Thesis (M.S.)--West Virginia University, 2005. / Title from document title page. Document formatted into pages; contains vii, 81 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 79-81).

Abstract The most common application of Distributed Generation (DG) is for reliability reasons. After experiencing an interruption, backup generators can be started to supply electricity to critical loads. The next most common application for DG is peak load shaving. During time periods of high energy demand or high energy prices, on-site generators are started up and used to serve part of the on-site loads. So DG can increase reliability of power supply, reduce loss of interruption and solve the problem of peak loads. Due to the high costs, only a few were installed. In order to investigate their economic values, in this thesis, several economic assessment methods are used to evaluate the cost-benefit of DG. Test results have revealed that, unless it is for environment protection reasons, the investment of DG is of little value if the fuel cost is high, and the electricity and the customer interruption costs are low. Keyword : Distributed Generation¡Mpeak load shaving.

The nature of electrical power requires specific infrastructure in order to operate adequately. Currently, the United States electricity grid contains a number of bottlenecking inefficiencies that arise from the aging infrastructure of the system. This paper examines the current state of the United States electricity grid, how potential changes in weather variables can affect the electricity consumption of residential consumers, and how implementation of Smart Grid technology can potentially mitigate these issues. It is determined through regression analysis that each weather variable that was tested proves significant for at least one of the consumers compared. This indicates that there is an enormous magnitude of individual variables that factor into residential electricity consumption and that more efficient and integrated electricity practices are necessary to optimize efficiency.

Residential buildings in the United States are responsible for the consumption of 38% of electricity, and for much of the fluctuations in the power demands on the electric grid, particularly in hot climates. Residential buildings are also where occupants spend nearly 69% of their time. As “smart” technologies, including electric grid-connected devices and home energy management systems are increasingly available and installed in buildings, this research focuses on the use of these technologies combined with available energy use data in accomplishing three main objectives. The research aims to: (a) better understand how residential buildings currently use electricity, (b) evaluate the use of these smart technologies and data to reduce buildings’ electricity use and their contribution to peak loads, and (c) develop a methodology to assess the impacts of these operational changes on occupant thermal comfort. Specifically this study focuses on two of the most significant electricity consumers in residential buildings: large appliances, including refrigerators, clothes washers, clothes dryers and dishwashers, and heating, ventilation and air conditioning (HVAC) systems. First, to develop an improved understanding of current electricity use patterns of large appliances and residential HVAC systems, this research analyzes a large set of field-collected data. This dataset includes highly granular electricity consumption information for residential buildings located in a hot and humid climate. The results show that refrigerators have the most reliable and consistent use, while the three user-dependent appliances varied more greatly among houses and by time-of-day. In addition, the daily use patterns of appliances vary in shape depending on a number of factors, particularly whether or not the occupants work from home, which contrasts with common residential building energy modeling assumptions. For the all-air central HVAC systems studied, the average annual HVAC duty cycle was found to be approximately 20%, and varied significantly depending on the season, time of day, and type of residential building. Duty cycle was also correlated to monthly energy use. This information provides an improvement to previously assumed values in indoor air modeling studies. Overall, the work presented here enhances the knowledge of how the largest consumers of residential buildings, large appliances and HVAC, operate and use energy, and identifies influential factors that affect these use patterns. The methodologies developed can be applied to determine use patterns for other energy consuming devices and types of buildings, to further expand the body of knowledge in this area. Expanding on this knowledge of current energy use, smart large appliances and residential HVAC systems are investigated for use in reducing peak electric grid loads, and building energy use, respectively. This includes a combination of laboratory testing, field-collected data, and modeling. For appliance peak load reduction, refrigerators are found to have a good demand response potential, in part due to the nearly 100% of residential buildings that have one or more of these appliances, and the predictability of their energy consumption behavior. Dryers provide less consistent energy use across all homes, but have a higher individual peak power demand during afternoon and evening peak use times. These characteristics also make dryers also a good candidate for demand response. The study of continuous commissioning of HVAC systems using energy data found that both runtime and energy use are increased, and cooling capacity and efficiency are reduced due to the presence of faults or inefficiencies. The correction of these faults have an estimated 1.4% to 5.7% annual impact on a residential building’s electricity use in a cooling-dominated climate such as the one studied. Overall, appliance peak load reduction results are useful for utility companies and policy makers in identifying what smart appliance may provide the most peak energy reduction potential through demand response programs. The results of the HVAC study provides a methodology that can be used with energy use data, to determine if an HVAC system has the characteristics implying an inefficiency may be present, and to quantify the annual savings resulting from its correction. The final aspect of this research focuses on the development of a tool to enable an assessment the effect of operational changes of a building associated with energy and peak load reduction on occupant comfort. This is accomplished by developing a methodology that uses the response surface methodology (RSM), combined with building performance data as input, and uncertainly analysis. A second-order RSM model constructed using a full-factorial design was generally found to provide strong agreement to in and out-of-sample building simulation data when evaluating the Average Percent of People Dissatisfied (PPD[subscript avg]). This 5-step methodology was applied to assess occupant thermal comfort in a residential building due to a 1-hour demand response event and a time-of-use pricing rate schedule for a variety of residential building characteristics. This methodology provides a model that can quickly assess, over a continuous range of values for each of the studied design variables, the effect on occupant comfort. This may be useful for building designers and operators who wish to quickly assess the effect of a change in building operations on occupants. / text

