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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Coordinated Control of Wind Turbine and Energy Storage System for Reducing Wind Power Fluctuation

This paper proposes a coordinated control of wind turbine and energy storage system (ESS). Because wind power (WP) is highly dependent on variable wind speed and could induce a severe stability problem to power system especially when the WP has high penetration level. To solve this problem, many power generation corporations or grid operators recently use the ESS. It has very quick response and good performance for reducing the impact of WP fluctuation but has high cost for its installation. Therefore, it is very important to design the control algorithm considering both ESS capacity and grid reliability. Thus, we propose the control algorithm to mitigate the WP fluctuation by using the coordinated control between wind turbine and ESS considering ESS state of charge (SoC) and the WP fluctuation. From deloaded control according to WP fluctuation and ESS SoC management, we can expect the ESS lifespan expansion and improved grid reliability. The effectiveness of the proposed method is validated in MATLAB/Simulink considering power system including both wind turbine generator and conventional generators which react to system frequency deviation.

17 WIND ENERGY↗
DOE OSTI2017

Coordinated Control of Wind Turbine and Energy Storage System for Reducing Wind Power Fluctuation

This paper proposes a method for the coordinated control of a wind turbine and an energy storage system (ESS). Because wind power (WP) is highly dependent on wind speed, which is variable, severe stability problems can be caused in power systems, especially when the WP has a high penetration level. To solve this problem, many power generation corporations or grid operators have begun using ESSs. An ESS has very quick response and good performance for reducing the impact of WP fluctuation; however, its installation cost is high. Therefore, it is important to design the control algorithm by considering both the ESS capacity and WP fluctuation. Thus, we propose a control algorithm to mitigate the WP fluctuation by using the coordinated control between the wind turbine and the ESS by considering the ESS capacity and the WP fluctuation. Using de-loaded control, according to the WP fluctuation and ESS capacity, we can expand the ESS lifespan and improve grid reliability by avoiding the extreme value of state of charge (SoC) (i.e., 0 or 1 pu). The effectiveness of the proposed method was validated via MATLAB/Simulink by considering a small power system that includes both a wind turbine generator and conventional generators that react to system frequency deviation. We found that the proposed method has better performance in SoC management, thereby improving the frequency regulation by mitigating the impact of the WP fluctuation on the small power system.

17 WIND ENERGY↗
DOE OSTI2012

Long-Term Wind Power Variability

The National Renewable Energy Laboratory started collecting wind power data from large commercial wind power plants (WPPs) in southwest Minnesota with dedicated dataloggers and communication links in the spring of 2000. Over the years, additional WPPs in other areas were added to and removed from the data collection effort. The longest data stream of actual wind plant output is more than 10 years. The resulting data have been used to analyze wind power fluctuations, frequency distribution of changes, the effects of spatial diversity, and wind power ancillary services. This report uses the multi-year wind power data to examine long-term wind power variability.

17 WIND ENERGY↗
DOE OSTI2015

Control voltage and power fluctuations when connecting wind farms

Voltage, frequency, active power and reactive power are very important parameters in terms of power quality. These parameters are followed when connecting any power plant, the more the connection of wind farms. Connecting wind farms to the electricity system must not cause interference outside the limits set by regulations. Modern solutions for fast and automatic voltage control and power fluctuations using electronic control systems of reactive power flows. FACTS (Flexible Alternating Current Transmision System) systems, established on the basis of power electronic circuits ensure control of electrical status quantities to achieve the necessary transfer of power to the power grid. FACTS devices can quickly control parameters and sizes of state power lines, such as impedance line voltages and phase angles of the voltages of the two ends of the line. Their use can lead to improvement in power system operation by increasing the transmission capacity of power lines, power flow control lines, improved static and transient stability reserve.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Bi-Level Arbitrage Potential Evaluation for Grid-Scale Energy Storage Considering Wind Power and LMP Smoothing Effect

Our paper deals with extended-term energy storage (ES) arbitrage problems to maximize the annual revenue in deregulated power systems with high penetration wind power. The conventional ES arbitrage model takes the locational marginal prices (LMP) as an input and is unable to account for the impacts of ES operations on system LMPs. This paper proposes a bi-level ES arbitrage model, where the upper level maximizes the ES arbitrage revenue and the lower level simulates the market clearing process considering wind power and ES. The bi-level model is formulated as a mathematical program with equilibrium constraints (MPEC) and then recast into a mixed-integer linear programming (MILP) using strong duality theory. Wind power fluctuations are characterized by the GARCH forecast model and the forecast error is modeled by forecast-bin based Beta distributions. Case studies are performed on a modified PJM 5-bus system and an IEEE 118-bus system with a weekly time horizon over an annual term to show the validity of the proposed bi-level model. The results from the conventional model and the bi-level model are compared under different ES power and energy ratings, and also various load and wind penetration levels.

