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At least 163 records · Page 9

Optimal Power Flow in DC Networks with Robust Feasibility and Stability Guarantees

With high penetrations of renewable generation and variable loads, there is significant uncertainty associated with power flows in DC networks such that stability and operational constraint satisfaction are of concern. Most existing DC network optimal power flow (DN-OPF) formulations assume exact knowledge of loading conditions and do not provide stability guarantees. Here, in contrast, this paper studies a DN-OPF formulation which considers both stability and operational constraint satisfaction under uncertainty. The need to account for a range of uncertainty realizations in this paper's robust optimization formulation results in a challenging semi-infinite program (SIP). The proposed solution algorithm reformulates this SIP into a computationally tractable problem by constructing a tight convex inner approximation of the stability set using sufficient conditions for the existence of a feasible and stable power flow solution. Optimal generator set-points are obtained by optimizing over the proposed convex stability set. The validity and effectiveness of the propose algorithm is demonstrated through various DC networks adapted from IEEE test cases.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Vehicle Powertrain Simulation Accuracy for Various Drive Cycle Frequencies and Upsampling Techniques

As connected and automated vehicle technologies emerge and proliferate, lower frequency vehicle trajectory data is becoming more widely available. In some cases, entire fleets are streaming position, speed, and telemetry at sample rates of less than 10 seconds. This presents opportunities to apply powertrain simulators such as the National Renewable Energy Laboratory's Future Automotive Systems Technology Simulator to model how advanced powertrain technologies would perform in the real world. However, connected vehicle data tends to be available at lower temporal frequencies than the 1-10 Hz trajectories that have typically been used for powertrain simulation. Higher frequency data, typically used for simulation, is costly to collect and store and therefore is often limited in density and geography. This paper explores the suitability of lower frequency, high availability, connected vehicle data for detailed powertrain simulation. A large data set of 1 Hz trajectories is used to quantify the accuracy loss when simulating energy consumption for conventional, hybrid, and battery electric powertrains using less than 1 Hz data. Techniques to upsample lower frequency drive cycle data in order to increase accuracy are also explored. Median energy consumption errors when simulating energy consumption for a 1/10 Hz trajectory are found to be 3-6% when compared to 1 Hz trajectories. Applying upsampling and interpolation techniques are shown to reduce the simulation errors by roughly 50%. The findings in this work can guide connected vehicle data collection specifications and processing techniques applied when using collected data for powertrain simulation.

ADVANCED PROPULSION SYSTEMS↗

Lessons Learned From Techno-Economic Analysis of Solar Photovoltaics and Battery Energy Storage at a Vietnam Industrial Park

Through the Clean Energy Investment Accelerator (CEIA), engineers from the United States (U.S.) National Renewable Energy Laboratory (NREL) conducted a case study analysis evaluating the techno-economic feasibility of battery energy storage systems (BESS) at an industrial park in Vietnam. The analysis uses NREL's REopt platform, a distributed energy modeling and optimization tool, to identify the cost-optimal system sizing for BESS, in conjunction with onsite renewables such as solar photovoltaics (PV), to reduce electricity costs, increase onsite renewable energy generation utilization, and improve resilience to grid outages.

batteries↗

Marine energy supported multi-energy system planning and operation optimization for sustainable coastal community

The growing need for sustainable energy solutions in coastal areas necessitates the development of integrated systems that leverage abundant marine resources. In this study, a standalone Marine Energy Supported Multi-Energy System (MRE-MES) is designed for sustainable coastal community development, utilizing renewable marine resources, including offshore wind, wave, and solar energy, to address the energy needs of electricity, heat, freshwater, and hydrogen. The proposed MRE-MES incorporates a co-optimization model that simultaneously balances capacity planning and operational efficiency to minimize costs and environmental impacts. The system is tested under different renewable energy penetration levels and demand uncertainties, using a two-stage stochastic programming to account for variability in renewable resources and consumption needs. The experimental results indicate that in the optimal system capacity configuration, the percentage of total renewable energy generation is around 80 %, with or without capacity limitation constraints on PV, water tank, and hydrogen storage. Compared to the worst-case scenario in Monte Carlo experiments, two-stage stochastic optimization results in a more robust decision that effectively mitigates the risks posed by future uncertain demand conditions. In conclusion, the findings highlight the viability of marine energy for providing a resilient, comprehensive energy solution to coastal communities.

