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Yinliang Xu - IEEE Xplore Author Profile
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Yinliang Xu

Also published under: Yingliang Xu

Publications
143
Citations
4,959
Publications by Year
20112025
Co-Authors:
Show All Co-Authors (202)
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Yinliang Xu

Also published under: Yingliang Xu

Affiliation

Tsinghua Shenzhen International Graduate School
Tsinghua University
Shenzhen, China

Publication Topics

Biography

Yinliang Xu (Senior Member, IEEE) received the B.S. and M.S. degrees in control science and engineering from the Harbin Institute of Technology, China, in 2007 and 2009, respectively, and the Ph.D. degree in electrical and computer engineering from New Mexico State University, Las Cruces, NM, USA, in 2013.,He is currently an Assistant Professor with Tsinghua–Berkeley Shenzhen Institute, Lab of Smart Grid and Renewable Energy, Shenzhen, China. From September 2015 to August 2018, he was an Adjunct Faculty with the Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA. From November 2013 to August 2017, he was with the School of Electronics and Information Technology, Sun Yat-sen University. From 20...
Publications
143
Citations
4,959
Publications by Year
20112025
Co-Authors:
Show All Co-Authors (202)
Author's Published Works

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Fast Charging Management of a Lithium-Ion Battery and Cooling System: A Stackelberg Game-Based Soft Actor Critic−Deep Reinforcement Learning Method

Hongrong Yang;Quanyi Chen;Xiaoying Shi;Yinliang Xu;Xuan Zhang

IEEE Transactions on Industrial Electronics
Year: 2025 | Early Access Article |
This article proposes a fast charging management strategy for lithium-ion batteries and cooling systems, tackling the challenge of achieving fast charging under multiple physical constraints while minimizing cooling energy consumption and battery aging. The complex coupling between the battery and cooling system is formulated as a Stackelberg game-based bilevel optimization framework, reflecting t...Show More

Fast Charging Management of a Lithium-Ion Battery and Cooling System: A Stackelberg Game-Based Soft Actor Critic−Deep Reinforcement Learning Method

Hongrong Yang;Quanyi Chen;Xiaoying Shi;Yinliang Xu;Xuan Zhang

Year: 2025 | Early Access Article |

Distribution Locational Marginal Emission for Carbon Alleviation in Distribution Networks: Formulation, Calculation, and Implication

Linwei Sang;Yinliang Xu;Hongbin Sun;Zaijun Wu;Qiuwei Wu;Wenchuan Wu

IEEE Transactions on Automation Science and Engineering
Year: 2025 | Volume: 22 | Journal Article |
Regulating the proper carbon-aware intervention policy is one of the keys to emission alleviation in the distribution network, whose basis lies in effectively attributing the emission responsibility using emission factors. This paper establishes the distribution locational marginal emission (DLME) to calculate the marginal change of emission from the marginal change of both active and reactive loa...Show More

Distribution Locational Marginal Emission for Carbon Alleviation in Distribution Networks: Formulation, Calculation, and Implication

Linwei Sang;Yinliang Xu;Hongbin Sun;Zaijun Wu;Qiuwei Wu;Wenchuan Wu

Year: 2025 | Volume: 22 | Journal Article |

Hydrogen Energy Storage System Participated Decentralized Voltage Control With Multi-Agent Deep Reinforcement Learning Method

Xian Zhang;Changlei Gu;Hong Wang;Guibin Wang;Yinliang Xu;Ahmed Rabee Sayed

IEEE Transactions on Industry Applications
Year: 2025 | Volume: 61, Issue: 2 | Journal Article |
With the development of power electronic technology, smart inverters and energy storage systems are progressively employed for voltage regulation in active distribution networks (ADNs). In this article, we incorporate hydrogen energy storage system (HESS) into distribution network voltage control and propose a cooperated voltage control framework. At first, we formulate a two-timescale voltage con...Show More

Hydrogen Energy Storage System Participated Decentralized Voltage Control With Multi-Agent Deep Reinforcement Learning Method

