The Swedish electricity market has seen a surge in demand over the past fifty years. Since the deregulation in 1996, both the demand and supply side of the market has evolved into a competitive space for market actors. This has resulted in unique conditions, characterizing the nordic electricity market as being very much dependent on demand predictability. Demand flexibility has been investigated frequently, but as certain sector developments constantly affect electricity consumption, such as price volatility and green technology development, the price- and temperature elasticities should be revised continuously. Hourly-price contracts specifically, are on the rise in price area SE1, with a rise from 6 percent to 8 percent during the middle of 2023, meaning consumption patterns are of interest for the future as the trend continues. This has, to the authors’ knowledge, not been investigated within the municipality of Luleå, which is the basis for this master’s thesis. Two methodologies were adopted to investigate the elasticities, (1) an ordinary least squares (OLS) regression model and (2) two weighted least squares (WLS) regression models. The results indicate that the price elasticity of demand in Luleå is still very inelastic, with temperature bearing the main explanatory power for household electricity consumption patterns.

This dissertation presents two essays that explore the antecedents of consumer decision-making when choices or behaviors have significant environmental consequences. The first essay involves theoretical development and experimental testing of a conceptual model describing the process by which a car-buyer evaluates the choice between an electric vehicle (EV) and a gasoline vehicle, while the second essay consists of empirical analysis of a large panel dataset of household-level 15-minute interval electricity consumption data to identify the drivers of different behavioral response patterns to electric utility requests for energy conservation on hot summer afternoons. The first essay is motivated by the observation that increased consumer adoption of battery-powered EVs is important for commercial and environmental reasons, but EV adoption is currently inhibited by both an up-front price disadvantage and the inconveniences associated with battery recharging. The research presented in the first essay leverages the Theory of Reasoned Action as well as the literature on identity signaling to develop a model on how consumers with interests in the environmental and/or technological implications of EV ownership evaluate the potential purchase of an EV versus a conventional automobile. The model generates ten pairs of hypotheses that are tested via estimation of a structural equation model using data from three online experiments. Bayesian pooling of the three sets of estimated path analysis coefficients finds considerable support for the conceptual model. These pooled results show that EV ownership signals the owner's concern about both environmental protection and technology advancement, but the effect of the environmental signal on EV purchase likelihood is positive whereas the effect of the technology signal on EV purchase likelihood is negative. Moreover, in addition to lowering EV purchase likelihood via a direct effect, the perceived inconveniences associated with EV ownership (e.g., needs for battery charging) offset the negative effect of technology signaling on EV purchase likelihood, while the corresponding interaction of inconvenience with environmental signaling value was found to be not significant. Meanwhile, a larger EV price premium had a direct negative effect on EV purchase likelihood but did not moderate the effects of either technology signaling value or environmental signaling value on EV purchase likelihood. Among other findings, specific knowledge about how EVs affect technological advancement has a direct positive influence on EV purchase likelihood. However, all downstream effects of specific knowledge about EVs effects on environmental protection are mediated by perceptions of EV effectiveness in benefitting the environment. Meanwhile, the second essay investigates consumer behavior concerning household electricity consumption. Utilities use demand response (DR) programs to induce customers to reduce electricity consumption during selected hot summer afternoons when power generation supplies may be challenged to satisfy regional demand levels. The research presented in the second essay leverages panel data on electricity consumption from households in a community where an experimental pro-social DR program was conducted to explore drivers of household responses to utility requests to voluntarily reduce electricity consumption. Analysis of the panel data shows that, on average, households with solar rooftops respond differently to utility DR notifications than non-solar households: solar households reduce electricity consumption as requested by the utility, whereas non-solar households receiving the same request actually increase electricity consumption. However, although solar households respond favorably to DR notification, they also consume significantly more electricity than non-solar households during most hours. These empirical results – greater responsiveness to DR notifications, but otherwise higher levels of electricity consumption – beg reconciliation and explanation. An experimental research study is proposed for a future examination of alternative psychological explanations for the observed differences in behavioral responses between solar and non-solar households. / Doctor of Philosophy / This dissertation presents two essays that explore how and why individuals make decisions with environmental consequences. The first essay investigates how and why individuals choose to purchase a higher-cost big-ticket durable good (i.e., an electric vehicle) that results in substantially lower air emissions over the lifetime of the product, while the second essay investigates how and why individuals make environmentally-friendly behavioral decisions (i.e., conserving electricity on a hot summer afternoon) when the stakes are modest and transitory. The first essay discusses the findings from three experiments in which on-line survey respondents were asked to imagine being in the market to buy a new car and then indicate how likely they would buy an electric vehicle (EV) rather than an otherwise identical gasoline automobile. Before indicating EV purchase likelihood, participants were informed to assume different levels of price premium and inconvenience (e.g., associated with battery recharging) resulting from EV ownership. Participants were also asked a series of questions to measure their attitudes about environmental protection and technology advancement, as well as the ability of EVs to help both of those dimensions of social progress. Of particular interest, participants were asked how much driving an EV sends a public signal of the owner's commitment to environmental and technology improvement. Among other findings, statistical analysis of the data collected from these experiments indicates that EV ownership sends a strong signal of the owner's commitment to both environmental protection and technology advancement. However, the environmental signal of EV ownership positively influences EV purchase likelihood, whereas the technology signal of EV ownership negatively influences EV purchase likelihood. Of further interest, this negative relationship between technology signaling value and EV purchase likelihood is offset by the perceived inconveniences associated with EV ownership (i.e., battery charging), such that the negative effect of technology signaling on EV purchase likelihood can be overcome if the prospective EV buyer also believes EV ownership is highly burdensome. The second essay presents the findings from analysis of 15-minute interval electricity consumption data during the summer of 2021 from 307 households in a master-planned community that was the site of an experimental utility demand response (DR) program. In this community, at 2 pm on seven particularly hot weekday afternoons that summer, the local electric utility issued a DR text message to a randomly-selected subset of households, asking them to conserve energy between 4 and 8 pm in order to help alleviate tight supplies of power generation. Any difference in average electricity consumption patterns between households that were asked to reduce electricity consumption (i.e., "treatment" households) and those that were not asked (i.e., "control" households) can be considered a "DR effect": a change in behavior induced by the utility's request to reduce electricity consumption. While initial analysis of the electricity consumption data revealed no DR effects, subsequent identification and segmentation of solar households (I.e., households with rooftop solar electricity production systems) from non-solar households enabled discovery of statistically-significant DR effects for both solar and non-solar households. Of particular interest, while solar households responded to the utility's DR text message in the intended manner by reducing electricity consumption, non-solar households responded by increasing electricity consumption instead. Experimental research is planned to investigate why solar households and non-solar households respond so differently to the same message from the utility.

