Addressing Energy Technologies and Policies that Shape Future Sustainability

A quickly growing global population presents a variety of challenges, and raises the critical question: How can we sustainably meet energy needs while considering — and preventing — environmental and human impacts? An ever-increasing demand for energy requires the development and maintenance of an underlying infrastructure providing important services and utilities, such as power and communications. As new energy technologies emerge, researchers need to consider factors such as materials and costs — but beyond the actual technologies, researchers also need to determine which policies and incentives will help ensure that the technologies are used efficiently. Research from MIT's Institute for Data, Systems, and Society uses data and models to better design and predict the outcomes of technologies and policies in the critical area of energy and environmental sustainability.

Smart grids and pricing

The increasing demand for energy — along with growing environmental concerns — have led to the engineering of modern power grids with the capacity to integrate renewable energy resources on a large scale. Although demand response and dynamic pricing are often considered a means of mitigating the uncertainties of renewable energy generation — and improving the system’s economic and environmental efficiency — the real-time coupling of supply and demand creates significant challenges for guaranteeing reliability and robustness in the power system.

Research by MIT professors Munther Dahleh, Sanjoy Mitter, and research scientist Mardavij Roozbehani addresses these challenges by providing a framework for modeling and analysis of the dynamics of supply, demand, and clearing prices in a power system with real-time retail pricing and information asymmetry. The team found that new technologies intended to increase reliance on renewable energy could actually result in bringing down the power grid if they are not matched with careful pricing policies. This result indicates the need for a deeper understanding of consumer behavior in response to real-time prices, and a thorough modeling and analysis of the dynamics of the system, based on actual usage data.

Research from MIT Professor John Tsitsiklis and Yunjian Xu of the Singapore University of Technology and Design explores pricing mechanisms that might mitigate the effect of demand fluctuations on the significant ancillary costs in an electric power system. In the future, a certain percentage of electricity production is required by law in many states to come from renewable sources. A demand surge or a decrease in renewable generation may result in higher energy costs due to the deployment of “peaking plants” — which only run when there is high demand, and are associated with a much higher cost per kilowatt hour. Through a dynamic game-theoretic formulation, they showed that a new pricing mechanism could create an effective incentive for consumers to shift their power consumption — potentially reducing the need for long-term investment in peaking plants.

Evaluating energy technologies

The rate of improvement of the cost or other aspect of the performance of a technology depends on a variety of factors often rooted in technology design, materials, institutional design, human behavior, and policy decisions. Some types of technologies evolve more quickly than others. Understanding which types of technologies improve the most rapidly — and have the best potential to be effective — is critical when determining which low-carbon energy technologies merit consideration and investment.

Research from Atlantic Richfield Career Development Assistant Professor Jessika Trancik and her team compares different formulas — Moore’s Law, Wright’s Law, and others — for predicting how rapidly technology will advance, and develops a forecasting model using data spanning across many different industries. Their findings indicate that these simple models have some predictive power — with Wright’s curve performing the best followed by Moore’s curve — which, in turn, can inform decisions about which technologies and policies to pursue toward climate change mitigation efforts and other sustainability goals. In other work, Trancik has shown how the design of a technology affects its rate of improvement and thereby why some technologies may improve faster than others. This research further enables private and public investors to sensibly invest time and money in developing low-carbon energy.

“The basic challenge we face in addressing climate change is to achieve a complete decarbonization of energy systems. I am interested in whether this is possible, and how we can make it more likely,” says Trancik. “Specifically, I ask: How should engineers, private investors, and policy makers invest inherently limited time and money to make this happen? Can we use data and models to help direct key areas of technology innovation to accelerate this transition?”

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