abstract: Electric power system security assessment is one of the most important requirements for operational and resource planning of the bulk power system ensuring safe operation of the power system for all credible contingencies. This deterministic approach usually provides a conservative criterion and can result in expensive bulk system expansion plans or conservative operating limits. Furthermore, with increased penetration of converter-based renewable generation in the electric grid, the dynamics of the grid are changing. In addition, the variability and intermittency associated with the renewable energy sources introduce uncertainty in the electricity grid. Since security margins have direct economic impact on the utilities; more clarity is required regarding the basis on which security decisions are made. The main objective of this work is to provide an approach for risk-based security assessment (RBSA) to define dynamic reliability standards in future electricity grids. RBSA provides a measure of the security of the power system that combines both the likelihood and the consequence of an event. A novel approach to estimate the impact of transient stability is presented by modeling several important protection systems within the transient stability analysis. A robust operational metric to quantify the impact of transient instability event is proposed that incorporates the effort required to stabilize any transiently unstable event. The effect of converter-interfaced renewable energy injection on system reliability is investigated us-ing RBSA. A robust RBSA diagnostics tool is developed which provides an interactive user interface where the RBSA results and contingency ranking reports can be explored and compared based on specific user inputs without executing time domain simulations or risk calculations, hence providing a fast and robust approach for handling large time domain simulation and risk assessment data. The results show that RBSA can be used effectively in system planning to select security limits. Comparison of RBSA with deterministic methods show that RBSA not only provides less conservative results, it also illustrates the bases on which such security decisions are made. RBSA helps in identifying critical aspects of system reliability that is not possible using the deterministic reliability techniques. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2017

The first part of this dissertation presents an innovative method for the assessment of generation system reliability. In this method, genetic algorithm (GA) is used as a search tool to truncate the probability state space and to track the most probable failure states. GA stores system states, in which there is generation deficiency to supply system maximum load, in a state array. The given load pattern is then convoluted with the state array to obtain adequacy indices. In the second part of the dissertation, a GA based method for state sampling of composite generation-transmission power systems is introduced. Binary encoded GA is used as a state sampling tool for the composite power system network states. A linearized optimization load flow model is used for evaluation of sampled states. The developed approach has been extended to evaluate adequacy indices of composite power systems while considering chronological load at buses. Hourly load is represented by cluster load vectors using the k-means clustering technique. Two different approaches have been developed which are GA parallel sampling and GA sampling for maximum cluster load vector with series state revaluation. The developed GA based method is used for the assessment of annual frequency and duration indices of composite system. The conditional probability based method is used to calculate the contribution of sampled failure states to system failure frequency using different component transition rates. The developed GA based method is also used for evaluating reliability worth indices of composite power systems. The developed GA approach has been generalized to recognize multi-state components such as generation units with derated states. It also considers common mode failure for transmission lines. Finally, a new method for composite system state evaluation using real numbers encoded GA is developed. The objective of GA is to minimize load curtailment for each sampled state. Minimization is based on the dc load flow model. System constraints are represented by fuzzy membership functions. The GA fitness function is a combination of these membership values. The proposed method has the advantage of allowing sophisticated load curtailment strategies, which lead to more realistic load point indices.