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At least 487 records · Page 27

Enhancing Lithium-Ion Battery Aging Simulations: Coupling a High-Resolution, 3D, Grain-Scale Electromechanical Model to a Single-Particle Model

One of the main goals in modeling lithium-ion batteries is to improve/predict longevity and resilience of new chemistries. Unfortunately, this requires simulation of thousands of charge/discharge cycles, which can be rather time consuming depending on the fidelity of the simulation. The purpose of this talk is to discuss a new modeling framework that couples a high-resolution, continuous damage model (CDM) to a single particle model (SPM) resulting in a good combination of speed and fidelity. In previous work, a 3D, continuum-level chemo-mechanical model was developed to investigate cracking within a single cathode particle comprised of hundreds to thousands of randomly oriented grains. The CDM predicted that particle fracture is primarily due to non-ideal grain interactions with slight dependence on high-rate charge demands. Essentially, when neighboring grains were misaligned, they expanded different rates relative to one another leading to high stresses and ultimately the formation of intraparticle cracks. The model predicted that small particles with large grains develop significantly less damage than larger particles with small grains. Finally, the model predicted most of the chemo-mechanical damage accumulates in the first charge after formation. This chemo-mechanical "damage saturation" effect indicated that initial particle fracture occurs within the first few cycles, while long-term cathode degradation is not solely chemo-mechanically induced. This led to a need for simulating fatigue-like mechanism that degrade the battery over longer time scales. In order to reach the time scales, need to resolve fatigue-like degradation, the CDM needs to be complemented by a faster model. Therefore, recent efforts have been focused on using results from the CDM to inform parameters within the SPM. These parameters are homogenization factors that are associated with diffusion, particle radius, and/or exchange current density. By coupling the CDM to the SPM, the aging simulation is broken up into two domains: short-term and long-term degradation. The short-term degradation occurs over a single cycle and is handled by the CDM due of its high fidelity, but relatively expensive computational cost. Such mechanism include break-in crack caused by mismatches in grain orientation. The long-term degradation occurs over tens of cycles and is handled by the SPM due it's computational efficiency. Most of the fatigue-like mechanism fall into this category. The eventual goal of this modeling framework is to upscale to a psudo-2D model allowing for full electrode simulation, which are informed by high-fidelity grain-scale simulations.

electrochemistry↗

System identity and transformation in petroleum jurisdictions: A multi-method approach for the North Slope Borough, Alaska

Capturing the multidimensionality of a bounded social-environmental system (SES) presents a range of challenges to interdisciplinary researchers due to the need to integrate divergent scientific paradigms, scalar data, and social theories. Contemporary Arctic circumpolar SESs studied under conditions of rapid and unprecedented climatic, ecological, economic, and sociopolitical change, defy any singular established methodological approach that aims to schematize and interpret the system for decision-making purposes. As a small interdisciplinary team working within a large Arctic SES modeling effort, we have found that developing systems models to support resilience in the Arctic requires an understanding of system dynamics that is attentive to holistic indicators of change, measured both quantitatively and qualitatively. Using the Alaska North Slope Borough as a case study, we apply three convergent frameworks to capture significant dimensions of the system for improved problem definition in confronting the challenges of Arctic climate change. We describe contemporary “oil and gas” social-ecological system components and dynamics, the historical processes and transformations that fundamentally altered the system, and the scientific projections for the most likely catalysts of future change. This analysis results in a typology for defining subnational Arctic hydrocarbon SESs. We conclude that the future of oil and gas development as a policy pathway in different locations experiencing rapid climate change can be evaluated when difficult-to-quantify variables are included.

Lovecraft, Amy Lauren↗

Capability for Integrated Systems Risk-Reduction Analysis

NASA's Human Research Program (HRP) is working to increase the likelihoods of human health and performance success during long-duration missions, and subsequent crew long-term health. To achieve these goals, there is a need to develop an integrated understanding of how the complex human physiological-socio-technical mission system behaves in spaceflight. This understanding will allow HRP to provide cross-disciplinary spaceflight countermeasures while minimizing resources such as mass, power, and volume. This understanding will also allow development of tools to assess the state of and enhance the resilience of individual crewmembers, teams, and the integrated mission system. We will discuss a set of risk-reduction questions that has been identified to guide the systems approach necessary to meet these needs. In addition, a framework of factors influencing human health and performance in space, called the Contributing Factor Map (CFM), is being applied as the backbone for incorporating information addressing these questions from sources throughout HRP. Using the common language of the CFM, information from sources such as the Human System Risk Board summaries, Integrated Research Plan, and HRP-funded publications has been combined and visualized in ways that allow insight into cross-disciplinary interconnections in a systematic, standardized fashion. We will show examples of these visualizations. We will also discuss applications of the resulting analysis capability that can inform science portfolio decisions, such as areas in which cross-disciplinary solicitations or countermeasure development will potentially be fruitful.

