This thesis presents a model that simulates and solves power system dispatch problems utilizing stochastic linear programming. The model features the ability to handle single period, multiple bus, linear DC approximated systems. It determines capacity, energy, and reserve quantities while accounting for N-1 contingency scenarios (single loss of either generator or line) on the network. Market systems applying to this model are also proposed, covering multiple real-time, day-ahead, and hybrid versions of consumer costing, transmission operator payment, and generator remuneration schemes. The model and its market schemes are applied to two test systems to verify its viability: a small 6-bus system and a larger 66-bus system representing the Ontario electricity network.

One of the main concerns with the introduction of competition in the power sector is the strategic behaviour of market participants. Computable models of strategic behaviour are becoming increasingly important to understand the complexities of competition. Such models can help analyze market designs and regulatory policies. In this thesis, further developments on the modelling and analysis of strategic behaviour in electricity markets are presented. This thesis work has been conducted along three research lines. <br /><br /> In the first research line, an oligopolistic model of a joint energy and spinning reserve market is formulated to analyze imperfect competition. Strategic behaviour is introduced by means of conjectured functions. With this integrated formulation for imperfect competition, the opportunity cost between generation and spinning reserve has been analytically derived. Besides, inter-temporal and energy constraints, and financial transmission rights are taken into account. Under such considerations, competition in electricity markets is modelled with more realism. The oligopolistic model is formulated as an equilibrium problem in terms of complementarity conditions. <br /><br /> In the second research line, a methodology to screen and mitigate the potential exacerbation of market power due to the ownership of financial transmission rights is presented. Hedging position ratios are computed to quantify the hedging level of financial transmission rights. They are based on the actual impact that each participant has in the energy market, and on the potential impact that it would have with the ownership of financial transmission rights. Thus, hedging position ratios are used to identify the potential gambling positions from the transmission rights bidders, and, therefore, used to prioritize critical positions in the auction for transmission rights. <br ><br /> In the last research line, alternative equilibrium models of markets for financial transmission rights are formulated. The proposed equilibrium framework is more natural and flexible for modelling markets than the classic cost-minimization markets. Different markets for financial transmission rights are modelled, namely: i) forwards, ii) options, and iii) joint forwards and options. Moreover, one-period, multi-period and multi-round markets for forwards are derived. These equilibrium models are proposed to analyze the bidding strategies of market participants. The potential impact of bidders on congestion prices is modelled by means of conjectured transmission price functions.

This thesis presents a model that simulates and solves power system dispatch problems utilizing stochastic linear programming. The model features the ability to handle single period, multiple bus, linear DC approximated systems. It determines capacity, energy, and reserve quantities while accounting for N-1 contingency scenarios (single loss of either generator or line) on the network. Market systems applying to this model are also proposed, covering multiple real-time, day-ahead, and hybrid versions of consumer costing, transmission operator payment, and generator remuneration schemes. The model and its market schemes are applied to two test systems to verify its viability: a small 6-bus system and a larger 66-bus system representing the Ontario electricity network.

This dissertation applies a variety of quantitative methods to European electricity market data to enable us to detect, understand, and eventually mitigate market imperfections. The empirical data indicate that market power and barriers to cross-border trade partially explain today’s market failures. Briefly, the five key findings of this dissertation are: First, we observe a decoupling between German electricity prices and fuel cost, even though British electricity prices are largely explained by short-run cost factors. Second, we demonstrate that rising prices of European Union emission allowances (EUA) have a greater impact on German wholesale electricity prices than falling EUA prices. Third, we reject the assumption of full integration of European wholesale electricity markets in 2002-2006; for several pairs of countries, the weaker hypothesis of (bilateral) convergence is accepted (i.e. efforts to develop a single European market for electricity have been only partially successful). Fourth, we observe that daily auction prices of scarce cross-border transmission capacities are insufficient to explain the persistence of international price differentials. Empirically, our findings confirm the insufficiency of explicit capacity auctions as stated in the theoretical literature. Fifth, we identify inefficiencies in the market behavior for the interconnector linking France and the United Kingdom (UK), for which several explanations, including market power, may be plausible.

