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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 ：
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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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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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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Abstract ：
The vigorous development of electric vehicles (EVs) is an important means of reducing carbon emissions and mitigating environmental problems such as the greenhouse effect. Battery swapping stations (BSSs) can both provide battery swapping services for large-scale EVs and charge batteries centrally. As the supply of fully charged batteries in the BSS shrinks, it becomes necessary to schedule the charging of the depleted batteries rapidly that users have swapped for fully-charged ones. The charging schedule for depleted batteries must be made without knowledge of future battery arrivals. In this context, this paper develops a mathematical model for online charging scheduling of BSSs, formulates the charging strategy as a two-dimensional rectangle packing problem, and quickly calculates the scheduling arrangement of batteries by partitioning the remaining available capacity of a BSS. Since there are limited battery types within the BSS which can provide battery replacement services, this paper supplements the proposed model with known battery types, which improves the utilization of the available capacity of BSSs. Finally, numerical results verify the effectiveness of the proposed model. Note to Practitioners-Electric vehicles (EVs) are becoming an alternative way to reduce carbon emissions in transportation systems. Herein, the optimal battery charging problem is the core problem when it comes to dispatching a huge number of EVs. Up to now, battery-swapping is widely used for EVs due to its simple, convenient way. Furthermore, a business model for the battery swapping stations (BSSs) is brought up, where EV users send their depleted batteries to the BSS and the BSS provides the users with a fully charged replacement battery from its warehouse, which only takes a few minutes. Since the maximum charging power of the BSS is limited by the capacity of the transformer connecting the BSS to the power grid, the BSS will adopt an optimal charging schedule that maximizes the charging benefit for large quantities of depleted batteries in the warehouse. However, the challenge is that the charging schedule for depleted batteries must be made without knowledge of future battery arrivals because the EV behaviors are difficult to predict. To address this problem, this paper developed an online charging scheduling algorithm, which formulates the charging strategy as a two-dimensional rectangle packing problem. The proposed method can provide battery replacement services in real-time and solve quickly without any information about incoming depleted EV batteries. The proposed model and method have been tested on the system with different numbers of batteries to show the effectiveness. Besides, the online two-dimensional rectangle packing problem can provide an online decision for BSSs.
Keyword ：
battery swapping station Electric vehicles online algorithm rectangle packing problem uninterrupted discrete-rate charging
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| GB/T 7714 | Bai, Jiawen , Ding, Tao , Jia, Wenhao et al. Online Rectangle Packing Algorithm for Swapped Battery Charging Dispatch Model Considering Continuous Charging Power [J]. | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2022 . |
| MLA | Bai, Jiawen et al. "Online Rectangle Packing Algorithm for Swapped Battery Charging Dispatch Model Considering Continuous Charging Power" . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2022) . |
| APA | Bai, Jiawen , Ding, Tao , Jia, Wenhao , Zhu, Shanying , Bai, Linquan , Li, Fangxing . Online Rectangle Packing Algorithm for Swapped Battery Charging Dispatch Model Considering Continuous Charging Power . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2022 . |
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Abstract ：
A virtual energy storage (VES) model is proposed in this paper to accommodate renewable energy under a special market regulation. Such VESs can provide or consume electricity to the main power grid under the premise that the daily net electricity energy is balanced. Furthermore, a multi-stage distributionally robust optimization (MSDRO) model is set up in this paper to address the temporal uncertainties in the day-ahead economic dispatch model. Compared with the traditional two-stage distributionally robust optimization, the proposed multi-stage approach provides more flexibilities so that the decision variables can be adjusted at each time period, leading to a complex nested formulation. To efficiently solve the MSDRO model, a stochastic dual dynamic programming method is employed to decompose the original large-scale optimization model into several sub-problems in the stages, as two steps: forward pass and backward pass. In the forward pass, the expected cost-to-go function is approximated by piecewise-linear functions and then several samples are used to generate a lower bound; the backward pass will generate Benders' cuts at each stage from the solution of the forward pass. The forward and backward passes are performed iteratively until the convergence is reached. Numerical results on an IEEE 118-bus system and a practical power system in China verify the proposed method.
Keyword ：
Computational modeling Distributionally robust optimization economic dispatch Load modeling multi-stage stochastic programming Optimization Power systems renewable energy Renewable energy sources stochastic dual dynamic programming Stochastic processes Uncertainty virtual energy storage
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| GB/T 7714 | Ding, Tao , Zhang, Xiaosheng , Lu, Runzhao et al. Multi-Stage Distributionally Robust Stochastic Dual Dynamic Programming to Multi-Period Economic Dispatch With Virtual Energy Storage [J]. | IEEE TRANSACTIONS ON SUSTAINABLE ENERGY , 2022 , 13 (1) : 146-158 . |
| MLA | Ding, Tao et al. "Multi-Stage Distributionally Robust Stochastic Dual Dynamic Programming to Multi-Period Economic Dispatch With Virtual Energy Storage" . | IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 13 . 1 (2022) : 146-158 . |
| APA | Ding, Tao , Zhang, Xiaosheng , Lu, Runzhao , Qu, Ming , Shahidehpour, Mohammad , He, Yuankang et al. Multi-Stage Distributionally Robust Stochastic Dual Dynamic Programming to Multi-Period Economic Dispatch With Virtual Energy Storage . | IEEE TRANSACTIONS ON SUSTAINABLE ENERGY , 2022 , 13 (1) , 146-158 . |
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Abstract ：
The emergence of nanogrid has led to active local energy markets. Renewable energy sources and flexible energy storages in nanogrids provide favorable conditions for establishing a sustainable bilateral trading market. However, due to the diversity of energy sources in nanogrids, it is difficult to establish a uniform scheduling scheme. To this end, this paper first establishes an effective peer-to-peer energy block contract market to provide a scheme for users to exchange energy at any time interval. The goal is to maximize contract volume while ensuring the economic profitability of all users. Beyond that, a garbled circuit-based price comparison mechanism is proposed based on a secure multi-party computation to achieve price comparison without revealing any individual user's data to other users for privacy preserving. Finally, a fully distributed algorithm is designed by constructing a local tracker to track the inequality amount in iteration. The algorithm also subtly parses the input data, which weakens the intertwined relationship in peer-to-peer transactions and improves computation efficiency. Case studies verify the privacy of the described method and the effectiveness of the energy block contract market. © 2010-2012 IEEE.
Keyword ：
distributed algorithm; Energy block; peer-to-peer; privacy preserving; secure multi-party computation
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| GB/T 7714 | Mu, C. , Ding, T. , Sun, Y. et al. Energy Block-Based Peer-to-Peer Contract Trading with Secure Multi-Party Computation in Nanogrid [J]. | IEEE Transactions on Smart Grid , 2022 , 13 (6) : 4759-4772 . |
| MLA | Mu, C. et al. "Energy Block-Based Peer-to-Peer Contract Trading with Secure Multi-Party Computation in Nanogrid" . | IEEE Transactions on Smart Grid 13 . 6 (2022) : 4759-4772 . |
| APA | Mu, C. , Ding, T. , Sun, Y. , Huang, Y. , Li, F. , Siano, P. . Energy Block-Based Peer-to-Peer Contract Trading with Secure Multi-Party Computation in Nanogrid . | IEEE Transactions on Smart Grid , 2022 , 13 (6) , 4759-4772 . |
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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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