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学者姓名：丁涛
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Abstract ：
Promoting the construction of the power market and the implementation of renewable energy policies to facilitate the use of renewable energy has become the industry consensus. In recent years, the renewable energy installed capacity in China has exploded, the varieties of market-oriented transactions for renewable energy have been continuously enriched, and the renewable energy policies also have been improved. However, the lack of quantitative analysis synthetically considering the impact of market, policy, and physical boundary on renewable energy utilization makes it difficult to measure and compare the effect of power market and policies. To solve this problem, this paper constructs a quantitative analysis model for renewable energy utilization and analyzes the influence mechanism of the market, policy, and physical boundary on renewable energy utilization to minimize system operating costs. The validity of the proposed model is verified by numerical results. © 2022 IEEE.
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| GB/T 7714 | Xu, Liang , Dong, Xiaoliang , Qiao, Ning et al. Quantitative Analysis of the Impact of Power Market and Policy on Renewable Energy Utilization [C] . 2022 : 976-981 . |
| MLA | Xu, Liang et al. "Quantitative Analysis of the Impact of Power Market and Policy on Renewable Energy Utilization" . (2022) : 976-981 . |
| APA | Xu, Liang , Dong, Xiaoliang , Qiao, Ning , Zhang, Chao , Sun, Yuge , Ding, Tao . Quantitative Analysis of the Impact of Power Market and Policy on Renewable Energy Utilization . (2022) : 976-981 . |
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Abstract ：
In this paper, a hybrid machine learning model is applied to evaluate the relationship between random initial states and the power system's vulnerability to cascading outages. A cascading outage simulator (CS), which uses off-line AC power flows, is proposed for generating training data. The initial states are randomly selected and the CS model is deployed for each initial state, where power system generation and loads are adjusted dynamically and power flows are redistributed to quantify the vulnerability metric. Furthermore, the proposed hybrid machine learning model deploys a combined Support Vector Machine (SVM) classification and Gradient Boosting Regression (GBR) to improve the learning precision. The classification model is trained by SVM, which divides the data into two categories with and without load shedding. Then, GBR is adopted only for the data with load shedding to determine the relationship between input power outage states and the vulnerability metric. The proposed vulnerability analysis approach is applied to several test systems and the results are analyzed. Note to Practitioners-The power system vulnerability can be quantified by cascading outage simulations. However, there are two challenges: i) there are a huge number of possible initial states and we cannot enumerate all these initial states for the cascading outage simulation. Neither can we precisely quantify the bus vulnerability. ii) The cascading outage simulation may be time-consuming for large-scale power systems, which is challenging for the online application. To address the above challenges, we expect to design a machine learning technique to predict the power system vulnerability, which can train the model in an offline way and then use it for the online application. Firstly, since there is not enough operation data from practical power systems, we develop a cascading outage simulator, using off-line AC power flows, for generating synthetic training data. Secondly, we observe that the training precision by directly applying the regression model may be very poor because the output of the machine learning model may take on an uneven distribution concerning input parameters. Thus, we propose a hybrid machine learning model with a combined classification and regression method, where the classification model is employed to remove the data without the load shedding, and the regression model then determines the relationship between input power outage states and the vulnerability metric. The proposed model and method have been tested on several systems including a practical large-scale Polish power system to show the effectiveness.
