Multi-Agent Sliding Mode Control for State of Charge Balancing Between Battery Energy Storage Systems Distributed in a DC Microgrid

This paper proposes the novel use of multi-agent sliding mode control for state of charge balancing between distributed dc microgrid battery energy storage systems. Unlike existing control strategies based on linear multi-agent consensus protocols, the proposed nonlinear state of charge balancing strategy: 1) ensures the battery energy storage systems are either all charging or all discharging, thus eliminating circulating currents, increasing efficiency, and reducing battery lifetime degradation; 2) achieves faster state of charge balancing; 3) avoids overloading the battery energy storage systems during periods of high load; and 4) provides plug and play capability. The proposed control strategy can be readily integrated with existing multi-agent controllers for secondary voltage regulation and accurate current sharing. The performance of the proposed control strategy was verified with an RTDS Technologies real-time digital simulator, using switching converter models and nonlinear lead-acid battery models.

[1]  Frank L. Lewis,et al.  Distributed Cooperative Secondary Control of Microgrids Using Feedback Linearization , 2013, IEEE Transactions on Power Systems.

[2]  Henrik W. Bindner,et al.  Model prediction for ranking lead-acid batteries according to expected lifetime in renewable energy systems and autonomous power-supply systems , 2007 .

[3]  Bill Rose,et al.  Microgrids , 2018, Smart Grids.

[4]  Florian Dörfler,et al.  Distributed control and optimization in DC microgrids , 2015, Autom..

[5]  Vassilios G. Agelidis,et al.  Distributed Cooperative Control of Microgrid Storage , 2015, IEEE Transactions on Power Systems.

[6]  Vassilios G. Agelidis,et al.  Communication delay robustness for multi-agent state of charge balancing between distributed AC microgrid storage systems , 2015, 2015 IEEE Conference on Control Applications (CCA).

[7]  Vassilios G. Agelidis,et al.  Cooperative Multi-Agent Control of Heterogeneous Storage Devices Distributed in a DC Microgrid , 2016, IEEE Transactions on Power Systems.

[8]  Juan C. Vasquez,et al.  A Distributed Control Strategy for Coordination of an Autonomous LVDC Microgrid Based on Power-Line Signaling , 2014, IEEE Transactions on Industrial Electronics.

[9]  E. Gubia,et al.  Boost DC-AC inverter: a new control strategy , 2005, IEEE Transactions on Power Electronics.

[10]  Richard M. Murray,et al.  DYNAMIC CONSENSUS FOR MOBILE NETWORKS , 2005 .

[11]  Francesco Bullo,et al.  Breaking the Hierarchy: Distributed Control and Economic Optimality in Microgrids , 2014, IEEE Transactions on Control of Network Systems.

[12]  Juan C. Vasquez,et al.  Multiagent based distributed control for state-of-charge balance of distributed energy storage in DC microgrids , 2014, IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society.

[13]  Vassilios G. Agelidis,et al.  Unified Distributed Control for DC Microgrid Operating Modes , 2016, IEEE Transactions on Power Systems.

[14]  Juan C. Vasquez,et al.  Supervisory Control of an Adaptive-Droop Regulated DC Microgrid With Battery Management Capability , 2014, IEEE Transactions on Power Electronics.

[15]  Abhijit Das,et al.  Cooperative Control of Multi-Agent Systems , 2014 .

[16]  Ju Lee,et al.  AC-microgrids versus DC-microgrids with distributed energy resources: A review , 2013 .

[17]  Sandro Zampieri,et al.  A Distributed Control Strategy for Reactive Power Compensation in Smart Microgrids , 2011, IEEE Transactions on Automatic Control.

[18]  M. Stanley Whittingham,et al.  History, Evolution, and Future Status of Energy Storage , 2012, Proceedings of the IEEE.

[19]  Kai Strunz,et al.  DC Microgrid for Wind and Solar Power Integration , 2014, IEEE Journal of Emerging and Selected Topics in Power Electronics.

[20]  Francesco Bullo,et al.  Synchronization and power sharing for droop-controlled inverters in islanded microgrids , 2012, Autom..

[21]  T. Kim,et al.  A Hybrid Battery Model Capable of Capturing Dynamic Circuit Characteristics and Nonlinear Capacity Effects , 2011, IEEE Transactions on Energy Conversion.

[22]  S.D.J. McArthur,et al.  Multi-Agent Systems for Power Engineering Applications—Part I: Concepts, Approaches, and Technical Challenges , 2007, IEEE Transactions on Power Systems.

[23]  H. Ikebe.  Power systems for telecommunications in the IT age , 2003, The 25th International Telecommunications Energy Conference, 2003. INTELEC '03..

[24]  Juan C. Vasquez,et al.  Multi-agent-based distributed state of charge balancing control for distributed energy storage units in AC microgrids , 2015, 2015 IEEE Applied Power Electronics Conference and Exposition (APEC).

[25]  Juan C. Vasquez,et al.  Hierarchical Control of Droop-Controlled AC and DC Microgrids—A General Approach Toward Standardization , 2009, IEEE Transactions on Industrial Electronics.

[26]  Frank L. Lewis,et al.  Distributed Cooperative Control of DC Microgrids , 2015, IEEE Transactions on Power Electronics.

[27]  Vassilios G. Agelidis,et al.  Cooperative control of DC microgrid storage for energy balancing and equal power sharing , 2014, 2014 Australasian Universities Power Engineering Conference (AUPEC).

[28]  Yogesh L. Simmhan,et al.  An Analysis of Security and Privacy Issues in Smart Grid Software Architectures on Clouds , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[29]  Lexuan Meng,et al.  Hierarchical control with virtual resistance optimization for efficiency enhancement and State-of-Charge balancing in DC microgrids , 2015, 2015 IEEE First International Conference on DC Microgrids (ICDCM).

[30]  Hermann Schweizer,et al.  Residential Battery Storage: Is the Timing Right? , 2015, IEEE Electrification Magazine.

[31]  Juan C. Vasquez,et al.  DC Microgrids—Part I: A Review of Control Strategies and Stabilization Techniques , 2016, IEEE Transactions on Power Electronics.

[32]  Robin J. Evans,et al.  Hybrid Dynamical Systems: Controller and Sensor Switching Problems , 2012 .