Paper [I] Using a unique and highly detailed data set on energy consumption at the appliance-level for 200 Swedish households, seemingly unrelated regression (SUR)-based end-use specific load curves are estimated. The estimated load curves are then used to explore possible restrictions on load shifting (e.g. the office hours schedule) as well as the cost implications of different load shift patterns. The cost implications of shifting load from "expensive" to "cheap" hours, using the Nord Pool spot prices as a proxy for a dynamic price, are computed to be very small; roughly 2-4% reduction in total daily costs from shifting load up to five hours ahead, indicating small incentives for households (and retailers) to adopt dynamic pricing of electricity. Paper [II] Using a detailed data set on appliance-level electricity consumption at the hourly level, we provide the first estimates of hourly and end-use-specific income elasticities for electricity. Such estimates are informative about how consumption patterns in general, and peak demand in particular, will develop as households’ income changes. We find that the income elasticities are highest during peak hours for kitchen and lighting, with point estimates of roughly 0.4, but insignificant for space heating. Paper [III] In this paper, I estimate the price elasticity of electricity as a function of the choice between fixed-price and variable-price contracts. Further, assuming that households have imperfect information about electricity prices and usage, I explore how media coverage of electricity prices affects electricity demand, both by augmenting price responsiveness and as a direct effect of media coverage on electricity demand, independent of prices. I also address the endogeneity of the choice of electricity contract. The parameters in the model are estimated using unique and detailed Swedish panel data on monthly household-level electricity consumption. I find that price elasticities range between −0.025 and −0.07 at the mean level of media coverage, depending on contract choice, and that households with monthly variation in electricity prices respond more to prices when there is extensive media coverage of electricity prices. When media coverage is high, for example 840 news articles per month (which corresponds to the mean plus two standard deviations), the price elasticity is −0.12, or 1.7 times the elasticity at the mean media coverage. Similarly, media coverage is also found to have a direct effect on electricity demand. Paper [IV] I explore how households switch between fixed-price and variable-price electricity contracts in response to variations in price and temperature, conditional on previous contract choice. Using panel data with roughly 54000 Swedish households, a dynamic probit model is estimated. The results suggest that the choice of contract exhibits substantial state dependence, with an estimated marginal effect of previous contractchoiceof0.96, andthattheeffectofvariationinpricesandtemperatureonthechoice of electricity contract is small. Further, the state dependence and price responsiveness are similar across housing types, income levels and other dimensions. A plausible explanation of these results is that transaction costs are larger than the relatively small cost savings from switching between contracts.

This thesis presents a residential thermostat that enables accurate aggregate load control systems for electricity demand response. The thermostat features a control strategy that can be modeled as a linear time-invariant system for short-term demand response signals from the utility. This control design gives rise to linear time-invariant models of aggregate load control and demand response, which is expected to facilitate the design of more accurate load-based regulation services for electricity interconnections and enable integration of more highly variable renewable electricity generation resources. A key feature of the new thermostat design is the elimination of aggregate short-term load control error observed with existing real-time pricing thermostats as they respond to price signals. / Graduate / 0548 / 0791 / 0544 / dchassin@uvic.ca

abstract: Energy consumption in buildings, accounting for 41% of 2010 primary energy consumption in the United States (US), is particularly vulnerable to climate change due to the direct relationship between space heating/cooling and temperature. Past studies have assessed the impact of climate change on long-term mean and/or peak energy demands. However, these studies usually neglected spatial variations in the “balance point” temperature, population distribution effects, air-conditioner (AC) saturation, and the extremes at smaller spatiotemporal scales, making the implications of local-scale vulnerability incomplete. Here I develop empirical relationships between building energy consumption and temperature to explore the impact of climate change on long-term mean and extremes of energy demand, and test the sensitivity of these impacts to various factors. I find increases in summertime electricity demand exceeding 50% and decreases in wintertime non-electric energy demand of more than 40% in some states by the end of the century. The occurrence of the most extreme (appearing once-per-56-years) electricity demand increases more than 2600 fold, while the occurrence of the once per year extreme events increases more than 70 fold by the end of this century. If the changes in population and AC saturation are also accounted for, the impact of climate change on building energy demand will be exacerbated. Using the individual building energy simulation approach, I also estimate the impact of climate change to different building types at over 900 US locations. Large increases in building energy consumption are found in the summer, especially during the daytime (e.g., >100% increase for warehouses, 5-6 pm). Large variation of impact is also found within climate zones, suggesting a potential bias when estimating climate-zone scale changes with a small number of representative locations. As a result of climate change, the building energy expenditures increase in some states (as much as $3 billion/year) while in others, costs decline (as much as $1.4 billion/year). Integrated across the contiguous US, these variations result in a net savings of roughly $4.7 billion/year. However, this must be weighed against the cost (exceeding $19 billion) of adding electricity generation capacity in order to maintain the electricity grid’s reliability in summer. / Dissertation/Thesis / Doctoral Dissertation Environmental Social Science 2016

