Bi-Level Coordinated Power System Restoration Model Considering the Support of Multiple Flexible Resources

When power systems encounter outages and large-scale blackouts, system restoration is critical and should be carried out with dedicated schemes. In this past, most studies divided the power system restoration into three stages (i.e., black-start zone partitioning, network reconfiguration, and load restoration) and deal with them separately. After that, few studies considering the three stages together were emerging while the support of multiple flexible resources, i.e., renewable energy source (RES), electric vehicle system (EVS) and energy storage system (ESS), were not considered comprehensively. Therefore, a bi-level coordinated power system restoration (BiCPSR) model is proposed in this work considering the support of multiple flexible resources. In the upper level, two network topology indices that describe the “reachability” and “shortest reachable distance” of buses in power systems, and the restoration characteristics of generators and loads are utilized for optimizing the start-up sequence of generators and network reconfiguration. In the lower level, the uncertainties of RES and EVS are considered by various scenarios and the support of multiple flexible resources is utilized cooperatively for accelerating the restoration process and maximizing the restorable load. Case studies on the revised IEEE 39-bus, WECC 179-bus and the actual Zhejiang power systems are performed to illustrate the basic features of the proposed model and its availability in bulk power systems. The comparisons between the proposed model and other models are also performed to illustrate the strengths of the proposed model.

[1]  Yusheng Xue,et al.  Decision Support System for Adaptive Restoration Control of Transmission System , 2021, Journal of Modern Power Systems and Clean Energy.

[2]  Dhruv Bhatnagar,et al.  Data-Driven Reliability Assessment for Marine Renewable Energy Enabled Island Power Systems , 2021, 2021 IEEE Power & Energy Society General Meeting (PESGM).

[3]  Fei Ding,et al.  Collaborative Distribution System Restoration Planning and Real-Time Dispatch Considering Behind-the-Meter DERS , 2021, IEEE Transactions on Power Systems.

[4]  Hongyu Li,et al.  Data-Driven Event Identification in the U.S. Power Systems Based on 2D-OLPP and RUSBoosted Trees , 2021, IEEE Transactions on Power Systems.

[5]  Qiuwei Wu,et al.  Optimal Generator Start-Up Sequence for Bulk System Restoration With Active Distribution Networks , 2021, IEEE Transactions on Power Systems.

[6]  Yi Ding,et al.  Bi-level Coordinated Planning of Active Distribution Network Considering Demand Response Resources and Severely Restricted Scenarios , 2021, Journal of Modern Power Systems and Clean Energy.

[7]  Fei Teng,et al.  Decentralized Data-Driven Load Restoration in Coupled Transmission and Distribution System With Wind Power , 2021, IEEE Transactions on Power Systems.

[8]  Zhaoyu Wang,et al.  A Two-Level Simulation-Assisted Sequential Distribution System Restoration Model With Frequency Dynamics Constraints , 2021, IEEE Transactions on Smart Grid.

[9]  Sarmad Hanif,et al.  Power System Resilience Metrics Augmentation for Critical Load Prioritization , 2021 .

[10]  Reza Roofegari nejad,et al.  Robust Distribution System Load Restoration With Time-Dependent Cold Load Pickup , 2020, IEEE Transactions on Power Systems.

[11]  Anmar Arif,et al.  Stochastic Pre-Event Preparation for Enhancing Resilience of Distribution Systems with High DER Penetration , 2020, Renewable and Sustainable Energy Reviews.

[12]  Ted K.A. Brekken,et al.  Connecting Risk and Resilience for a Power System Using the Portland Hills Fault Case Study , 2020, Processes.

[13]  Yi Ding,et al.  Robust System Separation Strategy Considering Online Wide-Area Coherency Identification and Uncertainties of Renewable Energy Sources , 2020, IEEE Transactions on Power Systems.

[14]  Weijia Liu,et al.  Availability Assessment Based Case-Sensitive Power System Restoration Strategy , 2020, IEEE Transactions on Power Systems.

