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This work presents a software framework for power system analysis, PowerFrame. It is composed of four layers. This four-layer architecture is designed for extensibility and reusability so that more complex power system problems can be tackled within the architecture. In the context of PowerFrame, this work explores complex power system problems. Included in these problems are parallel-placed cables with multiple conductors, and distributed resources operating in unbalanced power distribution systems. Mathematical models are derived. Errors between more exact models and conventional approaches are presented. PowerFrame is also designed to handle distributed computation for intensive power system calculations on multiple, networked computers. Distributed power flow algorithms are presented. Tests on Ethernet LANs show the feasibility of distributed computation under current computer network bandwidth. / Ph. D.

Distribution system analysis and design has experienced a gradual development over the past three decades. The once loosely assembled and largely ad hoc procedures have been progressing toward being well-organized. The increasing power of computers now allows for managing the large volumes of data and other obstacles inherent to distribution system studies. A variety of sophisticated optimization methods, which were impossible to conduct in the past, have been developed and successfully applied to distribution systems. Among the many procedures that deal with making decisions about the state and better operation of a distribution system, two decision support procedures will be addressed in this study: phase balancing and phase prediction. The former recommends re-phasing of single- and double-phase laterals in a radial distribution system in order to improve circuit loss while also maintaining/improving imbalances at various balance point locations. Phase balancing calculations are based on circuit loss information and current magnitudes that are calculated from a power flow solution. The phase balancing algorithm is designed to handle time-varying loads when evaluating phase moves that will result in improved circuit losses over all load points. Applied to radial distribution systems, the phase prediction algorithm attempts to predict the phases of single- and/or double phase laterals that have no phasing information previously recorded by the electric utility. In such an attempt, it uses available customer data and kW/kVar measurements taken at various locations in the system. It is shown that phase balancing is a special case of phase prediction. Building on the phase balancing and phase prediction design studies, this work introduces the concept of integrated design, an approach for coordinating the effects of various design calculations. Integrated design considers using results of multiple design applications rather than employing a single application for a distribution system in need of improvement relative to some system aspect. Also presented is a software architecture supporting integrated design. / Ph. D.

How to evaluate the performance of an electric power distribution system unambiguously and quantitatively is not easy. How to accurately measure the efficiency of it for a whole year, using real time hour-by-hour Locational Marginal Price data, is difficult. How to utilize distributed computing technology to accomplish these tasks with a timely fashion is challenging. This thesis addresses the issues mentioned above, by investigating feeder performance analysis of electric power distribution systems with distributed algorithm. Feeder performance analysis computes a modeled circuit's performance over an entire year, listing key circuit performance parameters such as efficiency, loading, losses, cost impact, power factor, three phase imbalance, capacity usage and others, providing detailed operating information for the system, and an overview of the performance of every circuit in the system. A diakoptics tearing method and Graph Trace Analysis based distributed computing technology is utilized to speed up the calculation. A general distributed computing architecture is established and a distributed computing algorithm is described. To the best of the author's knowledge, it is the first time that this detailed performance analysis is researched, developed and tested, using a diakoptics based tearing method and Graph Trace Analysis to split the system so that it can be analyzed with distributed computing technology. / Master of Science

Power distribution systems continue to evolve to accommodate the advancements in the field of microgrids and renewable energy resources. The future grids will be highly connected and will require increased reliability of the network. To this effect, low-voltage distribution systems with meshed or networked topology can be utilized. Currently, the use of low-voltage heavily-meshed distribution systems is restricted to urban areas with high load density that require increased reliability of power. A reason for this is the high cost of construction of such systems and complex topology which creates additional challenges. The direction of power flow in such systems is not unidirectional, which makes the power flow analysis difficult. Complicated network analysis techniques are required to determine the fault currents and protection settings in the network. Due to the aforementioned reasons, there is limited work analyzing the effectiveness of existing power flow algorithms to solve complex meshed systems. In this thesis, the robustness of two power flow algorithms is compared using an index called static stability breakdown margin parameter of circuit elements. For this study, a low-voltage heavily-meshed distribution test system is also proposed. Additionally, a study is conducted to show how reliable the meshed test system is against any fault in the system. The steady-state voltage stability of the test system is observed during the event of a fault. The stability margin parameter is then used to determine the vulnerable components in the system which need to be strengthened to increase the stability and voltage profile of the system. / Master of Science / Distribution systems carry electricity from the transmission system and deliver it to the customers. Distribution systems mainly operate using two topologies for their feeders: Radial and Meshed. The majority of customers are served using radial distribution systems, as in the radial feeders power flows in one direction (i.e. from substation to the end-user). They are simple in design and operation and are constructed at a moderate cost. However, if there is a fault along the main feeder, there will be an interruption of power to the end-use customer. On the other hand, meshed distribution systems involve multiple paths of power flow between all the points in the network. If a fault occurs along the feeder, the power flow is rerouted to the other available paths. Thus, Heavily Due to their complex topology, meshed systems are expensive to construct and deploy. The power flow analysis of these systems poses many challenges. Because of these reasons, their use is mainly restricted to urban areas with high load density which require very high reliability. The future grid is becoming increasingly complex and evolving to a meshed distribution topology has its own advantages. However, as presently the use of meshed systems is sparse, the work done on evaluating the stability of these systems is minimal. As a result of which, this thesis focuses on determining the optimal power flow solvers for these complex systems, analyzing their stability under abnormal operating conditions, and suggesting methods to reinforce the vulnerabilities in the system.

