The Swing options are the natural outcomes of the increasing uncertainty in the power markets, which came along with the deregulation process triggered by the UK government&rsquo / s action in 1990 to privatize the national electricity supply industry. Since then, the ways of handling the risks in the price generation process have been explored extensively. Producer-consumers of the power market felt confident as they were naturally hedged against the price fluctuations surrounding the large consumers. Companies with high power consumption liabilities on their books demanded tailored financial products that would shelter them from the upside risks while not preventing them from benefiting the low prices. Furthermore, more effective risk management practices are strongly dependent upon the successful parameterization of the underlying stochastic processes, which is also key to the effective pricing of derivatives traded in the market. In this thesis, we refer to the electricity spot price model developed jointly by Hambly, Howison and Kluge ([13]), which incorporates jumps and still maintains the analytical tractability. We also derive the forward curve dynamics implied by the spot price model and explore the effects on the forward curve dynamics of the spikes in spot price. As the main discussion of this thesis, the Grid Approach, which is a generalization of the Trinomial Forest Method, is applied to the electricity Swing options. We investigate the effects of spikes on the per right values of the Swing options with various number of exercise rights, as well as the sensitivities of the model-implied prices to several parameters.

Dans le contexte de l’augmentation de la part de la production énergétique provenant de sources renouvelables imprévisibles, les prix de l’électricité sont plus volatiles que jamais. Cette volatilité rend la prévision des prix plus difficile mais en même temps de plus grande valeur. Dans cette recherche, une analyse comparative de 8 modèles de prévision est effectuée sur la tâche de prédire les prix de gros de l’électricité du lendemain en France, en Allemagne, en Belgique et aux Pays-Bas. La méthodologie utilisée pour produire les prévisions est expliquée en détail. Les différences de précision des prévisions entre les modèles sont testées pour leur signification statistique. La méthode de gradient boosting a produit les prévisions les plus précises, suivi de près par une méthode d’ensemble. / In the context of the increase in the fraction of power generation coming from unpredictable renewable sources, electricity prices are as volatile as ever. This volatility makes forecasting future prices more difficult yet more valuable. In this research, a benchmark of 8 forecasting models is conducted on the task of predicting day-ahead wholesale electricity prices in France, Germany, Belgium and the Netherlands. The methodology used to produce the forecasts is explained in detail. The differences in forecast accuracy between the models are tested for statistical significance. Gradient boosting produced the most accurate forecasts, closely followed by an ensemble method.

The purpose of this study is to describe and analyze how manufacturing companies are affected by the electricity crisis and what measures they have taken to maintain liquidity in their operations. To achieve this, the empirical evidence will be based on interviews with manufacturing companies located in electricity area 4. To answer the questions “In what way have manufacturing companies in electricity area 4 been affected during the current electricity crisis?” and “How have these companies changed their operations and managed their liquidity and costs during the electricity crisis?” the study is based on an abductive research and on a qualitative research method. The results of the study shows that companies with variable electricity contracts in combination with an electricity cost that makes up a larger part of their manufacturing cost have suffered the most from a liquidity point of view. The most common measures taken by the companies were to increase the selling price, reduce their electricity use and become more self-sufficient in electricity.

To overcome the curse of dimensionality, dimension reduction is important and necessary for understanding the underlying phenomena in a variety of fields. Dimension reduction is the transformation of high-dimensional data into a meaningful representation in the low-dimensional space. It can be further classified into feature selection and feature extraction. In this thesis, which is composed of four projects, the first two focus on feature selection, and the last two concentrate on feature extraction. The content of the thesis is as follows. The first project presents several efficient methods for the sparse representation of a multiple measurement vector (MMV); some theoretical properties of the algorithms are also discussed. The second project introduces the NP-hardness problem for penalized likelihood estimators, including penalized least squares estimators, penalized least absolute deviation regression and penalized support vector machines. The third project focuses on the application of manifold learning in the analysis and prediction of 24-hour electricity price curves. The last project proposes a new hessian regularized nonlinear time-series model for prediction in time series.