Keyword ：
cascading outages Generators gradient boosting regression Indexes Load modeling Load shedding Machine learning Power systems Support vector machines vulnerability analysis
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| GB/T 7714 | Zhang, Hongji , Ding, Tao , Qi, Junjian et al. Model and Data Driven Machine Learning Approach for Analyzing the Vulnerability to Cascading Outages With Random Initial States in Power Systems [J]. | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2022 . |
| MLA | Zhang, Hongji et al. "Model and Data Driven Machine Learning Approach for Analyzing the Vulnerability to Cascading Outages With Random Initial States in Power Systems" . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2022) . |
| APA | Zhang, Hongji , Ding, Tao , Qi, Junjian , Wei, Wei , Catalao, Joao P. S. , Shahidehpour, Mohammad . Model and Data Driven Machine Learning Approach for Analyzing the Vulnerability to Cascading Outages With Random Initial States in Power Systems . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2022 . |
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Abstract ：
This article proposes an innovative integrated power and hydrogen distribution system (IPHDS) restoration model in response to multiple outages caused by natural disasters. During the restoration, repair crews and mobile battery-carried vehicles are considered to repair faulted lines and support critical power loads. Also, the network reconfiguration is taken into consideration in the restoration model to pick up loads. Besides, to address the different response time of hydrogen and power systems, the aerodynamic law-based dynamic hydrogen flow model is applied in the hydrogen system. The proposed model is presented as a mixed-integer linear program, which is verified on a 33-bus-48-node IPHDS with multiple outages. The present results verify the effectiveness of the proposed method.
Keyword ：
Aerodynamics Dynamic hydrogen flow Hydrogen integrated system restoration Load modeling Maintenance engineering mobile battery-carried vehicles (MBCVs) Natural gas network reconfiguration Power system dynamics repair crews (RCs) resilience Resilience
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| GB/T 7714 | Wang, Zekai , Ding, Tao , Jia, Wenhao et al. Multi-Period Restoration Model for Integrated Power-Hydrogen Systems Considering Transportation States [J]. | IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS , 2022 , 58 (2) : 2694-2706 . |
| MLA | Wang, Zekai et al. "Multi-Period Restoration Model for Integrated Power-Hydrogen Systems Considering Transportation States" . | IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 58 . 2 (2022) : 2694-2706 . |
| APA | Wang, Zekai , Ding, Tao , Jia, Wenhao , Mu, Chenggang , Huang, Can , Catalao, Joao P. S. . Multi-Period Restoration Model for Integrated Power-Hydrogen Systems Considering Transportation States . | IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS , 2022 , 58 (2) , 2694-2706 . |
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Abstract ：
Frequency regulation (FR) plays an important role in maintaining the power balance and stability of power systems. How to fairly and reasonably compensate generators and actively stimulate generators in the frequency regulation ancillary service market is an urgent problem to be solved by power market reforms. In this paper, a blockchain community thinking with decentralization, multi-party consensus and token incentive was proposed. Based on the token incentive feature, an FR credit was designed to integrate the global FR effect and individual participation performance, and then a frequency response model of the distributed FR system with decentralized features was established. Furthermore, the settlement process of the FR credit was designed by using multiparty consensus thinking, which can motivate generators to participate in FR and ensure the authenticity and reliability of the results. Case study verifies the effectiveness of the distributed FR system, which can effectively complete the system FR tasks and credit settlements, and fully mobilize the FR generators to enhance the efficiency of distributed FR resources. © 2022 Chin. Soc. for Elec. Eng.
Keyword ：
Blockchain Commerce Frequency response
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| GB/T 7714 | Mu, Chenggang , Ding, Tao , Ju, Chang et al. Power System Frequency Regulation Model Based on Blockchain Community Thinking [J]. | Proceedings of the Chinese Society of Electrical Engineering , 2022 , 42 (4) : 1375-1387 . |
| MLA | Mu, Chenggang et al. "Power System Frequency Regulation Model Based on Blockchain Community Thinking" . | Proceedings of the Chinese Society of Electrical Engineering 42 . 4 (2022) : 1375-1387 . |
| APA | Mu, Chenggang , Ding, Tao , Ju, Chang , Li, Li , Chi, Fangde , He, Yuankang et al. Power System Frequency Regulation Model Based on Blockchain Community Thinking . | Proceedings of the Chinese Society of Electrical Engineering , 2022 , 42 (4) , 1375-1387 . |
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Abstract ：
This paper proposes a cascading failure simulation (CFS) method and a hybrid machine learning method for vulnerability analysis of integrated power-gas systems (IPGSs). The CFS method is designed to study the propagating process of cascading failures between the two systems, generating data for machine learning with initial states randomly sampled. The proposed method considers generator and gas well ramping, transmission line and gas pipeline tripping, island issue handling and load shedding strategies. Then, a hybrid machine learning model with a combined random forest (RF) classification and regression algorithms is proposed to investigate the impact of random initial states on the vulnerability metrics of IPGSs. Extensive case studies are carried out on three test IPGSs to verify the proposed models and algorithms. Simulation results show that the proposed models and algorithms can achieve high accuracy for the vulnerability analysis of IPGSs.
