Big Data Analytics for Modelling the Impact of Wind Power Generation on Competitive Electricity Market Prices

Payam Zamani Dehkordi, Logan Rakai, Hamidreza Zareipour, William D. Rosehart

Abstract: There is vast potential value being stored in the massive amounts of data being produced each day in an electricity market. However, most of the value is not being realized due to the challenges in efficiently and intelligently processing and analyzing the large volumes data. These are the challenges of Big Data. Modern power systems are producing Big Data and better understanding it can lead to many business advantages. In this paper a data-driven approach is proposed for analyzing the effect of wind generation on the wholesale electricity price. It is a question of interest to know how electricity price is affected with higher level of wind penetration in a market. A model representing the quantitative effect of wind generation on electricity price would offer useful information to different sectors of electricity market from generators to consumers. Method is applied to the market of Alberta as a case study. The massive database is made based on the available public data from Alberta Electric System Operators (AESO). The impact of each MWh wind generation on the price of electricity is assessed. Results show that increased wind generation reduces wholesale market prices by a small, but economically-important amount. This impact is not constant and depends on the operating condition of the electricity market.