Browsing by Subject "Power system reliability"
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Item Forecasting congestion in transmission line and voltage stability with wind integration(2011-08) Kang, Han; Baldick, Ross; Grady, William M.Due to growth of wind power, system operators are being challenged by the integration of large wind farms into their electrical power systems. Large scale wind farm integration has adverse effects on the power system due to its variable characteristic. These effects include two main aspects: voltage stability and active line flow. In this thesis, a novel techniques to forecast active line flow and select pilot bus are introduced with wind power integration. First, this thesis introduces a methodology to forecast congestion in the transmission line with high wind penetration. Since most wind resources tend to be located far away form the load center, the active line flow is one of the most significant aspects when wind farm is connected to electrical grid. By providing the information about the line flow which can contribute to transmission line congestion, the system operators would be able to respond such as by requesting wind power or load reduction. The second objective of this thesis is to select the weakest bus, called pilot bus, among all load buses. System reliability, especially voltage stability, can be adversely affected by wind variability. In order to ensure reliable operation of power systems with wind power integration, the index to select the pilot bus is developed, and further prediction of voltage profile at the pilot bus is fulfilled. The objective function to select the pilot bus takes account of the N-1 contingency analysis, loading margin, and reactive power sensitivity. Through on the objective function, the pilot bus is representative of all load buses as well as controllable by reactive power regulation. Predicting the voltage profile at the pilot bus is also useful for system operators to determine wind power output.Item Integration of renewable energy sources: reliability-constrained power system planning and operations using computational intelligence(2009-05-15) Wang, LingfengRenewable sources of energy such as wind turbine generators and solar panels have attracted much attention because they are environmentally friendly, do not consume fossil fuels, and can enhance a nation?s energy security. As a result, recently more significant amounts of renewable energy are being integrated into conventional power grids. The research reported in this dissertation primarily investigates the reliability-constrained planning and operations of electric power systems including renewable sources of energy by accounting for uncertainty. The major sources of uncertainty in these systems include equipment failures and stochastic variations in time-dependent power sources. Different energy sources have different characteristics in terms of cost, power dispatchability, and environmental impact. For instance, the intermittency of some renewable energy sources may compromise the system reliability when they are integrated into the traditional power grids. Thus, multiple issues should be considered in grid interconnection, including system cost, reliability, and pollutant emissions. Furthermore, due to the high complexity and high nonlinearity of such non-traditional power systems with multiple energy sources, computational intelligence based optimization methods are used to resolve several important and challenging problems in their operations and planning. Meanwhile, probabilistic methods are used for reliability evaluation in these reliability-constrained planning and design. The major problems studied in the dissertation include reliability evaluation of power systems with time-dependent energy sources, multi-objective design of hybrid generation systems, risk and cost tradeoff in economic dispatch with wind power penetration, optimal placement of distributed generators and protective devices in power distribution systems, and reliability-based estimation of wind power capacity credit. These case studies have demonstrated the viability and effectiveness of computational intelligence based methods in dealing with a set of important problems in this research arena.Item Reliability Modeling and Evaluation in Aging Power Systems(2010-01-14) Kim, Hag-KwenRenewal process has been often employed as a mathematical model of the failure and repair cycle of components in power system reliability assessment. This implies that after repair, the component is assumed to be restored to be in as good as new condition in terms of reliability perspective. However, some of the components may enter an aging stage as the system grows older. This thesis describes how aging characteristics of a system may impact the calculation of commonly used quantitative reliability indices such as Loss of Load Expectation (LOLE), Loss of Load Duration (LOLD), and Expected Energy Not Supplied (EENS). To build the history of working and failure states of a system, Stochastic Point Process modeling based on Sequential Monte Carlo simulation is introduced. Power Law Process is modeled as the failure rate function of aging components. Power system reliability analysis can be made at the generation capacity level where transmission constraints may be included. The simulation technique is applied to the Single Area IEEE Reliability Test System (RTS) and the results are evaluated and compared. The results show that reliability indices become increased as the age of the system grows.Item Some optimization problems in power system reliability analysis(2009-05-15) Jirutitijaroen, PanidaThis dissertation aims to address two optimization problems involving power system reliabilty analysis, namely multi-area power system adequacy planning and transformer maintenance optimization. A new simulation method for power system reliability evaluation is proposed. The proposed method provides reliability indexes and distributions which can be used for risk assessment. Several solution methods for the planning problem are also proposed. The first method employs sensitivity analysis with Monte Carlo simulation. The procedure is simple yet effective and can be used as a guideline to quantify effectiveness of additional capacity. The second method applies scenario analysis with a state-space decomposition approach called global decomposition. The algorithm requires less memory usage and converges with fewer stages of decomposition. A system reliability equation is derived that leads to the development of the third method using dynamic programming. The main contribution of the third method is the approximation of reliability equation. The fourth method is the stochastic programming framework. This method offers modeling flexibility. The implementation of the solution techniques is presented and discussed. Finally, a probabilistic maintenance model of the transformer is proposed where mathematical equations relating maintenance practice and equipment lifetime and cost are derived. The closed-form expressions insightfully explain how the transformer parameters relate to reliability. This mathematical model facilitates an optimum, cost-effective maintenance scheme for the transformer.