Orientador: Pedro Magalhães Sobrinho / Coorientador: Jamil Haddad / Banca: Celso Eduardo Tuna / Banca: Teófilo Miguel de Souza / Banca: Roberto Akira Yamachita / Banca: Christian Jeremi Coronado Rodriguez / Resumo: O Programa de Eficiência Energética (PEE) das distribuidoras de energia elétrica é, atualmente, o principal mecanismo de destinação de recursos para a promoção do uso racional de energia no Brasil. Ao longo do período de 2000 a 2017, este instrumento proporcionou importantes alterações, como a implementação da Lei nº 12.212/2010, que determinou a aplicação da maior parcela dos recursos aos projetos de iluminação, troca de refrigeradores, reforma das instalações elétricas e aquecedores destinados a população de baixa renda. Neste contexto, para este estudo, foi desenvolvido um Software de Análise PEE responsável pela seleção das informações dos relatórios propostos e realizados do PEE, disponibilizado pela Agência Nacional de Energia Elétrica (ANEEL), possibilitando o acesso às informações relevantes contidas nos projetos. Em posse desses dados, foi possível determinar a economia de energia total de 3.190,12 GWh e redução de demanda na ponta de 1.168,05 MW para o período de análise abordado de 2009 a 2015, para os usos finais em iluminação e refrigeração. Na sequência do estudo, foi realizada uma avaliação sobre o impacto ambiental relacionado à redução de emissão de CO2, com a aplicação da metodologia proposta para o PEE da ANEEL, e pode-se verificar que foi evitada a emissão total de 1.244.457,02 tCO2 para o uso final em iluminação e 169.570,05 tCO2 para refrigeração (refrigerador), totalizando 1.414.027,07 tCO2 ao longo do período de 2009 a 2015. O foco principal desse estu... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The Energy Efficiency Program (PEE) of energy distributors is currently the main mechanism for allocating resources for the advancement of rational energy use in Brazil. Over the period from 2000 to 2017, this tool has undergone important changes, such as the implementation of Law 12,212 / 2010, which has determined the application of most of the resources to lighting projects, fridge replacement, electrical and heaters installation aimed at a low-income population. In this context, a PEE Analysis Software was developed for this study, which was used to select information from PEE proposed and performed reports, provided by the National Electric Energy Agency (ANEEL), allowing access to the relevant information contained in the projects. Based on these data, it was possible to determine a total energy saving of 3,190.12 GWh and demand reduction at peak of 1,168.05 MW for the analysis period from 2009 to 2015. Following the study, it was conducted an assessment on the environmental impact related to CO2 emission reduction, with the application of the methodology proposed for the ANEEL PEE, and it can be verified a total emission of 1,244,457.02 tCO2 for the end use in illumination and 169,570.05 tCO2 for cooling (fridge), totalizing 1,414,027.07 tCO2 over the period from 2009 to 2015. The study main focus was the development of the new Cost-benefit Ratio (RCB), where an RCB between 0.10 and 0.18 was obtained for the end use in lighting for traditional calculations and RCB between 0.08 to 0.17 adopting the Certified Emissions Reductions (CERs) calculated with the reduction of CO2, proving that both results were lower than the maximum established by ANEEL, thus providing a significant contribution to the other SES projects. For the end use in cooling, the results presented a traditional RCB between 0.97 and 1.62 and, increasing the CERs, ... (Complete abstract click electronic access below) / Doutor

Oregon utilities are replacing their portfolios of traditional fossil fuel generation with renewable generating sources. Stepping away from carbon-producing energy will leave a deficit of on-demand power, resulting in decreased reliability. To overcome these technical challenges, utilities must maximize the use of their present dispatchable resources. One such resource is the Portland General Electric (PGE) Dispatchable Standby Generation Program (DSG), which is an aggregated 105 MWs of distributed generation (DG). These resources are brought on-line when there is a critical need for power. Resources are added to the program if a transfer trip scheme is in place or a modeling study reveals that the feeder load is at least three times the generator capacity. If the load-to-capacity ratio were lower, more assets could be added to the DSG program. To investigate the impacts of lowering the DG load-to-capacity ratio on existing distribution feeders, we use Open-Source Distribution System Simulator (OpenDSS). We modeled the Oxford Rural feeder by converting a utility CYME database to instantiation files using several MATLAB programs. A MATLAB control program varies the load-to-capacity ratio of the OpenDSS feeder model and monitors the generator behavior immediately following a fault. We analyzed the results to determine the ideal load-to-capacity ratio that prevents unintentional islanding. The results show that the instantaneous (50) relay element settings dictate both the minimum load-to-capacity ratio and the maximum DG capacity. The present three-to-one ratio is very conservative and can be reduced. Additional dispatchable resources include a five MW battery-inverter system currently used as grid-back up. The battery is grid-tied to a 12.4 kV feeder making it an ideal candidate for conservation voltage reduction (CVR). Using the same feeder model, we investigated the effects of lowering the system voltage to the allowable minimum using injections of reactive power. A lower system voltage reduces the load at peak times. Conversely, increasing the voltage prevents generation conflicts. To determine the benefit of CVR by VAr-injection on the Oxford Rural feeder, we created a MATLAB optimization program to output the optimal feeder voltage for reduced system power. We use a Simulink feedback model to determine the appropriate reactive power needed to achieve the voltage change. We analyze the system model to reveal that the feeder is ideal for CVR but the system capacity must be increased to achieve the maximum power reduction.

