Coordinated Control of Distributed Energy Resources Using Features of Voltage Disturbances

Distributed energy resources (DERs) often rely on renewable sources whose random power fluctuations bring about voltage disturbances in distribution networks. Under such circumstances, voltage regulation through centralized control of DERs requires reliable detection and coordination mechanisms. This article proposes a new data-driven approach for event-triggered and coordinated control of DERs based on features of voltage disturbances. Synchrophasor datasets are processed to construct disturbance matrices that quantify spatio-temporal features of voltage disturbances. The estimated features are employed in clustering and control of DERs to suppress incipient events before exceeding a critical time. The proposed approach is tested in the IEEE 123-bus network, which has 15 solar photovoltaic sources with battery energy storage systems. The simulation results validate reliability and efficiency of the proposed method, and confirm that feature extraction combined with coordination of DERs can improve reliable and economic operation of distribution grids with renewables.

[1]  Thomas J. Overbye,et al.  Real-time event detection and feature extraction using PMU measurement data , 2015, 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[2]  Sami Repo,et al.  Coordinated Voltage Control in Distribution Networks Including Several Distributed Energy Resources , 2014, IEEE Transactions on Smart Grid.

[3]  Innocent Kamwa,et al.  Spatial–Temporal Feature Learning in Smart Grids: A Case Study on Short-Term Voltage Stability Assessment , 2020, IEEE Transactions on Industrial Informatics.

[4]  Chris Develder,et al.  An Information-Centric Communication Infrastructure for Real-Time State Estimation of Active Distribution Networks , 2015, IEEE Transactions on Smart Grid.

[5]  Bikash C. Pal,et al.  Distribution Voltage Control Considering the Impact of PV Generation on Tap Changers and Autonomous Regulators , 2014, IEEE Transactions on Power Systems.

[6]  K. T. Tan,et al.  Coordinated Control of Distributed Energy-Storage Systems for Voltage Regulation in Distribution Networks , 2016, IEEE Transactions on Power Delivery.

[7]  Michele De Santis,et al.  Linear method for steady-state analysis of radial distribution systems , 2018, International Journal of Electrical Power & Energy Systems.

[8]  Eung-Sang Kim,et al.  Frequency and Voltage Control Strategy of Standalone Microgrids With High Penetration of Intermittent Renewable Generation Systems , 2016, IEEE Transactions on Power Systems.

[9]  Zhehan Yi,et al.  A Unified Control and Power Management Scheme for PV-Battery-Based Hybrid Microgrids for Both Grid-Connected and Islanded Modes , 2018, IEEE Transactions on Smart Grid.

[10]  Jean Mahseredjian,et al.  An active distribution network model for smart grid control and protection studies—Model validation progress , 2017, 2017 IEEE Electrical Power and Energy Conference (EPEC).

[11]  Yi Wang,et al.  Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges , 2018, IEEE Transactions on Smart Grid.

[12]  Scott G. Ghiocel,et al.  Missing Data Recovery by Exploiting Low-Dimensionality in Power System Synchrophasor Measurements , 2016, IEEE Transactions on Power Systems.

[13]  Maarouf Saad,et al.  A Monitoring Technique for Reversed Power Flow Detection With High PV Penetration Level , 2015, IEEE Transactions on Smart Grid.

[14]  Jean Mahseredjian,et al.  On a new approach for the simulation of transients in power systems , 2007 .

[15]  Bri-Mathias Hodge,et al.  The Role of Concentrating Solar Power Toward High Renewable Energy Penetrated Power Systems , 2018, IEEE Transactions on Power Systems.

[16]  B. Kroposki,et al.  Steady-State Analysis of Maximum Photovoltaic Penetration Levels on Typical Distribution Feeders , 2013, IEEE Transactions on Sustainable Energy.

[17]  Tapan Kumar Saha,et al.  Real-Time Coordinated Voltage Control of PV Inverters and Energy Storage for Weak Networks With High PV Penetration , 2018, IEEE Transactions on Power Systems.

[18]  Jonathan Currie,et al.  Dynamic Event Detection Using a Distributed Feature Selection Based Machine Learning Approach in a Self-Healing Microgrid , 2018, IEEE Transactions on Power Systems.

[19]  Michele De Santis,et al.  Zoning Evaluation for Voltage Control in Smart Distribution Networks , 2018, 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe).

[20]  Chul-Hwan Kim,et al.  Coordinated Control Algorithm for Distributed Battery Energy Storage Systems for Mitigating Voltage and Frequency Deviations , 2016, IEEE Transactions on Smart Grid.

[21]  Anna Rita Di Fazio,et al.  Zoning Evaluation for Voltage Optimization in Distribution Networks with Distributed Energy Resources , 2019, Energies.

[22]  Matthew Rylander,et al.  It's All in the Plans: Maximizing the Benefits and Minimizing the Impacts of DERs in an Integrated Grid , 2015, IEEE Power and Energy Magazine.

[23]  Santiago Grijalva,et al.  Irregularity Detection in Output Power of Distributed Energy Resources Using PMU Data Analytics in Smart Grids , 2019, IEEE Transactions on Industrial Informatics.

[24]  Josep M. Guerrero,et al.  Centralized Disturbance Detection in Smart Microgrids With Noisy and Intermittent Synchrophasor Data , 2017, IEEE Transactions on Smart Grid.

[25]  Martin Ordonez,et al.  PV Energy Harvesting Under Extremely Fast Changing Irradiance: State-Plane Direct MPPT , 2019, IEEE Transactions on Industrial Electronics.

[26]  Ed Cortez,et al.  Distribution Synchrophasors: Pairing Big Data with Analytics to Create Actionable Information , 2018, IEEE Power and Energy Magazine.