Autonomous Power Control in a Reconfigurable 6.78-MHz Multiple-Receiver Wireless Charging System

This paper proposes and implements autonomous control of a multiple-receiver wireless charging system. The charging control problem is challenging due to the decentralized nature of the system, possible changing numbers and types of energy storage devices as loads of the receivers, and complexity in wireless power distribution mechanism. The game-theory-based control is developed that fully respects the unique characteristics of the transmitter (i.e., charger) and receivers. The preferences of the individual devices are first quantified using utility functions. Then, the charging control problem is formulated as a generalized Stackelberg game considering the leader–follower relationship between the transmitter and receivers, and the limited total charging power. At each control instant, the generalized Nash equilibrium among the receivers, i.e., charging power distribution here, is reached by searching the Lagrange multiplier while the total charging power from the transmitter is updated in a step-by-step manner. Both simulation and experimental results show that the proposed charging control autonomously manages and updates the power distribution in the cases where the receivers with different energy storage devices quit or join the wireless charging.

[1]  Walid Saad,et al.  Economics of Electric Vehicle Charging: A Game Theoretic Approach , 2012, IEEE Transactions on Smart Grid.

[2]  Tong Zhang,et al.  Efficiency and Optimal Loads Analysis for Multiple-Receiver Wireless Power Transfer Systems , 2015, IEEE Transactions on Microwave Theory and Techniques.

[3]  Chen Zhao,et al.  Utility Function-Based Real-Time Control of A Battery Ultracapacitor Hybrid Energy System , 2015, IEEE Transactions on Industrial Informatics.

[4]  T. Başar,et al.  Dynamic Noncooperative Game Theory , 1982 .

[5]  H. Vincent Poor,et al.  Three-Party Energy Management With Distributed Energy Resources in Smart Grid , 2014, IEEE Transactions on Industrial Electronics.

[6]  Mianxiong Dong,et al.  Game-theoretical energy management design for smart cyber-physical power systems , 2015 .

[7]  Nishantha C. Ekneligoda,et al.  Game-Theoretic Cold-Start Transient Optimization in DC Microgrids , 2014, IEEE Transactions on Industrial Electronics.

[8]  Walid Saad,et al.  Game-Theoretic Methods for the Smart Grid: An Overview of Microgrid Systems, Demand-Side Management, and Smart Grid Communications , 2012, IEEE Signal Processing Magazine.

[9]  Ankur A. Kulkarni,et al.  On the variational equilibrium as a refinement of the generalized Nash equilibrium , 2012, Autom..

[10]  G. Leitmann On generalized Stackelberg strategies , 1978 .

[11]  Francisco Facchinei,et al.  Generalized Nash Equilibrium Problems , 2010, Ann. Oper. Res..

[12]  He Yin,et al.  Loading and Power Control for a High-Efficiency Class E PA-Driven Megahertz WPT System , 2016, IEEE Transactions on Industrial Electronics.

[13]  Patrick P. Mercier,et al.  Wireless Power Transfer With Concurrent 200-kHz and 6.78-MHz Operation in a Single-Transmitter Device , 2016, IEEE Transactions on Power Electronics.

[14]  Tong Zhang,et al.  Compensation of Cross Coupling in Multiple-Receiver Wireless Power Transfer Systems , 2016, IEEE Transactions on Industrial Informatics.

[15]  Peter Bartal,et al.  Game Theoretic Approach for Achieving Optimum Overall Efficiency in DC/DC Converters , 2014, IEEE Transactions on Industrial Electronics.

[16]  Kibok Lee,et al.  Receivers for Multifrequency Wireless Power Transfer: Design for Minimum Interference , 2015, IEEE Journal of Emerging and Selected Topics in Power Electronics.

[17]  Anirban Mahanti,et al.  Game Theoretic Model Predictive Control for Distributed Energy Demand-Side Management , 2015, IEEE Transactions on Smart Grid.

[18]  Young-Jin Park,et al.  Analysis of Capacitive Impedance Matching Networks for Simultaneous Wireless Power Transfer to Multiple Devices , 2015, IEEE Transactions on Industrial Electronics.

[19]  Mo-Yuen Chow,et al.  Convergence Analysis of the Incremental Cost Consensus Algorithm Under Different Communication Network Topologies in a Smart Grid , 2012, IEEE Transactions on Power Systems.

[20]  Nishantha C. Ekneligoda,et al.  A Game Theoretic Bus Selection Method for Loads in Multibus DC Power Systems , 2014, IEEE Transactions on Industrial Electronics.

[21]  P.T. Krein,et al.  Game-Theoretic Control of Small-Scale Power Systems , 2009, IEEE Transactions on Power Delivery.

[22]  Mo-Yuen Chow,et al.  Cooperative Distributed Demand Management for Community Charging of PHEV/PEVs Based on KKT Conditions and Consensus Networks , 2014, IEEE Transactions on Industrial Informatics.

[23]  Martin Maier,et al.  Open Energy Market Strategies in Microgrids: A Stackelberg Game Approach Based on a Hybrid Multiobjective Evolutionary Algorithm , 2015, IEEE Transactions on Smart Grid.

[24]  Chengbin Ma,et al.  A Cascaded Boost–Buck Converter for High-Efficiency Wireless Power Transfer Systems , 2014, IEEE Transactions on Industrial Informatics.