Introducing more intermittent renewable energy sources in the energy system makes the role of balancing power more important. Furthermore, an increased infeed from intermittent renewable energy sources also has the effect of creating lower and more volatile electricity prices. Hence, investing in balancing power is prone to high risks with respect to expected profits, which is why a good representation of electricity prices is vital in order to motivate future investments. We propose a stochastic multi-factor model to be used for simulating the long-run dynamics of electricity prices as input to investment valuation of power generation assets. In particular, the proposed model is used to assess the impact of electricity price dynamics on investment decisions with respect to balancing power generation, where a combined heat and power plant is studied in detail. Since the main goal of the framework is to create a long-term representation of electricity prices so that the distributional characteristics of electricity prices are maintained, commonly cited as seasonality, mean reversion and spikes, the model is evaluated in terms of yearly duration which describes the distribution of electricity prices over time. The core aspects of the framework are derived from the mean-reverting Pilipovic model of commodity prices, but where we extend the assumptions in a multi-factor framework by adding a functional link to the supply- and demand for power as well as outdoor temperature. On average, using the proposed model as a way to represent future prices yields a maximum 9 percent overand underprediction of duration respectively, a result far better than those obtained by simpler models such as a seasonal profile or mean estimates which do not incorporate the full characteristics of electricity prices. Using the different aspects of the model, we show that variations of electricity prices have a large impact on the investment decision with respect to balancing power. The realized value of the flexibility to produce electricity in a combined heat and power plant is calculated, which yields a valuation close to historical realized values. Compared with simpler models, this is a significant improvement. Finally, we show that by including characteristics such as non-constant volatility and spiky behavior in investment decisions, the expected value of balancing power generators, such as combined heat and power plants, increases. / I takt med att fler intermittenta förnyelsebara energikällor tillför el i dagens energisystem, blir också balanskraftens roll i dessa system allt viktigare. Vidare så har en ökning av andelen intermittenta förnyelsebara energikällor även effekten att de bidrar till lägre men också mer volatila elpriser. Därmed är även investeringar i balanskraft kopplade till stora risker med avseende på förväntade vinster, vilket gör att en god representation av elpriser är central vid investeringsbeslut. Vi föreslår en stokastisk flerfaktormodell för att simulera den långsiktiga dynamiken i elpriser som bas för värdering av generatortillgångar. Mer specifikt används modellen till att utvärdera effekten av elprisers dynamik på investeringsbeslut med avseende på balanskraft, där ett kraftvärmeverk studeras i detalj. Eftersom huvudmålet med ramverket är att skapa en långsiktig representation av elpriser så att deras fördelningsmässiga karakteristika bevaras, vilket i litteraturen citeras som regression mot medelvärde, säsongsvariationer, hög volatilitet och spikar, så utvärderas modellen i termer av årlig prisvaraktighet som beskriver fördelningen av elpriser över tid. Kärnan i ramverket utgår från Pilipovic-modellen av råvarupriser, men där vi utvecklar antaganden i ett flerfaktorramverk genom att lägga till en länkfunktion till tillgång- och efterfrågan på el samt utomhustemperatur. Vid användande av modellen som ett sätt att representera framtida priser, fås en maximal över- och underprediktion av prisvaraktighet om 9 procent, ett resultat som är bättre än det som ges av enklare modellering såsom säsongsprofiler eller enkla medelvärdesestimat som inte tar hänsyn till elprisernas fulla karakteristika. Till sist visar vi med modellens olika komponenter att variationer i elpriser, och därmed antaganden som används i långsiktig modellering, har stor betydelse med avseende på investeringsbeslut i balanskraft. Det realiserade värdet av flexibiliteten att producera el för ett kraftvärmeverk beräknas, vilket ger en värdering nära faktiska realiserade värden baserade på historiska priser och som enklare modeller inte kan konkurrera med. Slutligen visar detta också att inkluderandet av icke-konstant volatilitet och spikkarakteristika i investeringsbeslut ger ett högre förväntat värde av tillgångar som kan producera balanskraft, såsom kraftvärmeverk.

