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Chartan, Erol Kevin

Publications and source records attributed to Chartan, Erol Kevin.

13 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Frequency Support Enhancement of a Permanent Magnet-Based Adjustable-Speed Pumped Hydropower Plant

When a permanent magnet (PM) -based adjustable-speed pumped storage hydropower (AS-PSH) plant delivers frequency response via the full-scaled power converter, there is power imbalance at the PM generator during the initial stage of the frequency response. This is because the AS-PSH has the different time constants between the power converter and wicket gate in the governor valve. Thus, this imbalance causes a sudden reduction of the rotational speed of the machine during the frequency response. As a result, a voltage deficit arises at the generator terminal in proportion to the decrement in the rotational speed, and thereby it is promising that the current flow at the converter will reach its limit when attempting to inject the commanded active power at a reduced voltage level. Thus, this causes the degradation of the frequency response of the AS-PSH due to the self-demagnetization at a reduced rotational speed. This paper proposes a frequency support scheme of a PM-based AS-PSH to enhance the frequency response by bridging the gap between the different time constants. To achieve this, the frequency support scheme includes a gate opening adjustment in the governor and a dynamic ramp-down in converter controller of the AS-PSH.

13 HYDRO ENERGY↗
DOE OSTI2019

Grid Integration and Market Analysis of Adjustable-Speed Pumped Storage Hydropower: Preprint

Many of the products and services on the electrical grid are currently provided by the conventional power plants, however the response time from conventional power plants is slow compared to hydropower. Similarly, some services such as spinning reserve can be provided by modern renewable generation (wind or PV), however the renewable generation must be curtailed down, thus there is loss of opportunity to harvest clean energy while operating in spinning reserve mode. On the other hand, Adjustable Speed - Pumped Storage Hydropower (AS-PSH) can readily perform these functions while pumping or generating, thus also taking benefit of energy arbitrage which can increase system efficiency. This paper will present results from production cost models showing the effect of adding AS-PSH 50 MW in size to three regional ISO areas which have different ancillary service markets available and different generation mixes. The benefits to these market areas from the ancillary services that can be provided by the AS-PSH and also the load shifting benefits will be measured with cost savings and additional system flexibility in the day-ahead and real-time markets. The main metrics to provide this insight will be savings in the total system production cost and in variable renewable energy curtailment respectively. The simulated operation of the additional hydropower will be analyzed to determine the breakdown of system cost reduction by ancillary services provided and load shifting.

13 HYDRO ENERGY↗
DOE OSTI2019

Grid Integration and Market Analysis of Adjustable-Speed Pumped Storage Hydropower

Variable renewable energy is increasing, affecting variability and range of energy prices and requiring extra power system flexibility. Adjustable-speed pumped storage hydropower (AS-PSH), can provide value to ancillary service markets and take benefit of energy arbitrage. Present results from production cost models showing the effects of adding 50 MW of AS-PSH to three Independent System Operators (ISOs).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

A two-step short-term probabilistic wind forecasting methodology based on predictive distribution optimization

With increasing wind penetrations into electric power systems, probabilistic wind forecasting becomes more critical to power system operations because of its capability of quantifying wind uncertainties. In this paper, a two-step probabilistic wind forecasting approach based on pinball loss optimization is developed. First, a multimodel machine learning-based ensemble deterministic forecasting framework is adopted to generate deterministic forecasts. The deterministic forecast is assumed to be the mean value of the predictive distribution at each forecasting time stamp. Then, the optimal unknown parameter (i.e., standard deviation) of the predictive distribution is estimated by a support vector regression surrogate model based on the deterministic forecasts. Lastly, probabilistic forecasts are generated from the predictive distribution. Numerical results of case studies at eight locations show that the developed two-step probabilistic forecasting methodology has improved the pinball loss metric score by up to 35% compared to a baseline quantile regression forecasting model.

17 WIND ENERGY↗
DOE OSTI2018

Comparing the Value of Improved Variable Renewable Energy Forecasting Accuracy in South African and United States Power Systems: Preprint

The value associated with an improved VRE forecast in has been quantified in this research. The value of improved VRE forecasts can increase with increasing VRE penetration levels with the range of this value becoming wider at increasing penetration levels. This value also saturates with high levels of improved VRE forecasts as there is relatively lower impact of improving VRE forecasts further. This paper discusses how the improvement of variable renewable energy (VRE) forecasting could impact the South African power system and a United States power system.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Characterizing forecastability of wind sites in the United States

