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Results for “active power control”

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 OSTI2023

Coordinated Wind Power Plant and Battery Control for Active Power Services

This article considers joint active power control of wind turbines and battery storage to follow a plant-level power reference signal. The joint control dynamically curtails the energy from a subset of the wind turbines and stores or withdraws energy from the battery to meet the power reference setpoint while accounting for wind plant aerodynamic interactions, such as wake losses. As a use case, we study the performance of the controller in maintaining a constant power output over hourly periods. A wind plant operating in this way would rely much less on other grid resources to meet its contractual agreements, thereby improving grid reliability, especially in grids with high penetration of wind and solar generation. We compare the operation of the wind plant under joint active power control to standard power-maximizing control with battery support. We present an analysis of the performance of the control system architecture. To study the impact of the battery size on performance, we simulate a 50-MW wind plant supported by batteries ranging from 8 to 64?MWh. We then evaluate the over and undergeneration penalties incurred by the plant during the simulation period.

17 WIND ENERGY↗
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 OSTI2022

Medium Voltage Energy Hub Based on Multilevel Cascaded H Bridge-Dual Active Bridge Back-to-Back Converter for Power Distribution Feeders Interconnection and Multiple Simultaneous Grid Services

This paper presents a medium voltage energy hub based on a modular design of a multilevel cascaded H bridge (CHB)-dual active bridge (DAB) converter. The energy hub composed of the two CHB-DAB modules with a back-to-back topology can be utilized as a grid interconnection component between distribution feeders. The energy hub can provide multiple simultaneous grid services such as voltage regulation, power factor correction, as well as active power flow control between the connected feeders, contributing to grid flexibility, efficiency, reliability, and resilience. Circuit structure and control schemes for the energy hub including both local controllers for each converter and outer loop controllers are proposed to enable simultaneous grid services with coordination to prevent the overloading of the hub. To validate the performance of the hub, the hub system is interconnected between two IEEE 4-bus feeders and provides voltage regulation for both feeders, while controlling active power flow between them. The system and the feeders are implemented in Real-Time Digital Simulator (RTDS) for real-time simulation verification.

Choi, Jongchan↗
DOE OSTI2021

Review of Power System Support Functions for Inverter-Based Distributed Energy Resources - Standards, Control Algorithms, and Trends

Penetration of renewable energy in power systems has been increasing in the past decades in response to increased global electricity demand and concerns for the environment. Distributed energy resources (DERs) based on renewables have experienced rapid growth thanks to the incentive programs and broad-based participation. With the growing prevalence of DERs, the risk of grid instability and vulnerability increases due to the intermittent nature of renewable energy. At the same time, the voltage and frequency deviation problems emerge more often when the reverse power flow occurs under supply-demand imbalance in distributed power systems. Standards and grid codes have been issued for DER inverters to interconnect with the distribution grid. The updated standard and grid codes expect DERs to provide a variety of power system support functions in order to incorporate higher DER penetration and to maximize DER value to the grid. This paper provides an overview of the power system support functions from renewable DER inverters, which are categorized as: voltage regulation by active/reactive power control, frequency regulation by active power control, voltage ride-through, and frequency ride-through. The benefits and drawbacks of each algorithm are presented and compared with its predecessor, manifesting the logic in the evolution of the algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

WHOC (Wind Hybrid Open Controller) [SWR-25-54]

The Wind Hybrid Open Controller (WHOC) is a python-based tool for real-time plant-level wind farm control and wind-based hybrid plant control. WHOC is primarily run in simulation, although we intend that it could be used for physical plants in future. WHOC provides simple farm-level (and hybrid plant-level) controls such as wake steering control, spatial filtering/consensus, active power control, and coordinated control of hybrid power plant assets; and creates an entry point for the development of more advanced controllers.

