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At least 145 records · Page 8

All sources NASA NTRS DOE OSTI

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

active power reserve↗

DOE OSTI2021

Enabling Interoperable SCADA Communications for PV Inverters through Embedded Controllers

The percentage integration of photovoltaic (PV) inverters in the field has increased significantly in the past 5 years. Regardless of the size of the PV plants and the inverters (residential vs. commercial), it is becoming crucial that these devices have the capability to communicate with peers (other smart devices) and with components that are at a hierarchy above the inverters (e.g., supervisory control and data acquisition (SCADA) systems, distributed controllers, and data managers). This project aims to develop a standard SCADA software code for inverters’ embedded controllers that will enable interoperability with other components in the system. To achieve this, the code will be developed using two different protocols: Distributed Network Protocol 3 and International Electrotechnical Commission 61850. The developed code is aimed to be deployed in simple embedded controllers. It will be tested in the National Renewable Energy Laboratory’s (NREL’s) Energy Systems Integration Facility. The tested code will then be made available through Triangle MicroWorks’s (TMW’s) software platform. The primary objectives of this project include training the NREL team with TMW’s embedded controller libraries, developing an interoperable communication code for embedded controllers, successfully testing and deploying the code, and demonstrating the newly developed code in a conference.

14 SOLAR ENERGY↗

DOE OSTI2020

Optimal Distribution System Voltage Regulation using State Estimation and DER Grid-Support Functions

Increasing solar energy penetrations may create challenges for distribution system operations because production variability can lead to large voltage deviations or protection system miscoordination. Instituting advanced management systems on distribution systems is one promising method for combating these challenges by intelligently controlling distribution assets to regulate voltage and ensure protection safety margins. While it is generally not the case today, greater deployment of power system sensors and interoperable distributed energy resources (DER)e.g., photovoltaic (PV) inverters, energy storage systems (ESS), electric vehicles (EVs)will enable situational awareness, control, and optimization of distribution systems. In this work, a control system was created which measures power system parameters to estimate the status of a feeder, forecasts the distribution state over a short-term horizon, and issues optimal set point commands to distribution-connected equipment to regulate voltage and protect the system. This two-year project integrated multiple research innovations into a management system designed to safely allow PV penetrations of 50% or greater. The integrated software was demonstrated through extensive real-time (RT) and power hardware-in-the-loop studies and a field demonstration on a live power system with a 684 kVA PV system.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

State-Constrained Grid-Forming Inverter Control for Robust Operation of AC Microgrids

The control of grid-forming inverters has become an important problem to achieve high efficiency, reliability, and resilience of the future power grid. The distributed control that utilizes a sparse communication network has been proposed for AC microgrids, in which each DG only has access to the information of a small number of its neighboring DGs, reducing the computational complexity and improving the resiliency to faults and unknown parameters. However, these approaches have not provided a clear analysis of the coupling between active/reactive power injections and the voltage magnitude/frequency or a distributed updating law with a solid theoretical foundation. Additionally, in the existing approaches, tracking the references from the secondary control may violate operational constraints.

Xu, Ying↗

DOE OSTI2025

Quantum Reinforcement Learning for Volt-VAR Control in Power Distribution Systems

Volt-VAR control (VVC) is crucial in active distribution networks for optimizing voltage profiles and minimizing network losses. While traditional deep reinforcement learning (DRL) algorithms exhibit promise for VVC, they often require extensive computational resources to handle such a high-dimensional problem. As a potential solution, quantum reinforcement learning (QRL) algorithms integrate the computational capabilities of quantum computing into the DRL framework. However, existing QRL algorithms struggle with complex VVC problems due to the limitations of current quantum hardware. To bridge this gap, this paper proposes an innovative QRL algorithm featuring an end-to-end architecture that integrates a classical autoencoder, variational quantum circuits (VQCs), and classical post-processing layers. This design efficiently compresses high-dimensional grid states, enabling VQCs to leverage quantum advantages while producing multiple control device outputs tailored for VVC tasks. Numerical studies on three representative distribution systems verify the effectiveness and scalability of the proposed QRL algorithm, and demonstrate its enhanced performance over classical approaches with only approximately 1% of the parameters. Additionally, the robustness of our developed algorithm is validated through noisy quantum environments.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Predictive and Cooperative Voltage Control with Probabilistic Load and Solar Generation Forecasting

