This paper examines the relationship between energy and economic growth in the case of Sweden.  It analyzes the role energy plays in the level of economic activity. The prevailing economic theories focus more on other factors as important for the economic growth. The included statistical data shows that the total energy use in Sweden has declined in the last couple of years. This is mainly as a result of the shift in energy use to higher quality fuels, electricity, optimized production process and machinery, and increased use of renewable energy sources. This paper investigates the connection between total energy use and levels of economic activity in Sweden. Furthermore, it discusses Sweden’s energy policy activities and their economic and environmental implications. Instead of looking at the entire economy, as some earlier papers, the focus is placed on the industrial and service sectors. This gives the possibility to better analyze the implemented energy policies, showing their effectiveness at these sectors. Time series analysis is employed following a four step procedure. First it is the Augmented Dickey-Fuller test performed, followed by the Johansen test and the Vector Error Correction Model (VECM). The results from VECM are interpreted with the help of the Wald test. The results from this four step procedure showed univariate cointegration between Industry`s output and energy consumption and bivariate cointegration between Service`s output and energy consumption. The paper further shows that there is a relation between the types of energy used in the economic sectors and the sectors` productivity levels. This paper also aims to demonstrate the environmental and economic effects from such relation.

Thesis (doctoral)--Rijksuniversiteit te Groningen, 1995. / Summary in Dutch. Material type: Dissertations. Includes bibliographical references (p. 283-291).

We analyze empirically the role played by energy inflation as a determinant of downward corrections in house prices. Using a dataset for 18 OECD economies spanning the last four decades, we identify periods of downward house price adjustment and estimate conditional logit models to measure the effect of energy inflation on the probability of these house price corrections after controlling for other relevant macroeconomic variables. Our results give strong evidence that increases in energy price inflation raise the probability of such corrective periods taking place. This phenomenon could be explained by various channels: through the adverse effects of energy prices on economic activity and income reducing the demand for housing; through the particular impact on construction and operation costs and their effects on the supply and demand of housing; through the reaction of monetary policy on inflation withdrawing liquidity and further reducing demand; through improving attractiveness of commodity versus housing investment on asset markets; or through a lagging impact of common factors on both variables, such as economic growth. Our results contribute to the understanding of the pass-through of oil price shocks to financial markets and imply that energy price inflation should serve as a leading indicator for the analysis of macro-financial risks. (authors' abstract)

Orientador: Alberto Luiz Francato / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Civil, Arquitetura e Urbanismo / Made available in DSpace on 2018-08-11T20:05:42Z (GMT). No. of bitstreams: 1 Souza_Heloisade_M.pdf: 1716129 bytes, checksum: a0c156b87935ffaf15054c1202f14091 (MD5) Previous issue date: 2008 / Resumo: A utilização da energia elétrica se faz presente na maioria das atividades humanas. Apesar de invisível, seus efeitos são fundamentais para garantir o pleno funcionamento dos eletrodomésticos, dos sistemas de iluminação, de comunicação, de segurança, etc. A forte dependência deste insumo fez com que o homem procurasse formas de garantir a produção de energia tanto em quantidade quanto em qualidade compatíveis com as tecnologias envolvidas nos equipamentos que fazem uso da energia. O forte incremento na demanda e a escassez de recursos nos meios para obtenção de energia trouxeram incrementos nos preços de energia elétrica, muitas vezes viabilizando a geração de energia por fontes alternativas. Neste sentido, estudar a matriz de custos do setor de geração de energia elétrica e tentar modelar métricas adequadas para fins de orçamento é algo de extremo valor seja para análise de novos investimentos ou mesmo para debater com o poder público as tarifas vigentes aplicadas no setor elétrico brasileiro. / Abstract: The use of electric energy is present in most of human activities. Although invisible, its effects are essential to ensure the full operation of household-electric ones, lighting systems, communication, security, etc. The strong dependence of this input has made the man look for ways to ensure energy production, both quantitatively and qualitatively, compatible with the technologies involved in equipment that make use of energy. The strong increase in demand and shortage of resources in the means of obtaining energy has brought increases in the prices of energy, often allowing the generation of energy by alternative sources. In this sense, consider the matrix of costs from electricity generation sector and try to shape metrics appropriate for the budget is extremely important for analysis of new investments or even to discuss the tariffs applied to the Brazilian electricity sector with the public power. / Mestrado / Recursos Hidricos, Energeticos e Ambientais / Mestre em Engenharia Civil

