Time-Series Modeling of Electricity Price Volatility in High-Renewable Power Grids: Evidence from Texas
1 Department of Information Systems and Quantitative Sciences, Rawls College of Business, Texas Tech University.
2 Department of Data Science, Rawls College of Business, Texas Tech University.
3 School of Computing and Data Science, Wentworth Institute of Technology, Boston, USA.
Research Article
Open Access Research Journal of Science and Technology, 2024, 12(01), 186-193.
Article DOI: 10.53022/oarjst.2024.12.1.0120
Publication history:
Received on 17 August 2024; revised on 15 September 2024; accepted on 18 September 2024
Abstract:
The rapid spread of renewable energy has changed the dynamics of electricity markets in a significant way, especially in areas with heavy use of wind and solar power. This research analyzes the price volatility of electricity in the Texas (ERCOT) power market by means of time-series modeling that allows one to determine the influence of increasing renewable integration on price behavior. Hourly wholesale electricity prices along with renewable generation data are scrutinized so that price dynamics and volatility patterns can be characterized. The results that are obtained empirically point to a strong clustering of volatility, non-normal price behavior, and persistent conditional variance, all of which signal the presence of varying risk in electricity prices over time. The results also indicate that times of high renewable penetration are linked to the increase in price variability, which is a reflection of the unpredictable nature of wind and solar generation. It is suggested by the Conditional volatility forecasts that fluctuation in renewable supply has an effect on the market in the short term only and not on the price level in the long term. This finding implies that the dynamic behaviour of renewable energy generation should be included as an input in modeling electricity prices and suggests that improved flexibility measures are necessary to cope with the unstable situation in a power system that relies heavily on renewables. The research is based on actual data from the Texas grid, and it appeals to and is useful for, besides the academic community, the market players and the authorities concerned with the electricity market under the scenario of energy transitions.
Keywords:
Electricity price volatility; Renewable energy integration; Time-series modeling; ERCOT; Wind and solar power; Energy markets
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