Mindock, J.↗

BESS Digital Assurance, Supply Chain Risk Management, and Emerging Regulation Session Two

The TADA BESS Supply Chain Workshops are designed to equip participants with the knowledge and tools necessary to address the evolving challenges at the intersection of battery energy storage systems (BESS), cybersecurity, and supply chain vulnerabilities. The workshops emphasize the application of Cyber-Informed Engineering (CIE) principles using INL’s procurement guide and the CIE-BAT tool. Attendees will develop risk-based security strategies and actionable compliance roadmaps tailored to their BESS projects. Additionally, the program fosters a collaborative network of practitioners and provides guidance on navigating emerging regulatory requirements, including FEOC rules under the OBBB framework, to assess and enhance organizational readiness. A comprehensive exploration of BESS cybersecurity supply chain risks, systematic vendor risk assessment through the BESS Procurement Guide, and practical application of the INL SCRM Chatbot for enhanced supply chain resilience. This is Session 2 of 3. (Full Version)

25 - ENERGY STORAGE↗

Why does cyber deterrence fail and when might it succeed? A framework for cyber scenario analysis

Through cyberattacks on information technology and digital communications systems, antagonists have increasingly been able to alter the strategic balance in their favor without provoking serious consequences. Conflict within and through the cyber domain is inherently different from conflict in other domains that house our critical systems. These differences result in new challenges for defending and creating resilient systems, and for deterring those who would wish to disrupt or destroy them. The purpose of this paper is to further examine the question of whether or not deterrence can be an effective strategy in cyber conflict, given our broad and varied interests in cyberspace. We define deterrence broadly as the creation of conditions that dissuade antagonists from taking unwanted actions because they believe that they will incur unacceptably high costs and/or receive insufficient benefits from taking that action. Deterrence may or may not be the most credible or effective strategy for achieving our desired end states in cybersecurity. Regardless of the answer here, however, it is important to consider why deterrence strategies might succeed under certain conditions, and to understand why deterrence is not effective within the myriad contexts that it appears fail. Deterrence remains a key component of U.S. cyber strategy, but there is little detail on how to operationalize or implement this policy, how to bring a whole-of-government and whole-of- private-sector approach to cyber deterrence, which types of antagonists can or should be deterred, and in which contexts. Moreover, discussion about how nations can and should respond to significant cyber incidents largely centers around whether or not the incident constitutes a "use of force," which would justify certain types of responses according to international law. However, we believe the "use of force" threshold is inadequate to describe the myriad interests and objectives of actors in cyberspace, both attackers and defenders. In this paper, we propose an approach to further examine if deterrence is an effective strategy and under which conditions. Our approach includes systematic analysis of cyber incident scenarios using a framework to evaluate the effectiveness of various activities in influencing antagonist behavior. While we only examine a single scenario for this paper, we propose that additional work is needed to more fully understand how various alternative thresholds constrain or unleash options for actors to influence one another's behavior in the cyber domain.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Building a Framework to Model Food Security Pressures and Shocks in a Changing World

• An Intersection of Challenges for Food Security • The Agricultural Model Intercomparison and Improvement Project (AgMIP) • Establishing and evaluating agricultural models • Responding to the transient signal of climate change • Understanding food systems and identifying potential for unexpected behaviors • Opportunities For a More Resilient Future

Food Security↗

Hydrophobic Metal–Organic Frameworks Enable Superior High-Pressure Ammonia Storage through Geometric Design

