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DOE OSTI2021

Model Based Predictive Control for Frequency Support in Multi-port Autonomous Reconfigurable Solar Plants

Primary frequency support functionality can be an added advantage for any new grid integrated power electronics resource. With increased penetration of power electronics resources, inertial and primary frequency response capabilities are decreasing. In this research paper, an advanced model-based predictive control (MBPC) algorithm for providing frequency support to a new topology of integrated photovoltaic(PV), batterybased energy storage systems (ESS), and high-voltage direct current (HV dc) systems called multi-port autonomus reconfigurable solar (MARS) plants is based on the synchronverter-based control. The simulation of MARS-HV dc system with proposed control strategy is simulated and validated for MARS connected to low short circuit ratio (SCR) grids in PSCAD/EMTDC simulation environment. The control algorithm proposed showcases better performance in terms of frequency nadir improvement and frequency steady state improvemnt with respect to no frequency control.

Marthi, Phani Ratna Vanamali↗

DOE OSTI2019

Community Microgrid Planning Considering Building Thermal Dynamics

In this paper, the building thermal dynamic characteristics are introduced in the community microgrid (MG) planning model. The proposed planning model is formulated as a mixed integer linear programming (MILP) which seeks to determine the optimal deployment strategy for various distributed energy resources (DER). The objective is to minimize the annualized cost of community MG, including investment cost in DER, operation cost for dispatchable fuel-generators (DFG), energy storage system (ESS) degradation cost, energy purchasing and peak demand charge at PCC, customer discomfort cost due to the room temperature deviation and load curtailment cost. Given the slow thermal dynamic characteristics of buildings, the heating, ventilation and air-conditioning (HVAC) system in the proposed model is treated as a demand side management (DSM) component, whose dispatch commands are provided by the central MG controller. Numerical results based on a community MG comprising of 20 residential buildings demonstrate the effectiveness of the proposed planning model and the benefits of introducing the building thermal dynamic model.

Zhang, Xiaohu↗

DOE OSTI2021

High-Fidelity Models and Fast EMT Simulation Algorithms for Isolated Multi-port Autonomous Reconfigurable Solar power plant (MARS)

The integration of hybrid photovoltaic (PV) and energy storage system (ESS) based plants has become a promising way of solving the intermittency of PV plants and providing frequency support to the power grid. The multi-port autonomous reconfigurable solar power plant (MARS) can integrate the PV systems and ESSs to an ac grid and dc lines. The proposed isolated MARS incorporates an isolated converter that connects to the PV arrays and is based on the dual active bridge (DAB) converter. The high frequency switching in the DAB and the means to control the DAB converter using delays between switching signals lead to the need for a small timestep in simulations. Moreover, several hundreds of modules that include a DAB converter are present in the MARS. The small timestep and the presence of several hundreds of modules lead to a significant rise in the overall simulation time. To address this issue, simulation algorithms like numerical stiffness-based hybrid discretization and the hysteresis relaxation technique are applied to the switched system model of isolated MARS. Additionally, an event-driven interpolating method is introduced to help increase the minimum timestep to simulate the conventional DAB converter model while maintaining high accuracy in the simulation results. The developed model is validated by comparison with its reference model built in the PSCAD/EMTDC and MATLAB software environments using library components.

Xia, Qian↗

DOE OSTI2025

Coordination of Energy Storage and Distributed Generation for Voltage Control and Peak-valley Filling

The increasing penetration of distributed energy resources (DERs) in distribution network (DN) poses challenges on voltage control. In addition, the growing integration of DERs also reshapes the traditional load profile. To comprehensively address the voltage control and peak-valley filling in DN, this paper proposes a model predictive control (MPC) based optimization framework. The proposed method can achieve coordinated voltage control and peak-valley filling by adjusting the reactive power output from distributed generations (DGs) and the charging/discharging of energy storage systems (ESS). The performance of the proposed method is demonstrated by simulations on a modified IEEE-123 bus system.