Load research is the study of load characteristics on a power distribution system which helps planning engineer make decisions about equipment ratings and future expansion decisions. As it is expensive to collect and maintain data across the entire system, data is collected only for a sample of customers, where the sample is divided into groups based upon the customer class. These sample measurements are used to calculate the load research factors like kWHr-to-peak kW conversion factors, diversity factors and 24 hour average consumption as a function of class, month and day type. These factors are applied to the commonly available monthly billing kW data to estimate load on the system. Among various customers on a power system, industrial customers form an important group for study as their annual kWHr consumption is among the highest. Also the errors with which the estimates are calculated are also highest for this class. Hence we choose the industrial class to demonstrate the Lawson-Hanson Non-Negative Least Square (NNLS) optimization technique to minimize the residual squared error between the estimated loads and the SCADA currents on the system. Five feeders with industrial dominant customers are chosen to demonstrate the improvement provided by the NNLS model. The results showed significant improvement over the Nonlinear Load Research Estimation (NLRE) method. / Master of Science

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Introduction: The global demand for renewable energy is expected to grow annually due to rising electricity consumption and economic and regulatory incentives. Efficient consumption and management of electricity consumption can support this trend (shifting from fossil resources to renewable resources) and benefit companies economically by reducing peak loads, reducing subscription costs, and protecting companies' power systems and networks. For effective and efficient electricity management that can help reduce peak electricity demand, resulting in lower demand charges and further reducing operational costs, it is necessary to understand the amount of electricity consumption and its influencing factors. Seasonal and daily variations and social behaviors influence changes in electricity consumption. Electric load variation management is essential for electricity consumers to control costs related to maximum load capacity and building electrical network equipment protection. This research presents an assessment method for describing daily electric load variations. It is applied to electricity consumption systems, but the technique is generic and can be applied to all activities where daily variations occur.Purpose: This thesis aims to show when and why peak load occurs in an office building and also to provide methods to improve the efficiency of electricity consumption during peak load.Method: This method implemented parameters like temperature, electricity consumption, and hours. It assessed a year's hourly electricity consumption in an office building to understand how load changes daily, weekly, and monthly. The case study was the AirSon office building. Data-driven from the efergy online portal was based on the hourly consumption every year.Results: The results show that the outdoor temperature, working hours, and consumption behavior can affect the overall electricity consumption and peak loads. Efficient building strategies are crucial for reducing peak loads by smartly controlling indoor temperature and managing electricity demand. The analysis offers insights into office building electricity consumption patterns and recommends strategies such as prioritizing car charging, optimizing the heat pump's operation of the HVAC system, improving the electricity management system, and using energy storage systems to reduce peak load.Discussion: The findings from this analysis carry implications for electricity consumption. It can provide some insights for reducing electricity consumption and enhancing efficiency in office buildings and similar facilities and also prioritization and rescheduling car chargers. In conclusion, it has interpreted and discussed the various electricity-saving strategies and their potential impact on electricity management.

