This paper outlines a strawman proposal for ensuring electricity supply adequacy by means of contracting obligations imposed on load serving entities (LSE). The mandatory contracts take the form of physically covered back stop call options with a high strike price so as not to interfere with normal risk management practices. These call options which can be covered by existing capacity, new investment in generation, or curtailable load contracts are of one year duration and a two year lead time so as to enable new entrant participation. The obligation, which is based on forecasted peak load plus adequate planning reserves, can be met with any forward or option contract that meets the physical cover requirement, has the same delivery period, and has a strike or forward price at or below the backstop strike price. The proposed mechanisms is intended to facilitate smooth transition to an energy only market where voluntary bilateral contracting and spot prices provide the needed incentives for adequate investment in generation capacity. We also describe a central procurement alternative that alleviates the credit problems associated with carrying long term option contracts. The features of the proposed approach are compared to those of the controversial LICAP mechanism proposed by the NEISO and endorsed by FERC.

This paper discusses alternative approaches that have been adopted around the world for guaranteeing the appropriate level of investment in electric generation capacity. We argue that long term reserves should be viewed as price insurance and be treated as a private good. However, political realities and asymmetries and distortions in risk management incentives may necessitate imposition of mandatory levels of such insurance on load serving entities. Furthermore, centralized markets may be needed as a supplement to bilateral contracting in order to facilitate efficient procurement of such insurance and to bridge the gap between the needs of generators and load serving entities with regard to duration of hedging instruments. We discuss the origins and shortcomings of capacity payments and capacity obligations and explain how long term supply contracts in the form of call options with premiums that depend on the contracts' strike prices can meet the need for ensuring supply adequacy and the financial health of the generator sector. We also outline a scheme where regulatory intervention in generation adequacy assurance takes the form of a hedging requirement imposed at the state level on load serving entities.

The prevalence of commercial activities whose profit and cost are correlated with weather risk makes weather derivatives valuable financial instruments that enable hedging of price or volumetric (quantity) risk in many industries. This paper proposes a multi-period equilibrium pricing model for weather derivative. In our stylized economy representative agents of weather-sensitive industries optimizes their hedging portfolios that drive the supply and demand for weather derivative which are dynamically determined based on a utility indifference pricing framework. At equilibrium the weather derivative market will be cleared and their market price can be obtained. Numerical examples illustrate the equilibrium prices and optimal choices for the weather derivative as function of the correlation between weather indices and demand for the underlying commodity. We also demonstrate the benefit of multiple trading opportunities which allows rebalancing of the hedging portfolio prior to the commodity delivery date, as compared to a single shot framework.

Many industries are exposed to weather risk. Weather derivatives can play a key role in hedging and diversifying such risk because the uncertainty in a company’s profit function can be correlated to weather condition which affects diverse industry sectors differently. Unfortunately the weather derivatives market is a classical example of an incomplete market that is not amenable to standard methodologies used for derivative pricing in complete markets. In this paper, we develop an equilibrium pricing model for weather derivatives in a multi-commodity setting. The model is constructed in the context of a stylized economy where agents optimize their hedging portfolios which include weather derivatives that are issued in a fixed quantity by a financial underwriter. The supply and demand resulting from hedging activities and the supply by the underwriter are combined in an equilibrium pricing model under the assumption that all agents maximize some risk averse utility function. We analyze the gains due to the inclusion of weather derivatives in hedging portfolios and examine the components of that gain attributable to hedging and to risk sharing.

This dissertation presents an equilibrium framework for analyzing the impact of cap-and-trade regulation on transmission-constrained electricity market. The cap-and-trade regulation of greenhouse gas emissions has gained momentum in the past decade. The impact of the regulation and its efficacy in the electric power industry depend on interactions of demand elasticity, transmission network, market structure, and strategic behavior of firms. I develop an equilibrium model of an oligopoly electricity market in conjunction with a market for tradable emissions permits to study the implications of such interactions. My goal is to identify inefficiencies that may arise from policy design elements and to avoid any unintended adverse consequences on the electric power sector.

