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Power-controllable variable refrigerant flow system with flexibility value for demand response Journal article
Ren, Peng, Chen, Lunshu, Hui, Hongxun. Power-controllable variable refrigerant flow system with flexibility value for demand response[J]. Energy, 2024, 313, 133820.
Authors:  Ren, Peng;  Chen, Lunshu;  Hui, Hongxun
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:9.0/8.2 | Submit date：2024/12/05
Demand Response  Fuzzy Control  Power Regulation  Predicted Mean Vote  Variable Refrigerant Flow System  
Adaptive Tie-line Power Smoothing with Renewable Generation Based on Risk-aware Reinforcement Learning Journal article
Peipei Yu, Hongcai Zhang, Yonghua Song. Adaptive Tie-line Power Smoothing with Renewable Generation Based on Risk-aware Reinforcement Learning[J]. IEEE Transactions on Power Systems, 2024, 39(6), 6819-6832.
Authors:  Peipei Yu;  Hongcai Zhang;  Yonghua Song
Favorite | TC[WOS]:2 TC[Scopus]:5  IF:6.5/7.4 | Submit date：2024/04/24
Tie-line Power Smoothing  Demand Response  Renewable Generation  Risk-aware Reinforcement Learning  
Bidding Mechanism Design for Building Virtual Power Plant to Participate in Demand Response Markets 建 筑 虚 拟 电 厂 参 与 需 求 响 应 市 场 的 报 量 报 价 机 制 设 计 Journal article
Qi, Taoyi, Hui, Hongxun, Ye, Chengjin, Ding, Yi, Zhao, Yuming, Song, Yonghua. Bidding Mechanism Design for Building Virtual Power Plant to Participate in Demand Response Markets 建 筑 虚 拟 电 厂 参 与 需 求 响 应 市 场 的 报 量 报 价 机 制 设 计[J]. Dianli Xitong Zidonghua/Automation of Electric Power Systems, 2024, 48(18), 14-24.
Authors:  Qi, Taoyi;  Hui, Hongxun;  Ye, Chengjin;  Ding, Yi;  Zhao, Yuming; et al.
Favorite | TC[Scopus]:0 | Submit date：2024/11/05
Bidding Mechanism  Demand Response  Electricity Market  Flexible Load  Urban Building  Virtual Power Plant  
A 6.5-to-6.9-GHz SSPLL with Configurable Differential Dual-Edge SSPD Achieving 44-fs RMS Jitter, -260.7-dB FOMJitter and -76.5-dBc Reference Spur Conference paper
Chen, Tianle, Ren, Hongyu, Yang, Zunsong, Huang, Yunbo, Meng, Xianghe, Yan, Weiwei, Zhang, Weidong, Zheng, Xuqiang, Guo, Xuan, Iizuka, Tetsuya, Mak, Pui In, Chen, Yong, Li, Bo. A 6.5-to-6.9-GHz SSPLL with Configurable Differential Dual-Edge SSPD Achieving 44-fs RMS Jitter, -260.7-dB FOMJitter and -76.5-dBc Reference Spur[C]:Institute of Electrical and Electronics Engineers Inc., 2024.
Authors:  Chen, Tianle;  Ren, Hongyu;  Yang, Zunsong;  Huang, Yunbo;  Meng, Xianghe; et al.
Favorite | TC[Scopus]:0 | Submit date：2024/10/10
Phase Noise  Power Demand  Laser Mode Locking  Prototypes  Detectors  Crystals  Very Large Scale Integration  
Exploiting demand-side heterogeneous flexible resources in risk management of power system frequency Journal article
Yao, Yu, Song, Yong Hua, Ye, Cheng Jin, Ding, Yi, Zhao, Yu Ming. Exploiting demand-side heterogeneous flexible resources in risk management of power system frequency[J]. SCIENCE CHINA-TECHNOLOGICAL SCIENCES, 2024, 67(5), 16120-1627.
Authors:  Yao, Yu;  Song, Yong Hua;  Ye, Cheng Jin;  Ding, Yi;  Zhao, Yu Ming
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:4.4/4.3 | Submit date：2024/05/16
Aggregation Method  Demand-side Heterogenous Flexible Resources  Mixed Integer Second-order Cone Programming  Performance Curve  Risk Management Of Power System Frequency  
The Race for the Extra Pico Second without Losing the Decibel: A Partial-Review of Single-Channel Energy-Efficient High-Speed Nyquist ADCs Conference paper
Chan, Chi Hana, Zhang, Minglei, Cao, Yuefena, Zhao, Honazhi, Martins, Rui P., Zhu, Yan. The Race for the Extra Pico Second without Losing the Decibel: A Partial-Review of Single-Channel Energy-Efficient High-Speed Nyquist ADCs[C]:Institute of Electrical and Electronics Engineers Inc., 2024, 28-1.