Conserving energy and reducing electricity consumption have become critical issues. Measuring when different appliances use electricity can be an effective way to save money on electricity bills. By providing information about hourly electricity prices and peak consumption times, people can subconsciously adopt energy saving habits to reduce the electricity consumption in their households. The challenge, however, lies in ensuring that all household members are informed and made aware of the right times to use electricity. This study will research how nudging can be utilized to reduce electricity consumption in households and what information the people in the households need to be able to make informed decisions to reduce their electricity consumption.

En gårdsbiogasanläggning har flera stora interna energiförbrukare. Tidigare forskning har visat att energibehovet för endast omblandning i en biogasanläggning kan uppgå till 1 % av biogasens energiinnehåll vilket är oerhört högt. En omrörningsstudie har genomförts där data som specifik omrörningseffekt (W/m3) och energiförbrukning (kWh/dag) har inhämtats ifrån verkliga fall och sedan jämförts emot varandra. Samband mellan större rötkammarvolymer och högre energiförbrukning per dag för omrörning kunde finnas. Samtidigt som kopplingar mellan mindre anläggningar och högre specifik omrörningseffekt (W/m3) också kunde finnas. En rötkammares värmebehov ligger teoretiskt på cirka 33 % för termofila processer och 20 % för mesofila processer av den teoretiska energiproduktionen för en anläggning med en rötkammare på 750 m3. En planerad anläggning med rötkammarvolym på cirka 3000 m3 och specifik omrörningseffekt på 22 W/m3, borde ha ett högre elbehov per år än 100 000 kWh/år. / A farm biogas plant has several large internal energy consumers. Previous research has shown that the energy for only mixing in a biogas plant may reach 1% of the biogas energy content which is extremely high. A mixing study was performed where data specific stirring power (W/m3) and energy consumption (kWh/day) has been obtained from real cases and then compared against each other. Correlation between larger reactor volumes and higher energy consumption per day for agitation could be. While connections between smaller plants and higher specific stirring power (W/m3) could also be. A digester heating demand is theoretically at around 33% for thermophilic processes and 20% for mesophilic processes of the theoretical energy output for a plant with a digester of 750 m3. A planned facility with reactor volume of approximately 3000 m3 and specific stirring power at 22 W/m3, should have a higher electricity demand per year than 100 000 kWh/year.

This paper examines the Environmental Kuznets Curve (EKC) hypothesis and tests for causality using Dynamic Ordinary Least Squares (DOLS) and the Vector Error Correction Model (VECM). There is evidence of long-run relationships in the three models under consideration. The Dynamic Ordinary Least Squares (DOLS) finds no evidence to support the existence of an environmental Kuznets curve (EKC) hypothesis for Zambia in the long-run. The evidence from the long-run suggests an opposite of the Environmental Kuznets Curve (EKC), in that the results indicate a U-shaped curve relationship between income and carbon emission. The conclusion on causality based on the VECM is that there is evidence of neutrality hypothesis between either total electricity and income or between industrial electricity and income in the short-run Additionally, there is evidence of conservation hypothesis in the context of residential and agricultural electricity consumption.