Keyword ：
Analytical models Biological system modeling cascading failures Generators Integrated power-gas system (IPGS) Load flow machine learning Natural gas Power system faults Power system protection vulnerability analysis
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| GB/T 7714 | Li, Shuai , Ding, Tao , Jia, Wenhao et al. A Machine Learning-Based Vulnerability Analysis for Cascading Failures of Integrated Power-Gas Systems [J]. | IEEE TRANSACTIONS ON POWER SYSTEMS , 2022 , 37 (3) : 2259-2270 . |
| MLA | Li, Shuai et al. "A Machine Learning-Based Vulnerability Analysis for Cascading Failures of Integrated Power-Gas Systems" . | IEEE TRANSACTIONS ON POWER SYSTEMS 37 . 3 (2022) : 2259-2270 . |
| APA | Li, Shuai , Ding, Tao , Jia, Wenhao , Huang, Can , Catalao, Joao P. S. , Li, Fangxing . A Machine Learning-Based Vulnerability Analysis for Cascading Failures of Integrated Power-Gas Systems . | IEEE TRANSACTIONS ON POWER SYSTEMS , 2022 , 37 (3) , 2259-2270 . |
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Abstract ：
Microgrids incorporate an increasing number of distributed energy resources (DERs), which induce a higher variability and faster dispatch capabilities in power systems. This paper proposes a two-layer real-time scheduling model for microgrids, based on approximate future cost function (AFCF), where the future cost represents the opportunity cost for the microgrid operation in subsequent periods. At the upper layer, the look-ahead rolling scheduling is adopted to optimize microgrid operations, in which the future cost function (FCF) in deterministic and stochastic scenarios is approximated by a piecewise linear function. At the lower layer, a real-time parameter updating strategy based on real-time data is proposed. In this case, the real-time scheduling readjusts the look-ahead schedule using the immediate cost in the current period and the future cost calculated by the updated AFCF. The proposed two-layer real-time scheduling model uses an offline optimization, in which most of the computation tasks are completed at the upper layer, and applies a real-time optimization, in which the time-consuming problem is avoided at the lower layer. The effectiveness of the proposed two-layer real-time scheduling model of microgrids is validated by using a grid-connected microgrid system. For comparison, other existing real-time scheduling methods are also implemented in the same microgrid system.
Keyword ：
Cost function Dynamic scheduling future cost function Load modeling Microgrid Microgrids Processor scheduling real-time scheduling Real-time systems State of charge two-layer scheduling
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| GB/T 7714 | Liu, Chunyang , Zhang, Hengxu , Shahidehpour, Mohammad et al. A Two-Layer Model for Microgrid Real-Time Scheduling Using Approximate Future Cost Function [J]. | IEEE TRANSACTIONS ON POWER SYSTEMS , 2022 , 37 (2) : 1264-1273 . |
| MLA | Liu, Chunyang et al. "A Two-Layer Model for Microgrid Real-Time Scheduling Using Approximate Future Cost Function" . | IEEE TRANSACTIONS ON POWER SYSTEMS 37 . 2 (2022) : 1264-1273 . |
| APA | Liu, Chunyang , Zhang, Hengxu , Shahidehpour, Mohammad , Zhou, Quan , Ding, Tao . A Two-Layer Model for Microgrid Real-Time Scheduling Using Approximate Future Cost Function . | IEEE TRANSACTIONS ON POWER SYSTEMS , 2022 , 37 (2) , 1264-1273 . |
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Total transfer capability evaluation is an effective method to analyze the backbone transmission capability among regions. To address renewable energy uncertainties, an interval total transfer capability model based on the multi-dimensional holomorphic embedding method and sum of squares relaxation technique is proposed to solve the regional total transfer capability in meshed high voltage direct current systems. First, the multi-dimensional holomorphic embedding method is used to derive the analytical expressions of regional tie lines. Second, the interval total transfer capability model can be reformulated by two bi-level optimization models. Third, sum of squares relaxation is employed to solve the two optimization problems. Numerical results on a 40-bus European Synthetic System and the Chinese meshed high voltage direct current system validate the effectiveness of the proposed model and method.
