The deregulation of energy markets around the world, including power markets has changed the way operating assets in these markets are managed. Independent power asset owners and even utilities operating in these markets no longer operate their assets based on the cost of service approach that prevailed under regulation. Just as in other competitive markets, the objectives of asset owners in power markets revolve around maximizing profit for their shareholders. To this end, financial valuation of physical assets in power markets should incorporate different strategies that are used by asset operators to maximize profit. A lot of observed strategies in power markets are driven by a number of factors, the key among which are: • asset operators are no longer obligated to supply service or manage their assets in certain prescribed ways, rather they have rights to operate, within applicable market rules, using techniques that maximize their profits, • revenues are driven by uncertain market factors, including power price, cost and/or availability of fuel stock and technical uncertainties, and • power assets have physical operating and equipment constraints and limits. Having flexibilties (“options”) to optimize their assets (inline with shareholders’ objectives), rational asset managers react strategically to gradual arrival of information , given applicable equipment constraints, by revising previous decisions in such a way that only optimal (or near optimal) decisions are implemented. As a result, the appropriate approach to valuing power assets in competitive markets must account for managerial flexibilities or “real options” in the presence of uncertainties and technical constraints. The focus of this work is to develop a robust valuation framework for physical power assets operating in competitive markets such as peaking or mid-merit thermal power plants and baseload power plants. The goal is to develop a modeling framework that can be adapted to different energy assets with different types of operating flexibilities and technical constraints and which can be employed for various purposes such as capital budgeting, business planning, risk management and strategic bidding planning among others. The valuation framework must also be able to capture the reality of power market rules and opportunities, as well as technical constraints of different assets. The modeling framework developed conceptualizes operating flexibilities of power assets as “switching options’ whereby the asset operator decides at every decision point whether to switch from one operating mode to another mutually exclusive mode, within the limits of the equipment constraints of the asset. As a current decision to switch operating modes (in the face of current realization of relevant uncertainty factors) may affect future operating flexibilities of the asset and hence cash flows , a dynamic optimization framework is employed. The developed framework accounts for the uncertain nature of key value drivers by representing them with appropriate stochastic processes. Specifically, the framework developed conceptualizes the operation of a power asset as a multi-stage decision making problem where the operator has to make a decision at every stage to alter operating mode given currently available information about key value drivers. The problem is then solved dynamically by decomposing it into a series of two-stage sub-problems according to Bellman’s optimality principle. The solution algorithm employed is the Least Squares Monte Carlo (LSM) method. The developed valuation framework was adapted for a gas-fired thermal power plant, a peaking hydroelectric power plant and a baseload power plant. This work built on previously published real options valuation methodologies for gas-fired thermal power plants by factoring in uncertainty from gas supply/consumption imbalance which is usually faced by gas-fired power generators. This source of uncertainty which has yet to be addressed in the literature, in the context of real options valuation, arises because of mismatch between natural gas and electricity wholesale markets. Natural gas markets in North America operate on a day-ahead basis while power plants are dispatched in real time. Inability of a power generator to match its gas supply and consumption in real time, leading to unauthorized gas over-run or under-run, attracts penalty charges from the gas supplier to the extent that the generator can not manage the imbalance through other means. A savvy gas-fired power plant operator will factor in the potential costs of gas imbalance into its operating strategies resulting in optimal operating decisions that may be different from when gas-imbalance is not considered. By considering an illustrative power plant operating in Ontario, we show effects of gas-imbalance on dispatch strategies on a daily cycling operation basis and the resulting impact on net revenue. Results show that a gas-fired power plant is over-valued by ignoring the impacts of gas imbalance on valuation. Similarly, we employ the developed valuation framework to value a peaking hydroelectric power plant. This application also builds on previous real options valuation work for peaking hydroelectric power plants by considering their operations in a joint energy and ancillary services market. Specifically, the valuation model is developed to capture the value of a peaking power plant whose owner has the flexibility to participate in a joint operating