Capacity planning↗

Empowering Rural Electrification in Honduras: An Integrated Assessment of PV/BESS and Productive Uses of Electricity in Gracias a Dios

Honduras faces significant challenges in its energy sector, particularly in rural areas where access to reliable and affordable electricity remains limited. The flagship rural electrification initiative for Honduras Secretary of Energy (SEN) is the Politica de Acceso Universal a la Electricidad (PAUEH - Universal Electricity Access Policy), a key solution for addressing this energy poverty is the deployment of more than 1700 distributed solar and hybrid mini-grid solutions. In late 2023 as a first step towards supporting SEN's electrification efforts, the National Renewable Energy Laboratory (NREL) developed a literature review of SEN electrification policy documents and conducted a series of technical capacity-building workshops with SEN and other energy sector stakeholders in Honduras focused on using NREL's open-source REopt tool to conduct techno-economic assessments and develop least-cost optimizations for potential solar + storage mini-grid systems. Building on some initial capacity building on mini-grid modeling, the National Renewable Energy Laboratory (NREL) worked with SEN to develop a detailed techno-economic assessment for electrifying two schools, a healthcare clinic, and a hospital in a hypothetical community within the department of Gracias a Dios. The analysis also evaluates the business case for cold storage productive use of energy (PUE) applications for the fisheries value chain and how the incorporation of these PUE loads potentially impacts both the viability of the PV+BESS solutions as well as local economic development. By assessing the potential for deployment of integrated PV/BESS systems to both support critical community services like education and healthcare, as well as potential for downstream enterprise and economic development, this analysis represents a first step that can help to inform specific strategies for development of pilot PV+BESS projects aligned with national priorities and sector level planning under PAUEH.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Fortalecimiento de la Electrificación Rural en Honduras: Una Evaluación Integrada de PV+BESS y Usos Productivos de la Electricidad en Gracias a Dios, Honduras [Empowering Rural Electrification in Honduras: An Integrated Assessment of PV/BESS and Productive Uses of Electricity in Gracias a Dios] (Spanish Translation)

Honduras faces significant challenges in its energy sector, particularly in rural areas where access to reliable and affordable electricity remains limited. The flagship rural electrification initiative for Honduras Secretary of Energy (SEN) is the Politica de Acceso Universal a la Electricidad (PAUEH - Universal Electricity Access Policy), a key solution for addressing this energy poverty is the deployment of more than 1700 distributed solar and hybrid mini-grid solutions. In late 2023 as a first step towards supporting SEN's electrification efforts, the National Renewable Energy Laboratory (NREL) developed a literature review of SEN electrification policy documents and conducted a series of technical capacity-building workshops with SEN and other energy sector stakeholders in Honduras focused on using NREL's open-source REopt tool to conduct techno-economic assessments and develop least-cost optimizations for potential solar + storage mini-grid systems. Building on some initial capacity building on mini-grid modeling, the National Renewable Energy Laboratory (NREL) worked with SEN to develop a detailed techno-economic assessment for electrifying two schools, a healthcare clinic, and a hospital in a hypothetical community within the department of Gracias a Dios. The analysis also evaluates the business case for cold storage productive use of energy (PUE) applications for the fisheries value chain and how the incorporation of these PUE loads potentially impacts both the viability of the PV+BESS solutions as well as local economic development. By assessing the potential for deployment of integrated PV/BESS systems to both support critical community services like education and healthcare, as well as potential for downstream enterprise and economic development, this analysis represents a first step that can help to inform specific strategies for development of pilot PV+BESS projects aligned with national priorities and sector level planning under PAUEH. This is the Spanish translation of NREL/TP-7A40-90865.

