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At least 91 records · Page 5

Organizational and psychological measures for data center energy efficiency: barriers and mitigation strategies

Abstract It was last estimated that in 2020, data centers comprised approximately 2% of total US electricity consumption, with an estimated annual growth rate of 4%. As our country increasingly relies on information technology (IT), our data centers (DCs) will need to increase their energy efficiency (EE) to stabilize their energy consumption. The task of studying EE in DCs is complicated by the interconnected nature of humans and mission-critical technical systems. Moreover, the literature tends to focus on technology solutions such as improvements to IT equipment, cooling infrastructure, and software, without addressing organizational and psychological drivers. Our research demystifies the complex interactions between humans and DCs, by asking What non-technical barriers impede EE investment decision-making and/or implementing energy management strategies? To begin to answer this question, we perform a literature review of 86 resources, ranging from peer-reviewed journal publications to handbooks. We also consider related fields such as organizational behavioral management and energy intensive buildings. We develop a public Zotero library, perform content coding, and complete a rudimentary network analysis. Our findings from the literature review suggest that (1) technological solutions are abundant in the literature but fall short of providing practical guidance on the pitfalls of implementation, (2) making energy efficiency a priority at the executive level of organizations will be largely ineffective if the IT and facilities staff are not directly incentivized to increase EE, and (3) there is minimal current understanding of how the individual psychologies of IT and facilities staff affect EE implementation in DCs. In the next phase of our research, we plan to interview data center operators/experts to ground-truth our literature findings and collaboratively design decarbonization policy solutions that target organizational structure, empower individual staff, and foster a supportive external market.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Expert and operator perspectives on barriers to energy efficiency in data centers

Abstract It was last estimated in 2016 that data centers (DCs) comprise approximately 2% of total US electricity consumption. However, this estimate is currently being updated to account for the massive increase in computing needs due to streaming, cryptocurrency, and artificial intelligence (AI). To prevent energy consumption that tracks with increasing computing needs, it is imperative we identify energy efficiency strategies and investments beyond the low-hanging fruit solutions. In a two-phased research approach, we ask: What non-technical barriers still impede energy efficiency (EE) practices and investments in the data center sector, and what can be done to overcome these barriers? In particular, we are focused on social and organizational barriers to EE. In Phase I, we performed a literature review and found that technical solutions are abundant in the literature, but fail to address the top-down cultural shifts that need to take place in order to adapt new energy efficiency strategies. In Phase II, reported here, we interviewed 16 data center operators/experts to ground-truth our literature findings. Our interview protocols focus on three aspects of DC decision-making: procurement practices, metrics and monitoring, and perceived barriers to energy efficiency. We find that vendors are the key drivers of procurement decisions, advanced efficiency metrics are facility-specific, and there is convergence in the design of advanced facilities due to the heat density of parallelized infrastructure. Our ultimate goals for our research are to design DC decarbonization policies that target organizational structure, empower individual staff, and foster a supportive external market.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

A comparison study on trading behavior and profit distribution in local energy transaction games

With the increasing integration of distributed energy resources, localized peer-to-peer energy transactions are receiving more attention since they could absorb more distributed renewables locally and stabilize the power grid. To better study the trading behavior and provide guidance for a viable local market design, different game-theoretical frameworks including cooperative and non-cooperative games are established and compared here. For the cooperative grand coalition, the maximum social welfare is guaranteed, the Shapley value and bargaining based approaches are implemented for further profit redistribution. It is shown that the asymmetrical bargaining power can be critical in the final payment clearing and it is equivalent to the Shapley value allocation if prosumers bargain based on their marginal contributions. Non-cooperative trading is formulated by using a Stackelberg bilevel model in which the upper level has the privilege in internal price setting. However, system optimum is not obtained due to competition, and the selfish leader could take away 89% of the collective benefit by price-gap based arbitraging. Aiming for a long-lasting trading market, a virtual benevolent leader and a uniform price structure in which purchase price equals selling price are tested. Experimental results indicate that maximum social welfare and a fairer profit allocation can be achieved with a uniform price structure, regardless of the selfish or benevolent attitude.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Challenges and Opportunities for Electric Utility Modeling and Asset Valuation Frameworks: Case Study on Valuing New Pumped Storage Hydropower

