As the demand for electricity increases throughout the globe while we want to reduce the use of fossil fuels, the need for renewable energy sources is bigger than ever. In countries where solar power makes up a large part of the total energy production, the overall electricity spot price level has become lower. This thesis investigates the underlying mechanism that drives the energy market, and in specific, how the solar power impacts the electricity spot price. We present results from studies made in other markets, and introduce a Regime Switching model for explaining the impact in Sweden. We show that an increase of photovoltaics power has a price lowering effect on the daily price pattern in price area SE3 and SE4.

Wind power has been growing rapidly in Sweden over the past decade as the country focuses on 100% renewable energy by 2040. The thesis seeks to investigate if increased wind power has had a dampening effect on the hourly day-ahead spot prices in Sweden’s bidding area SE3. An empirical approach is undertaken to estimate the impact that wind power has had onprices. Hourly spot prices for bidding area SE3 for the years 2016-2019 are analysed using a multivariate regression method. Other important variables like hydropower and nuclear production which are very significant in the Swedish electricity market are controlled for in the study. The results confirm that in the period 2016-2019, bidding SE3 experienced a merit order effect on price caused by increased wind power. The study shows that a 1% increase inwind power production is estimated to have decreased SE3 hourly spot prices by between 0.0268% and 0.059% between 2016 and 2019.

Sweden’s energy system is facing major changes in the near future in order to reducecarbon emissions and to switch to sustainable energy sources. PV systems havebecome a sensible alternative for homeowners that want to be a part of this changeand at the same time reduce the cost of their electricity bill. To further improve theutilization of their PV system and to handle the intermittent nature of solar power,battery storages have become an interesting system complement. This thesisinvestigates how batteries can provide smart services; load shift and peak price energyutilization to a household. This is done by developing an optimized battery algorithmmodel that can provide these smart services which is compared to a simple batteryalgorithm. The results show that the developed battery optimization model works asintended. It performs both load shift and peak price energy utilization. The economicanalysis shows that the most profitable PV system and battery configuration is a 20kW PV system with a 5 kWh battery. The system has an internal rate of return, IRR,of 2.3% which does not reach Vattenfall’s weighted average cost of capital, WACC, at7%. The results also show that the battery cost is an important factors for a system'sprofitability. A larger battery system is more expensive and the increased yield doesnot cover the increased cost. Further research is needed to implement the optimizedbattery as a functional application since the model has access to a perfect forecast andthus a method for forecasting PV production and load profile of the household arecrucial to get similar results.

Uma condição fundamental para que um mercado seja competitivo é que existam muitos compradores e, em especial, compradores que possam responder aos preços. Os consumidores reagem para se ajustarem aos preços de acordo com sua disposição em consumir um determinado bem. À medida que o preço se eleva, os consumidores tendem a reduzir a quantidade demandada e, quando o preço cai, os consumidores tendem a aumentar o volume demandado. A sensibilidade dos consumidores às mudanças de preços é caracterizada pela elasticidade-preço da demanda. Contudo, nos desenhos de mercados de energia elétrica, é comum a concentração de atenção no lado do suprimento, assumindo-se, implicitamente, que toda a demanda é inelástica. O presente trabalho contempla uma análise dos mecanismos de formação de preços de curto prazo adotados em mercados de energia elétrica (i.e. formação baseada em custos e formação baseada em ofertas) e, a partir desse contexto, avalia os benefícios da introdução de mecanismos de incentivo à participação da demanda na determinação dos preços do mercado de curto prazo como forma de elevar sua eficiência econômica. / Given an economic environmental, a fundamental condition for a market be suitable to competition is that must has a plenty of buyers and, in special, those who can react to price signals. The consumers reaction aims at to adjust their energy requirements to the prices according to their disposal to access a certain product or service. As the price increases, the consumers tend to reduce the demanded volume and, on the other hand, when the prices decreases, the consumers increase the demanded volume. The consumers´ reaction to the price changes is characterized by the price elasticity of demand. However, in the electric energy market design, it is common to pay attention to the supply side, taking into account, implicitly, that all demand is inelastic. This work performs an analysis of mechanisms of spot price formation adopted by electric energy markets (i.e. cost based and bid based prices) and, from this context, evaluates the benefits of incentive mechanisms to the demand participation in determining short-term market price as an option to improve the economic efficiency.

