Publications

Autonomous fuel synthesis in remote locations remains the Holy Grail of fuel delivery logistics. The burdened cost of delivering fuel to remote locations is often significantly more expensive than the purchase price. Here it is shown that newly developed solid aluminum metal fuel is suited for remote production of liquid diesel fuels. On a volumetric basis, aluminum has more than twice the energy of diesel fuel, making it a superb structural energy vector for remote applications. Once aluminum is treated with gallium, water of nearly any purity is used to rapidly oxidize the aluminum metal which spontaneously evolves hydrogen and heat in roughly equal energetic quantities. The benign byproduct of the reaction could, in theory, be taken to an off-site facility and recycled back into aluminum using standard smelting processes or it could be left onsite as a high-value waste. The hydrogen can easily be used as a feedstock for diesel fuel, via Fischer-Tropsch (FT) reaction mechanisms, while the heat can be leveraged for other processes, including synthesis gas compression. It is shown that as long as a carbon source, such as diesel fuel, is already present, additional diesel can be made by recovering and recycling the CO2 in the diesel exhaust. The amount of new diesel that can be made is directly related to the fraction of available CO2 that is recovered, with 100% recovery being equivalent to doubling the diesel fuel. The volume of aluminum required to accomplish this is lower than simply bringing twice as much diesel and results in a 50% increase in volumetric energy density. That is, 50% fewer fuel convoys would be required for fuel delivery. Moreover, aluminum has the potential to be exploited as a structural fuel that can be used as pallets, containers, etc., before being consumed to produce diesel. Furthermore, FT diesel production via aluminum and CO2 can be achieved without sacrificing electrical power generation.

Large power transformers (LPTs) are critical yet vulnerable components of the power grid. More frequent and intense heat waves or high temperatures can degrade their operational lifetime and increase the risk of premature failure. Without adequate preparedness, a widespread situation could ultimately lead to prolonged grid disruption and incur excessive economic costs. Here, we investigate the potential impact of climate warming and corresponding shifts in summertime "hot days" on a selected LPT located in the Northeast United States. We apply an analogue method, which detects the occurrence of hot days based on the salient, associated large-scale atmospheric conditions, to assess the risk of future change in their occurrence. Compared with the more conventional approach that relies on climate model simulated daily maximum temperature, the analogue method produces model medians of late twentieth century hot day frequency that are more consistent with observation and have stronger inter-model consensus. Under the climate warming scenarios, multi-model medians of both model daily maximum temperature and the analogue method indicate strong decadal increases in hot day frequency by the late twenty-first century, but the analogue method improves model consensus considerably. The decrease of transformer lifetime with temperature increase is further assessed. The improved inter-model consensus of the analogue method is viewed as a promising step toward providing actionable information for a more stable, reliable, and environmentally responsible national grid.

Ground personnel at the tactical edge often lack data and analytics that would increase their effectiveness. To address this problem, this work investigates methods to deploy cloud computing capabilities in tactical environments. Our approach is to identify representative applications and to design a system that spans the software/hardware stack to support such applications while optimizing the use of scarce resources. This paper presents our high-level design and the results of initial experiments that indicate the validity of our approach.

Superstorm Sandy illustrated the economic and human impact that severe weather can have on urban areas such as New York City. While flooding and wind damaged or destroyed some of the energy infrastructure, all installed microgrids in the New York City region remained operational during Sandy, including those at Princeton University, Goldman Sachs, New York University, and Co-op City. The resilience provided by these microgrids sparked renewed interest in pursuing more microgrid deployments as means to increase resiliency throughout the nation and in the face of many potential threats including severe weather events, and potentially terrorism. MIT Lincoln Laboratory has been engaged with the Department of Homeland Security (DHS), the Department of Energy (DoE), and the City of Boston in this Community Energy Study to explore the potential for microgrid deployment within Boston's thriving neighborhoods. Using hourly simulated building energy data for every building in Boston, provided by the Sustainable Design Lab on MIT campus, MIT Lincoln Laboratory was able to develop an approach that can identify zones within the city where microgrids could be implemented with a high return on investment in terms of resiliency, offering both cost savings and social benefit in the face of grid outages. An important part of this approach leverages a microgrid optimization tool developed by Lawrence Berkeley National Laboratory, with whom the MIT Lincoln Laboratory is now collaborating on microgrid modeling work. Using the microgrid optimization tool, along with building energy use data, forty-two community microgrids were identified, including ten multiuser microgrids, ten energy justice microgrids, and twenty-two emergency microgrids.