With the rapid growth of wind power, managing its uncertainty and variability becomes critical in power system operations. Wind forecasting is one of the enablers to partially tackle challenges associated with wind power uncertainty. To improve the 'forecasting ability', defined as forecastability, different forecasting methods have been developed to assist grid integration of wind energy. However, forecasting performance not only relies on the power of forecasting models, but is also related to local weather conditions and (known as wind characteristics) wind farm properties. In this study, geospatial and instance spatial distributions of six wind characteristics and two forecasting error metrics are first analyzed based on 126,000 + wind sites in the United States. Forecasts in different look-ahead times are generated by using a machine learning based multi-model forecasting framework model and the Weather Research and Forecasting model. A forecastability quantification method is developed by characterizing the relationship between forecastability and wind series entropy using three regression methods, i.e., linear approximation, locally weighted scatterplot smoother nonlinear nonparametric regression, and quantile regression. It is found that the forecastability of a wind site can be successfully characterized by wind series characteristics, thereby providing valuable information at different stages of wind energy projects.

17 WIND ENERGY↗
DOE OSTI2018

Probabilistic Short-Term Wind Forecasting Based on Pinball Loss Optimization

Probabilistic wind power forecasts that quantify the uncertainty in wind output have the potential to aid in the economic grid integration of wind power at large penetration levels. In this paper, a novel probabilistic wind forecasting approach based on pinball loss optimization is proposed, in conjunction with a multi-model machine learning based ensemble deterministic forecasting framework. By assuming the point-forecasted value as the mean at each point, one unknown parameter (i.e., standard deviation) of a predictive distribution at each forecasting point is determined by minimizing the pinball loss. A surrogate model is developed to represent the unknown distribution parameter as a function of deterministic forecasts. This surrogate model can be used together with deterministic forecasts to predict the unknown distribution parameter and thereby generate probabilistic forecasts. Numerical results of case studies show that the proposed method has improved the pinball loss by up to 35% compared to a baseline quantile regression forecasting model.

17 WIND ENERGY↗
DOE OSTI2018

Probabilistic Short-Term Wind Forecasting Based on Pinball Loss Optimization: Preprint

Probabilistic wind power forecasts that quantify the uncertainty in wind output have the potential to aid in the economic grid integration of wind power at large penetration levels. In this paper, a novel probabilistic wind forecasting approach based on pinball loss optimization is proposed, in conjunction with a multi-model machine learning based ensemble deterministic forecasting framework. By assuming the pointforecasted value as the mean at each point, one unknown parameter (i.e., standard deviation) of a predictive distribution at each forecasting point is determined by minimizing the pinball loss. A surrogate model is developed to represent the unknown distribution parameter as a function of deterministic forecasts. This surrogate model can be used together with deterministic forecasts to predict the unknown distribution parameter and thereby generate probabilistic forecasts. Numerical results of case studies show that the proposed method has improved the pinball loss by up to 35% compared to a baseline quantile regression forecasting model.

17 WIND ENERGY↗
DOE OSTI2018

Characterizing Time Series Data Diversity for Wind Forecasting: Preprint

Wind forecasting plays an important role in integrating variable and uncertain wind power into the power grid. Various forecasting models have been developed to improve the forecasting accuracy. However, it is challenging to accurately compare the true forecasting performances from different methods and forecasters due to the lack of diversity in forecasting test datasets. This paper proposes a time series characteristic analysis approach to visualize and quantify wind time series diversity. The developed method first calculates six time series characteristic indices from various perspectives. Then the principal component analysis is performed to reduce the data dimension while preserving the important information. The diversity of the time series dataset is visualized by the geometric distribution of the newly constructed principal component space. The volume of the 3-dimensional (3D) convex polytope (or the length of 1D number axis, or the area of the 2D convex polygon) is used to quantify the time series data diversity. The method is tested with five datasets with various degrees of diversity.

17 WIND ENERGY↗
DOE OSTI2018

Transforming the U.S. Market with a New Application of Ternary-Type Pumped-Storage Hydropower Technology: Preprint