I demonstrate this modeling framework with three case studies examining the impact of carbon cap-and-trade regulation. In the first case study, I study equilibrium results under various scenarios of resource ownership and emission targets using a 24-bus IEEE electric transmission system. The second and third case studies apply the equilibrium model to a realistic electricity market, Western Electricity Coordinating Council (WECC) 225-bus system with a detailed representation of the California market.

In the first and second case studies, I examine oligopoly in electricity with perfect competition in the permit market. I find that under a stringent emission cap and a high degree of concentration of non-polluting firms, the electricity market is subject to potential abuses of market power. Also, market power can occur in the procurement of non-polluting energy through the permit market when non-polluting resources are geographically concentrated in a transmission-constrained market.

In the third case study, I relax the competitive market structure assumption of the permit market by allowing oligopolistic competition in the market through a conjectural variation approach. A short-term equilibrium analysis of the joint markets in the presence of market power reveals that strategic permit trading can play a vital role in determining economic outcomes in the electricity market. In particular, I find that a firm with more efficient technologies can employ strategic withholding of permits, which allows for its increase in output share in the electricity market at the expense of other less efficient firms. In addition, strategic permit trading can influence patterns of transmission congestion.

These results illustrate that market structure and transmission congestion can have a significant impact on the market performance and environmental outcome of the regulation while the interactions of such factors can lead to unintended consequences. The proposed approach is proven useful as a tool for market monitoring purposes in the short run from the perspective of a system operator, whose responsibility has become indirectly intertwined with emission trading regulation.

There has been multiple national directives to enhance the economic operations of the power transmission system and promote the efficient use of the current grid configuration and resources. Transmission switching has been proposed as a new control method for various benefits, including improving the economic efficiency and meeting the reliability requirements. Optimal Transmission Switching model has been introduced to find an optimal generation dispatch and network topology to minimize the dispatch cost. Binary decision variables are used to denote the control of the transmission lines, which makes the model a nonlinear program. It suffers from curse of dimensionality and faces serious computational challenges.

To tackle the computational challenge of the Optimal Transmission Switching model, we propose three greedy algorithms in which only one line is switched at an iteration. In each iteration we solve a series of linear programs or smaller MIP programs, which can be implemented in parallel with the aid of high performance computing. The first algorithm enumerates all the possible line switching actions. The second algorithm produces a priority list ranking lines by a sensitivity factor based on dual criterion, and evaluate lines starting from the top of the list. The last one divides lines into small groups and consider each group at one time. We test the algorithms on the IEEE 118-Bus network and the FERC 13,867-Bus network which is representative of PJM Regional Transmission Organization. The results show that all three proposed algorithms result in cost reduction close to the best known optimal within a reasonable timeframe for IEEE network. For the FERC network which can not be solved directly by the OTS, the first two greedy algorithms are able to produce switching sequences which result in considerable cost reduction.

Furthermore we propose three machine learning based methods to produce priority lists for ranking possible line switching actions to facilitate faster searching. The algorithms take in the parameters from network status and network configuration to produce a standardized score representing the possible cost reduction of the line switching action. The numerical results on IEEE network and FERC network are presented. We evaluate the effectiveness of the priority lists based on dual criterion and machine learning methods by both regression analysis and comparison with random lists.

Based on the algorithms in literature and those we develop, we propose an algorithm selection method which selects the algorithm to optimize the cost improvement at each iteration. They show improvement in the cost reduction compared to the individual algorithms, especially for the FERC network.

The evolution of policy objectives and emergence of new technologies continually challenge the existing wholesale electricity market design. In the framework of standard market design based on locational marginal pricing (LMP) and two-settlement electricity markets, this dissertation investigates two topics for existing and future power systems -- Convergence Bidding (CB) and distribution locational marginal pricing (DLMP). The central theme that connects these two topics is market design -- determining how to create proper incentives to compensate market participants that ensures efficiency and reliability of the power grid.

