Modeling electricity prices for generation investment and scheduling analysis

Abstract

of thesis entitled “Modeling Electricity Prices for Generation Investment and Scheduling Analysis” Submitted by Yang HE for the degree of Doctor of Philosophy at The University of Hong Kong in February 2010 In the deregulated electric power industry, under the market environment, electricity spot prices are highly volatile and uncertain. For private generation companies, their profits are directly tied with and significantly affected by these fluctuations of electricity spot prices. To make decisions on building new power plants and scheduling the production thereafter, generation companies desire an electricity spot price model to assist their making these decisions. The system of electricity spot prices is of high complexity; it is driven by various physical underlying forces that play in different timescales. In the short time horizon of one week, the physical driving forces are intra-day and intra-week variations of electricity load, generation forced outages, etc.; in the mid-term of one year, it is the seasonal forces that are manifest, such as seasonal weather and temperature, annual generation maintenance, etc.; and in the time horizon of years and decades, the effecting physical forces are economic development and economic cycles, generation investment and retirement, fluctuations of fuel prices, etc. This work develops a Multi-granularity Framework to facilitate analyzing electricity spot prices, which views electricity spot prices in three timeperspectives, that is, multi-year yearly, intra-year weekly, and intra-week hourly. In each time-perspective, how the various underlying physical forces give rise to the very peculiar behaviors of electricity spot prices is carefully discussed. Because the physical forces that underlie electricity spot prices are independent to each other, play in different timescales, and affect electricity spot prices in different time horizons, this work adopts the methodology Divide and Conquer to build the price model: it decomposes the historical electricity spot price data into components that are driven by different and independent physical underlying forces, then models each price component respectively, and finally constructs a complete electricity spot price model out of the resulting sub-models. The overall price model explicitly considers the various physical forces that drive electricity spot prices, the model extends on a time horizon of multiple years and has a time unit of one hour, and its final result represents prices at each hour by a probability density function of Lognormal distribution. The model has been evaluated in the New-England and PJM electricity markets. Upon proper revisions, the same analysis framework and modeling methodology probably could be applied to many other electricity markets in the world. The proposed price model is physically grounded, mathematically simple, and computationally fast. It provides an analytical tool to generation companies for their making informed decisions in generation investment and scheduling analysis. Besides generation companies, the model could also be widely used by other players in electricity markets, like by power traders for pricing and trading electricity contracts, futures, options, and other electricity derivatives, and by power retailers and large power consumers for their power purchase and risk management.