Bojun Du

Spatial LLM Workload Shifting Needs Foresight: Model Commitment for AI Data Center Operation under Power Grid Constraints

Bojun Du, Hongyang Jia, Tonghui Li, Qingchun Hou et autres

AI data centers may face power supply shortages during certain periods, requiring operators to shift large language model (LLM) inference workloads spatially to maintain service rates. However, existing workload-shifting methods typically assume that any data center with sufficient computing resources can immediately …

Toward trustworthy security rules for MILP-based power system operation: An uncertainty-aware extraction framework

Xiao Cai, Mingyu Huang, Bojun Du, Qingchun Hou et autres

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From Tokens to Energy Flexibility: Quantization-Enabled Demand Response for Data Centers with LLM Inference Workloads

Bojun Du, Xiaoyi Fan, Ershun Du, Long Chen et autres

The rapid growth of large language model (LLM) inference is creating significant data-center loads that face increasing energy-management challenges under tightening grid conditions and demand response (DR) requirements. Conventional data-center energy management mainly relies on temporal and spatial workload shifting and campus-level …

Sustainable Artificial Intelligence: Power Through Computility–Electricity–Carbon Coordination

Xinxin Ge, Bojun Du, Ershun Du, Jianpei Han et autres

Artificial intelligence (AI) has soared to the top of the research area, business agenda, and people’s daily life, accelerating the construction of data centers and driving demand for more high-energy-intensity facilities. The energy-intensive operation of servers in data centers, along with the …

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Carbon-Aware Spatial-Temporal Scheduling for Multiple AI Data Center Parks with Training and Inference Workloads Characteristics

Jianpei Han, Ershun Du, Bojun Du, Yaowang Li et autres

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Evaluating the Dispatchable Capacity of Cross-Regional Data Center Clusters Toward Power System Operation

Jianpei Han, Ershun Du, Bojun Du, Yaowang Li et autres

Data center clusters (DCCs) are becoming a promising dispatchable resource for power system owing to the temporal and spatial flexibility. To characterize the dispatchable potential of DCCs for power system operation accurately, this study focuses on the cross-regional DCCs with communication links …

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Evaluating the carbon-emission reduction potential of employing low-carbon demand response to guide electric-vehicle charging: A Chinese case study

Bojun Du, Hongyang Jia, Bochao Zhang, Yaowang Li et autres

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Estimating the carbon emission reduction potential of using carbon-oriented demand response for data centers: A case study in China

Bojun Du, Hongyang Jia, Yaowang Li, Ershun Du et autres

The rapid advancement of artificial intelligence (AI) has significantly increased the computational load on data centers. AI-related computational activities consume considerable electricity and result in substantial carbon emissions. To mitigate these emissions, future data centers should be strategically planned and operated to …

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Vision, technology framework, and roadmap of the new power system in China

Bojun Du, Xiao Jing Cai, Qihuan Dong, Qiuyu Chen et autres

Power system decarbonization enables society-wide efforts to achieve carbon neutrality. This paper outlines the vision, pathways, and policy implications of constructing a new power system that is compatible with carbon neutrality goals. It analyzes eight key technological components of the new power …

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Evaluating the Carbon-Emission Reduction Potential of Employing Low-Carbon Demand Response to Guide Electric-Vehicle Charging: A Chinese Case Study

Bojun Du, Hongyang Jia, Bochao Zhang, Yaowang Li et autres

cn (code pays fourni par la source)