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Inaugural Workshop
Vision Session
At the beginning of the workshop, the participants considered priority energy analysis issues that would benefit from collaborative analysis over the next five years. Recommendations were developed for: 1) electricity, 2) transportation, and 3) energy efficiency and demand-side generation. Each group was also charged with the responsibility of identifying crosscutting issues that deserve attention. One such issue raised in several sessions was "improve behavioral factors in market/choice models and tools," which became the eighth and final energy analysis topic.
Access Vision Session Details for more specific information on the three areas listed above, or link from the individual topics.
Some common themes emerged across each of the three groups in thinking about priority analysis needs that might benefit from collaboration during the next five years.
- Need improved analysis of policy frameworks; interactions with technology deployment.
To better understand and predict the technology choices under different policies, workshop participants thought it was important to develop analysis tools that accurately simulate the complex and sometimes disjointed policy framework. Likewise, there was support for achieving a better understanding of what policy approaches are most effective so this could be applied to analysis of the policy impacts in the future, such as what incentive levels are required to make a project viable? This can be extended to research and development (R&D) portfolio management – there is a need for more information on which R&D investments have been the most successful in the past and why.
- Better ways to reflect and analyze behavioral factors that influence consumer, supplier and investment decisions.
Analytical tools should be improved to more closely model real-life consumer and investor decisions. The challenge will be to capture the serial nature of decision making and the effect of feedback that influences each step of the decision chain. For instance, a better understanding of utility decisions, such as how they are planning for carbon management, could improve forecasts of technology choices. We should not assume all decisions are analytically rigorous, and point out that the regulatory environment plays an important role that is not captured in many models. There also may be empirical data that could help inform analysis about consumer and investor behavior, but we need better mechanisms to synthesize and share that information with analysts.
- Policy decision makers and energy analysts need a better understanding and better approaches to uncertainty analysis.
Although the participants of these groups recognized that the National Energy Modeling System (NEMS) and other macro-level energy models use alternative cases to reflect the uncertainty in forecasts, they saw a need to better interpret the probability of those scenarios and the factors that contribute to the uncertainty. Better ways to analyze risk and better characterization of risk factors would help as well. These improvements might help decision makers answer questions such as how uncertainty around fossil fuel prices and carbon markets might impact the technology choices and cost of electricity.
- Energy models need to be more comprehensive and/or have better linkages to better represent market complexities and to understand the total projected impacts/value of energy.
There is a perceived need for models to evolve to better represent the complexities of the current energy systems in ways that allow for direct comparison of multiple attributes of energy choices. For instance, modeling tools should use standardized metrics and capture a broad spectrum of impacts including costs, reliability, equity, environmental impacts (including water), and economic-development impacts. There is a need for improvements in electricity system models to better simulate the complexities of the regional transmission system and contract-based markets. Models should be capable of incorporating the full supply chain, cross–sector analysis and to look at the interplay between the various demands in different sectors. Current models rely on oversimplified characterization through averages and prototypes and should be improved to rely more on probabilistic and agent-based modeling that better capture a distribution of responses.
In addition to these shared visions of the priority analysis needs and opportunities for collaboration over the next five years, each of these workshop groups generated a number of other suggestions. This information is accessible in the Vision Session Details section.
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