[pt] Os preços de energia elétrica, insumo básico para todo o Modelo Setorial, constituem uma das maiores incertezas do setor. Estas incertezas abrangem todos os elementos formadores de preços: a oferta, a demanda e as regras de mercado, tornando muitas vezes difícil ao agente a avaliação concreta e precisa do processo da formação de preços e do impacto que a variação de um dos elementos do processo produz no resultado final. O objetivo deste trabalho é apresentar a estrutura de formação de preços no mercado energético brasileiro de forma sistematizada, avaliando a composição das variáveis que afetam esta estrutura: a demanda por consumo, a expansão do sistema e as disponibilidades energéticas. O mercado é modelado em todos os seus detalhes físicos, e o cálculo é realizado a partir de todo o arcabouço regulatório, incluindo a reprodução do modelo de operação ótima responsável pelos preços de energia. Descreve-se inicialmente um modelo de previsão de demanda por subsistema, utilizando-se técnicas de Teoria de Análise Funcional. Focaliza-se em seguida o suprimento futuro de energia no país a partir da expansão da oferta. Finalmente, utiliza-se uma simulação da operação ótima do sistema a partir da reprodução dos resultados do modelo utilizado no setor - o Newave - a partir de uma implementação própria desenvolvida especialmente no escopo deste trabalho. De posse dos possíveis cenários futuros, pode-se mensurar o impacto que a variação de cada elemento formador (demanda, expansão e afluências) tem sobre os custos de energia. É possível observar que as incertezas nestas variáveis podem gerar grandes impactos nos custos marginais e, conseqüentemente, nos custos futuros de energia elétrica. / [en] Energy Prices, essential input for the Sectorial Model, consist on the biggest uncertainties of the Electric Sector. These uncertainties enclose all price elements: the supply, the demand and the market rules, making sometimes difficult for the agents to evaluate the price process and the impact that the variation of each process element can produce on the result. The objective is to present Brazilian price process in a structuralized way, evaluating the variables composition that affects this structure: the demand, the electric system expansion and the energy supply availability. The market is modeled in all its physical details, and the calculation is done into the regulatory environment, including a reproduction of the optimal operation model responsible for energy prices. First, a demand forecast model is described, based on Functional Analysis Theory. Then, the focus is on the energy future supply, analyzing the supply expansion in Brazil. Finally, an optimal operation system is simulated, reproducing the sector model (Newave) results from an implementation developed in this work. From these possible future settings, each element (demand, expansion and energy supply availability) variation impact on energy prices can be measured. The simulations show that uncertainties about these variables can have big impacts on marginal costs and, consequently, on the energy future prices.