Este trabalho tem como objetivo analisar a composição setorial do consumo de energia elétrica incorporado às exportações brasileiras em cada área de concessão de distribuição. Para tanto, foi elaborada uma matriz de insumo-produto inter-regional e coeficientes setoriais de consumo de energia elétrica por área de concessão. Foi constatado que o consumo de energia elétrica incorporado às exportações é maior nos setores industriais e que a produção destinada à exportação é mais eletrointensiva que a produção destinada ao consumo interno em 37 das 58 áreas de concessão analisadas. / The objective of this dissertation is to analyze the sectorial composition of the electric energy consumption used in the production of exported goods and services in each electricity distribution concession area in Brazil. To accomplish this goal, an input-output matrix was elaborated, along with sectorial coefficients of electric energy intensity. The results indicate that the sector which consumes the most energy in their exports is the industrial sector. There was also an indication that Brazilian exports consume more electric energy throughout their productive structure than the production absorbed internally in 37 of the 58 electricity distribution concession areas.

<p>Within the next years, the amount of consumption data will increase rapidly as old meters will be exchanged in favor of meters with hourly remote reading. A new refined supervision system must be developed. The main objective of this thesis is to investigate mathematical methods that can be used to find incorrect hourly measurements in district heat and electricity consumption, for each consumer. </p><p>A simulation model and a statistical model have been derived. The model parameters in the simulation model are estimated by using historical data of consumption and outdoor temperature. By using the outdoor temperature as input, the consumption can be simulated and compared to the actual consumption. Faults are detected by using a residual with a sliding window. The second model uses the fact that consumers with similar consumption patterns can be grouped into a collective. By studying the correlation between the consumers, incorrect measurements can be found. </p><p>The performed simulations show that the simulation model is best suited for consumers whose consumption is mostly affected by the outdoor temperature. These consumers are district heat consumers and electricity consumers that use electricity for space heating. The fault detection performance of the statistical model is highly dependent on finding a collective that is well correlated. If these collectives can be found, the model can be used on district heat consumers as well as electricity consumers.</p>

The electricity system is particularly susceptible to climate change due to the close interconnectedness between not only electricity production and consumption to climate, but also the interdependence of many European countries in terms of electricity imports and exports. This study provides a country based relative analysis of a number of selected European countries’ electricity system vulnerability to climate change. Taking into account a number of quantitative influencing factors, the vulnerability of each country is examined both for the current system and using some projected data. Ultimately the result of the analysis is a relative ranked vulnerability index based on a number of qualitative indicators. Overall, countries that either cannot currently meet their own electricity consumption demand with inland production (Luxembourg), or countries that experience and will experience the warmest national mean temperatures, and are expected to see increases in their summer electricity consumption are found to be the most vulnerable for example Greece and Italy. Countries such as the Czech Republic, France and Norway that consistently export surplus electricity and will experience decreases in winter electricity consumption peaks were found to be the least vulnerable to climate change. The inclusion of some qualitative factors however may subject their future vulnerability to increase. The findings of this study enable countries to identify the main factors that increase their electricity system vulnerability and proceed with adaptation measures that are the most eective in decreasing vulnerability.

Within the next years, the amount of consumption data will increase rapidly as old meters will be exchanged in favor of meters with hourly remote reading. A new refined supervision system must be developed. The main objective of this thesis is to investigate mathematical methods that can be used to find incorrect hourly measurements in district heat and electricity consumption, for each consumer. A simulation model and a statistical model have been derived. The model parameters in the simulation model are estimated by using historical data of consumption and outdoor temperature. By using the outdoor temperature as input, the consumption can be simulated and compared to the actual consumption. Faults are detected by using a residual with a sliding window. The second model uses the fact that consumers with similar consumption patterns can be grouped into a collective. By studying the correlation between the consumers, incorrect measurements can be found. The performed simulations show that the simulation model is best suited for consumers whose consumption is mostly affected by the outdoor temperature. These consumers are district heat consumers and electricity consumers that use electricity for space heating. The fault detection performance of the statistical model is highly dependent on finding a collective that is well correlated. If these collectives can be found, the model can be used on district heat consumers as well as electricity consumers.

A survey of residential solar photovoltaic (PV) adopters in Texas was administered and the results are presented and discussed. A 40% response rate was achieved and 365 complete responses were received. In addition to demographics, the survey uncovered aspects related to the decision-making process, information search, financial attractiveness of PV, and post-installation experience. Peer-effects did not have a large influence on the adoption of residential PV in Texas, but the potential for increasing the number of communication/information channels to increase the adoption rate of PV exists. Adopters experienced little uncertainty at the time of PV installation because sufficient dependable information was available during the search process. Overall, they are satisfied with PV. Contextual factors, such as income and the ability to purchase a PV system rather than lease one, influence behavior. Those who decreased electricity consumption post-adoption were more motivated to adopt by environmental concern and a general interest in energy than those who increased electricity consumption post-adoption. Those who experienced behavior changes also experienced an increase in awareness of electricity use post-adoption, while those who did not experience a behavior change reported no change in awareness post-adoption. Change in awareness of electricity use is less dependent on the attitudinal and contextual factors, such as environmental concern, motivation for adoption, age, and income, that influence consumption change. The potential for further analysis of the survey results is great and will likely yield additional conclusions about the consequences of the adoption of PV. Coupling the survey results with historical electricity bill data will yield stronger conclusions about behavior change. Surveying geographical areas outside of Texas is recommended. / text