A Data Market under a Lasso Regression Framework

As the complexity and uncertainty of modern energy systems grow, we are seeing a growing need for good quality data for improving system and market operations. Traditionally, data in energy operations has largely been regarded as a free and highly accessible commodity, which is in significant contrast to the rising concerns about data privacy. Therefore, recent academic research has been investigating various forms of data markets to incentivize data exchanges.

In this talk, I will be presenting our recent work on a specific type of data market that is based on linear regression frameworks. The chosen use case is wind power forecasting, in which wind agents have the potential to improve their forecasts through gaining access to measurement data from neighboring wind agents. Based on how data payments are determined, I will introduce two market clearing mechanisms: 1) a cooperative game theoretic profit allocation, and 2) a lasso (least absolute shrinkage and selection operator) payment scheme. A comparison of the two payment mechanisms will be presented to show the difference in their preserved market properties and in their levels of incentive for the data sellers and data buyers.

Joint work with Pierre Pinson and Jalal Kazempour.