[pt] O setor elétrico está passando por uma série de transformações para acomodar a transição energética. Dentre essas mudanças, destaca-se a liberalização do mercado, com maior autonomia dos consumidores e possibilidade de migração entre os mercados regulado e livre. Este contexto adiciona complexidade à tarefa das distribuidoras de energia de projetar a sua demanda futura e realizar as contratações de energia para o mercado cativo, uma vez que devem servir consumidores cada vez mais dinâmicos e, consequentemente, um portfólio mais instável. Ainda, a migração de consumidores para o mercado livre tem o potencial de gerar sobrecontratação das distribuidoras, incorrendo em sobrecustos a elas e aos consumidores cativos remanescentes. Este trabalho propõe um modelo de otimização para determinar os preços de equilíbrio para os mercados livre e regulado em um mercado liberalizado. Dada a interdependência das decisões dos agentes (distribuidora, consumidores cativos, consumidores livres e geradores), as suas interações serão modeladas como Múltiplos Problemas de Otimização com Restrições de Equilíbrio (MOPEC), em que os problemas de maximização de receitas de cada agente são combinados em um único problema de otimização, e conectados por restrições de equilíbrio. Demonstra-se ainda que o MOPEC proposto pode ser representado por um problema de programação linear. Os agentes são modelados avessos a risco, sendo suas funções objetivos individuais representadas como a combinação convexa do valor esperado e do Conditional Value at Risk (CVaR) de suas receitas. Entre os resultados do modelo, destacam-se os níveis ótimos de migração dos consumidores, decisões de contratação, e preços de contratos no mercado livre e tarifas no mercado regulado. São também apresentadas e modeladas diferentes propostas regulatórias para o tratamento dos passivos associados à sobrecontratação das distribuidoras. O modelo é aplicado em um estudo de caso simplificado e outro com dados realistas do sistema elétrico brasileiro. Finalmente, acopla-se o modelo em um processo iterativo que determina a expansão ótima do sistema, de forma que as decisões de contratação e preços resultantes geram sinais econômicos aos investimentos em expansão da capacidade de geração. Essa metodologia é aplicada em um estudo de caso, com exercícios de expansão da geração do sistema elétrico brasileiro. / [en] The power sector is undergoing a series of transformations to accommodate the energy transition. Among these changes, the market liberalization stands out, with greater consumer autonomy and the possibility of migration between the regulated and free markets. This context adds complexity to the task of energy distribution companies to project their future demand and to contract energy to serve the captive market, as these contracts should serve increasingly dynamic consumers and, consequently, a more unstable portfolio. Moreover, the migration of consumers to the free market may generate over-contracting of distributors, incurring extra costs for it and for the remaining captive consumers. This work proposes an optimization model to determine the equilibrium prices in the free and regulated environments in a liberalized market. Given the interdependence of agents decisions (distributors, captive consumers, free consumers and generators), their interactions are modeled as Multiple Optimization Problems with Equilibrium Constraints (MOPEC), in which each agent s revenue maximization problems are combined into a single optimization problem, and connected by equilibrium constraints. It is demonstrated that the proposed MOPEC can be represented by a linear programming problem. The agents are modeled as risk-averse, with their individual objective functions represented as the convex combination of the expected value and the Conditional Value at Risk (CVaR) of their revenues. Among the results of the model, we highlight the optimal levels of consumer migration, contracting decisions, and contract prices in the free market and tariffs in the regulated market, for a given system configuration. In addition, different regulatory proposals are presented and modeled for the treatment of liabilities associated with the over-contracting of distributors. The model is applied in a simplified case study and another one with realistic data of the Brazilian power system. Finally, the model is integrated in an iterative process that determines the optimal system expansion, so that the resulting contracting decisions and prices generate economic signals for investments in generation capacity expansion. This methodology is applied in a case study comprising generation expansion exercises of the Brazilian power system.

No description available.

The growth of electricity markets around the world has introduced new challenges in which one of the challenges is the uncertainty that has become a structural element in this new environment. Market players have to deal with it to guarantee the appropriate power system planning and operation as well as its own economical liquidity. Under an open access environment in a deregulated power system, transmission management holds a vital role in supporting transactions between suppliers and customers. Nevertheless, a transmission network has some constraints that should be addressed in order to ensure sufficient control to maintain the security level of a power system while maximizing market efficiency. The most obvious drawback of transmission constraints is a congestion problem that becomes an obstacle of perfect competition among the market participants since it can influence spot market pricing. The system becomes congested when the supplier and customer agree to produce and consume a particular amount of electric power, but this can cause the transmission network to exceed its thermal limits. Congestion can cause the market players to exercise market power that can result in price volatility beyond the marginal costs. Thus, it is important to manage congestion efficiently in the design of a power market. One mechanism that has direct correlation with transmission management is market clearing price (MCP). Under an open access environment, energy prices throughout the network will be different and measured based on transmission constraint and network losses. When network losses are ignored and there is no congestion on the transmission lines, the power price will be the same at all nodes. This is known as uniform marginal pricing (UMP). However, as the power flow violates transmission constraints, redispatching generating units is required and this will cause the price at every node to vary. This phenomenon is defined as locational marginal pricing (LMP). Therefore, the market clearing price has a strong relationship with transmission management, which is needed to be assessed in order to obtain an efficient and transparent price but satisfying all market participants. This project investigates an alternative solution to the dispatch mechanism, and then formulates a new Locational Marginal Price scheme using optimization technique that may well control congestion as the main issue. The model will vary and be improved, to be distilled into energy price, congestion revenue, cost of losses, as well as transmission usage tariff. The objective of the project is to support developing standard market design (SMD) in managing transmission systems which promotes economic efficiency, lowers delivered energy costs, maintains power system reliability and mitigates exercising market power.