Zhang, Zhengfa [University of Tennessee, Knoxville↗

DOE OSTI2021

On Secured Spinning Reserve Deployment of Energy-Limited Resources Against Contingencies

Featured with fast response abilities and high ramp rates, energy storage systems (ESS), such as pumped-storage hydropower (PSH) plants and battery storage systems (BSS), are considered as key first-responders to provide spinning reserve in response to system contingencies. However, ESSs are energy-limited resources, and their sustained spinning reserve deployment is restricted by the stored energy and available capacity. Indeed, the spinning reserve deployment against contingencies may deviate actual state-of-charge (SOC) from the scheduled value, inducing potential SOC boundary violations and non-dispatchability in later hours. This paper proposes the post-contingency operation model and the spinning reserve secure constraints to address these issues. The post-contingency operation model describes that spinning reserve from ESSs is promptly deployed when contingency occurs, and then gradually substituted by quick-start units when they are switched online. By leveraging this operation strategy, spinning reserve secure constraints in terms of SOC headrooms are adopted to guarantee its deplorability. Furthermore, a modified IEEE 118-bus system with multiple PSHs and BSSs is used to verify the proposed approach.

13 HYDRO ENERGY↗

DOE OSTI2015

Networked Microgrids for Self-healing Power Systems

This paper proposes a transformative architecture for the normal operation and self-healing of networked microgrids (MGs). MGs can support and interchange electricity with each other in the proposed infrastructure. The networked MGs are connected by a physical common bus and a designed two-layer cyber communication network. The lower layer is within each MG where the energy management system (EMS) schedules the MG operation; the upper layer links a number of EMSs for global optimization and communication. In the normal operation mode, the objective is to schedule dispatchable distributed generators (DGs), energy storage systems (ESs) and controllable loads to minimize the operation costs and maximize the supply adequacy of each MG. When a generation deficiency or fault happens in a MG, the model switches to the self-healing mode and the local generation capacities of other MGs can be used to support the on-emergency portion of the system. A consensus algorithm is used to distribute portions of the desired power support to each individual MG in a decentralized way. The allocated portion corresponds to each MG’s local power exchange target which is used by its EMS to perform the optimal schedule. The resultant aggregated power output of networked MGs will be used to provide the requested power support. Test cases demonstrate the effectiveness of the proposed methodology.

Consensus Algorithm↗

DOE OSTI2016

A Three-Phase Microgrid Restoration Model Considering Unbalanced Operation of Distributed Generation

Recent severe outages highlight the urgency of improving grid resiliency in the U.S. Microgrid formation schemes are proposed to restore critical loads after outages occur. Most distribution networks have unbalanced configurations that are not represented in sufficient detail by single-phase models. This study provides a microgrid formation plan that adopts a three-phase network model to represent unbalanced distribution networks. The problem formulation has a quadratic objective function with mixed-integer linear constraints. The three-phase network model enables us to examine the three-phase power outputs of distributed generators (DGs), preventing unbalanced operation that might trip DGs. Because the DG unbalanced operation constraint is non-convex, an iterative process is presented that checks whether the unbalanced operation limits for DGs are satisfied after each iteration of optimization. We also develop a relatively conservative linear approximation on the unbalanced operation constraint to handle larger networks. Compared with the iterative solution process, the conservative linear approximation is able to accelerate the solution process at the cost of sacrificing optimality to a limited extent. Simulation in the IEEE 34 node and IEEE 123 test feeders indicate that the proposed method yields more practical microgrid formations results. In addition, this paper explores the coordinated operation of DGs and energy storage (ES) installations. The unbalanced three-phase outputs of ESs combined with the relatively balanced outputs of DGs could supply unbalanced loads. In conclusion, the case study also validates the DG-ES coordination.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Optimal Energy Storage System and Smart Switch Placement in Dynamic Microgrids With Applications to Marine Energy Integration

Here this paper studies a dynamic microgrid (DMG) planning problem that places energy storage systems (ESSs) and smart switches (SSWs) optimally in the system. We apply the proposed methodology to applications concerning marine renewable energy (MRE). MRE is an emerging clean energy resource with enormous capacity but volatile and intermittent energy output profiles. Innovative grid-integration technologies designed to enhance the reliability of an MRE-integrated system are needed. However, there are still limited studies in this regard. Existing works have shown the promising prospect of using a dynamic microgrid (DMG) operational concept to accommodate renewable resources in distribution systems, but they usually assume fixed ESS and SSW installations. To further improve the operational flexibility of DMGs, we propose a DMG planning methodology that optimally places ESSs and SSWs so that a DMG with MRE is warranted with proper resource adequacy and topological flexibility in both the contingency and normal operations. We use realistic case studies based on a real-world distribution network and the U.S. Department of Energy's MRE dataset to verify the value and validity of the proposed work.

