Peer-Reviewed Journal Articles

  1. U. Munawar and Z. Wang, "Coordinated Integration of Distributed Energy Resources in Unit Commitment", International Journal of Electrical Power and Energy Systems, 2022. (pdf draft version)
  2. M. El-Hendawi, Z. Wang, R. Paranjape, S. Pederson, D. Kozoriz, J. Fick, "Electric Vehicle Charging Model in the Urban Residential Sector", Energies, vol. 15, issue. 13, pp. 4901, July 2022.
  3. M. El-Hendawi, Z. Wang, and X. Liu, "Centralized and Distributed Optimization for Vehicle-to-Grid Applications in Frequency Regulation", Energies, vol. 15, issue. 12, pp. 4446, June 2022.
  4. O Rezaei, R Habibifar, Z Wang, "A Robust Kalman Filter-Based Approach for SoC Estimation of Lithium-Ion Batteries in Smart Homes" Energies, vol. 15, issue. 10, pp. 3768, May 2022.
  5. M. Dehghani, M. Ghiasi, T. Niknam, K. Rouzbehi, Z. Wang, P. Siano, H. Alhelou, "Control of LPV Modeled AC-Microgrid Based on Mixed H2/H8 Time-Varying Linear State Feedback and Robust Predictive Algorithm", IEEE Access, vol. 10, pp., 3738 - 3755, 2021.
  6. Z. Wang, U. Munawar and R. Paranjape, "Stochastic Optimization for Residential Demand Response under Time of Use", IEEE Transactions on Industry Applications, vol. 57, pp. 1767 - 1778, 2020. (pdf version)
  7. K. Ginigeme and Z. Wang, "Distributed Optimal Vehicle-To-Grid Approaches with Consideration of Battery Degradation Cost under Real-Time Pricing", IEEE Access, vol. 8, pp. 5225 - 5235, 2020.
  8. U. Munawar and Z. Wang, "Framework of Using Machine Learning Approaches for Short-Term Solar Power Forecasting", Journal of Electrical Engineering and Technology, 2020.
  9. M. El-Hendawi and Z. Wang, "An ensemble method of full wavelet packet transform and neural network for short term electrical load forecasting", Electric Power Systems Research, vol. 182, pp. 106265, 2020.
  10. Z. Wang, R. Paranjape, Z. Chen and K. Zeng, Layered Stochastic Approach for Residential Demand Response Based on Real-Time Pricing and Incentive Mechanism” , IET Generation, Transmission & Distribution, vol. 4, issue. 3 pp. 423-431, 2019.
  11. Z. Wang, R. Paranjape, Z. Chen and K. Zeng, Multi-Agent Optimization for Residential Demand Response under Real-Time Pricingť, Energies, vol. 12, issue. 15, pp. 2867, Jul. 2019.
  12. Z. Wang and R. Paranjape, "Optimal Residential Demand Response for Multiple Heterogeneous Homes with Real-Time Price Prediction in a Multi-Agent Framework", IEEE Transactions on Smart Grid, vol. 8, issue 3, pp. 1173 - 1184, Oct. 2015 / May 2017.
  13. B. Tian, S. Liu, Y. Zhang, Z. Wang, "Analysis of fractal characteristic of fragments from rock burst tests under different loading rates”, Tehnički vjesnik, vol. 23, issue. 5, pp. 1269-1276, 2016.
  14. Z. Wang and R. Paranjape, "A Signal Processing Application for Evaluating Self-Monitoring Blood Glucose Strategies in a Software Agent Model", Computer Methods and Programs in Biomedicine, vol. 120, no. 2, pp. 77–87, Jul. 2015.
  15. Z. Wang and R. Paranjape, “The self-aware diabetic patient software agent model”, Computers in Biology and Medicine, vol. 43, no. 11, pp. 1900–1909, Nov. 2013

Book

  1. R. Paranjape, Z. Wang, and S. Gill, The Diabetic Patient Agent: Modeling Disease in Humans and the Healthcare System Response vol. 133, Springer, Dec. 2017.

Peer-Reviewed Conference Papers

  1. M. El-Hendawi and Z. Wang, "Multi-agent Optimization for Frequency Regulation through Vehicle-to-Grid Applications", 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall), Victoria, Canada, 2020.
  2. Z. Wang, U. Munawar and R. Paranjape, "Stochastic Optimization for Residential Demand Response under Time of Use", IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy, Cochin, Kerala, India, Jan. 2020.
  3. Z. Wang and R. Paranjape, “Evaluation of Electric Vehicle Penetration in a Residential Sector Under Demand Response Considering Both Cost and Convenience”, in Proc. IEEE Electrical Power Energy Conference, Saskatoon, SK, Canada, Oct. 2017.
  4. Z. Wang and R. Paranjape, "A Distributed Optimal Load Control Model for Heterogeneous Homes Responding to Time of Use," IEEE International Conference on Energy Internet (ICEI), Beijing, China, May 2017.
  5. Z. Wang and R. Paranjape, "A multi-agent system of evaluating residential demand response," in Proc. of the Workshop on Communications, Computation and Control for Resilient Smart Energy Systems, Waterloo, Ontario, Canada, June 2016 .
  6. Z. Wang and R. Paranjape, “Optimal scheduling algorithm for charging electric vehicle in a residential sector under demand response,” in Proc. IEEE Electrical Power Energy Conference, London, ON, Canada, 2015, pp. 1-5.
  7. Z. Wang and R. Paranjape, “An evaluation of electric vehicle penetration under demand response in a multi-agent based simulation,” in Proc. IEEE Electrical Power Energy Conference, Calgary, AB, Canada, Nov. 2014
  8. Z. Wang and R. Paranjape, “Agent-based simulation of home energy management system in residential demand response,” in Proc. IEEE Canadian Electrical and Computer Engineering Conference, Toronto, ON, Canada, May 2014, pp. 1–6
  9. Z. Wang, R. Paranjape, A. Sadanand, and Z. Chen, “Residential demand response: An overview of recent simulation and modeling applications,” in Proc. IEEE Canadian Electrical and Computer Engineering Conference, Regina, SK, Canada, May 2013, pp. 1–6
  10. Z. Wang and R. Paranjape, "Evaluating self-monitoring blood glucose strategies using a diabetic-patient software agent," in Proc. IEEE Canadian Electrical and Computer Engineering Conference, Regina, SK, Canada, May 2013
  11. R. Paranjape and Z. Wang, "The Diabetic Patient Software Agent: A Tool for Evaluating Self-Monitoring Blood Glucose Strategies," presented at the First IEEE Healthcare Innovation Conference, Houston, TX, USA, 2012
  12. R. Paranjape and Z. Wang, "The Self –Aware Diabetic Patient Agent," presented at the second Annual AMA-IEEE Medical Technology Conference, Boston, MA, USA, 2011