[pt] Nesta tese, apresentam-se estratégias de modelagem envolvendo modelos estruturais para a previsão do preço spot de energia elétrica do subsistema do Sudeste-Brasil. Foi utilizada a modelagem proposta por Harvey (1989), que extrai componentes não observáveis da série. Foram elaborados três modelos. No primeiro, utilizou-se somente o histórico da série. No segundo, inseriu-se uma variável de intervenção para o racionamento de energia ocorrido no Brasil no período de junho de 2001 a fevereiro de 2002. Por último, acrescentaram-se duas variáveis explicativas. / [en] In this thesis, modelling strategies are presented involving structural models to forecast the spot price of electric energy of Brazil. It had been used the modelling proposal of Harvey (1989) that extracts non-observable components of the series. Three models had been elaborated. In the first one, was adjusted only with the historical of the series. In the second, an intervention variable for the rationing occurred in Brazil in the period of June of 2001 till February of 2002 was inserted. Finally, in the last one, two explanatory variables were introduced.

Uma condição fundamental para que um mercado seja competitivo é que existam muitos compradores e, em especial, compradores que possam responder aos preços. Os consumidores reagem para se ajustarem aos preços de acordo com sua disposição em consumir um determinado bem. À medida que o preço se eleva, os consumidores tendem a reduzir a quantidade demandada e, quando o preço cai, os consumidores tendem a aumentar o volume demandado. A sensibilidade dos consumidores às mudanças de preços é caracterizada pela elasticidade-preço da demanda. Contudo, nos desenhos de mercados de energia elétrica, é comum a concentração de atenção no lado do suprimento, assumindo-se, implicitamente, que toda a demanda é inelástica. O presente trabalho contempla uma análise dos mecanismos de formação de preços de curto prazo adotados em mercados de energia elétrica (i.e. formação baseada em custos e formação baseada em ofertas) e, a partir desse contexto, avalia os benefícios da introdução de mecanismos de incentivo à participação da demanda na determinação dos preços do mercado de curto prazo como forma de elevar sua eficiência econômica. / Given an economic environmental, a fundamental condition for a market be suitable to competition is that must has a plenty of buyers and, in special, those who can react to price signals. The consumers reaction aims at to adjust their energy requirements to the prices according to their disposal to access a certain product or service. As the price increases, the consumers tend to reduce the demanded volume and, on the other hand, when the prices decreases, the consumers increase the demanded volume. The consumers´ reaction to the price changes is characterized by the price elasticity of demand. However, in the electric energy market design, it is common to pay attention to the supply side, taking into account, implicitly, that all demand is inelastic. This work performs an analysis of mechanisms of spot price formation adopted by electric energy markets (i.e. cost based and bid based prices) and, from this context, evaluates the benefits of incentive mechanisms to the demand participation in determining short-term market price as an option to improve the economic efficiency.

Day-ahead spot electricity markets are the most transparent spot markets where one can find integrated supply and demand curves of the market players for each settlement period. Since it is an indicator for the market players and regulators, in this thesis we model the spot electricity prices. Logarithmic daily average spot electricity prices are modeled as a summation of a deterministic function and multi-factor stochastic process. Randomness in the spot prices is assumed to be governed by three jump processes and a Brownian motion where two of the jump processes are mean reverting. While the Brownian motion captures daily regular price movements, the pure jump process models price shocks which have long term effects and two Ornstein Uhlenbeck type jump processes with different mean reversion speeds capturing the price shocks that affect the price level for relatively shorter time periods. After removing the seasonality which is modeled as a deterministic function from price observations, an iterative threshold function is used to filter the jumps. The threshold function is constructed on volatility estimation generated by a GARCH(1,1) model. Not only the jumps but also the mean reverting returns following the jumps are filtered. Both of the filtered jump processes and residual Brownian components are estimated separately. The model is applied to Austrian, Italian, Spanish and Turkish electricity markets data and it is found that the weekly forecasts, which are generated by the estimated parameters, turn out to be able to capture the characteristics of the observations. After examining the future contracts written on electricity, we also suggest a decision technique which is built on risk premium theory. With the help of this methodology derivative market players can decide on taking whether a long or a short position for a given contract. After testing our technique, we conclude that the decision rule is promising but needs more empirical research.