Xian Zhang;Changlei Gu;Hong Wang;Guibin Wang;Yinliang Xu;Ahmed Rabee Sayed

Year: 2025 | Volume: 61, Issue: 2 | Journal Article |

A Mimicking-Avoiding Short-Term Probabilistic Power Forecasting Method for Wave Energies

Haoxuan Chen;Yinliang Xu;Hongbin Sun

IEEE Transactions on Sustainable Energy
Year: 2025 | Volume: 16, Issue: 1 | Journal Article |
Wave energy is essential for sustainable marine development. However, complex marine weather conditions cause fluctuating wave power outputs, resulting in a mimicking phenomenon in predictions. Moreover, the lack of accurate numerical weather prediction (NWP) data sources aggravates the prediction inaccuracy. To address these obstacles, a series characteristic perception (SCP) method coordinated w...Show More

A Mimicking-Avoiding Short-Term Probabilistic Power Forecasting Method for Wave Energies

Haoxuan Chen;Yinliang Xu;Hongbin Sun

Year: 2025 | Volume: 16, Issue: 1 | Journal Article |

Electric Vehicles Management in Distribution Network: A Data-Efficient Bi-Level Safe Deep Reinforcement Learning Method

Hongrong Yang;Yinliang Xu;Hongbin Sun;Qinglai Guo;Qiong Liu

IEEE Transactions on Power Systems
Year: 2025 | Volume: 40, Issue: 1 | Journal Article |
Cited by: Papers (6)
In this paper, a deep reinforcement learning (DRL)-based electric vehicles (EVs) management strategy is proposed to achieve peak shaving and regulate the voltage violations in distribution networks. We present a new approach for modeling the EV user willingness considering the effect of multi-attribute attitudes and price incentives on user’ behaviors. The real-time optimal regulation problem is m...Show More

Electric Vehicles Management in Distribution Network: A Data-Efficient Bi-Level Safe Deep Reinforcement Learning Method

Hongrong Yang;Yinliang Xu;Hongbin Sun;Qinglai Guo;Qiong Liu

Year: 2025 | Volume: 40, Issue: 1 | Journal Article |

Robust Security Constrained Energy and Regulation Service Bidding Strategy for a Virtual Power Plant

Zhongkai Yi;Yinliang Xu;Xuan Wei;Hongbin Sun

CSEE Journal of Power and Energy Systems
Year: 2025 | Volume: 11, Issue: 2 | Journal Article |
In addition to renewable energy sources and market prices uncertainties, the regulation commands issued by the superior market operator are unpredictable factors within the virtual power plant (VPP) bidding range. To avoid branch power flow outage and voltage violation under uncertain regulation commands, a tri-level robust optimization-based day-ahead energy and regulation service bidding strateg...Show More

Robust Security Constrained Energy and Regulation Service Bidding Strategy for a Virtual Power Plant

Zhongkai Yi;Yinliang Xu;Xuan Wei;Hongbin Sun

Year: 2025 | Volume: 11, Issue: 2 | Journal Article |

Deep Clustering for Interpretable Electric-Carbon Potential Analysis in Smart Grid Resource Management

Yifan Lv;Yinliang Xu

2024 IEEE 8th Conference on Energy Internet and Energy System Integration (EI2)
Year: 2024 | Conference Paper |
The emergence of modern smart grids introduces a more distributed and diverse structure compared to traditional grids. The widespread adoption of distributed energy resources (DER), energy storage systems, and flexible loads, along with the implementation of virtual power plant (VPP) technology, enables the centralized coordination and control of these assets. This paper proposes a deep clustering...Show More

Deep Clustering for Interpretable Electric-Carbon Potential Analysis in Smart Grid Resource Management

Yifan Lv;Yinliang Xu

Year: 2024 | Conference Paper |

Low-carbon Optimal Energy Management of Integrated Electricity-Natural Gas Systems Considering Uncertainties

Shiqi Sun;Yinliang Xu

2024 IEEE 8th Conference on Energy Internet and Energy System Integration (EI2)
Year: 2024 | Conference Paper |
As global warming becomes increasingly severe, Integrated energy systems play an important role in carbon peaking and carbon neutrality goals. On the one hand, it can improve the security and reliability of energy supply through multi-energy complementary, on the other hand, it can also improve energy utilization efficiency and reduce carbon emissions through energy conversion at the same time. So...Show More