Keyword ：
high voltage direct current HVDC transmission Indexes Load flow Mathematical models Multi-dimensional holomorphic embedding method Renewable energy sources Security sum of squares relaxation Total transfer capability Uncertainty
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| GB/T 7714 | Sun, Yuge , Ding, Tao , Qu, Ming et al. Interval Total Transfer Capability for Mesh HVDC Systems Based on Sum of Squares and Multi-Dimensional Holomorphic Embedding Method [J]. | IEEE TRANSACTIONS ON POWER SYSTEMS , 2022 , 37 (6) : 4157-4167 . |
| MLA | Sun, Yuge et al. "Interval Total Transfer Capability for Mesh HVDC Systems Based on Sum of Squares and Multi-Dimensional Holomorphic Embedding Method" . | IEEE TRANSACTIONS ON POWER SYSTEMS 37 . 6 (2022) : 4157-4167 . |
| APA | Sun, Yuge , Ding, Tao , Qu, Ming , Wang, Fengyu , Shahidehpour, Mohammad . Interval Total Transfer Capability for Mesh HVDC Systems Based on Sum of Squares and Multi-Dimensional Holomorphic Embedding Method . | IEEE TRANSACTIONS ON POWER SYSTEMS , 2022 , 37 (6) , 4157-4167 . |
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Abstract ：
The autonomous mobility-on-demand (AMoD) system plays an important role in the urban transportation system. The charging behavior of AMoD fleet becomes a critical link between charging system and transportation system. In this paper, we investigate a strategic charging pricing scheme for charging station operators (CSOs) based on a non-cooperative Stackelberg game framework. The Stackelberg equilibrium investigates the pricing competition among multiple CSOs, and explores the nexus between the CSOs and AMoD operator. In the proposed framework, the responsive behavior of AMoD operator (order-serving, repositioning, and charging) is formulated as a multi-commodity network flow model to solve an energy-aware traffic flow problem. Meanwhile, a soft actor-critic based multi-agent deep reinforcement learning algorithm is developed to solve the proposed equilibrium framework while considering privacy-conservation constraints among CSOs. A numerical case study with city-scale real-world data is used to validate the effectiveness of the proposed framework.
Keyword ：
autonomous mobility-on-demand Charging stations deep reinforcement learning EV charging pricing power and transportation system Power systems Pricing Roads Routing Schedules soft actor-critic Transportation
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| GB/T 7714 | Lu, Ying , Liang, Yanchang , Ding, Zhaohao et al. Deep Reinforcement Learning-Based Charging Pricing for Autonomous Mobility-on-Demand System [J]. | IEEE TRANSACTIONS ON SMART GRID , 2022 , 13 (2) : 1412-1426 . |
| MLA | Lu, Ying et al. "Deep Reinforcement Learning-Based Charging Pricing for Autonomous Mobility-on-Demand System" . | IEEE TRANSACTIONS ON SMART GRID 13 . 2 (2022) : 1412-1426 . |
| APA | Lu, Ying , Liang, Yanchang , Ding, Zhaohao , Wu, Qiuwei , Ding, Tao , Lee, Wei-Jen . Deep Reinforcement Learning-Based Charging Pricing for Autonomous Mobility-on-Demand System . | IEEE TRANSACTIONS ON SMART GRID , 2022 , 13 (2) , 1412-1426 . |
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Abstract ：
The public safety power shutoff policy in California state of America is a new type of active blackout measures taken to prevent wildfire disasters, it is an urgent problem to be solved of how to ensure the safe operation of power system under public safety power shutoff policy and improve the operation resi-lience of system. The public safety power shutoff policy in California state is summarized and analyzed, and the basic process of safe operation of power grid under public safety power shutoff policy is reported, further the current existing problems and possible future research directions on security control under public safety power shutoff policy in California state are discussed. The proposed viewpoints can provide new insights of future resilient power system construction and wildfire prevention in China. © 2022, Electric Power Automation Equipment Press. All right reserved.