Vi står inför en förändring, att minska vårt beroende av fossil energi till förmån för en ökad användning av förnybar energi. Incitament i form av ökade punktskatter på fossilt bränsle påskyndar omställningen för företag till förnybar energi och- eller energieffektivisering av sin produktion. Konsekvensen blir en successivt fördyrad kostnad för produktion av fjärrvärme med eldningsolja till tillverkningsindustrin. Punktskatt på eldningsolja stiger med 41 procent, från 2010 till 2015 för företagen. Att konvertera befintliga oljepannor med träpulverbrännare är en lösning för att hålla produktionskostnaden nere och behålla sin konkurrenskraft. I detta examensarbete utreds möjligheten att konvertera oljepannor i storleksklassen 5-10 MW. Studien tar ett helhetsgrepp om RMT´s fjärrvärmeproduktion för att söka svaret på, om det är möjligt att göra en konvertering ur ett tekniskt, hållbarhets och ekonomiskt perspektiv. Det kända behovet av spetslast från oljepannorna vid -3°C visade sig inte stämma utan brytpunkten hamnade vid -6°C. Resultatet pekar på att en 10 MW oljepanna kan konverteras till 5 MW träpulver men ändå behålla sin ursprungliga effekt genom en kombibrännare. Tidigare problem som fanns med träpulveranläggningar är åtgärdade och referensanläggningar i den aktuella storleksklassen finns i drift. Ett förfrågningsunderlag skickades ut för att få in budgetpris på konvertering av 5 och 10 MW oljepannor för utvärdering. Trots att alla leverantörer gavs samma förutsättningar så varierade budgetpriserna väldigt mycket. En stor överraskning var den korta payoff tid på konverteringar med liten värmeeffekt. Att en konvertering av en 10 MW OP har en återbetalningstid på 20 månader var rätt osannolikt innan studien utfördes. Denna studie kan även ses som en inspirationskälla till andra företag som har mindre spetslastpannor och vill fasa ut sitt fossila beroende med förnybar energi, samtidigt behålla sin konkurrenskraft. I fjärrvärmebranschen är dessa 5 och 10 MW oljepannor vanliga som spetslastpannor. Det finns ca 1000 oljepannor i storleken 5-10 MW i Sverige så det bör finnas en stor potential i att konvertera oljepannor till träpulver. / We are facing a change, to reduce our dependency on fossil fuels in favor of increased use of renewable energy. Incentives in the form of increased excise taxes on fossil fuels for companies accelerate the conversion to renewable energy or energy efficiency of its production. The consequence is a progressively more expensive cost for district heating with fuel oil to manufacturing industry. Excise tax on fuel oil rises by 41 percent, from 2010 to 2015 for those companies. To convert existing oil boilers with woodpowderburner is a solution to keep production costs down and remain competitive. In this thesis studied the possibility of converting oil boilers in the size range of 5 to 10 MW. The study takes a holistic approach to RMT's district heating to search the answer, if it is possible to do a conversion from a technical, sustainability and economic perspective. The felt need for peak load from oil boilers at -3° C appeared not to sue; it ended up at -6° C. The results indicate that a 10 MW oil boiler can be converted to 5 MW wood powder and while retaining its original power through a combination burner. Previously problems with woodpowderplant are taken care of and reference facilities in the current magnitude are in operation. An inquiry was sent out to get budget prices at a conversion of 5 and 10 MW oil boilers for evaluation. Even though all the suppliers was given the same opportunities so varied budget prices very much. A big surprise was the short payoff time on conversions of small oil boilers. The conversion of a 10 MW OP has a payback period of 20 months and that was rather unlikely before the study was performed. This study can also be seen as a source of inspiration to other companies that have small peak load boilers and want to phase out their fossil fuel dependence with renewable energy, and retain their competitiveness. In the district heating sector, these 5 and 10 MW oil boilers are ordinary peak load boilers. There are about 1000 oil boilers in the size from 5 to 10 MW in Sweden so it should be a large potential in converting oil boilers to wood powder

