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At least 127 records · Page 7

Price equation captures the role of drug interactions and collateral effects in the evolution of multidrug resistance

Bacterial adaptation to antibiotic combinations depends on the joint inhibitory effects of the two drugs (drug interaction [DI]) and how resistance to one drug impacts resistance to the other (collateral effects [CE]). Here we model these evolutionary dynamics on two-dimensional phenotype spaces that leverage scaling relations between the drug-response surfaces of drug-sensitive (ancestral) and drug-resistant (mutant) populations. We show that evolved resistance to the component drugs – and in turn, the adaptation of growth rate – is governed by a Price equation whose covariance terms encode geometric features of both the two-drug-response surface (DI) in ancestral cells and the correlations between resistance levels to those drugs (CE). Within this framework, mean evolutionary trajectories reduce to a type of weighted gradient dynamics, with the drug interaction dictating the shape of the underlying landscape and the collateral effects constraining the motion on those landscapes. We also demonstrate how constraints on available mutational pathways can be incorporated into the framework, adding a third key driver of evolution. Our results clarify the complex relationship between drug interactions and collateral effects in multidrug environments and illustrate how specific dosage combinations can shift the weighting of these two effects, leading to different and temporally explicit selective outcomes.

Gjini, Erida↗

The Effects of Climate Change on Interregional Electricity Market Dynamics on the U.S. West Coast

The United States (U.S.) West Coast power system is strongly influenced by variability and extremes in air temperatures (which drive electricity demand) and streamflows (which constrain hydropower production). As hydroclimate changes across the West Coast, a combination of forces may work in tandem to make its bulk power system more vulnerable to physical reliability issues and market price shocks. In particular, a warmer climate is expected to increase summer cooling (electricity) demands and shift the average timing of peak streamflow (hydropower production) away from summer to the spring and winter, depriving power systems of hydropower when it is needed the most. Here, we investigate how climate change could alter interregional electricity market dynamics on the West Coast, including the potential for hydroclimatic changes in one region (e.g. Pacific Northwest (PNW)) to “spill over” and cause price and reliability risks in another (e.g. California). We find that the most salient hydroclimatic risks for the PNW power system are changes in streamflow, while risks for the California system are driven primarily by changes in summer air temperatures, especially extreme heat events that increase peak system demand. Altered timing and amounts of hydropower production in the PNW do alter summer power deliveries into California but show relatively modest potential to impact prices and reliability there. Instead, it is future climate conditions (extreme heat) in California that may exert a stronger significant influence on prices and reliability in the PNW, especially if California continues to rely on its northern neighbor for imported power to meet higher summer demands.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Development and Clustering of Rate-Oriented Load Metrics for Customer Price-Plan Analysis

One of the few methods electric utilities can use to motivate and change customer energy consumption is through retail rate structures. Utilities are increasingly moving toward more dynamic rate plans to encourage energy conservation, utilization of onsite renewable generation, peak demand reduction and flattening of demand profiles. This paper creates a set of rate-oriented load metrics that are the determinants of customers' bills under four unique rate plans. These metrics are not only indicative of which rate structure can provide customer bill reductions based on their load profile characteristics, but also convey useful information about load consumption behavior. With these metrics, utilities can analyze their customers and identify classes that are rewarded under each rate plan. This can help inform utilities whether the customers rewarded under each rate plans are meeting their original objectives. To develop these customer classes, we calculate these rate-oriented load metrics for each customer and perform k-means clustering. The analysis is conducted on a set of 300 customer profiles, examining four different rate plans, different numbers of clusters, customer bills and cluster load profile characteristics.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Use of Shallow Ice Core Measurements to Evaluate and Constrain 1980–1990 Global Reanalyses of Ice Sheet Precipitation Rates

Abstract Sea‐level rise (SLR) projections by Earth System Models (ESMs) depend on ice sheet surface mass balances. Accurate, global atmosphere reanalyses would be ideal for providing equilibrated ice sheet model initial conditions in fully coupled ESM simulations. Here we present the first evaluation of 1980–1990 global reanalysis precipitation over Greenland and Antarctica that uses independent observations of net accumulation rates derived from shallow ice cores. Precipitation distributions from both the European Centre for Medium‐Range Weather Forecast's Reanalysis (ERA5) and the Modern‐Era Retrospective Analysis for Research and Applications (MERRA‐2) are highly correlated with contemporaneous co‐located net accumulation rates from Greenland ( r 2 > 0.95) and West Antarctica ( r 2 > 0.7). Three other commonly used reanalyses (WFDE5, CRUNCEP, and GSWP3) exhibit significantly weaker correlations on one or both ice sheets. Our findings imply that ESMs should use ERA5 or MERRA‐2 in data‐forced simulations to validate ice sheet model dynamics and precondition firn for SLR projections.