Production of electric energy is continuously affected by many factors. Therefore, tools for predicting the future production are needed. In turn, the production affects the electric energy price, which is set on electric energy exchanges. This thesis is intended to find out if the software SDDP can be used for hydrothermal power production simulations in the Nord pool area. By building a simplified model of the electric energy production in Sweden with a focus on hydro, thermal and wind power, the intention is to see how the model is affected by different conditions. The investigated conditions are several; higher and lower water inflows to the hydro power reservoirs; different amounts of installed wind power production; different price levels of emission allowances for CO2. By using the simulation software SDDP, more wind power was seen to lower the electric energy prices, as well as reduce the need of transmission of power from the northern to the southern parts of Sweden. In the simulation, Sweden was divided into four areas, connected where the main bottlenecks in the power grid are located. Water inflows to the reservoirs are crucial in the model. Actual inflow data can be bought from SMHI. However, due to the limited thesis budget, estimations were constructed instead. The estimations were difficult to make and turned out to be too high. Consequently, no reliable evaluation of the SDDP software could be done using this data.

There is a demand for decision support tools that can model the electricity markets and allows to forecast the hourly electricity price. Many different ap- proach such as artificial neural network or support vector regression are used in the literature. This thesis provides comparison of several different estima- tors under one settings using available data from Czech electricity market. The resulting comparison of over 5000 different estimators led to a selection of several best performing models. The role of historical weather data (temper- ature, dew point and humidity) is also assesed within the comparison and it was found that while the inclusion of weather data might lead to overfitting, it is beneficial under the right circumstances. The best performing approach was the Lasso regression estimated using modified Lars. 1

The main topic of this thesis is the trading with one of the most important commodity in the market – electricity. This thesis will analyse the topic from the point of view of a natural person who potentially would like to start trading in the market. The description focuses on causes and consequences of the market liberalization. The electricity trading will be also analyzed from the point of view of a consumer and also of a producer. Possibilities will be listed on how to purchase and sell electricity, principles how particular markets work and also comparison of the price of electricity with the prices of other commodities. The aim is to give a detailed description about domestic market electricity price creation. The main purpose of this thesis is to describe the situation in the domestic market with electricity and future development in this branch, expressing also considerations about switching the electricity supplier.

