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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.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Consensus-Based Approach for Active Power Control and Reserve Estimation in Distributed PV Systems

With the increased adoption of distributed energy resources (DER), they are expected to contribute to power system reliability services and enhance power system stability. This paper presents a distributed consensus control approach for the real-time active power reserve estimation and power management in distributed PV (photovoltaic) systems. The proposed method estimates the cumulative active power reserve from numerous PV generators using only the sparse communication network for a DER aggregator to provide active power regulation services. The real-time active power reserve estimation using the proposed approach can be used by DER aggregators to manage curtailment in distributed PV systems to maintain stipulated reserves and further to provide power system frequency regulation. Using the proposed approach, the DER aggregator only requires measurements at the feeder substation and it does not require information from individual DER units, thereby improving system resiliency. The proposed distributed consensus control method is validated on IEEE 123 testbed using PSCAD simulations.

active power reserve↗
DOE OSTI2022

Consensus-Based Approach for Active Power Control and Reserve Estimation in Distributed PV Systems: Preprint

With the increased adoption of distributed energy resources (DER), they are expected to contribute to power system reliability services and enhance power system stability. This paper presents a distributed consensus control approach for the real-time active power reserve estimation and power management in distributed PV (photovoltaic) systems. The proposed method estimates the cumulative active power reserve from numerous PV generators using only the sparse communication network for a DER aggregator to provide active power regulation services. The real-time active power reserve estimation using the proposed approach can be used by DER aggregators to manage curtailment in distributed PV systems to maintain stipulated reserves and further to provide power system frequency regulation. Using the proposed approach, the DER aggregator only requires measurements at the feeder substation and it does not require information from individual DER units, thereby improving system resiliency. The proposed distributed consensus control method is validated on IEEE 123 testbed using PSCAD simulations.

active power reserve↗
DOE OSTI2019

Highly Accurate Method for Real-Time Active Power Reserve Estimation for Utility-Scale PV Power Plants: Preprint

This paper applies a robust technique for determining the available power reserve from a curtailed utility-scale photovoltaic (PV) power plant. The proposed technique does not require deploying any additional equipment or sensors and is based only on the addition of new control logic to the existing power plant controller. Also, the proposed method is universally applicable to PV plants with any type of smart inverters and PV modules. Accurate determination of available power is important for using curtailed PV generation as a resource for various types of active power controls, such as spinning reserves and primary and secondary frequency control. For PV plants to be able to maintain the desired regulation range, the plant controller must be able to estimate the available aggregate peak power that all the plant's inverters can produce at any point in time and ensure that the control error stays within the tolerance band at all times. In this paper, we explore a highly accurate control method that uses dedicated inverters within the plant as reference units and evaluates the available aggregate peak power for the whole plant under different cloud variability conditions.

14 SOLAR ENERGY↗
DOE OSTI2020

Data-Driven Day-Ahead PV Estimation Using Autoencoder-LSTM and Persistence Model

Inherent variability in photovoltaic (PV) and associated impacts on power systems is a challenging problem for both the PV owners and the grid operators. Existing statistical and machine learning algorithms typically work well for weather conditions similar to historical data. Furthermore, uncertain weather conditions pose a great challenge to the estimation accuracy of the estimation models. With the enhanced integration of intelligent electronic devices and the realization of associated automation in the power grid, renewable energy data is becoming more accessible, which can be utilized by deep learning models and improve the PV power generation estimation accuracy. In this paper, a hybrid deep learning model driven by external weather data is proposed to do day-ahead PV output forecasting at 15-minute-interval. The proposed model is motivated by the recent advancement of Long-Short-Term-Memory (LSTM) networks and AutoEncoder (AE), which estimates uncertainties in sequence while making the prediction for complex weather conditions. Meanwhile, the persistence model (PM) is used to predict continuous sunny weather conditions. The forecasting result is validated with data from multiple locations

42 ENGINEERING↗
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 OSTI2024

Method for Spatiotemporal Solar Power Profile Estimation for a Proposed U.S.–Caribbean–South America Super Grid under Hurricanes

