Chun Sing Lai

Prof. Dr. Chun Sing Lai

School of Electrical and Information Engineering, Tianjin University, Tianjin, China
orcid
SMIEEE, CEng, FHEA

Research Focus & Metrics

Academic Career Length

16Years (2011-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 March 2017 in Applied Energy
Verified
Journal Article
Closed access
Chun Sing LaiChun Sing Lai
Chun Sing Lai
MMMalcolm D. McCulloch
Malcolm D. McCulloch

4 February 2020 in IEEE Transactions on Smart Grid
Verified
Journal Article
Closed access
Xu XuXu Xu
Xu Xu
YJYouwei Jia
Youwei Jia
Yan XuYan Xu
Yan Xu
ZXZhao Xu
Zhao Xu
SCSongjian Chai
Songjian Chai
Chun Sing LaiChun Sing Lai
Chun Sing Lai
This paper proposes a novel framework for home energy management (HEM) based on reinforcement learning in achieving efficient home-based demand response (DR). The concerned hour-ahead energy consumption scheduling problem is duly formulated as a finite Markov decision process (FMDP) with discrete time steps. To tackle this problem, a data-driven method based on neural network (NN) and Q-learning algorithm is developed, which achieves superior performance on cost-effective schedules for HEM system. Specifically, real data of electricity price and solar photovoltaic (PV) generation are timely processed for uncertainty prediction by extreme learning machine (ELM) in the rolling time windows. The scheduling decisions of the household appliances and electric vehicles (EVs) can be subsequently obtained through the newly developed framework, of which the objective is dual, i.e. to minimize the electricity bill as well as the DR induced dissatisfaction. Simulations are performed on a residential house level with multiple home appliances, an EV and several PV panels. The test results demonstrate the effectiveness of the proposed data-driven based HEM framework.

17 August 2020 in Clean Technologies
Verified
Journal Article
Open access
PDF file
Chun Sing LaiChun Sing Lai
Chun Sing Lai
YJYouwei Jia
Youwei Jia
ZDZhekang Dong
Zhekang Dong
DWDongxiao Wang
Dongxiao Wang
Yingshan TaoYingshan Tao
Yingshan Tao
Loi Lei LaiLoi Lei Lai
Loi Lei Lai
Smart cities employ technology and data to increase efficiencies, economic development, sustainability, and life quality for citizens in urban areas. Inevitably, clean technologies promote smart cities development including for energy, transportation and health. The smart city concept is ambitious and is being refined with standards. Standards are used to help with regulating how smart cities function and contributing to define a smart city. Smart cities must be officially recognized by national and international authorities and organizations in order to promote societal advancement. There are many research and review articles on smart cities. However, technical standards are seldom discussed in the current literature. This review firstly presents the study of smart city definitions and domain. The well-known smart city standards will be presented to better recognize the smart city concept. Well-defined standards allow meaningful comparisons among smart cities implementation. How smart city initiatives make a city smarter and improve the quality of life will be discussed for various countries. This review highlights that technical standards are important for smart cities implementation. This paper serves as a guide to the most recent developments of smart cities standards.

28 January 2022 in IEEE Transactions on Smart Grid
Verified
Journal Article
Closed access
Zhuoli ZhaoZhuoli Zhao
Zhuoli Zhao
JGJuntao Guo
Juntao Guo
XLXi Luo
Xi Luo
Chun Sing LaiChun Sing Lai
Chun Sing Lai
PYPing Yang
Ping Yang
Mohammad ShahidehpourMohammad Shahidehpour
Mohammad Shahidehpour
A microgrid is considered to be a smart power system that can integrate local renewable energy effectively. However, the intermittent nature of renewable energy causes operating pressure and additional expense in maintaining the stable operation by the energy management system in a microgrid. The structure of multi-microgrids provides the possibility to construct flexible and various energy trading framework. In this paper, in order to reduce the adverse effects of uncertain renewable energy output, a distributed robust model predictive control (DRMPC)-based energy management strategy is proposed for islanded multi-microgrids. This strategy balances the robustness and economy of single-microgrid system operation by combining the advantages of robust optimization and model predictive control, while coping with the uncertainty of renewable energy sources. Furthermore, a dynamic energy trading market is formed among microgrids, which can enhance the overall economy of the multi-microgrids system. Simulation results verify the feasibility of the proposed DRMPC strategy.

1 October 2017 in Renewable and Sustainable Energy Reviews
Verified
Journal Article
Closed access
Chun Sing LaiChun Sing Lai
Chun Sing Lai
YJYouwei Jia
Youwei Jia
Loi Lei LaiLoi Lei Lai
Loi Lei Lai
ZXZhao Xu
Zhao Xu
MMMalcolm D. McCulloch
Malcolm D. McCulloch
KWKit Po Wong
Kit Po Wong

7 May 2018 in IEEE Access
Verified
Journal Article
Open access
CHChao Huang
Chao Huang
Zhenhua WangZhenhua Wang
Zhenhua Wang
ZZZihan Zhao
Zihan Zhao
Long WangLong Wang
Long Wang
Chun Sing LaiChun Sing Lai
Chun Sing Lai
DWDong Wang
Dong Wang
In this paper, the robustness of model-based state observers including extended Kalman filter (EKF) and unscented Kalman filter (UKF) for state of charge (SOC) estimation of a lithium-ion battery against unknown initial SOC, current noise, and temperature effects is investigated. To more comprehensively evaluate the performance of EKF and UKF, two battery models including the first-order resistor-capacitor equivalent circuit and combined model are considered. A novel method is proposed to identify the parameters of the equivalent circuit model. The performance of SOC estimation is evaluated by employing measurement data from a commercial lithium-ion battery cell. The experiment results show that UKF generally outperforms EKF in terms of estimation accuracy and convergence rate for each battery model. However, the advantages of UKF over EKF with the combined model is not as significant as with the equivalent circuit model. Both EKF and UKF demonstrate strong robustness against current noise. The updates of model parameters corresponding to operational temperatures generally improve the estimation accuracy of EKF and UKF for both models.