growing concern on achieving environmental sustainability and at the same time making economical savings has become a necessity in our society. The prices of different battery energy storage technologies together with PV cells are declining all around the globe which has led to the fact that there is an increased interest in investing and using these technologies to be able to reach environmental sustainability. The combined system however, must be accurately calculated both when it comes to the sizing and the different costs related to the combined system to be able to make an economical saving. This thesis addresses both of those aspects in Sweden where a residential building with roof-top installed PV system is assessed with a battery energy storage system. An investigation is necessary to be able to assess the different battery storage technologies available in the market today with their specific technical and economical specifications. The electricity market in Sweden, the role of the Distribution System Operator on the electricity pricing with different time tariffs and fuse size subscription, PV generation and battery specifications are investigated and modeled in this study. Sizing of the different battery technologies for the given system is accomplished through a methodology that is developed in this project for the Swedish system. The calculated size of the battery is then used in the Life Cycle Cost analysis, using Monte Carlo simulations for a chosen period of 25 years.Calculations shows that the most appropriate size for the battery system with the given parameters is 6 kWh for all the battery types investigated in this study. The size of the batteries is also shown to be mainly dependent on the charging/discharging time together with the set fuse size margin. Profitability of the Battery Energy Storage system is proven to be mainly dependent on the fuse size downgrade. Sulphur-Sodium battery result in the greatest savings while Vanadium Redox batteries in the least when sizing the batteries. Lithium-Ion battery technology however is most likely to result in the lowest Levelized Cost of Electricity, total- and cycle costs while the highest Net Present Value with 90 % probability in the Monte Carlo simulations. Lithium-Ion battery technology is also found to have the highest probability of having a positive NPV compared to the lowest probability for Sulphur-Sodium battery technology. Lead-Acid battery technology is however shown to have the least uncertainties compared to other Battery Energy Storage technologies due to its maturity. It is additionally shown that government subsidy plays a crucial role when investing in the battery storage system. However, even with the case of removed government subsidy, Lithium-Ion battery technology still results in the largest probability of having a positive NPV while Sulphur-Sodium battery technology results in the lowest probability of having a positive NPV. / Den växande oron för att uppnå miljömässig hållbarhet och samtidigt göra ekonomiska besparingar har blivit en nödvändighet i vårt samhälle. Priserna på olika energilagrings teknologier så som batterier tillsammans med PV-celler minskar runt om i världen vilket har lett till att det finns ett ökat intresse när det gäller att investera och använda dessa teknologier för att kunna nå miljömässig hållbarhet. Det kombinerade systemet måste dock noggrant beräknas både när det gäller storleken och de olika kostnaderna för det kombinerade systemet för att kunna göra en ekonomisk besparing. Denna avhandling behandlar båda dessa aspekter i Sverige där en bostadsbyggnad med takmonterat PV system utvärderas med ett batteri system. En undersökning är nödvändig för att kunna bedöma de olika batteri teknologier som finns tillgängliga på marknaden idag med sina specifika tekniska och ekonomiska specifikationer. Elmarknaden i Sverige, Distribution System Operatörs roll för elprissättning med olika tidstariffer och säkringsabonnemang, PV-generation och batterispecifikationer undersöks och modelleras i denna studie. Dimensionering av olika batteri teknologier för det givna systemet uppnås genom en metod som utvecklats i detta projekt för det svenska systemet. Den beräknade storleken på batteriet används sedan i livscykelkostnadsanalysen, med Monte Carlo-simuleringar under en vald period på 25 år. Beräkningar visar att den optimala storleken för batterisystemet med de angivna parametrarna är 6 kWh för alla batterityper som undersöktes i denna studie. Batteriets storlek visar sig också vara huvudsakligen beroende av laddning / urladdningstiden tillsammans med den inställda säkrings storleken. Lönsamheten hos batterilagringssystemet visar sig vara huvudsakligen beroende av säkringens nedgradering. Svavel-Natriumbatteriet resulterar i de största besparingarna medan Vanadium Redox batteriet i de minsta när dimensionering av batteriet äger rum. Litium-Ion batteriet är emellertid sannolikt att leda till den lägsta nivån av elkostnader, total- och cykelkostnader, medan det högsta nettoförsäljningsvärdet med 90% sannolikhet i Monte Carlo-simuleringarna. Litium-Ion batteriet befanns också ha den högsta sannolikheten att ha en positiv NPV jämfört med Svavel-Natriumbatteriet som resulterar i den lägsta sannolikheten. Lead-Acid batteriet visar sig ha den minsta osäkerheten i jämförelse med andra batterilagrings teknologier på grund av dess mognad. Det framgår dessutom att statlig subvention spelar en avgörande roll när man investerar i ett batteri lagrings system. Dock även med borttagna statliga subventioner, resulterar Litium-Ion batteriet fortfarande största sannolikheten för att ha en positiv NPV, medan Svavel-Natriumbatteriet resulterar den lägsta sannolikheten för att ha en positiv NPV.

