Protection of both the wind plant and the interconnecting transmission system during short-circuit faults is imperative for maintaining system structural integrity and reliability. The circuit breakers and protective relays used to protect the power system during such events are designed based upon calculations of the current that will flow in the circuit during the fault. Sequence-network models of various power-system components, such as synchronous generators, transformers, transmission lines, etc., are often used to perform these calculations. However, there are no such models widely accepted for certain types of wind-turbine generators used in modern wind plants. The problem with developing sequence-network models of wind plants is that several different wind-turbine generator designs exist; yet, each exhibit very different short-circuit behavior. Therefore, a “one size fits all” approach is not appropriate for modeling wind plants, as has been the case for conventional power plants based on synchronous-generator technology. Further, many of the newer wind-turbine designs contain proprietary controls that affect the short-circuit behavior, and wind-turbine manufacturers are often not willing to disclose these controls. Thus, protection engineers do not have a standard or other well-established model for calculating short-circuit currents in power systems with wind plants. Therefore, the research described in this dissertation involves the development of such models for calculating short-circuit currents from wind-turbine generators. The focus of this dissertation is on the four existing wind-turbine generator designs (identified as Types 1 – 4). Only AC-transmission-interconnected wind-turbine generators are considered in this dissertation. The primary objective of this research is the development of sequence-network models, which are frequency-domain analysis tools, for each wind-turbine generator design. The time-domain behavior of each wind-turbine generator is thoroughly analyzed through transient simulations, experimental tests on scaled wind-turbine generator test beds, and solutions to the system dynamic equations. These time-domain analyses are used to support the development of the sequence-network models.

abstract: Corrective transmission topology control schemes are an essential part of grid operations and are used to improve the reliability of the grid as well as the operational efficiency. However, topology control schemes are frequently established based on the operator's past knowledge of the system as well as other ad-hoc methods. This research presents robust corrective topology control, which is a transmission switching methodology used for system reliability as well as to facilitate renewable integration. This research presents three topology control (corrective transmission switching) methodologies along with the detailed formulation of robust corrective switching. The robust model can be solved off-line to suggest switching actions that can be used in a dynamic security assessment tool in real-time. The proposed robust topology control algorithm can also generate multiple corrective switching actions for a particular contingency. The solution obtained from the robust topology control algorithm is guaranteed to be feasible for the entire uncertainty set, i.e., a range of system operating states. Furthermore, this research extends the benefits of robust corrective topology control to renewable resource integration. In recent years, the penetration of renewable resources in electrical power systems has increased. These renewable resources add more complexities to power system operations, due to their intermittent nature. This research presents robust corrective topology control as a congestion management tool to manage power flows and the associated renewable uncertainty. The proposed day-ahead method determines the maximum uncertainty in renewable resources in terms of do-not-exceed limits combined with corrective topology control. The results obtained from the topology control algorithm are tested for system stability and AC feasibility. The scalability of do-not-exceed limits problem, from a smaller test case to a realistic test case, is also addressed in this research. The do-not-exceed limit problem is simplified by proposing a zonal do-not-exceed limit formulation over a detailed nodal do-not-exceed limit formulation. The simulation results show that the zonal approach is capable of addressing scalability of the do-not-exceed limit problem for a realistic test case. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2015

This study aims to build a method for validating a power system model in the PLEXOS software. Special emphasis is put on the carbon dioxide emissions modelling. A case study of Germany and Poland is formulated in order to apply the created procedure to a European power model. The verification of emissions being one of the outputs, is divided into two phases. The first one focuses on the historical results from 2016-2017, which are compared with the chosen reference statistics and the emissions results obtained in another optimization tool. The second phase looks into the trends of emissions in the near future, i.e. time period between 2019-2025. OSeMOSYS as the second piece of software is used for benchmarking the results obtained by the PLEXOS model.