As the deployment of wind and solar technologies increases at an unprecedented rate across the United States and in many world markets, the variability of power output from these technologies expands the need for increased power system flexibility. Energy storage can play an important role in the transition to a more flexible power system that can accommodate high penetrations of variable renewable technologies. This project focuses on how ternary pumped storage hydropower (T-PSH) coupled with dynamic transmission can help this transition by defining the system-wide benefits of deploying this technology in specific U.S. markets. T-PSH technology is the fastest responding pumped hydro technology equipment available today for grid services. T-PSH efficiencies are competitive with lithium-ion (Li-ion) batteries, and T-PSH can provide increased storage capacity with minimal degradation during a 50-year lifetime. This project evaluates T-PSH for grid services ranging from fast frequency response (FFR) for power system contingency events and enhanced power system stability to longer time periods for power system flexibility to accommodate ramping from wind and solar variability and energy arbitrage. In summary, this project: Compares power grid services and costs, including ancillary services and essential reliability services, for T-PSH and conventional pumped storage hydropower (PSH) - Evaluates the dynamic response of T-PSH and PSH technologies and their contribution to essential reliability services for grid stability by developing new power system model representations for T-PSH and performing simulations in the Western Interconnection - Evaluates production costs, operational impacts, and energy storage revenue streams for future power system scenarios with T-PSH focusing on time frames of 5 minutes and more - Assesses the electricity market-transforming capabilities of T-PSH technology coupled with transmission monitoring and dynamic control. This paper presents an overview of the methodology and initial, first-year preliminary findings of a 2-year in-depth study into how advanced PSH and dynamic transmission contribute to the transformation and modernization of the U.S. electric grid. This project is part of the HydroNEXT Initiative funded by the U.S. Department of Energy (DOE) that is focused on the development of innovative technologies to advance nonpowered dams and PSH. The project team consists of the National Renewable Energy Laboratory (project lead), Absaroka Energy, LLC (Montana-based PSH project developer), GE Renewable Energy (PSH pump/turbine equipment supplier), Grid Dynamics, and Auburn University (lead for NREL/Auburn dynamic modeling team).

13 HYDRO ENERGY↗
DOE OSTI2018

Evaluating the Impact of the 2017 Solar Eclipse on U.S. Western Interconnection Operations

With support from the U.S. Department of Energy (DOE) Solar Energy Technologies Office (SETO), the National Renewable Energy Laboratory (NREL) partnered with Peak Reliability to evaluate the impact of the August 21, 2017 total solar eclipse on the reliability and grid operations in the Western Electricity Coordinating Council (WECC) territory.

14 SOLAR ENERGY↗
DOE OSTI2018

Preliminary Findings of the South Africa Power System Capacity Expansion and Operational Modelling Study: Preprint

Wind and solar power contract prices have recently become cheaper than many conventional new-build alternatives in South Africa and trends suggest a continued increase in the share of variable renewable energy (vRE) on South Africa's power system with coal technology seeing the greatest reduction in capacity, see 'Figure 6: Percentage share by Installed Capacity (MW)' in [1]. Hence it is essential to perform a state-of-the-art grid integration study examining the effects of these high penetrations of vRE on South Africa's power system. Under the 21st Century Power Partnership (21CPP), funded by the U.S. Department of Energy, the National Renewable Energy Laboratory (NREL) has significantly augmented existing models of the South African power system to investigate future vRE scenarios. NREL, in collaboration with Eskom's Planning Department, further developed, tested and ran a combined capacity expansion and operational model of the South African power system including spatially disaggregated detail and geographical representation of system resources. New software to visualize and interpret modelling outputs has been developed, and scenario analysis of stepwise vRE build targets reveals new insight into associated planning and operational impacts and costs. The model, built using PLEXOS, is split into two components, firstly a capacity expansion model and secondly a unit commitment and economic dispatch model. The capacity expansion model optimizes new generation decisions to achieve the lowest cost, with a full understanding of capital cost and an approximated understanding of operational costs. The operational model has a greater set of detailed operational constraints and is run at daily resolutions. Both are run from 2017 through 2050. This investigation suggests that running both models in tandem may be the most effective means to plan the least cost South African power system as build plans seen to be more expensive than optimal by the capacity expansion model can produce greater operational cost savings seen only in the operational model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Characterizing Time Series Data Diversity for Wind Forecasting

Wind forecasting plays an important role in integrating variable and uncertain wind power into the power grid. Various forecasting models have been developed to improve the forecasting accuracy. However, it is challenging to accurately compare the true forecasting performances from different methods and forecasters due to the lack of diversity in forecasting test datasets. This paper proposes a time series characteristic analysis approach to visualize and quantify wind time series diversity. The developed method first calculates six time series characteristic indices from various perspectives. Then the principal component analysis is performed to reduce the data dimension while preserving the important information. The diversity of the time series dataset is visualized by the geometric distribution of the newly constructed principal component space. The volume of the 3-dimensional (3D) convex polytope (or the length of 1D number axis, or the area of the 2D convex polygon) is used to quantify the time series data diversity. The method is tested with five datasets with various degrees of diversity.

big data visualization↗