Grid integration is the practice of developing efficient ways to integrate various renewable energy sources and storages within micro & main grids. Due to the rapid growth of wind and solar distributed generations (DGs) and electric vehicles (EVs) technologies, we are interested in the integration of inverter-based DGs, grid-scale batteries, and EVs charging stations.

Studies show that EVs charging stations and grid-scale batteries can apply stress to distribution networks, overload transformers and feeders, and reduce the power quality by adding to voltage fluctuations, phase imbalance, and harmonics. These factors together with the power demand and price of the electricity that vary dynamically and stochastically, all emphasize the need for optimal grid integration systems.

Due to the unprecedented rate of cyberattacks, security of the grid integration systems has also become very important. In the near future, it is expected that a numerous number of EVs chargers are installed all around the world. Charging points are typically unmanned and partially located in remote areas, where cyber-physical protection is not guaranteed. Significant attempts have been made by different organizations such as the European network of cyber security (ENCS), and the European distribution system operators’ association for smart grids (E.DSO) to standardize cybersecurity requirements for EVs charging systems.

Our research aims to study and develop optimal and secured grid integration systems, by using advanced control theory and optimization techniques. 

Selected Publications

  • M Anvaripour, SMM Alavi, MJ Hayes, M Saif, “Cybersecurity of Electric Vehicles Charging Systems By Using Residual Generation Technique.” TechRxiv, 2022. [download]
  • RV Doyran, M Sedighizadeh, A Rezazadeh, SMM Alavi, “Optimal allocation of passive filters and inverter based DGs joint with optimal feeder reconfiguration to improve power quality in a harmonic polluted microgrid, ”, Renewable Energy Focus, 32, pp. 63-78, 2020.
  • M Sedighizadeh, SMM Alavi, AH Mohammadpour, “Stochastic Optimal Scheduling of Microgrids Considering Demand Response and Commercial Parking Lot by AUGMECON Method,” Iranian Journal of Electrical and Electronic Engineering, 16(3), pp. 393-411, 2020. 
  • M Sedighizadeh, AH Mohammadpour, SMM Alavi, “A daytime optimal stochastic energy management for EV commercial parking lots by using approximate dynamic programming and hybrid Big Bang Big Crunch algorithm,” Sustainable Cities and Society, 45, pp. 486 – 498, 2019.
  • M Sedighizadeh, AH Mohammadpour, SMM Alavi, “A two-stage optimal energy management by using ADP and HBB-BC algorithms for residential microgrids with renewable energy sources and storages,” Journal of Energy Storage, 21, pp. 460 – 480, 2019.