A brand new computer modeling tool developed by an MIT Energy Initiative (MITEI) research team will help infrastructure planners working within the electricity 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 mandatory infrastructure. The tool could reduce the period of time this planning takes and help be sure that the facility grid can proceed to offer customers with efficient, reliable, and low-cost electricity that meets emissions and regulatory standards. The tool was developed as a part of a philanthropically supported research project through MITEI, in collaboration with Princeton University and Latest York University.
Macro, the brand new tool, is specially designed for utility planners, regulators, and researchers who are attempting to know how electricity grids and other energy sectors might evolve given latest technologies and policies or alternative ways of using electricity and energy-intensive commodities, explains MITEI research scientist Ruaridh Macdonald. By entering details about available generating units, projected demand, costs, possible latest technologies, and potential policy constraints, planners can investigate various options for the design and operation of future infrastructure that can minimize prices and maximize value for everybody. Specifically, unlike traditional models, Macro accounts for co-dependencies between industrial sectors.
With further development, Macro will enable policymakers to explore — in real time — the impacts of potential policy options on outcomes starting from carbon emissions to grid reliability to commodity prices, and more.
Utility planners’ growing challenge and former MIT models
The demand for electricity is now skyrocketing, due partially to the increasing use of artificial intelligence and the electrification of every thing from vehicles to buildings. Consequently, more power generation and transmission will likely be required. 1000’s of wind and solar energy projects at the moment are coming online, but those units can’t be counted on to generate electricity on a regular basis, so complementary power sources and storage facilities are needed. As well as, energy consumers equivalent to data centers, manufacturing centers, and hospitals have strict reliability requirements that have to be met. Further complicating the planner’s task is the commitment to reducing, and even eliminating, carbon emissions.
Macro builds on a history of capability expansion models (CEMs), including GenX and DOLPHYN, which were developed by MITEI researchers to assist utilities plan for the long run. GenX was designed in 2017 to support decision-making related to power system investment, in addition to real-time grid operation, and to look at the impacts of possible policy initiatives on those decisions. DOLPHYN, released in 2021, has the identical core structure as GenX but with additional sectors added on, including production of hydrogen, biofuels, and more.
Nevertheless, Macdonald; Jesse Jenkins, one in all the creators of GenX and now a professor at Princeton University; and Dharik Mallapragada, one in all the creators of DOLPHYN and now a professor at Latest York University, realized that they needed to construct larger and higher-resolution models than GenX or DOLPHYN are able to to be able to get more accurate answers in regards to the impacts of policies and latest technologies.
Introducing Macro
Macdonald, Jenkins, and Mallapragada, alongside Princeton collaborators Filippo Pecci and Luca Bonaldo, got here up with a brand new architecture that gives the needed prolonged capabilities. In constructing Macro, they and their teams developed a set of 4 core components that might be combined to explain the energy system for any industrial process. “The components each describe basic actions in an energy system: transfer, storage, transformation, and entering or exiting the network,” explains Macdonald. “Since the components should not sector-specific, we’re capable of use them to construct networks of electricity, commodity, and data systems.” With Macro, users can deal with specific areas of the economy, for instance, for interregional transfer of electricity or commodities. This flexibility has led other research groups to start using Macro for their very own projects. “In truth, we have already got some people cement production and production of certain chemicals,” says Macdonald.
Furthermore, with Macro the user can break an issue into smaller pieces. Most software used for this sort of modeling is designed to run on one computer. “With Macro’s latest architecture, we will easily decompose a big problem into many small problems, which we will run on separate computers,” says Macdonald. That makes Macro well-suited to running on modern high-performance computing clusters. It also provides an additional benefit with regards to power system planning. Certain features of expansion — for instance, transmission — are too complex to be solved using conventional optimization methods, so most CEMs assume certain approximations. But with Macro, the transmission piece might be separated from the remaining of the issue and solved individually using AI techniques, generating a more accurate solution that may then be fed into the general model.
As well as, Macro’s developers placed great emphasis on ease of use. They developed a “taxonomy” of potential users and simplified the workflow of every group as much as possible. Most users just wish to plug of their data using Excel and other tools they’re aware of, do an evaluation of some problem, and get a solution. Others are modelers who wish to add a brand new technology or policy; those people might need to put in writing some added computer code — but not much. Finally, there are developers who wish to add latest features or large elements to the model and might want to do loads of coding. “We’ve structured things in Macro in order that life is loads easier for the primary two groups of users, at the associated fee of it being a bit harder for the developers,” says Macdonald. The team is now developing a graphical interface for the model so most individuals won’t ever need to use code. “They’ll just interact with it like they do with most software they use.”
Future plans: Using Macro to guide policymaking — in real time
Christopher Knittel, the George P. Shultz Professor on the MIT Sloan School of Management, plans to make use of Macro to design energy policy. His vision is inspired by the experience of Professor John Sterman of MIT Sloan, who led the event of the worldwide climate simulator “En-ROADS,” in addition to a system dynamics model that performs quick but approximate analyses, enabling users to check out — in real-time — different approaches to reducing carbon emissions.
As with the worldwide climate simulator, using Macro to perform an entire evaluation of a proposed policy can take days. But there are techniques for creating an “emulator” that would generate an approximate end in a matter of seconds. In his role as director of the “Enabling Latest Policy Approaches” mission of the MIT Climate Project, Knittel is exploring the potential for supporting a “flagship project” to construct an emulator to go on top of the complete Macro model that would 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 various policy steps would affect global temperatures, greenhouse gas concentrations, energy prices, sea-level rise, and so forth.
In using the emulator “you lose some accuracy or some capabilities of the complete Macro model,” Knittel notes, so he envisions letting members of Congress start by running the emulator to design a policy. “Then, before the legislator actually drafts the bill, the educational team would run the complete Macro model to verify the accuracy of the outcomes from the emulator,” says Knittel. “That exercise could help persuade policymakers what policy levers they must be pulling.”
Macro has been released as open-source software, freely available for research and business purposes. It has been tested by collaborators in the US, South Korea, India, and China. Several of those teams are developing country and regional models for others to utilize of their work.