reserve market and an energy market, which is currently the case in the Ontario wholesale power market. The model factors in water inflow uncertainty into the reservoir forebay of a hydroelectric facility and also considers uncertain energy and operating reserve prices. The switching options considered include (i) a joint energy and operating reserve bid (ii) an energy only bid and (iii) a do nothing (idle) strategy. Being an energy limited power plant, by doing nothing at a decision interval, the power asset operator is able to time-shift scarce water for use at a future period when market situations are expected to be better. An illustrative example considered shows the impact of the different value drivers on the plant’s value and dispatch strategies. Results show that by ignoring the flexibility of the asset owner to participate in an operating reserve market, a peaking hydroelectric power plant is undervalued. Finally, the developed valuation framework was employed to optimize life-cycle management decisions of a baseload power plant, such as a nuclear power plant. The applicability of real-options framework to the operations of baseload power plants has not attracted much attention in the literature given their inflexibility with respect to short-term operation. However, owners of baseload power plants, such as nuclear plants, have the right to optimize scheduling and spending of life cycle management projects such as preventative maintenance and equipment inspection. Given uncertainty of long-term value drivers, including power prices, equipment performance and the relationship between current life cycle spending and future equipment degradation, optimization is carried out with the objective of minimizing overall life-cycle related costs. These life-cycle costs include (i) lost revenue during planned and unplanned outages (ii) potential costs of future equipment degradation due to inadequate preventative maintenance and (iii) the direct costs of implementing the life-cycle projects. The switching options in this context include the option to shutdown the power plant in order to execute a given preventative maintenance and inspection project and the option to keep the option “alive” by choosing to delay a planned life-cycle activity. Results of an illustrative example analyzed show that the flexibility of the asset owner to delay spending or to suspend it entirely affects the asset’s value accordingly and should be factored into valuation. Applications can be found for the developed framework and models in different areas important to firms operating in competitive energy markets. These areas include capital budgeting, trading, risk management, business planning and strategic/tactitcal bidding among others.

The deregulation of energy markets around the world, including power markets has changed the way operating assets in these markets are managed. Independent power asset owners and even utilities operating in these markets no longer operate their assets based on the cost of service approach that prevailed under regulation. Just as in other competitive markets, the objectives of asset owners in power markets revolve around maximizing profit for their shareholders. To this end, financial valuation of physical assets in power markets should incorporate different strategies that are used by asset operators to maximize profit. A lot of observed strategies in power markets are driven by a number of factors, the key among which are: • asset operators are no longer obligated to supply service or manage their assets in certain prescribed ways, rather they have rights to operate, within applicable market rules, using techniques that maximize their profits, • revenues are driven by uncertain market factors, including power price, cost and/or availability of fuel stock and technical uncertainties, and • power assets have physical operating and equipment constraints and limits. Having flexibilties (“options”) to optimize their assets (inline with shareholders’ objectives), rational asset managers react strategically to gradual arrival of information , given applicable equipment constraints, by revising previous decisions in such a way that only optimal (or near optimal) decisions are implemented. As a result, the appropriate approach to valuing power assets in competitive markets must account for managerial flexibilities or “real options” in the presence of uncertainties and technical constraints. The focus of this work is to develop a robust valuation framework for physical power assets operating in competitive markets such as peaking or mid-merit thermal power plants and baseload power plants. The goal is to develop a modeling framework that can be adapted to different energy assets with different types of operating flexibilities and technical constraints and which can be employed for various purposes such as capital budgeting, business planning, risk management and strategic bidding planning among others. The valuation framework must also be able to capture the reality of power market rules and opportunities, as well as technical constraints of different assets. The modeling framework developed conceptualizes operating flexibilities of power assets as “switching options’ whereby the asset operator decides at every decision point whether to switch from one operating mode to another mutually exclusive mode, within the limits of the equipment constraints of the asset. As a current decision to switch operating modes (in the