Submitted by Geandra Rodrigues (geandrar@gmail.com) on 2018-06-14T13:10:24Z No. of bitstreams: 1 danielmoraisdesouza.pdf: 1277495 bytes, checksum: 7c2517a5b98a6f70aa787d954cd0c84a (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2018-06-27T15:04:32Z (GMT) No. of bitstreams: 1 danielmoraisdesouza.pdf: 1277495 bytes, checksum: 7c2517a5b98a6f70aa787d954cd0c84a (MD5) / Made available in DSpace on 2018-06-27T15:04:32Z (GMT). No. of bitstreams: 1 danielmoraisdesouza.pdf: 1277495 bytes, checksum: 7c2517a5b98a6f70aa787d954cd0c84a (MD5) Previous issue date: 2018-02-19 / Eletricidade é um insumo de uso generalizado nas economias modernas, penetrando nas mais variadas atividades produtivas e de consumo na sociedade. No entanto, as dificuldades de armazenamento em larga escala dessa forma de energia fazem com que a eletricidade seja muito sensível às condições de oferta, a ponto de que problemas de abastecimento rapidamente se convertem em apagões. Dentre vários dispositivos implementados na re-estrutuação do setor elétrico brasileiro (SEB) ao longo dos últimos 17 anos, estão sistemas de previsão de médio e longo-prazos usados por parte dos agentes públicos e privados do setor para reduzir as incertezas dos processos de abastecimento e expansão. A ANEEL chegou a recomendar na NT 292/2008-SER o uso de três metodologias multivariadas alternativas nesses sistemas de previsão, a saber: modelos VAR e VCE, modelos autorregressivos com defasagens distribuídas (ARDL) e modelos estruturais a espaço de estados. A literatura especializada, em que pese a presença de vários estudos propondo modelos de previsão do consumo de eletricidade para os três segmentos residencial, industrial e comercial, apresenta majoritariamente modelos de tipo VAR e VCE. Este estudo atualiza a literatura no que concerne ao uso de modelos VAR e VCE e ao mesmo tempo os compara em termos preditivos com os modelos ARDL e estruturais a espaço de estados. Os resultados encontrados na análise do desempenho preditivo dos modelos mostraram que para o segmento residencial, o modelo com melhor capacidade preditivo foi o modelo estrutural, enquanto que para o segmento comercial foi o modelo VCE e, para o segmento industrial, foi o modelo ARDL. Previsões de 2014 a 2025 foram feitas com o intuito de informar ao mercado brasileiro a demanda de energia para cada segmento. Foram usadas bases de dados disponíveis e atualizadas provenientes das mesmas fontes usadas nos estudos da literatura. / Electricity is an input of widespread use in modern economies, penetrating in the most varied productive and consumption activities in society. However, the difficulties of large-scale storage make electricity very sensitive to supply conditions, to the point that supply problems quickly turns into blackouts. Among several devices implemented in the re-structuring of the Brazilian electricity sector (SEB) over the last 17 years, medium and long-term forecasting systems are used by public and private sector agents to reduce the uncertainties of the supply processes and expansion. ANEEL recommend in NT 292/2008-SER the use of three alternative multivariate methodologies in these prediction systems, namely: VAR and VCE models, autoregressive models with distributed lags (ARDL), and state space structural models. The specialized literature, despite the presence of several studies proposing models of prediction of the consumption of electricity for the three residential, industrial and commercial segments, mainly presents models of type VAR and VCE. This study updates the literature regarding the use of VAR and VCE models and at the same time compares them in predictive terms with the ARDL and structural state space models. The results found in the predictive model analysis showed that for the residential segment, the model with the best predictive capacity was the structural model, while for the commercial segment it was the VCE model and, for the industrial segment, it was the ARDL model. Forecasts from 2014 to 2025 were made with the intention of informing the Brazilian market the energy demand for each segment. Available and updated databases from the same sources used in literature studies were used.

