Three Essays on Empirical Analysis of United States Electricity Markets

Open Access
Gautam, Suman
Graduate Program:
Energy and Mineral Engineering
Doctor of Philosophy
Document Type:
Date of Defense:
September 19, 2014
Committee Members:
  • Seth Adam Blumsack, Dissertation Advisor
  • Seth Adam Blumsack, Committee Chair
  • Jonathan P Mathews, Committee Member
  • Zhen Lei, Committee Member
  • Robert D Weaver, Committee Member
  • Luis F Ayala H, Committee Member
  • Electricity Markets
  • RPS
  • Coal Plants
  • Selection Bias
  • demand response
  • RCT Design
  • extreme value theory
  • AR/GARCH Models
In this Dissertation, three independent studies analyze the impact of recent changes in both supply and demand sides of the U.S. electricity sector. Below is a brief description of three essays. I. Coal Plant’s Response to Renewable Portfolio Standards Renewable Portfolio standards require load-serving entities to purchase a given percentage of their electricity sales from eligible renewable energy technologies. This study analyzes the impact of RPS on the coal utilization by coal plants of Pennsylvania, New Jersey, and Maryland (PJM) electricity market. We develop a panel dataset of 259 unique PJM coal-fired utility plants’ integrating their fuel purchases with state-level RPS energy mandates, electricity prices, and fuel prices from 2001 to 2011, covering both pre-RPS and post-RPS era. Since selection of RPS policies may be non-random, we employ a two-step Heckit model to control for states’ decision to adopt an RPS and choose yearly RPS levels. The results show that a percentage point increase in state’s yearly energy target increases the average plant’s coal purchase by 45 thousand tons. These results are approximately consistent across selection-corrected models. The analysis showing the positive impact of RPS yearly targets on PJM coal plants’ coal purchases suggests a few things. There are fewer coal plants operating at the margin. Moreover, RPS yearly energy targets are fairly low at present; they are scheduled to increase considerably in coming years. Renewable Portfolio Standards may decrease the amount of fuel utilized by coal plants when RPS mandates increase in future. II. Residential Customers Response to Critical Peak Events of Electricity: Green Mountain Power Experience Demand response (DR) programs, usually through peak pricing and incentive-based approaches, can encourage customers to reduce or shift consumption during peak periods. This benefits utilities by lowering short-run generation costs and reducing the need for some long-run peak-driven investments. This paper analyzes the impact of Vermont’s Green Mountain Power’s (GMP) emergency DR programs on residential customers’ electricity consumption during a two-year pilot study program in 2012–2013. The 3,735 single-home residents of Central Vermont area were separated into six treatment groups and two control groups resulting into 26 million hourly load observations during the period of the study. Our analysis shows that incentive-based demand response programs have statistically significant impacts on reducing peak load. Specifically, CPR rates reduced peak load usage 6% to 7.7% and CPP rates reduced peak load between 6.8% and 10.3% during critical peak events. Moreover, on average, IHD-equipped participants’ monthly energy consumption was 2.0% to 5.3% lower than the monthly energy usage of non-IHD customers. However, none of the CP rate and IHD treatments induced a persistent response across multiple critical events and none of the treatment groups exhibited a consistent response to critical peak events. Based on our evaluation of GMP’s DR programs during 2012 and 2013, neither critical peak pricing nor rebates are themselves sufficient to substitute for new capacity to meet resource adequacy requirements. III. Analysis of Load and Price patterns in the U.S. Electricity Sector The study analyzes hourly electricity loads and marginal costs of electric entities with of extreme value theory (EVT), a concept widely used in the financial sector. For each year’s hourly data of balancing authorities and utilities, we fit generalized extreme value (GEV) distribution and estimate the parameters of the distribution with an aim of comparing how these parameters have changed over time and market regions. We also account for the time dependencies, seasonalities, and near-time clustering present in the electricity markets – both for electricity load and prices – with the help of autoregressive conditional hetereskedastic models. The results show that the distributions of hourly load and lambda values are fat tailed. Hourly lambda values have more extreme values generating fatter tails than hourly electricity load. We also show that extreme tail quantiles estimated with the GEV parameters at different percentile levels are comparable with the percentiles of actual observations.