14 SOLAR ENERGY↗

Representing the Future Role of Hydropower and Pumped Storage Hydropower (PSH) in Electricity Planning Tools

Existing tools for long-term electric sector planning struggle to represent hydropower's nuanced site-specific technical and operating characteristics, which depend on technical specifications as well as water management practices and regulations. As a result, long-term planning models and tools insufficiently characterize hydropower value and incentives, and they cannot fully represent the role hydropower can play in a future electricity system that could include a high penetration of variable wind and solar generation, battery storage, and other low-carbon technologies. This presentation demonstrates the culmination of a multi-year effort to enhance hydropower representations in electricity planning models at the National Renewable Energy Laboratory (NREL), as part of the U.S. Department of Energy (USDOE) HydroWIRES Initiative. New modeling techniques are demonstrated using the NREL Regional Energy Deployment System (ReEDS), an open-access electric sector capacity expansion model used extensively in a wide range of technology deployment and integration analysis, including the 2016 USDOE Hydropower Vision. ReEDS uses a least-cost optimization approach to understand investment and operation of electricity generation, storage, and transmission technologies under future scenarios of electricity technology innovation, demand, policy, and other sectoral drivers. ReEDS was modified to better represent value and opportunities for both pumped storage hydropower (PSH) and hydropower systems without pumping. We incorporated a new national closed-loop PSH resource and cost assessment to explore new PSH deployment opportunities and added plant-level data to better represent the existing PSH fleet. New upgrade pathways enable opportunities for enhanced hydropower flexibility by adding pumps, upgrading dispatchability, increasing capacity, or increasing energy availability. The model was also modified to better represent the value of long-duration energy storage beyond diurnal time scales, allowing both hydropower and PSH to better balance energy supply and demand variations in high-renewable systems. These new features are demonstrated under reference and high-renewable futures and a range of sensitivity scenarios to understand which hydropower and PSH deployment and upgrade opportunities are the most attractive. These scenarios indicate potential for new closed-loop PSH deployment and for hydropower flexibility improvements to have important impacts on long-term electricity system emissions and economic outcomes. Increasing flexibility of the existing hydropower fleet can reduce the need to invest in new flexible grid technologies and help achieve decarbonization goals. Systems with sufficient energy storage could also be valuable for balancing seasonal differences in renewable energy availability, particularly from solar energy. The methods developed for ReEDS and subsequent scenario results reveal important considerations for future hydropower and grid system planning, and all data and code is freely available in a public code repository for use throughout the hydropower industry.

capacity expansion↗

Linear Approximation Line Pack Model for Integrated Electricity and Natural Gas Systems OPF

With the increasing installation of renewable energy electricity generation capacity, the penetration of natural gas fired generation is also substantailly increasing in power systems due to their fast response capability to mitigate system variability and uncertainty. The dependencies between the electricity system and the natural gas system are becoming more important to maintain the reliability of both systems. In integrated electricity and natural gas systems (IEGSs) operations, the nonlinearity of the natural gas system brings challenges to the system operators. In this paper, the IEGSs optimal power flow (OPF) model is proposed that includes the line pack of the gas system via linear approximation. The simulation results demonstrate that the proposed model can obtain optimal solutions efficiently that are comparable in accuracy as the nonlinear line pack model.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Control of Low-Inertia Power Systems

Electric power systems are undergoing an unprecedented transition from fossil fuel–based power plants to low-inertia systems that rely predominantly on power electronics and renewable energy resources. This article reviews the resulting control challenges and modeling fallacies, at both the device and system level, and focuses on novel aspects or classical concepts that need to be revised in light of the transition to low-inertia systems. To this end, we survey the literature on modeling of low-inertia systems, review research on the control of grid-connected power converters, and discuss the frequency dynamics of low-inertia systems. Moreover, we discuss system-level services from a control perspective. Overall, we conclude that the system-theoretic mindset is essential to bridge different research communities and understand the complex interactions of power electronics, electric machines, and their controls in large-scale low-inertia power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Long-term basin-scale hydropower expansion under alternative scenarios in a global multisector model

