Smart Meter Data-Driven Load Forecasting and Price Design in the Retail Market
The widespread popularity of smart meters enables an immense amount of fine-grained smart meter data to be collected. Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide. Electricity retailer is one of the main participants in the retail market. This presentation will discuss how an electricity retailer can increase the competitiveness in the retail market by making full use of the fine-grained smart meter data. Since load forecasting is fundamental for various businesses of the retailer, the first part of this presentation will study how to enhance the performance of aggregated load forecasting using the fine-grained smart meter data. Designing customizing prices is an effective way to promote consumer interactions and increase customer stickiness for retailers. The second part of this presentation will introduce a novel data-driven approach for incentive-compatible customizing time-of-use (ToU) price design so that the benefits of both the retailer and consumers can be gained.
Overview
Abstract
The widespread popularity of smart meters enables an immense amount of fine-grained smart meter data to be collected. Meanwhile, the deregulation of the power industry, particularly on the delivery side, has continuously been moving forward worldwide. Electricity retailer is one of the main participants in the retail market. This presentation will discuss how an electricity retailer can increase the competitiveness in the retail market by making full use of the fine-grained smart meter data. Since load forecasting is fundamental for various businesses of the retailer, the first part of this presentation will study how to enhance the performance of aggregated load forecasting using the fine-grained smart meter data. Designing customizing prices is an effective way to promote consumer interactions and increase customer stickiness for retailers. The second part of this presentation will introduce a novel data-driven approach for incentive-compatible customizing time-of-use (ToU) price design so that the benefits of both the retailer and consumers can be gained.
Brief Biography
Yi Wang is a postdoctoral researcher in the Power Systems Laboratory, ETH Zurich, since Feb. 2019. He received his Bachelor's degree at Huazhong University of Science and Technology (HUST) in June 2014 and Ph.D. degree at Tsinghua University in Jan. 2019. From March 2017 to April 2018, he was a visiting student at the University of Washington. His research interests include data analytics in the smart grid, multiple energy systems, and cyber-physical power distribution systems. He is the first author of over 20 journal papers and gains over 2500 citations in Google Scholar. His doctoral thesis was selected as the Excellent Doctoral Thesis of Tsinghua University. He was awarded as an Excellent Graduate Student of Tsinghua University and Siebel Scholar. He currently serves as the secretary of the IEEE PES Working Group on Energy Forecasting and Analytics and the vice-chair of IEEE PES Working Group on Load Aggregator and Distribution Market. He is the reviewer of over 20 journals and has been recognized as the best reviewer of IEEE Transactions journals several times. He is an associate editor of IET Renewable Power Generation and IET Smart Grid.