Schneider, Adam↗

Operating-Envelopes-Aware Decentralized Welfare Maximization for Energy Communities

We propose an operating-envelope-aware, prosumer-centric, and efficient energy community that aggregates individual and shared community distributed energy resources and transacts with a regulated distribution system operator (DSO) under a generalized net energy metering tariff design. To ensure safe network operation, the DSO imposes dynamic export and import limits, known as dynamic operating envelopes, on end-users' revenue meters. Given the operating envelopes, we propose an incentive-aligned community pricing mechanism under which the decentralized optimization of community members' benefit implies the optimization of overall community welfare. The proposed pricing mechanism satisfies the cost-causation principle and ensures the stability of the energy community in a coalition game setting. Numerical examples provide insights into the characteristics of the proposed pricing mechanism and quantitative measures of its performance.

distributed energy resources aggregation↗

Opportunities and Challenges to Capturing Distributed Battery Value via Retail Utility Rates and Programs

Distributed battery deployment is increasing with advanced metering, control, and communication technologies, leaving electric utilities with an under-utilized, flexible grid resource in aggregate. Rates can reflect locational and temporal prices while utility incentive-based programs allow DERs to provide direct grid services. However, utilities must balance accurately reflecting dynamic grid conditions versus simple and feasible design that encourages customer participation. Currently, most rates and incentive-based programs are simple, but as penetration of DER and advanced controls increase, dynamic designs could become prevalent. Utilities could encourage providing multiple services to optimize distributed battery dispatch and value streams, however, challenges persist when stacking services across distribution and bulk systems. A DER committed to multiple discrete services concurrently necessitates coordination between operators and a clear hierarchy of commitments. One way to address this is to separate commitments by time or capacity. For services that follow cyclic, predictable patterns, or those that are peak driven with predictability, an operator could ensure sufficient state of charge for participation, leaving time where a distributed battery could otherwise provide different services by segmenting participation temporally. To provide continuous or unexpected services, a battery operator may use state of charge management to reserve some percentage of the battery and segment participation by capacity. Macroeconomic trends, load patterns, generation profiles, and grid configurations drive variation in value and the subsequent implications for utility offerings and how a customer might participate. As distributed battery adoption increases, both regulators and utilities will need to ensure no adverse grid impacts and encourage provision of societal value beyond the customer domain.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Impacts of aluminum-cerium alloy deployment on different cerium commodities

An imbalance problem exists in the rare earth element (REE) market with cerium and lanthanum being more abundant and less valuable than REEs used in permanent magnets such as neodymium, praseodymium, and dysprosium. Research has been conducted on new applications for abundant REEs to improve mining economics. However, potential market impacts of these applications on individual REE commodities are unknown. A system dynamics model was built to investigate the impacts of a new aluminum-cerium (Al-Ce) alloy that can theoretically utilize three separate cerium forms: cerium carbonate, cerium oxide, or cerium metal. Cerium carbonate currently possesses the lowest price of the three available forms making it initially the most favorable choice. However, model results showed that cerium oxide could provide the best economics for future Al-Ce alloy deployment because its larger market size buffers major shocks to price.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Comparing Delay-, Distance-, and Cordon-Based Congestion Pricing Strategies Via Large-Scale Simulation

This study compares the impacts of delay-, distance-, and cordon-based congestion pricing strategies for Austin, Texas, using the POLARIS agent-based activity-based travel demand simulation model. This approach enables agent-level heterogeneity and realistic choice options (including destination, mode, and activity scheduling) for dynamic traffic assignment and congestion feedbacks across a major metro region, which are features lacking in past work. To ensure comparability, distance-based tolls were set to generate the same revenue as delay-based tolling of $3.5 M/day, averaging $1.17/resident/day or $0.42/vehicle-trip. Delay-based pricing delivers 44% lower network delay and 13% lower VHT compared to the no-toll baseline, levels unmatched by other pricing strategies. At the height of the AM peak, drivers pay up to $0.13/mile on average, though most links in the network remain untolled. Distance-based pricing is the most effective at reducing VMT (by 4%), but VHT reductions (of 6%) primarily stem from drivers selecting closer destinations, achieving only one-fourth the delay reduction of delay-based pricing. Across various implementations of delay- and distance-based pricing, the results suggest that spatial variations of tolls are far more important than temporal variations. Cordon tolls produce minimal impacts at the network-wide level, but offer substantial delay reductions inside the cordon. Other major findings include: 1) delay-based pricing increases trip-making during the PM peak period due to backward shifts in discretionary-activity start times by higher-income residents; and 2) tolls’ spatial impacts, including changes in network flows and tolls paid by residents, vary substantially between delay- and distance-based pricing strategies.

