanova, and Jasur Abdubannaev

Subjects
Consumption (economics), business.industry, Computer science, media_common.quotation_subject, Turbine, Renewable energy, Power (physics), Electric power system, Electricity generation, Risk analysis (engineering), Psychological resilience, business, Natural disaster, media_common
Abstract

Modern society is becoming more dependent on secure and reliable power energy and increasing consumption power energy simultaneously. Rising fuel prices and growing environmental damage motivate Generation companies to use renewable energy. In this regard, Microgrids are being considered to build a future power network system and are being implemented widely due to its various advantages. It is made more promising by connecting it in parallel with nonrenewable distributed generators (e.g. Micro turbine and Fuel cell). Modern society needs more reliable, secure, and safe power. A Rising number of natural disasters that caused large blackouts put change of structure of the current power system. This paper investigates the impact of Microgrids to enhance the power system resiliency. The impact of networked Microgrids and distributed generators to enhance resilience and using theirs effectively during and after outages are also considered in this paper.

Published
2020

41. Optimal scheduling of asymmetrical AC/DC micro-grid based on PET

Author

Zhang Tao, Sun Yingyun, Pu Tianjiao, Chen Naishi, and Dong Lei

Subjects
Correctness, Computer science, 020209 energy, Micro grid, 02 engineering and technology, Network topology, law.invention, Control theory, law, Optimal scheduling, Power electronics, 0202 electrical engineering, electronic engineering, information engineering, Transformer, Low voltage, Voltage
Abstract

Aiming at the AC/DC hybrid micro-grid with power electronic transformer (PET) and considering the asymmetric operation mode of three-phase load in low voltage AC system, a multi-objective optimal scheduling model of AC/DC system based on PET is proposed in this paper. In view of the topology of PET establish its steady state model then analyzing the three-phase unbalance mitigation mechanism. Make full use of the flexibility of multi-port PET to coordinate the controlled DGs in AC/DC networks. Then a long-time scale optimal scheduling model was established to minimize the operating costs and suppression the three-phase voltage unbalance factor. The economy and reliability of the system have been improved. Finally through the simulation analysis of the constructed example, the results verify the correctness and effectiveness of the proposed model in this paper.

Published
2018

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