This thesis examines how an econometric model, which allows for simultaneity, performs when estimating electricity supply and demand on the Swedish aggregated electricity market, divided into its four price areas. Previous research and theory points to the importance of taking simultaneity into consideration when estimating simultaneous equation models. The purpose of the thesis is to clarify whether a simultaneous equation model is adequate for estimation of the Swedish electricity market. To answer the question, the existence of simultaneity between electricity price and quantity (produced and consumed electricity) is examined. Regression analyses are performed for each price area and the coefficients are estimated with 2SLS, while Hausman tests and F-tests are carried out to unveil potential simultaneity. The results show that the performance of the model of the thesis varies: the coefficients of the price variables in the demand functions are never statistically significant, while the coefficients of the monthly dummy variables in most cases have expected signs and are statistically significant, especially during summer. Regarding the supply side, the results show that net export have the expected positive statistically significant effect on quantity of electricity supplied in price areas 2, 3, and 4, while the signs and significance of the coefficients of the price variables varies. A price increase in area 3 has a statistically significant positive effect on supply in all areas, while the price of area 4 has a statistically significant negative effect on supply in area 3 and 4. A price increase in area 1 leads to a statistically significant positive effect on supply only in area 2, while such a change in the price of area 2 is associated with a statistically significant decrease of the supply in that same area. As to simultaneity between price and quantity demanded, the Hausman tests gives sufficient evidence to conclude that simultaneity exists in all price areas. Similarly, on the supply side the performed F-tests result in clear evidence of existence of simultaneity. / Denna uppsats undersöker hur en ekonometrisk modell som tar hänsyn till simultanitet presterar vid estimering av utbjuden och efterfrågad kvantitet av elektricitet på den svenska aggregerade elmarknaden, uppdelad efter de fyra elområdena. Tidigare forskning och teori visar på betydelsen av att ta simultanitet i beaktning när simultana ekvationsmodeller estimeras. Uppsatsen syftar till att klarlägga om en simultan ekvationsmodell är lämplig för estimering av den svenska elmarknaden. För att få svar på frågan undersöks om det förekommer simultanitet mellan elpris och kvantitet (producerad och konsumerad el). Regressionsanalyser utförs för varje elområde och koefficienterna skattas med hjälp av 2SLS, medan Hausmantest och F-test utförs för att upptäcka eventuell simultanitet. Uppsatsens modell levererar varierande resultat: i efterfrågeekvationerna är prisvariabeln aldrig statistiskt signifikant medan månadsvariablerna i de flesta fall har förväntat tecken och är statistiskt signifikanta, framför allt under sommarhalvåret. För utbudssidan visar resultatet att nettoexport har väntad positiv statistiskt signifikant effekt på utbjuden kvantitet i elområde 2, 3 och 4, medan tecken och signifikans för prisvariablernas koefficienter varierar. En ökning av priset i område 3 leder till en statistiskt signifikant utbudsökning i alla områden, medan priset i område 4 har en statistiskt signifikant negativ effekt på utbudet i område 3 och 4. En prisökning i elområde 1 leder endast till en statistiskt signifikant positiv utbudsökning i område 2, medan en prisökning i elområde 2 i stället endast leder till en statistiskt signifikant utbudsminskning i samma område. Vad gäller simultanitet mellan pris och kvantitet på efterfrågesidan visar Hausmantesten att det finns tillräckligt starka statistiska bevis för att påstå att simultanitet förekommer i alla elområden. Även för utbudssidan resulterar de utförda F-testen i tydliga bevis för förekomst av simultanitet.

High electricity prices have become the title of several news articles recently in Sweden and the prices have experienced large sudden fluctuations during certain periods. In this thesis work, a smart control model for the electricity usage in three different households has been developed with the main purpose to minimize the electricity cost. This has been implemented by using mixed-integer linear programming (MILP) to optimize the cost 24 hours ahead, and by forecasting two of the main inputs; the load and the electricity spot prices for bidding zone three (SE3) in Sweden. The units included in the model are the photovoltaic system, the batteries, the electricity consumption in the house and the electric vehicles. However, the main task of the smart control was to determine when and in which amount the energy should flow from one unit to another, or to/from the grid. In other words, it decides the charging/discharging of the batteries, the selling/buying of electricity and the charging of the electric vehicle (EV). Different amounts of cost savings/profits have been obtained when applying the smart control on the three houses, which have different annual consumption, capacities of the components, heating systems and more. The results showed that it is most optimal to run the model between the time interval 13.00-00.00, when the spot prices for the next day are known, in order to avoid the remarkable impact accompanied with the use of forecasted electricity prices as input to the model. The forecasting of the load is, on the other hand, required to run the model, but this thesis showed that the effect of the uncertainties in this forecast is relatively small. Three types of machine learning methods were implemented to perform the forecasts, namely linear regression (LR), decision tree regression and random forest regression. After measuring especially the mean absolute error (MAE) to validate the results, the random forest regression showed the least error and the other methods showed close results when looking at the electric load prognosis.