Multidimensional Intelligent Distribution Network Load Analysis and Forecasting Management System Based on Multidata Fusion Technology

In order to improve the work efficiency of load characteristic analysis and realize lean management, scientific prediction, and reasonable planning of the distribution networks, this paper develops a multidimensional intelligent distribution network load analysis and prediction management system based on the fusion of multidimensional data for the application of multidimensional big data in the smart distribution network. First, the framework of the software system is designed, and the functional modules for multidimensional load characteristic analysis are designed. Then, the method of multidimensional user load characterization is introduced; furthermore, the application functions and the design process of some important function modules of the software system are introduced. Finally, an application example of the multidimensional user load characterization system is presented. Overall, the developed system has the features of interoperability of data links between functional modules, information support between different functions, and modular design concept, which can meet the daily application requirements of power grid enterprises and can respond quickly to the issued calculation requirements.

[1]  Tao Yu,et al.  Smart dispatching for energy internet with complex cyber‐physical‐social systems: A parallel dispatch perspective , 2019, International Journal of Energy Research.

[2]  Yang Zhang,et al.  A novel integrated price and load forecasting method in smart grid environment based on multi-level structure , 2020, Eng. Appl. Artif. Intell..

[3]  Efstratios N. Pistikopoulos,et al.  A hierarchical clustering decomposition algorithm for optimizing renewable power systems with storage , 2020 .

[4]  Wenjing Zhao,et al.  Power quality disturbance classification based on time-frequency domain multi-feature and decision tree , 2019 .

[5]  Xiaoming Huang,et al.  Adaptive distributed auction-based algorithm for optimal mileage based AGC dispatch with high participation of renewable energy , 2021 .

[6]  Tang Wai-wen Development of load modeling platform for electric power system , 2005 .

[7]  Zhang Jie,et al.  Equilibrium analysis of general N-population multi-strategy games for generation-side long-term bidding: An evolutionary game perspective , 2020 .

[8]  Qian Ai,et al.  A Big Data Architecture Design for Smart Grids Based on Random Matrix Theory , 2015, IEEE Transactions on Smart Grid.

[9]  Libing Zhou,et al.  A novel coast-down no-load characteristic test and curve conversion method for large-scale synchronous condenser , 2019, Electric Power Systems Research.

[10]  Jun Wang,et al.  Edge sensing data-imaging conversion scheme of load forecasting in smart grid , 2020 .

[11]  Bo Yang,et al.  Parallel Cyber-Physical-Social Systems Based Smart Energy Robotic Dispatcher and Knowledge Automation: Concepts, Architectures, and Challenges , 2019, IEEE Intelligent Systems.

[12]  Tao Yang,et al.  Design of a novel modal space sliding mode controller for electro-hydraulic driven multi-dimensional force loading parallel mechanism. , 2019, ISA transactions.

[13]  Zhiqiang Wang,et al.  Modeling of regional electrical heating load characteristics considering user response behavior difference , 2020 .

[14]  Tao Yu,et al.  A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems , 2019, International Journal of Energy Research.

[15]  Oana Dumitrascu,et al.  Performance Evaluation for a Sustainable Supply Chain Management System in the Automotive Industry Using Artificial Intelligence , 2020, Processes.

[16]  Tao Yu,et al.  Optimal Mileage Based AGC Dispatch of a GenCo , 2020, IEEE Transactions on Power Systems.

[17]  Ness B. Shroff,et al.  Flexible Load Balancing with Multi-dimensional State-space Collapse , 2018, Perform. Evaluation.

[18]  Changgang Li,et al.  Development approach of a programmable and open software package for power system frequency response calculation , 2017 .

[19]  Tao Yu,et al.  Consensus Transfer ${Q}$ -Learning for Decentralized Generation Command Dispatch Based on Virtual Generation Tribe , 2018, IEEE Transactions on Smart Grid.