Abstract Hydropower is an important source of renewable, low-carbon energy. Global and regional energy systems, including hydropower, may evolve in a variety of ways under different scenarios. Representation of hydropower in global multisector models is often simplified at the country or regional level. Some models assume a fixed hydropower supply, which is not affected by economic drivers or competition with other electricity generation sources. Here, we implement an endogenous model of hydropower expansion in the Global Change Analysis Model, including a representation of hydropower potential at the river basin level to project future hydropower production across river basins and explore hydropower’s role in evolving energy systems both regionally and globally, under alternative scenarios. Each scenario utilizes the new endogenous hydropower implementation but makes different assumptions about future low-carbon transitions, technology costs, and energy demand. Our study suggests there is ample potential for hydropower to expand in the future to help meet growing demand for electricity driven by socioeconomic growth, electrification of end-use sectors, or other factors. However, hydropower expansion will be constrained by resource availability, resource location, and cost in ways that limit its growth relative to other technologies. As a result, all scenarios show a generally decreasing share of hydroelectricity over total electricity generation at the global level. Hydropower expansion varies across regions, and across basins within regions, due to differences in resource potential, cost, current utilization, and other factors. In sum, our scenarios entail hydropower generation growth between 36% and 119% in 2050, compared to 2015, globally.

54 ENVIRONMENTAL SCIENCES↗

High- and Mid-Fidelity Modeling Comparison for a Floating Marine Turbine System

There is a lack of suitable numerical tools, particularly open-source tools, that can be used for designing and optimizing marine turbine systems. The National Renewable Energy Laboratory has added features to their widely used mid-fidelity wind turbine modeling code, OpenFAST, to enable modeling of axial-flow marine turbines. This necessitated the addition of several physical effects relevant to marine turbines that are neglected for wind turbines. These include buoyancy, added mass and inertial loads, wave-current superposition, and changes to the coordinate systems. This updated version of OpenFAST allows for the modeling of both fixed and floating marine turbines at a speed comparable to real time. While efficient for large sets of load cases and design studies, mid-fidelity codes make simplifying assumptions that may impact their accuracy. High-fidelity computational fluid dynamics (CFD) simulations can capture more flow effects with fewer assumptions and provide detailed body pressure mapping and flow-field information. It is important to compare predictions between mid-fidelity and high-fidelity codes, both to verify the models and to understand the limitations. A floating marine turbine system was modeled both with OpenFAST and with the commercial CFD code STAR-CCM+. The CFD model used a three-dimensional unsteady Reynolds-averaged Navier-Stokes solver for a volume-of-fluid numerical wave and current tank. The blade-resolved simulations used the sliding-interface technique for the spinning rotor and an overset grid to accommodate the rigid-body motion of the floating system. The mooring system was modeled with a custom coupling of the CFD solver with the open-source code MoorDyn. This improves upon the existing quasi-static catenary solver in STAR-CCM+, which lacks seabed contact or line-to-line connections. Simulation results for a floating marine turbine are compared between OpenFAST and CFD, highlighting the capabilities of the mid-fidelity code and identifying the areas where a high-fidelity approach is needed.

CFD↗

Power System Frequency Dynamics Modeling, State Estimation, and Control using Neural Ordinary Differential Equations (NODEs) and Soft Actor-Critic (SAC) Machine Learning Approaches

With the global energy transition of the electric power system, grid control, supervision, and protection is becoming more challenging. With the increasing integration of renewable energy sources (RES), the system dynamics are changing, causing traditional power system dynamic modeling with swing equation-based modeling approaches to fail. Additionally, the converter-dominated power grid is decreasing the system inertia, making the power system more fragile to the frequency swings. This paper first investigates and compares the application of a model-based Kalman filter state estimation approach with (i) a model-free machine learning approach --- neural ordinary differential equations (NODEs) --- and (ii) a data-driven system identification (SysId) approach to model and infer critical state values of the power system frequency dynamics. Then a model predictive control (MPC) framework is compared to a model-free Soft Actor-Critic (SAC) reinforcement learning (RL) control algorithm in providing efficient fast frequency response (FFR) to the power system frequency dynamics. The approaches are compared in terms of their performance goals as well as their per-timestep computational efficiency. Furthermore, the comparative study for state estimation shows that for the model-free requirement, both NODEs and SysId can provide accurate state estimates; however, with increasing model complexity, NODEs can be a better choice for model identification. Similarly, the results from the FFR comparative study show that the SAC RL-based FFR, once trained, outperforms MPC with better control signals and faster computation time, making the SAC RL-based FFR better option for providing FFR to the power system.

