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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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26 records · Page 2
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DOE OSTI2023

Operando Temperature Measurements of Photovoltaic Laser Power Converter Devices Under Continuous High-Intensity Illumination

Photovoltaic devices that operate under extremely high irradiances, such as laser power converters (LPCs), may also operate at elevated temperatures, even under active cooling, as the result of large temperature gradients. We demonstrate the operation of GaAs LPC devices under orders of magnitude of irradiances up to 150 W/cm 2 in a monochromatic laser simulator with an active cooling stage. The steady-state open-circuit voltage (V OC ) as a function of irradiance is known to droop at high irradiance as the result of junction heating, but the junction temperature can be difficult to measure by conventional methods. Fast, transient V OC measurements under these extreme operating conditions are used here to determine the junction temperature. Empirical V OC temperature coefficients of the devices at each irradiance of interest are determined and used as an integral part of this technique. We show that the thermal design of different LPC devices strongly affects the operating temperature of the junctions. Knowledge of the operating temperature can be a strong tool for understanding the nature of loss mechanisms and improving the design for the operation of photovoltaic LPCs at high irradiances. Furthermore, this technique can be used for laboratory devices during initial design as well as to characterize mass-produced and packaged devices for quality control.

14 SOLAR ENERGY↗
DOE OSTI2021

Event-Driven Predictive Approach for Real-Time Volt/VAR control with CVR in solar PV rich Active Distribution Network

The focus of this paper is on analyzing the impact of conservation voltage reduction in the presence of active devices such as solar photovoltaic (PV) and developing controls that leverage these distributed energy resources. An event-driven predictive approach for real-time volt/volt-ampere reactive (VAR) optimization, along with local two-level adaptive volt/VAR droop-based control algorithm for advanced distribution management systems, is introduced. The methodology covers aggregated and autonomous controls under different timescale operations, including the impact and effect of unpredicted events such as cloud transients on PV power production. In addition, the control schemes include the uncertainties in PV power generation and load power demand. The proposed methodology is validated in a real-time framework using the real-time digital simulator platform through co-simulation with models based on Python and OpenDSS (Open Distribution System Simulator). The developed methodology is tested on the modified IEEE 123-feeder test system. The results reveal that the proposed methodology works well in the presence of high penetrations of PV power, produces significant energy savings, and mitigates over-/undervoltage problems.

14 SOLAR ENERGY↗
DOE OSTI2023

Deep reinforcement learning assisted co-optimization of Volt-VAR grid service in distribution networks

With the increasing penetration of distributed energy resources in distribution networks, Volt-VAR control and optimization (VVC/VVO) have become very important to ensure an acceptable quality of service to all customers. System operators can rely on slow-responding utility devices, including capacitor banks and on-load tap changing transformers, along with fast-responding battery and photovoltaic (PV) inverters for the VVC/VVO implementation. Because of variations in response time of these two classes of devices, and different control actions (discrete versus continuous), coordinated and optimal scheduling and operation have become of utmost importance. Here, this paper develops a look-ahead deep reinforcement learning (DRL)-based multi-objective VVO technique to improve the voltage profile of active distribution networks, decrease network and inverter power loss, and save the operational cost of the grid. It proposes a deep deterministic policy gradient (DDPG)-based approach to schedule the optimal reactive and/or active power set-points of fast-responding inverters, and a deep Q-network (DQN)-based DRL agent to schedule the discrete decisions variables of slow-responding assets. The reactive power output of PV and battery smart inverters are scheduled at 30-minute intervals and the capacitors’ commitment status is scheduled with several hour intervals. The proposed framework is validated on the modified IEEE 34-bus and 123-bus test cases with embedded PV and PV-plus-storage. To validate the efficacy of the proposed VVO, it is compared with several scenarios, including the base case without VVO, localized droop control of DERs, DDPG-only, and twin delayed DDPG (TD3) agent-based DRL techniques. The results justify the superior performance of the proposed method to improve the voltage profile, reduce network power loss, and minimize the look-ahead grid operational cost while minimizing the undesirable power losses in inverters as a result of power factor adjustments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Frequency Restoration of Grid-Forming Inverters in Pulse Load and Plug-in Events

This article presents a restoration technique using grid-forming inverters in an islanded microgrid during pulse load and plug-in events. In a microgrid powered by droop-controlled inverters, frequency is the variable that is accessible to all inverters for adjusting their power contributions. The problem is that the microgrid frequency deviates from its nominal value after a load change. The presented method enables the inverters to restore the frequency to the nominal value and regulate their bus voltage amplitudes. The frequency and voltage restorations are performed without communication while achieving the desired power-sharing between grid-forming inverters. The restoration is activated after detecting any active or reactive power changes. In this article, the dynamic model of an inverter equipped with enabled restoration paths is developed to verify the stability of the inverter controller under various conditions. The frequency and voltage restorations are examined during pulse load and plug-in events. Furthermore, this study is performed using a laboratory-scale islanded microgrid powered by two 208 V, 60 Hz, 5 kVA inverters.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Impedance Analysis and PHIL Demonstration of Reactive Power Oscillations in a Wind Power Plant Using a 4-MW Wind Turbine

