News from Warsaw on the just-concluded 19th round of global climate talks suggests that there has been little progress towards a binding agreement on either cutting emissions or paying the rising costs of climate change. Nonetheless, even without a global agreement requiring them to cut emissions from power plants, which account for about a third of the problem, 130 countries have set renewable energy targets. Some of these targets are quite ambitious.
Ambitious renewable targets are great but sun and wind are only available in particular places and fluctuate depending on by time of day, weather, and season. As the share of renewables in a power grid increases, this intermittency problem gets worse: you may have extra power when you don’t need it and not enough power when demand is high. Overcoming intermittency usually requires additional back-up capacity—such as natural gas plants that can be fired up at short notice—raising total costs.
In this week’s Wonkcast I discuss this problem with CGD visiting senior associate Kevin Ummel and explore with him his ingenious, data-intensive solution. Kevin’s plan for making large-scale wind and solar power a reality begins with a simple insight: by taking into account spatiotemporal characteristics of wind and solar—where and when the wind blows and the sun shines—and matching this information against the where the power is needed, the location of the grid, and daily and seasonal fluctuations in demand, it is possible to build renewable power facilities in places that will minimize intermittency, thus reducing costs.
Read the brief here.
Read the paper here.
While the concept is simple, execution of such planning is hugely data intensive and devilishly complex. Kevin is well-suited for this task, having among other things overseen the development and roll out of CGD’s Carbon Monitoring for Action (CARMA) website, the world’s only comprehensive source of information on the location, ownership, and emissions of the 60,000 power plants around the world the 20,000 entities that own them.
To demonstrate the his spatiotemporal approach, Kevin applied these techniques to South Africa, which has committed to an ambitious renewables target: wind and solar power to provide 20% of generating capacity by 2030. His paper, Planning for Large Scale Wind and Solar Power in South Africa: Identifying Cost Effective Deployment Strategies Using Spatiotemporal Modeling, shows why such planning is critical and, for those with the data and modeling skills, a handbook on how to proceed. For the less technical among us, including people like me and presumably many policymakers and government planners, his CGD brief provides an overview of the process and a concise seven-point plan on how to proceed, starting with step one:
Determine wind and solar resource levels and identify geographic areas suitable for potential project or transmission siting. The latter should consider technological, economic, environmental, and sociopolitical constraints through consultation with stakeholders.
Sounds easy, right? Kevin understands that this is easier said than done, and that it requires massive computing power. Fortunately, such power is now low-cost and widely available.
“We have lots of experience planning power systems with no renewables in them,” Kevin explains.
“I like to use the analogy of a Rubik's cube. Solving a traditional Rubik’s cube is like planning a conventional power system with fossil fuels and no renewables. Now, imagine I took that Rubik’s cube and modified 2-3 % of the squares so that they changed every few seconds, and then asked you to solve the cube so that you had solid colors on every side to achieve a maximum percentage of solid colors for a certain period of time. That's like planning a power system that has a low level of wind and solar power penetration. Now, imagine that I took the same Rubik’s cube and instead of just 2-3% of the squares changing colors, imagine 40-50% of the squares. Trying to arrange that in a way that maximizes the probability of having solid colors on all sides at any one time is like planning a high penetration wind and solar power system: very complex.
Complexity notwithstanding, Kevin thinks it entirely possible, given the large potential benefits, that countries will invest in initial spatial and temporal modeling to get an idea of which renewable energy technology makes sense, given their energy needs and geography.
“I'd like to see investors like the World Bank, governments, and energy ministries approach the power system planning problem the way a financially savvy person approaches retirement planning,” he says.
“The future is uncertain. We don't know what's going to happen with costs of technologies, but we should try to develop deployment strategies and transmission plans that leave open as many low-cost possibilities as we can, so that as we learn more we have the ability to go down those paths that make the most sense.”
I invite you to listen in on my conversation with Kevin. After the interview ended, I kept the recorder running in a post-interview chat that offered some surprising insights. With Kevin’s kind permission, we have added that as a bonus audio clip at the end of the Wonkcast.
My thanks to Sophia Bernazzani for a draft of this blog post, and to Kristina Wilson for recording and editing the Wonkcast. You can follow me on Twitter at @LMacDonaldDC and the Center for Global Development’s institutional feed at @CGDev.