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

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.

Extra High Voltage (EHV) transmission corridor siting studies are complex and costly procedures, which are often prolonged by technical, public, institutional, and state regulatory factors. The primary goal of this research is to contribute to a more predictable and expedient siting study. The following objectives are accomplished: - Exploration and description of technical and methodological aspects of siting in terms of the general approach to the siting study, impact assessment techniques, data collection and mapping considerations. - Exploration and description of the following public and institutional considerations in the siting study: public participation, active opposition, media coverage, attitudes of affected agencies, and communication among involved organizations. Determination of the effects of these considerations on the siting process. - Review and evaluation of the state siting regulations in terms of: clarity of requirements, technical siting requirements, coordination of actions in the study, coordination with other relevant regulations, and public and agency participation in the study. Identification of the effects of the state regulations on the siting study process. - Development of guidelines for improved EHV transmission corridor siting studies. The principal methodology of the research is the single case study of Wyoming-Cloverdale 765 kV siting project, which represents a model of a contemporary, interstate, EHV siting study. The results of the case study are complemented by the review of state siting regulations and the literature. The major outcome of the research are the guidelines for improved corridor siting studies. The guidelines are developed for corridor siting study consultants, electric utility companies, and state regulatory commissions. Findings of the research indicate that technical, public, institutional, and state regulatory factors interactively affect the process of the corridor siting study. Furthermore, the siting study has dominant political overtones, and as such cannot be treated as a merely technical project. Public opposition to new EHV transmission lines can significantly increase the effects of technical, public, institutional, and regulatory deficiencies, reducing the probability of line approval. The testing of the guidelines in siting study practice, and a multiple case study research dealing with the same considerations and their interactions, are suggested for future research. / Ph. D.

The thesis addresses electronic power distribution systems for the residential applications. Presented are both, renewable energy ac-nanogrid system along with the vehicle-to-grid technology implementation, and envisioned structure and operation of dc-nanogrid addressing all system components chosen as an inherent part of the future electrical architecture. The large-scale model is built and tested in the laboratory environment covering a few operational modes of the ac-nanogrid, while later in the thesis is shown how dc bus signaling technique could be contemplated for the energy management of the renewable energy sources and their maximal utilization. Thesis however puts more focus on the dc-nanogrid system to explore its benefits and advantages for the electrical systems of the future homes that can easily impact not only residential, but also microgrid, grid and intergrid levels. Thus, presented is low frequency terminal behavioral modeling of the system components in dc-nanogrid motivated by the fact that system engineers working on the system-level design rarely have access to all the information required to model converters and system components, other than specification and data given in the datasheets. Using terminal behavioral modeling, converters are measured on-line and their low frequency dynamics is identified by the means of the four transfer functions characteristically used in two port network models. This approach could significantly improve system-level design and simulations. In addition to previously mentioned, thesis addresses terminal behavioral modeling of dc-dc converters with non-linear static behavior showing hybrid behavioral models based on the Hammerstein approach. / Master of Science

Demand Side Management (DSM) has begun to emerge as a major component of utility planning, with more utilities than ever before using it to help meet their own needs and those of their customers. DSM encompasses utility and customer activities aimed at modifying load shape, which embodies the timing and level of customer electricity demand. Future load shapes will result from the combined effect of individual DSM programs seeking specific load shape objectives. Load Duration Curve (LDC) is the vehicle through which DSM impacts are incorporated into power system planning and operation. Models of the LDC is one of the most important tools in the analysis of electric power system. The DSM will affect the peak load, the base load and total energy demand of the load duration curve. Those three impacts have to be explicitly modeled into the load duration curve for properly representing the effects of demand side management activities. However, the available models cannot properly represent the impacts of demand side management into load duration curve, because they do not explicitly model those three variables into their load duration curve. A new model that can incorporate the effects of demand side management is needed by utilities to help them with planning and operation. A new way to directly model the inverted load duration curve (ILDC) is presented in this study thus facilitating the representation of DSM impacts. Peak Load, base load and total energy demand are the variables of the new model. Using DSM activities as case studies, the new model produced good results compared to other widely used models, in term of reliability indices (LOLP and ENS) and total energy under the load duration curve. The flexibility, simplicity and the speed of execution of the new model in calculating the reliability indices are demonstrated. The capability of the new model to calculate the capacity credit is also presented. As a result of its ability to represent energy under load duration curve, the new model is inserted into WASP computer program to calculate the production cost. Results obtained from the new model (modified WASP) compared to results from original WASP are very close. Based on these capabilities it can be claimed that the new ILDC model is a better overall model and can be used as an alternative load model in utility planning and operation. / Ph. D.

