November 17, 2017
Link to Original Article
Ian Schneider has spent much of his academic career thinking about renewable energy and where it sits in the larger picture of social and financial infrastructures. His interest began during his undergraduate days at Dartmouth College, where he studied engineering. In the course of his studies, he found himself digging into the question of how to make renewable energy viable — not just from an engineering perspective, but also from an economic one.
The variability in clean energy supplies, which frequently come from unpredictable sources like the wind and sun, makes them particularly tricky to plug into traditional pricing and distribution models. However, understanding that the power grid is impacted as much by the workings of financial markets as it is by functions of energy supply, Schneider began exploring research questions about the interaction between the two.
These kinds of questions, with strong engineering and economic components, drew him to MIT’s Laboratory for Information and Decision Systems (LIDS) for his graduate research in the fall of 2014. It also made him a natural fit, two years later, for the new PhD program in Social and Engineering Systems (SES) offered by MIT's Institute for Data, Systems, and Society (IDSS). Schneider became one of just 17 candidates admitted to the inaugural class of the SES program, which began in the fall of 2016.
To understand Schneider's current research, it's important to first know a bit about how energy is traded. Typically, the electricity sent to our homes on a day-to-day basis is treated as a commodity that is bought and sold in wholesale energy markets, then resold to consumers at retail prices. However, the wholesale market model is not designed with grid stability in mind. In the energy market, unpredictable gaps between supply and demand are problematic, leading to unsteady power provision.
To avoid this, grid operators have begun to use markets that trade in future energy availability, called capacity markets. In capacity markets a bid and auction system is used to compensate suppliers in the present for energy they commit to providing in the future. The aim is to ensure, by incentivizing investment in facilities and equipment well in advance of needing it, that the power supplied to the grid will always meet the demand. This kind of stability is important for maintaining reliable infrastructure, and becomes especially important during peak demand periods, when an overloaded grid could fail and, in the worst case, create a ripple effect of power outages throughout a region.
Conventional capacity markets, which are designed to accommodate the steady power supplies that come from traditional sources such as coal, natural gas, or other fossil fuels, require a great deal of reliable prediction to work well — an approach that falters when renewable energy is added to the mix.
"Suppliers of wind and solar power have an uncertain source of energy — which means the amount of energy they produce varies at any given time," Schneider explains. "Some might say this means they have no capacity value: Since you can't trust the electricity to be available at any given time, the supplier shouldn't be able to earn money in a capacity market. Others might disagree, arguing that the supplier's bid can sufficiently account for the likelihood that it will operate at certain hours, as well as the costs if there isn't any wind or solar energy available."
The solution to this prediction problem lies in how the market is structured. This is where Schneider's research comes into play.
His work focuses on market design and optimization. Using tools from game theory, economics, and probability, he examines and develops market mechanisms that account for underlying uncertainties in energy availability. This helps him to develop a nuanced and realistic understanding of the ways changes in the market's design — the rules by which it operates — influence the behaviors of its participants, and to get a sense of the features most critical to their choices. The hope is to build efficiency into the system, because in a market that can manage uncertainty well, both traditional and renewable generators become better bidders, neither overpromising nor underestimating what they can give to the grid operators and at what price. This in turn makes it easier for operators to figure out whether it's worth it to accept a bidder's offer, ultimately moving the market toward self-regulation.
While Schneider's overarching goal is to make it easier for electricity markets to accommodate the uncertainty of renewable energy, he also hopes that market improvements can open up space for innovation that will further ease the integration of renewable resources. For instance, improvements to market design could encourage innovative companies to harness demand flexibility, a means of incentivizing consumers to shift their energy use to periods when electricity is less expensive or more renewable energy is available.
Ultimately, the aim is for Schneider’s work to inform the policy makers and regulators who drive the grid's mechanics.
"It would be amazing if we could give step-by-step guidelines about how the market should be run," he says. "But the more likely outcome is that we explain the key drivers of value and risk in this market. If we use the underlying mathematical features, and assume that both buyers and sellers are trying to make as much money as they can, we can help identify the features that will give us the greatest understanding of how market design impacts grid efficiency and reliability."
For Schneider, who is supervised by Professor Munther Dahleh and Principal Research Scientist Mardavij Roozbehani, LIDS is the perfect place to study these questions, especially because of the diversity of research happening at the lab. He calls it an exciting place to work, and a great place to learn.
"We have the same sort of tools and language for how we do things, but we're working on extremely different problems," he says. "People at LIDS really understand the mathematical foundations of optimization and statistics, and there's support and knowledge here for using these tools to tackle big societal problems."
It is these big problems, Schneider says, that will keep him working for years to come, engineering a cleaner, brighter future.
Reprinted with permission of MIT News.