Cap-and-trade is the most discussed CO2 emissions control scheme in the U.S. It is a market-based mechanism that has been used previously to successfully reduce the levels of SO2 and NOx emitted by power generators. Since electricity generators are responsible for about 40% of the CO2 emissions in the U.S., the implementation of CO2 cap-and-trade will have a significant impact on electric power generation systems. In particular, cap-and-trade will influence the investment decisions made by power generators. These decisions in turn, will affect electricity prices and demand. If the allowances (or emission permits) created by a cap-and-trade program are auctioned, the government will collect a significant amount of money that can be redistributed back to the electricity market participants to mitigate increases on electricity prices due to cap-and-trade and also, to increase the market share of low-emission generators. In this dissertation, we develop two models to analyze the impact of CO2 cap-and-trade on electric power generation systems. The first model is intended to be used by power generators in a restructured market to evaluate investment decisions under different CO2 cap-and-trade programs for a given time horizon and a given forecast in demand growth. The second model is intended to aid policymakers in developing optimal CO2 revenue redistribution policies via subsidies for low-emission generators. Through the development of these two models, our underlying objective is to provide analysis tools for policymakers and market participants so that they can make informed decisions about the design of cap-and-trade programs and about the market actions they can take if such programs are implemented.

With a significant number of states in the U.S. and countries around the world trading electricity in restructured markets, a sizeable proportion of capacity expansion in the future will have to take place in market-based environments. However, since a majority of the literature on capacity expansion is focused on regulated market structures, there is a critical need for comprehensive capacity expansion models targeting restructured markets. In this research, we develop a two-level game-theoretic model, and a novel solution algorithm that incorporates risk due to volatilities in profit (via CVaR), to obtain multi-period, multi-player capacity expansion plans. To solve the matrix games that arise in the generation expansion planning (GEP) model, we first develop a novel value function approximation based reinforcement learning (RL) algorithm. Currently there exist only mathematical programming based solution approaches for two player games and the N-player extensions in literature still have several unresolved computational issues. Therefore, there is a critical void in literature for finding solutions of N-player matrix games. The RL-based approach we develop in this research presents itself as a viable computational alternative. The solution approach for matrix games will also serve a much broader purpose of being able to solve a larger class of problems known as stochastic games. This RL-based algorithm is used in our two-tier game-theoretic approach for obtaining generation expansion strategies. Our unique contributions to the GEP literature include the explicit consideration of risk due to volatilities in profit and individual risk preference of generators. We also consider transmission constraints, multi-year planning horizon, and multiple generation technologies. The applicability of the twotier model is demonstrated using a sample power network from PowerWorld software. A detailed analysis of the model is performed, which examines the results with respect to the nature of Nash equilibrium solutions obtained, nodal prices, factors affecting nodal prices, potential for market power, and variations in risk preferences of investors. Future research directions include the incorporation of comprehensive cap-and-trade and renewable portfolio standards components in the GEP model.

This thesis consists of four essays, mainly related to the fields of industrial organization and political economy. The focus is on deregulation of electricity retail markets and on the continued regulation of parts of such markets after the introduction of competition. The first essay is an empirical essay on the causes of deregulation. The timing of implementation of competition in retail electricity markets in the United States, Canada, Europe, Australia and New Zealand is studied. One conclusion is that there exist important qualitative differences between the United States and Europe. While deregulation in the United States to a large extent seems to have been driven by consumer interest concerns, the influence from interest groups is more pronounced in Europe.The second (theoretical) and third (empirical) essays deal with the interaction between the regulation of distribution networks and the retail market. When the regulated and unregulated operations are conducted within vertically integrated companies the regulation may not only have an effect on the regulated market, but also affect the behavior in the unregulated market. In the third essay a test that uses prices to detect patterns of cross-subsidization is developed and used on Norwegian data. Especially the effects of a regulatory change on cross-subsidization behavior are analyzed. The results both highlight the importance of a well-designed regulation of the regulated market and give support to requirements of vertical separation between regulated and unregulated operations.The fourth essay is a study of domestic electricity demand. It is thus somewhat different than the other papers since it is not directly connected to the issue of electricity market deregulation. Since the energy sector is an essential part of any modern economy and energy production has considerable environmental effects, the sector has for a long time been subject to political interventions. To some extent the policy instruments available to the legislator are reduced by deregulations. The use of taxes to affect prices and thus the demand for electricity may at the same time become an even more important policy instrument and more difficult to implement due to the internationalization of the electricity market. / Diss. Stockholm : Handelshögsk., 2003

abstract: This work presents research on practices in the day-ahead electric energy market, including replication practices and reliability coordinators used by some market operators to demonstrate the impact these practices have on market outcomes. The practice of constraint relaxations similar to those an Independent System Operator (ISO) might perform in day-ahead market models is implemented. The benefits of these practices are well understood by the industry; however, the implications these practices have on market outcomes and system security have not been thoroughly investigated. By solving a day-ahead market model with and without select constraint relaxations and comparing the resulting market outcomes and possible effects on system security, the effect of these constraint relaxation practices is demonstrated. Proposed market solutions are often infeasible because constraint relaxation practices and approximations that are incorporated into market models. Therefore, the dispatch solution must be corrected to ensure its feasibility. The practice of correcting the proposed dispatch solution after the market is solved is known as out-of-market corrections (OMCs), defined as any action an operator takes that modifies a proposed day-ahead dispatch solution to ensure operating and reliability requirements. The way in which OMCs affect market outcomes is illustrated through the use of different corrective procedures. The objective of the work presented is to demonstrate the implications of these industry practices and assess the impact these practices have on market outcomes. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2016