Hydrophobic metal–organic frameworks (MOFs) are typically overlooked for ammonia storage due to weak host–guest interactions. Here, we demonstrate that four structurally analogous aluminum-based MOFs exhibit a counterintuitive behavior whereby framework geometry, rather than ligand hydrophilicity, determines high-pressure NH 3 adsorption performance. The hydrophobic CAU-23 achieved an exceptional capacity matching hydrophilic analogs despite its poor low-pressure uptake. This pressure-dependent enhancement stems from the unique 4-cis-4-trans geometry of CAU-23 compared to the purely cis arrangement of MIL-160 and KMF-1 and the alternating cis-trans configuration of MOF-303. Critically, CAU-23 retained 95% capacity over three high-pressure cycles, whereas hydrophilic MOFs suffered 39–46% irreversible losses due to strong NH 3 -framework interactions that compromise structural integrity. Grand canonical Monte Carlo simulations reveal that high pressure enables NH 3 clustering through intermolecular hydrogen bonding, bypassing the need for strong host–guest interactions. High-pressure powder X-ray diffraction measurements confirm the exceptional mechanical resilience of CAU-23, showing complete structural recovery upon decompression despite exhibiting the highest pressure sensitivity among the studied MOFs. An extended analog, HE-CAU-23, validates this design principle with further enhanced capacity. Furthermore, these findings reveal a paradigm shift toward hydrophobic MOFs with optimized geometry for high-performance and regenerable gas storage applications.

MOFs↗

Investigating Resiliency of Transportation Network under Targeted and Potential Climate Change Disruptions

Ensuring robustness and resilience in intermodal transportation systems is essential for the continuity and reliability of global logistics. These systems are vulnerable to various disruptions, including natural disasters and technical failures. Despite significant research on freight transportation resilience, investigating the robustness of the system after targeted and climate-change-driven disruption remains a crucial challenge. Drawing on network science methodologies, this study models the interdependencies within the rail and water transport networks and simulates different disruption scenarios to evaluate system responses. Here, we use the data from the U.S. Department of Energy Volpe Center for network topology and tonnage projections. The proposed framework quantifies deliberate, stochastic, and climate-driven infrastructure failure, using higher resolution downscaled multiple Earth System Models’ simulations from Coupled Model Intercomparison Project Phase version 6. We show that the disruptions of a few nodes could have a larger impact on the total tonnage of freight transport than on network topology. For example, the removal of targeted 20 nodes can bring the total tonnage carrying capacity to 30% with about 75% of the rail freight network intact. This research advances the theoretical understanding of transportation resilience and provides practical applications for infrastructure managers and policymakers. By implementing these strategies, stakeholders and policymakers can better prepare for and respond to unexpected disruptions, ensuring sustained operational efficiency in transportation networks.

Climate Change Disruptions↗

Enhancing Security and Resiliency in Operational Technology Environments Through Network Slicing and Federated Learning

The growing convergence of Information Technology (IT) and Operational Technology (OT) within Industry 4.0 environments has introduced new demands on industrial network infrastructure. As cyber-physical systems become increasingly interconnected, ensuring the secure, timely, and efficient exchange of critical data is essential. This thesis explores how network slicing, a method of creating isolated virtual network segments, can be applied within OT environments to address challenges such as latency, security, and resource allocation. The first research question addressed in this thesis is: How can OT networks take advantage of NFV and SDN technology to become cyber resilient? This study examines the operational, security, and architectural implications of introducing network slicing into traditionally static OT infrastructures such as Industrial Control Systems (ICS) and SCADA. Through simulated deployments and case studies, the research demonstrates how slicing enables better isolation between critical and non-critical services, thereby improving response time, throughput, and security in sensitive environments. The second question considers: How to dynamically implement network slicing and take advantage of network resources towards integrating decentralized machine learning? In response, this thesis proposes a framework that combines Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Federated Learning (FL) to enable real-time analytics while maintaining data locality. The proposed approach reduces the burden on centralized infrastructure and minimizes privacy risks by supporting on-site training of models across distributed OT nodes, coordinated through dynamically allocated network slices. The third focus explores: How slicing helps to increase the resiliency of OT networks through the orchestration of a dynamic DMZ? To answer this, the thesis presents a method for creating and managing Dynamic Demilitarized Zones (DMZs) using network slicing. This enables flexible and automated isolation of sensitive subsystems during threat scenarios or high-risk operations. Coupled with intelligent orchestration and containerized security services, the dynamic DMZ significantly enhances the system's ability to respond to cyber incidents without halting production. Ultimately, this thesis contributes a comprehensive architecture that blends network slicing with machine learning, secure segmentation, and automation, paving the way for resilient, adaptive, and intelligent OT environments. Performance evaluations across multiple scenarios show improvements in system reliability, threat response time, model accuracy, and resource utilization, providing a strong foundation for future industrial automation systems.

