Abstract
During the past two decades, the power systems witnessed vital changes in terms of centralized paradigm versus more decentralized and market driven approaches; technical advances on communications and computation; and generation technologies which collectively lead to the advancement of microgrids (MGs). In this thesis, a novel dynamic adaptive simulations (DAS) approach is introduced for addressing major challenges in the operation and control of MGs, such as solving the economic and environmental load dispatch problem, achieving a sophisticated autonomous control of microgrids, and promoting the cooperation between individual microgrids to increase the power network reliability and energy surety. Initially, a first version of dynamic adaptive simulation was designed, namely DAS-EELD, for the efficient real-time economic and environmental load dispatching . The DAS-EELD framework was illustrated and validated via a modified IEEE-30 bus test system and as the experiments revealed, it is capable of reducing the computational resource usage for the reliable power dispatch without compromising the quality of the solution. Moreover, for the operation and control of MGs a second version of DAS was developed, namely DAS-CONTROL, in order to speed up significantly the real-time computation of the resource allocation and control decisions to optimize the operational cost, energy surety, and emissions. For validating the DAS-CONTROL framework a realistic MG was utilized to prove that DAS-CONTROL significantly reduces the computational burden of a considerably complex multi-objective problem. Finally, a third version of DAS was developed, namely DAS-SH, to provide distributed microgrids with a protocol of self-healing, both when they are operating collaboratively and competitively (in an isolated mode) while increasing the reliability of the network by pledging energy surety. DASSH framework was applied to a realistic case study that includes three microgrids and has been tested under four different emergency incidents. The results reveal that the cooperative collection of distributed microgrids were able to meet the critical and priority loads to a higher extent at all times while sacrificing from the less important non-critical loads. With the combination of the results from the different dynamic adaptive simulation versions that were created, this thesis reveals that DAS is a promising method to model microgrid systems as it provides means to find the most efficient method to optimize and enhance the microgrids’ operation and control and attain several benefits.