Zur Umsetzung der Energiewende ist in den kommenden Jahren ein substantieller Netzausbau notwendig, der jedoch regional unterschiedlich stark ausfallen wird. Nach gegenwärtiger Gesetzeslage werden die folglich sehr unterschiedlich hohen Netzkosten von den Endkunden des jeweiligen Netzgebietes über die Netznutzungsentgelte getragen. Mittels eines detaillierten Modells der Kostenbestandteile der Netzkosten in den einzelnen Regionen wurden unter Berücksichtigung des erwarteten Netzausbaus sowie der demographischen Entwicklung die Netznutzungsentgelte, geschlüsselt nach den Kundengruppen Haushalt und Gewerbe sowie Industrie bis zum Jahr 2023 prognostiziert. Die anschließende Analyse eines bundesweiten Wälzens von Übertragungs- und Verteilungsnetzbestandteilen der Entgelte kommt zu dem Ergebnis, dass in Zukunft neben den ostdeutschen Flächenländern auch die Küstenländer Schleswig-Holstein und Niedersachsen sowie Teile Bayerns von einem bundeseinheitlichen Entgelt profitieren würden. Dabei stellt sich eine asymmetrische Verteilung von Be- und Entlastungen dar. Den zum Teil erheblichen jährlichen Entlastungen von bis zu 130 EUR pro 3-Personenhaushalt stehen in den süd- und westdeutschen Flächenländern vergleichsweise geringe Mehrbelastungen von maximal 30 EUR gegenüber. Gleichzeitig zeigt die Analyse, dass ein alleiniges Wälzen der Übertragungsnetzkosten zum heutigen Stand für Industriekunden in Ostdeutschland zwar merkliche Entlastungen mit sich bringen würde, diese aber zukünftig abnehmen und im Haushaltskundenbereich sehr gering ausfallen. Insgesamt lässt sich aus den Ergebnissen der Analyse schlussfolgern, dass die regionale Ungleichverteilung der Netzkosten tendenziell zunimmt und es Regionen in Deutschland gibt, in denen hohe Netzausbaukosten, eine negative demographische Entwicklung und eine geringe Kaufkraft zusammentreffen und so Privathaushalte sowie Industriebetriebe stark belasten.

With the aim for higher economical efficiency, considerable and radical changes have occurred in the worldwide electricity sector since the beginning of 1980s. By that time, the electricity sector has been controlled by the state-owned vertically integrated monopolies which manage and control all generation, transmission, distribution and retail activities and the consumers buy electricity with a price set by these monopolies in that system. After the liberalization and restructuring of the electricity power sector, separation and privatization of these activities have been widely seen. The main purpose is to ensure competition in the market where suppliers and consumers compete with each other to sell or buy electricity from the market and the consumers buy the electricity with a price which is based on competition and determined according to sell and purchase bids given by producers and customers rather than a price set by the government. Due to increasing competition in the electricity market, accurate electricity price forecasts have become a very vital need for all market participants. Accurate forecast of electricity price can help suppliers to derive their bidding strategy and optimally design their bilateral agreements in order to maximize their profits and hedge against risks. Consumers need accurate price forecasts for deriving their electricity usage and bidding strategy for minimizing their utilization costs. This thesis presents the determination of system day ahead price (SGOF) at the day ahead market and system marginal price (SMF) at the balancing power market in detail and develops artificial neural network models together with multiple linear regression models to forecast these electricity prices in Turkish electricity market. Also the methods used for price forecasting in the literature are discussed and the comparisons between these methods are presented. A series of historical data from Turkish electricity market is used to understand the characteristics of the market and the necessary input factors which influence the electricity price is determined for creating ANN models for price forecasting in this market. Since the factors influencing SGOF and SMF are different, different ANN models are developed for forecasting these prices. For SGOF forecasting, historical price and load values are enough for accurate forecasting, however, for SMF forecasting the net instruction volume occurred due to real time system imbalances is needed in order to increase the forecasting accuracy.

