Journal of Shanghai Jiao Tong University

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    Application and Prospect of Two-Part Tariff Mechanism in Context of Transmission and Distribution Price Reform
    REN Xijun, SONG Zhumeng, WANG Bao, YE Yutong, PAN Sijia, WANG Mengyuan, XU Xiaoyuan
    Journal of Shanghai Jiao Tong University    2024, 58 (10): 1479-1488.   DOI: 10.16183/j.cnki.jsjtu.2023.102
    Abstract （618）   HTML （51）    PDF（pc） （1057KB）（588）       Save

    In the context of the reform of transmission and distribution tariff mechanism, the drawbacks of the existing two-part tariff system which cannot reasonably reflect the real cost of electricity consumption by power users have gradually emerged. The two-part tariff mechanism is responsible for allocating the space for electricity generation, transmission, distribution and sale tariffs, and regulating the resources of the power system. Therefore, it is urgent to improve the existing two-part tariff mechanism. This paper, focusing on the two-part tariff mechanism, first, introduces the basic theory and billing ratio of the two-part tariff, and studies the method of apportioning transmission and distribution costs based on different load rates and voltage levels. Then, it summarizes the electricity tariff mechanisms such as load rate packages and time-of-use tariffs and the basic tariff mechanisms such as tariff, load adjustments, and improved billing ratios respectively for the collection methods of two-part tariffs. Afterwards, it analyzes the implementation mode of two-part tariff mechanism theory by combining the practical experience of two-part system in the United States, France, Japan, and other foreign countries. Finally, it proposes the future development direction and suggestions of China’s two-part tariff mechanism.

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    Spatio-Temporal Adaptation Assessment of Key Technologies of New Distribution Network Based on 3D Space
    LIU Dongming, ZENG Qingbin, ZHANG Yongjun, ZHANG Jun, FAN Wei, LIU Yu
    Journal of Shanghai Jiao Tong University    2024, 58 (10): 1489-1499.   DOI: 10.16183/j.cnki.jsjtu.2023.053
    Abstract （326）   HTML （16）    PDF（pc） （3873KB）（213）       Save

    In the context of the development of new power systems, a three-dimensional spatio-temporal adaptation assessment model based on grid satisfaction, spatio-temporal resources, and effectiveness improvement is proposed to address the problems of distribution network planning and construction, the adaptability of key distribution network technologies to the space and time in which they are applied, and the difficulty of quantifying application defects. Subjective assignment in hierarchical analysis is improved using continuous interval ordered weighted average operator, and the problem of bias of the single assignment method is solved by the introduction of a subjective-objective combination assignment method constructed by the conflicting correlation among criteria method. The degree of affiliation of each indicator is determined using the fuzzy integrated evaluation method and the evaluation level is then obtained. The case studies show that the proposed method can quantify the degree of fit between the key technologies of the distribution network and the space-time, identify the adaptability of the key technologies to different space-times and the degree of satisfaction of the application of the technologies in different space-times, and reveal the weaknesses of the key technologies of the distribution network, which can help improve the efficiency and effectiveness of the investment in the distribution network and better serve the high-quality development of the economy and society.

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    A Prediction Method of New Power System Frequency Characteristics Based on Convolutional Neural Network
    LU Wen’an, ZHU Qingxiao, LI Zhaowei, LIU Hui, YU Yiping
    Journal of Shanghai Jiao Tong University    2024, 58 (10): 1500-1512.   DOI: 10.16183/j.cnki.jsjtu.2023.071
    Abstract （516）   HTML （18）    PDF（pc） （2722KB）（565）       Save

    In order to solve the problems existing in the traditional frequency analysis method for the frequency analysis of grids with a high proportion of new energy, such as the large amount of calculation, the difficulty of modeling, and the prominent contradiction between the calculation speed and the calculation accuracy, this paper proposes a new frequency characteristic prediction method for the new power system based on convolutional neural network (CNN). First, the main frequency indexes of the power system with a high proportion of new energy under power disturbances are predicted using one-dimensional CNN, including the initial frequency change rate, frequency extremum, and frequency steady-state value. The prediction accuracy is improved by setting reasonable input characteristics and optimizing the parameters of the neural network. Then, the impact of disturbance location and disturbance type is further considered, and the power system characteristic data set containing disturbance information is established by the method of data dimensionality reduction. The input characteristics are constructed by using the principle of three primary channels for reference, and the extended two-dimensional CNN is used to predict the frequency security index, which improves the adaptability of CNN in the frequency analysis of grids with a high proportion of new energy. Finally, the method is verified by an example in the improved BPA 10-machine 39-node model, and the results are compared with the prediction results of the recurrent neural network, which proves that the proposed method has a high accuracy and adaptability.

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