Thesis (Ph. D.)--Rutgers University, 2007. / "Graduate Program in Economics." Includes bibliographical references (p. 112-114).

Despite the extensive deployment of smart-meters (SMs) at the low-voltage (LV) level, which are either fully operational or will be in the near future, distribution network operators (DNOs) are still relying on a limited number of permanently installed monitoring devices at primary and secondary medium-voltage (MV) substations, for purposes of network operation and control, as well as to inform and facilitate trading interactions between generators, distributors and suppliers. Accordingly, improved and sufficiently developed models for the analysis of aggregate demands at the MV-level are required for the correct assessment of load variability, composition and time-dependent evolution, necessary for: addressing issues of robustness, security and reliability; accomplishing higher penetration levels from renewable/distributed generation; implementing demand-side-management (DSM) schemes and incorporating new technologies; decreasing environmental and economic costs and aiding towards the realisation of automated and proactive ''smart-grid'' networks. The analysis of MV-demand measurements provides an independent source of information that can capture network characteristics that do not manifest in the data collected at the LV-level, or when such data is restricted or altogether unavailable. This information describes the supply/demand interactions at the mid-level between high-voltage (HV) transmission and LV end-user consumption and opens possibilities for validation of existing bottom-up aggregation approaches, while addressing issues of reliance on survey-based data for technical and economic power system studies. This thesis presents improved and novel methodologies for the analysis of aggregate demands, measured at MV-substations, aimed at more accurate and detailed load profiling, temporal decomposition and identification of the drivers of demand variability, classification of grid-supply- points (GSPs) according to consumption patterns, disaggregation with respect to customer-classes and load-types and load forecasting. The developed models are based on a number of traditional and modern analytical and statistical techniques, including: data mining, correlational and regression analysis, Fourier analysis, clustering and pattern recognition, etc. The approaches are demonstrated on demand datasets from UK and European based DNOs, thus providing specific information for the demand characteristics, the dependencies to external parameters and to socio-behavioural factors and the most likely load composition at the corresponding geographical locations, while the approaches are also intendent to be easily adaptable for studies at equivalent voltage and demand aggregation levels.

abstract: A major problem faced by electric utilities is the need to meet electric loads during certain times of peak demand. One of the widely adopted and promising programs is demand response (DR) where building owners are encouraged, by way of financial incentives, to reduce their electric loads during a few hours of the day when the electric utility is likely to encounter peak loads. In this thesis, we investigate the effect of various DR measures and their resulting indoor occupant comfort implications, on two prototype commercial buildings in the hot and dry climate of Phoenix, AZ. The focus of this study is commercial buildings during peak hours and peak days. Two types of office buildings are modeled using a detailed building energy simulation program (EnergyPlus V6.0.0): medium size office building (53,600 sq. ft.) and large size office building (498,600 sq. ft.). The two prototype buildings selected are those advocated by the Department of Energy and adopted by ASHRAE in the framework of ongoing work on ASHRAE standard 90.1 which reflect 80% of the commercial buildings in the US. After due diligence, the peak time window is selected to be 12:00-18:00 PM (6 hour window). The days when utility companies require demand reduction mostly fall during hot summer days. Therefore, two days, the summer high-peak (15th July) and the mid-peak (29th June) days are selected to perform our investigations. The impact of building thermal mass as well as several other measures such as reducing lighting levels, increasing thermostat set points, adjusting supply air temperature, resetting chilled water temperature are studied using the EnergyPlus building energy simulation program. Subsequently the simulation results are summarized in tabular form so as to provide practical guidance and recommendations of which DR measures are appropriate for different levels of DR reductions and the associated percentage values of people dissatisfied (PPD). This type of tabular recommendations is of direct usefulness to the building owners and operators contemplating DR response. The methodology can be extended to other building types and climates as needed. / Dissertation/Thesis / M.S. Architecture 2012

