Search NASA⌕ Search

SEARCH · Search NASA

Results for “PV variability”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Reducing Demand Charges and Onsite Generation Variability Using Behind-the-Meter Energy Storage

Electric utilities in the United States are increasingly employing demand charges and/or real-time pricing. This directive is bringing potential opportunities in deploying behindthe-meter energy storage (BMES) systems for various grid functionalities. This study quantifies techno-economic benefits of BMES in reducing demand charge and smoothing load/generation intermittencies, and determines how those benefits vary with onsite distributed photovoltaic. We proposed a two-stage control algorithm, whereby the first stage proactively determines costoptimal BMES configuration for reducing peak-demands and demand charges, and the second stage adaptively compensates intermittent generations and short load spikes that may otherwise increase the demand charges. The performance of the proposed algorithm is evaluated through a 24 hours time sweep simulation performed using data from smart microgrid testbed at Idaho National Laboratory (INL). The simulation results demonstrated that this research provides a simple but effective solution for peak shaving, demand charge reductions, and smoothing onsite PV variability.

25 ENERGY STORAGE↗
DOE OSTI2019

The Potential Role of Concentrating Solar Power within the Context of DOE's 2030 Solar Cost Targets

To continue the momentum for cost reduction in solar technologies, DOE recently established 2030 cost targets for that would make solar one of the lowest-cost sources of new electricity in the United States. The potential impacts of achieving the 2030 cost targets for solar photovoltaics (PV) were recently explored in a related publication, which found significant potential for solar PV deployment and generation, particularly when coupled with low-cost storage. Building on this previous report, the present analysis evaluates the potential impacts of simultaneously achieving the 2030 cost targets for both PV and concentrating solar power with thermal energy storage (CSP-TES) systems in the contiguous United States, with a detailed evaluation of the role that CSP-TES plays in realizing those impacts. The evolution of the contiguous U.S. electricity system is evaluated with NREL's Renewable Energy Deployment System (ReEDS) model, which has been specifically designed to represent the time and locational value of renewable generation technologies in the U.S. power system. This analysis suggests achievement of the SunShot cost targets for CSP-TES might lead to dramatic growth in CSP-TES capacity, with accelerated deployment after the 2030 target is achieved. Based on our representation of the 2030 cost targets in ReEDS, economically-competitive CSP-TES could primarily be deployed in a dispatch able, high-capacity factor configuration, which allows it to provide valuable services to the power sector and results in a generation profile for CSP-TES that is complementary to that of variable PV. Aggressive cost reductions for CSP-TES could also make such a configuration economically competitive across the contiguous United States, which is a a result that warrants additional analysis in terms of potential siting, permitting, regulatory, and construction challenges in these new regions and markets. The modeled capacity expansion of both CSP-TES and PV in our scenarios that achieve the 2030 cost targets requires most growth in transmission capacity at a pace that is consistent with historical buildout rates, and could result in reduced electricity prices and power system costs, water usage, and air emissions. Finally, sensitivity analysis suggests that the cumulative capacity and geographic extent of CSP-TES deployment in our low-cost solar scenarios is highly sensitive to the post-2030 cost reductions for CSP-TES and future price of natural gas, and that significant competition exists among renewable energy and energy storage technologies, assuming aggressive cost reductions for each. For example, significant competition between CSP-TES and PV with battery storage indicates the similar role that these systems play in capacity planning and grid operations, as well as the fact that low-cost battery storage would help improve the capacity value of low-cost PV into the future.

14 SOLAR ENERGY↗
DOE OSTI2017

Fast Quasi-Static Time-Series (QSTS) for yearlong PV impact studies using vector quantization