La croissance du marché des pompes à chaleur contribue à l'augmentation de la thermosensibilité de la demande électrique. Il devient nécessaire de mieux comprendre l'impact des usages thermosensibles de l'électricité, notamment concernant ceux qui sont corrélés de manière non linéaire à la température extérieure. Dans cette optique, cette thèse vise à construire un cadre de modélisation qui permette i) d'analyser les facteurs d'influence de la thermosensibilité à partir d'une description physique des usages thermosensibles, et ii) de réaliser des diagnostics de ces paramètres d'influence tout en tenant compte des incertitudes associées. Une approche de modélisation hybride qui bénéficie des avantages de modèles statistiques et de modèles physiques est principalement employée pour répondre à ces questions.La première étape consiste à estimer la part thermosensible de la demande réelle par un modèle prédictif top-down. On développe ensuite un modèle d'analyse physique de la thermosensibilité à l'échelle régionale à partir de la thermique du bâtiment. On s'appuie notamment sur des modèles pseudo-physiques de performance de pompes à chaleur qui sont régressés sur des données constructeur ou des mesures de performances réelles. Un COP régional est déterminé pour l'ensemble des PAC installées. Enfin, les paramètres d'influence du modèle de thermosensibilité ainsi développé sont estimés à l'aide de l'approche bayésienne, qui offre un cadre pour le traitement de l'incertitude sous la forme de probabilités. Des coefficients équivalents de déperditions thermiques, une température intérieure équivalente ainsi que les parts du chauffage Joule et par PAC pour le parc de bâtiments régional ont été obtenus. / The growing heat pump market contributes to the increase in temperature sensitivity of electricity demand. It becomes necessary to understand the impact of temperature sensitive end-uses of electricity, including those which are correlated non-linearly to the outside temperature. In this context, this thesis aims to build a modeling framework to i) analyze the influencing factors of the temperature sensitivity of electricity demand from a physical description of temperature-sensitive equipment, and ii) to perform diagnoses of these parameters of influence by taking into account the associated uncertainties. A hybrid modeling approach that benefits the advantages of statistical models and physical models is used to answer these questions.Firstly, the temperature-sensitive part of electricity demand is estimated by a predictive top-down model. Then a physical model to analyze the temperature sensitivity at regional level is developed based on building thermal energy needs. A regional coefficient of performance (COP) is determined for the whole installed heat pumps by using pseudo-physical models which are regressed on manufacturer data or actual performance measures. Finally, the parameters of influence of the developed temperature sensitivity model are estimated using the Bayesian approach which provides a framework for the treatment of uncertainty in the form of probabilities. Equivalent coefficients of heat loss, an equivalent internal temperature, as well as the share of Joule heating and the share of heat pumps for the regional building stock are obtained.

[pt] O objetivo deste trabalho é inserir variáveis exógenas no modelo Holt-Winters com múltiplos ciclos, genuinamente univariado. Serão usados dados horários de demanda de energia elétrica provenientes de uma cidade da região sudeste do Brasil e dados de temperatura, tanto em sua forma primitiva quanto derivada, por exemplo, indicadores de dias quentes, o chamado cooling degree days (CDD). Com isso, pretende-se melhorar o poder preditivo do modelo, gerando previsões com maior acurácia. / [en] The aim of this thesis is to insert exogenous variables in the model Holt-Winters with multiple cycles, genuinely univariate. Hourly data will be used for electricity demand from a city in southeastern Brazil and temperature data, both in its original form as derived, for example, indicators of hot days, cooling degree days called (CDD). With this, we intend to improve the predictive power of the model, generating predictions with greater accuracy.

