A brand new computer modeling tool developed by a research team on the MIT Energy Initiative (MITEI) will help infrastructure planners working in the electrical power industry and other energy-intensive sectors higher predict and prepare for future needs and conditions as they develop plans for power generation capability, transmission lines and other crucial infrastructure. The tool could reduce the time required for this planning and help be certain that the grid can proceed to supply customers with efficient, reliable and cost-effective electricity that meets emissions and regulatory standards. The tool was developed as a part of a philanthropically supported research project by MITEI in collaboration with Princeton University and New York University.
Macro, the brand new tool, is designed specifically for utility planners, regulators and researchers who want to know how power grids and other energy sectors might evolve in light of latest technologies and policies or alternative ways of using electricity and energy-intensive goods, explains MITEI researcher Ruaridh Macdonald. By inputting details about available generation units, projected demand, costs, possible latest technologies and potential policy constraints, planners can explore various options for designing and operating future infrastructure that minimize prices and maximize value for all. In contrast to traditional models, Macro particularly takes into consideration the interdependencies between industrial sectors.
As development continues, Macro will enable policymakers to look at in real-time the impact of potential policy options on outcomes starting from carbon emissions to grid reliability to commodity prices and more.
The growing challenge for care planners and former MIT models
The demand for electricity is now increasing rapidly, which is due, amongst other things, to the increasing use of artificial intelligence and the electrification of vehicles and buildings. As a result, more electricity generation and transmission shall be required. Thousands of wind and solar energy projects are actually coming online, but these units can’t be relied upon to repeatedly generate electricity, requiring supplementary power sources and storage facilities. In addition, energy consumers comparable to data centers, production centers and hospitals must meet strict reliability requirements. The planner's task is further complicated by the commitment to scale back and even eliminate carbon emissions.
Macro builds on a history of capability expansion models (CEMs), including GenX and DOLPHYN, developed by MITEI researchers to assist utilities plan for the long run. GenX was developed in 2017 to support decision-making related to investments in energy systems and grid operations in real time and to look at the impact of possible policy initiatives on these decisions. DOLPHYN, released in 2021, has the identical core structure as GenX but has additional sectors including hydrogen production, biofuels and more.
However, Macdonald; Jesse Jenkins, certainly one of the inventors of GenX and now a professor at Princeton University; and Dharik Mallapragada, certainly one of DOLPHYN's founders and now a professor at New York University, realized that they needed to create larger and higher-resolution models than GenX or DOLPHYN are able to as a way to get more precise answers concerning the impact of policies and latest technologies.
Introduction to macro
Macdonald, Jenkins, and Mallapragada, together with Princeton collaborators Filippo Pecci and Luca Bonaldo, developed a brand new architecture that gives the required advanced features. In constructing Macro, she and her teams developed a set of 4 core components that could be combined to explain the energy system for any industrial process. “The components each describe fundamental processes in an energy system: transmission, storage, transformation and entry or exit from the network,” explains Macdonald. “Because the components usually are not industry-specific, we are able to use them to construct networks of power, raw material and data systems.” With Macro, users can deal with specific areas of the economy, for instance for the supra-regional transfer of electricity or raw materials. This flexibility has led other research groups to make use of Macro for their very own projects. “In fact, some persons are already involved in cement production and the production of certain chemicals,” says Macdonald.
Additionally, macro allows the user to interrupt down an issue into smaller pieces. Most software used for such a modeling is designed to run on a pc. “With Macro's latest architecture, we are able to easily break down a big problem into many small problems that we are able to run on separate computers,” says Macdonald. This makes Macro well suited to running on modern high-performance computing clusters. It also provides an extra advantage when planning energy systems. Certain facets of expansion – for instance, transmission – are too complex to be solved using traditional optimization methods, so most CEMs assume certain approximations. But with Macro, the transmission part could be separated from the remainder of the issue and solved individually using AI techniques, generating a more accurate solution that may then be fed into the general model.
In addition, the developers of Macro attached great importance to ease of use. They developed a “taxonomy” of potential users and simplified each group’s workflow as much as possible. Most users just wish to enter their data using Excel and other tools they’re comfortable with, perform an issue evaluation, and get a solution. Others are modelers who wish to add a brand new technology or policy; These people could have to write down some additional computer code – but not much. Finally, there are developers who wish to add latest features or large elements to the model and wish to do a whole lot of coding to do that. “We've structured things in Macro in a way that makes life rather a lot easier for the primary two groups of users, on the expense of constructing it slightly harder for the developers,” says Macdonald. The team is currently developing a graphical user interface for the model so that almost all users never need to make use of code. “They simply interact with it as they do with most software they use.”
Future Plans: Using Macros to Drive Policy Making – in Real Time
Christopher Knittel, George P. Shultz Professor on the MIT Sloan School of Management, plans to make use of Macro to shape energy policy. His vision is inspired by the experiences of MIT Sloan Professor John Sterman, who led the event of the En-ROADS global climate simulator and a system dynamics model that performs rapid but approximate evaluation and allows users to try different approaches to reducing carbon emissions in real time.
As with the worldwide climate simulator, it may well take days to conduct a full evaluation of a proposed policy using Macro. However, there are techniques for creating an “emulator” that may generate an approximate end in seconds. In his role as director of the MIT Climate Project's Enabling New Policy Approaches mission, Knittel is exploring the potential of supporting a “flagship project” to develop an emulator that sits on top of the total macro model and will run in real time. Knittel and his team would then meet with select policymakers and invite them to make use of Macro to see how different policy moves would affect global temperatures, greenhouse gas concentrations, energy prices, sea level rise, etc.
By using the emulator, “you lose some accuracy or some functionality of the total macro model,” notes Knittel. So he envisions having members of Congress first run the emulator to draft policy. “Before the legislature actually drafts the bill, the tutorial team would run the total macro model to substantiate the accuracy of the emulator’s results,” says Knittel. “This exercise could help persuade policymakers what policy levers they need to use.”
Macro was released as open source software and is freely available for research and business purposes. It has been tested by employees within the US, South Korea, India and China. Several of those teams are developing country and regional models that others can use of their work.
