Research Interests

    My research interests focus on Computational Methods, Simulation and Modelling for Energy Systems Integration and Smart Grid, which include:
  1. Demand Response as a component of smart grid: simulation and modeling optimal control of electricity usage in homes and buildings to reduce peak demand and improve energy efficiency, evaluation of electric vehicle penetration and energy storage facilities (e.g., batteries and pumped-hydro);
  2. Renewable energy integration (e.g., wind and solar power): evaluation of regulation and reserve requirements for renewable penetration, developing novel methods for optimal renewable sources integration, and developing renewable forecasting models;
  3. Optimal dispatch of power generation: modeling optimal power flow in large scale power systems to minimize/maximize selected objective functions (e.g., generation cost, CO2 emission and system security);
  4. Optimization theory and application: linear programming, convex optimization and none-linear optimization;
  5. Multi-agent system: simulation and modeling, distributed control algorithms, and game theory applications.

Research Funding

  • NSERC Discovery Grant, "Integration of distributed energy resources into electricity systems and markets," 2021-2026 (PI)
  • Mitacs Accelerate Grant, "Integration of electric vehicles into the power distribution system," 2021-2023 (PI)
  • Mitacs Research Training Award, "Multi-agent optimization for vehicle-to-grid applications in frequency regulation," 2020 (PI)
  • Mitacs Research Training Award, "Vehicle to Grid Application in Unit Commitment," 2020 (PI)
  • President's Research Seed Grant at the University of Regina, "Stochastic Optimization for Residential Demand Response in Power Systems and Electricity Markets," 2020-2022 (PI)
  • Research Opportunities Fund at the University of Regina, "Demand Response for Solar PV Energy Integration," (PI)
  • Research Start-Up Fund at the University of Regina (PI)

Sponsors and Partners (in alphabetical order)

I would like to express my sincere gratitude to our sponsors and partners. Without their generous support, the research in our group would never be possible.

Mitacs       Mitacs
NSERC       Natural Sciences and Engineering Research Council of Canada (NSERC)

SaskPower       Saskatchewan Power Corporation

Research Projects

    In this section, we would like to share some models and algorithms developed in our research. Click on each title, you can download the instruction, codes and data. Our Github site is under development. You may also check up later on.
  1. Optimization Workshop
  2. This workshop introduces basic concepts, models and algorithms in linear programming, convex optimization and stochastic optimization. A MATLAB-based modeling system for convex optimization, CVX, is covered. Case studies are presented including an production plan problem, smart electric vehicle charging, a newsvendor problem, and a regression model. The codes are provided for practice.

    The workshop is organized by IEEE South Sask section & PES/IAS Joint Chapter in collaboration with Engineering Graduate Student Association (EGSA) and the Faculty of Engineering and Applied Science at the University of Regina.

  3. Vehicle-to-Grid Application
  4. Vehicle-to-Grid (V2G) features with bi-directional power flow and two-way communication, which allows EVs act as both controllable loads under demand response (DR) and distributed energy resources. This practice is based on our publication indicated below. In the practice, you will learn how to develop an optimal V2G model to reduce the peak demand and/or the variance of the load profile meanwhile obtain monetary benefit.

    Ref: 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.

  5. ML framework for solar
  6. Various machine learning approaches are widely applied for short-term solar power forecasting, which is highly demanded for renewable energy integration and power system planning. However, selecting an appropriate machine learning approach and feature selection method is a significant challenge. In this study, a framework is developed to quantitatively evaluate various approaches and discover the best model for short-term solar power forecasting.

    Ref: U. Munawar and Z. Wang, "Framework of Using Machine Learning Approaches for Short-Term Solar Power Forecasting", Journal of Electrical Engineering and Technology, 2020.

  7. Residential Demand Response
  8. Demand Response (DR) is designed to reduce peak demand and encourage electric consumption when renewable energy is available in response to market price and/or power availability over time. In the provided link, we have shared the experimental/simulated data and optimal control algorithms from this Project.

    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.