We first empirically test whether the California Independent System Operator's (CAISO) existing two-settlement electricity markets are efficient, and if not, to what extent CB improves market efficiency. We examine the theoretical and empirical tools intended for other financial markets to help us understand the efficacy of CB in the forward and spot electricity markets. In the light of the efficient market hypothesis, Jensen uses the zero-profit competitive equilibrium to describe the condition for market efficiency. This definition of market efficiency directly converts the test of market efficiency into the assessment of return behavior. Following this methodology, we empirically test for market efficiency by evaluating the performance of trading strategies based on market data in the CAISO electric power markets. Our backtest results show that profitable trading opportunities continue to exist in the post-CB period, but the profitability decreases substantially. The decrease in profitability in the post-CB period indicates the improvement of market efficiency, and demonstrates the benefit of CB. The profitability in the post-CB period, however, conveys empirical implications that can be interpreted differently, depending on the level of competition and the level of risk aversion of virtual traders.

We further examine the use of DLMP to improve system efficiency in future power systems. DLMP is a modified form of LMP to alleviate congestion induced by electric vehicle (EV) loads on the distribution network. The distribution system operator (DSO) determines distribution locational marginal prices (DLMPs) by solving the social welfare optimization for both the conventional household demand and the EV demand with marginal costs exogenously set to LMPs. We show mathematically that the socially optimal charging schedule can be implemented through a decentralized mechanism where retailers and EV aggregators respond autonomously to the posted DLMPs by maximizing their individual net surplus in the perfectly competitive local DSO market. We further investigate the problem of designing pricing mechanism when LMPs are uncertain. A robust DLMP method is developed for EV charging management under price uncertainty. The efficacy of the proposed use of DLMP is demonstrated by means of case studies using the Bus 4 distribution system of the Roy Billinton Test System (RBTS) and the Danish driving data.

The large-scale integration of renewable energy sources is advancing rapidly in numerous power systems. However, utilizing renewable resources at a bulk scale is hindered by the fact that these resources are neither controllable nor accurately predictable. Our analysis focuses on the cost of balancing power system operations in the presence of renewable resources and on the amount of operating reserves that is necessary for ensuring the reliable operation of the system. We also explore the extent to which demand-side flexibility can mitigate these impacts. We present a contract that couples the operations of renewable energy resources with deferrable loads that can shift a fixed amount of energy demand over a given time window. Various flexible energy consumption tasks can be characterized in this way, including electric vehicle charging or agricultural pumping.

We use a two-stage stochastic unit commitment model for our analysis. The use of this model is justified by the fact that it is capable of quantifying the operating costs of the system and the amount of required capacity in order to face the increased uncertainty of daily operations. We present a dual decomposition algorithm for solving the model and various scenario selection algorithms for representing uncertainty.

We present results for a reduced network of the California power system that consists of 124 generators, 225 buses and 375 lines. We fist validate the stochastic unit commitment policy that we derive from the stochastic optimization model by demonstrating that it outperforms deterministic unit commitment rules commonly used in practice. We demonstrate this superior performance for both a transmission-constrained as well as an unconstrained system for various types of uncertainty including network element failures as well as two levels of wind integration that roughly correspond to the 2012 and 2020 renewable energy integration targets of California. We then use the stochastic unit commitment model to quantify the impacts of coupling renewable energy supply with deferrable demand on operating costs and reserve requirements. We also demonstrate the superiority of coupling contracts to demand-side bidding in the day-ahead market which is due to the fact that demand bids fail to account for the inter-temporal dependency of deferrable demand.

We present an empirical analysis of a supply function equilibrium model in the Texas spot electricity market. We derive condititions for optimal bidding behavior in a spot market with ex ante bilaterally contracted sales. By using generation cost information, we are able to derive a set of ex post- and ex ante-optimal supply functions and use a nonparametric model of firm behavior to compare our theoretically-optimal supply functions to actual offers made in years 2002 and 2003. Our results show that with markups and markdowns far in excess of what a model of profit-maximizing behavior suggests. For small generators, municipalities, and cogenerators we find evidence suggesting these firms may be acting to exclude themselves from the market by economically witholding their generation.By using partial-linear behavior model we demonstrate some learning effects to have taken place during the first quarter of 2002.