Low-carbon Optimal Energy Management of Integrated Electricity-Natural Gas Systems Considering Uncertainties

Shiqi Sun;Yinliang Xu

Year: 2024 | Conference Paper |

Active Distribution Network Planning Considering Flexibility of Distributed Energy Resources

Jiongwei Lin;Yinliang Xu

2024 IEEE 8th Conference on Energy Internet and Energy System Integration (EI2)
Year: 2024 | Conference Paper |
As the penetration of distributed resources (DRs), such as electric vehicles (EVs) and renewable energy systems, continues to rise, effective distribution network planning faces increasing complexity. This paper presents an active distribution network planning model that takes into account network topology and distributed resources. The proposed model incorporates a wide range of flexible resource...Show More

Active Distribution Network Planning Considering Flexibility of Distributed Energy Resources

Jiongwei Lin;Yinliang Xu

Year: 2024 | Conference Paper |

Three-objective Capacity Planning of Distributed Energy Resources in Distribution Network

Yuqi Wang;Yinliang Xu;Hongbin Sun

2024 IEEE Power & Energy Society General Meeting (PESGM)
Year: 2024 | Conference Paper |
The wide deployment of distributed energy resources provokes the concerns with the impossible energy triangle. A three-objective capacity planning model of distributed energy resources regarding economic cost, carbon emission and voltage deviation is proposed. The capacity planning includes two stages. Firstly, the capacity of PV unit and energy storage system (ESS) is determined. Secondly, the op...Show More

Three-objective Capacity Planning of Distributed Energy Resources in Distribution Network

Yuqi Wang;Yinliang Xu;Hongbin Sun

Year: 2024 | Conference Paper |

Pricing Frequency Ancillary Services for Low-Inertia Power Systems under Model Nonconvexity

Zhihao Li;Yinliang Xu

2024 3rd Conference on Fully Actuated System Theory and Applications (FASTA)
Year: 2024 | Conference Paper |
To enhance the frequency stability in low-inertia power systems, system operators incentivize controllable energy resources to defiver the frequency ancillary service in the wholesale electricity market. In this paper, a pricing scheme for frequency ancillary service is established for low-inertia power systems in the day-ahead electricity market. The distinct response characteristics of thermal g...Show More

Pricing Frequency Ancillary Services for Low-Inertia Power Systems under Model Nonconvexity

Zhihao Li;Yinliang Xu

Year: 2024 | Conference Paper |

Incentivizing EVs to Participate in V2B Interactions using the Stackelberg Game

Zixuan Wei;Wai Kin Victor Chan;Yi Zhang;Yinliang Xu;Linwei Sang;Rongtao Ye

2024 7th International Conference on Energy, Electrical and Power Engineering (CEEPE)
Year: 2024 | Conference Paper |
V2B(Vehicle-to-Building) is an economic proposition of the V2X(Vehicle-to-Everything) technology. It has been demonstrated that V2B technology may effectively increase the usage of renewable energy. However, there is scant evidence that Grid incentives might indirectly encourage EVs to engage in V2B activities by affecting building behavior. In this paper, the Stackelberg game model is used to des...Show More

Incentivizing EVs to Participate in V2B Interactions using the Stackelberg Game

Zixuan Wei;Wai Kin Victor Chan;Yi Zhang;Yinliang Xu;Linwei Sang;Rongtao Ye

Year: 2024 | Conference Paper |

Learning-Based Real-Time Aggregate Flexibility Provision and Scheduling of Electric Vehicles

Mingyang Zhang;Hongrong Yang;Yinliang Xu;Hongbin Sun

IEEE Transactions on Smart Grid
Year: 2024 | Volume: 15, Issue: 6 | Journal Article |
Cited by: Papers (2)
It is recognized that large-scale electric vehicles (EVs) can be aggregated and behave as a controllable storage to provide flexibility for power systems. To provide high-quality services to both the system and EV users, it is critical to accurately estimate the aggregate flexibility of EVs, which is highly challenging due to the uncertainties from regulation signals and EV behaviors. Thus, this p...Show More