Pays en développement avec une demande d’électricité croissante, le Vietnam a instauré la tarification progressive de l’électricité résidentielle. La fixation du tarif de l’énergie est toujours une question délicate, entre gestion de la demande, lutte contre la pauvreté, effets sur l’inflation, besoins d’investissement pour assurer la sécurité énergétique et le développement des technologies vertes. Cette action nécessite une maîtrise très profonde du comportement des consommateurs ainsi que la demande des ménages. La thèse a pour but d’explorer les facteurs qui impactent la demande d’électricité Vietnamienne au niveau résidentielle en se basant sur : les prix, les revenus, la démographie (comprenant la taille et la composition des foyers) et les vagues de chaleur. Les données de « pool et panel » sont collectées à partir des trois micro enquêtes sur le niveau de vie des foyers vietnamiens en 2012, 2014, 2016.Cette thèse estime économétriquement la demande d’électricité des ménages. Elle innove sur deux points de méthode.Premièrement, elle utilise les données individuelles issues des enquêtes nationales, avec le détail de la structure des tarifs et des factures d’électricité des ménages répondants. Cela dépasse donc les limites de beaucoup de recherches passées qui étaient basées soit sur données nationales agrégées, soit sur données individuelles mais avec une quantité et un prix imputés, soit sur données individuelles avec le détail de la structure des tarifs et des factures d’électricité mais au niveau régional seulement. Cette innovation est possible car le marché de l’électricité au Vietnamien est monopolistique, avec un seul vendeur – Electricité de Vietnam (EVN), à qui le gouvernement commande d’utiliser une grille tarifaire homogène pour tout le pays.Deuxièmement, la thèse propose une nouvelle façon d’explorer l’impact des hautes températures sur la demande d’électricité. La méthode propose d’ajouter une variable muette qui représente l’occurrence d’une vague de chaleur. Cette variable est complémentaire de la notion « Degrés-jours de refroidissement » qui représente la température dans la plupart des études précédentes.Les conclusions principales sont que: (i) Les ménages réagissent aux prix marginaux, la demande est élastique par rapport au prix. (ii) Il existe un seuil de revenu à partir duquel la consommation d'électricité des foyers augmente quand le revenu augmente : la consommation d'électricité des foyers ayant ce revenu peut être considérée comme le niveau de besoin fondamental, un seuil de pauvreté pour l’électricité. (iii) La progressivité de la tarification ne pénalise pas les familles nombreuses : le tarif progresse moins vite que les d’économies d'échelle des dépenses d'électricité. (iv) Nous n’observons pas d’effet de la composition du foyer en termes enfants / adultes / personnes âgés sur la dépense d'électricité. (v) Les vagues de chaleur - un phénomène lié au changement climatique - impactent la demande d’électricité et devraient être mieux prises en compte dans l’estimation de la demande. / As a developing country with surging demand in electricity, Vietnam has implemented demand-side management in the residential electricity market, such as increasing block tariffs to balance the tension between energy security and the development of clean technology. The implementation of demand-side management requires a deep understanding of customer behaviors and household demand. The thesis aims to explore the factors impacting on Vietnamese residential electricity demand in the period of 2012–16. The exploration focuses on four main factors: prices, income, demographics (including household size and composition), and heatwaves. The data are a pool data set and a panel data set which have been constructed from the three rounds of the micro survey Vietnam Household Living Standard Survey (VHLSS) in 2012, 2014 and 2016.The thesis has two novel points in estimating household electricity demand function.First, it uses micro survey data at national level, with detailed tariff structures and private electricity billing. In the past, researches have often used national aggregate data or national micro survey data with imputed quantity or price. Researches that use micro survey data with detail tariff schedules and electricity bills are often at a regional level rather than at a national level due to the absence of national data on tariff structures. The residential electricity market in Vietnam is a monopoly with a single seller, Vietnam Electricity (EVN). Electricity tariff schedules are proposed by EVN and set by the Government and are thus uniform in national scale. This provides a chance to estimate demand function from national micro survey data, with full detail of electricity prices and billings.Second, the thesis proposes a new way to capture the impact of high temperature on electricity demand. That is, to include an additional dummy variable to represent the extreme distribution of temperature. The additional dummy variable is a complement to the concept of cooling degree days which is a popular representation of temperature in previous researches.The estimate results lead to five main conclusions. (i) Households do respond to marginal prices and demand is elastic to price. (ii) There exists an income threshold from which household electricity consumption increases as income increases. The electricity consumption of households in the income group is the reference level of electricity poverty threshold. (iii) The increasing block tariff does not cancel out economies of scale in electricity expenditure of households. (iv) There is no difference in electricity expenditure across children, adults and elders. (v) Heatwaves – a climate change related phenomenon – do have impacts on electricity demand and need to be addressed carefully in estimating electricity demand in the future.

No description available.