Asset valuation by electric utilities is becoming increasingly difficult in the rapidly changing electric sector. Rapid deployment of variable generation and inverter-based storage systems along with uncertain demand growth, climate, policies, and other factors create a challenging environment for understanding the value proposition of a new potential asset. This report describes an effort between the Tennessee Valley Authority (TVA) and three U.S. Department of Energy laboratories to perform a detailed review of utility modeling and analysis practices for asset valuation and identify challenges and opportunities for advancing its methods into the future. It focuses on a case study of new potential pumped storage hydropower (PSH) because of growing interest in new PSH capacity to provide energy balancing, firm capacity, and a range of ancillary services. Staff from the DOE labs conducted systematic interviews about current practices in capacity expansion modeling, production-cost modeling, hydrological modeling, and transmission stability modeling while also discussing how scenario analysis is conducted and how models and data are integrated. The effort resulted in a set of model, integration, and scenario recommendations that could be valuable to TVA, other utilities, system operators, and other stakeholders conducting integrated grid analysis. Individual model recommendations suggest exploring computational tradeoffs with detail and resolution across spatiotemporal structure, supply- and demand-side details, transmission overlays, market interactions, and ancillary services. Automated processes to pass data between models and conduct larger scenario suites could also enhance valuation practices by enabling a more consistent study of asset value across a broader range of uncertain future grid conditions where PSH could be particularly valuable. TVA and other industry stakeholders can learn from and adapt applied research-grade methods developed by DOE laboratories and other research institutions to improve decision making and accelerate progress towards a reliable, economic, sustainable energy system.

13 HYDRO ENERGY↗

MN Solar Pathways: Solar Deployment Strategies-Modeling and Case Studies

As part of the Minnesota Solar Pathways project, funded in part by the Department of Energy’s Solar Energy Technologies Office, Clean Power Research (CPR) developed a Solar Deployment Strategies modeling tool to calculate the economic impacts and market incentives created by different solar portfolios and rate structures. CPR developed the tool using Minnesota-specific data and assumptions. The case studies described in this report provide insight into possible pathways toward the state goal of 10% solar, but the emphasis of the report is the demonstration of the tool in use cases that can be expanded and modified in future years as needs change.

14 SOLAR ENERGY↗

HIPPO – A Software Platform for Electricity Market Research and Development

The goal of this project is to provide Regional transmission organizations (RTOs) and independent system operators (ISOs) a market design and prototyping software, High-Performance Power-Grid Optimization (HIPPO), that they can evaluate electricity market design options, calculate market planning strategies and operational performance. With the high standards and strict reliability requirements for operating power systems, impacts of new technologies need to be fully investigated prior to any consideration for adoption. A market design and prototyping software tool which can be used to prototype electricity market design options, to calculate market planning strategies and operational performance with high precision, and to investigate the impacts for integrating future power grid technologies will be valuable to RTOs/ISOs who operate power systems, to vendors like GE and ABB who provide the market solvers, and to market participants and researchers who are actively doing market research. HIPPO is a such tool that can be used to improve the current market operations and provide capabilities for rigorous forward-looking design and prototyping of next-generation energy markets. HIPPO has a high-resolution model for the day-ahead SCUC, which was validated with MISO and GE-Grid Solutions. HIPPO is built with parallel and distributed computing capabilities and can be executed in both multi-thread and high-performance computing (HPC) settings. This capability provides fast solution speed necessary to handle the larger and more complex SCUC problems of real-world cases and the potentially growing size and complexity of future scenarios. In addition, HIPPO has a concurrent optimizer (CO) which manages multiple algorithm executions simultaneously and leverages the advantages from different algorithms. This structure provides flexibility to better benchmark competing approaches. Highly accurate market model, fast solution technologies and flexible model and algorithm control are the features which will make HIPPO an extensible platform for developing and testing multiple approaches to meet a wide range of future market needs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Magnetic Gears: The Key to Robust, Cost-Effective Hydropower Drivetrains

