abstract: Due to economic and environmental reasons, several states in the United States of America have a mandated renewable portfolio standard which requires that a certain percentage of the load served has to be met by renewable resources of energy such as solar, wind and biomass. Renewable resources provide energy at a low variable cost and produce less greenhouse gases as compared to conventional generators. However, some of the complex issues with renewable resource integration are due to their intermittent and non-dispatchable characteristics. Furthermore, most renewable resources are location constrained and are usually located in regions with insufficient transmission facilities. In order to deal with the challenges presented by renewable resources as compared to conventional resources, the transmission network expansion planning procedures need to be modified. New high voltage lines need to be constructed to connect the remote renewable resources to the existing transmission network to serve the load centers. Moreover, the existing transmission facilities may need to be reinforced to accommodate the large scale penetration of renewable resource. This thesis proposes a methodology for transmission expansion planning with large-scale integration of renewable resources, mainly solar and wind generation. An optimization model is used to determine the lines to be constructed or upgraded for several scenarios of varying levels of renewable resource penetration. The various scenarios to be considered are obtained from a production cost model that analyses the effects that renewable resources have on the transmission network over the planning horizon. A realistic test bed was created using the data for solar and wind resource penetration in the state of Arizona. The results of the production cost model and the optimization model were subjected to tests to ensure that the North American Electric Reliability Corporation (NERC) mandated N-1 contingency criterion is satisfied. Furthermore, a cost versus benefit analysis was performed to ensure that the proposed transmission plan is economically beneficial. / Dissertation/Thesis / M.S. Electrical Engineering 2012

In an attempt to thwart global warming in a concerted way, more than 130 countries have committed to becoming carbon neutral around 2050. In the United States, the Biden ad- ministration has called for 100% clean energy by 2035. It is estimated that in order to meet that target, the energy production from solar and wind should increase to 50-70% from the current 11% share. Under higher penetration of solar and wind, the intermittency of the energy source poses critical problems in forecasting, uncertainty quantification, reserve man- agement, unit commitment, and economic dispatch, and presents unique challenges to the distribution system, including predicting solar adoption by the user as well as forecasting end-use load profiles. While these problems are complex, advances in machine learning and artificial intelligence provide opportunities for novel paradigms for addressing the challenges. The overall aim of the dissertation is to harness data-driven and model-based techniques and develop computationally efficient tools for improved power systems operation under high re- newables penetration in the next-generation electric grid. Some of the salient contributions of this work are the reduction in the number of uncertain scenarios by 99%; dramatic reduc- tion in the computational overhead to simulate stochastic unit commitment and economic dispatch on a single-node electric-grid system to merely 10 seconds from 24 hours; reduc- tion in the total monthly operating cost of two-stage stochastic economic dispatch by an average of 5%, and reduction in average overall reserve due to intermittency in renewables by 50%; and improvement in the existing end-use load prediction and rooftop PV adopter identification tools by a considerable margin. / Doctor of Philosophy / In an attempt to thwart global warming in a concerted way, more than 130 countries have committed to becoming carbon neutral around 2050. In the United States, the Biden ad- ministration has called for 100% clean energy by 2035. It is estimated that in order to meet that target, the energy production from solar and wind should increase to 50-70% from the current 11% share. Under higher penetration of solar and wind, the intermittency of the energy source poses critical problems in forecasting, uncertainty quantification, reserve man- agement, unit commitment, and economic dispatch, and presents unique challenges to the distribution system, including predicting solar adoption by the user as well as forecasting end-use load profiles. While these problems are complex, advances in machine learning and artificial intelligence provide opportunities for novel paradigms for addressing the challenges. The overall aim of the dissertation is to harness data-driven and model-based techniques and develop computationally efficient tools for improved power systems operation under high re- newables penetration in the next-generation electric grid. Some of the salient contributions of this work are the reduction in the number of uncertain scenarios by 99%; dramatic reduc- tion in the computational overhead to simulate stochastic unit commitment and economic dispatch on a single-node electric-grid system to merely 10 seconds from 24 hours; reduc- tion in the total monthly operating cost of two-stage stochastic economic dispatch by an average of 5%, and reduction in average overall reserve due to intermittency in renewables by 50%; and improvement in the existing end-use load prediction and rooftop PV adopter identification tools by a considerable margin.