The rapidly growing penetration levels of distributed photovoltaic (PV) systems requires more comprehensive studies to understand their impact on distribution feeders. IEEE P.1547 highlights the need for Quasi-Static Time Series (QSTS) simulation in conducting distribution impact studies for distributed resource interconnection. Unlike conventional scenario-based simulation, the time series simulation can realistically assess time-dependent impacts such as the operation of various controllable elements (e.g. voltage regulating tap changers) or impacts of power fluctuations. However, QSTS simulations are still not widely used in the industry because of the computational burden associated with running yearlong simulations at a 1-s granularity, which is needed to capture device controller effects responding to PV variability. This study presents a novel algorithm that reduces the number of times that the non-linear 3-phase unbalanced AC power flow must be solved by storing and reassigning power flow solutions as it progresses through the simulation. Each unique power flow solution is defined by a set of factors affecting the solution that can easily be queried. We demonstrate a computational time reduction of 98.9% for a yearlong simulation at 1-s resolution with minimal errors for metrics including: number of tap changes, capacitor actions, highest and lowest voltage on the feeder, line losses, and ANSI voltage violations. Finally, the key contribution of this work is the formulation of an algorithm capable of: (i) drastically reducing the computational time of QSTS simulations, (ii) accurately modeling distribution system voltage-control elements with hysteresis, and (iii) efficiently compressing result time series data for post-simulation analysis.

14 SOLAR ENERGY↗
DOE OSTI2026

redis-pvxs-ioc

redis-pvxs-ioc is a standalone PVAccess service for hot-reloadable, Redis-backed EPICS process variables (PV) defined by YAML. The core runtime is PVA-only. It does not load EPICS databases, call iocInit(), or serve Channel Access.

Steinkamp, Derek [Fermi National Accelerator Labor↗
DOE OSTI2018

A Conservative Approach to Defining Photovoltaic System Hazards to Firefighters

Sandia National Laboratories performed analysis to develop conservative hazard guidelines regarding firefighters working near photovoltaic (PV) arrays. Assuming implementation of NFPA 70 system shutdown requirements, the analysis focused on DC hazards only. Several different PV variables were considered, including system grounding and DC voltage classes. The hazard scenarios considered the contact conditions, current paths through the body, and PPE. Guidelines for the hazard definitions for men and women were based on the IEC TS 60479-1 guidelines. The importance of PPE was illustrated in the results.

14 SOLAR ENERGY↗
DOE OSTI2019

Data-Driven Day-Ahead PV Estimation Using Hybrid Deep Learning

Ongoing smart grid activities and associated automation resulted in rich set of data. Here, these data can be utilized for monitoring and estimation of real time photovoltaic (PV) generation. Inherent variability in PV and related impact on power systems is a challenging problem. Improving the accuracy of PV generation estimation is beneficial for both the PV owners and the grid operators. Recently, deep learning algorithms possible by the availability of data have shown its advantages for time series estimation; however, its application on PV generation estimation is still in the early stage. In this paper, a hybrid estimation model with a combination of long-short-term-memory network (LSTM) and persistence model (PM) is developed to provide day-ahead PV estimation at 15-minute time interval with high accuracy and robustness. Simulation results show the superior performance of the proposed method over existing methods for most of the test cases.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Hierarchical Control of Utility-Scale Solar PV Plants for Mitigation of Generation Variability and Ancillary Service Provision

This paper presents a hierarchical control system to mitigate the variability of solar photovoltaic (PV) power plant and provide ancillary services to the electric grid without the need for additional non-solar resources. With coordinated management of each inverter in the system, the control system commands the power plant to proactively curtail a small fraction of its instantaneous maximum power potential, which gives the plant enough headroom to ramp up production from the overall power plant, for a service such as regulation reserve. This control system is practical for continuously changing cloud cover conditions in partially cloudy days. A case study from a site in Hawaii with one-second resolution solar irradiance data is used to verify the efficacy of the proposed control system. The proposed control algorithm is subsequently compared with the alternative control technology from the literature, the grouping control algorithm; the results show that the proposed hierarchical control system is over 10 times more effective in reducing generator mileage to support power fluctuations from solar PV power plants.

14 SOLAR ENERGY↗
DOE OSTI2017

Fast Determination of Distribution-Connected PV Impacts Using a Variable Time-Step Quasi-Static Time-Series Approach: Preprint

The increasing deployment of distribution-connected photovoltaic (DPV) systems requires utilities to complete complex interconnection studies. Relatively simple interconnection study methods worked well for low penetrations of photovoltaic systems, but more complicated quasi-static time-series (QSTS) analysis is required to make better interconnection decisions as DPV penetration levels increase. Tools and methods must be developed to support this. This paper presents a variable-time-step solver for QSTS analysis that significantly shortens the computational time and effort to complete a detailed analysis of the operation of a distribution circuit with many DPV systems. Specifically, it demonstrates that the proposed variable-time-step solver can reduce the required computational time by as much as 84% without introducing any important errors to metrics, such as the highest and lowest voltage occurring on the feeder, number of voltage regulator tap operations, and total amount of losses realized in the distribution circuit during a 1-yr period. Further improvement in computational speed is possible with the introduction of only modest errors in these metrics, such as a 91 percent reduction with less than 5 percent error when predicting voltage regulator operations.