Agent-based modeling↗

A Kinetic Model-Driven Techno-Economic Analysis of Plastic Pyrolysis: Linking Process Dynamics to Economic Viability

This study employs a kinetic model integrated into Aspen Plus to predict pyrolysis product distribution under various conditions. A techno-economic assessment calculated the minimum selling price (MSP) of pyrolysis oil under different operating conditions for the baseline capacity of 100 kta, and across eight processing capacities ranging from 30 to 150 kta. The lowest MSP under the baseline capacity is estimated at $\$$420/ton, which is 33% lower than the 2023 average US crude oil price ($\$$74.6/bbl, equivalent to $\$$634/ton based on the density of pyrolysis oil). Under Monte Carlo simulation, accounting for variability in key economic and technical parameters, the mean MSP is estimated at $\$$1137/ton. The economic viability depends on feedstock price remaining below $\$$320/ton, defining the break-even feedstock price threshold. Sensitivity analysis further identifies capital investment and transportation cost as key economic drivers. Capacities beyond 90 kta show limited economies of scale benefits. Reducing product storage time cuts capital costs by 7% but raises operational risk. Uncertainty analysis suggests the economic feasibility of pyrolysis oil is unlikely to compete with crude oil without policy incentives.

petrochemicals↗

Drivers of Future Physical Water Scarcity and Its Economic Impacts in Latin America and the Caribbean

Future water scarcity is a global concern with significant impacts on the energy, water, and land (EWL) sectors. Countries in Latin America and the Caribbean (LAC) are significant producers of agricultural goods consumed around the world, so disruptions resulting from land and water scarcity in LAC are a source of global supply chain risk. Understanding where water scarcity in LAC could occur and what could drive that scarcity to emerge is critical for strategic resource management and planning, both regionally and globally. Assessing future water scarcity impacts in LAC is challenging given the multisector dynamic interactions among the EWL sectors and the multiple uncertainties acting across different spatial scales. We use scenario discovery to explore the drivers of future water scarcity, considering assumptions related to climate, socioeconomics, and the EWL sectors. Understanding what factors drive uncertainty across outcomes can help stakeholders in targeting long-term solutions and future data collection efforts. To illuminate these dynamics, we use a database consisting of a large ensemble of scenarios representing diverse worlds simulated using the Global Change Analysis Model. We quantify future water scarcity and its economic impacts across the scenarios using three metrics: (1) physical water scarcity, (2) water price, and (3) crop profit. We find that physical water scarcity and water price are driven primarily by reservoir storage capacity assumptions, highlighting the importance of strategic water infrastructure development in maintaining future water availability and accessibility. Crop profit is driven by a combination of water supply and demand assumptions, which emphasizes the complex nature of EWL multisector dynamics. While most of LAC is poised to have abundant land and water resources available for development, water basins in Mexico and along the Pacific coast of South America experience high levels of severity and uncertainty across the future scenarios for at least one of the metrics. We find that the drivers of extreme scarcity vary spatially and across the metrics, further highlighting the heterogeneity of the region and the importance of considering multiple metrics to assess water scarcity vulnerability.

