Smart energy management system for microgrid planning and operation

Abstract

A smart grid refers to an electricity transmission and distribution system that incorporates elements of traditional and cutting-edge power engineering, information technology, and communications, which can provide better grid performance and to support a wide array of additional custom services to consumers. A smart grid would facilitate the full use of sustainable energy technologies like solar power, wind power and fuel cells with the help of distributed energy storage systems (ESS). To understand the behavior of a smart grid, the author develops models suitable for overall analysis and design. The final goal is to lay the groundwork which would allow efficient management of the smart grid by solving all kinds of optimization problems, i.e., minimizing the operating costs, enhancing efficiency and reducing emission level while meeting the load demand. Smart Energy Management System (SEMS) is a core part for a smart grid system, which can make this system more intelligent. The optimal placement of the capacitors with the renewable energy is also discussed in this thesis. To handle the multi-objective optimization in the smart grid, a Jump and Shift method is proposed in this thesis. It aims to solve a large scale linear/nonlinear programming problem where the constraints are related to another large scale linear/nonlinear programming. A 14-bus and 112-bus power systems are tested to verify the multi-objective optimization algorithm based on the Jump and Shift method. ESS plays an important role in the smart grid. It is desirable to shave the peak demand and store the surplus electrical/renewable energy. A new method based on the cost-benefit analysis for the optimal sizing of an ESS in a microgrid (MG) is proposed. The Unit Commitment problem with the required spinning reserve capacity for the MG is considered in this method. To deal with the optimal control of ESS, the author presents an online management energy system for the lithium-ion (Li-ion) battery based on the proposed mathematical battery model and the adaptive Extended Kalman Filter (EKF) method. The proposed technique can be used to predict the state of charge (SOC) of the Li-ion battery via the online measured voltage and current.