Der zur Umsetzung der Energiewende notwendige Netzausbau fällt regional sehr unterschiedlich hoch aus. Durch die bestehende Entgeltsystematik ergeben sich hierbei potentielle Mehrbelastungen für Stromkunden in Regionen mit einem hohen Anteil an Erneuerbaren Energien. Aktuell sind vor allem in den neuen Bundesländern höhere Entgelte zu verzeichnen. Im Rahmen dieses Kurzgutachtens werden mittels eines detaillierten Modells der Netzkosten auf den unterschiedlichen Spannungsebenen nach Landkreisen aufgeschlüsselte Netznutzungsentgelte bis zum Jahr 2024 prognostiziert. Darüber hinaus werden fünf Anpassungsvarianten der bestehenden Entgeltsystematik quantitativ analysiert und diskutiert:  Einheitliches Übertragungsnetzentgelt  Streichung der vermiedenen Netznutzungsentgelte für dargebotsabhängige Erzeuger  Preiskorridore für Endkundenentgelte  Bundeseinheitliche Entgelte für Endkunden  Wälzen der durch Erneuerbare Energien (EE) bedingten Verteilernetzkosten Aus den Analysen ergeben sich vor allem für die Varianten Entgeltkorridore, bundeseinheitliche Entgelte sowie für das Wälzen der EE-bedingten Verteilernetzkosten signifikante Entlastungseffekte für Kunden mit sehr hohen Entgelten bei moderater Mehrbelastung der übrigen Stromkunden. Obwohl die letzte Variante zu einer verursachungsgerechteren Kostenverteilung führen würde, ist deren Umsetzbarkeit äußerst fraglich. Erste Maßnahmen um ein Auseinanderdriften der Entgelte abzuschwächen, die deutlich einfacher umzusetzen sind, wären die Einführung eines einheitlichen Übertragungsnetzentgelts sowie die Streichung vermiedener Netznutzungsentgelte für dargebotsabhängige Erzeuger.

Zur Umsetzung der Energiewende ist in den kommenden Jahren ein substantieller Netzausbau notwendig, der jedoch regional unterschiedlich stark ausfallen wird. Nach gegenwärtiger Gesetzeslage werden die folglich sehr unterschiedlich hohen Netzkosten von den Endkunden des jeweiligen Netzgebietes über die Netznutzungsentgelte getragen. Mittels eines detaillierten Modells der Kostenbestandteile der Netzkosten in den einzelnen Regionen wurden unter Berücksichtigung des erwarteten Netzausbaus sowie der demographischen Entwicklung die Netznutzungsentgelte, geschlüsselt nach den Kundengruppen Haushalt und Gewerbe sowie Industrie bis zum Jahr 2023 prognostiziert. Die anschließende Analyse eines bundesweiten Wälzens von Übertragungs- und Verteilungsnetzbestandteilen der Entgelte kommt zu dem Ergebnis, dass in Zukunft neben den ostdeutschen Flächenländern auch die Küstenländer Schleswig-Holstein und Niedersachsen sowie Teile Bayerns von einem bundeseinheitlichen Entgelt profitieren würden. Dabei stellt sich eine asymmetrische Verteilung von Be- und Entlastungen dar. Den zum Teil erheblichen jährlichen Entlastungen von bis zu 130 EUR pro 3-Personenhaushalt stehen in den süd- und westdeutschen Flächenländern vergleichsweise geringe Mehrbelastungen von maximal 30 EUR gegenüber. Gleichzeitig zeigt die Analyse, dass ein alleiniges Wälzen der Übertragungsnetzkosten zum heutigen Stand für Industriekunden in Ostdeutschland zwar merkliche Entlastungen mit sich bringen würde, diese aber zukünftig abnehmen und im Haushaltskundenbereich sehr gering ausfallen. Insgesamt lässt sich aus den Ergebnissen der Analyse schlussfolgern, dass die regionale Ungleichverteilung der Netzkosten tendenziell zunimmt und es Regionen in Deutschland gibt, in denen hohe Netzausbaukosten, eine negative demographische Entwicklung und eine geringe Kaufkraft zusammentreffen und so Privathaushalte sowie Industriebetriebe stark belasten.

