Rising levels of CO2, a finite supply of fossil fuels, national vulnerability to disruptions in energy supplies due to weather, geologic events and terrorists, and the critical importance of an aging electric power grid have made the robust supply of energy a major national priority. Recent events reinforce this situation. A sudden freeze on Feb 2, 2011 produced rolling blackouts in Texas. The 2008 blackout brought the Northeast to its knees. And the recent earthquake off the coast of Japan demonstrated the impact of unanticipated events on even the most sophisticated, well prepared countries.
It is essential that we make the correct decisions about investments to meet our energy requirements over the long term. In addition, we need to learn how to make the most of the resources we have, using the existing infrastructure to ensure a robust, cost-effective supply of energy to meet the demands of a growing economy that strikes a balance between costs and environmental constraints. Over the past 20 years, dramatic savings have been achieved in transportation and logistics using algorithmic advances in linear, nonlinear and integer programming. In the 1980s, we could solve problems with dozens of integer variables; today, we have algorithms that can solve complex problems with tens of thousands of integer variables.
We do not have general purpose algorithms for solving problems that combine optimization and uncertainty. Even simple forms of uncertainty can make small deterministic optimization problems into problems that are beyond the power of our largest computers. Yet we have to address the many forms of uncertainty that can arise in the planning of our energy systems. For example, we have good models of the uncertainty of wind, yet we do not have even the basic mathematics to deal with the heavy-tailed behavior of electricity spot prices. Yet, these problems pale in comparison to what we face to model the events that led up to the nuclear plant failures in Japan, or the Northeast blackout in 2008.
Uncertainty is one of the most intractable challenges we face in the design of new algorithms, but it is not the only challenge. We would like to model the effect of residential consumption behaviors on the grid. We do not have the tools to model the dynamics of 50 million households on the PJM power grid. We have to plan investments over decades while also dealing with dynamics that evolve on the scale of hours, minutes and seconds. We have to capture the twin towers of complex physical systems that evolve in the presence of different forms of uncertainty. All of these questions introduce issues that cannot be solved with current modeling and algorithmic technologies.
A series of themes cut across this Special Focus. These themes combine methodological challenges such as the need to deal with different time and spatial scales, metrics for risk and reliability, and the challenges of managing large datasets and sharing models for algorithmic testing:
We will advance our knowledge in these areas through a series of activities that help to bring together the different communities in energy and mathematics with the goal of identifying the needs that we face, showcasing promising research, highlighting areas that need additional research and then taking steps to enable this research. Workshops in this Special Focus will highlight specific problem domains in the design and control of energy systems and identify modeling and algorithmic challenges that must be addressed in order to solve important operational and policy problems. Research activities expected to come out of the workshops includes the development and testing of algorithms to solve a wide range of stochastic optimization problems arising in energy operations and planning, algorithms for multiscale models, and the development of repositories for data and models to support algorithmic research. Advancing the state of the art in models and algorithms requires the ability to share data and, in some cases, models that can serve as benchmarks. We plan to identify a series of grand challenges that can serve as benchmarks to advance algorithms for major problem classes. We will also produce tutorials to help the community with the challenge of modeling complex problems, and in particular stochastic optimization problems.
Opportunities to Participate: