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Assisting households with maintaining adequate energy supply is one method for improving overall quality of life. Households experiencing energy insecurity may be unable to afford to use energy for necessary services at home (e.g., unable to purchase air conditioners). Energy efficiency (EE) can reduce energy costs for low-income households–requiring less energy for essential activities. While existing research has identified the groups that are less likely to participate in energy efficiency programs, there is limited research on how participation impacts energy insecurity among vulnerable households when they participate. Using over 138,000 households in Tallahassee, Florida we study participants in a neighborhood program that targeted underserved communities. We conduct quasi-experimental difference-in-difference comparisons for seasonal energy consumption, energy bills, and energy burden during the cooling season in response to air conditioning (AC) appliance purchases. We compare impacts for households in the program (REACH) and higher income non-REACH qualified households. We find that REACH homes, on average, save approximately 300kWh-eq on energy or $25 seasonally after purchasing an AC unit. While AC rebates reduce seasonal energy burden by 0.6 % in non-REACH homes, there is no statistically significant change in seasonal energy burden for REACH homes. The difference in energy reduction between REACH and non-REACH qualified homes could be due to increases in AC use among REACH homes after rebates. Further work could explore this trend of potential increases in efficient appliance use among low-income homes. 

Ensuring an equitable energy transition requires models and tools that can account for equity and energy justice goals. Power system models (PSMs) are widely used throughout industry, government, and academia to simulate or optimize the operations and planning of current and future electricity systems under different scenarios, parameter assumptions and policy frameworks. These models are important tools that allow users to understand how the power system may evolve under different future conditions, but importantly, they are also used to inform policy implementation and investment decisions across all aspects of the power system. However, existing models seldom include energy justice considerations and therefore energy justice priorities are not reflected in the policies and other decision-making processes that are informed by these models. The purpose of this review is to provide a framework that energy modelers can draw upon to integrate energy justice and equity goals into PSMs. To this end, 99 papers that examine the intersection of energy justice and power system models are summarized and ten core aspects of the power system that can impact energy justice outcomes, and therefore require new modeling approaches, are identified. This review then establishes key current practices, challenges, and opportunities associated with capturing energy justice considerations in power system models across these ten aspects. This review concludes by proposing four key research directions that should be pursued to improve the representation of energy justice and equity in power system modeling. Finally, this review also addresses challenges raised by United Nations Sustainable Development Goal 7, which aims to ensure affordable energy access to everyone and Sustainable Development Goal 13, which aims to take urgent action to address climate change. 

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. 

With increasing focus on equitable and just energy transition, it is critical to understand the trade-offs of different decarbonization outcomes across economic, environmental, and social sustainability criteria. In this analysis, we use a multi-criteria decision analysis to quantify sustainability outcomes across 32 decarbonization outcomes in 2050 in the U.S. The economic sustainability criteria we use are system cost, national average retail rate, and electricity system employment. The environmental sustainability criteria we use are life cycle greenhouse gas emissions, life cycle water depletion, life cycle land transformation, and air pollution fatalities. The social sustainability (distributional impacts) criteria we use are retail rate equality across states, electricity employment equality across low-income households, and air pollution disparities across census tracts. We evaluate performance across these criteria under eleven different stakeholder preference scenarios. We find that decarbonization policies with indefinitely extended tax credits have the highest sustainability score under equal criteria weighting, with greater investments in renewable energy technologies, and result in better environmental, system cost, job, and air pollution disparities compared to mid-case scenarios, that only include current policies and CO2 reduction targets. We also see that our multi-criteria decision analysis identifies decarbonization outcomes that would not have been identified as optimal under a single objective, which highlights the importance of trade-off analyses to understand decarbonization outcomes more holistically. 

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. 

Sustainable Development Goal 7 of the United Nations is to achieve universal access to clean, modern and affordable electricity by 2030. However, 600 million people in sub-Saharan Africa (SSA) currently do not have access to electricity. As a result of this energy inequality, countries in SSA need to plan electricity systems that provide access in an equitable manner. The research question we explore in this paper is how integrating elicited preferences for equality into an electricity system planning model affects investment decisions regarding technology deployment. Our novel contribution is proposing a framework in the form of a discrete choice experiment and a statistical estimation model to determine decision makers’ preferences for equality. In our study, we find that higher preferences for equality result in an increased deployment of solar diesel mini-grids. These hybrid mini-grids, in turn, drive the carbon emissions intensity of the electricity system fourfold. As such, there is a need for stakeholders in Africa’s energy sector to consider the potential divergence between a carbon-minimizing electrification strategy and equitable electrification. 

Energy equity and justice have become priority considerations for policymakers, practitioners, and scholars alike. To ensure that energy equity is incorporated into actual decisions and analysis, it is necessary to design, use, and continually improve energy equity metrics. In this article, we review the literature and practices surrounding such metrics. We present a working definition for energy justice and equity, and connect them to both criteria for and frameworks of metrics. We then present a large sampling of energy equity metrics, including those focused on vulnerability, wealth creation, energy poverty, life cycle, and comparative country-level dynamics.We conclude with a discussion of the limitations, gaps, and trade-offs associated with these various metrics and their interactions thereof. 

Abstract Energy transitions and decarbonization require rapid changes to a nation’s electricity generation mix. There are many feasible decarbonization pathways for the electricity sector, yet there is vast uncertainty about how these pathways will advance or derail the nation’s energy equality goals. We present a framework for investigating how decarbonization pathways, driven by a least-cost paradigm, will impact air pollution inequality across vulnerable groups (e.g., low-income, minorities) in the US. We find that if no decarbonization policies are implemented, Black and high-poverty communities may be burdened with 0.19–0.22 μg/m³higher PM_2.5concentrations than the national average during the energy transition. National mandates requiring more than 80% deployment of renewable or low-carbon technologies achieve equality of air pollution concentrations across all demographic groups. Thus, if least-cost optimization capacity expansion models remain the dominant decision-making paradigm, strict low-carbon or renewable energy technology mandates will have the greatest likelihood of achieving national distributional energy equality. Decarbonization is essential to achieving climate goals, but myopic decarbonization policies that ignore co-pollutants may leave Black and high-poverty communities up to 26–34% higher PM_2.5exposure than national averages over the energy transition.