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- Genre: Academic theses
- Creators: Vittal, Vijay
Proposed market solutions are often infeasible because constraint relaxation practices and approximations that are incorporated into market models. Therefore, the dispatch solution must be corrected to ensure its feasibility. The practice of correcting the proposed dispatch solution after the market is solved is known as out-of-market corrections (OMCs), defined as any action an operator takes that modifies a proposed day-ahead dispatch solution to ensure operating and reliability requirements. The way in which OMCs affect market outcomes is illustrated through the use of different corrective procedures. The objective of the work presented is to demonstrate the implications of these industry practices and assess the impact these practices have on market outcomes.
Constraint relaxations practice was replicated in this work using a test case and a real-life large-scale system, while capturing both energy market aspects and AC real-time system performance. System performance investigation included static and dynamic security analysis for base-case and post-contingency operating conditions. PJM peak hour loads were dynamically modeled in order to capture delayed voltage recovery and sustained depressed voltage profiles as a result of reactive power deficiency caused by constraint relaxations. Moreover, impacts of constraint relaxations on operational system security were investigated when risk based penalty prices are used. Transmission lines in the PJM system were categorized according to their risk index and each category was as-signed a different penalty price accordingly in order to avoid real-time overloads on high risk lines.
This work also extends the investigation of constraint relaxations to post-contingency relaxations, where emergency limits are allowed to be relaxed in energy market models. Various scenarios were investigated to capture and compare between the impacts of base-case and post-contingency relaxations on real-time system performance, including the presence of both relaxations simultaneously. The effect of penalty prices on the number and magnitude of relaxations was investigated as well.
The cost of education is increasing, and the use of mandatory fees to offset costs is increasingly becoming more prevalent. Mandatory fees in higher education are not a new occurrence and have been associated with higher education institutions since their inception. However, the use and number of mandatory fees have grown, especially within the last decade, to include more fees that support core initiatives that were once covered by higher education institutions. Despite the vast amount of research concerning costs associated with attendance at higher education institutions, there is less research on how undergraduate students understand these costs, and how understanding of educational expenses may influence students’ behavior. Moreover, there is a dearth of research that explores students' engagement in services and programs supported by mandatory fees at higher education institutions.
This investigation fills the gaps, as it studies undergraduate students’ understandings of and attitudes toward mandatory fees while addressing their engagement in fee-supported services and programs. The data collection process utilizes a survey given to undergraduate students at a large research institution in the southwest United States. The survey uses multiple formats (i.e., Likert-scale, open-ended questions, multiple choice), to measure students’ understandings of costs and information about mandatory fees, frequency of use of services, and students’ prior knowledge about higher education institutions before enrollment.
Students’ perceptions of costs differ by individual and family, and the costs associated with fees can be a surprise for many students entering institutions of higher education. While fees are utilized to help retain and graduate all students, increasing fees change the total price for students. There are relatively few studies that measure the extent to which students engage in services or programs funded by the mandatory fees. While price is at the forefront for many federal and state policymakers, the need to make college more affordable for everyone without losing quality services and programs, must be addressed.
The first part of the dissertation introduces a new framework of graph signal processing (GSP) for the power grid, Grid-GSP, and applies it to voltage phasor measurements that characterize the overall system state of the power grid. Concepts from GSP are used in conjunction with known power system models in order to highlight the low-dimensional structure in data and present generative models for voltage phasors measurements. Applications such as identification of graphical communities, network inference, interpolation of missing data, detection of false data injection attacks and data compression are explored wherein Grid-GSP based generative models are used.
The second part of the dissertation develops a model for a joint statistical description of solar photo-voltaic (PV) power and the outdoor temperature which can lead to better management of power generation resources so that electricity demand such as air conditioning and supply from solar power are always matched in the face of stochasticity. The low-rank structure inherent in solar PV power data is used for forecasting and to detect partial-shading type of faults in solar panels.
The key interdependencies between the two systems are the requirements of water for the cooling cycle of traditional thermal power plants as well as electricity for pumping and/or treatment in the WDS. While previous work has considered the dependency of thermoelectric generation on cooling water requirements at a high-level, this work considers the impact from limitations of cooling water into network simulations in both a short-term operational framework as well as in the long-term planning domain.
The work completed to set-up simulations in operational length time-scales was the development of a simulator that adequately models both systems. This simulation engine also facilitates the implementation of control schemes in both systems that take advantage of the knowledge of operating conditions in the other system. Initial steps for including the influence of anticipated water availability and water rights attainability within the combined generation and transmission expansion planning problem is also presented. Lastly, the framework for determining the infrastructural-operational resilience (IOR) of the interdependent systems is formulated.
Adequately modeling and studying the two systems and their interactions is becoming critically important. This importance is illustrated by the possibility of unforeseen natural or man-made events or by the likelihood of load increase in the systems, either of which has the risk of putting extreme stress on the systems beyond that experienced in normal operating conditions. Therefore, this work addresses these concerns with novel modeling and control/policy strategies designed to mitigate the severity of extreme conditions in either system.
Adequacy assessment becomes more complex due to the variability introduced by renewable energy generation. The traditionally used reserve margin only considers projected peak demand and would be inadequate since it does not consider an evaluation of off-peak conditions that could also be critical due to the variable renewable generation. Therefore, in order to address the impact of variable renewable generation, a probabilistic evaluation that studies all hours of a year based on statistical characteristics is a necessity to identify the adequacy risks. On the other hand, the system dynamic behavior is also changing. Converter interfaced generation resources have different dynamic characteristics from the conventional synchronous units and inherently do not participate in grid regulation functions such as frequency control and voltage control that are vital to maintaining operating reliability. In order to evaluate these evolving grid characteristics, comprehensive reliability evaluation approaches that consider system stochasticity and evaluate both adequacy and dynamic security are important to identify potential system risks in this transforming environment.
The impact of the failure of transmission assets operated by a major utility company in the Southwest United States on its power system network is studied. A methodology is used to quantify the impact and subsequently rank transmission assets in decreasing order of their criticality. The analysis is carried out on the power system network using a node breaker model and steady state analysis. The light load case of spring 2019, peak load case of summer 2023 and two intermediate load cases have been considered for the ranking. The contingency simulations and power flow studies have been carried out using a commercial power flow study software package, Positive Sequence Load Flow (PSLF). The results obtained from PSLF are analyzed using Matlab to obtain the desired ranking. The ranked list of transmission assets will enable asset managers to identify the assets that have the most significant impact on the overall power system network performance. Therefore, investment and maintenance decisions can be made effectively. A conclusion along with a recommendation for future work is also provided in the thesis.