Rodiles Delgado, Brian G↗

Addressing Consequence within Operational Risk: An Approach for Understanding an Organization’s Unique Infrastructure Environment

The endeavor of tackling operational risk focused on consequences is challenging even for the most resourced entity but can be advanced though a simplified approach: identifying, binning, and prioritizing the infrastructure environment. While no two entities within a single element of the 16 critical infrastructure sectors are exactly alike when it comes to risk, there is a basic process to move toward a greater understanding of operational risk through becoming more informed about the infrastructure landscape in which the entity exists. The process starts with bringing internal and external stakeholders and subject matter experts together to analyze key areas such as Information Technology (IT) and Operational Technology (OT) components and points of convergence, analyzing internal and external cyber and physical dependencies, accounting for explosive growth in devices and wireless technology, and leveraging the contributions of people inside and outside the operational environment. Attaining a common understanding of the infrastructure landscape as part of addressing consequences within operational risk is not easy to do or resource light, but the process outlined provides the framework to further any entity’s efforts in this space.

99 GENERAL AND MISCELLANEOUS↗

Distributed Software-Defined Network Architecture for Smart Grid Resilience to Denial-of-Service Attacks

An important challenge for smart grid security is designing a secure and robust smart grid communications architecture to protect against cyber-threats, such as Denial-of-Service (DoS) attacks, that can adversely impact the operation of the power grid. Researchers have proposed using Software Defined Network frameworks to enhance cybersecurity of the smart grid, but there is a lack of benchmarking and comparative analyses among the many techniques. In this work, a distributed three-controller software-defined networking (D3-SDN) architecture, benchmarking, and comparative analysis with other techniques is presented. The selected distributed flat SDN architecture divides the network horizontally into multiple areas or clusters, where each cluster is handled by a single Open Network Operating System (ONOS) controller. A case study using the IEEE 118-bus system is provided to compare the performance of the presented ONOS-managed D3-SDN, against the POX controller. In addition, the proposed architecture outperforms a single SDN controller framework by a tenfold increase in throughput; a reduction in latency of > 20%; and an increase in throughput of approximately 11% during the DoS attack scenarios.

Agnew Jr., Dennis↗

5G integrated edge computing platform for efficient component monitoring in coal-fired power plants

This project developed a cutting-edge 5G-integrated edge computing framework to enhance operational efficiency and reliability in coal-fired power plants through real-time component monitoring and anomaly detection. The initiative focused on leveraging distributed machine learning, federated learning, and 5G-based dynamic network slicing to support scalable, fault-tolerant monitoring environments to meet the operational requirements in industrial control systems. With a Distributed Edge Computing Service (DECS) orchestration, this project enabled federated learning at edge for condition monitoring and introduced adaptive client selection strategies to minimize communication overhead. Scalable distributed training was achieved using the Horovod framework, thus enhancing performance across edge nodes. In the realm of 5G networking, the project designed and deployed reconfigurable, QoS-aware network slicing tailored for operational technology (OT) environments, integrating software-defined networks to bolster cyber-resilience and enabling dynamic slicing for federated learning workloads. A significant milestone was the development of a virtualized ICS environment with 5G core integration—which allowed elastic and fault tolerant distributed training on real-world datasets such as NASA Bearings, Hydraulic Systems, and TEP. To broaden the impact of the project, a TRL-3 virtualized ICS testbed for research and education was designed. This project engaged several graduate and undergraduate students to conduct research on the cutting-edge technology, and it resulted in one PhD dissertation, one MS thesis, and over 14 peer-reviewed publications. With the support of this project students also participated in national cybersecurity competitions to improve their professional development skills.

20 FOSSIL-FUELED POWER PLANTS↗

Future Intensity‐Duration‐Frequency Curves of Extreme Precipitation in the Midwest United States From Convection‐Permitting Modeling