In a deregulated electricity market, offering the appropriate amount of electricity at the right time with the right bidding price is of paramount importance. The forecasting of electricity market clearing price (MCP) is a prediction of future electricity price based on given forecast of electricity demand, temperature, sunshine, fuel cost, precipitation and other related factors. Currently, there are many techniques available for short-term electricity MCP forecasting, but very little has been done in the area of mid-term electricity MCP forecasting. The mid-term electricity MCP forecasting focuses electricity MCP on a time frame from one month to six months. Developing mid-term electricity MCP forecasting is essential for mid-term planning and decision making, such as generation plant expansion and maintenance schedule, reallocation of resources, bilateral contracts and hedging strategies. Six mid-term electricity MCP forecasting models are proposed and compared in this thesis: 1) a single support vector machine (SVM) forecasting model, 2) a single least squares support vector machine (LSSVM) forecasting model, 3) a hybrid SVM and auto-regression moving average with external input (ARMAX) forecasting model, 4) a hybrid LSSVM and ARMAX forecasting model, 5) a multiple SVM forecasting model and 6) a multiple LSSVM forecasting model. PJM interconnection data are used to test the proposed models. Cross-validation technique was used to optimize the control parameters and the selection of training data of the six proposed mid-term electricity MCP forecasting models. Three evaluation techniques, mean absolute error (MAE), mean absolute percentage error (MAPE) and mean square root error (MSRE), are used to analysis the system forecasting accuracy. According to the experimental results, the multiple SVM forecasting model worked the best among all six proposed forecasting models. The proposed multiple SVM based mid-term electricity MCP forecasting model contains a data classification module and a price forecasting module. The data classification module will first pre-process the input data into corresponding price zones and then the forecasting module will forecast the electricity price in four parallel designed SVMs. This proposed model can best improve the forecasting accuracy on both peak prices and overall system compared with other 5 forecasting models proposed in this thesis.

This thesis presents a collection of papers that has been published, accepted or submitted for publication. They assess price, volatility and market relationships in the five regional electricity markets in the Australian National Electricity Market (NEM): namely, New South Wales (NSW), Queensland (QLD), South Australia (SA), the Snowy Mountains Hydroelectric Scheme (SNO) and Victoria (VIC). The transmission networks that link regional systems via interconnectors across the eastern states have played an important role in the connection of the regional markets into an efficient national electricity market. During peak periods, the interconnectors become congested and the NEM separates into its regions, promoting price differences across the market and exacerbating reliability problems in regional utilities. This thesis is motivated in part by the fact that assessment of these prices and volatility within and between regional markets allows for better forecasts by electricity producers, transmitters and retailers and the efficient distribution of energy on a national level. The first two papers explore whether the lagged price and volatility information flows of the connected spot electricity markets can be used to forecast the pricing behaviour of individual markets. A multivariate generalised autoregressive conditional heteroskedasticity (MGARCH) model is used to identify the source and magnitude of price and volatility spillovers within (intra-relationship) and across (inter-relationship) the various spot markets. The results show evidence of the fact that prices in one market can be explained by their own price lagged one-period and are independent of lagged spot prices of any other markets when daily data is employed. This implies that the regional spot electricity markets are not fully integrated. However, there is also evidence of a large number of significant ownvolatility and cross-volatility spillovers in all five markets indicating that shocks in some markets will affect price volatility in others. Similar conclusions are obtained when the daily data are disaggregated into peak and off-peak periods, suggesting that the spot electricity markets are still rather isolated. These results inspired the research underlying the third paper of the thesis on modelling the dynamics of spot electricity prices in each regional market. A family of generalised autoregressive conditional heteroskedasticity (GARCH), RiskMetrics, normal Asymmetric Power ARCH (APARCH), Student APARCH and skewed Student APARCH is used to model the time-varying variance in prices with the inclusion of news arrival as proxied by the contemporaneous volume of demand, time-of-day, day-of-week and month-of-year effects as exogenous explanatory variables. The important contribution in this paper lies in the use of two latter methodologies, namely, the Student APARCH and skewed Student APARCH which take account of the skewness and fat tailed