Electric utilities are constantly seeking ways to reduce costs, and one way is to defer the construction of major new facilities. Such a deferal can be instituted by automating the power distribution system in an effort to make the system operate more efficiently and effectively. Increased efficiency on the distribution level improves the use of existing facilities on the distribution, transmission, and generation levels. The stumbling block for justifying distribution automation is often at the economic evaluation stage. This is due to the difficulty of incorporating the effects of technologies which have not been implemented in the past. In this research, a new method of economic analysis of utility distribution systems is proposed. The method will utilize interval analysis to determine the effects of uncertainty in data in utility revenue requirement studies. One of the frequently encountered problems in applying interval analysis is the resulting overly large bounds which in turn reduce the usefulness of results. Therefore, a method of obtaining sharp bounds is presented. The economic calculations will incorporate results from reliability analysis as well as reconfiguration studies. Thus, an explicit consideration of engineering design aspects is included. In addition, a cost/effectiveness analysis of distribution automation is presented in terms of several proposed economic indices associated with system cost, reliability, efficiency, and peak. A method of incorporating value of service considerations into revenue requirement studies is also presented. The capability to analyze automation expansion plans as well as conventional expansion plans will be discussed. Accordingly, utility distribution planning can be more precise with regard to potential economic benefits. / Ph. D.

No description available.

Electric power distribution network is an important part of electrical power systems for delivering electricity to consumers. Electric power utilities worldwide are increasingly adopting the computer aided monitoring, control and management of electric power distribution systems to provide better services to the electrical consumers. Therefore, research and development activities worldwide are being carried out to automate the electric power distribution system. The power distribution system consists of a three-phase source supplying power through single-, two-, or three-phase distribution lines, switches, and transformers to a set of buses with a given load demand. In addition, unlike transmission systems, single-, two-, and three-phase sections exist in the network and single-, two-, and three-phase loads exist in the distribution networks. Further, most distribution systems are overhead systems, which are susceptible to faults caused by a variety of situations such as adverse weather conditions, equipment failure, traffic accidents, etc. When a fault occurs on a distribution line, it is very important for the utility to identify the fault location as quickly as possible for improving the service reliability. Hence, one of the crucial blocks in the operation of distribution system is that of fault detection and it’s location. The achievement of this objective depends on the success of the distribution automation system. The distribution automation system should be implemented quickly and accurately in order to isolate those affected branches from the healthy parts and to take alternative measures to restore normal power supply. Fault location in the distribution system is a difficult task due to its high complexity and difficulty caused by unique characteristics of the distribution system. These unique characteristics are discussed in the present work. In recent years, some techniques have been discussed for the location of faults, particularly in radial distribution systems. These methods use various algorithmic approaches, where the fault location is iteratively calculated by updating the fault current. Heuristic and Expert System approaches for locating fault in distribution system are also proposed which uses more measurements. Measurements are assumed to be available at the sending end of the faulty line segment, which are not true in reality as the measurements are only available at the substation and at limited nodes of the distribution networks through the use of remote terminal units. The emerging techniques of Artificial Intelligence (AI) can be a solution to this problem. Among the various AI based techniques like Expert systems, Fuzzy Set and ANN systems, the ANN approach for fault location is found to be encouraging. In this thesis, an ANN approaches with limited measurements are used to locate fault in long distribution networks with laterals. Initially the distribution system modeling (using actual a-b-c phase representation) for three-, two-, and single-phase laterals, three-, two-, and single- phase loads are described. Also an efficient three-phase load flow and short circuit analysis with loads are described which is used to simulate all types of fault conditions on distribution systems. In this work, function approximation (FA) is the main technique used and the classification techniques take a major supportive role to the FA problem. Fault location in distribution systems is explained as a FA problem, which is difficult to solve due to the various practical constraints particular to distribution systems. Incorporating classification techniques reduce this FA problem to simpler ones. The function that is approximated is the relation between the three-phase voltage and current measurements at the substation and at selected number of buses (inputs), and the line impedance of the fault points from the substation (outputs). This function is approximated by feed forward neural network (FFNN). Similarly for solving the classification problems such as fault type classification and source short circuit level classification, Radial Basis Probabilistic Neural Network (RBPNN) has been employed. The work presented in this thesis is the combinational use of FFNN and RBPNN for estimating the fault location. Levenberg Marquardt learning method, which is robust and fast, is used for training FFNN. A typical unbalanced 11-node test system, an IEEE 34 nodes test system and a practical 69- bus long distribution systems with different configurations are considered for the study. The results show that the proposed approaches of fault location gives accurate results in terms of estimated fault location. Practical situations in distribution systems such as unbalanced loading, three-, two-, and single- phase laterals, limited measurements available, all types of faults, a wide range of varying source short circuit levels, varying loading conditions, long feeders with multiple laterals and different network configurations are considered for the study. The result shows the feasibility of applying the proposed method in practical distribution system fault diagnosis.