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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 ：
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 ：
The uncertainty of renewable generation makes the operating status of distribution systems more volatile, as fully controllable resource at the distribution level is rare. A dispatchable region refers to the set that consists of all admissible patterns of nodal renewable power injections under which the power flow is solvable without violating security bound constraints. This article studies the dispatchable region in distribution networks under alternating current power flow model. A rank minimization problem is proposed to test power flow solvability under a fixed nodal power injection pattern, providing basic operation to construct the exact dispatchable region. A sequential low-order semidefinite programming procedure is developed to solve the problem. Furthermore, based on a global outer approximation of the second-order conic relaxation of the distflow model, a linear programming-based polyhedral projection algorithm is developed to calculate an outer approximation of the dispatchable region. The projection algorithm is also applied to the traditional linearized distflow model. Combining the feasibility test procedure, it is shown that the intersection of the respective dispatchable regions obtained from two linearized power flow models produces a fairly accurate approximation for the true dispatchable region under the exact nonlinear distflow model. The proposed method is an extension of existing studies on security assessment for distribution systems under uncertainty.
Keyword ：
Dispatchable region distflow model Distribution networks distribution system Load modeling Mathematical models Renewable energy sources renewable generation security assessment Uncertainty Visualization Voltage
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| GB/T 7714 | Shen, Ziqi , Wei, Wei , Ding, Tao et al. Admissible Region of Renewable Generation Ensuring Power Flow Solvability in Distribution Networks [J]. | IEEE SYSTEMS JOURNAL , 2022 , 16 (3) : 3982-3992 . |
| MLA | Shen, Ziqi et al. "Admissible Region of Renewable Generation Ensuring Power Flow Solvability in Distribution Networks" . | IEEE SYSTEMS JOURNAL 16 . 3 (2022) : 3982-3992 . |
| APA | Shen, Ziqi , Wei, Wei , Ding, Tao , Li, Zhigang , Mei, Shengwei . Admissible Region of Renewable Generation Ensuring Power Flow Solvability in Distribution Networks . | IEEE SYSTEMS JOURNAL , 2022 , 16 (3) , 3982-3992 . |
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Abstract ：
Developing solar power generation technology is an efficient approach to relieving the global environmental crisis. However, solar energy is an energy source with strong uncertainty, which restricts large-scale photovoltaic (PV) applications until accurate solar energy predictions can be achieved. PV power forecasting methods have been widely researched based on existing predictions of satellite-derived solar irradiance, whereas modeling cloud motion directly from satellite images is still a tough task. In this study, an end-to-end short-term forecasting model is proposed to take satellite images as inputs, and it can learn the cloud motion characteristics from stacked optical flow maps. In order to reduce the huge size of measurements, static regions of interest (ROIs) are scoped based on historical cloud velocities. With its well-designed deep learning architecture, the proposed model can output multi-step-ahead prediction results sequentially by shifting receptive attention to dynamic ROIs. According to comparisons with related studies, the proposed model outperforms persistence and derived methods, and enhances its learning capability relative to conventional learning models via the novel architecture. The model can be applied to PV plants or arrays in different areas, suitable for forecast horizons within three hours.
Keyword ：
Cloud motion Clouds Computational modeling Data models deep learning Forecasting photovoltaic forecasting Predictive models regions of interest satellite images Satellites Weather forecasting
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| GB/T 7714 | Cheng, Lilin , Zang, Haixiang , Wei, Zhinong et al. Short-term Solar Power Prediction Learning Directly from Satellite Images With Regions of Interest [J]. | IEEE TRANSACTIONS ON SUSTAINABLE ENERGY , 2022 , 13 (1) : 629-639 . |
| MLA | Cheng, Lilin et al. "Short-term Solar Power Prediction Learning Directly from Satellite Images With Regions of Interest" . | IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 13 . 1 (2022) : 629-639 . |
| APA | Cheng, Lilin , Zang, Haixiang , Wei, Zhinong , Ding, Tao , Xu, Ruiqi , Sun, Guoqiang . Short-term Solar Power Prediction Learning Directly from Satellite Images With Regions of Interest . | IEEE TRANSACTIONS ON SUSTAINABLE ENERGY , 2022 , 13 (1) , 629-639 . |
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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 ：
Electric vehicles (EVs) have attracted enormous attention in recent years due to their potentials in mitigating energy crisis and air pollutions. However, the long battery charging time and lack of sufficient charging infrastructure highly restrict the popularization of EVs. In this context, it is promising to establish battery charging and swapping systems (BCSSs) based on the concept of battery swapping services. To optimally achieve the combined operation of BCSSs, this paper proposes a hybrid swapped battery charging and logistics dispatch model in continuous time domain. Identifying the special structure of the mathematical models of the two problems, this paper innovatively formulated the swapped battery charging strategy as the rectangle packing problem and the battery logistics model as the vehicle routing problem. The two models are closely linked by the delivery time of transporting the well-charged batteries from battery charging stations to battery swapping stations. A hybrid optimal operation model of BCSSs is further formulated as a mixed-integer linear programming model by incorporating the interaction between the battery charging and battery logistics. Finally, case studies are conducted on several BCSSs and numerical results validate the effectiveness of the proposed model.
