Real-time digital co-simulation method of smart grid for integrating large-scale demand response resources

With study of activating the immense potentials of demand response (DR) in load side attracts widespread attention, a simulation platform with the ability of “Source-Grid-Load” interactive operation is essential. Nevertheless it’s an enormous challenge to incorporate the DR resources in real-time simulation as the characteristics of mass, diversity and responsiveness. This paper presents a method of digital real-time cosimulation (DRTCoS) applied to smart grid integrated with large-scale DR resources. Firstly, the strategy that decoupling the network with DR resources from the rest parts according to different dynamic responses is proposed, building the multi-rate digital simulation, and an interface algorithm for the co-simulation is devised. Then DRTCoS that combines Real Time Digital Simulator (RTDS) and the power distribution system simulation, GridLAB-D is built, with typical application scenarios on micro-grid and high-voltage grid studied. Finally, case study validates the effectiveness of the cosimulation based on large-scale air-conditioningloads.

[1]  Hui Li,et al.  Coordinated Control of Distributed Energy Storage System With Tap Changer Transformers for Voltage Rise Mitigation Under High Photovoltaic Penetration , 2012, IEEE Transactions on Smart Grid.

[2]  Kai Strunz,et al.  Real-Time Simulation Technologies for Power Systems Design, Testing, and Analysis , 2015, IEEE Power and Energy Technology Systems Journal.

[3]  Thillainathan Logenthiran,et al.  Multiagent System for Real-Time Operation of a Microgrid in Real-Time Digital Simulator , 2012, IEEE Transactions on Smart Grid.

[4]  Chen Fang,et al.  A hardware-in-the-loop simulation of AGC for large scale wind-gas coordinating power generation , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[5]  K. Schneider,et al.  GridLAB-D: An open-source power systems modeling and simulation environment , 2008, 2008 IEEE/PES Transmission and Distribution Conference and Exposition.

[6]  Goran Andersson,et al.  Scheduling and Provision of Secondary Frequency Reserves by Aggregations of Commercial Buildings , 2016, IEEE Transactions on Sustainable Energy.

[7]  C. Dufour,et al.  Hardware-In-the-Loop Simulation of Power Drives with RT-LAB , 2005, 2005 International Conference on Power Electronics and Drives Systems.

[8]  Ning Lu,et al.  An Evaluation of the HVAC Load Potential for Providing Load Balancing Service , 2012, IEEE Transactions on Smart Grid.

[9]  Francisco Gonzalez-Longatt,et al.  PowerFactory Applications for Power System Analysis , 2014 .

[10]  Junjie HU,et al.  Transactive control: a framework for operating power systems characterized by high penetration of distributed energy resources , 2017 .

[11]  Ning Lu,et al.  A Demand Response and Battery Storage Coordination Algorithm for Providing Microgrid Tie-Line Smoothing Services , 2014, IEEE Transactions on Sustainable Energy.

[12]  Thoshitha T. Gamage,et al.  Analyzing the Cyber-Physical Impact of Cyber Events on the Power Grid , 2015, IEEE Transactions on Smart Grid.

[13]  D. P. Chassin,et al.  Multi-State Load Models for Distribution System Analysis , 2011, IEEE Transactions on Power Systems.