14 SOLAR ENERGY↗
DOE OSTI2022

Enhanced Control, Optimization, and Integration of Distributed Energy Applications (ECO-IDEA)

With support from the U.S. Department of Energy Solar Energy Technologies Office, the National Renewable Energy Laboratory (NREL) partnered with Xcel Energy, Schneider Electric, Varentec, and Electric Power Research Institute (EPRI) to meet the goals of the Enabling Extreme Real-Time Grid Integration of Solar Energy (ENERGISE) program. This project developed and validated an innovative data-enhanced hierarchical control architecture that enables the efficient, reliable, resilient, and secure operation of future distribution systems with a high penetration of distributed energy resources like solar energy. The architecture enables a hybrid control approach where a centralized control layer is complemented by distributed control algorithms for solar inverters and autonomous control of grid edge devices. It is fully interoperable and includes all the cybersecurity aspects necessary for reliable and secure system operation. The hybrid approach can seamlessly integrate multiple voltage-regulation technologies, both at central and grid-edge levels, which enables reliable and efficient system operation in the face of unpredictable conditions. The overarching goal of the Eco-Idea project is to develop, validate, and deploy a unique and innovative Data-Enhanced Hierarchical Control (DEHC) architecture that comprehensively addresses the formidable challenges associated with proliferation of high penetration of distributed PV such as reverse power flows, transients from variability of PV systems, feeder load balancing, and voltage stability. These issues are exposing the weaknesses of existing grid operations and controls - including, but not limited to, lack of grid situational awareness, heuristic and slow-acting control actions, latency of control for emergency situations, and points of failure in communications. The proposed architecture will comprehensively resolve the deficiencies of current operational settings - where monitoring and control solutions proposed across industry and academia may not be interoperable and may not coexist in the same system - and will enable an efficient, reliable, resilient, and secure operation of future distribution systems with penetration of solar energy well beyond current limits. The DEHC architecture was developed and validated rigorously through hardware-in-loop simulations in the laboratory environment and deployed on the field.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Eastern Renewable Generation Integration Study

The Eastern Interconnection (EI) is one of the largest power systems in the world, and its size and complexity have historically made it difficult to study in high levels of detail in a modeling environment. In order to understand how this system might be impacted by high penetrations (30% of total annual generation) of wind and solar photovoltaic (PV) during steady state operations, the National Renewable Energy Laboratory (NREL) and the U.S. Department of Energy (DOE) conducted the Eastern Renewable Generation Integration Study (ERGIS). This study investigates certain aspects of the reliability and economic efficiency problem faced by power system operators and planners. Specifically, the study models the ability to meet electricity demand at a 5-minute time interval by scheduling resources for known ramping events, while maintaining adequate reserves to meet random variation in supply and demand, and contingency events. To measure the ability to meet these requirements, a unit commitment and economic dispatch (UC&ED) model is employed to simulate power system operations. The economic costs of managing this system are presented using production costs, a traditional UC&ED metric that does not include any consideration of long-term fixed costs. ERGIS simulated one year of power system operations to understand regional and sub-hourly impacts of wind and PV by developing a comprehensive UC&ED model of the EI. In the analysis, it is shown that, under the study assumptions, generation from approximately 400 GW of combined wind and PV capacity can be balanced on the transmission system at a 5-minute level. In order to address the significant computational burdens associated with a model of this detail we apply novel computing techniques to dramatically reduce simulation solve time while simultaneously increasing the resolution and fidelity of the analysis. Our results also indicate that high penetrations of wind and PV (collectively variable generation (VG)), significantly impact the operation of traditional generating resources and cause these resources to be used less frequently and operate across a broader output range because wind and PV have lower operating costs and variable output levels.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Fast Determination of Distribution-Connected PV Impacts Using a Variable-Time-Step Quasi-Static Time-Series Approach