Keyword ：
Disaster prevention Disasters Electric load flow Electric power system control Electric power system security Electric power transmission networks Fires
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| GB/T 7714 | Wang, Zekai , Ding, Tao , Li, Li et al. Policies and measures of public safety power shutoff for enhancing grid resilience under catastrophic wildfire in California state of America [J]. | Electric Power Automation Equipment , 2022 , 42 (3) : 36-44 . |
| MLA | Wang, Zekai et al. "Policies and measures of public safety power shutoff for enhancing grid resilience under catastrophic wildfire in California state of America" . | Electric Power Automation Equipment 42 . 3 (2022) : 36-44 . |
| APA | Wang, Zekai , Ding, Tao , Li, Li , Wang, Kang , Chi, Fangde . Policies and measures of public safety power shutoff for enhancing grid resilience under catastrophic wildfire in California state of America . | Electric Power Automation Equipment , 2022 , 42 (3) , 36-44 . |
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Abstract ：
Unit commitment is one of the most important models for power system economics and operations. It usually takes the minimum cost as the objective function to meet the physical and security constraints of the power system operation. Mathematically, unit commitment is mixed-integer programming, which is essentially an NP-hard problem. As the scale of the system increases, the number of integer variables increases, and its computational complexity will also increase dramatically. In order to address the challenge of 'the curse of dimensionality', this paper, based on the convex hull theory of single-unit, extended the convex hull of single-unit to multi-unit systems and established a convex hull model for large-scale unit commitment problems considering security constraints. Meanwhile, the strategy of dual convex hull embedded in multi-units commitment and the method of constructing a feasible solution were designed to solve the adaptability problem of units to convex hull and the non-0-1 solution problem of the optimal solution caused by the relaxation of the multi-units convex hull. Furthermore, two convex hulls were applied to multi-unit security-constrained unit commitment where the mixed-integer programming model was approximately transformed into linear programming, and integers were omitted. This idea has achieved an important breakthrough in the computational complexity from the NP-hard to the polynomial time, suitable for large-scale power system unit commitment models. Finally, simulation results of several provincial power systems show that the computational efficiency of the proposed method is 1~2 orders of magnitude higher than that of pure mixed-integer programming. © 2022 Chin. Soc. for Elec. Eng.
Keyword ：
Computational complexity Computational efficiency Computational geometry Dynamic programming Integer programming Linear programming Polynomial approximation
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| GB/T 7714 | Qu, Ming , Ding, Tao , Li, Li et al. An Approximate Linear Program From an NP-hard to a Polynomial Time Complexity for a Large-scale Unit Commitment: Dual Convex Hull Model [J]. | Proceedings of the Chinese Society of Electrical Engineering , 2022 , 42 (9) : 3261-3275 . |
| MLA | Qu, Ming et al. "An Approximate Linear Program From an NP-hard to a Polynomial Time Complexity for a Large-scale Unit Commitment: Dual Convex Hull Model" . | Proceedings of the Chinese Society of Electrical Engineering 42 . 9 (2022) : 3261-3275 . |
| APA | Qu, Ming , Ding, Tao , Li, Li , Chi, Fangde , He, Yuankang , Chen, Tian'en et al. An Approximate Linear Program From an NP-hard to a Polynomial Time Complexity for a Large-scale Unit Commitment: Dual Convex Hull Model . | Proceedings of the Chinese Society of Electrical Engineering , 2022 , 42 (9) , 3261-3275 . |
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