Authors:  Chan, Chi Hana;  Zhang, Minglei;  Cao, Yuefena;  Zhao, Honazhi;  Martins, Rui P.; et al.
Favorite | TC[WOS]:0 TC[Scopus]:0 | Submit date：2024/06/05
Technological Innovation  Quantization (Signal)  Power Demand  Energy Resolution  Reliability Engineering  Energy Efficiency  Trajectory  Time-domain Analysis  Task Analysis  Signal Resolution  
A 28nm 314.6TLFOPS/W Reconfigurable Floating-Point Analog Compute-In-Memory Macro with Exponent Approximation and Two-Stage Sharing TD-ADC Conference paper
He, Pengyu, Zhao, Yuanzhe, Xie, Heng, Wang, Yang, Yin, Shouyi, Li, Li, Zhu, Yan, Martins, R. P., Chan, Chi Hang, Zhang, Minglei. A 28nm 314.6TLFOPS/W Reconfigurable Floating-Point Analog Compute-In-Memory Macro with Exponent Approximation and Two-Stage Sharing TD-ADC[C]:Institute of Electrical and Electronics Engineers Inc., 2024.
Authors:  He, Pengyu;  Zhao, Yuanzhe;  Xie, Heng;  Wang, Yang;  Yin, Shouyi; et al.
Favorite | TC[WOS]:0 TC[Scopus]:2 | Submit date：2024/06/05
Power Demand  In-memory Computing  Throughput  Energy Efficiency  Hardware  Common Information Model (Computing)  Artificial Intelligence  
Customer baseline load estimation for virtual power plants in demand response: An attention mechanism-based generative adversarial networks approach Journal article
Wang, Zhenyi, Zhang, Hongcai. Customer baseline load estimation for virtual power plants in demand response: An attention mechanism-based generative adversarial networks approach[J]. Applied Energy, 2024, 357, 122544.
Authors:  Wang, Zhenyi;  Zhang, Hongcai
Favorite | TC[WOS]:3 TC[Scopus]:4  IF:10.1/10.4 | Submit date：2024/04/02
Attention Mechanism  Baseline Load Estimation  Demand Response  Generative Adversarial Networks  Virtual Power Plant  
Constraint learning-based optimal power dispatch for active distribution networks with extremely imbalanced data Journal article
Yonghua Song, Ge Chen, Hongcai Zhang. Constraint learning-based optimal power dispatch for active distribution networks with extremely imbalanced data[J]. CSEE Journal of Power and Energy Systems, 2024, 10(1), 51-65.
Authors:  Yonghua Song;  Ge Chen;  Hongcai Zhang
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:6.9/6.9 | Submit date：2024/04/24
Deep Learning  Demand Response  Distribution Networks  Imbalanced Data  Optimal Power Flow  
A 28-nm 19.9-to-258.5-TOPS/W 8b Digital Computing-in-Memory Processor With Two-Cycle Macro Featuring Winograd-Domain Convolution and Macro-Level Parallel Dual-Side Sparsity Journal article
Wu, Hao, Chen, Yong, Yuan, Yiyang, Yue, Jinshan, Wang, Xinghua, Li, Xiaoran, Zhang, Feng. A 28-nm 19.9-to-258.5-TOPS/W 8b Digital Computing-in-Memory Processor With Two-Cycle Macro Featuring Winograd-Domain Convolution and Macro-Level Parallel Dual-Side Sparsity[J]. IEEE Journal of Solid-State Circuits, 2024.
Authors:  Wu, Hao;  Chen, Yong;  Yuan, Yiyang;  Yue, Jinshan;  Wang, Xinghua; et al.
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:4.6/5.6 | Submit date：2024/07/04
Accuracy  Artificial Intelligence  Artificial Intelligence (Ai)  Circuits  Cmos  Computing-in-memory (Cim)  Energy Efficiency  Energy Efficiency  Look-up Table (Lut)  Multiply-accumulation (Mac)  Neural Network (Nn)  Power Demand  Radix16  Table Lookup  Throughput  Unstructured Sparsity  Winograd Convolution