This paper proposes predictive cooperative voltage control method in a power system with high penetration of photovoltaic (PV) units. Cooperative distributed control of the reactive power output of PV inverters is coordinated with operation of voltage regulators (VRs) to maintain system voltages within an appropriate bandwidth. Probabilistic forecasting of the solar power generation and the loads is applied to estimate voltage changes which, in turn, are used to set the VR tap positions for preventing large voltage fluctuations with the lowest risk considering the voltage distribution estimation. The fine tuning of voltage adjustment is achieved by cooperative control of PV inverters to maintain a uniform voltage profile across the system. The proposed method is tested on the modified IEEE 123-node test feeder with high PV penetration using real insolation data and with constant loads replaced by several different load profiles. Simulation results demonstrate the effectiveness of the coordinated approach for voltage control with cooperative PV and predictive VR controls taking into account probabilistic load and solar power forecasts.

Cooperative Control↗

DOE OSTI2020

Comparing Cetane Number Measurement Methods

Cetane number is one of the most important fuel performance metrics for mixing controlled compression-ignition “diesel” engines, quantifying a fuel’s propensity for autoignition when injected into end-of-compression-type temperature and pressure conditions. The historical default and referee method on a Cooperative Fuel Research (CFR) engine configured with indirect fuel injection and variable compression ratio is cetane number (CN) rating. A subject fuel is evaluated against primary reference fuel blends, with heptamethylnonane defining a low-reactivity endpoint of CN = 15 and hexadecane defining a high-reactivity endpoint of CN = 100. While the CN scale covers the range from zero (0) to 100, typical testing is in the range of 30 to 65 CN. Alternatively, several constant-volume combustion chamber (CVCC)-based cetane rating devices have been developed to rate fuels with an equivalent derived cetane number (DCN) or indicated cetane number (ICN). These devices measure ignition delay for fuel injected into a fixed volume of high-temperature and high-pressure air to simulate end-of-compression-type conditions. In this study, a range of novel fuel compounds are evaluated across three CVCC methods: the Ignition Quality Tester (IQT), Fuel Ignition Tester (FIT), and Advanced Fuel Ignition Delay Analyzer (AFIDA). Resulting DCNs and ICNs are compared for fuels within the normal diesel fuel range of reactivity, as well as very high (~100) and very low DCNs/ICNs (~5). Distinct differences between results from various devices are discussed. This is important to consider because some new, high-efficiency advanced compression-ignition (CI) engine combustion strategies operate with more kinetically controlled distributed combustion as opposed to mixing controlled diffusion flames. These advanced combustion strategies may benefit from new fuel chemistries, but current rating methods of CN, DCN, and ICN may not fully describe their performance. In addition, recent evidence suggests ignition delay in modern on-road diesel engines with high-pressure common rail fuel injection systems may no longer directly correlate to traditional CN fuel ratings. Simulated end-of-compression conditions are compared for CN, DCN, and ICN and discussed in the context of modern diesel engines to provide additional insight. Results highlight the potential need for revised and/or multiple fuel test conditions to measure fuel performance for advanced CI strategies.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Hierarchical Model-Free Transactive Control of Building Loads to Support Grid Services