Renewable energy integration will become a significant issue as renewable penetration levels increase, and will require new generation support infrastructure; Energy storage provides one solution to this issue. Specifically, battery technologies offer a wide range of energy and power output abilities, making them ideal for a variety of integration applications. Distributed energy storage on distribution grids may be required in many areas of Canada where renewables will be installed. Peak shaving using distributed small (residential) energy storage can provide a reduction in peak loads and help renewable energy integration. To this end, a peak shaving model was developed for typical houses in several regions in Canada which provided sizing and performance results. An experimental battery bank and cycling apparatus was designed and constructed using these sizing results. This battery bank and cycling apparatus was then used to calibrate and validate a lithium iron phosphate battery energy storage system model.

This dissertation presents the design, analysis, and validation of a hierarchical transactive control system that engages demand response resources to enhance the integration of renewable electricity generation resources. This control system joins energy, capacity and regulation markets together in a unified homeostatic and economically efficient electricity operation that increases total surplus while improving reliability and decreasing carbon emissions from fossil-based generation resources. The work encompasses: (1) the derivation of a short-term demand response model suitable for transactive control systems and its validation with field demonstration data; (2) an aggregate load model that enables effective control of large populations of thermal loads using a new type of thermostat (discrete time with zero deadband); (3) a methodology for optimally controlling response to frequency deviations while tracking schedule area exports in areas that have high penetration of both intermittent renewable resources and fast-acting demand response; and (4) the development of a system-wide (continental interconnection) scale strategy for optimal power trajectory and resource dispatch based on a shift from primarily energy cost-based approach to a primarily ramping cost-based one. The results show that multi-layer transactive control systems can be constructed, will enhance renewable resource utilization, and will operate in a coordinated manner with bulk power systems that include both regions with and without organized power markets. Estimates of Western Electric Coordinating Council (WECC) system cost savings under target renewable energy generation levels resulting from the proposed system exceed US$150B annually by the year 2024, when compared to the existing control system. / Graduate

abstract: High Voltage Direct Current (HVDC) Technology has several features that make it particularly attractive for specific transmission applications. Recent years have witnessed an unprecedented growth in the number of the HVDC projects, which demonstrates a heightened interest in the HVDC technology. In parallel, the use of renewable energy sources has dramatically increased. For instance, Kuwait has recently announced a renewable project to be completed in 2035; this project aims to produce 15% of the countrys energy consumption from renewable sources. However, facilities that use renewable sources, such as solar and wind, to provide clean energy, are mostly placed in remote areas, as their installation requires a massive space of free land. Consequently, considerable challenges arise in terms of transmitting power generated from renewable sources of energy in remote areas to urban areas for further consumption. The present thesis investigates different transmission line systems for transmitting bulk energy from renewable sources. Specifically, two systems will be focused on: the high-voltage alternating current (HVAC) system and the high-voltage direct current (HVDC) system. In order to determine the most efficient way of transmitting bulk energy from renewable sources, different aspects of the aforementioned two types of systems are analyzed. Limitations inherent in both HVAC and HVDC systems have been discussed. At present, artificial intelligence plays an important role in power system control and monitoring. Consequently, in this thesis, the fault issue has been analyzed in transmission systems, with a specific consideration of machine learning tools that can help monitor transmission systems by detecting fault locations. These tools, called models, are used to analyze the collected data. In the present thesis, a focus on such models as linear regression (LR), K-nearest neighbors (KNN), linear support vector machine (LSVM) , and adaptive boost (AdaBoost). Finally, the accuracy of each model is evaluated and discussed. The machine learning concept introduced in the present thesis lays down the foundation for future research in this area so that to enable further research on the efficient ways to improve the performance of transmission line components and power systems. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2019