This dissertation contains three independent essays relating energy economics. The first essay investigates price asymmetry of diesel in South Korea by using the error correction model. Analyzing weekly market prices in the pass-through of crude oil, this model shows asymmetric price response does not exist at the upstream market but at the downstream market. Since time-variant residuals are found by the specified models for both weekly and daily retail prices at the downstream level, these models are implemented by a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) process. The estimated results reveal that retail prices increase fast in the rise of crude oil prices but decrease slowly in the fall of those. Surprisingly, retail prices rarely respond to changes of crude oil prices for the first five days. Based on collusive behaviors of retailers, this price asymmetry in Korea diesel market is explained. The second essay aims to evaluate the new incentive system for biodiesel in South Korea, which keeps the blend mandate but abolishes tax credits for government revenues. To estimate changed welfare from the new policy, a multivariate stochastic simulation method is applied into time-series data for the last five years. From the simulation results, the new biodiesel policy will lead government revenues to increases with the abolishment of tax credit. However, increased prices of blended diesel will cause to decrease demands of both biodiesel and blended diesel, so consumer and producer surplus in the transport fuel market will decrease. In the third essay, the Regression - Seasonal Autoregressive Integrated Moving Average (REGSARIMA) model is employed to predict the impact of air temperature on daily peak load demand in Houston. Compared with ARIMA and Seasonal Model, a REGARIMA model provides the more accurate prediction for daily peak load demand for the short term. The estimated results reveal air temperature in the Houston areas causes an increase in electricity consumption for cooling but to save that for heating. Since the daily peak electricity consumption is significantly affected by hot air temperature, this study makes a conclusion that it is necessary to establish policies to reduce urban heat island phenomena in Houston.

La thèse répond à deux problématiques, d'une part la quantification des effacements de consommation d'électricité par technologies gaz dans l'habitat et d'autre part de l'intégration de leurs valorisations dans une perspective de planification des infrastructures. Ces travaux se justifient dans un contexte d'augmentation de la pointe électrique, à l'origine d'une hausse du risque de défaillance du système, et de la baisse des consommations de gaz naturel conduisant à une sous utilisation du réseau de distribution. Pourtant, alors que la demande en gaz naturel croît du fait de l'installation de centrales à cycle combiné sur le réseau de transport, l'interaction des réseaux de distribution gaz/électricité n'est pas exploitée.Ce manuscrit envisage l'intégration des technologies gaz comme moyen de Maîtrise de la Demande en Électricité dans le processus de planification des réseaux. Ainsi les effacements de consommations d'électricité lors des périodes dimensionnantes par des micro-cogénérateurs ou des chaudières hybrides sont des solutions alternatives aux solutions de renforcement de réseaux.Pour quantifier le gisement d'effacement, nous nous intéressons à l'impact marginal des systèmes sur la demande en termes de modification de la quantité d'Énergie Non Distribuée potentielle. Les estimations des impacts de systèmes de chauffage sur la demande sont donc des prérequis à cette approche. Nous modélisons les courbes de charge régionales par une approche Bottom-Up permettant de déterminer les profils de demande marginale de chauffage en fonction des systèmes. La mise en application de cette méthode est à fiabiliser par des études socio-technico-économiques permettant de réduire les incertitudes sur les déterminants des besoins de chauffage. Une calibration en puissance des profils générés a été proposée mais n'a pu être réalisée. En revanche, nous apportons une contribution à l'analyse des courbes de charge agrégées en montrant que le modèle d'estimation actuellement utilisé par le gestionnaire de réseau s'apparente à un modèle simplifié de bâtiment / This PhD thesis addresses two issues: Firstly, the assessment of Demand Side Management (DSM) opportunity of gas and electricity technologies in dwellings, and secondly, the integration of their valuations in infrastructure planning schemes.This work originaites from a context of the growth of electricity peaks (which increased risk of system failure) and the natural gas consumption decrease which leads to an under-utilization of the gas distribution network.This manuscript focuses on the integration of gas technologies as DSM solution to contribute to the planning of electricity grid. Indeed, relieving the electricity consumption during constrained periods by diffusing micro-cogeneration or hybrid boiler, is an actual alternative to network reinforcement solutions. To quantify the load shedding capacity, we are interested in the marginal impact of demand systems on the amount of Energy Not Supplied potential. Estimating systems' impacts on heating demand is a prerequisite to this approach. So we model the regional heating load curves by a Bottom-Up approach to simulate marginal demand profiles depending on heating systems. The implementation of this method requires socio-technico-economic studies to reduce uncertainty of the determinants of heating needs. A load calibration methodology has been proposed but has not been performed. However, we make a contribution to the analysis of aggregated load curves emphasizing that the load model currently used by network operator similar to a simplified building model.