25 ENERGY STORAGE↗

DOE OSTI2022

Hierarchical Control of Megawatt-Scale Charging Stations for Electric Trucks with Distributed Energy Resources

Electrifying medium- and heavy-duty trucks is critical to decarbonizing the transportation sector. Energy needs of electric trucks will likely require megawatt-scale charging stations, which could significantly stress the electric distribution grid. Distributed energy resources (DER) can alleviate this stress and reduce charging costs with proper management. To that end, this work develops a hierarchical predictive control algorithm for future multi-port megawatt-scale charging stations that can provide real-time energy management for stations, decide charging rates, dispatch energy storage system (ESS), and provide grid voltage support. We integrate three algorithmic components: (i) an energy management optimization (EMO) that provides supervisory control to DER assets and charging loads at minute scale, (ii) a real-time energy management system (RT-EMS) that heuristically compensates for fast disturbances at sub-second scale, and (iii) a model predictive control (MPC)-based battery management system (BMS) that communicates future charging demands to the EMO, to manage the overall megawatt-scale site. Additionally, validation in a controller hardware-in-the-loop (CHIL) environment shows that the hierarchical controller can reduce the total energy consumption from the grid by approximately 28% compared to an uncontrolled case for the station configuration in this paper, without impacting charging time.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

EMOS (Energy Management Optimization System) [SWR-21-46]

EMOS software performs a real-time hierarchal optimal control for energy systems like multi-port electric vehicles charging site with distributed energy resources (DERs) and energy storage systems (ESSs). It gathers information in real-time from electric vehicles, power grid, and DERs, solves a multi-objective energy management optimization problem, and output setpoint for chargers, ESS converters, DERs converters, and grid converters. The software incorporates a novel integration of two control tasks: a) An Energy Management Optimization (EMO), which is the brain of EMOS controller that gathers information in real-time from EVs [e.g., battery size, state-of-charge (SOC), desired SOC, and charge acceptance curve], grid (e.g., electricity price, allowed feeder capacity, ramp rate limit, and reactive power), and DERs (e.g., prediction for solar generation for PV systems). It solves a control optimization problem in real-time to find optimal setpoint for ESSs power dispatch, EVs charging rate, and grid inverters. The objectives are to (1) minimize the charging cost considering grid energy, demand charges, and battery energy, (2) minimize charging time to meet fast charging criterion, (3) keep high energy level on ESSs at the end of an operating period, while satisfying constraints related to grid, EVs, and power converters. b) Real-Time Energy Management System (RT-EMS) is a rule-based algorithm that has faster response than EMO. It receives optimal setpoint from EMO and actual measurements from the system and modify the setpoint to compensate for any fast disturbance in the system, until a new optimum solution is received. Fast disturbances may include vehicle connect/disconnect, unpredicted variation in DERs profiles, variation in grid voltage, errors in PV generation prediction, and others. In addition, RT-EMS regulates voltage at point of common coupling (PCC) by managing reactive power of grid converters.

Mohamed, Ahmed↗

DOE OSTI2025

EVI-EnSitePy (Electric Vehicle Infrastructure – Energy Estimation and Site Optimization Tool in Python) [EVI-X Modeling Suite] [SWR-25-07]

EVI-EnSitePy is a comprehensive agent-based tool designed for the analysis and design of high-power charging sites, encompassing a wide array of site agents including Electric Vehicles (EVs), chargers, energy storage units (ESS), renewable energy resources (DER), and loads. This versatile tool offers diverse functionalities and a modular modeling approach, allowing detailed configuration of agents based on power ratings, port numbers, energy capacities, demand requirements, charger interfaces, and flexibility to customize the tool for project specific goals. By simulating agent interactions and employing various metrics, EVI-EnSitePy enables the assessment of site performance, exploration of energy management systems (EMS), and implementation of innovative EV charging policies. Utilizing EV charge schedules and arrival states, the tool performs thorough charging site simulations, with outputs consisting of agent and site-level power profiles and statistical metrics. Employing a tree graph structure, EVI-EnSitePy supports nested site structures and power distribution modeling. The tool's ability to generate charging schedules deterministically or via stochastic analysis further enhances its versatility. Through its features and capabilities, EVI-EnSitePy offers a powerful platform for informed decision-making in the realm of high-power charging site design and operation.

