|dc.description.abstract||Smart grids have emerged as dominant platforms for effectively accommodating high penetration of renewable-based distributed generation (DG) and electric vehicles (EVs). These smart paradigms play a pivotal role in the advancement of distribution systems and pave the way for active distribution networks (ADNs). However, the large number of smart meters deployed in the distribution system (e.g., 200 million smart meters will be installed in Europe by 2020) represents one of the main challenges facing the management and control of distribution networks and thus the enabling of smart grids. In addition to the data tsunami flooding central controllers, the concerns about privacy and system vulnerability are fast becoming a key restraint for the implementation of the smart grids. These concerns are prompting utilities to be more reluctant to adopt new techniques, leaving the distribution system mired in relatively old-fashioned routines. Microgrids provide an ideal paradigm to form smart grids, thanks to their limited size and ability to ‘island’ when supplying most of their loads during emergencies, which improves system reliability. However, preserving load-generation balance is comprehensively challenging, given that microgrids are dominated by renewable-based DGs, which are characterized by their probabilistic nature and intermittent power. Although microgrids are now well-established and have been extensively studied, there is still some debate over having microgrids that are solely ac or solely dc, with the consensus tending toward hybrid ac-dc microgrids. Furthermore, while some research has addressed using solely ac microgrids, the planning of hybrid ac-dc microgrids has not yet been investigated, despite the many benefits these types of microgrids offer. Additionally, developing steady-state analysis tools capable of handling grid-connected mode and islanded mode for the operation of ac microgrids and hybrid ac-dc microgrids still has uncertainties about their computational burden, complexity, and convergence. The high R/X ratio characterized distribution systems result in ill-condition that hinders the convergence of conventional Newton Raphson (NR) techniques. Moreover, calculating the inversion of the Jacobian matrix that is formed from the calculation of derivatives adds to the complexity of these techniques. Therefore, developing a simple, accurate, and fast steady-state analysis tool is crucial for enabling microgrids and hence smart grids.
Driven by the aforementioned challenges, the broad goal of this thesis is to enable microgrids as building clusters to smooth and accelerate the realization of smart grids. Achieving this objective involves a number of stages, as follows: 1) The development of probabilistic models for loads and renewable DG-based output power. These models are then integrated with the load flow analysis techniques to form a probabilistic power flow (PPF) tool. 2) The proposal of a novel operational
philosophy that divides existing bulky grids into manageable clusters of self-adequate microgrids that adapt their boundaries to keep load-generation balance at different operating scenarios. 3) The proposal of planning a framework for the newly constructed grids as hybrid ac-dc microgrids with minimum levelized investment costs and consideration of the probabilistic nature of load and renewable generation. 4) The development of a branch-based power flow algorithm for steady-state analysis of ac microgrids and hybrid ac-dc microgrids.||en