Today's utilities face new challenges due to the continually increasing penetration of residential solar and other distributed, stochastic generation sources. In order to maintain balance and stability in the grid without building costly, large-scale generation plants, utilities are turning to distributed energy resources for use in demand response programs. Demand response is a cost-efficient way to balance grid load/generation without the need for increased capital investment in traditional generation resources. Demand response programs are excellent exploiters of end-user devices that otherwise further accentuate the daily load curve and thus, add to the difficulties created by daily load peaks. Electric water heaters are excellent candidates for use in demand response programs for a variety of reason. One, electric water heaters represent a large portion of daily household loads due to their high nominal power ratings (1.5 kW - 5.5kW), and frequent use estimated to account for approximately one third of all daily residential power demand. Two, they are composed of strictly resistive elements, which greatly simplifies modeling, aggregation and control. And third, they can be used for load "shedding" during periods of high electrical demand as well as load "absorbing" during periods of excess generation due to their thermal storage capabilities. With improved access and control, electric water heaters could become a major distributed energy resource for utilities. In order to properly control and use a distributed energy resource, it is important to know how these resources operate and their patterns of behavior in different environments. This thesis presents a single-element, single mass electric water heater model for analyzing the effectiveness of using electric water heaters as distributed energy resources and for participation in demand response programs. The CTA-2045 communication protocol was used for testing demand response signals. The electric water heater is modeled in Python and the communication pathway was built in C++ and Python.

Increasing penetration of non-dispatchable renewable energy resources and greater peak power demand present growing challenges to Bulk Power System (BPS) reliability and resilience. This research investigates the use of an Internet of Things (IoT) framework for large scale Distributed Energy Resource (DER) aggregation and control to reduce energy imbalance caused by stochastic renewable generation. The aggregator developed for this research is Distributed Energy Resource Aggregation System (DERAS). DERAS comprises two AllJoyn applications written in C++. The first application is the Energy Management System (EMS), which aggregates, emulates, and controls connected DERs. The second application is the Distributed Management System (DMS), which is the interface between AllJoyn and the physical DER. The EMS runs on a cloud-based server with an allocated 8 GB of memory and an 8 thread, 2 GHz processor. Raspberry Pis host the simulated Battery Energy Storage System (BESS) or electric water heater (EWH) DMSs. Five Raspberry Pis were used to simulate 250 DMSs. The EMS used PJM's regulation control signals, RegA and RegD, to determine DERAS performance metrics. PJM is a regional transmission organization (RTO). Their regulation control signals direct power resources to negate load and generation imbalances within the BPS. DERAS's performance was measured by the EMS server resource usage, network data transfer, and signal delay. The regulation capability of aggregated DER was measured using PJM's resource performance assessment criteria. We found the use of an IoT framework for DER aggregation and control to be inadequate in the current network implementation. However, the emulated modes and aggregation response to the regulated control signal demonstrates an excellent opportunity for DER to benefit the BPS.

The increasing demand for and prevalence of distributed energy resources (DER) such as solar power, electric vehicles, and energy storage, present a unique set of challenges for integration into a legacy power grid, and accurate models of the low-voltage distribution systems are critical for accurate simulations of DER. Accurate labeling of the phase connections for each customer in a utility model is one area of grid topology that is known to have errors and has implications for the safety, efficiency, and hosting capacity of a distribution system. This research presents a methodology for the phase identification of customers solely using the advanced metering infrastructure (AMI) voltage timeseries. This thesis proposes to use Spectral Clustering, combined with a sliding window ensemble method for utilizing a long-term, time-series dataset that includes missing data, to group customers within a lateral by phase. These clustering phase predictions validate over 90% of the existing phase labels in the model and identify customers where the current phase labels are incorrect in this model. Within this dataset, this methodology produces consistent, high-quality results, verified by validating the clustering phase predictions with the underlying topology of the system, as well as selected examples verified using satellite and street view images publicly available in Google Earth. Further analysis of the results of the Spectral Clustering predictions are also shown to not only validate and improve the phase labels in the utility model, but also show potential in the detection of other types of errors in the topology of the model such as errors in the labeling of connections between customers and transformers, unlabeled residential solar power, unlabeled transformers, and locating customers with incomplete information in the model. These results indicate excellent potential for further development of this methodology as a tool for validating and improving existing utility models of the low-voltage side of the distribution system.