Learning-Based Real-Time Aggregate Flexibility Provision and Scheduling of Electric Vehicles

Mingyang Zhang;Hongrong Yang;Yinliang Xu;Hongbin Sun

Year: 2024 | Volume: 15, Issue: 6 | Journal Article |

Peer-to-Peer Energy Trading of Carbon-Aware Prosumers: An Online Accelerated Distributed Approach With Differential Privacy

Xuan Wei;Yinliang Xu;Hongbin Sun;Wai Kin Chan

IEEE Transactions on Smart Grid
Year: 2024 | Volume: 15, Issue: 6 | Journal Article |
Cited by: Papers (2)
Peer-to-peer (P2P) energy trading offers a promising way for prosumers to achieve multi-bilateral trades, further aids the integration of distributed energy resources into distribution networks and facilitates the low-carbon operation of the system. But realizing this potential requires overcoming challenges in model formulation and distributed optimization. This paper presents a novel P2P energy ...Show More

Peer-to-Peer Energy Trading of Carbon-Aware Prosumers: An Online Accelerated Distributed Approach With Differential Privacy

Xuan Wei;Yinliang Xu;Hongbin Sun;Wai Kin Chan

Year: 2024 | Volume: 15, Issue: 6 | Journal Article |

Carbon-Aware Peer-to-Peer Joint Energy and Reserve Trading Market for Prosumers in Distribution Networks

Zehao Song;Yinliang Xu;Lun Yang;Hongbin Sun

IEEE Internet of Things Journal
Year: 2024 | Volume: 11, Issue: 14 | Journal Article |
Cited by: Papers (1)
The increasing penetration of distributed energy resources (DERs) has facilitated the development of peer-to-peer (P2P) trading mechanism. An efficient P2P trading market framework is essential to integrate various kinds of DERs into new power systems while ensuring network security constraints (NSCs). This article proposes a carbon-aware P2P trading market to realize joint energy and reserve trad...Show More

Carbon-Aware Peer-to-Peer Joint Energy and Reserve Trading Market for Prosumers in Distribution Networks

Zehao Song;Yinliang Xu;Lun Yang;Hongbin Sun

Year: 2024 | Volume: 11, Issue: 14 | Journal Article |

Networked Multiagent-Based Safe Reinforcement Learning for Low-Carbon Demand Management in Distribution Networks

Jichen Zhang;Linwei Sang;Yinliang Xu;Hongbin Sun

IEEE Transactions on Sustainable Energy
Year: 2024 | Volume: 15, Issue: 3 | Journal Article |
Cited by: Papers (5)
This paper proposes a multiagent-based bilevel operation framework for low-carbon demand management in distribution networks considering the carbon emission allowance on the demand side. In the upper level, the load agents optimize the regulation signals for various types of loads to maximize profits; in the lower level, the distribution network operator makes optimal dispatching decisions to mini...Show More

Networked Multiagent-Based Safe Reinforcement Learning for Low-Carbon Demand Management in Distribution Networks

Jichen Zhang;Linwei Sang;Yinliang Xu;Hongbin Sun

Year: 2024 | Volume: 15, Issue: 3 | Journal Article |

Two-Stage Carbon-Oriented Scheduling of an Active Distribution Network With Thermostatically Controlled Load Aggregators

Mingyang Zhang;Yinliang Xu;Zhongkai Yi

IEEE Transactions on Sustainable Energy
Year: 2024 | Volume: 15, Issue: 3 | Journal Article |
Cited by: Papers (3)
Thermostatically controlled loads (TCLs) are deemed as essential flexible resources on the demand side that can facilitate the cost-effective transition to a low-carbon power system. However, integrating massive TCLs into the optimal operation of active distribution network (ADN) is challenging due to uncertainty and computation complexity. A reliable and efficient TCLs aggregation technique is th...Show More