No presente trabalho são aplicadas ferramentas estatísticas quantitativas clássicas no setor de telefonia móvel brasileiro a fim de comparar os seus resultados. Uma metodologia é proposta para a aplicação destas ferramentas de um modo prático em uma de uma operadora de telefonia celular brasileira. Após são aplicados os métodos de previsão às séries históricas de demanda de acessos da telefonia móvel brasileira, estratificadas de acordo com as tecnologias utilizadas (TDMA, CDMA e GSM), no período de agosto de 2002 a julho de 2007. De acordo com o desempenho, são obtidos os modelos de cada método estatístico proposto. Entre estes, se escolheu aquele que melhor descreveu cada série e previsões foram geradas. Os modelos ARIMA apresentaram o melhor desempenho dentre os métodos aplicados nas séries estudadas. / The activity of planning ahead their systems in an appropriate manner is fundamental to telecommunication sector agents in order to manage the resources allocation and to meet the quality requirements in the provision of mobile telephony services. As the networks and production systems deployment takes time, there is the need of a service demand forecast. Therefore, in this study, classical quantitative statistics tools are applied to the Brazilian mobile telephony sector to compare their results. A methodology for the application of such tools in a practical way within a business environment of this sector is proposed. Afterwards, forecasting methods are applied to the time series referred to Brazilian mobile telephony demand, stratified according to the technologies (TDMA, CDMA and GSM) in the period from August 2002 to July 2007. The models of each statistical method proposed, based on the performance results, are obtained and, among these methods, it is chosen a model that best described each time series. The ARIMA model had the best performance among the methods applied in the time series studied and forecasts were made.

No presente trabalho são aplicadas ferramentas estatísticas quantitativas clássicas no setor de telefonia móvel brasileiro a fim de comparar os seus resultados. Uma metodologia é proposta para a aplicação destas ferramentas de um modo prático em uma de uma operadora de telefonia celular brasileira. Após são aplicados os métodos de previsão às séries históricas de demanda de acessos da telefonia móvel brasileira, estratificadas de acordo com as tecnologias utilizadas (TDMA, CDMA e GSM), no período de agosto de 2002 a julho de 2007. De acordo com o desempenho, são obtidos os modelos de cada método estatístico proposto. Entre estes, se escolheu aquele que melhor descreveu cada série e previsões foram geradas. Os modelos ARIMA apresentaram o melhor desempenho dentre os métodos aplicados nas séries estudadas. / The activity of planning ahead their systems in an appropriate manner is fundamental to telecommunication sector agents in order to manage the resources allocation and to meet the quality requirements in the provision of mobile telephony services. As the networks and production systems deployment takes time, there is the need of a service demand forecast. Therefore, in this study, classical quantitative statistics tools are applied to the Brazilian mobile telephony sector to compare their results. A methodology for the application of such tools in a practical way within a business environment of this sector is proposed. Afterwards, forecasting methods are applied to the time series referred to Brazilian mobile telephony demand, stratified according to the technologies (TDMA, CDMA and GSM) in the period from August 2002 to July 2007. The models of each statistical method proposed, based on the performance results, are obtained and, among these methods, it is chosen a model that best described each time series. The ARIMA model had the best performance among the methods applied in the time series studied and forecasts were made.

Energy assessment in buildings is an essential topic in order to achieve the set goals for energy efficiency. This thesis investigated the energy consumption in various scenarios in Husmuttern’s buildings. Different purposes (school and apartment), locations (Spain and Sweden) and materials combinations are modelled and analysed. The models were created in the building performance simulation tool IDA ICE. After the yearly energy demand results were obtained they were processed and analysed. Then several factors were changed in the model in order to investigate different impacts in the energy consumption of the building, such as the overall heat transfer, hot water consumption, windows and doors. Also, PV panels were installed in the model to obtain the potential penetration of renewable energy in the buildings. The results showed the different consumption in the buildings depending on the purpose and location, and the impact of the changed factors in the overall energy consumption. The change of windows to more efficient ones showed that the apartments improve their consumption more than the schools, especially in when the Spanish location is considered. This case also had the biggest possible change when the hot water demand is varied. Whereas if the door was the changed, the Swedish apartment has the most possible improvement.