The last full review of load models used for power system studies occurred in the 1980s. Since then, new types of loads have been introduced and system load mix has changed considerably. The examples of newly introduced loads include drive-controlled motors, low energy consumption light sources and other modern power electronic loads. Their numbers have been steadily increasing in recent years, a trend which is expected to escalate. Accordingly, the majority of load models used in traditional power system studies are becoming outdated, as they are unable to accurately represent power demand characteristics of existing and future loads. Therefore, in order to accurately predict both active and non-active power demand characteristics of aggregated modern power system loads in different load sectors (e.g. residential, commercial or industrial), existing load models should be updated and new models developed. This thesis aims to fill this gap by developing individual, generic and aggregated steady state models of the most common loads in use today, as well as of those expected to show significant growth in the future. The component-based approach is adopted for load modelling, where individual load models are obtained in detailed simulations of physical devices. Whenever possible, the developed individual load models are validated by measurements. These detailed individual load models are then simplified and expressed as equivalent circuit and analytical models, which allowed the establishment of generic load models that can be easily aggregated. It should be noted that since all non-active power characteristics are correctly represented, the developed aggregated load models allow for a full harmonic analysis, which is not the case with the standard steady state load models. Therefore, the proposed load models form an extensive library of comprehensive load models that are suitable for use in multiple areas of power system research. Based on the results of research related to typical domestic/residential sector load mix, the newly developed load models are aggregated and then applied to a typical UK/Scotland distribution network. Considerable differences are seen between network characteristics of newly proposed and previously developed models. The voltage distortion of a typical distribution system bus is investigated, and it is shown that distortion of the system voltage is likely to increase significantly in the future. The results of the presented research also suggest that neglecting the harmonic characteristics from the set of general load attributes may introduce errors in standard load flow studies.

The advent of Wide-Area measurement Systems has spurred interest in the use of non-local feedback signals for power swing damping control. Although damping can be improved through generator excitation systems, dc links and other grid connected power electronic converters, the full potential of wide-area measurements can be realized by coordinating the strategies used for multiple controllable devices in a grid. These strategies also need to be robust to partial or complete loss of communication, changes in operating points, topology and equipment outages, improve damping of all the controllable swing modes, and have adequate stability margins to avoid destabilization of untargeted modes. This thesis investigates a control strategy for multi-infeed and multi-terminal (also referred to as multiple embedded dc links in this thesis) dc links using local frequency difference signals as well as the frequency difference signals obtained from other dc links. This strategy combines the advantages of the local frequency difference signal with the additional degrees of freedom provided by the use of non-local frequency difference signals, to achieve targeted and enhanced swing mode damping for the poorly damped modes. Since the strategy uses only a limited set of non-local signals, the signals may be directly communicated to the dc links without having to be centrally collated with other system-wide measurements. The key aspect of the proposed strategy is the use of a symmetric positive definite (spd) gain matrix. This results in enhanced damping for all controllable swing modes. Furthermore, loss of communication between the dc links does not destroy the symmetric positive definiteness and the gain elements can be tuned to selectively enhance damping of poorly damped modes. Eigenvalue sensitivity analysis and case studies on a 3 machine 9 bus and 16 machine 68 bus system with multiple HVDC links are presented to demonstrate the key attributes and the effectiveness of this strategy. / October 2016

Thesis (Ph. D. electrical engineering)--Washington State University, December 2007. / Includes bibliographical references (p. 112-116).

This 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.

This thesis describes and evaluates a set of multiobjective generation expansion planning models that include four objectives and importance given to renewable generation technologies while considering location of generation units. Using multicriteria decision making theory, these models provide results which indicate the most recommendable amount of each type of generating technology to install at each location. A framework to solve and generate alternative solutions is provided for each model, and representative case studies from the Mexican Electric Power System are used to show the performance of the proposed models and solution methods. The models include a single-period model, a multi-period model, single-period mixed-integer non-linear model, and a fuzzy multi-criteria model. Among the attributes considered are the investment and operation cost of the units, the environmental impact, the amount of imported fuel, and the portfolio investment risk. The approaches to solve the models are based on multiobjective linear programming, analytical hierarchy process, and evolutionary algorithms. The incorporation of more than three criteria to generate the expansion alternatives, the importance given to renewable generation technologies, and the geographical location of the new generation units are some features of the proposed models which have not been considered simultaneously in the literature. A novel multiobjective evolutionary programming algorithm has been proposed in this thesis. / "July 2006."