Solar photovoltaic (PV) generation technology stands out as a scalable and cost-effective solution to enable the transition toward decarbonization. However, PV solar output, beyond the daily solar irradiance variability and unavailability during nights, is very sensitive to weather events like hurricanes. Hurricanes nucleate massive amounts of clouds around their centers, shading hundreds of kilometers in their path, reducing PV power output. This research proposes a spatiotemporal method, implemented in MATLAB R2023b coding, to estimate the shading effect of hurricanes over a wide distribution of PV solar plants connected to a high-voltage power infrastructure called the U.S.–Caribbean–South America super grid. The complete interconnection of the U.S., the Caribbean, and South America results in the lowest power valley levels, i.e., an overall percentual reduction in PV power output caused by hurricane shading. The simulations assess the impact of hurricanes in 10 synthetic trajectories spanning from Texas to Florida. The Caribbean would also experience lower power valleys with expanded interconnectivity schemes. The U.S.–Caribbean–South America super grid reduces Caribbean variability from 37.8% to 8.9% in the case study. The proposed spatiotemporal method for PV power profile estimation is a valuable tool for future solar power generation expansion, transmission planning, and system design considering the impact of hurricanes.

14 SOLAR ENERGY↗
DOE OSTI2024

Using spatio-temporal graph neural networks to estimate fleet-wide photovoltaic performance degradation patterns

Accurate estimation of photovoltaic (PV) system performance is crucial for determining its feasibility as a power generation technology and financial asset. PV-based energy solutions offer a viable alternative to traditional energy resources due to their superior Levelized Cost of Energy (LCOE). A significant challenge in assessing the LCOE of PV systems lies in understanding the Performance Loss Rate (PLR) for large fleets of PV systems. Estimating the PLR of PV systems becomes increasingly important in the rapidly growing PV industry. Precise PLR estimation benefits PV users by providing real-time monitoring of PV module performance, while explainable PLR estimation assists PV manufacturers in studying and enhancing the performance of their products. However, traditional PLR estimation methods based on statistical models have notable drawbacks. Firstly, they require user knowledge and decision-making. Secondly, they fail to leverage spatial coherence for fleet-level analysis. Additionally, these methods inherently assume the linearity of degradation, which is not representative of real world degradation. To overcome these challenges, we propose a novel graph deep learning-based decomposition method called the Spatio-Temporal Graph Neural Network for fleet-level PLR estimation (PV-stGNN-PLR). PV-stGNN-PLR decomposes the power timeseries data into aging and fluctuation components, utilizing the aging component to estimate PLR. PV-stGNN-PLR exploits spatial and temporal coherence to derive PLR estimation for all systems in a fleet and imposes flatness and smoothness regularization in loss function to ensure the successful disentanglement between aging and fluctuation. We have evaluated PV-stGNN-PLR on three simulated PV datasets consisting of 100 inverters from 5 sites. Experimental results show that PV-stGNN-PLR obtains a reduction of 33.9% and 35.1% on average in Mean Absolute Percent Error (MAPE) and Euclidean Distance (ED) in PLR degradation pattern estimation compared to the state-of-the-art PLR estimation methods.

14 SOLAR ENERGY↗
DOE OSTI2020

Generalized and Mechanistic PV Module Performance Prediction From Computer Vision and Machine Learning on Electroluminescence Images

Electroluminescence (EL) imaging of photovoltiac (PV) modules offers high-speed, high-resolution information about device performance, affording opportunities for greater insight and efficiency in module characterization across manufacturing, research and development, and power plant operations and management. Predicting module electrical properties from EL image features is a critical step toward these applications. In this article, we demonstrate quantification of both generalized and performance mechanism-specific EL image features, using pixel intensity-based and machine learning classification algorithms. From EL image features, we build predictive models for PV module power and series resistance, using time-series current-voltage (I-V) and EL data obtained stepwise on five brands of modules spanning three Si cell types through two accelerated exposures: damp heat (DH) (85°C/85% RH) and thermal cycling (TC) (IEC 61215). Overall, 195 pairs of EL images and I-V characteristics were analyzed, yielding 11700 individual PV cell images. A convolutional neural network was built to classify cells by the severity of busbar corrosion with high accuracy (95%). Generalized power predictive models estimated the maximum power of PV modules from EL images with high confidence and an adjusted-R 2 of 0.88, across all module brands and cell types in extended DH and TC exposures. Mechanistic degradation prediction was demonstrated by quantification of busbar corrosion in EL images of three module brands in DH, and subsequent modeling of series resistance using these mechanism-specific EL image features. For modules exhibiting busbar corrosion, we demonstrated series resistance predictive models with adjusted-R 2 of up to 0.73.