Keyword ：
Batteries Costs Electric vehicles Load modeling Logistics logistics dispatch Mathematical models Numerical models Optimization rectangle packing problem swapped battery charging
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| GB/T 7714 | Jia, Wenhao , Ding, Tao , Bai, Jiawen et al. Hybrid Swapped Battery Charging and Logistics Dispatch Model in Continuous Time Domain [J]. | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2022 , 71 (3) : 2448-2458 . |
| MLA | Jia, Wenhao et al. "Hybrid Swapped Battery Charging and Logistics Dispatch Model in Continuous Time Domain" . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 71 . 3 (2022) : 2448-2458 . |
| APA | Jia, Wenhao , Ding, Tao , Bai, Jiawen , Bai, Linquan , Yang, Yongheng , Blaabjerg, Frede . Hybrid Swapped Battery Charging and Logistics Dispatch Model in Continuous Time Domain . | IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY , 2022 , 71 (3) , 2448-2458 . |
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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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Abstract ：
Clean energy heating electrification programs provide a promising way to reduce carbon emissions from fossil fuel combustion and consumption. This work studies the cost competitiveness of clean energy heating technologies under three dynamic mechanisms: investment costs, subsidy policies, and operating costs with real data. It provides key insights into the cost competitiveness of the different heating technologies deployed in different areas, as well as their sensitivity to the three dynamic mechanisms. The results show that currently, the distinct heating programs are more cost-efficient in the urban area with existing heating networks. The average payback period of all district clean energy heating programs in the urban area is 14.9 years, while that of the individual clean heating programs is 24.7 years. The individual heating programs are becoming increasingly cost-competitive with the incentive mechanisms, especially the electricity pricing mechanisms. Moreover, individual heating technologies present remarkable advantages on flexibility and sustainability in the long run. According to the technology diffusion model proposed in this paper, the individual clean heating programs will occupy more than 50% of the market share in 2050 under the comprehensive effect of CAPEX, government subsidies, and OPEX. The real-world results and analysis render references to shape the pathway of clean energy heating electrification in Northwest China and other regions with a similar situation. © 2022 Elsevier Ltd
Keyword ：
Carbon Competition Cost benefit analysis Dynamics Electric utilities Fossil fuels Investments Operating costs Sustainable development
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| GB/T 7714 | Ding, Tao , Sun, Yuge , Huang, Can et al. Pathways of clean energy heating electrification programs for reducing carbon emissions in Northwest China [J]. | Renewable and Sustainable Energy Reviews , 2022 , 166 . |
| MLA | Ding, Tao et al. "Pathways of clean energy heating electrification programs for reducing carbon emissions in Northwest China" . | Renewable and Sustainable Energy Reviews 166 (2022) . |
| APA | Ding, Tao , Sun, Yuge , Huang, Can , Mu, Chenlu , Fan, Yuqi , Lin, Jiang et al. Pathways of clean energy heating electrification programs for reducing carbon emissions in Northwest China . | Renewable and Sustainable Energy Reviews , 2022 , 166 . |
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Abstract ：
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 stochastic cloud cover on photovoltaic (PV) panels affects the solar power outputs, producing high instability in the integrated power systems. It is an effective approach to track the cloud motion during short-term PV power forecasting based on data sources of satellite images. However, since temporal variations of these images are noisy and non-stationary, pixel-sensitive prediction methods are critically needed in order to seek a balance between the forecast precision and the huge computation burden due to a large image size. Hence, a graphical learning framework is proposed in this study for intra-hour PV power prediction. By simulating the cloud motion using bi-directional extrapolation, a directed graph is generated representing the pixel values from multiple frames of historical images. The nodes and edges in the graph denote the shapes and motion directions of the regions of interest (ROIs) in satellite images. A spatial-temporal graph neural network (GNN) is then proposed to deal with the graph. Comparing with conventional deep-learning-based models, GNN is more flexible for varying sizes of input, in order to be able to handle dynamic ROIs. Referring to the comparative studies, the proposed method greatly reduces the redundancy of image inputs without sacrificing the visual scope, and slightly improves the prediction accuracy.
Keyword ：
Bidirectional control Brightness temperature Clouds deep learning Extrapolation Forecasting graphical learning Predictive models satellite images Satellites Solar PV power prediction
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| GB/T 7714 | Cheng, Lilin , Zang, Haixiang , Wei, Zhinong et al. Solar Power Prediction Based on Satellite Measurements - A Graphical Learning Method for Tracking Cloud Motion [J]. | IEEE TRANSACTIONS ON POWER SYSTEMS , 2022 , 37 (3) : 2335-2345 . |
| MLA | Cheng, Lilin et al. "Solar Power Prediction Based on Satellite Measurements - A Graphical Learning Method for Tracking Cloud Motion" . | IEEE TRANSACTIONS ON POWER SYSTEMS 37 . 3 (2022) : 2335-2345 . |
| APA | Cheng, Lilin , Zang, Haixiang , Wei, Zhinong , Ding, Tao , Sun, Guoqiang . Solar Power Prediction Based on Satellite Measurements - A Graphical Learning Method for Tracking Cloud Motion . | IEEE TRANSACTIONS ON POWER SYSTEMS , 2022 , 37 (3) , 2335-2345 . |
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