Modeling the Economic Cost of Transmission Bottlenecks

The purpose of this paper is to model the stochastic behavior of nodal prices and use the predicted price differences between zones as the basis for measuring the magnitude and riskiness of congestion costs using the New York State electricity market as an example. The first step uses a principal components analysis to simplify the structure of nodal electricity prices in New York State, reducing 405 hourly prices in 2005 to less than 10 factors that explain 99% of the total price variability and correspond closely to established zones in the State. The analysis focuses on the Hudson Valley and New York City because these two zones are linked by one of the most important transmission bottlenecks in the State. A multivariate time series model for hourly temperature in different zones is estimated using data from January 1st 2000 to December 31st 2005 to represent the primary source of variability due to the weather. Multivariate models for the hourly electricity load in different zones conditionally on the temperature, and the hourly spot price of electricity in different zones conditionally on temperature, the load and the price of natural gas. By focusing on the zonal prices in the Hudson Valley and New York City, it is possible to simulate different realizations of temperature and to determine the financial riskiness of revenues from the price differences due to transmission congestion between these two zones. This in turn is an essential piece of information for evaluating the incentives for investing in merchant transmission upgrades between these two zones.