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In this dissertation, I study the welfare consequences of environmental and price regulations in the electricity industry. I address two important questions. One is that what are the welfare consequences of allowing firms to self-select between different types of environmental regulations. Another is how does the choice of transmission congestion pricing structure affect the emission externalities and fuel efficiency in the wholesale markets. I answer these questions by exploring several policy experiments in the state of Texas in the United States. The first chapter is a general introduction to the Texas electricity industry and the conceptual framework of analysis in this dissertation. It consists of the institutional details of the industry, including market organizations, transmission congestion pricing structures, and emission regulations. Based on the institutional details, I discuss the theoretical implications and propose the empirical hypothesis for above research questions. In the second chapter, I evaluate the welfare consequences of allowing firms to self-select between cap-and-trade regulation and intensity standards using the data from a unique voluntary NO [subscript x] emission cap-and-trade program in Texas from 2001 to 2005. The welfare evaluation focuses on the effects of such mixed policy instruments on emissions, industry profits, and market exit. I construct and estimate a structural model of power generating units equilibrium choices of policy instrument, emission abatement, and production to recover their abatement costs. With the estimated parameters, I simulate the equilibrium outcomes under a counterfactual mandatory cap-and-trade regulation. Results reveal that the mixed policy framework mainly benefits small and high-cost generating units. However, the aggregate emissions are lower and the aggregate profits are higher under the mandatory cap-and-trade regulation. I also document that the mixed policy instruments lead to a higher exit rate of older generating units. In the year 2010, the Electric Reliability Council of Texas (ERCOT) changed from a zonal market structure to a nodal market structure to incorporate the cost of transmission congestion into the wholesale price. The third chapter compares the emission intensities and fuel efficiency of power generating units in the ERCOT before and after this regulatory change, to investigate its efficiency and environmental impacts in the congested areas. I find that the new nodal market structure has heterogeneous impacts on areas with different causes of transmission congestion. For counties located along the path to transferring wind generation from west Texas to east Texas, the nodal pricing leads to increases in emissions from fossil-fired power plants, although the total increase in emission cost is not economically significant. Contrarily, the nodal pricing increases the fuel efficiency by 2-9.6% for power plants located around congested areas with excess load, and the estimated fuel cost saving is around $154.8m. The results provide important policy implications for future transmission network planning.

Emergency control in electric power systems requires rapid identification and implementation of corrective actions. Typically, system operators have performed this service while relying on rules-of-thumb and predetermined control sequences with limited decision support tools. Automatic control schemes offer the potential to improve this process by quickly analyzing large, complex problems to identify the most effective actions. Model-predictive control (MPC) is one such scheme which has a strong record of success in the process industry and has begun receiving attention in power systems applications. Incorporating flexibility into the MPC model using energy storage and temperature-based transmission line limits has shown promising results for relieving transmission overloads on small networks with linear active power models. Separately, MPC has demonstrated its capabilities in correcting transformer-driven voltage collapse behaviors. However, a comprehensive solution combines both aspects into a single controller formulation with knowledge of active and reactive power and voltage magnitude and angle. Additionally, most power system networks are large and result in computationally challenging problem formulations. This work considers these practical limitations and suggests techniques to enable an MPC process capable of operating reliably in the real-world. A new linear controller model is proposed which considers voltage magnitude and angle and both active and reactive power. The new model provides greater accuracy when predicting system behavior and better identifies the actual control needs of the system. The problem size is reduced by limiting the model to only those devices which are significantly affected by the emergency conditions. The new approach is shown to identify controls more rapidly and better suppresses undesirable thermal behavior on overloaded transmission lines while avoiding potential voltage collapse situations.