This issue of The Journal of Energy Markets focuses on market microstructure details and the factor dynamics of price formation in power and gas. These are crucial in determining both profitability in trading and accuracy in asset valuation.
In our first paper, “Parameter variation and the components of natural gas price volatility”, Matthew Brigida starts by observing that the parameters linking the demand and supply of natural gas to its price are likely to be dynamic throughout the year and over time. Thus, he models natural gas returns as a linear function of gas storage and weather variables, allowing the coefficients of this function to vary continuously over time. He also uses this model to calculate conditional natural gas volatility and the proportion of volatility attributable to each factor. He finds that natural gas return volatility is higher in the winter, showing that this increase is due to weather and natural gas storage. Evidently, the time series estimates of the changing proportion of volatility attributable to each factor will be useful for hedging and derivatives trading in natural gas markets.
“Pricing fast-responding electric storage assets in the presence of negative prices and price spikes: a simulation-and-regression approach”, the issue’s second paper, finds Sang Baum Kang, Mark T. Klein and Jialin Zhao analyzing how electric storage can smooth the imbalance between power supply and demand in order to fully utilize renewable resources. They seek to give an accurate valuation of storage technology, focusing on the use of fast-responding electric storage for real-time power trading and proposing a novel valuation model that considers the presence of price spikes and negative prices within a simulation-and-regression framework. By calibrating the model against California data, they assess the economic feasibility of four specific battery technologies: sodium sulfur (Na-S) molten salt, zinc bromide flow, lithium iron phosphate and lithium nickel cobalt aluminum batteries. Although none of these batteries reaches breakeven of its merchant-trading value, Na-S molten salt and zinc bromide flow require less progression in cost reduction. Further, the authors find that real-time storage optimization is more valuable than day-ahead optimization, emphasizing that fast-responding electric storage technologies are potentially more valuable if their capital costs become sufficiently low in the future.
Finally, in the third paper in the issue, “Electricity market prices for day-ahead ancillary services and energy: Texas”, J. Zarnikau, C. K. Woo, S. Zhu, R. Baldick, C. H. Tsai and J. Meng apply a regression-based approach to hourly data for the seven-year period 2011–17, exploring the determinants of day-ahead market prices for ancillary services (ASs) and energy in the Electric Reliability Council of Texas (ERCOT) market. For each gigawatt increase in responsive reserve or nonspinning reserve procured, they find price increases of about US$3.47 per MW per hour and US$5.63 per MW per hour, respectively. Meanwhile, the cost of an additional 1 GW of regulation up and down is much higher: US$17 per MW per hour and US$31 per MW per hour, respectively. A US$1/MWh increase in the DAM energy price tends to increase reserve and regulation prices by about the same amount. An increase in wind generation tends to decrease AS prices because it reduces the day-ahead energy price via the merit-order effect. Hence, Texas’s wind generation expansion has not raised ERCOT’s AS prices over that period. Going forward, however, the authors suggest that Texas could face AS cost escalation due to the high regulation up and down prices, should ERCOT’s requirement and procurement of those services increase due to rising renewable production.
It is precisely the economic impact of these fine details of price formation in energy markets that makes detailed analysis and the development of new methodologies so valuable.
Derek W. Bunn
London Business School
This paper models natural gas returns explicitly, allowing for market participants to learn over time and to react differently to present changes in economic variables. This learning and adaptation, and the attendant parameter uncertainty, constitutes…
Pricing fast-responding electric storage assets in the presence of negative prices and price spikes: a simulation-and-regression approach
This study focuses on the use of batteries for real-time power trading and proposes a simulation-and-regression-based valuation model.
This paper explores determinants of day-ahead market prices for ancillary services and energy in the Electric Reliability Council of Texas (ERCOT).