In this dissertation, several volt-var optimization methods have been proposed to improve the expected performance of the distribution system using distributed renewable energy sources and conventional volt-var control equipment: photovoltaic inverter reactive power control for chance-constrained distribution system performance optimisation, integrated distribution system optimization using a chance-constrained formulation, integrated control of distribution system equipment and distributed generation inverters, and coordination of PV inverters and voltage regulators considering generation correlation and voltage quality constraints for loss minimization. Distributed generation sources (DGs) have important benefits, including the use of renewable resources, increased customer participation, and decreased losses. However, as the penetration level of DGs increases, the technical challenges of integrating these resources into the power system increase as well. One such challenge is the rapid variation of voltages along distribution feeders in response to DG output fluctuations, and the traditional volt-var control equipment and inverter-based DG can be used to address this challenge. These methods aim to achieve an optimal expected performance with respect to the figure of merit of interest to the distribution system operator while maintaining appropriate system voltage magnitudes and considering the uncertainty of DG power injections. The first method is used to optimize only the reactive power output of DGs to improve system performance (e.g., operating profit) and compensate for variations in active power injection while maintaining appropriate system voltage magnitudes and considering the uncertainty of DG power injections over the interval of interest. The second method proposes an integrated volt-var control based on a control action ahead of time to find the optimal voltage regulation tap settings and inverter reactive control parameters to improve the expected system performance (e.g., operating profit) while keeping the voltages across the system within specified ranges and considering the uncertainty of DG power injections over the interval of interest. In the third method, an integrated control strategy is formulated for the coordinated control of both distribution system equipment and inverter-based DG. This control strategy combines the use of inverter reactive power capability with the operation of voltage regulators to improve the expected value of the desired figure of merit (e.g., system losses) while maintaining appropriate system voltage magnitudes. The fourth method proposes a coordinated control strategy of voltage and reactive power control equipment to improve the expected system performance (e.g., system losses and voltage profiles) while considering the spatial correlation among the DGs and keeping voltage magnitudes within permissible limits, by formulating chance constraints on the voltage magnitude and considering the uncertainty of PV power injections over the interval of interest. The proposed methods require infrequent communication with the distribution system operator and base their decisions on short-term forecasts (i.e., the first and second methods) and long-term forecasts (i.e., the third and fourth methods). The proposed methods achieve the best set of control actions for all voltage and reactive power control equipment to improve the expected value of the figure of merit proposed in this dissertation without violating any of the operating constraints. The proposed methods are validated using the IEEE 123-node radial distribution test feeder.

Several demand side management (DSM) techniques and algorithms have been used in the literature. These algorithms show that by adopting DSM and Time-of-Use (TOU) price tariffs; electricity cost significantly decreases, and optimal load scheduling is achieved. However, the purpose of the DSM is to not only lower the electricity cost, but also to avoid the peak load even if the electricity prices low. To address this concern, this dissertation starts with a brief literature review on the existing DSM algorithms and schemes. These algorithms can be suitable for Direct Load Control (DLC) schemes, Demand Response (DR), and load scheduling strategies. \end{abstract} Secondly, the dissertations compares two of DSM algorithms to show the performance based on cost minimization, voltage fluctuation, and system power loss [see in Chapter 5]. The results show the importance of balance between objectives such as electricity cost minimization, peak load occurrence, and voltage fluctuation evolution while simultaneously optimizing the cost.