Sinner, Michael (Misha) [National Renewable Energy↗
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 OSTI2023

Real-Time Regional PV Spinning Reserve Estimator with AGC Look-Ahead Windows

Curtailed PV generation is a zero-marginal cost spinning reserve that can be used for a number of active power control services. However, unlike the traditional spinning reserve providers, i.e., fossil-fueled generators, who 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 forecasts of the PV PHL are essential. A novel reference-control grouping based scaling method has been proposed by NREL to estimate the PV PHL in real-time. This work further enhances the methodology by: 1) improving the model accuracy through machine learning; 2) considering look-ahead windows introduced by the computation and communication latencies; 3) applying the method to regional spinning reserve estimation. A significant performance improvement, over 99% of estimation error reduction, has been observed based on real-world data collected by CAISO and PV plant operators.

potential high limit↗
DOE OSTI2023

Real-Time Regional PV Spinning Reserve Estimator with AGC Look-Ahead Windows: Preprint

Curtailed PV generation is a zero-marginal cost spinning reserve that can be used for a number of active power control services. However, unlike the traditional spinning reserve providers, i.e., fossil-fueled generators, who 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 forecasts of the PV PHL are essential. A novel reference-control grouping based scaling method has been proposed by NREL to estimate the PV PHL in real-time. This work further enhances the methodology by: 1) improving the model accuracy through machine learning; 2) considering look-ahead windows introduced by the computation and communication latencies; 3) applying the method to regional spinning reserve estimation. A significant performance improvement, over 1.6% of estimation error reduction, has been observed based on real-world data collected by CAISO and PV plant operators.

potential high limit↗
DOE OSTI2020

Decoupled Active and Reactive Power Control without PLL Requirement for Differential Buck Converter

This paper presents a decoupled active and reactive power control for the differential buck converter. This control does not require phase locked loop (PLL) for synchronization. Also, it achieves active and reactive power control in a simple single control loop structure. The differential buck converter has the capability to compensate the double line frequency power ripple without relying on additional hardware. Particularly, the Common Mode (CM) operation is used for active double line frequency power ripple absorption. Whereas, the Differential Mode (DM) operation is used as an active and reactive power exchanging port between the converter and the grid at the point of common coupling (PCC). The motivation behind this paper proposal are: (i) to avoid PLL induced instabilities, especially, when connecting the differential buck converter with weak grid, and (ii) direct active and reactive power control for the differential buck in a single loop control. The theoretical analyses are verified with a 5 kW converter with only 50 μF decoupling capacitor connected to a weak grid.

Khan, Ahmad↗
DOE OSTI2023

Robust Adaptive Control for Large-scale Inverter-based Resources with Partial and Complete Loss of Inverters

This article proposes an approach to address the current and the aggregated active power control challenge for large-scale inverter-based resources subjected to partially or completely loss of inverters and grid voltage variations. To address this problem, a distributed active power mechanism is proposed which generates desired inverters current. Then an adaptive mechanism distributes the voltage control input automatically between inverters in response to the partial or complete loss of inverters. An L2-gain-based controller is designed for each inverter to track the desired current and rejects the grid voltage disturbance. Here, simulation results show a significant robust tracking for collective active power and current.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Evaluating large scale aqueous organic redox flow battery performance with a hybrid numerical and machine learning framework

Aqueous organic redox flow battery (AORFB) is a promising cost-competitive technology for large-scale energy storage. Among existing work, the dihydroxyphenazine (DHP)-based AORFB has demonstrated high energy density and low-capacity degradation in 10 cm$^2$ cells during lab tests. However, its commercial-scale performance in more complex environments remains unknown, posing a barrier to commercialization. To address this gap, this work presents a comprehensive performance evaluation of a 780 cm$^2$ DHP-based AORFB by combining a physics-based numerical model, machine learning (ML)-based surrogate models, and ML-derived sensitivity quantification. Specifically, we first select 12 key battery parameters that include 10 physicochemical and 2 operation quantities, then select 6 performance metrics that include energy efficiency (EE), discharging capacity, charging energy, and power losses due to concentration, activation, and ohmic over-potentials. With such selection, 12800 combinations of the 12 parameters are subsequently generated using the Latin Hypercube Sampling method. Such combinations, together with 38 pre-defined State of Charge, are then integrated to a validated AORFB model developed in COMSOL to compute the performance metrics. With both input parameters and performance metrics, 60 deep neural network (DNN) surrogate models are then trained to approximate the relationship between the 10 physicochemical quantities and 6 performance metrics at each flow rate and current density. Sensitivity scores are then calculated based on the DNN models. Two additional sensitivity analysis tools, i.e., MARS, and SHAP, are also used to cross-validate the sensitivity scores from the DNN. The results demonstrate that 1) the standard potential ranks first in controlling EE and charging energy, 2) the membrane conductivity is most critical for power loss and EE, and 3) specific area and reaction rate control activation power loss.

