Adaptive Hierarchical Control in V2G Integrated Micro-grids
Date
2021-03-03
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Abstract
An adaptive hierarchical control system for utilizing electric vehicle (EV) batteries to provide energy storage service in a Vehicle-to-Grid (V2G) integrated micro-grid is presented in this thesis. The control system consists of an on-line self-tuning (ST) adaptive inverter controller at the primary level and an Adaptive Neuro-Fuzzy Inference System (ANFIS) based energy management system (EMS) at the secondary level of the micro-grid hierarchical control system. A micro-grid test system based on a dc fast charging station is developed in order to utilize electric vehicle batteries for energy management. A grid connected voltage source inverter is used to connect the charging station to the point of common coupling. Development and implementation of an on-line self-tuning adaptive controller for the grid-connected inverter, that overcomes the limitations of conventional fixed gain PI controllers is described in the first part of the thesis. The proposed ST controller uses recursive least squares identification with a variable forgetting factor for system identification, and pole-shifting control with a variable pole-shift factor for control synthesis. In Part II of the thesis, an ANFIS based EMS is proposed for micro-grid energy management at the secondary level of the hierarchical control system. The control algorithm takes into account various uncertainties associated with micro-grid operation like intermittent power generation from renewable energy sources, random availability of EVs for storage, dynamic load demand etc., and allocates the power optimally among the available resources. After independently analyzing the performance of the primary and secondary level controllers, comprehensive case studies are carried out to analyze the performance of the overall hierarchical control system in Part III of the thesis. The coordinated operation of the primary and secondary level controllers was able to maintain an optimal power balance in the micro-grid by utilizing the fast response and reserve capacity of EV batteries, while also minimizing the power transfer to/from the main grid. A comparison between self-tuning and PI based inverter controllers at the primary level is done to re-emphasize the superiority of adaptive controllers over fixed gain controllers in giving an optimal performance during changes in system operating conditions.
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Keywords
Micro-grids, Self-tuning control, Grid-tied inverter, Electric vehicle, Vehicle-to-grid
Citation
Mohammed Shakeel, F. (2021). Adaptive Hierarchical Control in V2G Integrated Micro-grids (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.