Yuanzheng Li
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View article: Effect of Cu Content on Corrosion Resistance of 3.5%Ni Weathering Steel in Marine Atmosphere of South China Sea
Effect of Cu Content on Corrosion Resistance of 3.5%Ni Weathering Steel in Marine Atmosphere of South China Sea Open
The influence of the copper (Cu) content on the corrosion resistance of 3.5%Ni low-carbon weathering steel was investigated using periodic dry–wet cycle accelerated corrosion tests. The mechanical properties of the steels were assessed via…
View article: Parameter Optimization of Grid‐Forming Converters by Maximizing Domain of Attraction Estimates of Converter‐Integrated Power Systems
Parameter Optimization of Grid‐Forming Converters by Maximizing Domain of Attraction Estimates of Converter‐Integrated Power Systems Open
Grid‐forming converters (GFMCs) are expected to replace the function of synchronous generators (SGs) in the future power system with high integration rate of power electronics converters. Transient stability of such systems will be signifi…
View article: ZTFed-MAS2S: A Zero-Trust Federated Learning Framework With Verifiable Privacy and Trust-Aware Aggregation for Wind Power Data Imputation
ZTFed-MAS2S: A Zero-Trust Federated Learning Framework With Verifiable Privacy and Trust-Aware Aggregation for Wind Power Data Imputation Open
Wind power data often suffers from missing values due to sensor faults and unstable transmission at edge sites. While federated learning enables privacy-preserving collaboration without sharing raw data, it remains vulnerable to anomalous …
View article: Physical Informed-Inspired Deep Reinforcement Learning Based Bi-Level Programming for Microgrid Scheduling
Physical Informed-Inspired Deep Reinforcement Learning Based Bi-Level Programming for Microgrid Scheduling Open
To coordinate the interests of operator and users in a microgrid under complex and changeable operating conditions, this paper proposes a microgrid scheduling model considering the thermal flexibility of thermostatically controlled loads a…
View article: Industry demand response in dispatch strategy for high-proportion renewable energy power system
Industry demand response in dispatch strategy for high-proportion renewable energy power system Open
On the power supply side, renewable energy (RE) is an important substitute to traditional energy, the effective utilization of which has become one of the major challenges in risk-constrained power system operations. This paper proposes a …
View article: A computational efficient approach for distributionally robust unit commitment with enhanced disjointed layered ambiguity set
A computational efficient approach for distributionally robust unit commitment with enhanced disjointed layered ambiguity set Open
To achieve the sustainable development of the society, renewable energy dominated power systems are gradually formed. However, the uncertainty of renewable power poses challenges for power system operations, such as balancing the load and …
View article: A Demand–Supply Cooperative Responding Strategy in Power System With High Renewable Energy Penetration
A Demand–Supply Cooperative Responding Strategy in Power System With High Renewable Energy Penetration Open
Industrial demand response (IDR) plays an important role in promoting the\nutilization of renewable energy (RE) in power systems. However, it will lead to\npower adjustments on the supply side, which is also a non-negligible factor in\naff…
View article: Enhancing Cyber-Resilience in Integrated Energy System Scheduling with Demand Response Using Deep Reinforcement Learning
Enhancing Cyber-Resilience in Integrated Energy System Scheduling with Demand Response Using Deep Reinforcement Learning Open
Optimally scheduling multi-energy flow is an effective method to utilize renewable energy sources (RES) and improve the stability and economy of integrated energy systems (IES). However, the stable demand-supply of IES faces challenges fro…
View article: Interpretable data‐driven contingency classification for real‐time corrective security‐constrained economic dispatch
Interpretable data‐driven contingency classification for real‐time corrective security‐constrained economic dispatch Open
High penetrations of renewable energy are crucial for low‐carbon power systems. However, the higher volatility of renewable power generation pushes real‐time operations closer to equipment limits. It is thus important to utilize flexibilit…
View article: Tight power and energy coupling constraints of energy storage resources for unit commitment
Tight power and energy coupling constraints of energy storage resources for unit commitment Open
Energy Storage Resources (ESRs) can help promote high penetrations of renewable generation and shift the peak load. However, the increasing number of ESRs and their features different from conventional generators bring computational challe…
View article: Federated Multiagent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multimicrogrid Energy Management
Federated Multiagent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multimicrogrid Energy Management Open
The utilization of large-scale distributed renewable energy (RE) promotes the development of the multimicrogrid (MMG), which raises the need of developing an effective energy management method to minimize economic costs and keep self energ…
View article: Resilient Power Systems Operation with Offshore Wind Farms and Cloud Data Centers
Resilient Power Systems Operation with Offshore Wind Farms and Cloud Data Centers Open
To enhance the resilience of power systems with offshore wind farms (OWFs), a proactive scheduling scheme is proposed to unlock the flexibility of cloud data centers (CDCs) responding to uncertain spatial and temporal impacts induced by hu…
View article: PMU Measurements-Based Short-Term Voltage Stability Assessment of Power Systems via Deep Transfer Learning
PMU Measurements-Based Short-Term Voltage Stability Assessment of Power Systems via Deep Transfer Learning Open
Deep learning has emerged as an effective solution for addressing the\nchallenges of short-term voltage stability assessment (STVSA) in power systems.\nHowever, existing deep learning-based STVSA approaches face limitations in\nadapting to…
View article: Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approach
Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approach Open
In a modern power system with an increasing proportion of renewable energy, wind power prediction is crucial to the arrangement of power grid dispatching plans due to the volatility of wind power. However, traditional centralized forecasti…
View article: Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach
Detection of False Data Injection Attacks in Smart Grid: A Secure Federated Deep Learning Approach Open
As an important cyber-physical system (CPS), smart grid is highly vulnerable to cyber attacks. Amongst various types of attacks, false data injection attack (FDIA) proves to be one of the top-priority cyber-related issues and has received …
View article: Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach
Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach Open
The electric vehicle (EV) and electric vehicle charging station (EVCS) have been widely deployed with the development of large-scale transportation electrifications. However, since charging behaviors of EVs show large uncertainties, the fo…