O presente trabalho pretende estudar a disposição a pagar do consumidor brasileiro por produtos com maior eficiência energética. Entender o quanto o consumidor está disposto a pagar por um ganho de eficiência pode contribuir na elaboração de políticas públicas para o setor elétrico, e na criação de incentivos à indústria para produção e investimentos neste tipo de tecnologia. O trabalho utiliza-se de uma pesquisa de campo sobre posse de equipamentos e hábitos de uso, realizada pela Eletrobrás/Procel em 2005. A metodologia empregada segue a linha de trabalhos já realizados em outros países, a exemplo dos estudos realizados por Dubin e McFadden (1984) e Sammer e Wüstenhagen (2006). Os resultados encontrados apontam para uma subestimação dos consumidores quanto à avaliação da economia proporcionada por produtos mais eficientes. Sendo assim, abre-se espaço para campanhas de conscientização e valorização dessas tecnologias ou incentivos monetários para consumo dos mesmos. / The focus of this research is to investigate the Brazilian consumer\'s willingness to pay for products with more energy efficiency. Measuring how much the consumer is willing to pay could provide us with good insights that may help in discussing politics regarding the electric sector, plus it may guide politics in taking actions and decisions on incentives in order to promote this kind of more efficient technological products. This investigation is based on a field research regarding the possess of appliances and their uses, consisting of 4310 household questionnaires. The methodology applied follows previous work on this field and similar studies made for other countries. Examples are Dubin e McFadden (1984) for space and water heating in the USA and Sammer e Wüstenhagen (2006) for washing machines in the Switzerland. The results make clear an underestimation of the monetary economy due to the use of more efficient products. Therefore, there\'s an opportunity for the government to motivate and promote this kind of technology, either through advertising for and awareness of the general population, or through subsides to the industry to produce more efficient products or monetary incentives for the their purchases.

No presente trabalho são aplicadas ferramentas estatísticas quantitativas clássicas no setor de telefonia móvel brasileiro a fim de comparar os seus resultados. Uma metodologia é proposta para a aplicação destas ferramentas de um modo prático em uma de uma operadora de telefonia celular brasileira. Após são aplicados os métodos de previsão às séries históricas de demanda de acessos da telefonia móvel brasileira, estratificadas de acordo com as tecnologias utilizadas (TDMA, CDMA e GSM), no período de agosto de 2002 a julho de 2007. De acordo com o desempenho, são obtidos os modelos de cada método estatístico proposto. Entre estes, se escolheu aquele que melhor descreveu cada série e previsões foram geradas. Os modelos ARIMA apresentaram o melhor desempenho dentre os métodos aplicados nas séries estudadas. / The activity of planning ahead their systems in an appropriate manner is fundamental to telecommunication sector agents in order to manage the resources allocation and to meet the quality requirements in the provision of mobile telephony services. As the networks and production systems deployment takes time, there is the need of a service demand forecast. Therefore, in this study, classical quantitative statistics tools are applied to the Brazilian mobile telephony sector to compare their results. A methodology for the application of such tools in a practical way within a business environment of this sector is proposed. Afterwards, forecasting methods are applied to the time series referred to Brazilian mobile telephony demand, stratified according to the technologies (TDMA, CDMA and GSM) in the period from August 2002 to July 2007. The models of each statistical method proposed, based on the performance results, are obtained and, among these methods, it is chosen a model that best described each time series. The ARIMA model had the best performance among the methods applied in the time series studied and forecasts were made.