17 WIND ENERGY↗
DOE OSTI2022

Rolling Optimization of Transmission Network Recovery and Load Restoration Considering Hybrid Wind-Storage System and Cold Load Pickup

A common solution to deal with the stochasticity introduced by fast-ramping wind power integration is to equip wind farms (WFs) with energy storage systems (ESSs) to formulate hybrid WF-ESSs. In addition to leveling off wind power fluctuations during normal operations, a hybrid WF-ESS can be a flexible power source to accumulate the power system restoration. In this paper, we propose a rolling optimization model for transmission network recovery and load restoration considering the contributions of WF-ESSs. The proposed model is formulated as a mixed integer linear programming problem that simultaneously optimizes the amount and location of restorable load blocks as well as the restoration lines. The cold load pickup features of interrupted loads considering the outage duration are modeled in detail. A chance-constrained method is employed to deal with the uncertainty of wind power, and a rolling horizon-based framework is adopted to reduce the influence of forecast error. Case studies are conducted on both New England 39-bus system and part of a provincial power system in China. The results show that the load restoration process can be significantly accelerated by employing the proposed method and contributions of hybrid WF-ESSs to power system restoration are validated.

chance-constrained optimization↗
DOE OSTI2023

A Sustainable Wind–Biogas Hybrid System for Remote Areas in Jordan: A Case Study of Mobile Hospital for a Zaatari Syrian Refugee Camp

Access to reliable and sustainable energy in remote areas remains a pressing global challenge, significantly affecting economic development and the quality of life. This study focuses on the implementation of fully off-grid wind–biogas hybrid power systems to address this issue, with a focus on remote healthcare camp facilities. This paper investigates the performance of a hybrid renewable energy system within the context of one of Jordan’s northern remote areas, the Zaatari Syrian Refugee Camp, assessing its efficiency and environmental impact by taking the Zaatari hospital as the case study. Simulations were conducted to evaluate system components, including wind turbines, biogas generators, and diesel generators. A comprehensive evaluation was conducted, encompassing both the operational efficiency of the system and its impact on the environment. This study also considered various scenarios (SC#), including grid availability and autonomy levels, to optimize system configurations. The techno-economic assessment employed the levelized cost of energy (LCOE) as a key performance indicator, and sensitivity analyses explored the impact of diesel costs and wind power fluctuations on the system. Additionally, environmental assessment was conducted to evaluate the environmental effects of hybrid systems, with a specific focus on reducing greenhouse gas emissions. This investigation involved an examination of emissions in three different scenarios. The results indicate that the lowest LCOE that could be achieved was 0.0734 USD/kWh in SC#1 with 72.42% autonomy, whereas achieving 100% autonomy increased the LCOE to 0.1756 USD/kWh. Additionally, the results reveal that in scenarios SC#2 and SC#3, which have a higher proportion of diesel generator usage, there were elevated levels of NO x and CO 2 emissions. Conversely, in SC#1, which lacks diesel generators, emissions were notably lower. The proposed hybrid system demonstrates its potential to provide a reliable energy supply to healthcare facilities in remote regions, emphasizing both economic feasibility and environmental benefits. These findings contribute to informed decision making for sustainable energy solutions in similar contexts, promoting healthcare accessibility and environmental sustainability.

09 BIOMASS FUELS↗
DOE OSTI2017

Power-Smoothing Scheme of a DFIG Using the Adaptive Gain Depending on the Rotor Speed and Frequency Deviation

In an electric power grid that has a high penetration level of wind, the power fluctuation of a large-scale wind power plant (WPP) caused by varying wind speeds deteriorates the system frequency regulation. This paper proposes a power-smoothing scheme of a doubly-fed induction generator (DFIG) that significantly mitigates the system frequency fluctuation while preventing over-deceleration of the rotor speed. The proposed scheme employs an additional control loop relying on the system frequency deviation that operates in combination with the maximum power point tracking control loop. To improve the power-smoothing capability while preventing over-deceleration of the rotor speed, the gain of the additional loop is modified with the rotor speed and frequency deviation. The gain is set to be high if the rotor speed and/or frequency deviation is large. In conclusion, the simulation results based on the IEEE 14-bus system clearly demonstrate that the proposed scheme significantly lessens the output power fluctuation of a WPP under various scenarios by modifying the gain with the rotor speed and frequency deviation, and thereby it can regulate the frequency deviation within a narrow range.

