In the last two decades, more than half of the countries in the world have introduced a reform process in their power industries and billions of dollars have been spent on liberalizing electricity markets around the world. This thesis presents a doctoral research concerned with the cross-country empirical analysis of the electricity market reforms. The thesis is in three-paper format; that is, we present three independent but related stand-alone papers. The first paper focuses on the impact of power market reforms on electricity price-cost margins and industrial/residential price ratios. It investigates this issue by looking at the impact of the electricity industry reforms on residential and industrial electricity price-cost margins and their effect on industrial/residential price ratios. Using panel data from 63 developed and developing countries covering the period 1982–2009, empirical models are developed and analysed. The results suggest that each individual reform step has different impact on price-cost margins and industrial/residential price ratios for each consumer and country group. That is to say, our findings imply that similar reform steps may have different impacts in different countries, which supports the idea that reform prescription for a specific country cannot easily be transferred to another one with similar success. The second paper explores whether the question of why some countries are able to implement more extensive reforms is closely related to the question of why some countries have better institutions than others. It analyses this question by using an empirical econometric model based on Poisson regression with cross-section data covering 51 states in US, 13 provinces in Canada and 51 other countries. The study concludes that both the background of the chairperson and the minister/governor and institutional endowments of a country are important determinants of how far reforms have gone in a country. Considering the fact that ideological considerations, political composition of governments and educational/professional background of leaders have played and will play a crucial role throughout the reform process; the third paper attempts to discover the impact of political economic variables on the liberalization process in electricity markets. It develops and analyses empirical models using panel data from 55 developed and developing countries covering the period 1975–2010. The results suggest that a portion of the differences in the reform experiences of reforming countries in the past three decades can be explained by differences in the political structure, in the ideology of the government and in the professional and educational backgrounds of the political leaders.

In modern days electric power systems, the penetration of renewable resources and the introduction of free market principles have led to new challenges facing the power producers and regulators. Renewable production is intermittent which leads to fluctuations in the grid and requires more control for regulators, and the free market principle raises the challenge for power plant producers to operate their plants in the most profitable way given the fluctuating prices. Those problems are addressed in the literature as the Economic Dispatch, and they have been discussed from both regulator and producer view points. Combined Cycle Power plants have the privileges of being dispatchable very fast and with low cost which put them as a primary solution to power disturbance in grid, this fast dispatch-ability also allows them to exploit price changes very efficiently to maximize their profit, and this sheds the light on the importance of prices forecasting as an input for the profit optimization of power plants. In this project, an integrated solution is introduced to optimize the dispatch of combined cycle power plants that are bidding for electricity markets, the solution is composed of two models, the forecasting model and the optimization model. The forecasting model is flexible enough to forecast electricity and fuel prices for different markets and with different forecasting horizons. Machine learning algorithms were used to build and validate the model, and data from different countries were used to test the model. The optimization model incorporates the forecasting model outputs as inputs parameters, and uses other parameters and constraints from the operating conditions of the power plant as well as the market in which the plant is selling. The power plant in this mode is assumed to satisfy different demands, each of these demands have corresponding electricity price and cost of energy not served. The model decides which units to be dispatched at each time stamp to give out the maximum profit given all these constraints, it also decides whether to satisfy all the demands or not producing part of each of them.