The increasing deployment of distribution-connected photovoltaic (DPV) systems requires utilities to complete ever more complex interconnection studies. Relatively simple interconnection study methods worked well for low penetrations of photovoltaic systems, but more complicated quasistatic time-series (QSTS) analysis is required to make better interconnection decisions as DPV penetration levels increase. Tools and methods must be developed to support this type of complex analysis to enable efficient completion of PV impact studies. This paper presents a variable-time-step solver for QSTS analysis that significantly shortens the computational time and effort to complete a detailed analysis of the operation of a distribution circuit with many DPV systems. Specifically, it demonstrates that the proposed variable-time-step solver can reduce the required computational time by as much as 84% without introducing any important errors to metrics, such as the number of voltage regulator tap operations and highest and lowest voltage occurring on the distribution circuit during a 1-yr simulation period. Further improvement in computational speed is possible with the introduction of only modest errors in these metrics, such as a 91% reduction with less than 5% error when predicting voltage regulator operations. Furthermore, an alternate variable time-step method is also shown to perform adequately and shows promise of even greater reductions in simulation runtime.

14 SOLAR ENERGY↗
DOE OSTI2024

Machine Learning-Driven Reliability Estimation of PV Inverters Considering Alert-Ambient Variability

Weather-induced spatio-temporal degradation limits outdoor PV inverter lifetime and reliability, necessitating advanced data analysis. This study employs a top-down, data-driven approach utilizing multiple machine learning (ML) algorithms to estimate inverter reliability in a 1.4 MW PV power plant, considering factors such as irradiance, humidity, temperature, time of day, and weather conditions. An extensive alert dataset from 17 identical inverters, including alert types, propagation, and frequency, reveals significant correlations with environmental factors and inverter output power, enabling the construction of a performance reliability model. Dual-stage supervised-ML models are evaluated for accuracy, with the ‘classification-regression’ model by an artificial neural network (ANN) tested on the averaged “Alert-Ambient” dataset, which is outperformed by ‘clustering-regression’ models using random forest (RF) and K-Nearest Neighbors (KNN) on individual inverter datasets. K-means clustering applies principal component analysis to reduce dimensions, achieving improved accuracy beyond the 80% achieved by ANN on the averaged dataset. Second-stage regression estimates inverter reliability with a mean square error of 0.0195 on the averaged dataset and as low as 0.002 on individual inverter datasets using RF. Furthermore, these findings highlight the method's suitability for estimating PV inverter output reliability under ambient conditions, essential for digital twin development and related applications.

14 SOLAR ENERGY↗
DOE OSTI2020

Closing the Loops on Solar Photovoltaics Modules: An Agent-Based Modeling Approach for the Study of Circular Economy Strategies

Solar photovoltaics (PV) installed capacity have grown exponentially since the early 2000s (average annual growth rate of 50%). With 4,700 GW projected installed capacity by 2050, the volume of PV panels waste is also expected to become substantial. Though renewables are a sine qua non to the establishment of a truly circular economy (CE), the issue arising from their end-of-life management needs to be resolved. The management of PV end-of-life also represent a singular opportunity to create value, from recovered valuable, rare or critical materials (e.g. silver, tellurium, indium). Moreover, the PV case illustrates some of the current barriers to CE; for instance, the need for a common definition of waste (PV are defined as e-waste in the European Union but as general waste in the United States) and potential loss of innovations' advantages (PV average efficiency has continuously grown the past 10 years). In this study, an agent-based modeling (ABM) approach is proposed to simulate PV end-of-life management in the United States. The model explores how the decisions of the various actors involved in handling PV waste affect the quantities of PV that are reused, recycled, or land-filled. In the model, manufacturers, residential, and nonresidential PV owners, installers, and recyclers are represented by agents while governmental policies and regulations are treated as exogenous variables. PV-market data are used to define agents' characteristics (e.g., installed PV capacities or producing costs). Agents' decision rules draw on the literature related to industrial symbiosis (IS) and peoples' waste behaviors as they appear as the most widely used model to implement CE principles at a meso level. Specifically, the concepts of mutual trust between IS actors and knowledge about the IS philosophy are yielded to define the agents' decision process. The primary outputs of the ABM are the costs and volume of waste associated with each end-of-life pathway. Preliminaries results indicate recycling of residential PV is affected by the cost, perceived difficulty of recycling behaviors, and social norms. Moreover, the type of network connecting agents and the initial recycling rate strongly influence results. The model also highlights the crucial role of recycling behavior adoption: in case of early failure of PV, recycled volumes increase by about 40%. This represents an additional 5.7 billion USD from potential recovered silver. Finally, although results for the PV case are presented here, the developed ABM aims at being a general tool for the study of CE strategies and the CE transition. Furthermore, steps of this research include extending the model to encompass circular economy strategies (e.g., design for recycling or lifetime extension) and validating its general architecture from various case studies.