Jackson, Derek [National Renewable Energy Laborato↗

DOE OSTI2025

QuESt PCM

SAND2025-14369O QuESt PCM is a tool for modeling power system production cost. It is designed for high-fidelity representation of energy storage systems (ESS) and is part of QuESt 2.0: Open-source Platform for Energy Storage Analytics. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Nguyen, Tu Anh [Sandia National Lab. (SNL-CA), Liv↗

DOE OSTI2021

Quantifying the Uncertainty of the Future Hydrological Impacts of Climate Change: Comparative Analysis of an Advanced Hierarchical Sensitivity in Humid and Semiarid Basins

Comparison and quantification of different uncertainties of future climate change involved in the modeling of a hydrological system are highly important for both hydrological modelers and policy-makers. However, few studies have accurately estimated the relative importance of different sources of uncertainty at different spatiotemporal scales. Here, a hierarchical sensitivity analysis framework (HSAF) incorporated with a variance-based global sensitivity analysis is developed to quantify the spatiotemporal contributions of different uncertainties in hydrological impacts of climate change in two different climatic (humid and semiarid) basins in China. The uncertainty sources include three emission scenarios (ESs), 20 global climate models (GCs), three hydrological models (HMs), and the associated sensitive hydrological parameters (PAs) screened and sampled by the Morris and Latin hypercube sampling methods, respectively. Further, the results indicate that the overall trend of uncertainty is PA > HM > GC > ES, but their uncertainties have discrepancies in projections of different hydrological variables. The HM uncertainty in annual and monthly discharge projections is generally larger than the PA uncertainty in the humid basin than semiarid basin. The PA has greater uncertainty in extreme hydrological event (annual peak discharge) projections than in annual discharge projections for both basins (particularly for the humid basin), but contributes larger uncertainty to annual and monthly discharge projections in the semiarid basin than humid basin. The GC contributes larger uncertainty in all the hydrological variables projections in the humid basin than semiarid basin, while the ES uncertainty is rather limited in both basins. Overall, our results suggest there is greater spatiotemporal variability of hydrological uncertainty in more arid regions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

WHONDRS 48 Hour Diel Cycling Study at the Nisqually River, WA

This dataset is from a sampling campaign completed over the course of 48 hours to investigate the effects of glacial freeze/thaw cycle-induced water level variation on aqueous chemistry, dissolved organic matter chemistry, and metabolic potential of microbial communities at the Nisqually River, WA, USA. Filtered surface water (SW) and pore water (PP) samples were collected every three hours in August 2018. This dataset is one of seven companion datasets that were part of the WHONDRS 48 Hour Diel Cycling Study of freshwater rivers. All seven datasets are available on ESS-DIVE; they can be found by searching for WHONDRS 48 Hour.This package contains a variety of chemical and hydrologic variables. All data types are provided in a standard format. Basic geochemistry analyses (e.g., non-purgeable organic carbon concentration, cations, anions, SpC, pH) were conducted, standardized photos were taken, surface water hydrographs were collated from existing instrumentation, and extensive metadata were captured. Aqueous chemistry variables include USGS codes in the first portion of the variable name (e.g., 00940_Cl_mg_per_L). These codes allow for interoperability with USGS water quality data. In addition, samples were delivered to the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory) for high resolution characterization of dissolved organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). The data package contains instructions to process the FTICR data based on the user’s specific needs, and sample-level summary indices derived from the FTICR data will be added to the data package. Beyond the data in this data package, metagenomic data are being generated on associated microbial communities. When these data are available, the data package will be updated with information on accessing them. Please contact WHONDRS@pnnl.gov for more information. Please use the data package’s DOI to cite the data package. We ask that you email us at WHONDRS@pnnl.gov to let us know that you're using the data and acknowledge WHONDRS and the U.S. Department of Energy's Subsurface Biogeochemical Research program—which generously provides funding to WHONDRS—in your publications, proposals, presentations, etc. All data are free to be used for any purpose, such as for manuscripts, presentations, and grant proposals. Note that the included hydrograph has a separate DOI that should also be cited. There is no obligation to include WHONDRS members as co-authors.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

