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Loutan, Clyde

Publications and source records attributed to Loutan, Clyde.

10 records
All sourcesNASA NTRSDOE OSTI
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 OSTI2018

Ensuring System Reliability: Distributed Energy Resources and Bulk Power System Considerations

Rooftop solar energy, storage batteries, and electric vehicles are changing the way people use and produce electricity. These are among the growing set of technologies called distributed energy resources (DERs) because they are typically connected to the utility at the medium-voltage distribution system. In general, the utility distribution system connects substations to homes and businesses. The effect of DE Rs on operating the power grid has been dramatic and exciting, and they provide both challenges and benefits to the entire grid: spanning utilization, distribution, and transmission voltage ranges. The zero-carbon resources among the broader family of DE Rs are critical in complying with state and federal energy policies. This, in turn, has required grid operators, utilities, regulators, consumer advocates, and lawmakers to reach out across traditional domains to engage in the robust dialog and coordination needed to integrate distribution-level resources to the larger high-voltage grid and the networked bulk power systems (BPSs).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Geographic smoothing of solar photovoltaic electric power production in the Western USA

We examined the geographic smoothing of solar photovoltaic generation from 15 utility-scale plants in California, Nevada, and Arizona and from 19 commercial building installations in California. This is the first comparison of geographic smoothing from utility-scale and building-mounted PV and the first examination of solar PV smoothing in this region. Our research questions were (1) how does geographic smoothing of commercial building rooftop PV compare to that of utility scale PV?, (2) is the geographic smoothing found for utility-scale plants the same for the western US as in India?, and (3) how does the geographic smoothing for PV compare to that of wind? By examining the power output of these generators in the frequency domain, we quantified the smoothing obtained by combining the output of geographically separated plants. We found that utility-scale and commercial rooftop plants exhibited similar geographic smoothing, with 10 combined plants reducing the amplitude of fluctuations at 1 h to 18%–28% of those seen for a single plant. We find that combining a few PV sites together reduces fluctuations, but that the point of quickly diminishing returns is reached after ∼5 sites, and that for all the locations and plant sizes considered, PV does not exhibit as much geographic smoothing as is seen for combining wind plants. We present preliminary theoretical arguments for why geographic smoothing of PV plants is less effective than that for wind plants. The slope of the high-frequency part of the PV power spectrum can at best be geographically smoothed (steepen) to an asymptotic spectrum of [Formula: see text]. This limit for PV has considerably less smoothing than that for wind's geographic smoothing, shown theoretically and from observed data to be [Formula: see text].

Klima, Kelly↗
DOE OSTI2018

Improving BA Control Performance Through Advanced Regulation Requirements Prediction

The paper presents a comprehensive approach to predict balancing authority (BA) regulation requirements in order to improve BAs control performance. The proposed probabilistic approach takes into account multiple uncertain and time dependent factors affecting the system balance (e.g., wind and solar generation, electrical loads). A set of methods to predict the regulation has been developed: (1) probabilistic analysis of regulation requirements with respect to time of the day, (2) linear or nonlinear regression models linking regulation requirements to different influencing factors, (3) adopting time series forecasting techniques (e.g., autoregressive integrated moving average (ARIMA)) to account for temporal continuity and auto-correlation in the regulation requirements data. Proposed methodology has been tested and validated using actual California Independent System Operator (ISO) data.

Solar Generation, Wind Generation, Area Control Er↗
DOE OSTI2018

Benefit Cost Analysis of Improved Forecasting for Day-Ahead Hourly Regulation Requirements

A benefit cost analysis (BCA) of the Day-Ahead Regulation Prediction (DARP) tool found substantial savings to providing an improved forecast of the California Independent System Operators (CAISO) reserve requirements for regulation services with increasing amounts of solar generation. DARP predicts day-ahead reserve requirements for Regulation Up (Reg Up) and Regulation Down (Reg Down) using the balancing authority’s Area Control Error (ACE) and actual frequency. The BCA approach statistically estimated the price-quantity relationships for Reg Up and Reg Down. The estimated changes in Regulation reserve prices along with the estimated changes in day-ahead reserve requirements, forecast by DARP, provide a cost savings estimate. The results indicate that more savings were found for reducing Reg Up than reducing Reg Down purchases. A similar approach may be used to estimate savings from improved forecasting of reserves for other ancillary services, or to estimate benefits from other forecasting tools and methodologies.

Ancillary Services, Benefit Cost Analysis, Frequen↗
DOE OSTI2018

Balancing Needs Assessment Using Advanced Probabilistic Forecasts

This paper presents a comprehensive approach to predict Balancing Authority (BA) regulation and load following requirements in order to improve BA control performance. In this paper the Pacific Northwest National Laboratory’s (PNNL) “ramp and uncertainty prediction tool (RUT) and day-ahead regulation prediction (DARP) tool” were upgraded to incorporate advanced probabilistic forecast information provided by AWS Truepower. The proposed methodology has been tested and validated using actual California Independent System Operator (CAISO) data. Simulation confirmed that integration probabilistic forecast information can reduce the predicted regulation range by about 12-31%. This means that BAs can procure fewer balancing resources without compromising their reliability and control performance requirements.