This paper presents impedance-based analysis, mitigation, and power-hardware-in-the-loop (PHIL) demonstration of reactive power oscillations in a wind power plant using a 4-MW Type III wind turbine drivetrain. Because such low-frequency oscillations result from interactions among slower control loops of wind turbines regulating phasor quantities—active and reactive power output of the wind turbine and the magnitude of voltages at the point of interconnection (POI)—a new type of admittance is defined in terms of phasor quantities for their analysis. The so-called power-domain admittance of a wind turbine is defined as the transfer function from the frequency and magnitude of voltages at the POI to the active and reactive power output of the turbine. The power-domain admittance responses of the 4-MW wind turbine are measured using a 7-MVA grid simulator to identify the source of the reactive power oscillations. Power-domain impedance analysis and PHIL experiments are performed to explain how a resonant mode manifests as turbine-to-turbine and plant-to-grid reactive power oscillations. It is discovered that weaker grids exhibiting high inductive impedance mitigate oscillations in the reactive power output of wind power plants; however, a higher grid impedance does not help in damping turbine-to-turbine reactive power oscillations. This paper presents a simple droop-based solution to eliminate both turbine-to-turbine and plant-to-grid reactive power oscillations.

17 WIND ENERGY↗
DOE OSTI2022

Soft Actor Critic Based Volt-VAR Co-optimization in Active Distribution Grids

Modern distribution networks are undergoing several technical challenges, such as voltage fluctuations, because of high penetration of distributed energy resources (DERs). This paper proposes a deep reinforcement learning (DRL)-based Volt VAR co-optimization technique for reducing voltage fluctuations as well as power loss under high penetration of DERs. In addition, the proposed approach minimizes the operational cost of the grid. A stochastic policy optimization based soft actor critic (SAC) agent is proposed to configure the optimal set-points of the reactive power outputs of the inverters. The performance of the proposed model is verified on the modified IEEE 34- and 123-bus systems and compared with a base case scenario with no reactive supply by inverters, and a local droop control approach. The results demonstrate that the proposed framework outperforms the conventional droop control method in improving the voltage profile, minimizing the network power loss, and reducing grid operational cost.

—Distribution grids, deep reinforcement learning, ↗
DOE OSTI2024

Multi-agent voltage control in distribution systems using GAN-DRL-based approach

Active distribution grids can experience voltage fluctuations and violations due to the high penetration of variable distributed energy resources (DERs). These problems might occur because of the uncertain and variable generation natures of these resources, especially solar photovoltaic resources, during panel shadowing scenarios. Volt-VAR control (VVC) is an efficient method that controls the reactive power set-points of the inverters to regulate the voltage of distribution grids. Although several VVC approaches have been proposed recently, the performance of these approaches degrades significantly if behind-the-meter solar generation data are unobservable/missing. Therefore, it is necessary to impute missing/unobservable PV data accurately to be utilized in VVC approaches. Further, this paper proposes a model-free, data-driven, centrally trained, and decentrally executed multi-agent deep reinforcement learning-based VVC architecture to regulate the voltage of distribution networks. A generative adversarial network (GAN) is incorporated to impute the unobservable PV data accurately, which improves the performance of the proposed control architecture. The proposed multi-agent-soft-actor–critic algorithm (MASAC)-based VVC technique utilizes the actual PV dataset as well as the imputed dataset from the GAN framework to learn the optimal coordinated control policy for controlling the optimal reactive power set-points of PV inverters. The effectiveness of the proposed approach is analyzed on a modified IEEE 34-bus test case with added PV inverters. The results are compared and analyzed with a base case model with no VVC and VVC with a local droop control approach, genetic algorithm optimization, and a centralized soft actor–critic-based approach. Moreover, the performance of the proposed approach is compared with that of a multi-agent VVC framework without using the PV generation data and load information as the system state. The results illustrate that the proposed method with more state input improves the voltage profile and reduces the power loss of the network across various loading and PV generation scenarios.

14 SOLAR ENERGY↗
DOE OSTI2025

A Secondary Control Framework for Microgrid Interoperability With Vendor-Agnostic Grid-Forming Units: Design, Implementation, and Demonstration via Large-Scale Hardware Setup

The reliable operation of islanded microgrids increasingly depends on secondary controls that restore voltage and frequency to nominal values and ensure accurate active and reactive power sharing. Centralized secondary control architectures achieve high accuracy through global coordination at the cost of single-point failures and limited scalability compared with decentralized/distributed approaches. But a critical gap remains in addressing the interoperability and vendor-agnostic operation of secondary controls in real-world microgrids where heterogeneous diesel generator(s) and grid-forming (GFM) inverter(s) from multiple manufacturers always coexist. Practical and vendor-agnostic interoperability guidelines for the secondary control architecture of microgrids with multiple GFM units have not yet been developed; therefore, this paper proposes an interoperable and vendor-agnostic secondary control framework that operates seamlessly across GFM units from different vendors without relying on proprietary controls and protocols, hardware, or lock-ins. The framework leverages existing communication infrastructures (e.g., Modbus TCP/IP) to enable cost-effective deployment while addressing practical challenges, such as packet loss and quantization errors. Mitigation strategies-including data averaging, situational event-triggered control, and finite-iteration execution-are introduced to enhance reliability under real-world conditions. A generalized modeling and design framework is also presented, supported by robustness analysis to demonstrate independence from vendor-specific implementations. The proposed framework is validated through a large-scale hardware demonstration using a 3-$\phi$, 480-V, 60-Hz, 713-kVA laboratory hardware microgrid involving a heterogeneous diesel generator and multiple GFM inverters, showcasing its effectiveness in achieving stable voltage and frequency restoration and accurate power sharing under practical constraints. The results highlight the framework's potential as a scalable and practical solution for next-generation microgrids requiring openness, standard framework, and interoperability.

24 POWER TRANSMISSION AND DISTRIBUTION↗