face of current realization of relevant uncertainty factors) may affect future operating flexibilities of the asset and hence cash flows , a dynamic optimization framework is employed. The developed framework accounts for the uncertain nature of key value drivers by representing them with appropriate stochastic processes. Specifically, the framework developed conceptualizes the operation of a power asset as a multi-stage decision making problem where the operator has to make a decision at every stage to alter operating mode given currently available information about key value drivers. The problem is then solved dynamically by decomposing it into a series of two-stage sub-problems according to Bellman’s optimality principle. The solution algorithm employed is the Least Squares Monte Carlo (LSM) method. The developed valuation framework was adapted for a gas-fired thermal power plant, a peaking hydroelectric power plant and a baseload power plant. This work built on previously published real options valuation methodologies for gas-fired thermal power plants by factoring in uncertainty from gas supply/consumption imbalance which is usually faced by gas-fired power generators. This source of uncertainty which has yet to be addressed in the literature, in the context of real options valuation, arises because of mismatch between natural gas and electricity wholesale markets. Natural gas markets in North America operate on a day-ahead basis while power plants are dispatched in real time. Inability of a power generator to match its gas supply and consumption in real time, leading to unauthorized gas over-run or under-run, attracts penalty charges from the gas supplier to the extent that the generator can not manage the imbalance through other means. A savvy gas-fired power plant operator will factor in the potential costs of gas imbalance into its operating strategies resulting in optimal operating decisions that may be different from when gas-imbalance is not considered. By considering an illustrative power plant operating in Ontario, we show effects of gas-imbalance on dispatch strategies on a daily cycling operation basis and the resulting impact on net revenue. Results show that a gas-fired power plant is over-valued by ignoring the impacts of gas imbalance on valuation. Similarly, we employ the developed valuation framework to value a peaking hydroelectric power plant. This application also builds on previous real options valuation work for peaking hydroelectric power plants by considering their operations in a joint energy and ancillary services market. Specifically, the valuation model is developed to capture the value of a peaking power plant whose owner has the flexibility to participate in a joint operating reserve market and an energy market, which is currently the case in the Ontario wholesale power market. The model factors in water inflow uncertainty into the reservoir forebay of a hydroelectric facility and also considers uncertain energy and operating reserve prices. The switching options considered include (i) a joint energy and operating reserve bid (ii) an energy only bid and (iii) a do nothing (idle) strategy. Being an energy limited power plant, by doing nothing at a decision interval, the power asset operator is able to time-shift scarce water for use at a future period when market situations are expected to be better. An illustrative example considered shows the impact of the different value drivers on the plant’s value and dispatch strategies. Results show that by ignoring the flexibility of the asset owner to participate in an operating reserve market, a peaking hydroelectric power plant is undervalued. Finally, the developed valuation framework was employed to optimize life-cycle management decisions of a baseload power plant, such as a nuclear power plant. The applicability of real-options framework to the operations of baseload power plants has not attracted much attention in the literature given their inflexibility with respect to short-term operation. However, owners of baseload power plants, such as nuclear plants, have the right to optimize scheduling and spending of life cycle management projects such as preventative maintenance and equipment inspection. Given uncertainty of long-term value drivers, including power prices, equipment performance and the relationship between current life cycle spending and future equipment degradation, optimization is carried out with the objective of minimizing overall life-cycle related costs. These life-cycle costs include (i) lost revenue during planned and unplanned outages (ii) potential costs of future equipment degradation due to inadequate preventative maintenance and (iii) the direct costs of implementing the life-cycle projects. The switching options in this context include the option to shutdown the power plant in order to execute a given preventative maintenance and inspection project and the option to keep the option “alive” by choosing to delay a planned life-cycle activity. Results of an illustrative example analyzed show that the flexibility of the asset owner to delay spending or to suspend it entirely affects the asset’s value accordingly and should be factored into valuation. Applications can be found for the developed framework and models in different areas important to firms operating in competitive energy markets. These areas include capital budgeting, trading, risk management, business planning and strategic/tactitcal bidding among others.