54 ENVIRONMENTAL SCIENCES↗

Sustainable hydrogen manufacturing via renewable-integrated intensified process for refueling stations

The widescale consumer adoption of hydrogen fuel cell electric vehicles (HFCEVs) is currently hindered by the high cost of small-scale hydrogen generation and the lack of extensive hydrogen refueling infrastructure. Natural gas-based hydrogen is cheaper when produced in large volumes but is also associated with high CO 2 emissions. To counter these challenges, we propose a hybrid approach where both natural gas and renewables are integrated in a synergistic manner using a dynamic process intensification technology that can be deployed on-site for meeting local demands of refueling stations. The technology is based on sorption enhanced steam methane reforming (SE-SMR) that utilizes a combination of reaction with in-situ CO 2 adsorption for enhancing process modularity, productivity and efficiency thereby outperforming conventional SMR at small scale. We develop a mixed integer linear programming (MILP)-based optimization framework for simultaneous design and scheduling of the SE-SMR process. The simultaneous optimization provides a synergistic combination whereby the renewables allow sustainable hydrogen manufacturing and the dynamic SE-SMR allows optimal use of the intermittency of the renewables. The U.S. nationwide analysis indicates that for futuristic renewable prices and a hydrogen production capacity of 2 ton/day, hydrogen can be produced at 50% less cost compared to the current cost of small-scale hydrogen generation. Finally, the city-wise analysis with varying hydrogen demand shows that even with just 5% HFCEV market penetration level, hydrogen production cost less than $3/kg can be obtained at small scales across the United States with even cheaper hydrogen for large cities.

08 HYDROGEN↗

Technology Pathways Could Help Drive the U.S. West Coast Grid's Exposure to Hydrometeorological Uncertainty

Abstract Previous studies investigating deep decarbonization of bulk electric power systems and wholesale electricity markets have not sufficiently explored how future grid pathways could affect the grid's vulnerability to hydrometeorological uncertainty on multiple timescales. Here, we employ a grid operations model and a large synthetic weather ensemble to “stress test” a range of future grid pathways for the U.S. West Coast developed by ReEDS, a well‐known capacity planning model. Our results show that gradual changes in the underlying capacity mix from 2020 to 2050 can cause significant “re‐ranking” of weather years in terms of annual wholesale electricity prices (with “good” years becoming bad, and vice versa). Nonetheless, we find the highest and lowest ranking price years in terms of average electricity price remain mostly tied to extremes in hydropower availability (streamflow) and load (summer temperatures), with the strongest sensitivities related to drought. Seasonal dynamics seen today involving spring snowmelt and hot, dry summers remain well‐defined out to 2050. In California, future supply shortfalls in our model are concentrated in the evening and occur mostly during periods of high temperature anomalies in late summer months and in late winter; in the Pacific Northwest, supply shortfalls are much more strongly tied to negative streamflow anomalies. Under our more robust sampling of stationary hydrometeorological uncertainty, we also find that the ratio of dis‐patchable thermal (i.e., natural gas) capacity to wind and solar required to ensure grid reliability can differ significantly from values reported by ReEDS.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Spatially explicit terrestrial carbon densities for calibrating the carbon cycle in human-Earth system Models

Soil and vegetation carbon stocks play a critical role in human-Earth system models. These stocks (denominated as densities in MgC/ha) affect variables such as land use change emissions and also influence land use change pathways under climate forcing scenarios where terrestrial carbon is assigned a carbon price. Here we present reharmonized soil and vegetation carbon densities both at the 5-arcmin resolution grid cell level and also aggregated to 235 water sheds for 4 land use types (Cropland, Grazed land, Urban land and unmanaged vegetation) and 15 unmanaged land cover types. Moreover, we use the distribution of carbon within and across pixels to define statistical "states" of carbon, once again differentiated by land type. These statistical states are used to define a range of possible carbon values that can be used for defining initial conditions of soil and vegetation carbon in human-Earth system models. We implement these data in a state-of-the-art multi sector dynamics model, namely the Global Change Analysis Model (GCAM), and show that these new data improve several land use responses, especially when terrestrial carbon is assigned a carbon price.

54 ENVIRONMENTAL SCIENCES↗

Optimal operation of solid-oxide electrolysis cells considering long-term chemical degradation

Optimizing the performance of solid oxide electrolysis cells (SOECs) for long-term hydrogen (H 2 ) production at high temperatures is crucial, as prolonged operation leads to efficiency losses and shorter cell lifespans due to chemical degradation. Here, in this work, we adopt a quasi-steady state approach for dynamic optimization over extended operational periods to address the disparity in timescales between cell operation and degradation. Integrating a 2-D non-isothermal SOEC model with balance-of-plant (BOP) equipment, we explore three optimization objectives: minimizing terminal degradation, maximizing integral efficiency, and minimizing the levelized cost of H 2 (LCOH). Our dynamic optimization algorithm reduces LCOH by 9.5% and 16% compared to strategies focusing solely on terminal degradation and integral efficiency, respectively. For electricity prices of 0.03 $\$$/mWh and 0.3 $\$$ mWh optimal replacement schedules range from 5 to 2 years, depending on the operational mode. Furthermore, a flexible operational mode yields additional improvements in LCOH over traditional galvanostatic and potentiostatic modes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Quantifying Membrane Structure and Dynamics during Bioproduct Production in Zymomonas mobilis by Molecular Simulation

