Financial Transmission Rights (FTRs) have been a feature of competitive electricity markets for nearly 20 years. FTRs are financial derivatives sold in periodic auctions. Revenues from the sale of these derivatives are passed through to electricity ratepayers to compensate them for transmission congestion payments they make in the spot energy market. FTR purchasers are effectively swapping their auction payment for an uncertain revenue stream over the life of the FTR. In recent years, industry market monitors have become concerned with the auctions’ performance in adequately compensating ratepayers – FTRs sell, on average, for a price less than the revenue they generate for the purchaser. This dissertation contributes to our understanding of FTR market functioning by studying the Auction Revenue Rights (ARR) management process, which is the predominate mechanism used in U.S. electricity markets to distribute FTR auction revenue to electricity ratepayers. This dissertation is organized into three essays, detailed below. The first essay demonstrates how the ARR process influences fundamental supply conditions in the FTR auction market and show how divergent auction equilibria emerge under different ARR decision-making regimes. Using market data from the PJM Interconnection, this essay finds empirical evidence that variation in ARR management strategies helps explain differences between an FTR’s auction price and its realized ex post value. The second essay studies the interaction of affiliated subsidiaries in auctions for FTRs. The essay documents a setting where a regulated utility routinely sells FTRs (through the ARR process) to an affiliated generation company in an auction where a portion of the revenue is passed through to the regulated utility’s retail customers. It appears that the affiliated generator may be placing strategic bids in the auction to minimize the price they pay for the derivatives, which would also minimize the revenue passed through to the regulated utility’s retail customers. The third essay studies the relationship between the long-term FTR auction market and the annual auction market in terms of ARR prices. Long-term auction clearing prices systematically overvalue FTRs that are along the paths of an ARR, thus providing electricity ratepayers with a biased signal of the potential value of their ARR allocations. Collectively, these three essays demonstrate the role of the ARR process in determining equilibrium FTR auction prices. Not only do ARR management decisions directly affect equilibrium prices, but ARRs constitute the mechanism by which auction revenues are passed through to ratepayers. Thus, any analysis of FTR auction revenue deficiency must include a thorough understanding and empirical incorporation of the ARR process into the analysis. <br><br>

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.

Participants in deregulated electric power markets compete for financial transmission rights (FTRs) to hedge against losses due to transmission congestion by submitting bids to the independent system operator (ISO). The ISO obtains an FTR allocation, that maximizes sales revenue while satisfying simultaneous feasibility. This FTR allocation remains in place for a length of time during which the participants compete in the energy market to maximize their total payoff from both FTR and energy markets. Energy markets (bi-lateral, day ahead, real time) continue until the the end of the current FTR period, at which time the participants can choose to modify their FTR holdings for the next FTR period. As in any noncooperative game, finding Nash equilibrium bidding strategies is of critical importance to the participants in both FTR and energy markets. In this research, a two-tier matrix game theoretic modeling approach is developed that can be used to obtain equilibrium bidding behavior of the participants in both FTR and energy markets considering the total payoff from FTR and energy. The matrix game model presents a significant deviation from the bilevel optimization approach commonly used to model FTR and energy allocation problems. A reinforcement learning (RL) algorithm is also developed which uses a simulation model and a value maximization approach to obtain the equilibrium bidding strategies in each market. The model and the RL based solution approach allow consideration of multi-dimensional bids (for both FTR and energy markets), network contingencies, varying demands, and many participants. The value iteration based RL algorithm obtains pure strategy Nash equilibrium for FTR and energy allocation. A sample network with three buses and four participants is considered for demonstrating the viability of the game theoretic model for FTR market. A PJM network example with five buses, five generators and three loads is also considered to analyze equilibrium bidding behavior in joint FTR and energy markets. Several numerical experiments on the sample networks are conducted using the approach of statistical design of experiments (DOE) to assess impacts of variations of bid and network parameters on the market outcomes like participant payoffs and equilibrium strategies.

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.

<p>In the US, seven regional transmission organizations (RTOs) operate wholesale electricity markets within three largely independent transmission systems, the largest of which includes five RTO regions and many vertically integrated utilities.</p> <p>RTOs operate a day-ahead and a real-time market. In the day-ahead market, generation and demand-side resources are optimally scheduled based on bids and offers for the next day. Those schedules are adjusted according to actual operating conditions in the real-time market. Both markets involve a unit commitment calculation, a mixed integer program that determines which generators will be online, and an economic dispatch calculation, an optimization determines the output of each online generator for every interval and calculates locational marginal prices (LMPs).</p> <p>The use of LMPs for the management of congestion in RTO transmission systems has brought efficiency and transparency to the operation of electric power systems and provides price signals that highlight the need for investment in transmission and generation. Through this work, we aim to extend these efficiency and transparency gains to the coordination across RTOs. Existing market-based inter-regional coordination schemes are limited to incremental changes in real-time markets. </p> <p>We propose a multi-regional unit-commitment that enables coordination in the day-ahead timeframe by applying a distributed approach to approximate a system-wide optimal commitment and dispatch while allowing each region to largely maintain their own rules, model only internal transmission up to the boundary, and keep sensitive financial information confidential. A heuristic algorithm based on an extension of the alternating directions method of multipliers (ADMM) for the mixed integer program is applied to the unit commitment. </p> The proposed coordinated solution was simulated and compared to the ideal single-market scenario and to a representation of the current uncoordinated solution, achieving at least 58% of the maximum potential savings, which, in terms of the annual cost of electric generation in the US, could add up to nearly $7 billion per year. In addition to the coordinated day-ahead solution, we develop a distributed solution for financial transmission rights (FTR) auctions with minimal information sharing across RTOs that constitutes the first known work to provide a viable option for market participants to seamlessly hedge price variability exposure on cross-border transactions.