Abstract During the last four decades, global warming has statistically significant intensified extreme precipitation events in the Midwestern United States (defined here as the region covering Illinois, Indiana, Ohio, and Kentucky), leading to increased risks to human life, property, and infrastructure. To enable climate change adaptation and resilience across various economic and social sectors in this region, updated information about future climate changes, specifically at finer spatial scales, is essential. Leveraging a new 150‐year dynamical downscaling data set at convection‐permitting resolution, this study introduces a framework to construct the projected future intensity‐duration‐frequency (IDF) curves of heavy precipitation, which are prominent tools for infrastructure design and water resources management. This framework generates IDF curves at both sub‐daily and multi‐day duration utilizing hourly in situ observations as well as quantile‐based statistical techniques in bias‐correction and return levels selection. The assumption of non‐stationarity in the distribution parameter fitting process is also implemented in this workflow. Compared to historical IDF curves for 1980–2022, future projected IDF curves for 2058–2100 under Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios indicate an average intensity increase of approximately 15% and 25%, respectively, across 74 stations, considering both annual and seasonal timescales. Future projections suggest that extreme precipitation events may become more severe across six investigated return periods, with longer return periods showing a greater increase. The frequency of future extreme precipitation events in the Midwest region is also projected to double. Furthermore, current results reveal spatial heterogeneity of future trends across stations owing to the high‐resolution input data set. Plain Language Summary This study investigates the evolving nature of extreme precipitation events in the Midwestern United States under a changing climate. By leveraging a high‐resolution dynamical downscaling data set, we construct projected intensity‐duration‐frequency (IDF) curves for future extreme rainfall events. These curves serve as vital tools for infrastructure planning and water resource management. Our analysis reveals a significant increase in both the intensity and frequency of extreme precipitation events in the region. Future projected IDF curves for the late century indicate an average intensity increase of approximately 15%–25% compared to historical values. Moreover, the frequency of such events is expected to double. Spatial heterogeneity in future trends is observed across different stations within the Midwest, highlighting the importance of high‐resolution modeling in capturing localized climate variability. These findings underscore the urgent need for climate adaptation strategies to mitigate the increasing risks associated with extreme precipitation events in the region. Key Points This study introduces a workflow to construct future intensity‐duration‐frequency (IDF) curves over the Midwest United States using a new convection‐permitting modeling data set The current IDF construction workflow reproduces well the historical observed IDF 30 curves in summer months with median relative errors of 2.4% among 74 stations and 6 investigated durations The projected IDF curves show diverse future trends of extreme precipitation across stations, with intensity increases of approximately 15% and 25% under RCP4.5 and RCP8.5 climate scenarios, respectively, and a doubling of frequency on average

Nguyen, Trung↗

An Open-Source Virtual Testbed for a Real Net-Zero Energy Community

Net zero energy communities (NZECs) are critical to ensure sustainability and resilience of modernized power systems. System modeling helps overcome technical challenges in designing and operating NZECs. In this paper, we present the modeling work based on a real NZEC. Two sets of models are developed: higher-fidelity physics-based models considering the interaction between subsystems of the studied NZEC and capturing fast-dynamics; and lower-fidelity data-driven models requiring less resource to establish and/or run. All models are validated against measurements from this real NZEC. In addition, we create a simulation framework which streamlines the processes for simulation and thus allows using developed models to form a virtual testbed. To demonstrate the usage of the virtual testbed, a case study is conducted where a building-to-grid integration control is evaluated via simulation. The evaluation results suggest that the tested control significantly smooths the power draw of the studied community and doesn’t sacrifice the thermal comfort to a great extent.

Huang, Sen↗

A Markov framework for generalized post-event systems recovery modeling: From single to multihazards

State-dependent models can be used to represent the system recovery process as a series of stochastic transitions from lower to higher functional states. However, the applications of these models have been limited in scope and there is a lack of a generalized recovery modeling framework. A generalized framework would permit a robust forecasting of systems and system-of-systems recovery under multiple hazards, and more broadly, would contribute to community disaster preparedness. This paper develops a generalized post hazard-event recovery modeling framework based on state-dependent Markov-type processes. We then apply the proposed framework to solve a spectrum of problems that range from hind-casting single-system recovery following a single hazard event to forecasting post-event trajectories under multiple hazards and modeling the recovery of a system-of-systems. First, Markov chains are used to hind-cast the observed recovery for a portfolio of buildings affected by the 2014 South Napa, California, earthquake. Next, Markov processes are used to formulate a parametric post hazard-event recovery model, which can be updated using Bayesian statistics when relevant datasets become available. Semi-Markov processes are then used to develop a more general model of single hazard recovery, which accounts for the intensity of the loading and level of damage caused by the event. Semi-Markov processes with non-renewal features are then used to account for multihazard interactions in a post-event recovery model, and applied to a case study that involves a community in Charleston, South Carolina. Lastly, Markov-type processes are combined with Bayesian networks to model the recovery of residential, commercial, educational, and industrial buildings (system-of-systems) following a hazard event. Overall, these applications demonstrate the versatility of the Markov framework towards handling recovery problems with varying levels of complexity.