Based on previous demonstrated success at fabricating 5 and 10 kW scale magnetic gearbox (MGB) prototypes, Emrgy and its partners (the project team) proposed to design and construct a 100 kW scale MGB with a 30:1 gear ratio for the low-head hydro applications. The Statement of Project Objectives included tasks covering: 1) Market Applicability; 2) Technical Metrics; 3) Design (initial); 4) Electromagnetic (EM) Load and Structural Analysis; 5) Modal Analysis; 6) Sealing Design and 7) Final Design during Budget Period 1. Budget Period 2 included tasks covering: 1) Materials Procurement and Test Plan Development; 2) Assembly; and 3) Testing. The Market Applicability study (Task 1) led to a clear conclusion and recommendation toward “Low Head” technologies for maximum market share of both New Stream Reach development as well as powering Non-Powered Dams. The findings of this study also identified the opportunity for a larger scale magnetic gearbox-based drive train as a function of increased torque, as opposed to increased speed. The Technical Metrics Study (Task 2) concluded a horizontal orientation was preferred, examined potential loss mechanisms, concluded that a Halbach Array magnetic design was preferred, established a 30:1 gear ratio as optimal, and established a power rating of 100 kW as optimal. The subsequent initial and final detailed design process included electro-magnetic (EM) load and structural analysis (Task 4), a Modal analysis (for vibration) (Task 5), and a sealing design (Task 6) to assure water impermeability. The final design package (Task 7) included 729 individual parts, 117 unique part numbers, and 15 assemblies. In order to facilitate procurement, the full bill of materials was broken down into several sub-components: 1) custom magnetic parts; 2) custom machined parts; 3) custom casted parts; and 4) commercial off the shelf (COTS) parts. The casted parts were fabricated by Oak Ridge National Laboratory (ORNL) via a Cooperative Research and Development Agreement (CRADA) with Emrgy and funded by the Advanced Manufacturing Office (AMO). The procurement effort (Task 8) ultimately covered three time periods based on challenges encountered in meeting the budgeted cost for the prototype. Following the first effort in the early stages of Budget Period 2 in 2017, a no-cost time extension was granted to seek alternative fabrication and procurement options. The project was re-booted in 2020 based on the new ORNL CRADA that would focus on five (5) of the more difficult and expensive parts using their advanced manufacturing expertise. Procurement efforts for the other custom machined parts resulted in quotations that still exceeded the budget by more than $\$$100k. This was, in part, also due to the concurrent COVID-19 pandemic that caused both supply chain disruptions and labor shortages. As the project continued, pricing and availability degraded further. In Q2 FY’22, it was decided to not proceed with the fabrication of the prototype (Task 9) based on budgetary limitations. Outcomes included a full and detailed design of a 100 kW magnetic gearbox and associated indented bill of materials (BOM) and CAD drawings, a full assembly instruction manual with an associated BOM for materials necessary to support assembly, the fabrication of the double Halbach magnetic array for the rotor/stator system, fabrication of five (5) sand-casted/machined parts (via CRADA with ORNL) and an initial draft of a comprehensive testing plan. The most significant non-outcome was the actual fabrication and testing of the prototype gearbox based on budget limitations. Lessons learned included the need for an Application / Design / Cost trade analysis to better elucidate the cost potential of the MGB in the projected volumes anticipated for future demand. This would better establish the efficacy of the original cost target ($\$$0.80/Watt) and/or the need for reconsideration of designs and applications. Likewise, additional consideration of the prototype nature of the gearbox – single use, short lifetime, etc. - either as a separate exercise or in place of the design process completed, to reduce the cost of the demonstration prototype device. Additionally, project continuity was cited as a significant risk based on the loss of the primary design engineering firm after Budget Period 1. A design analysis exercise was conducted at the conclusion of the project to identify potential areas for cost reduction. One concept considered was the removal of the inner ring of magnets (with associated changes in the outer ring magnets) to enable a horizontal collapse of the design. It was estimated this could reduce cost by 10-25% without impacting performance.

