No description available.

No description available.

Since the onset of the recession in the 1970's, consumers have frequently expressed frustration with what appear to be ever-increasing utility bills, blaming what they perceive as unnecessarily high rates on industry inefficiency. From the industry perspective, inefficiency is not only the problem which has developed since the recession. The more critical issue is the industry's transition from a noncompetitive environment to a competitive one. In the past, the electric utility industry did not have to compete because each utility operated in an exclusive service territory, and each was regulated by the government. However, currently the industry is experiencing increased competition, both indirect and direct. The indirect competition has taken the form of alternative energy sources such as natural gas and such new technology sources as solar, wind, co-generation power, etc. Electric utility companies have also experienced direct competition among themselves for industrial and commercial customers. The latter has resulted because the price of electricity significantly influences management decisions about where to locate their plants. Thus, efficient operation of electric generation is an extremely important task both for customers and industry. Productivity measures, then, are vital to the industry's economic well-being. This study used three different models to measure and compare the total factor productivity of 95 electric utility companies from 1974 to 1984: the translog econometric model, the superlative index model, and the Craig and Harris model. First, the translog econometric model was applied to Investigate characteristics of the production structure for the electric utility industry. Next, the total factor productivity was calculated using each of the three models. Finally, the superlative index model was applied for bilateral and multilateral comparisons to the following categories: industry as a whole, six regions, five types of generation, and four different output levels.

Thesis. 1980. Ph.D.--Massachusetts Institute of Technology. Dept. of Economics. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND DEWEY. / Bibliography : leaves 182-186. / Ph.D.

The purpose of this study is to examine a two-part hypothesis. The first part is that underdepreciation is the cause of serious financial problems which have beset investor-owned electric utilities in recent years. The second part is that depreciation adjusted for changes in the general level of prices would do much to alleviate these problems.

Electricity prices in competitive markets are extremely volatile with salient features such as mean-reversion and jumps and spikes. Modeling electricity spot prices is essential for asset and project valuation as well as risk management. I introduce the mean-reversion feature into a classical variance gamma model to model the electricity price dynamics as a mean-reverting variance gamma (MRVG) process. Derivative pricing formulae are derived through transform analysis and model parameters are estimated by the generalized method of moments and the Markov Chain Monte Carlo method. A real option approach is proposed to value a tolling contract incorporating operational characteristics of the generation asset and contractual constraints. Two simulation-based methods are proposed to solve the valuation problem. The effects of different electricity price assumptions on the valuation of tolling contracts are examined. Based on the valuation model, I also propose a heuristic scheme for hedging tolling contracts and demonstrate the validity of the hedging scheme through numerical examples. Autoregressive Conditional Heteroscedasticity (ARCH) and Generalized ARCH (GARCH) models are widely used to model price volatility in financial markets. Considering a GARCH model with heavy-tailed innovations for electricity price, I characterize the limiting distribution of a Value-at-Risk (VaR) estimator of the conditional electricity price distribution, which corresponds to the extremal quantile of the conditional distribution of the GARCH price process. I propose two methods, the normal approximation method and the data tilting method, for constructing confidence intervals for the conditional VaR estimator and assess their accuracies by simulation studies. The proposed approach is applied to electricity spot price data taken from the Pennsylvania-New Jersey-Maryland market to obtain confidence intervals of the empirically estimated Value-at-Risk of electricity prices. Several directions that deserve further investigation are pointed out for future research.

Through integrated modeling of power system operations and market risks, this thesis addresses a variety of important issues on market signals modeling, generation capacity scheduling, and electricity forward trading. The first part of the thesis addresses a central problem of transmission investment which is to model market signals for transmission adequacy. The proposed system simulation framework, combined with the stochastic price model, provides a powerful tool for capturing the characteristics of market prices dynamics and evaluating transmission investment. We advocate the use of an AC power flow formulations instead since it allocates transmission losses correctly and reveals the economic incentives of voltage requirements. By incorporating reliability constraints in the market dispatch, the resulting market prices yield incentives for market participants to invest in additional transmission capacity. The second part of the thesis presents a co-optimization modeling framework that incorporates market participation and market price uncertainties into the capacity allocation decision-making problem through a stochastic programming formulation. Optimal scenario-dependent generation scheduling strategies are obtained. The third part of the thesis is devoted to analyzing the risk premium present in the electricity day-ahead forward price over the real-time spot price. This study establishes a quantitative model for incorporating transmission congestion into the analysis of electricity day-ahead forward risk premium. Evidences from empirical studies confirm the significant statistical relationship between the day-ahead forward risk premium and the shadow price premiums on transmission flowgates.