iSpace Robots

Intelligent Space (iSpace) is a relatively new concept to effectively use distributed sensors, actuators, robots, computing processors, and information technology over communication networks. iSpace is a large scale Mechatronics System by integrating sensors, actuators, and control algorithms in a communication system using knowledge from various engineering disciplines such as automation, control, hardware and software design, image processing, communication and networking.



Collaborative Distributed Energy Management Systems (CoDEMS)


Typically, the distributed energy resources (DER) are controlled by the utility distribution management system (DMS) or DER management system (DERMS). If hosted by microgrid, the microgrid energy management system (MG-EMS) will be added between the DMS/DERMS and DERs. This type of top-down hierarchical control chain is heavily constrained by the communication latency, quality, bandwidth, and availability. These systems are not positioned to embrace the DER boom and will be a bottle-neck for undergoing DER integration. The solution to the scalability is decentralization. Current academic and industry efforts are made to push control to the “edge”, namely on on-site DERs. With built-in edge autonomy in DERs, they can seamlessly work together and the system becomes more scalable. Another downside of the conventional centralized control scheme is the lack of resilience against natural and man-made disasters. The typical industry practice for resilience is by adding redundant central controllers. However, this redundancy is expensive yet cannot rapidly restore electric service in parallel. Therefore, the distributed control technologies have attracted significant academic and industry attention in recent years. Our lab has been developing distributed EMS, called Collaborative Distributed Energy Management Systems (CoDEMS), since 2008.



[1]Z. Cheng, J. Duan, and M.-Y. Chow, “To Centralize or to Distribute: That Is the Question: A Comparison of Advanced Microgrid Management Systems,” EEE Ind. Electron. Mag., vol. 12, no. 1, pp. 6–24, Mar. 2018, doi: 10.1109/MIE.2018.2789926.

[2]N. Rahbari-Asr, Y. Zhang, and M.-Y. Chow, “Consensus-based distributed scheduling for cooperative operation of distributed energy resources and storage devices in smart grids,” IET Generation, Transmission & Distribution, vol. 10, no. 5, pp. 1268–1277, Apr. 2016, doi: 10.1049/iet-gtd.2015.0159.

[3]Y. Zhang, N. Rahbari-Asr, J. Duan, and M.-Y. Chow, “Day-Ahead Smart Grid Cooperative Distributed Energy Scheduling With Renewable and Storage Integration,” IEEE Trans. Sustain. Energy, vol. 7, no. 4, pp. 1739–1748, Oct. 2016, doi: 10.1109/TSTE.2016.2581167.


Resilient Energy Magament in ugrid Simulator (REMμS)



The microgrid is envisioned to be the building block of the future smart grid, for its abilities to host distributed energy resources, to improve grid reliability, and to enhance system resiliency. One of the most studied research topics of the microgrid is the distributed microgrid energy management system. However, the algorithm prototyping and hardware validation still remain great challenges at the current stage. Our lab has been developing a highly scalable, customizable, and low-cost DC microgrid testbed framework that enables fast distributed MG-EMS prototyping and provides proof-of-concept validation.



[1]Cheng and M. Chow, “The Development and Application of a DC Microgrid Testbed for Distributed Microgrid Energy Management System,” IECON 2018 – 44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, 2018, pp. 300-305, doi: 10.1109/IECON.2018.8591816.


Neighborhood watch


One of the pressing challenges in the distributed EMS field is cybersecurity. Energy system is one of the most critical infrastructures. Its security is of paramount significance to the well-being of the society. Most state-of-the-art distributed EMS algorithms lack security features that can withstand cyber threats, e.g. data integrity attacks. From our interaction with the microgrid operators, we learned that the cybersecurity of the system and the resilience against cyber-attacks are must-have features for the distributed DER controller. The two critical cybersecurity aspects of the distributed microgrid EMS are 1) data integrity; and 2) data confidentiality. Our lab has been investigating these cybersecurity issues since 2012.



[1] Z. Cheng and M. -Y. Chow, “Resilient Collaborative Distributed Energy Management System Framework for Cyber-Physical DC Microgrids,” in IEEE Transactions on Smart Grid, vol. 11, no. 6, pp. 4637-4649, Nov. 2020, doi: 10.1109/TSG.2020.3001059.

[2] F. Ye, Z. Cheng, X. Cao and M. -Y. Chow, “A Random-Weighted Privacy-Preserving Distributed Algorithm for Energy Management in Microgrid with Energy Storage Devices,” 2020 2nd IEEE International Conference on Industrial Electronics for Sustainable Energy Systems (IESES), Cagliari, Italy, 2020, pp. 249-254, doi: 10.1109/IESES45645.2020.9210675.

[3] Z. Cheng and M. -Y. Chow, “An Augmented Bayesian Reputation Metric for Trustworthiness Evaluation in Consensus-based Distributed Microgrid Energy Management Systems with Energy Storage,” 2020 2nd IEEE International Conference on Industrial Electronics for Sustainable Energy Systems (IESES), Cagliari, Italy, 2020, pp. 215-220, doi: 10.1109/IESES45645.2020.9210638.

[4] Z. Cheng and M. Chow, “Reputation-based Collaborative Distributed Energy Management System Framework for Cyber-physical Microgrids: Resilience against Profit-driven Attacks,” 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 2020, pp. 1-5, doi: 10.1109/ISGT45199.2020.9087737.


Samsung Project – Big Data Based Remaining Driving Range Estimation


The amount of data collected in Electric Vehicles has been growing fast because we have many more sensors, higher bandwidth communication systems, and cheaper memory to monitor and measure real-time driving range related data and store the data on the vehicles, in connected clouds, etc. This massive amount of data can have different levels of accuracy, resolutions, and relevance in unstructured ways. Big Data technologies have been emerging to address huge, diverse and unstructured data to substantially improve the overall system performance. With proper use of Big Data concepts and techniques, the remaining driving range estimation of the vehicle can be substantially improved.

The range estimation needs the incorporation and synchronization of all standard, real-time and historical data. Usually, the standard and historical data provides an initial prediction of the driving range; and the real-time data updates the estimation during the driving. However, under different conditions, some data are more relevant than others for the range estimation. This data can be historical, standard, or real-time depending on different situations. The big data analytics helps us identify the relevant data and discover its correlation to the remaining driving range estimation.



[1] H. Rahimi-Eichi and M.-Y. Chow, “Big-Data Framework for Electric Vehicle Range Estimation,”  presented at the 40th Annual Conference of the IEEE Industrial Electronics Society (IECON2014), IEEE, Dallas, TX , 2014.

[2] Z. Cheng, M. Chow, D. Jung and J. Jeon, “A big data based deep learning approach for vehicle speed prediction,” 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE), Edinburgh, 2017, pp. 389-394, doi: 10.1109/ISIE.2017.8001278.

[3] D. Jung, M. Chow, Z. Cheng, and J. Jeon, “Method and apparatus for estimating driving information,“US10215579B2, 2019.