characteristics of the electricity spot price series. The results indicate significant innovation spillovers (ARCH effects) and volatility spillovers (GARCH effects) in the conditional standard deviation equation, even with market and calendar effects included. Intraday prices also exhibit significant asymmetric responses of volatility to the flow of information (that is, positive shocks or good news are associated with higher volatility than negative shocks or bad news). The fourth research paper attempts to capture salient feature of price hikes or spikes in wholesale electricity markets. The results show that electricity prices exhibit stronger mean-reversion after a price spike than the mean-reversion in the normal period, suggesting the electricity price quickly returns from some extreme position (such as a price spike) to equilibrium; this is, extreme price spikes are shortlived. Mean-reversion can be measured in a separate regime from the normal regime using Markov probability transition to identify the different regimes. The fifth and final paper investigates whether interstate/regional trade has enhanced the efficiency of each spot electricity market. Multiple variance ratio tests are used to determine if Australian spot electricity markets follow a random walk; that is, if they are informationally efficient. The results indicate that despite the presence of a national market only the Victorian market during the off-peak period is informationally (or market) efficient and follows a random walk. This thesis makes a significant contribution in estimating the volatility and the efficiency of the wholesale electricity prices by employing four advanced time series techniques that have not been previously explored in the Australian context. An understanding of the modelling and forecastability of electricity spot price volatility across and within the Australian spot markets is vital for generators, distributors and market regulators. Such an understanding influences the pricing of derivative contracts traded on the electricity markets and enables market participants to better manage their financial risks.

[pt] A busca por fontes energéticas alternativas tornou-se questão crucial para o desenvolvimento econômico mundial, sendo a biomassa uma alternativa a ser considerada. Neste estudo analisamos o caso de uma indústria de chapas de fibras de madeira, na qual cavacos de madeira podem ser utilizados tanto como matéria prima quanto como combustível para geração de energia térmica. Neste segmento, durante o processo produtivo são gerados grandes volumes de resíduos florestais que podem ser usados como combustível. O objetivo do presente trabalho é determinar a viabilidade econômico-financeira de se instalar um processo de cogeração de energia tendo como combustíveis resíduos florestais e gás natural. Assumimos que os gestores possuem duas alternativas: usar os resíduos e gás na geração de energia, liberando os cavacos para produção de MDF e HDF ou empregar os resíduos florestais, gás natural e cavacos de madeira como combustível, comercializando o excedente de energia no mercado de curto prazo. A avaliação financeira foi baseada na Teoria das Opções Reais considerando a flexibilidade gerencial de selecionar otimamente o destino final dos cavacos de madeira (chapa de madeira ou energia) ao longo do tempo. Uma importante inovação do trabalho consiste na incorporação de fatores de sazonalidade na volatilidade do preço de energia, adaptando o processo estocástico as especificidades do mercado brasileiro. Foi considerada como incerteza o preço da energia (PLD) e adotou-se como base o Modelo Geométrico de Reversão a Média com Saltos de Clewlow, Strickland e Kaminski (2000). Os resultados indicam que a opção de comercializar o excedente de energia não é viável financeiramente e em média não agrega valor ao projeto. / [en] The search for alternative energy sources has become critical issue for the economic development of the world, and biomass is an alternative to be considered. In this study we analyze the case of a producer of wood fiber boards, in which wood chips may be either used as raw material for the wood boards or as fuel to generate energy. In this segment, the production process generates large volumes of forest residues that can be used as fuel. The objective of this study is to determine the economic feasibility of installing a cogeneration energy process fueled with forest residues and natural gas. We assume that managers have two alternatives: use residues and gas for heat energy generation, releasing the wood chips for the production of MDF and HDF, or use forest residues, gas and wood chips as fuel, selling the surplus energy in the short term market. The valuation was based on the Real Options Theory considering the managerial flexibility to select the optimal final destination of the wood chips (fiber board or energy) along time. One of the innovations of this work is the incorporation of seasonal factors in the energy price volatility, adapting the stochastic process to the specificities of the Brazilian market. The main uncertainty, energy price (PLD), was based on the Mean Reversion Model with Jumps of Clewlow, Strickland and Kaminski (2000). The results indicate that the option to sell the surplus power is not financially viable and on average adds no value to the project.