14 SOLAR ENERGY↗
DOE OSTI2022

A Self-Organizing Nano Grid (SONG) for Energy Access Clusters

In this paper, a bottom-up dc Self-Organizing Nano Grid (SONG) for rural electrification at ultra-low-cost through a flexible and scalable topology is presented. The proposed nano grid requires no communication and uses dc-bus signaling to convey excess energy in the system through the bus voltage, allowing independent agents to conduct energy transactions based on their needs in a decentralized manner. Moreover, this article proposes a concept that uses a novel multi-source, multi-segment droop control strategy with demand coordination, allowing for dynamic self-balancing of the system under fluctuating operating conditions. The unique use of a surplus photovoltaic (PV) power droop mode precisely estimates energy availability to minimize load chattering that helps in stabilizing the dc-bus. In addition, this architecture uses standard dc/dc converters for a low-cost, modular, Plug and Play ( PnP ) solution with highly automated operation, enabling local assembly, operation, and maintenance by non-technical personnel. The proposed SONG concept was validated in simulation and with a 700W, laboratory-scale, custom made nano grid system. The simulation and experimental test cases show the feasibility of the SONG system by demonstrating the proposed multi-source, multi-segment droop control strategy without load chattering under varying operating conditions.

dc-dc converter↗
DOE OSTI2026

PV Systems Modelling with Python

PVSC tutorial demonstrating how to download irradiance data and sun position for a specific location, determine the orientation of a single-axis tracker system, calculate Plane of Array irradiance, and estimate the power output of a PV system.

14 SOLAR ENERGY↗
DOE OSTI2019

Validating Performance Models for Hybrid Power Plant Control Assessment

The need for simple, but accurate performance models of wind turbine generators (WTGs), photovoltaic (PV) plants, and battery energy storage systems (BESS) for various hybrid power plant (HPP) studies motivates the present work. Particularly, the development and verification stage of HPP controls requires reduced-order models to minimize the complexity and computation effort of simulation platforms. In this paper, such models are proposed, and the most essential parts of the models are validated through field measurements. The models target power system integration studies involving active and reactive power, as well as frequency and voltage regulation where detailed models, as proposed in the standards, can be cumbersome. Field measurements of two Vestas WTGs, one 1-MW PV plant, and one 1-MW/1-MWh BESS are used for model validation. The results show that the WTG and PV performance models correctly estimate the power generation variability according to fluctuations in wind speed and solar irradiance. The BESS performance model provides satisfactory results related to grid-forming control performance and estimation of state-of-charge. The presented validation work enables using the proposed performance models for power system studies and HPP control design in all model-based design stages, that is, preliminary analysis, design, verification, and validation with a high level of confidence.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Regional Real-Time PV Spinning Reserve Estimator