WHONDRS 48 Hour Diel Cycling Study at the Erpe River, Germany

This dataset is from a sampling campaign completed over the course of 48 hours to investigate the effects of wastewater treatment plant effluent induced water level variation on aqueous chemistry, dissolved organic matter chemistry, and metabolic potential of microbial communities at the Erpe River, Germany. Filtered surface water (SW) and pore water (PP) samples were collected every three hours in September 2018. This dataset is one of seven companion datasets that were part of the WHONDRS 48 Hour Diel Cycling Study of freshwater rivers. All seven datasets are available on ESS-DIVE; they can be found by searching for WHONDRS 48 Hour.This package contains a variety of chemical and hydrologic variables. All data types are provided in a standard format. Basic geochemistry analyses (e.g., non-purgeable organic carbon concentration, cations, anions, SpC, pH) were conducted, standardized photos were taken, surface water hydrographs were collated from existing instrumentation, and extensive metadata were captured. Aqueous chemistry variables include USGS codes in the first portion of the variable name (e.g., 00940_Cl_mg_per_L). These codes allow for interoperability with USGS water quality data. In addition, samples were delivered to the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory) for high resolution characterization of dissolved organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). The data package contains instructions to process the FTICR data based on the user’s specific needs, and sample-level summary indices derived from the FTICR data will be added to the data package. Beyond the data in this data package, metagenomic data are being generated on associated microbial communities. When these data are available, the data package will be updated with information on accessing them. Please contact WHONDRS@pnnl.gov for more information. Please use the data package’s DOI to cite the data package. We ask that you email us at WHONDRS@pnnl.gov to let us know that you're using the data and acknowledge WHONDRS and the U.S. Department of Energy's Subsurface Biogeochemical Research program—which generously provides funding to WHONDRS—in your publications, proposals, presentations, etc. All data are free to be used for any purpose, such as for manuscripts, presentations, and grant proposals. There is no obligation to include WHONDRS members as co-authors.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

WHONDRS 48 Hour Diel Cycling Study at the Altamaha River in Georgia, USA

This dataset is from a sampling campaign completed over the course of 48 hours to investigate the effects of tidally induced water level variation on aqueous chemistry, dissolved organic matter chemistry, and metabolic potential of microbial communities at the Altamaha River, GA, USA. Filtered surface water (SW) and pore water (PP) samples were collected every three hours in October 2018. This dataset is one of seven companion datasets that were part of the WHONDRS 48 Hour Diel Cycling Study of freshwater rivers. All seven datasets are available on ESS-DIVE; they can be found by searching for WHONDRS 48 Hour. This package contains a variety of chemical and hydrologic variables. All data types are provided in a standard format. Basic geochemistry analyses (e.g., non-purgeable organic carbon concentration, cations, anions, SpC, pH) were conducted, standardized photos were taken, surface water hydrographs were collated from existing instrumentation, and extensive metadata were captured. Aqueous chemistry variables include USGS codes in the first portion of the variable name (e.g., 00940_Cl_mg_per_L). These codes allow for interoperability with USGS water quality data. In addition, samples were delivered to the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory) for high resolution characterization of dissolved organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). The data package contains instructions to process the FTICR data based on the user’s specific needs, and sample-level summary indices derived from the FTICR data will be added to the data package. Beyond the data in this data package, metagenomic data are being generated on associated microbial communities. When these data are available, the data package will be updated with information on accessing them. Please contact WHONDRS@pnnl.gov for more information. Please use the data package’s DOI to cite the data package. We ask that you email us at WHONDRS@pnnl.gov to let us know that you're using the data and acknowledge WHONDRS and the U.S. Department of Energy's Subsurface Biogeochemical Research program—which generously provides funding to WHONDRS—in your publications, proposals, presentations, etc. All data are free to be used for any purpose, such as for manuscripts, presentations, and grant proposals. Note that the included hydrograph has a separate DOI that should also be cited. There is no obligation to include WHONDRS members as co-authors. Please note that this data package was originally published in November 2019. It was updated in July 2020. See the readme file in the data package for details. None of the data values were altered. The units were updated in one column of the main metadata file and in one of the methods codes descriptions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