Electricity markets may become more sensitive to weather conditions because of higher penetration of renewable energy sources and climatic changes. Albania is 100% reliant on hydropower for its domestic energy generation, making this country compelling to investigate as it is highly sensitive to changing weather conditions. We use an ARMA-GARCH model to investigate whether weather and economic factors had a relationship with monthly hydroelectricity prices in the Albanian Energy Market in the period 2013-2018. We find that electricity price is affected by variations in weather and is not utterly robust to extreme hydrological changes. Generally, our dependent variable appears to be particularly influenced by air pressure followed by temperature and rainfall. We also perceive that there is a relationship between economic factors and hydroelectricity prices, where residual supply appears to have a significant negative relationship with our dependent variable. However, we were originally anticipating a higher dependency of electricity prices on weather conditions, due to the inflated hydro-power reliance for electricity production in the Albanian Energy Market. This effect is offset by several factors, where the state monopolized behaviour of the energy sector occupies a predominant influence on our results.

Market prices in the liberalized European electricity system play a crucial role in promoting competition, ensuring grid stability, and maximizing profits for market participants. Accurate electricity price forecasting algorithms have, therefore, become increasingly important in this competitive market. However, existing evaluations of forecasting models primarily focus on overall accuracy, overlooking the underlying causality of the predictions. The thesis explores two state-of-the-art forecasters, the deep neural network (DNN) and the Lasso Estimated AutoRegressive (LEAR) models, in the EPEX-BE and Nord Pool markets. The aim is to understand if their predictions can be trusted in more general settings than the limited context they are trained in. If the models produce poor predictions in extreme conditions or if their predictions are inconsistent with reality, they cannot be relied upon in the real world where these forecasts are used in downstream decision-making activities. The results show that for the EPEX-BE market, the DNN model outperforms the LEAR model in terms of overall accuracy. However, the LEAR model performs better in predicting negative prices, while the DNN model performs better in predicting price spikes. For the Nord Pool market, a simpler DNN model is more accurate for price forecasting. In both markets, the models exhibit behaviours inconsistent with reality, making it challenging to trust the models’ predictions. Overall, the study highlights the importance of understanding the underlying causality of forecasting models and the limitations of relying solely on overall accuracy metrics. / Priserna på den liberaliserade europeiska elmarknaden spelar en avgörande roll för att främja konkurrens, säkerställa stabilitet i elnätet och maximera aktörernas vinster. Exakta prisprognoalgoritmer har därför blivit allt viktigare på denna konkurrensutsatta marknad. Existerande utvärderingar av prognosverktyg fokuserar emellertid på den övergripande noggrannheten och förbiser de underliggande orsakssambanden i prognoserna. Denna rapport utforskar två moderna prognosverktyg, DNN (Deep Neural Network) och LEAR (Lasso Estimated AutoRegressive) på elmarknaderna i Belgien respektive Norden. Målsättningen är att förstå om deras prognoser är pålitliga i mer allmänna sammanhang än det begränsade sammahang som de är tränade i. Om modellerna producerar dåliga prognoser under extrema förhållanden eller om deras prognoser inte överensstämmer med verkligheten så kan man inte förlita sig på dem i den verkliga världen, där prognoserna ligger till grund för beslutsfattande aktiviteter. Resultaten för Belgien visar att DNN-modellen överträffar LEAR-modellen när det gäller övergripande noggrannhet. LEAR-modellen presterar dock bättre när det gäller att förutse negativa priser, medan DNN-modellen presterar bättre när det gäller prisspikar. På den nordiska elmarknaden är en enklare DNN-modell mer noggrann för prisprognoser. På båda marknaden visar modellerna beteenden som inte överensstämmer med verkligheten, vilket gör det utmanande att lita på modellernas prognoser. Sammantaget belyser studien vikten av att förstå de underliggande orsakssambanden i prognosmodellerna och begränsningarna med att enbart förlita sig på övergripande mått på noggrannhet.