@article{sun_lu_li_lubkeman_lu_2018, title={Modeling Combined Heat and Power Systems for Microgrid Applications}, volume={9}, ISSN={["1949-3061"]}, url={http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=ORCID&SrcApp=OrcidOrg&DestLinkType=FullRecord&DestApp=WOS_CPL&KeyUT=WOS:000443200700023&KeyUID=WOS:000443200700023}, DOI={10.1109/TSG.2017.2652723}, abstractNote={This paper presents the modeling of combined heat and power (CHP) systems for microgrid applications. When generating electricity, a CHP unit can recycle waste heat to supply building thermal loads to improve the overall efficiency of a traditional generation system. The ramping capability of a CHP unit makes it an ideal resource for load following and frequency regulation in microgrid operation. In this paper, a CHP model built in Simulink is developed. The CHP model includes three key components: generator, turbine, and absorption chiller. A new isochronous governor control strategy is proposed to provide zero-steady-state-error frequency regulation. The supply of building thermal loads is modeled to facilitate the calculation of the overall CHP system efficiency. The impact of ambient temperature on the maximum electrical output is considered. The developed model is implemented on OPAL-RT for testing the microgrid controller performance in a microgrid system.}, number={5}, journal={IEEE TRANSACTIONS ON SMART GRID}, author={Sun, Tiankui and Lu, Jian and Li, Zhimin and Lubkeman, David Lee and Lu, Ning}, year={2018}, month={Sep}, pages={4172–4180} }
 @misc{sun_li_rong_lu_li_2017, title={Effect of load change on the Thevenin equivalent impedance of power system}, volume={10}, number={3}, journal={Energies}, author={Sun, T. K. and Li, Z. M. and Rong, S. and Lu, J. and Li, W. X.}, year={2017} }
 @inproceedings{sun_li_lu_lu_2016, title={A preliminary study on the Thevenin equivalent parameters of DFIG wind farms}, DOI={10.1109/pesgm.2016.7741429}, abstractNote={It is an important but challenging issue to represent a wind farm using an equivalent model. This paper tries to develop an equivalent representation of a wind farm for power system planning and operation studies using Thevenin equivalent circuit. In the paper, a series string configuration of wind farms with doubly-fed induction generator (DFIG) wind turbines is emphasized, and case studies have been conducted to investigate the characteristics of the equivalent parameters of such a wind farm under different control strategies and wind speeds. Simulation results show that the equivalent parameters are strongly related to the wind speed, particularly when the constant power factor control strategy is employed for wind turbines. It can also be concluded that, compared to the constant power factor case, the constant voltage control scheme possesses better performance and thus is more popular for power system operation and control.}, booktitle={2016 ieee power and energy society general meeting (pesgm)}, author={Sun, T. K. and Li, Z. M. and Lu, J. and Lu, N.}, year={2016} }
 @inproceedings{wu_he_yip_lu_lu_2016, title={A two-stage random forest method for short-term load forecasting}, DOI={10.1109/pesgm.2016.7741295}, abstractNote={Machine learning methods are the main stream algorithms applied in short term load forecasting. However, typical machine learning methods consisting of Artificial Neural Network (ANN) and Support Vector Regression (SVR) have deficiencies hard to overcome, such as easy to be trapped in local optimization (for ANN) or hard to decide kernel parameter and penalty parameter (for SVR). On the other hand, grey relational analysis is an effective method to select proper historical data as training set for training machine learning models. But it is not comprehensive and accurate enough. In this paper, a new two-stage hybrid algorithm aimed to solve these two problems is proposed. Random Forest (RF) method is introduced as the machine learning method, which will not cause overfitting problem and parameters are easy to be tuned. Furthermore, Grey Relational Projection (GRP) is introduced to select similar historical data to train random forest models. The final forecasting results based on real load data prove this new two-stage method performs better than the other two common methods.}, booktitle={2016 ieee power and energy society general meeting (pesgm)}, author={Wu, X. Y. and He, J. H. and Yip, T. and Lu, J. and Lu, N.}, year={2016} }
 @inproceedings{lu_lu_wu_he_2016, title={Short-term HVAC load forecasting algorithms for home energy management}, DOI={10.1109/pesgm.2016.7741349}, abstractNote={This paper presents the forecasting algorithms for determining the electricity usage and operation status of residential heating, ventilating, and air conditioning (HVAC) systems. Two algorithms are presented based on what types of measured data can be received by the home energy management system (HEMS). Algorithm 1 is developed assuming only HVAC status is available to forecast the future HVAC usage. Algorithm 2 is developed for cases that the HVAC operation status, room temperature and outdoor temperature time series are known. The sensitivity of the room temperature change rate to outdoor temperature is derived and used to forecast the HVAC operation status. Results show that Algorithm 1 performs well for very short term forecast (less than 1 hour) and Algorithm 2 outperforms Algorithm 1 when forecasting HVAC behaviors for longer periods (from one hour to several hours) under a broader operation conditions such as continuously running or cold start. Both algorithms are measurement-based and require little computational resources and time to implement so that they fit well for providing HVAC status estimation to the HEM system for scheduling HVAC loads.}, booktitle={2016 ieee power and energy society general meeting (pesgm)}, author={Lu, J. and Lu, N. and Wu, X. Y. and He, J. H.}, year={2016} }