97 MATHEMATICS AND COMPUTING↗

System Advisor Model (SAM) and PVWatts

The System Advisor Model (SAM) is a free techno-economic software model that facilitates decision-making for people in the renewable energy industry. PVWatts estimates the energy production of grid-connected photovoltaic energy systems throughout the world. It allows homeowners, small building owners, installers, and manufacturers to easily develop estimates of the performance of potential photovoltaic installations.

energy planning↗

Coupled Aerodynamic and Hydrodynamic Hybrid Simulation of Floating Offshore Wind Turbines

The development and innovation of floating offshore wind energy in the U.S. requires detailed high-fidelity observations and measurements of turbine and platform loading due to wind, waves, and currents. However, full-scale and quasi-full-scale experiments require significant financial and temporal investments for construction, experimental testing, and long-term field campaigns. To support the commercial advancement of the offshore wind energy industry, specialized wind tunnel and wave basin experimental facilities are critical to be able to test FOWT designs at small scale under controlled conditions prior to full-scale deployment. Oregon State University (OSU) is internationally known as a leader in water and energy research, development, and testing. The O.H. Hinsdale Wave Research Laboratory (HWRL) and the Wallace Energy Systems and Renewables Facility (WESRF) at OSU have extensive experience building, modeling, monitoring, controlling, and actuating scaled systems. Experiments on wave-structure interaction have been performed at the HWRL since its establishment in 1972. Studies have included the interaction of waves with coastal structures (breakwaters, seawalls, buildings, cylinders, bridges, fixed foundations of offshore wind turbines, etc.) and with floating structures (e.g., wave energy converters, maneuvering of vessels, etc.). Hinsdale is actively used by marine energy technology developers, both for private testing and OSU-collaborative research projects. However, despite the availability of several large-scale facilities for hydrodynamic testing (at OSU and elsewhere in the U.S.), existing experimental laboratories are generally limited in their ability to accurately generate combined wind and wave conditions. The simulation of both wind and waves in experimental testing is complicated due to a number of constraints, including: [i] incompatible similitude laws governing the wind and waves for scaled experiments, [ii] producing accurate wind over a large enough control volume via fans, and [iii] generating wind that reasonably represents the atmospheric boundary layer in existing wave basins/flumes. Hence, physical test data providing insight into the simultaneous wave- and wind-structure response of floating offshore wind components can be difficult to generate. Given the aforementioned challenges in classic hydrodynamic experiments, the motivation of this project is to establish a real-time hybrid simulation (RTHS) approach that can apply aero- and hydro-dynamic loading by augmenting wave-only experimental facilities with virtual aerodynamic forces through numerical models representing the remaining dynamic forces. RTHS is a physical-numerical approach that partitions a prototype system into physical and numerical sub-assemblies that interact with each other through actuators and sensors in real time. In coupling physical and numerical models, the hybrid simulation approach applied herein is ideal for problems with: (1) structures subjected to different scaling laws, such as floating offshore wind turbines subjected to combined aero/hydro-dynamic loading, (2) structures that are too large or complex to be tested entirely in a laboratory setting, such as deep-water mooring applications, and (3) component testing, where the behavior of a portion of the assembly is uncertain but still interacts with other portions of the structure, such as testing the fatigue life of turbine blades. Few U.S. experimental facilities are able to test simultaneous aero- and hydro-dynamic loading and none can accurately produce aero/hydro-dynamic response on scaled FOWT models due to conflicting similitude laws between the wind (commonly Reynolds) and the waves (commonly Froude). To aid in accelerating the development of the U.S. floating offshore industry, there is a significant need to develop a flexible, modular framework that can expand the capacities of existing wave-only laboratories. The project goal is to demonstrate a hydrodynamic real-time hybrid simulation (hydro-RTHS) framework that couples numerical wind and physical waves acting on a FOWT, thus representing simultaneous aero/hydro-dynamic loading. The FOWT is partitioned into a full-scale numerical sub-assembly associated with the aerodynamics and a model-scale physical sub-assembly associated with the hydrodynamics. The numerical-physical partition associated with hydro-RTHS mitigates scaling constraints by supplying different scaling laws to the physical and numerical sub-assemblies. Herein, length, force, and time are scaled and exchanged between the sub-assemblies using Froude scaling to represent the open-channel flow in the physical sub-assembly. Other similitude laws could also be utilized depending on the problem definition. It is envisioned that the ability to model FOWTs under waves and wind, with mitigation of similitude distortions, would result in reduced development costs (currently, FOWT concept development is performed with full-size pro- totypes at enormous expense and risk) and increase the reliability of the FOWT industry (since extreme wave and wind conditions and contingency events can be tested safely in a controlled environment).