The conversion of lignocellulosic biomass into biofuels and bioproducts by microbial biorefineries is central to a sustainable chemical industry. Zymomonas mobilis is one such biorefinery chassis and is resistant to ethanol stress, leading to its use in biomass conversion to biofuels and bioproducts. However, Z. mobilis growth is often inhibited by organic acids, aldehydes, alcohols, ketones, and amides found in biomass hydrolysate. The resulting slow growth inhibits production and as a result drives up the price for the resulting products. One hypothesis is that these molecules interact with or disrupt the bacterial membrane, triggering stress responses and hindering growth. To test this hypothesis at the molecular level, we employ all-atom molecular dynamics (MD) simulations to investigate lignocellulose-derived small molecules and their impact on a biologically relevant Z. mobilis membrane model. Simulations were conducted across a range of inhibitor concentrations from 0 to 2.5 mol %, analyzing key membrane properties such as area per lipid (APL), membrane thickness, lipid-order parameter (−S CH ), lateral diffusion coefficient (D xy ), and permeability coefficient (Pm). From simulation, we observed altered membrane structure and dynamics at these modest small molecule concentrations commonly found in hydrolysates. Generally, the membranes become thinner, with a higher area per lipid and lower-order parameter as the small molecule concentration increases. These trends are stronger for more hydrophobic molecules with greater hydrophobic bulk, as isobutanol, propanol, and propanoic acid showed greater membrane perturbations as the concentration increased compared to other small molecules. Tracking small molecule distributions directly in our equilibrium simulations allows us to determine concentration-dependent free energy profiles for these molecules. While the trends are noisy, generally the barriers to crossing the membrane decrease as the concentration increases, indicating that the membranes become leakier as small molecule concentrations rise. Comparing between native Z. mobilis membranes with hopanoids and membranes sharing the same phospholipid composition but without hopanoids, hopanoids stabilize and order the membrane for smaller molecules to maintain membrane structure but appear insufficient for larger hydrophobic molecules like isobutanol. These findings provide a mechanistic understanding of how small molecules found in biomass degradation streams interact with the Z. mobilis membrane, offering valuable insights for future strain engineering efforts to optimize biofuel and bioproduct synthesis from biomass feedstocks by highlighting limits to small molecule tolerance. This knowledge can guide the modification of membrane composition to develop more robust microbes, thereby improving microbial survival and yields in industrial contexts.

Singh, Nitin Kumar [Michigan State Univ., East Lan↗

Economic dispatch for electricity merchant with energy storage and wind plant: State of charge based decision making considering market impact and uncertainties

Here this paper investigates how the market impact of electricity merchants and uncertainty of wind generation affect their co-optimized scheduling policy, specifically for merchants who have both energy storage and wind plants. In the existing literature, merchants' trading actions are usually assumed not to affect market prices; however, a large-scale energy storage merchant's actions can affect market prices. To this end, we approximate the electricity price by a linear function of the quantity of power traded by the merchant in the reward function to achieve decision-making incorporating the market impact. This paper utilizes the dynamic programming approach to analyze merchants' optimal multi-period decision-making incorporating market impact, uncertain wind generation, and energy storage constraints. First, our results demonstrate that for a merchant with co-located energy storage facilities and wind power plants, the energy storage's feasible state of charge (SOC) range can be segmented into four possible sub-ranges by three analytically developed SOC reference points. The unique optimal trading decision can be achieved by comparing the current energy inventory and the SOC references of the next period. Second, our results show that market impact and uncertainties substantially change the optimal storage scheduling policy by impacting the values of the reference points. To mitigate the negative effect of the merchant's market impact on buying and selling actions, the merchant may reduce the amount of generating or pumping electricity each period to maximize profit. Moreover, we identify and investigate the trade-off between market price and transaction quantity. Our findings provide co-optimized scheduling guidance for electricity merchants with co-located energy storage and renewable power plants systems.

17 WIND ENERGY↗