A Smart Self-healing Strategy for Electric Microgrids

The future of power systems is motivated by the necessity to diminish the impact of global climate change and reducing the concentration of greenhouse gases in the atmosphere. This can present an extraordinary challenge for the business of electric generation and delivery. The future power system is commonly referred as a smart grid, which it can be defined as the electrical network that uses intelligent monitoring, control, and communication technologies to improve the quality of service and increase the reliability and security of the system. The microgrid is one of the future sub-sets of the smart grid that have been realized at the distribution voltage level. The microgrid is a small-scale power grid that can operate independently or in conjunction with the area's main electrical grid. It combines different renewable energy resources with conventional generation and energy storage to improve the reliability of the system and reduce the cost. The microgrid has the capability of disconnecting itself during interruptions and operates in islanding mode to supply the loads during interruption. An important concept of microgrids is the self-healing, or smart restoration, which can be defined as the capability of the microgrid to isolate and restore the system, or part of it, to its normal operation using advanced monitoring and control systems and utilizing all the local available distributed sources. The main objective of this project is to propose a smart self-healing strategy for microgrids that includes multiple hybrid distributed generation (DG) systems. The DG system will be is a hybrid system that includes renewable resources, conventional DGs, and storage units. In the first phase of this study, the wind speed and solar radiation will be forecasted using Markov models and the auto-regressive moving average (ARMA) method. Then, speed-power and intensity-power models will be used to predict the forecasted wind and solar power for a practical wind turbine and PV panels. Finally, the availability model for all the main components of the hybrid DG system will be modelled. In the second phase, the hybrid DG system will be modelled. The hybrid DG system may consist of renewable DGs, conventional DGs, and storage units. In the third phase, a priority list model of all the customers in the system will be proposed. The priority list will rank the customers based on different factors such as: the type and criticality of the load, cost of interruption, load management programs implemented in the system, and system and load reliability indices. This is a multi-objective optimization problem where the solution will be a restoration priority list for any operational state of the system. In the fourth phase, the smart self-healing strategy will be proposed. The protection and switching devices and the configuration of the microgrid system will be modeled where both radial and networked configurations are considered in this study. Both the priority list and smart self-healing models will be modeled as multi-objective optimization problems. Different technique will be considered in this study to solve the optimization problem such as Lagrangian relaxation, mixed integer linear programming, and particle swarm optimization. Finally, sensitivity analysis will be performed to study the impact of different parameters on the priority list and smart restoration strategy.