[15]  Can Zhang,et al.  Optimal Skeleton-Network Restoration Considering Generator Start-Up Sequence and Load Pickup , 2019, IEEE Transactions on Smart Grid.

[16]  Peter A. Lindahl,et al.  An Energy Buffer for Controllable Input Impedance of Constant Power Loads , 2019, IEEE Transactions on Industry Applications.

[17]  Tao Ding,et al.  Optimal black start strategy for microgrids considering the uncertainty using a data‐driven chance constrained approach , 2019, IET Generation, Transmission & Distribution.

[18]  Tong Liu,et al.  Global optimisation model and algorithm for unit restarting sequence considering black‐start zone partitioning , 2019, IET Generation, Transmission & Distribution.

[19]  Gino J. Lim,et al.  A Parallel Sectionalized Restoration Scheme for Resilient Smart Grid Systems , 2019, IEEE Transactions on Smart Grid.

[20]  Wei Sun,et al.  Incorporating Wind Energy in Power System Restoration Planning , 2019, IEEE Transactions on Smart Grid.

[21]  Karen L. Butler-Purry,et al.  Multi-Time Step Service Restoration for Advanced Distribution Systems and Microgrids , 2018, IEEE Transactions on Smart Grid.

[22]  Lei Sun,et al.  A Model Predictive Control Based Generator Start-Up Optimization Strategy for Restoration With Microgrids as Black-Start Resources , 2018, IEEE Transactions on Power Systems.

[23]  Kai Sun,et al.  Identifying the Timing of Controlled Islanding Using a Controlling UEP Based Method , 2018, IEEE Transactions on Power Systems.

[24]  Wei Sun,et al.  Advanced power system partitioning method for fast and reliable restoration: toward a self-healing power grid , 2018 .

[25]  Feng Qiu,et al.  An Integrated Approach for Power System Restoration Planning , 2017, Proceedings of the IEEE.

[26]  Yutian Liu,et al.  Power system restoration: a literature review from 2006 to 2016 , 2016 .

[27]  Bin Wang,et al.  A test cases library for methods locating the sources of sustained oscillations , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[28]  Di Wu,et al.  Fast assessment of frequency response of cold load pickup in power system restoration , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[29]  Can Zhang,et al.  Network partitioning strategy for parallel power system restoration , 2016 .

[30]  Fushuan Wen,et al.  Multi-objective restoration optimisation of power systems with battery energy storage systems , 2016 .

[31]  Fushuan Wen,et al.  Optimisation model for power system restoration with support from electric vehicles employing battery swapping , 2016 .

[32]  Amany El-Zonkoly,et al.  Renewable energy sources for complete optimal power system black-start restoration , 2015 .

[33]  Chong Wang,et al.  OBDD-Based Sectionalizing Strategies for Parallel Power System Restoration , 2011, IEEE Transactions on Power Systems.

[34]  Wei Sun,et al.  Optimal Generator Start-Up Strategy for Bulk Power System Restoration , 2011, IEEE Transactions on Power Systems.

[35]  Hao Zhou,et al.  Division algorithm and interconnection strategy of restoration subsystems based on complex network theory , 2011 .

[36]  R. Billinton,et al.  Application of adverse and extreme adverse weather: modelling in transmission and distribution system reliability evaluation , 2006 .

[37]  Thomas H. Ortmeyer,et al.  Propagation-Based Network Partitioning Strategies for Parallel Power System Restoration With Variable Renewable Generation Resources , 2021, IEEE Access.

[38]  Jinghan He,et al.  Parallel Restoration Method for AC-DC Hybrid Power Systems Based on Graph Theory , 2019, IEEE Access.

[39]  Ye Lin,et al.  A Two-stage Strategy for Network Reconfiguration Based on Concept of Regret , 2013 .

[40]  J. Lofberg,et al.  YALMIP : a toolbox for modeling and optimization in MATLAB , 2004, 2004 IEEE International Conference on Robotics and Automation (IEEE Cat. No.04CH37508).