17 WIND ENERGY↗
DOE OSTI2019

Optimizing wind farm siting to reduce power system impacts of wind variability

We introduce and solve two variants of a biobjective optimization model to reduce the negative impact of wind variability on the power system by strategically locating wind farms. The first model variant considers average changes in wind power over time; the second captures extreme fluctuations in wind power. A complementary set of wind sites is selected with the aim of minimizing both residual demand and the variability in residual demand. Because exact optimization is computationally intensive, we develop two heuristics—forward and backward greedy algorithms—to find approximate solutions. The results are compared with the exact optimization results for a well-selected subset of the data as well as to the results from selecting sites based on average wind alone. The two models are solved using demand data and potential wind sites for the Southwest Power Pool. Though both objectives can be improved by adding more sites, for a fixed number of sites, minimizing residual demand and variability in residual demand are competing objectives. Here, we find an approximate efficient frontier to compare trade-offs between the two objectives. We also vary the parameter in the heuristic that controls how the two objectives are prioritized. For the case study, the backward greedy algorithm is more effective at reducing the wind power variability than the forward greedy algorithm. Furthermore, using the backward algorithm for the full dataset is more effective than solving the exact optimization on a subset of the data when the results are evaluated using the full dataset.

17 WIND ENERGY↗
DOE OSTI2026

Differences in cluster and internal wake effects from mesoscale and large-eddy simulations off the US East Coast

Mesoscale simulations are increasingly used to estimate wake effects within and between large wind farms, despite limited validation for large-scale wake effects. This study evaluates the capabilities and limitations of mesoscale simulations in capturing wake-induced impacts on wind turbine power production through a direct comparison with large-domain large-eddy simulations (LESs) for three planned offshore wind farms under realistic atmospheric conditions and a range of atmospheric stabilities. We assess mesoscale performance in replicating wake characteristics behind single and multiple turbine clusters and quantify the resulting variability in mean turbine power. Results show that mesoscale Weather Research and Forecasting simulations with the Fitch wind farm parameterization capture key features of the velocity deficit downstream of both single and multiple wind farms, with mean root-mean-square errors near 5 % and good agreement with stability-driven wake behavior. However, in these simulations, the mesoscale Fitch parameterization underestimates power losses from internal wake effects, particularly when turbines align with the prevailing wind direction or under stable stratification. In these conditions, individual wakes persist and dominate downstream power deficits. The coarse resolution of the mesoscale simulations limits their ability to resolve individual wind turbine wakes that drive power fluctuations within wind farms. Nonetheless, mesoscale simulations can yield accurate estimates of combined wake losses from internal and cluster effects across some wind direction sectors, where errors in wake representation may cancel each other out. These findings underscore the strengths of mesoscale simulations for capturing broader wake patterns while highlighting their limitations for modeling turbine-level power losses. Future work should explore hybrid modeling approaches to capture both long-range cluster wake propagation and localized internal wake dynamics.

17 WIND ENERGY↗
NASA NTRS1974

The power associated with density fluctuations and velocity fluctuations in the solar wind

Direct observations from Pioneer 6 of solar-wind-proton fluctuations have been used to obtain the power spectra associated with solar-wind-proton number density and velocity fluctuations in the frequency range of 0.001 to 0.01 Hz, extending previous analyses by an order of magnitude at the higher frequencies. The slopes of the power spectra associated with the density fluctuations and the velocity fluctuations are similar and are in agreement with the shape of the power spectra found at the lower frequencies. The power spectra indicate that the power-law density spectrum observed at lower frequencies extends to at least 0.01 Hz. This smooth variation in the spectrum at these frequencies is consistent with previous extrapolations of both spacecraft and interplanetary scintillation observations.

Intriligator, D. S.↗
NASA NTRS1979

Spacecraft radio scattering observations of the power spectrum of electron density fluctuations in the solar wind

Solar wind electron density power spectra in the solar equatorial region are inferred from observations of phase scintillations and spectral broadening made with the Viking, Helios, and Pioneer spacecraft. The heliocentric distance range covered is 2-215 solar radii and for some observations close to the sun the spectra extend to fluctuation frequencies as high as 100 Hz. For heliocentric distances of about 20 solar radii the equivalent spacecraft-measured one-dimensional density spectrum is well modeled by a single power law in the frequency range 0.0001-0.05 Hz. The flattening of the density spectrum within 20 solar radii is presumably associated with energy deposition in the near-sun region and acceleration of the solar wind.