25 ENERGY STORAGE↗
DOE OSTI2025

A hybrid numerical and machine learning framework for evaluating the performance of a 780 cm 2 aqueous organic redox flow battery

Aqueous organic redox flow battery (AORFB) is a promising cost-competitive technology for large-scale energy storage. Among existing work, the dihydroxyphenazine (DHP)-based AORFB has demonstrated high energy density and low capacity degradation in 10 cm2 cells during lab tests. However, its commercial-scale performance in more complex environments remains unknown, posing a barrier for commercialization. To address this gap, this work presents a comprehensive performance evaluation of a 780 cm 2 DHP-based AORFB by combining physics-based numerical model, machine learning (ML)-based surrogate models, and ML-derived sensitivity quantification. Specifically, we first select 12 key battery parameters that include 10 physicochemical quantities and 2 operation quantities, then select 6 performance metrics that include energy efficiency (EE), discharging capacity, charging energy, and power losses due to concentration, activation, and ohmic over-potentials. With such selection, 12800 combinations of the 12 parameters are subsequently generated using the Latin Hypercube Sampling method. These combinations, together with 38 pre-defined State of Charge, are then integrated to a validated AORFB model developed in COMSOL to compute the performance metrics. With both input parameters and performance metrics, 60 deep neural network (DNN) surrogate models are then trained to approximate the relationship between the 10 physicochemical quantities and 6 performance metrics at each flow rate and current density. Sensitivity scores are then calculated based on the DNN models. Two additional sensitivity analysis tools, i.e., MARS, and SHAP, are also used to cross-validate the sensitivity scores from the DNN. The results demonstrate that 1) the standard potential ranks the first in controlling EE and charging energy, 2) the membrane conductivity is most critical for power loss and EE, and 3) specific area and reaction rate control activation power loss.

25 ENERGY STORAGE↗
DOE OSTI2022

Optimal operational controls for power grid

Power grid operation is facing increased penetration of renewables and distributed energy resources at both transmission and distribution levels. This presents an increased level of randomness and uncertainty for its operational quality, where the challenges are mainly how controls and optimizations can be performed so that the operational quality of the power grid can be maintained. An interesting feature is also that we need to make a good use of uncertainties embedded in the swing equation from the renewable contributions rather than simply trying to minimize the impact of uncertainties. Furthermore, this requires effective solutions on system estimation, control, and optimization. Indeed, in past decades, work has been carried out on the development of advanced controls and optimization for such complicated dynamic systems. Examples are optimal operational control for both performance and stability enhancement considering random renewables penetration, frequency, and active power control for microgrids and parameter estimation for various parts of the power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Single Stage PLL-less Decoupled Active and Reactive Power Control for Weak Grid Interactive Inverters

This paper presents a single stage phase locked loop-less (PLL-less) active and reactive power (PQ) control for single-phase weak grid interactive inverters. The absence of the PLL requirement in the proposed PQ control enhances the stability margin comparing to conventional current control approaches in both the stationary or the synchronous reference frame. Additionally, the proposed control scheme enables direct PQ control with a single loop control structure. This paper demonstrates that the proposed PLL-less PQ control scheme is asymptotically stable if the controller gains are positive. The necessary conditions for supplying the rated active power into the weak grid are derived. The provided analysis shows that certain amount of reactive power injection is necessary to avoid point of common coupling (PCC) voltage collapse when the rated active power is injected into the weak grid. Several case studies are provided to demonstrate the performance of the proposed PLL-less PQ control scheme under weak grid condition.

Khan, Ahmad↗
DOE OSTI2020

A machine learning-based fast frequency response control for a VSC-HVDC system

An HVDC system can realize a very fast frequency response to the disturbed system under a contingency because its active power control is decoupled from the frequency deviation. However, most of existing HVDC frequency control strategies are coupled with system primary frequency control and secondary frequency control. Since the traditional system frequency control is dominated by the thermal generators, the advantage of the fast response of the HVDC system is not made fully used. The development of a frequency response estimation based on a machine learning algorithm provides another approach to improve the frequency response capability of the HVDC system. Different from other frequency deviation tracking strategies, a machine learning based HVDC frequency response control can directly increase the power flow of a HVDC system by estimation of the system generator or load lost. In this paper, a fast frequency response control using a HVDC system for a large power system disturbance based on the multivariate random forest regression (MRFR) algorithm is proposed. The simulation is carried out with an integrated power system model based on the North American interconnections. The simulation results indicate that the proposed MRFR based frequency response control can significantly improve the frequency low point during an event, while stabilizing the frequency in advance.

42 ENGINEERING↗