13 HYDRO ENERGY↗

HYBRID MANUFACTURING TECHNIQUE USING LARGE-SCALE ADDITIVE MANUFACTURING AND COMPRESSION MOLDING FOR HIGH PERFORMANCE COMPOSITES

Additive Manufacturing (AM) or 3-D printing has moved from small-scale prototypes to large-scale functional structures. With the introduction of large-scale 3D printers into the market a new industry has been born. For the first time, we are presenting the idea of preparing large-scale 3-D printed β-stage preforms to be used in traditional compression molding (CM) manufacturing technique to prepare final functional structures. Short carbon fiber (CF) reinforced material acrylonitrile butadiene Styrene i.e. ABS with 20% CF is used in present work. It is shown that highly aligned 3D printed short carbon fiber composite maintains its orientation after compression molding. A sandwich panel is prepared to show the capability to prepare complicated parts by integrating AM and CM. Interphase of the prepared dissimilar material is also studied using SEM. Samples prepared using AM-CM integration showed 11% and 79 % improvement in tensile strength and tensile modulus respectively compared to the traditional extrusion compression molding (ECM) process.

Kumar, Vipin↗

Financial Exposure After a Sealed-Source Release: Insurance Limits, Federal Cost Pathways, and Implications for Gamma Irradiator Substitution

This report assesses whether private insurance and existing federal authorities would likely provide meaningful financial protection to private operators of self-shielded gamma irradiators after a major sealed-source release. It does not answer this question quantitatively because publicly observable data on premiums, limits, uptake, and claims outcomes for this risk class appear sparse. Instead, it uses a qualitative structural analysis based on prior literature, review of relevant federal authorities, limited observable insurance-market evidence, expert outreach, and historical analogs. The analysis finds that available public and private mechanisms do not combine into a clear, dependable, or readily verifiable compensation structure for ordinary private sealed-source operators. Institutions therefore should not assume that either insurance or government response will make them financially whole after a severe incident. Source reduction and replacement remain more dependable than post-event financial mechanisms for reducing institutional exposure and broader radiological risk.

99 GENERAL AND MISCELLANEOUS↗

Addressing Critical Problems in Materials Science Through Multiscale and Multimode Characterization (Project 1); Characterization and Optimization of Novel Triple-Conducting Oxide Materials for Energy Applications (Project 2) (CRADA Final Report)

PROJECT 1: Address critical problems in materials science and simultaneously advance the state-of-the-art in multiscale and multimode characterization using the combined advanced analytical capabilities and expertise of Colorado School of Mines (CSM) and the National Renewable Energy Laboratory (NREL). The primary effort of the Phase I of this CRADA is to establish the International Center for Multiscale Characterization using shared resources at both NREL and CSM. Phase II will focus on capability development and marketing, choosing candidate materials science issues in the areas of structure imaging, chemical composition mapping, and correlating properties and performance of materials for impact in energy-related, environmental and critical materials areas. The CRADA will be modified to include specific topics of concern in materials science to industry member partners. Advanced analytical capabilities and expertise at CSM and NREL will be used to advance materials understanding and performance through characterization of multiscale phenomena including structural imaging, chemical composition mapping, and other techniques correlating properties and performance of materials. PROJECT 2: As part of the International Center for Materials Characterization, work under Modification #1 will be led by Colorado School of Mines (CSM), working in collaboration with NREL staff to mentor and advise CSM postdoctoral researchers on set up of diffusion annealing experiments. The purpose of the modification is to provide for NREL staff to mentor and advise CSM postdoctoral researchers on set up of diffusion annealing experiments, including mentoring and advising the CSM-NREL team on proper Secondary Ion Mass Spectrometry (SIMS) data analysis as needed. SIMS measurements of 10-20 samples will be performed at NREL during the project duration.