Sverige och Europa genomgår en omställning till högre andel förnybar el, samtidigt har Europas elpriser varit rekordhöga de senaste åren och den sammankopplade elhandeln i EU har bidragit till höga elpriser i Sverige. Litteraturstudien har avsett att redogöra för hur strategier som begränsar elhandeln mellan Sverige och EU påverkar Sveriges försörjningstrygghet, elpriser och konkurrenskraft samt hur miljön påverkas av dessa strategier. Studien kartlägger elhandeln i Sverige och Europa, både den fysiska och marknadsmässiga samt de legala faktorer som påverkar den. Balansen mellan försörjningstrygghet, miljö, ekonomiska faktorer och internationell elhandel undersöks. Dessutom introduceras några strategier för att begränsa och kontrollera den internationella elhandeln, de främsta var fysisk begränsning av elöverföringen samt olika prissättningsstrategier. Resultaten visar att en fysisk begränsning av gränsöverskridande elhandel inte är möjlig givet de regelverk som finns på plats men skulle drabba försörjningstryggheten samt medföra osäkerheter på lång sikt i ekonomiska faktorer som konkurrenskraft och elpriser i Sverige. Dessutom skulle en fysisk begränsning försvåra övergången till mer förnybar energi i Sverige och Europa och på så vis påverka miljön negativt. Det framstår därför som mer gynnsamt på kort sikt att istället kontrollera elhandeln med olika prissättningsstrategier såsom lägre priser för inhemska konsumenter, för att på så sätt dra nytta av inkomsterna och miljönyttan som internationell elhandel innebär utan att de nationella elpriserna drabbas i samma utsträckning. Beroende på prissättningsstrategi innebär det att den negativa påverkan på antingen hushållens eller industriernas elpriser reduceras, varav framför allt det senare innebär att konkurrenskraften inte påverkas lika negativt. Dock visar studien att en sådan modell kräver ytterligare forskning för att utvärdera dess genomförbarhet och potentiella effekter. Slutsatsen av studien indikerar att både bibehålla den nuvarande situationen och implementera alternativa strategier medför olika för- och nackdelar avseende försörjningstrygghet, elpriser, den svenska industrins konkurrenskraft och miljöpåverkan. För att identifiera den mest optimala strategin krävs ytterligare forskning samt en noggrann avvägning av de potentiella konsekvenserna. / Sweden and Europe are increasingly shifting towards renewable electricity. However, Europe has recently seen record-high electricity prices, with the interconnected electricity trade within EU contributing to high prices in Sweden. This literature review aims to elucidate the impacts of strategies restricting electricity trade between Sweden and the EU on Sweden’s energy security, electricity prices, competitiveness, and the environment. The study maps the physical and market dynamics of electricity trade in Sweden and Europe and examines the legal factors influencing it. It also explores the balance between energy security, environmental considerations, economic factors, and international electricity trade. Various strategies for limiting and controlling international electricity trade are introduced, focusing on physical restrictions on electricity transmission and different pricing strategies. The findings suggest that physically restricting cross-border electricity trade is not feasible under current regulations and would harm energy security, leading to long-term uncertainties in economic factors such as competitiveness and electricity prices in Sweden. Additionally, such restrictions would impede the transition to renewable energy in Sweden and Europe, adversely affecting the environment. Therefore, controlling electricity trade through various pricing strategies, such as lower prices for domestic consumers, seems more advantageous in the short run. This approach allows for leveraging the economic and environmental benefits of international electricity trade without significantly impacting national electricity prices. Depending on the pricing strategy, the negative impact on either household or industrial electricity prices is mitigated, with the latter primarily ensuring competitiveness remains less affected. However, further research is needed to evaluate the feasibility and potential effects of this model. The study concludes that both maintaining the current situation and implementing alternative strategies entail various advantages and disadvantages concerning energy security, electricity prices, the competitiveness of Swedish industry, and environmental impact. To identify the most optimal strategy, further research and a careful assessment of the potential consequences are required.

Financial processes may possess long memory and their probability densities may display heavy tails. Many models have been developed to deal with this tail behaviour, which reflects the jumps in the sample paths. On the other hand, the presence of long memory, which contradicts the efficient market hypothesis, is still an issue for further debates. These difficulties present challenges with the problems of memory detection and modelling the co-presence of long memory and heavy tails. This PhD project aims to respond to these challenges. The first part aims to detect memory in a large number of financial time series on stock prices and exchange rates using their scaling properties. Since financial time series often exhibit stochastic trends, a common form of nonstationarity, strong trends in the data can lead to false detection of memory. We will take advantage of a technique known as multifractal detrended fluctuation analysis (MF-DFA) that can systematically eliminate trends of different orders. This method is based on the identification of scaling of the q-th-order moments and is a generalisation of the standard detrended fluctuation analysis (DFA) which uses only the second moment; that is, q = 2. We also consider the rescaled range R/S analysis and the periodogram method to detect memory in financial time series and compare their results with the MF-DFA. An interesting finding is that short memory is detected for stock prices of the American Stock Exchange (AMEX) and long memory is found present in the time series of two exchange rates, namely the French franc and the Deutsche mark. Electricity price series of the five states of Australia are also found to possess long memory. For these electricity price series, heavy tails are also pronounced in their probability densities. The second part of the thesis develops models to represent short-memory and longmemory financial processes as detected in Part I. These models take the form of continuous-time AR(∞) -type equations whose kernel is the Laplace transform of a finite Borel measure. By imposing appropriate conditions on this measure, short memory or long memory in the dynamics of the solution will result. A specific form of the models, which has a good MA(∞) -type representation, is presented for the short memory case. Parameter estimation of this type of models is performed via least squares, and the models are applied to the stock prices in the AMEX, which have been established in Part I to possess short memory. By selecting the kernel in the continuous-time AR(∞) -type equations to have the form of Riemann-Liouville fractional derivative, we obtain a fractional stochastic differential equation driven by Brownian motion. This type of equations is used to represent financial processes with long memory, whose dynamics is described by the fractional derivative in the equation. These models are estimated via quasi-likelihood, namely via a continuoustime version of the Gauss-Whittle method. The models are applied to the exchange rates and the electricity prices of Part I with the aim of confirming their possible long-range dependence established by MF-DFA. The third part of the thesis provides an application of the results established in Parts I and II to characterise and classify financial markets. We will pay attention to the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), the NASDAQ Stock Exchange (NASDAQ) and the Toronto Stock Exchange (TSX). The parameters from MF-DFA and those of the short-memory AR(∞) -type models will be employed in this classification. We propose the Fisher discriminant algorithm to find a classifier in the two and three-dimensional spaces of data sets and then provide cross-validation to verify discriminant accuracies. This classification is useful for understanding and predicting the behaviour of different processes within the same market. The fourth part of the thesis investigates the heavy-tailed behaviour of financial processes which may also possess long memory. We consider fractional stochastic differential equations driven by stable noise to model financial processes such as electricity prices. The long memory of electricity prices is represented by a fractional derivative, while the stable noise input models their non-Gaussianity via the tails of their probability density. A method using the empirical densities and MF-DFA will be provided to estimate all the parameters of the model and simulate sample paths of the equation. The method is then applied to analyse daily spot prices for five states of Australia. Comparison with the results obtained from the R/S analysis, periodogram method and MF-DFA are provided. The results from fractional SDEs agree with those from MF-DFA, which are based on multifractal scaling, while those from the periodograms, which are based on the second order, seem to underestimate the long memory dynamics of the process. This highlights the need and usefulness of fractal methods in modelling non-Gaussian financial processes with long memory.