Two-Stage Carbon-Oriented Scheduling of an Active Distribution Network With Thermostatically Controlled Load Aggregators

Mingyang Zhang;Yinliang Xu;Zhongkai Yi

Year: 2024 | Volume: 15, Issue: 3 | Journal Article |

Dynamic Incentive Pricing on Charging Stations for Real-Time Congestion Management in Distribution Network: An Adaptive Model-Based Safe Deep Reinforcement Learning Method

Hongrong Yang;Yinliang Xu;Qinglai Guo

IEEE Transactions on Sustainable Energy
Year: 2024 | Volume: 15, Issue: 2 | Journal Article |
Cited by: Papers (3)
This paper concerns the pricing strategy for real-time distribution network congestion management, aiming at maximizing the total social welfare while eliminating congestion. The difficulty lies in dealing with the complex time-varying relationship between price and charging demand in the integrated power system and transportation network, and ensuring the safe application of the proposed method w...Show More

Dynamic Incentive Pricing on Charging Stations for Real-Time Congestion Management in Distribution Network: An Adaptive Model-Based Safe Deep Reinforcement Learning Method

Hongrong Yang;Yinliang Xu;Qinglai Guo

Year: 2024 | Volume: 15, Issue: 2 | Journal Article |

Online Voltage Regulation of Active Distribution Networks: A Deep Neural Encoding-Decoding Approach

Linwei Sang;Yinliang Xu;Wenchuan Wu;Huan Long

IEEE Transactions on Power Systems
Year: 2024 | Volume: 39, Issue: 2 | Journal Article |
Cited by: Papers (5)
Voltage regulation plays a vital role in active distribution networks, which can be posed as mixed-integer quadratic programming (MIQP). To alleviate the real-time MIQP solution burden, this article proposes a deep neural encoding-decoding approach for online voltage regulation, including offline encoding and online decoding processes. Offline encoding extracts the integer variables and active con...Show More

Online Voltage Regulation of Active Distribution Networks: A Deep Neural Encoding-Decoding Approach

Linwei Sang;Yinliang Xu;Wenchuan Wu;Huan Long

Year: 2024 | Volume: 39, Issue: 2 | Journal Article |

Multi-Objective Planning of Distributed Energy Resources Based on Enhanced Adaptive Weighted-Sum Algorithm

Yuqi Wang;Yinliang Xu;Hongbin Sun

IEEE Transactions on Power Systems
Year: 2024 | Volume: 39, Issue: 2 | Journal Article |
Cited by: Papers (6)
A multi-objective planning model of distributed energy resources is proposed in this article to find the balance between carbon emission and economic cost. The economic cost is in the two-stage form including investment and operation cost. A novel enhanced adaptive weighted-sum algorithm (EAWS) with only one sparse-preference parameter is proposed to generate an informative Pareto front. A new cal...Show More

Multi-Objective Planning of Distributed Energy Resources Based on Enhanced Adaptive Weighted-Sum Algorithm

Yuqi Wang;Yinliang Xu;Hongbin Sun

Year: 2024 | Volume: 39, Issue: 2 | Journal Article |

Virtual Power Plants Peer-to-Peer Energy Trading in Unbalanced Distribution Networks: A Distributed Robust Approach Against Communication Failures

Xuan Wei;Jiajin Liu;Yinliang Xu;Hongbin Sun

IEEE Transactions on Smart Grid
Year: 2024 | Volume: 15, Issue: 2 | Journal Article |
Cited by: Papers (14)
In recent years, virtual power plants (VPPs) have been undergoing a rapid development to aggregate mushrooming distributed energy resources. In this paper, a distributed robust algorithm for VPPs’ peer-to-peer (P2P) energy trading is proposed which can improve the robustness against communication failures such as packet losses and computing node failures in the cyber layer. Firstly, a P2P energy t...Show More

Virtual Power Plants Peer-to-Peer Energy Trading in Unbalanced Distribution Networks: A Distributed Robust Approach Against Communication Failures

Xuan Wei;Jiajin Liu;Yinliang Xu;Hongbin Sun

Year: 2024 | Volume: 15, Issue: 2 | Journal Article |

An Augmented Lagrangian-Based Safe Reinforcement Learning Algorithm for Carbon-Oriented Optimal Scheduling of EV Aggregators