08 HYDROGEN↗

Long-Duration Energy Storage Grid Integration-Valuation Framework and Incentive Gaps

Given these challenges and current modeling gaps on Long Duration Energy Storage (LDES), enhancing the structure and design of existing planning, operations, and organized wholesale markets can better characterize the value of LDES to the power system. To more thoroughly assess the gaps and barriers to LDES investment and readiness for integration into a future grid, we conducted stakeholder outreach through an online survey, interviews with individual independent system operators/regional transmission organizations, and a literature review. Based on this assessment, we identified a set of opportunities for LDES focused development, including a framework to quantify the contributions of LDES on resource adequacy, reliability, and resiliency. Specifically, we identify the potential demand for and benefits of an open-source, LDES-centric evaluation framework that can guide future planning, operations, market design, and policy reforms.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Power market models for the clean energy transition: State of the art and future research needs

As power systems around the world are rapidly evolving to achieve decarbonization objectives, it is crucial that power system planners and operators use appropriate models and tools to analyze and address the associated challenges. This paper provides a detailed overview of the properties of power market models in the context of the clean energy transition. We review common power market model methodologies, their readiness for low- and zero-carbon grids, and new power market trends. Based on the review, we suggest model improvements and new designs to increase modeling capabilities for future grids. The paper highlights key modeling concepts related to power system flexibility, with a particular focus on hydropower and energy storage, as well as the representation of grid services, price formation, temporal structure, and the importance of uncertainty. We find that a changing resource mix, market restructuring, and growing price uncertainty require more precise modeling techniques to adequately capture the new technology constraints and the dynamics of future power markets. In particular, models must adequately represent resource opportunity costs, multi-horizon flexibility, and energy storage capabilities across the full range of grid services. Moreover, at the system level, it is increasingly important to consider sub-hourly time resolution, enhanced uncertainty representation, and introduce co-optimization for dual market clearing of energy and grid services. Likewise, models should capture interdependencies between multiple energy carriers and demand sectors.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Multi-Source Machine Learning and Thermoplastics Enhanced Aerostructure Manufacturing (mTEAM)

RTX Technology Research Center (RTRC), together with Collins Aerospace (Collins) and Oak Ridge National Laboratory (ORNL) has developed an Artificial Intelligence (AI) / Machine Learning (ML) guided solution to advance the manufacturing and assembly of high performance and lightweight thermoplastic composite (TPC) aerospace products. The solution aims to lower risk, cost and lead time for induction heating based welding and consolidation processes for TPC structure. The cost and lead time of part and material specific process development for induction welding (IW) and induction consolidation will be reduced by replacing traditional empirical methods with optimization methods that merge AI/ML and physics-based process simulations and process experiments with sensing and controls. TPC-IW process development is empirical in nature, and uncertainties in material & process behavior exist near & far from the induction coil. Physics-based simulations can be leveraged directly for process optimization but can be too computationally expensive to run in high fidelity and real time to do robust process optimization. The key impact of successful TPC induction consolidation and welding is cost & lead time reduction for part & material specific consolidation and welding recipes. This is an enabler for more rapid deployment of TPC structures via joining assembly, which can reduce energy & cost intensive usage of autoclaves & ovens. The solution aimed to advance the U.S. Department of Energy’s interests in using thermoplastics and automation in composite manufacturing for improvement of products for existing markets via increased production speeds, reduced costs, and lowered use of energy. Welded TPC structures can offer significant weight & energy savings for high-value commercial aerospace & industrial applications compared to metal & thermoset composite structures assembled by mechanical fastening and/or adhesive bonding. The project was organized into two Budget Periods. Budget Period 1 (BP1) was 15 months and its goal was to perform ML process optimization framework development & deployment on lab-coupon aerostructure components. A Go/No-Go Review was performed at the end of BP1 to verify fulfilment of key tasks & milestones to justify a Go Decision to move into the next Budget Period. Budget Period 2 (BP2) was 12 months and its goal was the deployment of the ML framework for ML process optimization of pilot industrial scale aerostructure components. The overall project aim was to develop & demonstrate ML-enhanced modeling framework that learns process-property mapping from multiple data sources at different fidelities. During BP1, the team accomplished key tasks & milestones to demonstrate the concept of multi-source ML for TPC aerostructure consolidation and assembly. First, the team completed documentation of induction based TPC heating requirements including baseline metrics to compare measured results against. Next the team completed demonstration of data generation from physics-based simulations for ML surrogate model generation and demonstrated the integration of physics-based simulation data into multi-source AI/ML algorithms. In parallel, the team established the lab-coupon scale induction welding system and completed a process to label and reduce generated data from physics-based simulation and experiments for ML surrogate models to enable multi-source ML model training & testing. To complete BP1, the team integrated physics-based simulation data and experimental data into multi-source ML algorithms. This was based on the team completing ML deployment of the induction welding on a lab system at RTRC and AI/ML deployment on existing induction welding line at Collins. ORNL visited both Collins and RTRC sites to witness the TPC induction welding process. Then, ORNL designed and constructed a new version of their vision-based sensing system better adapted to acquire process signals of the TPC induction welding process for process anomaly and defect detection. In BP2, the team accomplished key tasks & milestones to scale up multi-source ML for TPC aerostructure consolidation and assembly from the lab-coupon scale to the pilot-industrial scale. In BP2, the team demonstrated real time anomaly & defect detection via experiments performed by ORNL & RTRC. The team completed ML-optimization heating trials for TPC induction consolidation at Collins, and the team confirmed pilot industrial scale experimental data from Collins was compatible with the developed ML pipeline from RTRC. The team completed sub-element scale ML process optimization demonstration at RTRC, where the team leveraged RTRC’s robotic TPC welding setup to de-risk the ML process optimization by performing ML analysis of recorded temperatures to account for complex part features. Then, the team applied its ML-derived control strategies and ML process optimization framework at Collins to the pilot-industrial scale on a demo skin-stiffener part representative of a nacelle aerostructure fan cowl section. The key innovation is the AI/ML framework enabling effective process development of high performance, lightweight, energy efficient TPCs for composite aircraft structures.