Energy poverty is a major obstacle to human development. Energy services supply the benefits that “trigger” wider social and economic changes and create the conditions for improving social equality and economic growth. The South African government has scaled up electricity access to its poor population to such an extent that 85% of the country’s population had access to electricity in 2017. Nevertheless, access to electricity is not the same as the ability to use it, as the poor find the price of electricity unaffordable. The government therefore embarked on a programme to provide households’ that they consider ‘indigent’ or ‘poor’ 50kWh of free energy. This is criticized by many as not being enough to sufficiently satisfy household energy needs. Even the generous supply of 100kWh electricity provided by the City of Tshwane and the City of Johannesburg municipalities is considered to be insufficient. This study investigates the impact of the 100kWh free basic electricity subsidy on the energy use of the urban poor in the township of Soshanguve in the City of Tshwane municipality. It focuses on whether the 100kWh is enough to meet these household’s’ energy needs. The finding of the study is that the 100kWh FBE is enough to meet these households’ needs for lighting, some cooking and appliance use, but not for space and water heating. / Development Studies / M.A. (Development Studies)

There has recently been an increased interest in hydrogen, both as a solution for seasonal energy storage but also for implementations in various industries and as fuel for vehicles. The transition to a society less dependent on fossil fuels highlights the need for new solutions where hydrogen is predicted to play a key role. This project aims to investigate technical and economic outcomes of different strategies for production and storage of hydrogen based on hydrogen demand and source of electricity. This is done by simulating the operation of different systems over a year, mapping the storage level, the source of electricity, and calculating the levelized cost of hydrogen (LCOH). The study examines two main cases. The first case is a system integrated with offshore wind power for production of hydrogen to fuel the operations in the industrial port Gävle Hamn. The second case examines a system for independent refueling stations where two locations with different electricity prices and traffic flows are analyzed. Factors such as demand, electricity prices, and component costs are investigated through simulating cases as well as a sensitivity analysis. Future potential sources of income are also analyzed and discussed. The results show that using an alkaline electrolyzer (AEL) achieves the lowest LCOH while PEM electrolyzer is more flexible in its operation which enables the system to utilize more electricity from the offshore wind power. When the cost of wind electricity exceeds the average electricity price on the grid, a higher share of wind electricity relative to electricity from the grid being utilized in the production results in a higher LCOH. The optimal design of the storage depends on the demand, where using vessels above ground is the most beneficial option for smaller systems and larger systems benefit financially from using a lined rock cavern (LRC). Hence, the optimal design of a system depends on the demand, electricity source, and ultimately on the purpose of the system. The results show great potential for future implementation of hydrogen systems integrated with wind power. Considering the increased share of wind electricity in the energy system and the expected growth of the hydrogen market, these are results worth acknowledging in future projects.