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Universal, affordable and reliable electricity remains a key pillar towards achieving Sustainable Development Goals. It is low income countries that find bridging gaps in electricity access particularly challenging. Making judicious financial investments is critical in a low income setting as there are multiple competing compelling areas in which to make resource allocations. A data driven approach that can leverage prior data from electricity service providers can guide decision making. This dissertation presents approaches that leverage such data, to assist utilities and national bodies with insights that could be useful. There are five unique contributions made. These are in the form of key results about electricity consumption patterns, novel methodologies for electricity demand prediction and relevant metrics for estimating the cost of a grid connection. First, this thesis, through in-depth analysis of electricity data from thousands of households, sheds light on electricity consumption patterns in Rwanda and Kenya. This work revealed that utilities are increasingly connecting low consuming households whose consumption peaks sooner and plateaus lower than their peers who were connected earlier. While the previous focus of research has been on addressing electricity supply-side constraints, this work is the first of it's kind to show that electricity consumption for the newly electrified is very low, thereby making capital cost recovery of a grid connection even harder to achieve. This mismatch between supply and demand emphasizes the need for utilities to better quantify expected demand upon connection. Secondly, this thesis makes methodological contributions that support electricity demand prediction for the yet-to-be grid-connected households. Specifically, Convolutional Neural Network (CNN) models were designed to take as inputs pre-grid-access daytime satellite image patches and output electricity consumption levels. Results from this work show that the proposed methodologies perform better than utility based estimates of anticipated demand. This methodology shows that rapid large scale evaluation of latent demand can be effectively performed using daytime satellite imagery, thereby giving guidance on which sites or regions are more suitable for grid versus off-grid technologies. Outputs from the models have been utilized by energy planners in Kenya. The third unique contribution made in this dissertation is in the development of key metrics to estimate the cost of grid-access. Complementary to the evaluation of electricity demand, this thesis also develops an electricity grid network optimization model, connecting 9.2 million structures in Kenya. Given transformer placement and the estimates for low and medium voltage line, an approximation for the per household wire requirement is obtained. The work shows that traditional rural/urban classification based on population density may not be enough and is often deceiving in estimating the cost of grid-access and a new categorization based on our proposed per household wire requirement metrics provides more relevant estimates on the total cost. Fourthly, this dissertation also demonstrates methods to re-purpose electricity data in order to provide insights to new domains such as household wealth. This work illustrates how household overall expenditure can be obtained from electricity usage data and how electricity usage can be obtained from daytime satellite imagery. This methodological contribution provides a pathway for stakeholders to estimate household overall expenditure from daytime satellite imagery. The work shows the value of electricity data in answering other questions in new domains without the deployment of additional surveys or hardware. The final research contribution discussed in this thesis focuses on methods to make smart modifications to existing machine learning models to support analysis in settings where label availability is small and label quality is poor. This concept is illustrated with a building segmentation task given misaligned and omitted building footprints. Our proposed end-to-end learning pipeline demonstrates how data constrained regions can learn about building characteristics despite having incomplete and noisy labels. In addition, this work is used to provide explanatory features to the CNNs used for prediction in the earlier parts of the work. While the focus of the research was on Kenya and Rwanda, this work transcends multiple domains such as water and internet access and can be extending to countries seeking evidence-based approaches to inform sustainable development.

Reliability and restoration are important considerations in electric distribution systems. Reliability analysis is generally considered as a design tool to be used to improve the performance of the system. Restoration analysis is generally considered as a tool to be used for outaged situations. Reliability and restoration analysis are related, and some of the relationships are pointed to in this work. / Ph. D.

Comprehensive analysis, modeling, and design techniques are developed for distributed power systems. Dynamic interactions caused by paralleling, stacking, and cascading converter modules are analyzed. Incorporating the effects of all subsystem interactions, systematic design procedures are established in order to optimize the dynamic performance of large-scale distributed power systems. An advanced three-loop control scheme is developed to optimize the dynamics of multimodule converters. A design-oriented model reduction technique is employed to design power supplies utilizing a stacked configuration of multi-module converters. An unterminated modeling and design approach is proposed to optimize the dynamics of cascaded converter stages, while ensuring the stability and compatibility of the integrated system. Systematic design procedures for intermediate filters are developed. / Ph. D.