abstract: This work introduces self-organizing techniques to reduce the complexity and burden of coordinating distributed energy resources (DERs) and microgrids that are rapidly increasing in scale globally. Technical and financial evaluations completed for power customers and for utilities identify how disruptions are occurring in conventional energy business models. Analyses completed for Chicago, Seattle, and Phoenix demonstrate site-specific and generalizable findings. Results indicate that net metering had a significant effect on the optimal amount of solar photovoltaics (PV) for households to install and how utilities could recover lost revenue through increasing energy rates or monthly fees. System-wide ramp rate requirements also increased as solar PV penetration increased. These issues are resolved using a generalizable, scalable transactive energy framework for microgrids to enable coordination and automation of DERs and microgrids to ensure cost effective use of energy for all stakeholders. This technique is demonstrated on a 3-node and 9-node network of microgrid nodes with various amounts of load, solar, and storage. Results found that enabling trading could achieve cost savings for all individual nodes and for the network up to 5.4%. Trading behaviors are expressed using an exponential valuation curve that quantifies the reputation of trading partners using historical interactions between nodes for compatibility, familiarity, and acceptance of trades. The same 9-node network configuration is used with varying levels of connectivity, resulting in up to 71% cost savings for individual nodes and up to 13% cost savings for the network as a whole. The effect of a trading fee is also explored to understand how electricity utilities may gain revenue from electricity traded directly between customers. If a utility imposed a trading fee to recoup lost revenue then trading is financially infeasible for agents, but could be feasible if only trying to recoup cost of distribution charges. These scientific findings conclude with a brief discussion of physical deployment opportunities. / Dissertation/Thesis / Doctoral Dissertation Systems Engineering 2019

The world is right now in a global transition from a fossil fuel dependency towards an electrified society based on green and renewable energy. Investments in power grid capacity are therefore needed to meet the increased future demand which this transition implicates. One part of this is the expansion of intermittent energy sources, such as wind and solar power. Even though these sources have benefits in form of cheap and green energy, they have other characteristics that need to be addressed. Per definition, intermittent power sources cannot produce energy on demand since they are dependent on weather conditions such as wind and sun. This induces a second problem which is that it can be hard to predict the production from intermittent power sources, especially wind, which increases the volatility in the power market. Because of these characteristics, the expansion of wind power has increased the volume traded on the intraday power market. The intermittent energy surge, emphasizes the need of a good trading strategy for balance responsible parties to handle the increased trading volume and volatility. The prupose of this report is to introduce the elements which affect intraday power trading, formulate the fundamentals of a power trading strategy and thereafter explore how predictive models can be used in such a strategy. This includes predicting regulating and intraday market prices using linear regression models, neural networks and LSTM-models. Furthermore, the report highlights underlying properties which affects the predictive power of a prediction model used to forecast wind power production. Regulating prices can be predicted well using both linear regression models and more complex deep learning models based on weather and market data. Both approaches are better than using a simple model based on the latest regulating and market price, since the simple model tends to fall short in a volatile market. Overall, the deep learning models performs the best.  The difference in result when predicting the volume weighted average price on the intraday market, using linear regression and machine learning, are not as substantial. In fact, the linear models tends to outperform the machine learning models in some instaces. The conclusion when analyzing how underlying properties affect wind power prediction models is that how far ahead the model predicts is not the key factor affecting predictive power. Instead, the production volume predicted has a larger effect.

As regras definidas pelo Decreto 5.163/2004 trazem incentivos e penalidades aos Distribuidores no processo de apresentação de suas declarações de necessidades de compra de energia ao Ministério de Minas e Energia. Nesse sentido, é importante para os Geradores estabelecer uma metodologia robusta para prever o comportamento dos agentes de distribuição com confiabilidade razoável, de forma a permitir uma preparação adequada para os leilões de que pretendem participar e, adicionalmente, simular os cenários pós-leilões de modo a compreender os efeitos dos preços e volumes contratados no ambiente regulado sobre as condições de contratação no ambiente livre. Este trabalho propõe-se a analisar as referidas regras, apresentando um modelo de otimização utilizando Algoritmos Genéticos que simula o comportamento das distribuidoras, obtendo-se uma importante ferramenta de apoio à definição de estratégias de comercialização de uma empresa geradora. / The rules defined by the Decree 5.163/2004 bring incentives and penalties for Distribution companies to present their power purchase necessity declaration for the Ministry of Mines and Energy. In this sense, it is important for the Generation companies to establish a robust methodology for predicting Distribution companies behavior with enough accountability in order to allow an adequate preparation for the auctions in which those agents intend to participate and, additionally, simulate post auctions scenarios in order to understand the effects of prices and contracted volumes in the regulated environment over the free market contracting conditions. This work is supposed to analyze those rules, presenting an optimization model using Genetic Algorithms, which simulates Distribution companies behavior, getting an important power trading strategy decision support tool for a Generation Company.