This dissertation analyzes pricing under uncertainty focusing on the U.S. airline industry. It sets to test theories of price dispersion driven by uncertainty in the demand by taking advantage of very detailed information about the dynamics of airline prices and inventory levels as the flight date approaches. Such detailed information about inventories at a ticket level to analyze airline pricing has been used previously by the author to show the importance of capacity constraints in airline pricing. This dissertation proposes and implements many new ideas to analyze airline pricing. Among the most important are: (1) It uses information about inventories at a ticket level. (2) It is the first to note that fare changes can be explained by adding dummy variables representing ticket characteristics. Therefore, the load factor at a ticket level will lose its explanatory power on fares if all ticket characteristics are included in a pricing equation. (3) It is the first to propose and implement a measure of Expected Load Factor as a tool to identify which flights are peak and which ones are not. (4) It introduces a novel idea of comparing actual sales with average sales at various points prior departure. Using these deviations of actual sales from sales under average conditions, it presents is the first study to show empirical evidence of peak load pricing in airlines. (5) It controls for potential endogeneity of sales using dynamic panels. The first essay tests the empirical importance of theories that explain price dispersion under costly capacity and demand uncertainty. The essay calculates a measure of an Expected Load Factor, that is used to calibrate the distribution of demand uncertainty and to identify which flights are peak and which ones are off-peak. It shows that different prices can be explained by the different selling probabilities. The second essay is the first study to provide formal evidence of stochastic peak-load pricing in airlines. It shows that airlines learn about the demand and respond to early sales setting higher prices when expected demand is high and more likely to exceed capacity.

A rapid growth in the national electricity demand is placing an ever-increasing demand on the national electricity supply utility, Eskom. Projections show that the load demand in South Africa may exceed the installed capacity by as early as 2007. This is mainly due to the increase in demand in the residential sector as a result of the electrification of rural and previously disadvantaged communities. However, the industrial and commercial sectors also have a role in this increase. In an attempt to reduce the demand for electricity Eskom has adopted its Demand Side Management (DSM) initiative. This initiative is aimed at lowering the electricity demand in peak times through energy efficiency (EE) or load shift, out of peak demand times. Eskom is implementing the DSM strategy by financing Energy Service Companies (ESCOs) to reduce the demand load of major electricity end-users during peak times. Buildings consume a large percentage of the total energy supply in the world. Most of the energy consumed in buildings is used by the heating, ventilation and air-conditioning (HVAC) systems, as well as lighting. However, a large potential for energy savings exists in buildings. Studies have shown that up to 70% of the electricity consumption of a building can be saved through retrofit studies. However, to capitalise on these opportunities, the ESCOs require tools and procedures that would enable them to accomplish energy savings studies quickly and efficiently. It should be a holistic approach to the typical ESCO building audit. A study of current available software programs showed the lack of holistic tools aimed specifically at retrofit audits, and therefore also the need for such a program. The building simulation program most suited to the retrofit study was chosen and it was used in a retrofit audit. By emulating a retrofit audit with this software, its performance in the field, both positive and negative, could be established. With the experience gained from the retrofit study, as well as input from ESCOs in the industry, a need for such a retrofit tool was established. The simulation program that was tested in the retrofit study is the tool Quickcontrol, as well as the newer version of the program, entitled QEC. The case study showed that even though these packages are well suited to ESCO work, they have certain drawbacks in view of the holistic project approach. The ESCOs require a simple, fast, and integrated procedure for energy audits. This procedure should be embodied in a software program. This study proposes a new integrated procedure for energy audits and the analyses of buildings, in the form of a software tool. This new tool is geared towards the ESCO building audit, in both South A6ica and internationally. It is designed to enable a diplomate engineer to accomplish a building energy and retrofit analysis in two weeks, leading the user through all the main project steps, from data acquisition to writing of the final project report. This is a significant improvement, since it normally takes 50 man-days for an experienced and trained engineering team to complete a full building audit. This tool was used in a case study to test its validity and accuracy. It was found that certain situations would arise in which the criteria that were set for the program would not be adequate. The results from the case study were favourable and satisfied the criteria that were set for the procedure. / Thesis (Ph.D. (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2004.