Woo, R.↗
DOE OSTI2010

Analysis of Wind Power and Load Data at Multiple Time Scales

In this study we develop and apply new methods of data analysis for high resolution wind power and system load time series, to improve our understanding of how to characterize highly variable wind power output and the correlations between wind power and load. These methods are applied to wind and load data from the ERCOT region, and wind power output from the PJM and NYISO areas. We use a wavelet transform to apply mathematically well-defined operations of smoothing and differencing to the time series data. This approach produces a set of time series of the changes in wind power and load (or ?deltas?), over a range of times scales from a few seconds to approximately one hour. A number of statistical measures of these time series are calculated. We present sample distributions, and devise a method for fitting the empirical distribution shape in the tails. We also evaluate the degree of serial correlation, and linear correlation between wind and load. Our examination of the data shows clearly that the deltas do not follow a Gaussian shape; the distribution is exponential near the center and appears to follow a power law for larger fluctuations. Gaussian distributions are frequently used in modeling studies. These are likely to over-estimate the probability of small to moderate deviations. This in turn may lead to an over-estimation of the additional reserve requirement (hence the cost) for high penetration of wind. The Gaussian assumption provides no meaningful information about the real likelihood of large fluctuations. The possibility of a power law distribution is interesting because it suggests that the distribution shape for of wind power fluctuations may become independent of system size for large enough systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2013

Algorithm for Screening Phasor Measurement Unit Data for Power System Events and Categories and Common Characteristics for Events Seen in Phasor Measurement Unit Relative Phase-Angle Differences and Frequency Signals

A network of multiple phasor measurement units (PMU) was created, set up, and maintained at the University of Texas at Austin to obtain actual power system measurements for power system analysis. Power system analysis in this report covers a variety of time ranges, such as short- term analysis for power system disturbances and their effects on power system behavior and long- term power system behavior using modal analysis. The first objective of this report is to screen the PMU data for events. The second objective of the report is to identify and describe common characteristics extracted from power system events as measured by PMUs. The numerical characteristics for each category and how these characteristics are used to create selection rules for the algorithm are also described. Trends in PMU data related to different levels and fluctuations in wind power output are also examined.

17 WIND ENERGY↗
NASA NTRS1978

Plasma fluctuations in the solar wind

Using Ogo 5 plasma and magnetic field data, the power spectra of solar wind fluctuations are calculated over the 10 to the -3 to 10 to the -1 Hz range. It is found that the spectra calculated from flux measurements equal the density power spectra times the square of the average solar wind speed. The power spectrum relative density is of the same order of magnitude as the power spectrum of speed fluctuations relative to the Alfven speed. In cases where density and field fluctuations are inconsistent with magnetosonic waves, static inhomogeneities with a balance between electron thermal and magnetic pressures is assumed. It is also felt that a resonant proton cyclotron instability driven by the solar wind's proton thermal anisotropy may cause the observed power enhancements near 1 Hz.

Neugebauer, M.↗
DOE OSTI2022

Physics-Informed Gaussian Process Regression for States Estimation and Forecasting in Power Grids

Real-time state estimation and forecasting are critical for the efficient operation of power grids. In this paper, a physics-informed Gaussian process regression (PhI-GPR) method is presented and used for forecasting and estimating the phase angle, angular speed, and wind mechanical power of a three-generator power grid system using sparse measurements. In standard data-driven Gaussian process regression (GPR), parameterized models for the prior statistics are fit by maximizing the marginal likelihood of observed data. In the PhI-GPR method, we propose to compute the prior statistics offline by solving stochastic differential equations (SDEs) governing the power grid dynamics. The short-term forecast of a power grid system dominated by wind generation is complicated by the stochastic nature of the wind and the resulting uncertainty in wind mechanical power. Here, we assume that the power grid dynamics are governed by swing equations, with the wind mechanical power fluctuating randomly in time. We solve these equations for the mean and covariances of the power grid states using the Monte Carlo simulation method. We demonstrate that the proposed PhI-GPR method can accurately forecast and estimate observed and unobserved states. For the considered problem, PhI-GPR has computational advantages over the ensemble Kalman filter (EnKF) method: In PhI-GPR, ensembles are computed offline and independently of the data acquisition process, whereas for EnFK, ensembles are computed online with data acquisition, rendering real-time forecast more challenging. We also demonstrate that the PhI-GPR forecast is more accurate than the EnKF forecast when the random mechanical wind power is non-Markovian. In contrast, the two methods produce similar forecasts for the Markovian mechanical wind power. For observed states, we show that PhI-GPR provides a forecast comparable to the standard data-driven GPR; both forecasts are significantly more accurate than the autoregressive integrated moving average (ARIMA) forecast. We also show that the ARIMA forecast is more sensitive to observation frequency and measurement errors than the PhI-GPR forecast.

24 POWER TRANSMISSION AND DISTRIBUTION↗