Thesis (Ph. D.)--University of Missouri--Rolla, 2007. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed December 7, 2007). Includes bibliographical references.

Favorable price trends and increasing demand for renewable energy sources portend accelerating integration of solar photovoltaic (PV) generation into traditional electric power system networks. Managing the variable output of massive PV resources makes system frequency regulation more complex and expensive. ISOs must procure additional regulation and load following capacity, while power plants must supply more regulation work. In contrast to costly physical storage solutions, this thesis proposes to address the issue by reconfiguring the electricity market pricing structure to translate all power imbalances into real-time market price signals. More accurately determining the instantaneous value of energy, electric power markets could reward participants who can quickly respond to frequency fluctuations. By utilizing short term forward markets to monetize the risk associated with intermittency, the true cost of reliability is determined and could reduce wasteful capacity payments. This market redesign is an ideal open platform for disparate smart grid technologies which could encourage all suppliers, loads and generator, to offer supply or reduce consumption when it is needed most and could vastly improve frequency performance metrics.

An optimally designed thermosiphon for power electronics cooling is developed. There exists a need for augmented grid assets to facilitate power routing and decrease line losses. Power converter augmented transformers (PCATs) are critically limited thermally. Conventional active cooling system pumps and fans will not meet the 30 year life and 99.9% reliability required for grid scale implementation. This approach seeks to develop a single-phase closed-loop thermosiphon to remove heat from power electronics at fluxes on the order of 10 - 15 W/cm2. The passive thermosiphon is inherently a coupled thermal-fluid system. A parametric model and multi-physics design optimization code will be constructed to simulate thermosiphon steady state performance. The model will utilize heat transfer and fluid dynamic correlations from literature. A particle swarm optimization technique will be implemented for its performance with discrete domain problems. Several thermosiphons will be constructed, instrumented, and tested to verify the model and reach an optimal design.

The performance of a novel micro-thermodielectric power generation device (MTDPG) was investigated in order to determine if thermodielectric power generation can compete with current portable power generation technologies. Thermodielectric power generation is a direct energy conversion technology that converts heat directly into high voltage direct current. It requires dielectric (i.e., capacitive) materials whose charge storing capabilities are a function of temperature. This property is exploited by heating these materials after they are charged; as their temperature increases, their charge storage capability decreases, forcing them to eject a portion of their surface charge to an appropriate electronic storage device. Previously, predicting the performance of a thermodielectric power generator was hindered by a poor understanding of the materials thermodynamic properties and the affect unsteady heat transfer losses have on system performance. In order to improve predictive capabilities in this study, a thermodielectric equation of state was developed that describes the relationship between the applied electric field, the surface charge stored by the thermodielectric material, and its temperature. This state equation was then used to derive expressions for the material's thermodynamic states (internal energy, entropy), which were subsequently used to determine the optimum material properties for power generation. Next, a numerical simulation code was developed to determine the heat transfer capabilities of a micro-scale parallel plate heat recuperator (MPPHR), a device designed specifically to a) provide the unsteady heating and cooling necessary for thermodielectric power generation and b) minimize the unsteady heat transfer losses of the system. The previously derived thermodynamic equations were then incorporated into the numerical simulation code, creating a tool capable of determining the thermodynamic performance of an MTDPG, in terms of the thermal efficiency, percent Carnot efficiency, and energy/power density, when the material properties and the operating regime of the MPPHR were varied. The performance of the MTDPG was optimized for an operating temperature range of 300 500 K. The optimization predicted that the MTDPG could provide a thermal efficiency of 29.7 percent. This corresponds to 74.2 percent of the Carnot efficiency. The power density of this MTDPG depends on the operating frequency and can exceed 1,000,000 W/m3.

Many automation and power control systems are integrated into the 'Smart Grid' concept for efficiently managing and delivering electric power. This integrated approach created several challenges that need to be taken into consideration such as cyber security issues, information sharing, and regulatory compliance. There are several issues that need to be addressed in the area of cyber security. Currently, there are no metrics for evaluating cyber security and methodologies to detect cyber attacks are in their infancy. There is a perceived lack of security built into the smart grid systems, but there is no mechanism for information sharing on cyber security incidents. In this thesis, we discuss the vulnerabilities in power system devices, and present ideas and a proposal towards multiple-threat system intrusion detection. We propose to test the multiple-threat methods for cyber security monitoring on a multi-laboratory test bed, and aid the development of a SCADA test bed, to be constructed on the Georgia Tech Campus.