14 SOLAR ENERGY↗
DOE OSTI2018

Photoluminescence-Imaging-Based Evaluation of Non-Uniform CdTe Degradation

A control algorithm is designed to smooth the variability of PV power output using distributed batteries. The tradeoff between smoothing and battery size is shown. It is also demonstrated that large numbers of highly distributed current, voltage, and irradiance sensors can be utilized to control the distributed storage in a more optimal manner. It is also demonstrated that centralized energy storage control for PV ramp rate smoothing requires very fast communication, typically less than a 15-second update rate. Finally, advanced inverter dynamic reactive current is shown to provide voltage variability smoothing, hence reducing the number of voltage regulator tap changes without energy storage.

14 SOLAR ENERGY↗
DOE OSTI2016

On the Path to SunShot. Advancing Concentrating Solar Power Technology, Performance, and Dispatchability

This report examines the remaining challenges to achieving the competitive concentrating solar power (CSP) costs and large-scale deployment envisioned under the U.S. Department of Energy's SunShot Initiative. Although CSP costs continue to decline toward SunShot targets, CSP acceptance and deployment have been hindered by inexpensive photovoltaics (PV). However, a recent analysis found that thermal energy storage (TES) could increase CSP's value--based on combined operational and capacity benefits--by up to 6 cents/kWh compared to variable-generation PV, under a 40% renewable portfolio standard in California. Thus, the high grid value of CSP-TES must be considered when evaluating renewable energy options. An assessment of net system cost accounts for the difference between the costs of adding new generation and the avoided cost from displacing other resources providing the same level of energy and reliability. The net system costs of several CSP configurations are compared with the net system costs of conventional natural-gas-fired combustion-turbine (CT) and combined-cycle plants. At today's low natural gas prices and carbon emission costs, the economics suggest a peaking configuration for CSP. However, with high natural gas prices and emission costs, each of the CSP configurations compares favorably against the conventional alternatives, and systems with intermediate to high capacity factors become the preferred alternatives. Another analysis compares net system costs for three configurations of CSP versus PV with batteries and PV with CTs. Under current technology costs, the least-expensive option is a combination of PV and CTs. However, under future cost assumptions, the optimal configuration of CSP becomes the most cost-effective option.

14 SOLAR ENERGY↗
DOE OSTI2017

Time Series Analysis of Photovoltaic Soiling Station Data: Version 1.0, August 2017

The time series data from PV soiling stations, operating in the USA, at different time periods are analyzed and presented. The current version of the paper includes twenty stations operating between 2013 and 2016, but the paper is intended to be periodically updated as more stations and more data become available. The challenges in working with soiling stations data are discussed, including measurement methodology, quality controls, and measurement uncertainty. The soiling profiles of the soiling stations are made available so that the PV community can make use of this data to guide operations and maintence decisions, estimate soiling derate in performance models, and more generally come to a better understanding of the challenges associated with the variability of PV soiling.

14 SOLAR ENERGY↗
DOE OSTI2018

Fast QSTS for Distributed PV Impact Studies Using Vector Quantization and Variable Time-Steps

As the penetration level of PV systems increases, utilities are in great need of methods to quickly and effectively analyze PV's impact on electric distribution systems. Compared to other conventional methods, quasi-static time-series (QSTS) based analysis has demonstrated its advantages of accurately capturing the impact of interest, e.g. control device operations, and min and max voltages. However, yearlong QSTS simulation with 1-second granularity is currently too computationally expensive for regular use. Therefore, vector quantization (VQ) and variable time-step (VTS) solution methods have been developed to accelerate QSTS simulation. This paper proposes the combination of VQ with VTS. We investigate the effectiveness of implementing VQ in combination with 2 VTS algorithms, predetermined time-step (PT) and VTS with backtrack downsampling (VTS-BD) to demonstrate a runtime reduction of 99.55% with error metrics below acceptable thresholds.

14 SOLAR ENERGY↗