As regras definidas pelo Decreto 5.163/2004 trazem incentivos e penalidades aos Distribuidores no processo de apresentação de suas declarações de necessidades de compra de energia ao Ministério de Minas e Energia. Nesse sentido, é importante para os Geradores estabelecer uma metodologia robusta para prever o comportamento dos agentes de distribuição com confiabilidade razoável, de forma a permitir uma preparação adequada para os leilões de que pretendem participar e, adicionalmente, simular os cenários pós-leilões de modo a compreender os efeitos dos preços e volumes contratados no ambiente regulado sobre as condições de contratação no ambiente livre. Este trabalho propõe-se a analisar as referidas regras, apresentando um modelo de otimização utilizando Algoritmos Genéticos que simula o comportamento das distribuidoras, obtendo-se uma importante ferramenta de apoio à definição de estratégias de comercialização de uma empresa geradora. / The rules defined by the Decree 5.163/2004 bring incentives and penalties for Distribution companies to present their power purchase necessity declaration for the Ministry of Mines and Energy. In this sense, it is important for the Generation companies to establish a robust methodology for predicting Distribution companies behavior with enough accountability in order to allow an adequate preparation for the auctions in which those agents intend to participate and, additionally, simulate post auctions scenarios in order to understand the effects of prices and contracted volumes in the regulated environment over the free market contracting conditions. This work is supposed to analyze those rules, presenting an optimization model using Genetic Algorithms, which simulates Distribution companies behavior, getting an important power trading strategy decision support tool for a Generation Company.

This report explores the impacts of variable renewable energy (VRE) on power trading in the European wholesale electricity market. The intricate operation of a typical power exchange in Europe is accompanied by an equally complex balancing system. The increasing amount of VRE in the power system, such as wind and solar power, has far-reaching impacts for power traders in both this electricity market and the corresponding balancing system. As a result, the electricity market is evolving in unprecedented ways and new participants are entering the playing field to capitalize on the changing dynamics caused by VRE generators. One novel participant, the virtual power plant (VPP), possesses an advantage over other market participants by aggregating VRE generators with controllable renewable energy generators, like biogas and hydro plants, into one entity. This allows the VPP to both gain access to live VRE production data that larger plants don’t have, which it then utilizes to remotely dispatch various subpools of assets, and to provide balancing services to the grid. Subsequently, VPPs are able to trade VRE and other renewable electricity superiorly on the same spot markets and balancing systems as large central power plants and industrial consumers. The report asserts that VPP traders can earn profits through means of innovative trading strategies that exploit predictable market impacts caused by VRE power through a robust understanding of the electricity market and their unique access to data. / Denna rapport undersöker effekterna av variabel förnybar energi (VRE) på krafthandeln på den europeiska elhandelsmarknaden för stora aktörer. Den komplicerade driften av ett typiskt kraftutbyte i Europa åtföljs av ett lika komplicerat balanseringssystem. Den ökande mängden VRE i kraftsystemet, såsom vind- och solkraft, har långtgående effekter för krafthandlare på både denna elmarknad och motsvarande balanseringssystem. Som ett resultat utvecklas elmarknaden på enastående sätt och nya deltagare kommer in på spelplanen för att dra nytta av den förändrade dynamiken som orsakas av VRE-generatorer. En ny spelare, det virtuella kraftverket (VPP), har en fördel jämfört med andra marknadsaktörer genom att samla VRE-generatorer med styrbara förnybara energiproducenter, som biogas och vattenkraftverk, till en enhet. Detta gör att VPP både kan få tillgång till live VRE-produktionsdata som större anläggningar inte har, som den sedan använder för att distribuera olika underpooler av tillgångar och för att tillhandahålla balanstjänster till nätet. Därefter kan VPP: er handla med VRE och annan förnybar el på ett överlägset sätt på samma spotmarknader och balanseringssystem som stora centrala kraftverk och industrikonsumenter. Rapporten visar att VPP-handlare kan göra vinster genom innovativa handelsstrategier som utnyttjar förutsägbara marknadseffekter orsakade av VRE-kraft genom en detaljerad förståelse för elmarknaden och unik tillgång till data för produktionen av förnybar energi / <p>QC 20201118</p>