Curtailed photovoltaic (PV) generation is a zero-marginal-cost spinning reserve that can be used for a number of active power control services. Unlike traditional spinning reserve providers, however, i.e., fossil-fueled generators, which have well-defined operating characteristics, e.g., available headroom or potential high limit (PHL), PV plants have by nature variable and uncertain operating characteristics. To ensure the effective coordination between PV plants and the system operator during an active power control event, accurate knowledge of the PV PHL is essential. It ensures that enough headroom is reserved by the PV plants to deliver the award services in real time and informs feasible dispatch decisions made by the market operator. To tackle this challenge, a novel reference-control grouping-based PV plant reserve estimation method has been proposed by the National Renewable Energy Laboratory under past projects funded by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Office. The estimation method separates inverters within a plant into two groups: a control group and a reference group. While the reference group is reserved to operate at its PHL, the control group can be curtailed to provide the grid services. Real-time outputs from the reference inverters are used to estimate the PHL for the whole plant based on the ratio between capacities of the reference group and of the plant. This work further enhances the methodology by (1) improving the model accuracy through machine learning; (2) automating the reference inverter selection through correlation analysis; (3) considering estimation look-ahead windows; and (4) applying to regional spinning reserve estimation. Significant performance improvement has been observed based on real-world data collected by CAISO, Southern Company, and Terabase Energy. Compared with the original scaling method, the newly proposed machine learning-based approach reduces the estimation errors by 30% and 13% at the plant level and region level, respectively. Results obtained from this project are intended to be used by grid operators, market operators, balancing authorities, and PV plant owners and operators to facilitate PV participation in ancillary service markets. Regulators, policymakers, and system planners can also consider the results of this work in their decision-making processes. In addition to the performance improvement on the existing reference-control based grouping method, we also investigated how the variability of PV generation from a single PV inverter can be used to represent the variability of PV generation at the plant level.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Frequency Analysis of Solar PV Power to Enable Optimal Building Load Control

In this paper, we present a flexibility estimation mechanism for buildings’ thermostatically controlled loads (TCLs) to enable the distribution level consumption of the majority of solar photovoltaic (PV) generation by local building TCLs. The local consumption of PV generation provides several advantages to the grid operation as well as the consumers, such as reducing the stress on the distribution network, minimizing voltage fluctuations and two-way power flows in the distribution network, and reducing the required battery storage capacity for PV integration. This would result in increasing the solar PV generation penetration levels. The aims of this study are twofold. First, spectral (frequency) analyses of solar PV power generation together with the power consumption of multiple building TCLs (such as heating, ventilation, and air conditioning (HVAC) systems, water heaters, and refrigerators) are performed. These analyses define the bandwidth over which these TCLs can operate and also describe the PV generation frequency bandwidth. Such spectral analyses, in frequency domain, can help identify the flexible components of PV generation that can be consumed by the various TCLs through optimal building load utilization. Second, a quadratic optimization problem based on model predictive control is formulated to allow consuming most of the low and medium frequency content of the PV power locally by building TCLs, while maintaining occupants’ comfort and TCLs’ physical constraints. The solution to the proposed optimization problem is achieved using optimal control strategies. Numerical results show that most of the low and medium frequency content of the PV generation can be consumed locally by building TCLs. The remaining high-frequency content of the PV generation can then be stored/offset using energy storage systems.

14 SOLAR ENERGY↗
DOE OSTI2023

Probing-Based Inertia Estimation Method Using Hybrid Power Plants

With the displacement of synchronous generation by inverter-based resources (IBRs), power systems could face the challenge of reduced inertia since IBRs do not inherently contribute to system inertia. Therefore, there is rising interest in monitoring system inertia in real-time applications for situational awareness. In addition, there is a growing number of IBRs that provide fast frequency responses (FFR) in the form of synthetic inertia and P-f droop. It is desirable to quantify the contribution of these FFR controls as equivalent inertia. This paper proposes a probing-based inertia estimation method using a PV-battery hybrid power plant in the Kauai island power system. The method is validated under different operating conditions.

hybrid power plant↗
DOE OSTI2022

Power Electronics Based Self-Monitoring and Diagnosing for Photovoltaic Systems

Faults in photovoltaic (PV) systems can seriously affect the efficiency, energy yield, cost, safety, and reliability of PV plants. Condition monitoring of PV plants is, therefore, a very important approach to estimating the health condition of PV modules and power electronics in the system. However, additional hardware for PV system monitoring adds cost to the system's operation; delayed maintenance service also causes additional energy production loss. The Center for Power Electronics Systems (CPES) at the Virginia Polytechnic Institute and State University and Siemens Cooperate Research developed the online impedance measurement for a PV panel self-monitoring and diagnosing technology using the DC-DC converter connected to the panel. Small-signal impedances of a monocrystalline silicon PV panel were modeled and simulated to reflect fault conditions such as the short-circuit, hot-spot, and junction box faults. Modeling and simulation results were validated firstly using a test setup consisting of a solar simulator, a network analyzer, small-signal injectors, and a monocrystalline PV panel rated at 300 W.

14 SOLAR ENERGY↗