WHONDRS 48 Hour Diel Cycling Study at the Columbia River in Washington, USA

This dataset is from a sampling campaign completed over the course of 48 hours to investigate the effects of the upstream Priest Rapids Dam-induced water level variation on aqueous chemistry, dissolved organic matter chemistry, and metabolic potential of microbial communities at the Columbia River, WA, USA. Filtered surface water (SW) and pore water (PP) samples were collected every three hours in August 2018. This dataset is one of seven companion datasets that were part of the WHONDRS 48 Hour Diel Cycling Study of freshwater rivers. All seven datasets are available on ESS-DIVE; they can be found by searching for WHONDRS 48 Hour.This package contains a variety of chemical and hydrologic variables. All data types are provided in a standard format. Basic geochemistry analyses (e.g., non-purgeable organic carbon concentration, cations, anions, SpC, pH) were conducted, standardized photos were taken, surface water hydrographs were collated from existing instrumentation, and extensive metadata were captured. Aqueous chemistry variables include USGS codes in the first portion of the variable name (e.g., 00940_Cl_mg_per_L). These codes allow for interoperability with USGS water quality data. In addition, samples were delivered to the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory) for high resolution characterization of dissolved organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). The data package contains instructions to process the FTICR data based on the user’s specific needs, and sample-level summary indices derived from the FTICR data will be added to the data package. Beyond the data in this data package, metagenomic data are being generated on associated microbial communities. When these data are available, the data package will be updated with information on accessing them. Please contact WHONDRS@pnnl.gov for more information. Please use the data package’s DOI to cite the data package. We ask that you email us at WHONDRS@pnnl.gov to let us know that you're using the data and acknowledge WHONDRS and the U.S. Department of Energy's Subsurface Biogeochemical Research program—which generously provides funding to WHONDRS—in your publications, proposals, presentations, etc. All data are free to be used for any purpose, such as for manuscripts, presentations, and grant proposals. There is no obligation to include WHONDRS members as co-authors.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

WHONDRS 48 Hour Diel Cycling Study at the Jordan River, Israel

This dataset is from a sampling campaign completed over the course of 48 hours to investigate the effects of groundwater pumping induced water level variation on aqueous chemistry, dissolved organic matter chemistry, and metabolic potential of microbial communities at the Jordan River, Israel. Filtered surface water (SW) and pore water (PP) samples were collected every three hours in August 2018. This dataset is one of seven companion datasets that were part of the WHONDRS 48 Hour Diel Cycling Study of freshwater rivers. All seven datasets are available on ESS-DIVE; they can be found by searching for WHONDRS 48 Hour.This package contains a variety of chemical and hydrologic variables. All data types are provided in a standard format. Basic geochemistry analyses (e.g., non-purgeable organic carbon concentration, cations, anions, SpC, pH) were conducted, standardized photos were taken, surface water hydrographs were collated from existing instrumentation, and extensive metadata were captured. Aqueous chemistry variables include USGS codes in the first portion of the variable name (e.g., 00940_Cl_mg_per_L). These codes allow for interoperability with USGS water quality data. In addition, samples were delivered to the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory) for high resolution characterization of dissolved organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). The data package contains instructions to process the FTICR data based on the user’s specific needs, and sample-level summary indices derived from the FTICR data will be added to the data package. Beyond the data in this data package, metagenomic data are being generated on associated microbial communities. When these data are available, the data package will be updated with information on accessing them. Please contact WHONDRS@pnnl.gov for more information. Please use the data package’s DOI to cite the data package. We ask that you email us at WHONDRS@pnnl.gov to let us know that you're using the data and acknowledge WHONDRS and the U.S. Department of Energy's Subsurface Biogeochemical Research program—which generously provides funding to WHONDRS—in your publications, proposals, presentations, etc. All data are free to be used for any purpose, such as for manuscripts, presentations, and grant proposals. There is no obligation to include WHONDRS members as co-authors.

54 ENVIRONMENTAL SCIENCES↗