Regulation, Solar Generation, Wind Generation, Con↗
DOE OSTI2017

Demonstration of Essential Reliability Services by a 300-MW Solar Photovoltaic Power Plant

The California Independent System Operator (CAISO), First Solar, and the National Renewable Energy Laboratory (NREL) conducted a demonstration project on a large utility-scale photovoltaic (PV) power plant in California to test its ability to provide essential ancillary services to the electric grid. With increasing shares of solar- and wind-generated energy on the electric grid, traditional generation resources equipped with automatic governor control (AGC) and automatic voltage regulation controls -- specifically, fossil thermal -- are being displaced. The deployment of utility-scale, grid-friendly PV power plants that incorporate advanced capabilities to support grid stability and reliability is essential for the large-scale integration of PV generation into the electric power grid, among other technical requirements. A typical PV power plant consists of multiple power electronic inverters and can contribute to grid stability and reliability through sophisticated 'grid-friendly' controls. In this way, PV power plants can be used to mitigate the impact of variability on the grid, a role typically reserved for conventional generators. In August 2016, testing was completed on First Solar's 300-MW PV power plant, and a large amount of test data was produced and analyzed that demonstrates the ability of PV power plants to use grid-friendly controls to provide essential reliability services. These data showed how the development of advanced power controls can enable PV to become a provider of a wide range of grid services, including spinning reserves, load following, voltage support, ramping, frequency response, variability smoothing, and frequency regulation to power quality. Specifically, the tests conducted included various forms of active power control such as AGC and frequency regulation; droop response; and reactive power, voltage, and power factor controls. This project demonstrated that advanced power electronics and solar generation can be controlled to contribute to system-wide reliability. It was shown that the First Solar plant can provide essential reliability services related to different forms of active and reactive power controls, including plant participation in AGC, primary frequency control, ramp rate control, and voltage regulation. For AGC participation in particular, by comparing the PV plant testing results to the typical performance of individual conventional technologies, we showed that regulation accuracy by the PV plant is 24-30 points better than fast gas turbine technologies. The plant's ability to provide volt-ampere reactive control during periods of extremely low power generation was demonstrated as well. The project team developed a pioneering demonstration concept and test plan to show how various types of active and reactive power controls can leverage PV generation's value from being a simple variable energy resource to a resource that provides a wide range of ancillary services. With this project's approach to a holistic demonstration on an actual, large, utility-scale, operational PV power plant and dissemination of the obtained results, the team sought to close some gaps in perspectives that exist among various stakeholders in California and nationwide by providing real test data.

14 SOLAR ENERGY↗
DOE OSTI2016

Comprehensive Solutions for Integration of Solar Resources into Grid Operations

The need for proactive closed-loop integration of uncertainty information into system operations and probability-based controls is widely recognized, but rarely implemented in system operations. Proactive integration for this project means that the information concerning expected uncertainty ranges for net load and balancing requirements, including required balancing capacity, ramping and ramp duration characteristics, will be fed back into the generation commitment and dispatch algorithms to modify their performance so that potential shortages of these characteristics can be prevented. This basic, yet important, premise is the motivating factor for this project. The achieved project goal is to demonstrate the benefit of such a system. The project quantifies future uncertainties, predicts additional system balancing needs including the prediction intervals for capacity and ramping requirements of future dispatch intervals, evaluates the impacts of uncertainties on transmission including the risk of overloads and voltage problems, and explores opportunities for intra-hour generation adjustments helping to provide more flexibility for system operators. The resulting benefits culminate in more reliable grid operation in the face of increased system uncertainty and variability caused by solar power. The project identifies that solar power does not require special separate penetration level restrictions or penalization for its intermittency. Ultimately, the collective consideration of all sources of intermittency distributed over a wide area unified with the comprehensive evaluation of various elements of balancing process, i.e. capacity, ramping, and energy requirements, help system operators more robustly and effectively balance generation against load and interchange. This project showed that doing so can facilitate more solar and other renewable resources on the grid without compromising reliability and control performance. Efforts during the project included developing and integrating advanced probabilistic solar forecasts, including distributed PV forecasts, into closed –loop decision making processes. Additionally, new uncertainty quantifications methods and tools for the direct integration of uncertainty and variability information into grid operations at the transmission and distribution levels were developed and tested. During Phase 1, project work focused heavily on the design, development and demonstration of a set of processes and tools that could reliably and efficiently incorporate solar power into California’s grid operations. In Phase 2, connectivity between the ramping analysis tools and market applications software were completed, multiple dispatch scenarios demonstrated a successful reduction of overall uncertainty and an analysis to quantify increases in system operator reliability, and the transmission and distribution system uncertainty prediction tool was introduced to system operation engineers in a live webinar. The project met its goals, the experiments prove the advancements to methods and tools, when working together, are beneficial to not only the California Independent System Operator, but the benefits are transferable to other system operators in the United States.

14 SOLAR ENERGY↗
DOE OSTI2013

Prediction of Regulation Reserve Requirements in California ISO Control Area based on BAAL Standard

This paper presents new methodologies developed at Pacific Northwest National Laboratory (PNNL) to estimate regulation capacity requirements in the California ISO control area. Two approaches have been developed: (1) an approach based on statistical analysis of actual historical area control error (ACE) and regulation data, and (2) an approach based on balancing authority ACE limit control performance standard. The approaches predict regulation reserve requirements on a day-ahead basis including upward and downward requirements, for each operating hour of a day. California ISO data has been used to test the performance of the proposed algorithms. Results show that software tool allows saving up to 30% on the regulation procurements cost .

Etingov, Pavel V.↗