Carbon dioxide is a main greenhouse gas that is responsible for global warming and climate change. The reduction in greenhouse gas emission is required to comply with the Kyoto Protocol. Looking at CO2 emissions distribution in Canada, the electricity and heat generation sub-sectors are among the largest sources of CO2 emissions. In this study, the focus is to reduce CO2 emissions from electricity generation through capacity expansion planning for utility companies. In order to reduce emissions, different mitigation options are considered including structural changes and non structural changes. A drawback of existing capacity planning models is that they do not consider uncertainties in parameters such as demand and fuel prices. Stochastic planning of power production overcomes the drawback of deterministic models by accounting for uncertainties in the parameters. Such planning accounts for demand uncertainties by using scenario sets and probability distributions. However, in past literature different scenarios were developed by either assigning arbitrary values or by assuming certain percentages above or below a deterministic demand. Using forecasting techniques, reliable demand data can be obtained and can be inputted to the scenario set. The first part of this thesis focuses on long term forecasting of electricity demand using autoregressive, simple linear, and multiple linear regression models. The resulting models using different forecasting techniques are compared through a number of statistical measures and the most accurate model was selected. Using Ontario electricity demand as a case study, the annual energy, peak load, and base load demand were forecasted, up to year 2025. In order to generate different scenarios, different ranges in economic, demographic and climatic variables were used. The second part of this thesis proposes a robust optimization capacity expansion planning model that yields a less sensitive solution due to the variation in the above parameters. By adjusting the penalty parameters, the model can accommodate the decision maker’s risk aversion and yield a solution based upon it. The proposed model is then applied to Ontario Power Generation, the largest power utility company in Ontario, Canada. Using forecasted data for the year 2025 with a 40% CO2 reduction from the 2005 levels, the model suggested to close most of the coal power plants and to build new natural gas combined cycle turbines and nuclear power plants to meet the demand and CO2 constraints. The model robustness was illustrated on a case study and, as expected, the model was found to be less sensitive than the deterministic model.

Carbon dioxide is a main greenhouse gas that is responsible for global warming and climate change. The reduction in greenhouse gas emission is required to comply with the Kyoto Protocol. Looking at CO2 emissions distribution in Canada, the electricity and heat generation sub-sectors are among the largest sources of CO2 emissions. In this study, the focus is to reduce CO2 emissions from electricity generation through capacity expansion planning for utility companies. In order to reduce emissions, different mitigation options are considered including structural changes and non structural changes. A drawback of existing capacity planning models is that they do not consider uncertainties in parameters such as demand and fuel prices. Stochastic planning of power production overcomes the drawback of deterministic models by accounting for uncertainties in the parameters. Such planning accounts for demand uncertainties by using scenario sets and probability distributions. However, in past literature different scenarios were developed by either assigning arbitrary values or by assuming certain percentages above or below a deterministic demand. Using forecasting techniques, reliable demand data can be obtained and can be inputted to the scenario set. The first part of this thesis focuses on long term forecasting of electricity demand using autoregressive, simple linear, and multiple linear regression models. The resulting models using different forecasting techniques are compared through a number of statistical measures and the most accurate model was selected. Using Ontario electricity demand as a case study, the annual energy, peak load, and base load demand were forecasted, up to year 2025. In order to generate different scenarios, different ranges in economic, demographic and climatic variables were used. The second part of this thesis proposes a robust optimization capacity expansion planning model that yields a less sensitive solution due to the variation in the above parameters. By adjusting the penalty parameters, the model can accommodate the decision maker’s risk aversion and yield a solution based upon it. The proposed model is then applied to Ontario Power Generation, the largest power utility company in Ontario, Canada. Using forecasted data for the year 2025 with a 40% CO2 reduction from the 2005 levels, the model suggested to close most of the coal power plants and to build new natural gas combined cycle turbines and nuclear power plants to meet the demand and CO2 constraints. The model robustness was illustrated on a case study and, as expected, the model was found to be less sensitive than the deterministic model.