David Daniels is a visiting professor at Chalmers University, where he focuses on global energy systems. He is also a non-resident fellow of the Payne Institute for Public Policy at Colorado School of Mines, and he serves on the international advisory panel of the Sustainable Gas Institute at Imperial College London. Prior to moving to Sweden, Dr. Daniels spent 20 years in Washington, DC.
He was the chief energy modeler at the U.S. Energy Information Administration (EIA), where he helped formulate the government’s long-term energy outlooks; he developed the U.S. Department of Homeland Security’s quantitative terrorism risk analysis methodology; and he worked as technical advisor to several programs at the Defense Advanced Research Projects Agency (DARPA). David began his career as a strategy consultant at The Boston Consulting Group (BCG). His academic background is in high energy particle physics, earning graduate degrees from Oxford and Harvard.
Keynote: Does electric power need continuous futures markets?
Date and time: Friday, 12th November at 10:00 – 10:30 (Helsinki time)
The relationship between power system physical and financial markets has always been an uneasy one, which is different from other energy markets (i.e., oil, gas). Explanations for this difference usually involve some combination of the following: 1) electricity can’t be stored, 2) physical power networks must be balanced on very short time scales, 3) power systems are technically complex and difficult to manage. At the same time, financial markets are recognized for their ability to convey price signals efficiently to market participants. Many power systems have used financial markets of various configurations to help solve the dispatch problem efficiently. Day-ahead power markets are nearly ubiquitous, and intra-day markets are becoming more common as well. But, the financial signals that help ensure efficient dispatch markets do not necessarily lead to the right long-term capacity being built. Can a single set of financial market signals be used to help manage power systems from both very short and very long time scales?