Data-Driven Adaptive Operation of Soft Open Points in Active Distribution Networks

The integration of soft open point (SOP) effectively improves the flexibility of active distribution networks (ADNs). However, in practical operation, accurate network parameters are difficult to obtain and the operation state changes rapidly with distributed generators (DGs). With the development of information technologies, massive operation data can be acquired in ADNs. How to utilize multisource data has become the key to realize the intelligent operation of ADNs. This article proposes a data-driven operation strategy of SOP based on model-free adaptive control (MFAC). First, considering the inaccurate parameters and frequent change of operation states, a data-driven framework is formulated for the real-time operation of SOP. Then, the operation strategies of multiple SOPs are further improved with interarea coordination. The results of case studies show that driven by the measurement data, the potential benefits of SOPs are explored to adaptively respond to system state changes and improve the operational performance of ADNs.

[1]  Geev Mokryani,et al.  Planning and operation of LV distribution networks: a comprehensive review , 2019, IET Energy Systems Integration.

[2]  Jianzhong Wu,et al.  Coordinated Control Method of Voltage and Reactive Power for Active Distribution Networks Based on Soft Open Point , 2017, IEEE Transactions on Sustainable Energy.

[3]  Josep M. Guerrero,et al.  Data-Driven Control for Interlinked AC/DC Microgrids Via Model-Free Adaptive Control and Dual-Droop Control , 2017, IEEE Transactions on Smart Grid.

[4]  Jian Zhao,et al.  Distributed Online Voltage Control in Active Distribution Networks Considering PV Curtailment , 2019, IEEE Transactions on Industrial Informatics.

[5]  Elias Kyriakides,et al.  Identification and Estimation of Erroneous Transmission Line Parameters Using PMU Measurements , 2017, IEEE Transactions on Power Delivery.

[6]  Jianzhong Wu,et al.  Robust Operation of Soft Open Points in Active Distribution Networks With High Penetration of Photovoltaic Integration , 2019, IEEE Transactions on Sustainable Energy.

[7]  Chao Huang,et al.  Data-Driven Short-Term Solar Irradiance Forecasting Based on Information of Neighboring Sites , 2019, IEEE Transactions on Industrial Electronics.

[8]  Sean Hughes,et al.  Clustering by Fast Search and Find of Density Peaks , 2016 .

[9]  Ying Chen,et al.  Improved model-free adaptive wide-area coordination damping controller for multiple-input–multiple-output power systems , 2016 .

[10]  Xin Shi,et al.  Adversarial Feature Learning of Online Monitoring Data for Operational Risk Assessment in Distribution Networks , 2018, IEEE Transactions on Power Systems.

[11]  Lin Guan,et al.  Data-Driven-Based Optimization for Power System Var-Voltage Sequential Control , 2019, IEEE Transactions on Industrial Informatics.

[12]  Zhongsheng Hou,et al.  Controller-Dynamic-Linearization-Based Model Free Adaptive Control for Discrete-Time Nonlinear Systems , 2013, IEEE Transactions on Industrial Informatics.

[13]  Fei Jiang,et al.  Big data issues in smart grid – A review , 2017 .

[14]  Jianzhong Wu,et al.  Operating principle of Soft Open Points for electrical distribution network operation , 2016 .

[15]  Shangtai Jin,et al.  A Novel Data-Driven Control Approach for a Class of Discrete-Time Nonlinear Systems , 2011, IEEE Transactions on Control Systems Technology.

[16]  Shangtai Jin,et al.  Data-Driven Model-Free Adaptive Predictive Control for a Class of MIMO Nonlinear Discrete-Time Systems With Stability Analysis , 2019, IEEE Access.

[17]  Peng Li,et al.  Combined decentralized and local voltage control strategy of soft open points in active distribution networks , 2019, Applied Energy.

[18]  Xue Li,et al.  Day-ahead scheduling of multi-carrier energy systems with multi-type energy storages and wind power , 2018, CSEE Journal of Power and Energy Systems.

[19]  Hongbin Sun,et al.  A deep spatial-temporal data-driven approach considering microclimates for power system security assessment , 2019, Applied Energy.

[20]  Yi Wang,et al.  Data-Driven Power Flow Linearization: A Regression Approach , 2017, IEEE Transactions on Smart Grid.

[21]  Bu Xuhui,et al.  Data driven multiagent systems consensus tracking using model free adaptive control , 2016, 2016 Chinese Control and Decision Conference (CCDC).

[22]  Lin Guan,et al.  Noise Effect and Noise-Assisted Ensemble Regression in Power System Online Sensitivity Identification , 2017, IEEE Transactions on Industrial Informatics.

[23]  Zhao Xu,et al.  Daily Clearness Index Profiles Cluster Analysis for Photovoltaic System , 2017, IEEE Transactions on Industrial Informatics.

[24]  Jianzhong Wu,et al.  Optimal operation of soft open points in medium voltage electrical distribution networks with distributed generation , 2016 .

[25]  T. C. Green,et al.  Benefits of Distribution-Level Power Electronics for Supporting Distributed Generation Growth , 2013, IEEE Transactions on Power Delivery.

[26]  Gabriela Hug,et al.  Data-Driven Local Control Design for Active Distribution Grids Using Off-Line Optimal Power Flow and Machine Learning Techniques , 2019, IEEE Transactions on Smart Grid.