abstract: The main objective of this research is to develop an integrated method to study emergent behavior and consequences of evolution and adaptation in engineered complex adaptive systems (ECASs). A multi-layer conceptual framework and modeling approach including behavioral and structural aspects is provided to describe the structure of a class of engineered complex systems and predict their future adaptive patterns. The approach allows the examination of complexity in the structure and the behavior of components as a result of their connections and in relation to their environment. This research describes and uses the major differences of natural complex adaptive systems (CASs) with artificial/engineered CASs to build a framework and platform for ECAS. While this framework focuses on the critical factors of an engineered system, it also enables one to synthetically employ engineering and mathematical models to analyze and measure complexity in such systems. In this way concepts of complex systems science are adapted to management science and system of systems engineering. In particular an integrated consumer-based optimization and agent-based modeling (ABM) platform is presented that enables managers to predict and partially control patterns of behaviors in ECASs. Demonstrated on the U.S. electricity markets, ABM is integrated with normative and subjective decision behavior recommended by the U.S. Department of Energy (DOE) and Federal Energy Regulatory Commission (FERC). The approach integrates social networks, social science, complexity theory, and diffusion theory. Furthermore, it has unique and significant contribution in exploring and representing concrete managerial insights for ECASs and offering new optimized actions and modeling paradigms in agent-based simulation. / Dissertation/Thesis / Ph.D. Industrial Engineering 2013

La chaine d’approvisionnement énergétique a fortement évolué aux cours des 20 dernières années. La libéralisation des marchés de l’électricité et les nouvelles technologies ont fortement influencé la manière d’envisager la production et la transmission d’électricité. Les modèles mathématiques classiques utilisés dans les problèmes lié à l’énergie ont besoin d’être revus pour intégrer les contraintes pratiques modernes.Un problème classique pour un Compagnie Génératrice (CG) est le problème de Unit Commitment (UC) qui consiste à établir un plan de production pour une demande en électricité connue. Lorsque ce problème fut considéré, le prix de l’électricité et la demande étaient relativement simple à estimer comme une seule CG nationale avait le monopole du marché. Ce problème a été étudié de manière extensive en utilisant de la Programmation Mathématique (PM). Aujourd’hui, le prix de l’électricité est relativement volatile à cause de l’introduction de marchés dérégulés et la demande du marché est répartie entre plusieurs CGs en compétition sur divers marchés. Une CG ne peut se limiter à considérer un problème de UC seul pour envisager sa production. Il y a un besoin d’intégrer les incertitudes liées au marché de l’électricité et aux quantités à produire aux modèles utilisés pour qu’une CG puisse établir un plan de production rentable.La technologie a aussi permis d’envisager de nouveaux concept tel que les Micro-Grilles (MGs). Une MG est composée d’un ensemble de consommateurs reliés à travers un réseau de transmission, possédant des générateurs d’électricité et optimisant leur consommation interne. Ce concept est possible grâce à l’utilisation croissante d’énergies renouvelables locales ainsi que l’utilisant croissante d’appareils interconnectés. Cependant, étant donné que les énergies renouvelables ont un faible rendement, sont intermittentes et que les appareils de stockage d’énergie sont encore peu efficaces, les MGs ne peuvent pas envisager d’être pleinement autonome en électricité. Il y a donc une nécessité d’avoir un fournisseur d’électricité externe pour avoir suffisamment d’électricité disponible à tout moment. Une CG jouant le rôle de fournisseur auprès d’une MG fait face énormément d’incertitude concernant la demande à cause de la gestion interne de la MG sur laquelle elle n’a pas de contrôle.Dans cette thèse, des problèmes d’optimisation intégrant de nouvelles contraintes modernes liés à l’approvisionnement énergétique sont étudiés via la PM. Plusieurs problèmes considèrant des interactions entre plusieurs acteurs sont modélisés via des formulations bi-niveau. Nous illustrons comment les difficultés liées aux contraintes modernes peuvent être exploitées pour obtenir des propriétés permettant de reformuler les problèmes étudiés en formulation linéaire en nombre entiers. Des heuristiques performantes sont obtenus à partir des formulations exactes dont certaines sont applicables à des problèmes plus généraux. Une analyse extensive de la performance des méthodes de résolution ainsi que de l’influence des contraintes modernes sont présentées dans diverses expériences numériques. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished