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Multiple studies call for engineering education to integrate social justice into classroom instruction. Yet, there is uncertainty regarding whether integrating these social topics into engineering curriculum will support or detract from the learning of technical concepts. This study focuses on evaluating how reframing technical assessments to include social justice concepts impacts student learning and investigates how well students integrate social justice into engineering decision making. Using a within-subject design, in which students were exposed to both conditions (questions with and without social justice context), we evaluate how social justice framing impacts overall student learning of technical topics. Social justice prompts are added to homework questions, and we assess students’ demonstration of knowledge of original technical content of the course, as well as their ability to consider social justice implications of engineering design. In the earlier homework assignment, the experimental group showed a significant decrease in learning when technical concepts were framed to include social justice. As the students became more familiar with social justice considerations, their learning of technical concepts became comparable to that of students who did not have the social justice components in their assignment. Their evaluation of the social implications of technical decisions also improved. History: This paper has been accepted for the INFORMS Transactions on Education Special Issue on DEI in ORMS Classrooms. Funding: This work was supported by the Carnegie Mellon University’s Wimmer Faculty Fellowship and the National Science Foundation [Grant 2053856]. D. Nock also acknowledges support from the Wilton E. Scott Institute for Energy Innovation, where she is an energy fellow. 

Sustained power outages are growing in scale and number primarily due to i) the increasing number and intensity of disasters and ii) decarbonization- and electrification-related grid changes. Outage mitigation technologies (e.g., backup diesel generators, and solar panels) increasingly provide vital electricity access during disasters. However, their adoption is inequitable due to individual- or community-level barriers and historic underinvestment in certain communities. We postulate that community-based Resilience Hubs (RHs), which are being increasingly deployed to provide on-site services during disasters, can be expanded to address this inequity by supplying backup power to vulnerable communities through islanded operations. To that end, we present Grid-Aware Tradeoff Analysis (GATA) framework to identify the best backup power systems for expanded RHs. To include technical, economic, and social facets in the framework, we will use three-phase power flow (TPF) and multi-criteria decision analysis (MCDA). TPF will enforce the electrical feasibility of islanded RH operation, and MCDA will quantify the economic, environmental, and equity-weighted outage mitigation performance. As a use case for GATA, we will evaluate multiple representative RHs in Richmond, California, and highlight the non-dominated systems for the electrically feasible RHs. We show the value of GATA's detailed grid simulation, its ability to quantify tradeoffs across scenarios, and its possible extensions. 

The ability to (re)establish basic community infrastructure and governmental functions, such as medical and communication systems, after the occurrence of a natural disaster rests on a continuous supply of electricity. Traditional energy-generation systems consisting of power plants, transmission lines, and distribution feeders are becoming more vulnerable, given the increasing magnitude and frequency of climate-related natural disasters. We investigate the role that fuel cells, along with other distributed energy resources, play in post-disaster recovery efforts. We present a mixed-integer, non-linear optimization model that takes load and power-technology data as inputs and determines a cost-minimizing design and dispatch strategy while considering operational constraints. The model fails to achieve gaps of less than 15%, on average, after two hours for realistic instances encompassing five technologies and a year-long time horizon at hourly fidelity. Therefore, we devise a multi-phase methodology to expedite solutions, resulting in run times to obtain the best solution in fewer than two minutes; after two hours, we provide proof of near-optimality, i.e., gaps averaging 5%. Solutions obtained from this methodology yield, on average, an 8% decrease in objective function value and utilize fuel cells three times more often than solutions obtained with a straight-forward implementation employing a commercial solver.