Dynamic Modeling of Electric Springs

The use of “Electric Springs” is a novel way of distributed voltage control while simultaneously achieving effective demand-side management through modulation of noncritical loads in response to the fluctuations in intermittent renewable energy sources (e.g., wind). The proof-of-concept has been successfully demonstrated on a simple 10-kVA test system hardware. However, to show the effectiveness of such electric springs when installed in large numbers across the power system, there is a need to develop simple and yet accurate simulation models for these electric springs which can be incorporated in large-scale power system simulation studies. This paper describes the dynamic simulation approach for electric springs which is appropriate for voltage and frequency control studies at the power system level. The proposed model is validated by comparing the simulation results against the experimental results. Close similarity between the simulation and experimental results gave us the confidence to use this electric spring model for investigating the effectiveness of their collective operation when distributed in large number across a power system. Effectiveness of an electric spring under unity and non-unity load power factors and different proportions of critical and noncritical loads is also demonstrated.

[1]  J. M. Noworolski,et al.  Generalized averaging method for power conversion circuits , 1990, 21st Annual IEEE Conference on Power Electronics Specialists.

[2]  Laszlo Gyugyi,et al.  Understanding FACTS: Concepts and Technology of Flexible AC Transmission Systems , 1999 .

[3]  J. Dixon,et al.  Reactive Power Compensation Technologies: State-of-the-Art Review , 2005, Proceedings of the IEEE.

[4]  D. Westermann,et al.  Demand Matching Wind Power Generation With Wide-Area Measurement and Demand-Side Management , 2007, IEEE Transactions on Energy Conversion.

[5]  M. Pedrasa,et al.  Scheduling of Demand Side Resources Using Binary Particle Swarm Optimization , 2009, IEEE Transactions on Power Systems.

[6]  Vincent W. S. Wong,et al.  Autonomous Demand-Side Management Based on Game-Theoretic Energy Consumption Scheduling for the Future Smart Grid , 2010, IEEE Transactions on Smart Grid.

[7]  Masood Parvania,et al.  Demand Response Scheduling by Stochastic SCUC , 2010, IEEE Transactions on Smart Grid.

[8]  Alec Brooks,et al.  Demand Dispatch , 2010, IEEE Power and Energy Magazine.

[9]  S C Lee,et al.  Demand Side Management With Air Conditioner Loads Based on the Queuing System Model , 2011, IEEE Transactions on Power Systems.

[10]  Peter Palensky,et al.  Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads , 2011, IEEE Transactions on Industrial Informatics.

[11]  Felix F. Wu,et al.  Electric Springs—A New Smart Grid Technology , 2012, IEEE Transactions on Smart Grid.

[12]  Siew-Chong Tan,et al.  General Steady-State Analysis and Control Principle of Electric Springs With Active and Reactive Power Compensations , 2013, IEEE Transactions on Power Electronics.

[13]  Nilanjan Ray Chaudhuri,et al.  Droop Control of Distributed Electric Springs for Stabilizing Future Power Grid , 2013, IEEE Transactions on Smart Grid.

[14]  Ron Shu-Yuen Hui,et al.  Reduction of Energy Storage Requirements in Future Smart Grid Using Electric Springs , 2013, IEEE Transactions on Smart Grid.

[15]  Julia Frankfurter,et al.  Reactive Power Control In Electric Systems , 2016 .