36 MATERIALS SCIENCE↗

One Year In: Tracking the Impacts of NEM 3.0 on California’s Residential Solar Market

On December 15, 2022, the California Public Utilities Commission passed an overhaul of the net metering program for the state’s investor-owned utilities. The changes replaced the long-standing net energy metering (NEM) tariffs with a net billing tariff (NBT) structure—colloquially known as “NEM 3.0”—which significantly reduces the compensation for behind-the-meter solar photovoltaic (PV) systems. The NEM tariffs remained open for new interconnection applications until April 15, 2023, but after that date, all new interconnection applications were submitted under NBT. Now, one year later, we have an opportunity to evaluate how the California solar market has evolved under this new compensation regime. As a precursor to its annual Tracking the Sun report, Berkeley Lab has released a short technical brief describing key trends in the California residential solar market since the roll-out of the new NBT structure. The purpose of this analysis is to provide empirical insights into how the market has evolved over the past year, confirming some expectations while also revealing several striking surprises.

14 SOLAR ENERGY↗

Reinventing CEMENT: Carbonation-Enabled Mineralization to Engender Novel Technology

Cement is the most important building material used in civil infrastructure but it is extremely energy intensive to produce. We developed novel ways to make cements at low temperatures by mimicking the chemistry of the calcium silicate mineral pseudowollastonite. Pseudowollastonite is interesting because when it is exposed to hot (90-150oC), alkaline conditions with CO2 and water it reacts to form mineral phases that are much stronger than commercial cements. The resulting phases are also much more resistant to acid attack and have lower permeability, which will make them last longer. Many of the mineral phases in our cements are similar to those that exist in ancient Roman cements that have lasted thousands of years. Our objective was to understand how waste materials from industrial processes (such as power production, municipal solid waste treatment, or power plants) can be used to replicate the conditions that give pseudowollastonite cement its remarkable properties. We worked to optimize our formulations and incorporate the cements into concrete blends that the industry can test and consider using in different applications. Our cements could be used in pre-cast cement structural elements, which makes up nearly 20% of the total cement market and is growing rapidly. This work supports ARPA-E program objectives to significantly improve the industrial efficiency of materials production.