No presente trabalho, é analisado o comportamento da demanda por transporte coletivo em quatro cidades de porte médio do interior do Estado de São Paulo (Araraquara, São Carlos, Jaú e São José do Rio Preto), visando a identificar os principais fatores que afetam o volume de passageiros, como: população, valor da tarifa, índice de motorização total e desagregada (automóveis e motocicletas), quantidade de empregos formais, produto interno bruto (PIB) per capita, etc. As análises efetuadas mostram que a demanda total por transporte coletivo urbano é influenciada principalmente pelas seguintes variáveis sócio-econômicas: população, índice de motorização e PIB per capita. Outros fatores que também influenciam são: tamanho da cidade, qualidade do transporte coletivo, cultura de utilização da bicicleta, etc.. São desenvolvidos dois modelos matemáticos simples, utilizando a técnica de regressão linear múltipla, que estabelecem relação entre a demanda por transporte coletivo e as principais variáveis sócio-econômicas que a afetam, como ferramenta para a previsão da demanda em diferentes cenários conformados pelas variáveis sócio-econômicas. Os resultados apresentados pelo modelo matemático que utiliza como índice de motorização, a relação entre a frota de veículos de duas rodas e a população refletem de maneira mais próxima a realidade que o modelo que utiliza a frota total. Este modelo é adequado para ser utilizado na previsão da demanda por transporte coletivo no universo das quatro cidades analisadas com erro máximo menor que 10%. / In this paper we analyzed the behavior of demand for public transportation in four mid-sized cities in the state of São Paulo (Araraquara, São Carlos, Jau and Sao Jose do Rio Preto), to identify key factors affecting the volume of passengers, such as population, amount of fare, motorization rate and total breakdown (automobiles and motorcycles), amount of formal employment, gross domestic product (GDP) per capita, etc. In general, the following conclusions can be inferred from the analysis performed: the total demand for transportation in a city depends mainly on the following parameters: population, motorization rate and GDP per capita. Other indirect factors such as quality public transport, culture, use of transport, etc. They also developed two simple mathematical models using multiple linear regression technique with the objective of establishing relations between the demand for public transportation and the following key social-economic variables that were identified as those that most influence the demand: population, motorization rate and GDP per capita, which is useful for predicting the demand for public transport in different scenarios conformed by social-economic variables. The results presented by the mathematical model show that uses such as motorization rate, the relation between the fleet of two-wheeled vehicles and the population, more closely reflect the reality that the model uses the total fleet. The model uses the rate of motorization in considering two vehicles suitable for use in forecasting demand for public transportation in the universe of the four cities analyzed maximum error <10%.

Abstract A great deal of research in transportation economics has been motivated by the need to solve traffic congestion problems and to diminish negative environmental effects of road transport. The question, whether the economic measures are efficient, motivates this dissertation on the value of travel time, the rules of optimal pricing and the demands for public transportation and private car use. Three concepts of the marginal value of travel time (MVT) are specified in this thesis. The first concept involves only the direct disutility of the travel time to work in addition to the utility of market goods and leisure. The second concept also includes the disutility from the time spent at work. The third concept furthermore takes into account the effect of the length of working hours, travel time, cost and income. The length of travel time, gender, family structure and flexibility of working hours have different effects on empirical MVTs, but travel costs and income affect them in a similar fashion. The pricing decisions of the firms providing bus services are analysed with and without public subsidies. The consumption externality, i.e. the quantity demanded by other users, affects the individual bus demand. The results indicate that under uniform pricing, a socially optimal subsidy equals the increase in consumer's surplus minus the fare revenue lost from current users due to lower fare. Under nonlinear pricing, the optimal pricing can be achieved when the regulator sets the subsidy so that it is inversely proportional to the network elasticity. The welfare loss due to increasing tax burden and the opportunity cost of providing cash fare service is also taken into account in the optimal pricing rule. A model of bus demand with asymmetric information on the characteristics of bus users is developed. The model allows for habit formation and network effects. The latter effect is due to the positive influence of the aggregate demand for Regional Bus Cards (RBC) on an individual's own demand. The empirical results indicate that in RBC services positive network effects are present and the elasticity of network size is less than one, which implies that the regional bus card is an impure public good. The own price elasticity of RBC in the short run is within the range of -0.3 and -1.1. The demand for RBC cards is more elastic than demand for RBC trips or passenger kilometres. The estimated price elasticity of urban bus demand is in line with that of RBC. A reasonably high cross-price elasticity of RBC trips and the ticket of 40 trips but a lower reverse elasticity were found. A weakly separable demand for car mileage from car ownership and labour supply was rejected as was the exogeneity of car ownership in the mileage model. Therefore, the price elasticity of car mileage with respect to fuel price was estimated from the two equation model of car mileage with endogenous car ownership. The estimated parameters of the Tobit model are consistent but slightly higher than those estimated from the least squares. The fuel price elasticity varies from -0.2 to -0.9 with exogenous and endogenous car ownership, respectively. The findings of this study can be applied in the analysis and implementation of different pricing and subsidy schemes for public transportation, as well as in the evaluation of the effectiveness of economic instruments for managing the growth of private car use.