[pt] Esta dissertação tem como objetivo principal introduzir uma formulação de modelo não-linear multivariado, a qual combina o modelo STVAR (Smooth Transition Vector Autoregressive) com a metodologia CART (Classification and Regression Tree) a fim de utilizá-lo para geração de cenários e de previsões. O modelo resultante é um Modelo Vetorial Auto-Regressivo com Transição Suave Estruturado por Árvores, denominado STVAR-Tree e tem como base o conceito de múltiplos regimes, definidos por árvore binária. A especificação do modelo é feita através do teste LM. Desta forma, o crescimento da árvore é condicionado à existência de não-linearidade nas séries, que aponta a divisão do nó e a variável de transição correspondente. Em cada divisão, são estimados os parâmetros lineares, por Mínimos Quadrados Multivariados, e os parâmetros não-lineares, por Mínimos Quadrados Não-Lineares. Como forma de avaliação do modelo STVARTree, foram realizados diversos experimentos de Monte Carlo com o objetivo de constatar a funcionalidade tanto do teste LM quanto da estimação do modelo. Bons resultados foram obtidos para amostras médias e grandes. Além dos experimentos, o modelo STVAR-Tree foi aplicado às séries brasileiras de Vazão de Rios e Preço Spot de energia elétrica. No primeiro estudo, o modelo foi comparado estatisticamente com o Periodic Autoregressive (PAR) e apresentou um desempenho muito superior ao concorrente. No segundo caso, a comparação foi com a modelagem Neuro-Fuzzy e ganhou em uma das quatro séries. Somando os resultados dos experimentos e das duas aplicações conclui-se que o modelo STVAR-Tree pode ser utilizado na solução de problemas reais, apresentando bom desempenho. / [en] The main goal of the dissertation is to introduce a nonlinear multivariate model, which combines the model STVAR (Smooth Transition Vector Autoregressive) with the CART (Classification and Regression Tree) method and use it for generating scenarios and forecasting. The resulting model is a Tree- Structured Vector Autoregressive model with Smooth Transition, called STVARTree, which is based on the concept of multiple regimes, defined by binary tree. The model specification is based on Lagrange Multiplier tests. Thus, the growth of the tree is conditioned on the existence of nonlinearity in the time series, which indicates the node to be split and the corresponding transition variable. In each division, linear parameters are estimated by Multivariate Least Squares, and nonlinear parameters by Non-Linear Least Squares. As a way of checking the STVAR-Tree model, several Monte Carlo experiments were performed in order to see the functionality of both the LM test and the model estimation. Best results were obtained with medium and large samples. Besides, the STVAR-Tree model was applied to Brazilian time series of Rivers Flow and electricity spot price. In the first study, the model was statistically compared to the Periodic Autoregressive (PAR) model and had a much higher performance than the competitor. In the second case, the model comparison was with Neural-Fuzzy Modeling and the STVAR-Tree model won in one of the four series. Adding both the experiments and the two applications results we conclude that the STVARTree model may be applied to solve real problems, having good results.

This diploma thesis deals with spot energy trading in the Czech Republic. The first section focuses on legal market environments, market members, and types of individual markets. This serves as an introduction to the second and the final section, which answers the question of the advantageousness of spot energy trading. The final section illustrates a practical demonstration of an optimization analysis of energy costs in a particular company. The contribution of this thesis, in my opinion, is the practical description of the calculation of energy costs in one company or an association of companies, which are considering the spot energy purchase, and a transfer of the responsibility for the deviation.

Today, the control of heat pumps aims to first and foremost maintain a comfortable indoor temperature. This is primarily done by deciding input power based on outside temperature. The cost of electricity, which can be rather volatile, is not taken into account. Electricity price can be provided on an hourly rate, and since a house can store thermal energy for a duration of time, it is possible to move electricity consumption to hours when electricity is cheap. In this thesis, the strategy used in the developed controller is Model Predictive Control (MPC). It is a suitable strategy because of the ability to incorporate an objective function that can be designed to take the trade-off between indoor temperature and electricity cost into account. The MPC prediction horizon is dynamic as the horizon of known electricity spot prices varies between 12 and 36 hours throughout the day. We model a residential house heated with a ground source heat pump for use in a case analysis. Sampled weather and spot price data for three different weeks are used in computer simulations. The developed MPC controller is compared with a classic \textit{heat curve} controller, as well as with variations of the MPC controller to estimate the effects of prediction and model errors.  The MPC controller is found to be able to reduce the electricity cost and/or provide better comfort and the prioritization of these factors can be changed depending on user preferences. When shifting energy consumption in time it is necessary to store thermal energy somewhere. If the house itself is used for this purpose, variations in indoor temperature must be accepted. Further, accurate modeling of the Coefficient of Performance (COP) is essential for ground source heat pumps. The COP varies significantly depending on operating conditions and the MPC controller must therefore have a correct perception of the COP. Publicly available weather forecasts are of sufficient quality to be usable for future prediction of outside temperature. For future studies, it would be advantageous if better models can be developed for prediction of global radiation. Including radiation in the MPC controller model would enable better comfort with very similar operating costs compared to when the MPC controller does not take radiation into account.