36 MATERIALS SCIENCE↗

Bridging Control and Deployment: A Cross-Layer Analysis of Scalable Building Cluster Control

Building cluster control has emerged as a promising approach for enabling flexible and coordinated operation of distributed building systems, yet its transition from pilot demonstrations to routine grid-interactive operation remains limited. This paper argues that this gap cannot be explained by control algorithms alone. Instead, it arises from interacting barriers in communication infrastructure, data and semantic interoperability, uncertainty management, stakeholder participation, market design, and policy support. Accordingly, the paper reviews both technical and non-technical barriers to building cluster control. Technical challenges include heterogeneous devices and protocols, communication latency and reliability, distributed decision-making, and uncertainty propagation across aggregated loads. Non-technical barriers include user participation, stakeholder coordination, incentive allocation, and data governance. Existing solution approaches are synthesized, including semantic interoperability frameworks, edge and hierarchical communication architectures, distributed and transactive control strategies, uncertainty-aware optimization, policy mechanisms, and market reforms. Based on this analysis, two research directions are identified: testing infrastructures that can evaluate control performance under realistic multi-building conditions, and abstraction methods that allow building clusters to interact with other energy sectors through standardized flexibility representations. Overall, the paper provides a structured review of how building cluster control can move from isolated demonstrations toward reproducible, market-compatible, and grid-relevant implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Solar cities: A case study analysis of city-level enablers of expanded solar energy access

Rooftop solar photovoltaic (PV) adoption can benefit households by reducing electricity bills and enhancing energy resiliency. Low and moderate-income (LMI) households have been less likely to adopt PV and experience these benefits in the United States than higher-income households. Adopter income trends are often explored through quantitative analysis with limited explanatory power. Our quantitative analysis only explains around one-third of city-level variation in LMI adoption trends through socioeconomic factors such as median home values and income inequality and PV market factors such as cumulative adoption and incentives. We implement semi-structured interviews in three case studies of cities with relatively high rates of LMI PV adoption to better understand the factors that explain PV adopter income trends. The case studies partly reiterate findings from quantitative analysis, such as the role of PV incentives. The case studies reveal a broader set of LMI adoption drivers that are missed in quantitative analyses. The case studies show how city contexts can affect LMI adoption, such as the role of supportive city governments. The case studies also reveal the importance of partnerships, such as partnerships between city governments and state LMI PV program implementers. Finally, interviewees emphasized the importance of building trust among prospective LMI PV adopters. Interviewees suggested that partnerships, outreach, and consumer protection measures were crucial to building trust in PV installers among LMI households.

Adoption↗

Comparative life cycle assessment of a modular cross-laminated timber residential building designed for disassembly and reuse versus traditional wood frame construction

There is a need for affordable housing across the U.S., with high-performance modular and prefabricated buildings providing a logical avenue for meeting some of this demand. However, there is a need to balance high performance construction – including low emissions – with affordability. To provide a proof-of-concept in meeting these goals, the Circular Home is a cross-laminated timber (CLT)-based deconstructible and reconfigurable single-family residence that meets high performance targets in moisture, energy, design, economics, and life cycle assessment (LCA). This study focuses on the LCA, presenting a cradle-to-cradle whole-building life cycle assessment (WBLCA) for the Circular Home and a functionally equivalent Baseline Home constructed with traditional materials and methods. The functional unit is 1 m 2 of gross floor area across 60 years. Revit building information models (BIM) provided material quantities and Tally LCA was utilized for impact data (inclusive of biogenic carbon sequestration), supplemented with manufacturer environmental product declarations (EPDs). The Circular Home outperforms the baseline residence in most measured impact categories, including global warming potential (GWP), producing −2.73 kgCO 2 eq/m 2 in embodied emissions, whereas the modeled baseline has an embodied GWP of 428 kgCO 2 eq/m 2 . The careful material selection and advanced building design optimizes performance, with the Circular Home containing only −0.006 times the embodied emissions and −0.02 times the operational emissions of its traditional counterpart. Finally, the unique contribution of this work is in the environmental impact comparison of a high-performance modular CLT structure that can be affordably scaled and mass produced in a U.S. market, compared to typical single family home construction.

Circularity↗