Project

Project 4. Development of Adaptive Control for Microgrids and Distributed Control for Networked Microgrids

Microgrids (MGs) are independent distribution power systems with dedicated control systems that provide guaranteed power quality for various loads. The MG allows for the scalable integration of local generators and loads into the main grid and enables a high penetration of distributed energy resources (DERs). MGs operate in both islanded and grid-connected modes and they connect to the main grid at only one point. The MG energy management system (EMS) is responsible for stable and economically efficient operation. MG-EMS (1) regulates voltage and frequency in both grid-connected and islanded modes, (2) shares the load among various DERs, (3) controls the exchange of power between the MG and the main grid, and (4) optimizes the MG operating cost.

Aiming at addressing the challenges during the transition from grid-connected mode to islanding mode, I proposed a neural network based intelligent controller as an adaptive secondarily controller for voltage and frequency stabilization in MGs with system uncertainties and disturbances. The learning capability of the proposed approach is validated for stabilizing the voltage and frequency of the microgrids with promising performance.

Related Publications

[A3] Jafari, M., Ghasemkhani, A., Sarfi, V., Livani, H., Yang, L., & Xu, H. (2019). Biologically inspired adaptive intelligent secondary control for MGs under cyber imperfections. IET Cyber-Physical Systems: Theory & Applications, 4(4), 341-352.

[A2] Jafari, M., Sarfi, V., Ghasemkhani, A., Livani, H., Yang, L., & Xu, H. (2019, August). Adaptive Intelligent Secondary Control of Microgrids Using a Biologically-Inspired Reinforcement Learning. In 2019 IEEE Power & Energy Society General Meeting (pp. 1-5). IEEE. PDF

[A1] Jafari, M., Sarfi, V., Ghasemkhani, A., Livani, H., Yang, L., Xu, H., & Koosha, R. (2018, February). Adaptive neural network based intelligent secondary control for microgrids. In 2018 IEEE Texas Power and Energy Conference (TPEC) (pp. 1-6). IEEE.