The growth of electricity markets around the world has introduced new challenges in which one of the challenges is the uncertainty that has become a structural element in this new environment. Market players have to deal with it to guarantee the appropriate power system planning and operation as well as its own economical liquidity. Under an open access environment in a deregulated power system, transmission management holds a vital role in supporting transactions between suppliers and customers. Nevertheless, a transmission network has some constraints that should be addressed in order to ensure sufficient control to maintain the security level of a power system while maximizing market efficiency. The most obvious drawback of transmission constraints is a congestion problem that becomes an obstacle of perfect competition among the market participants since it can influence spot market pricing. The system becomes congested when the supplier and customer agree to produce and consume a particular amount of electric power, but this can cause the transmission network to exceed its thermal limits. Congestion can cause the market players to exercise market power that can result in price volatility beyond the marginal costs. Thus, it is important to manage congestion efficiently in the design of a power market. One mechanism that has direct correlation with transmission management is market clearing price (MCP). Under an open access environment, energy prices throughout the network will be different and measured based on transmission constraint and network losses. When network losses are ignored and there is no congestion on the transmission lines, the power price will be the same at all nodes. This is known as uniform marginal pricing (UMP). However, as the power flow violates transmission constraints, redispatching generating units is required and this will cause the price at every node to vary. This phenomenon is defined as locational marginal pricing (LMP). Therefore, the market clearing price has a strong relationship with transmission management, which is needed to be assessed in order to obtain an efficient and transparent price but satisfying all market participants. This project investigates an alternative solution to the dispatch mechanism, and then formulates a new Locational Marginal Price scheme using optimization technique that may well control congestion as the main issue. The model will vary and be improved, to be distilled into energy price, congestion revenue, cost of losses, as well as transmission usage tariff. The objective of the project is to support developing standard market design (SMD) in managing transmission systems which promotes economic efficiency, lowers delivered energy costs, maintains power system reliability and mitigates exercising market power.

The growth of electricity markets around the world has introduced new challenges in which one of the challenges is the uncertainty that has become a structural element in this new environment. Market players have to deal with it to guarantee the appropriate power system planning and operation as well as its own economical liquidity. Under an open access environment in a deregulated power system, transmission management holds a vital role in supporting transactions between suppliers and customers. Nevertheless, a transmission network has some constraints that should be addressed in order to ensure sufficient control to maintain the security level of a power system while maximizing market efficiency. The most obvious drawback of transmission constraints is a congestion problem that becomes an obstacle of perfect competition among the market participants since it can influence spot market pricing. The system becomes congested when the supplier and customer agree to produce and consume a particular amount of electric power, but this can cause the transmission network to exceed its thermal limits. Congestion can cause the market players to exercise market power that can result in price volatility beyond the marginal costs. Thus, it is important to manage congestion efficiently in the design of a power market. One mechanism that has direct correlation with transmission management is market clearing price (MCP). Under an open access environment, energy prices throughout the network will be different and measured based on transmission constraint and network losses. When network losses are ignored and there is no congestion on the transmission lines, the power price will be the same at all nodes. This is known as uniform marginal pricing (UMP). However, as the power flow violates transmission constraints, redispatching generating units is required and this will cause the price at every node to vary. This phenomenon is defined as locational marginal pricing (LMP). Therefore, the market clearing price has a strong relationship with transmission management, which is needed to be assessed in order to obtain an efficient and transparent price but satisfying all market participants. This project investigates an alternative solution to the dispatch mechanism, and then formulates a new Locational Marginal Price scheme using optimization technique that may well control congestion as the main issue. The model will vary and be improved, to be distilled into energy price, congestion revenue, cost of losses, as well as transmission usage tariff. The objective of the project is to support developing standard market design (SMD) in managing transmission systems which promotes economic efficiency, lowers delivered energy costs, maintains power system reliability and mitigates exercising market power.