This research focuses on improving the efficiency of power market operations by providing system operators additional tools for managing the costs of supplying and delivering electricity. A transmission topology control (TC) framework for production cost reduction based on a shift factor (SF) representation of branch and breaker flows is proposed. The framework models topology changes endogenously while maintaining linearity in the overall Mixed Integer Linear Programming (MILP) formulation. This work develops the DC lossless, and loss-adjusted TC formulations that can be used in a Day Ahead or intra-day market framework as well as an AC-based model that can be used in operational settings. Practical implementation choices for the Shift Factor formulation are discussed as well as the locational marginal prices (LMPs) under the TC MIP setting and their relation to LMPs without TC. Compared to the standard B-theta alternative used so far in TC research, the shift factor framework has significant computational complexity advantages, particularly when a tractably small switchable set is optimized under a representative set of contingency constraints. These claims are supported and elaborated by numerical results.

As renewable energy becomes increasingly cost competitive and Renewable Portfolio Standards (RPS) push states to produce more and more of their steadily growing power demands from renewable sources, the need to solve the problems associated with renewable penetration becomes a priority. The intermittent nature of solar and wind power generation require additional cost that inhibit their implementation as penetration levels grow. Reliability remains power utilities' top priority while they struggle to upgrade their systems. Old generation facilities will be decommissioned, renewable energy projects will come on line and transmission upgrades become inevitable. Variability on the grid is currently mitigated through the use of Operational Reserves. These units are costly and utilities are currently looking for ways to reduce the amount of reserves required. Balancing Area cooperation is currently being considered by many as the most economical and environmentally conscience method to mitigate variability. Many aspects of Balancing Area cooperation will be discussed along with the motivations for their implementation. A 22.5 square mile area of land in Clovis, NM will be the home of the Tres Amigas project designed to unite the three asynchronous grids of our Nation with the purpose of improving reliability and reducing cost through the exchange of power and ancillary services such as VAR support and Operating Reserves. This paper will investigate the implications of this project on the Operational Reserves required to mitigate variability due to increasing renewable energy penetration by enabling Balancing Areas to cooperate across regions that are currently not assessable.

Zur Umsetzung der Energiewende ist in den kommenden Jahren ein substantieller Netzausbau notwendig, der jedoch regional unterschiedlich stark ausfallen wird. Nach gegenwärtiger Gesetzeslage werden die folglich sehr unterschiedlich hohen Netzkosten von den Endkunden des jeweiligen Netzgebietes über die Netznutzungsentgelte getragen. Mittels eines detaillierten Modells der Kostenbestandteile der Netzkosten in den einzelnen Regionen wurden unter Berücksichtigung des erwarteten Netzausbaus sowie der demographischen Entwicklung die Netznutzungsentgelte, geschlüsselt nach den Kundengruppen Haushalt und Gewerbe sowie Industrie bis zum Jahr 2023 prognostiziert. Die anschließende Analyse eines bundesweiten Wälzens von Übertragungs- und Verteilungsnetzbestandteilen der Entgelte kommt zu dem Ergebnis, dass in Zukunft neben den ostdeutschen Flächenländern auch die Küstenländer Schleswig-Holstein und Niedersachsen sowie Teile Bayerns von einem bundeseinheitlichen Entgelt profitieren würden. Dabei stellt sich eine asymmetrische Verteilung von Be- und Entlastungen dar. Den zum Teil erheblichen jährlichen Entlastungen von bis zu 130 EUR pro 3-Personenhaushalt stehen in den süd- und westdeutschen Flächenländern vergleichsweise geringe Mehrbelastungen von maximal 30 EUR gegenüber. Gleichzeitig zeigt die Analyse, dass ein alleiniges Wälzen der Übertragungsnetzkosten zum heutigen Stand für Industriekunden in Ostdeutschland zwar merkliche Entlastungen mit sich bringen würde, diese aber zukünftig abnehmen und im Haushaltskundenbereich sehr gering ausfallen. Insgesamt lässt sich aus den Ergebnissen der Analyse schlussfolgern, dass die regionale Ungleichverteilung der Netzkosten tendenziell zunimmt und es Regionen in Deutschland gibt, in denen hohe Netzausbaukosten, eine negative demographische Entwicklung und eine geringe Kaufkraft zusammentreffen und so Privathaushalte sowie Industriebetriebe stark belasten.