الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
The pressing demand for more electric power, the environmental problems
resulting from centralized conventional fossil fuel-based power plants, and advances in power electronic converters and renewable energy technology have led to a significant penetration of renewable energy and conversion systems in electric power systems, and are now cost-competitive with fossil
fuels-based power plants. Also, micro-grids (MGs) are considered the best
option to electrify the remote or rural areas to avoid long transmission lines with the associated losses and cost. The inaccurate prediction of solar irradiance, wind speed and load demand may significantly influence the operation of micro-grids. This condition causes unbalance between generation and load that leads to variations in DC and AC bus voltages, and may affect system stability. Previous energy management studies are based on historical data and assume perfect forecast of the weather conditions and the demand, that is difficult to achieve in practice. This may lead to uneconomic and undesired operation due to the error in forecast data. The system should maintain its stability and good performance under these uncertainties. The uncertainty in weather prediction and load demand during real-time operation is taken into account in this research. Optimum operation of two islanded inter-connected MGS is achieved through two stages in this thesis. The first stage is the day-ahead optimal scheduling of MG sources. The second stage is maintaining the system balance considering economic operation during real-time operation. The first contribution of the thesis is introducing an efficient energy
management system (EMS) based on three phases using advanced metaheuristic techniques with taking into account the technical operational
constraints such as the generated
between the two MGs and the voltage magnitudes of buses. Phase 1 is an
efficient day-ahead unit commitment based on two steps, phase 2 is hourahead scheduling and phase 3 is real-time operation for an autonomous two connected MGs system.
Also, the research provides an efficient hierarchical control structure for two connected MGs isolated from the main grid that is able to maintain the balance between generation and load, and system stability in an economical
manner under different operating conditions. Another contribution of this thesis is an improvement of the dynamic performance and stability for two islanded inter-connected MGs and a gridtied PV MG by using an adaptive self-tuning controller based on on-line system identification and pole shift control (PS controller) and a non-linear adaptive controller that is fuzzy PI controller based model reference adaptive control (MRAFPI) instead of conventional PI controller, the most common controller in MG applications. Various studies prove that the proposed controllers, PS controller and MRAFPI controller, in general realize favorable dynamic performance of the system, can perform robustly over a wide range of operating conditions and show better performance when compared with the conventional PI controller.
A new effective heuristic optimization technique, called Global Porcellio
Scaber algorithm (GPSA), that improves and addresses the shortcomings of
the basic Porcellio Scaber algorithm (PSA), is also proposed to optimally
tune the used controllers by minimizing the integral of time multiplied by .