Good Decision-Making: Recognizing and Evaluating Risks and Opportunites
The energy industry is in a period of rapid change. There are challenges and opportunities related to smart grids, cybersecurity, disruptive technologies, distributed renewable energy, and customer choice, among other things. Because decision-making is becoming increasingly complex, the process of identifying decisions and uncertainties must be more formalized to identify and mitigate risks, as well as seize opportunities.
As part of the municipal exploration study, the city is analyzing the risks and opportunities that come with creating a municipal utility and with staying with Xcel Energy. By identifying both risks and opportunities, the city will begin to see how different decisions influence each other and how they should best be packaged into distinct strategies.
City staff established three key goals for decision analysis:
- Ensure that decision-making is credible and understandable, based on accurate information.
- Effectively incorporate expert guidance and public input.
- Recognize risks and opportunities associated with taking particular action or sets of actions.
Decision analysis is the practice of addressing decision-making in a formal manner. Risk is the likelihood of a negative outcome, and it can be analyzed probabilistically by looking at the severity of adverse consequences and the likelihood of each consequence. Risk analysis helps identify the factors that could jeopardize the success of a project. It also helps develop procedures to reduce the likelihood of these factors occurring or institute countermeasures for those factors that cannot be mitigated.
The city’s feasibility modeling incorporated decision analysis principles. The purpose was to identify variables that had a large potential to impact whether the city would be able to meet the metrics within the City Charter , and in particular, the requirement that a city utility would not charge rates that exceed those charged by Xcel Energy at the time of acquisition. Those variables were then treated probabilistically; in other words, they were given high, median, and low cost ranges that were modeled in combination with each other.
Based on discussions with the working groups, several assumptions were identified that could have significant impacts on the cost to operate the local electric utility and its ability to reduce carbon emissions and decrease renewable energy. Those assumptions include power supply costs (wind prices and natural gas prices), stranded and acquisition costs, operations and management costs, interest rates, debt service coverage ratio, and carbon prices. More information on how those assumptions were selected and tested is available here Feb. 26 Attachment H.
The Decision Analysis Working Group was tasked with reviewing the framework of the decision analysis model and with vetting data and assumptions to be included within it.
The working group met three times from December 2012 to March 2013.
Pete Baston – IDEAS, LLC
Tom Feiler – Clipper Windpower, Inc.
David Kline – National Renewable Energy Laboratory
Tom Leifer – QI Path
Frank Selto – University of Colorado
Zane Selvans – Clean Energy Action
JoAnn Silverstein – University of Colorado
Edith Zagona – University of Colorado
Greg Hamm – Stratelytics, LLC
City of Boulder Staff
Heather Bailey – Executive Director of Energy Strategy and Electric Utility Development
Kelly Crandall – Sustainability Specialist
Sarah Huntley – Media Relations/Communications Coordinator