Este trabalho aborda a inserção das fontes incentivadas de energia - PCHs, Biomassa, Eólica e Solar - no ambiente de comercialização de energia elétrica do setor elétrico brasileiro, mais especificamente na CCEE. Apesar de a legislação ter criado o consumidor especial em 1998, a falta de definição do processo de comercialização das fontes incentivadas impediu, durante anos, o crescimento deste mercado. Porém, para propor uma solução para esta implementação, é importante conhecer as atuais regras que regem a comercialização de energia, identificando, assim, as possibilidades de adequação. Por se tratar de um problema atual, várias propostas de solução foram apresentadas por agentes do setor através da Audiência Pública 33/05. Essas propostas também são analisadas no trabalho, de forma que a solução apresentada considera as vantagens e desvantagens do que foi discutido pelo mercado. Por fim, a simulação da solução proposta indica a sua viabilidade de implantação e permite uma análise crítica do mercado de fontes incentivadas e das Regras de Comercialização da CCEE. / This work approaches the insertion of the renewable energy sources - SHP, Biomass, Wind and Solar - in the Brazilian power trading environment, more specifically in Wholesale Market Administrator. Although the legislation created the special consumer in 1998, the lack of definition in the renewable energy trading process hindered this market development during years. However, to consider a solution for this implementation, it is important to know the current rules that conduct the power trading, therefore, identifying the possibilities of adjustment. Since this is a current subject, some proposals had been presented by sector agents through the Public Hearing 33/05. In this work, these proposals are also analyzed, so the presented solution considers the advantages and disadvantages of what was discussed by the market agents. Finally, the simulation of the proposed solution indicates its implementation viability and allows a critical analysis of the renewable energy sources market and the Trading Rules of the Wholesale Market Administrator.

Este trabalho aborda a inserção das fontes incentivadas de energia - PCHs, Biomassa, Eólica e Solar - no ambiente de comercialização de energia elétrica do setor elétrico brasileiro, mais especificamente na CCEE. Apesar de a legislação ter criado o consumidor especial em 1998, a falta de definição do processo de comercialização das fontes incentivadas impediu, durante anos, o crescimento deste mercado. Porém, para propor uma solução para esta implementação, é importante conhecer as atuais regras que regem a comercialização de energia, identificando, assim, as possibilidades de adequação. Por se tratar de um problema atual, várias propostas de solução foram apresentadas por agentes do setor através da Audiência Pública 33/05. Essas propostas também são analisadas no trabalho, de forma que a solução apresentada considera as vantagens e desvantagens do que foi discutido pelo mercado. Por fim, a simulação da solução proposta indica a sua viabilidade de implantação e permite uma análise crítica do mercado de fontes incentivadas e das Regras de Comercialização da CCEE. / This work approaches the insertion of the renewable energy sources - SHP, Biomass, Wind and Solar - in the Brazilian power trading environment, more specifically in Wholesale Market Administrator. Although the legislation created the special consumer in 1998, the lack of definition in the renewable energy trading process hindered this market development during years. However, to consider a solution for this implementation, it is important to know the current rules that conduct the power trading, therefore, identifying the possibilities of adjustment. Since this is a current subject, some proposals had been presented by sector agents through the Public Hearing 33/05. In this work, these proposals are also analyzed, so the presented solution considers the advantages and disadvantages of what was discussed by the market agents. Finally, the simulation of the proposed solution indicates its implementation viability and allows a critical analysis of the renewable energy sources market and the Trading Rules of the Wholesale Market Administrator.