Research Description by Topics

Power and Energy Systems

One of the central issues confronting grid operators is that the current design of power distribution system is unaccustomed to intermittency, a major characteristic of distributed energy resources (DERs). In the absence of effective tools and methodologies to manage high levels of DERs, the power grid will experience frequency and voltage variations, overloads of transformers and transmission lines, phase load imbalances, and other variations from operating standards of power grids. Given these challenges, new tools and methodologies must be developed for the technical and economic management of power grids with high penetration of DERs.

One of the most promising solutions to cope with the intermittency of DERs is the integration of distributed battery energy storage (BES). In recent years, we have been working on many problems regarding planning and operation of BES. We propose new models and algorithms for analyzing the economic and lifetime performance of distributed BES systems in smart power grids. We also study how to integrate renewable energy in future smart power grids with EV battery charging stations. Meanwhile, we have also developed novel distributed algorithms for the energy management of cooperative microgrids with integration of BES.

Selected Publications
  • X. Tan, Y. Wu and D.H.K. Tsang, “Pareto Optimal Operation of Distributed Battery Energy Storage Systems for Energy Arbitrage under Dynamic Pricing”, IEEE Transactions on Parallel and Distributed Systems, Vol. 27, No. 7, 2103-2115, July 2016. [PDF]

  • X. Tan, Y. Wu and D.H.K. Tsang, “A Stochastic Shortest Path Framework for Quantifying the Value and Lifetime of Battery Energy Storage under Dynamic Pricing”, IEEE Transactions on Smart Grid, vol. 8, no. 2, pp. 769-778, March 2017. [PDF]

  • B. Sun, Zhe Huang, X. Tan, and D.H.K. Tsang, “Optimal Scheduling for Electric Vehicle Charging with Discrete Charging Levels in Distribution Grid”, IEEE Transactions on Smart Grid, to appear. [PDF ]

  • Y. Wu, X. Tan, L. Qian, D.H.K. Tsang, W. Song, and L. Yu, “Optimal Pricing and Energy Scheduling for Hybrid Energy Trading Market in Future Smart Grid”, IEEE Transactions on Industrial Informatics, vol. 11, no. 6, pp. 1585-1596, Dec. 2015. [PDF]

  • T. Liu, X. Tan, B. Sun, Y. Wu, and D.H.K. Tsang, “Energy Management of Cooperative Microgrids: A Distributed Optimization Approach“, International Journal of Electrical Power and Energy Systems, to appear. [PDF]

  • W. Li, X. Tan, B. Sun, and D.H.K. Tsang, “Optimal Power Dispatch of a Centralized Electric Vehicle Battery Charging Station with Renewables”, IET Communications, to appear.

Smart and Sustainable Cities

The concept of smart cities is an urban development vision aiming to improve the cities’ sustainability and the citizens’ quality of life by means of information and communication technology. By 2050, 70% of the world's population is projected to live and work in cities, with transportation and energy as major constituents. Battery electric vehicles have significantly higher energy efficiency when compared to gasoline- and diesel-fueled vehicles, which are widely believed to become mainstream in the coming decades. Therefore, it is projected that there will be a large number of mobile batteries, facilitated by electric vehicles either with or without drivers, moving around in future smart cities. As a result, two orginally separated large systems, i.e., the power system and the transportation system, will become increasingly correlated both in their design and operation, which yields many new challenges and requires a substantial research.

We are interested in all kinds of applications in smart cities, but primarily focus on transportation and energy. We have been working on smart homes, smart buildings, and smart power grids, in which charging and discharging scheduling of electric vehicles plays a very important role. In particular, we investigate the planning and operation of energy refueling infrastructures of electric vehicles (including battery-swapping mode and plug-in charging mode) in smart cities. Meanwhile, autonomous electric vehicles will also be a very important part of future energy-transportation nexus, which is a prominent example of smart city application.

Selected Publications
  • X. Tan, D.H.K. Tsang et al, “Harnessing Multi-dimensional Flexibility of Autonomous Vehicles for Energy Management Optimization in Smart Cities,” working paper.

  • X. Tan, B. Sun, Y. Wu and D.H.K. Tsang, “Asymptotic Performance Evaluation of Battery Swapping and Charging Station for Electric Vehicles”, accepted by Performance Evaluation (Elsevier), December 2017. [ArXiv Preprint]

  • X. Tan, G. Qu, B. Sun, N. Li, and D.H.K. Tsang, “Optimal Scheduling of Battery Charging Stations Serving Electric Vehicles Based on Battery Swapping”, IEEE Transactions on Smart Grid, to appear. [PDF]

  • B. Sun, X. Tan, and D.H.K. Tsang, “Optimal Charging Operation of Battery Swapping and Charging Stations with QoS Guarantee“, IEEE Transactions on Smart Grid, to appear. [PDF]

  • W. Li, X. Tan, and D.H.K. Tsang, “Smart Home Energy Management Systems Based on Non-Intrusive Load Monitoring“, in Proceedings of IEEE International Conference on Smart Grid Communications (IEEE SmartGridComm 2015), Nov. 2015. [PDF]

  • S. Agheb, X. Tan, and D.H.K. Tsang, “Model Predictive Control of Integrated Room Automation Considering Occupants Preference“, in Proceedings of IEEE International Conference on Smart Grid Communications (IEEE SmartGridComm 2015), Nov. 2015. [PDF]

Energy-Efficient Networking and Computing

Fog computing is a decentralized computing infrastructure in which data, computation, storage and applications are distributed in the most logical, efficient place between the data source and the cloud. Fog computing essentially extends cloud computing and services to the edge of the network, bringing the advantages and power of the cloud closer to where data is created and acted upon. The goal of fog computing is to improve efficiency and reduce the amount of data transported to the cloud for processing, analysis and storage.

With the increasing development of Internet of Things, e.g., tremendous networked and heterogeneous devices in smart cities or connected vehicles, we have an excellent opportunity to bring the ‘cloud’ closer to the edge and users as ‘fog’. Previously, we have worked on the optimal resource allocation for LET-A downlink with heterogeneous traffic types. Recently, we start to investigate many interesting engineering and economic problems in vehicular fog computing with slow-driving and/or parked vehicles.

Sample Publications
  • X. Tan, and D.H.K. Tsang et al,“Temporal Flexibility Pricing in Vehicular Fog Computing based on Parked Vehicles,” working papers.

  • Y. Wu, L. Qian, H. Mao, X. Yang, H. Zhou, X. Tan, and D.H.K. Tsang, “Secrecy-Driven Resource Management for Vehicular Computation-Offloading Networks”, IEEE Network, to appear.

  • S. Niafar, X. Tan, and D.H.K. Tsang, “Optimal Downlink Scheduling for Heterogeneous Traffic in LET-A Based on MDP and Chance-Constrained Approaches”, ACM Springer Mobile Networks and Applications (MONET) Journal, 2015. [PDF]

  • S. Niafar, X. Tan and D.H.K. Tsang, “The Optimal User Scheduling for LTE-A Downlink with Heterogeneous Traffic Types”, [invited paper], in Proceedings of 10th International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness (Qshine 2014), Rhodes, Greece, 2014. [PDF]