Residential buildings consume 4.4 quads of electricity annually, approximately 37% of the total electricity consumption in the United States. This represents a vast resource that can be used for demand management and other ancillary services. This project aims to develop a robust, scalable hierarchical transactional control mechanism incorporating elements of model-free control (MFC) and game theory to harness buildings to provide ancillary services to the grid. This approach is being taken to address the challenges of incorporating traditional transactional control schemes into existing buildings. The challenges include small individual building sizes requiring aggregation of many buildings, unpredictable energy usage that makes model identification difficult, and satisfying the sensitive occupant comfort constraints. In the proposed approach, by separating the control mechanism into two layers above and below the load aggregator, MFC can be used below the aggregator to modulate flexible building loads in response to pricing signals with guaranteed performance. This allows the burden of identifying an accurate model of the system to be shifted to the above-aggregator layer, where fluctuations in individual building usage have less impact on predicted building system behavior. Game theory concepts can then be used to determine pricing curves and control signals among regional aggregators. Managing this control in a game-theoretic approach will allow us to build in financial incentives that increase customer engagement. Additionally, the usage of MFC necessitates less burdensome computational and communication requirements, thus, it is easily deployable on small, embedded devices. In a broader sense, developing a strategy capable of effectively incorporating residential and small commercial buildings will allow greater throughput of existing and emerging grid services in addition to future transactive energy grid management methods. Using MFC within a hierarchical control architecture will allow the shifting of existing forecasting challenges to an aggregate level, where dynamics are slower and more predictable. This will enable a smooth interface between the grid services requests of utilities and the reliable control required by participating buildings. MFC, which supports distributed control architecture, permits a scalable solution that can be deployed to neighborhood-size systems as well as individual buildings. This project focuses on three objectives: (1) developing the mathematical framework, algorithm toolkit, and software toolset of the two-layer transactive control testbed; (2) developing a scalable solution for application over many residential and small-size commercial buildings with sparse distributed communication; and (3) field testing and implementation on hardware of the control strategies developed in the previous two objectives. The research and development activities are focused and designed to be impactful within the relevant 2025 targets timeframe. An open-source control framework for exploiting variability and dispatchability of building loads will be delivered as the outcome of the project. This capability enables greater participation of loads in electricity markets and ancillary services that are both useful for the utility and financially beneficial for building owners.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Specific Ion and Electric Field Controlled Diverse Ion Distribution and Electroosmotic Transport in a Polyelectrolyte Brush Grafted Nanochannel

Controlling ion distribution inside a charged nanochannel is central to using such channels in diverse applications. Here, we show the possibility of using a charged polyelectrolyte (PE) brush grafted nanochannel for triggering diverse nanoscopic ion distribution and nanofluidic electroosmotic transport by controlling the valence and size of the counterions (that screen the charges of the PE brushes) and the strength of an externally applied axial electric field. We atomistically simulate separate cases of fully charged Polyacrylic acid (PAA) brush functionalized nanochannels with Na + , Cs + , Ca 2+ , Ba 2+ , and Y 3+ counterions screening the PE charges. Four key findings emerge from our simulations. First, we find that the counterions with a greater valence and a smaller size prefer to remain localized inside the brush layer. Second, for the case where there is an added chloride salt with the same cation (as the screening counterions), there are more coions (Cl - ions) in the brush-free bulk than counterions (for counterions Na + , Ca 2+ , Ba 2+ , Y 3+ ): this is a manifestation of the overscreening (OS) of the PE brush layer. Contrastingly, the number of Cs + ions remain higher than the Cl - ions inside the brush-free bulk, ensuring that there is no OS effect for this case. Third, large applied electric field enables a few Na + , Cs + , and Ba 2+ counterions to leave the brush layer and to go to the bulk: this makes the OS of the PE brush layer disappear for the cases of PE brushes being screened by the Na+ and Ba 2+ ions. On the other hand, no such electric-field-mediated disappearance of OS is observed for the cases of Ca 2+ and Y 3+ screening counterions; we attribute this to the firm attachment of these counterions to the negatively charged monomers. Free energy associated to a counterion binding to a PE chain corroborates this diversity in the counterion-specific response to the applied electric field. Lastly, we demonstrate that such diverse ion distributions, along with specific electric-field-strength-dependent ion properties, lead to (1) EOS transport in nanochannels grafted with PAA brushes screened with Cs + ions to be always counterion dominated, (2) EOS transport in nanochannels grafted with PAA brushes screened with Ca 2+ and Y 3+ ions to be always coion dominated, and (3) EOS transport in nanochannels grafted with PAA brushes screened with Na + and Ba 2+ ions to be coion dominated for smaller electric fields and counterion dominated for larger electric fields.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Environmental control and air distribution system and method of using the same

An indoor environmental control and air distribution system for a building includes: an air handling unit; a manifold connected to the air handling unit having a chamber formed by a plurality of walls and a plurality of orifices formed through at least one of the walls; air distribution conduits each independently having a first end connected to the orifices of the manifold and a second end extending out from the manifold into different zones throughout the building; and an airflow modulating device having one or more airflow regulating dampers independently configured to move into at least two positions in which each position provides a different percentage of total air volume to each air distribution conduit. A method of using the indoor environmental control and air distribution system is also included.