16 TIDAL AND WAVE POWER↗

Airport Infrastructure Planning Using Multi-Stage Stochastic Programming

The Athena project, funded by the Department of Energy, has worked to identify the critical infrastructure at Dallas Fort Worth (DFW) Airport which influences mobility between the airport and the surrounding city of Dallas. Using scalable methods that can leverage HPC resources we have developed a multi-stage stochastic infrastructure expansion model for determining parking and curb modifications to the DFW Airport over a 20-year horizon. Additionally, we have explored the impacts of congestion pricing in conjunction with infrastructure modifications. Our multi-stage stochastic model is implemented using the mpi-sppy software and solved in parallel using progressive hedging on the National Renewable Energy Laboratory's HPC system Eagle. In this talk we present results from solving this model at scale.

airport planning↗

High- and Mid-Fidelity Modeling Comparison for a Floating Marine Turbine System: Preprint

There is a lack of suitable numerical tools, particularly open-source tools, that can be used for designing and optimizing marine turbine systems. The National Renewable Energy Laboratory (NREL) has added features to their widely used mid-fidelity wind turbine modeling code, OpenFAST, to enable modeling of axial flow marine turbines. This necessitated the addition of several physical effects relevant to marine turbines that can be neglected for wind turbines. These include buoyant loads, added mass and inertia loads, wave-current superposition, and changes to the coordinate systems. This updated version of OpenFAST allows for the modeling of both fixed and floating marine turbine systems at a speed comparable to real time. While efficient for long simulations, large sets of load cases, and design studies, mid-fidelity codes cannot capture all of the potentially important physical phenomenon impacting marine turbine systems. High-fidelity computational fluid dynamics (CFD) simulations can capture more flow effects with fewer assumptions and provide detailed body pressure mapping and flow-field information. It is important to compare predictions between mid-fidelity and high-fidelity codes, both to verify the models and to understand the limitations. A floating marine turbine system designed by NREL was modeled both with OpenFAST and with the commercial CFD code, STARCCM+. The CFD model used a 3-D unsteady Reynolds-averaged Navier-Stokes solver for a volume-of-fluid numerical wave and current tank. The blade-resolved simulations used the sliding-interface technique for the spinning rotor and an overset grid to accommodate the rigid-body motion of the floating system. The mooring system was modelled with a custom coupling of the CFD solver with the open-source code, MoorDyn. This improves upon the existing quasi-static catenary solver in STARCCM+, which lacks seabed contact or line-to-line connections. Spatial and temporal convergence studies were conducted. The simulation results for a combined current and wave condition are compared between OpenFAST and CFD, highlighting the capabilities of the mid-fidelity code and identifying the areas where a high-fidelity approach is needed.

CFD↗

Wattile: Probabilistic Deep Learning-based Forecasting of Building Energy Consumption [SWR-20-94]

Accurate energy forecasting is becoming critical due to many reasons: i ) optimal distributed energy resources operations and dispatch, ii) fault detection and diagnostics, and iii) meeting operational energy efficiency targets. Wattile uses deep learning (DL) for the building's short-term load forecasting application. Two specific types of neural networks called, Long Short Term Memory (LSTM) and Sequence-to-Sequence (S2S) models are used to make predictions. Forecasting models are trained using online historical weather and occupancy indicator data streams from the Intelligent Campus Program's data acquisition systems at the National Renewable Energy Laboratory (NREL) for main meters and sub-meters of multiple building types. These models use probabilistic methods to provide quantile-based forecasts in addition to nominal conditional median predictions of electricity consumption.

Frank, Stephen↗