Xiaoying Shi;Yinliang Xu;Guibin Chen;Ye Guo

IEEE Transactions on Smart Grid
Year: 2024 | Volume: 15, Issue: 1 | Journal Article |
Cited by: Papers (17)
This paper proposes an augmented Lagrangian-based safe off-policy deep reinforcement learning (DRL) algorithm for the carbon-oriented optimal scheduling of electric vehicle (EV) aggregators in a distribution network. First, practical charging data are employed to formulate an EV aggregation model, and its flexibility in both emission mitigation and energy/power dispatching is demonstrated. Second,...Show More

An Augmented Lagrangian-Based Safe Reinforcement Learning Algorithm for Carbon-Oriented Optimal Scheduling of EV Aggregators

Xiaoying Shi;Yinliang Xu;Guibin Chen;Ye Guo

Year: 2024 | Volume: 15, Issue: 1 | Journal Article |

Low-Carbon Oriented Collaborative Planning of Electricity-Hydrogen-Gas-Transportation Integrated System Considering Hydrogen Vehicles

Xian Zhang;Xiaoyan Gao;Yu Zheng;Yinliang Xu;Edward Chung;Ahmed Rabee Sayed

IEEE Transactions on Network Science and Engineering
Year: 2024 | Volume: 11, Issue: 6 | Journal Article |
Cited by: Papers (1)
Under the pressures of the energy crisis and climate change, hydrogen vehicles (HVs) have experienced significant growth in many countries recently. To promote the penetration of HVs, a low-carbon collaborative planning model based on carbon emission flow (CEF) and a modified user equilibrium theory is proposed for the electricity-hydrogen-gas-transportation integrated system. New carbon capture a...Show More

Low-Carbon Oriented Collaborative Planning of Electricity-Hydrogen-Gas-Transportation Integrated System Considering Hydrogen Vehicles

Xian Zhang;Xiaoyan Gao;Yu Zheng;Yinliang Xu;Edward Chung;Ahmed Rabee Sayed

Year: 2024 | Volume: 11, Issue: 6 | Journal Article |

An Analytical Polytope Approximation Aggregation of Electric Vehicles Considering Uncertainty for the Day-Ahead Distribution Network Dispatching

Jing Jian;Mingyang Zhang;Yinliang Xu;Wenjun Tang;Shan He

IEEE Transactions on Sustainable Energy
Year: 2024 | Volume: 15, Issue: 1 | Journal Article |
Cited by: Papers (15)
The integration of large-scale heterogeneous electric vehicles (EVs) into the distribution network increases the system management complexity significantly. Due to the flexible charging/discharging operation and shiftable energy consumption during the parking time, EVs possess a great dispatching potential for the distribution network. This article develops an analytical polytope approximation (AP...Show More

An Analytical Polytope Approximation Aggregation of Electric Vehicles Considering Uncertainty for the Day-Ahead Distribution Network Dispatching

Jing Jian;Mingyang Zhang;Yinliang Xu;Wenjun Tang;Shan He

Year: 2024 | Volume: 15, Issue: 1 | Journal Article |

Encoding Carbon Emission Flow in Energy Management: A Compact Constraint Learning Approach

Linwei Sang;Yinliang Xu;Hongbin Sun

IEEE Transactions on Sustainable Energy
Year: 2024 | Volume: 15, Issue: 1 | Journal Article |
Cited by: Papers (15)
Decarbonizing the energy supply is essential and urgent to mitigate the increasingly visible climate change. Its basis is identifying emission responsibility during power allocation by the carbon emission flow (CEF) model. However, the main challenge of CEF application is the intractable nonlinear relationship between carbon emission and power allocation. So this article leverages the high approxi...Show More

Encoding Carbon Emission Flow in Energy Management: A Compact Constraint Learning Approach

Linwei Sang;Yinliang Xu;Hongbin Sun

Year: 2024 | Volume: 15, Issue: 1 | Journal Article |

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