42 ENGINEERING↗

Watershed response of the Feather River Basin, California, United States of America to future climate changes

Increasing mean annual temperatures under climate change are expected to reduce seasonal snowpack, increase evapotranspiration (ET), and alter summer baseflow in headwater watersheds worldwide. Strong regional variability in hydrologic responses highlights the need for catchment-scale, physically based models to assess future flood risk and water availability. This study examines climate-driven changes in the hydrologic response of the Upper Feather River watershed in the Sierra Nevada Mountains, California. Four priority climate models and two representative concentration pathways (RCP4.5 and RCP8.5) are used to evaluate future hydrologic responses of the watershed. Hydrologic processes are simulated using an objectively calibrated Soil and Water Assessment Tool Plus model for a historical baseline (1986–2005) and a future period 2070–2099), driven by observed and projected precipitation and temperature. Results indicate a declining contribution of snowfall to annual precipitation, with peak snowfall and water yield shifting 1–3 months earlier. Long-term annual maximum flows are projected to increase considerably, whereas low-flow responses are mixed, with both increases and decreases projected by the end of the century. These findings highlight the need for adaptive watershed management to enhance flood protection, water storage, and drought resilience. Future water resource planning should also account for one-to-three-month shifts in peak water yield and surface runoff due to changes in snowmelt timing and a lower snowfall-to-rainfall ratio under climate change. The modeling framework and insights are transferable to other snow-dominated headwater watersheds experiencing climate-driven change.

Tigabu, T [UC Davis]↗

Reinforcement Learning for Intentional Islanding in Resilient Power Transmission Systems

Intentional islanding is the process of identifying and deliberately decomposing the transmission network to form self-sustained islands from an endangered network during disruptions to improve resilience and security. Most existing intentional islanding models are offline resilience decision tools and hence do not provide outage responses in a timely manner. In this paper, a reinforcement learning (RL) based model for intentional islanding is developed, which offers real-time switching control, online deployability, and adaptability to varying system conditions. The intentional islanding process is formulated as a Markov decision process, where the optimal transmission switching policy is learned using the RL approach. The control policy is learned over an environment that encompasses a Power System Simulator for Engineering (PSS/E) model of the transmission network, facilitated by an interface to the standard openAI Gym framework. The proposed RL-based methodology aims to form stable and self-sustainable islands by ensuring voltage stability while reducing the power mismatch in the formed islands. A proximal policy optimization algorithm is designed, which is suitable for controlling the on/off status of the switches with multi-layer perceptron as value and actor networks. The effectiveness of the proposed framework in the self-recovery of the grid by island formation is applied on the modified IEEE 39-bus test network and validated by dynamic simulations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

A Comprehensive Framework for Valuation of Ecosystem Services in the Context of Hydropower

While models to value ecosystem services exist, they suffer from a lack of “portability” in the sense that a single modelling framework developed for one site cannot be imported and applied to another. Additionally, benefit estimations are often fragmented, incomplete, incomparable, and in heterogenous metrics, thus preventing aggregation to arrive at one comprehensive value. In the context of hydropower, the methods, as well as the metrics used to value the associated ecosystem services, vary between stakeholders (e.g., competing water users, agencies, etc.), detracting from the ability to assess the total value of a hydropower project, water management schemes, or comparing value between competing water users. Further, current methods to value water neglect non-market ecosystem services’ values by failing to account for externalities, resulting in misallocation of costs (e.g., obligatory stakeholder payments for water use) and misinterpretation of hydropower benefits. The benefit-cost ratios in hydropower development are often incomplete because non-market benefits are excluded (e.g., benefits, such as fish habitat resiliency and agricultural water reliability, derived from hydropower development). An approach to enable consistent, standardized hydropower benefit-cost analyses does not currently exist. This paper conceptualizes a methodology to standardize ecosystem services valuation in the context of hydropower projects through the integration of the U.S. Environmental Protection Agency’s existing Final Ecosystem Goods and Services Classification System framework and economic valuation techniques, and demonstrates the applicability of this methodology through a limited proof-of-concept application (the New Waddell Plant, a pumped hydroelectric energy storage facility in Phoenix, AZ). The beauty of our proposed framework lies in its ability to be imported and applied to any other pumped storage facility after accounting for site-specific features.

13 HYDRO ENERGY↗