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  题目:A Data-Driven Approach for Managing Price and Quantity Risks in Electricity Wholesales Markets

  报告人:Shijie Deng,Georgia Institute of Technology,Associate Professor




  In the restructured electricity industry, electricity is traded in forward markets with varying time horizons. A major challenge for large industrial power consumers and load-serving entities is to procure electricity in these markets to minimize the total cost while meeting all the consumption demands. Two sources of risk arising from the electricity market price and the consumption quantity at the time of use not only make the procurement decision difficult but also can cause significant penalty cost if the realized power consumption deviates from the procured quantities by certain margins. Furthermore, the distributions of the market prices and the real-time power demand may not be known. We propose a data-driven approach to tackle this problem through a reinforcement learning framework.


  Dr. Shijie Deng is a tenured faculty member in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology. He holds a Ph.D. in Industrial Engineering and Operations Research from the University of California at Berkeley. Dr. Deng actively researches and teaches in financial modeling in energy markets, statistical learning and data analytics with energy and financial applications, electricity transmission pricing, carbon emission trading, financial asset pricing and real options valuation. He was the director of the Master of Science Program in Quantitative & Computational Finance at Georgia Tech from 2007 to 2013. He served as an Associate Editor for Operations Research. He received the CAREER Award from the National Science Foundation in 2002.  Dr. Deng has consulted with several private and public companies on issues of energy derivative pricing, structured transactions, and risk management in the deregulated electricity industry.

发布时间:2018-12-11 14:33:36 浏览人数:
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