Poerschke, Andrew↗

DOE OSTI2024

Environmental control and air distribution system and method of using the same

Poerschke, Andrew↗

DOE OSTI2022

A hybrid architecture for volt-var control in active distribution grids

Modern active distribution grids are characterized by the increasing penetration of distributed energy resources (DERs). The proper coordination and scheduling of a large numbers of these small-scale and spatially distributed DERs is necessary, and warrants the use of novel distributed approaches. In this paper, we propose a hybrid volt-var control architecture for the distribution grid, which leverages existing centralized and local approaches to planning, decision making, and control, and augments it with distributed optimization and distributed control for DER management. First, we propose a convex model to describe the power physics of distribution grids of meshed topology and unbalanced structure, based on current injection and McCormick Envelopes. Second, we employ the distributed proximal atomic coordination (PAC) algorithm to coordinate DERs to provide voltage support. We implement volt-var optimization by optimally coordinating DERs including PV smart inverters and demand response. We present results using the IEEE-34 bus network, using real data from a distribution feeder in Hawaii, to model load and PV generation. Different levels of DER penetration and objective functions are simulated. Finally, our results show the need for the coordination of DERs to improve voltage profiles, even in networks with existing voltage control devices. Further, we show the need for flexible reactive power capabilities to achieve desired grid performance.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Integrated Coordination of Voltage Regulators with Distributed Cooperative Inverter Control in Systems with High Penetration of DGs

Increased penetration of distributed generation (DG) in recent years has a significant impact on operation and control of power systems. One of the main issues with the increased penetration of DGs is the effect of voltage rise. This paper investigates integration of coordinated voltage regulator (VRs) operation with cooperative distributed inverter control to maintain voltages in appropriate limits in distribution systems with high penetration of DGs. The coordinated VR control is used to optimize the settings of multiple nested VRs while the cooperative distributed control optimally dispatches reactive power of the DGs. The integrated approach is compared with the case without coordinated VRs control (uncoordinated local control). Simulation results illustrate the effective performance of the proposed method to achieve unified voltage profile.

coordinated voltage regulator control↗

DOE OSTI2025

Topology-Aware Reinforcement Learning for Voltage Control: Centralized and Decentralized Strategies

Volt-VAR control (VVC) methods based on deep reinforcement learning (DRL) can effectively control distribution grid voltage and minimize power loss by implementing corrective and preventive control measures on the reactive power output of inverter-based distributed energy resources (DERs). However, model-free DRL-based VVC approaches usually cannot capture the important topological feature of the power system since they use a fully-connected network (FCN) to deliver the action. Therefore, this paper proposes a graph convolutional network (GCN)-based DRL approach that can employ the topological information of the network to take better control action for regulating the voltage. Our implementation allows for both centralized and decentralized configurations, utilizing a single agent and multiple agents respectively. Although the centralized GCN-based DRL approach has its advantages of minimizing voltage fluctuation and power loss, it is not suitable for large scale power systems due to its challenges in terms of scalability, computation speed and potential single points of failure. Therefore, these problems can be resolved using the decentralized GCN-based DRL approach. Moreover, to ensure the safe operation of the model, our proposed approach incorporates an exponential barrier function while formulating the reward function for each agent. To validate performance of the proposed approaches, the proposed model is tested on modified IEEE test systems and the performances are measured in terms on voltage fluctuation reduction, minimization of power loss and computational speed. Finally, the results show that the proposed topology-aware approach outperforms the FCN-based DRL approach in terms of reducing voltage fluctuation and minimizing power loss of the network. Moreover, it is shown that the decentralized GCN-based DRL has faster computational speed than other approaches.

42 ENGINEERING↗

DOE OSTI2024

Distributed Automatic Generation Control Considering DPV Using T&D Dynamic Co-Simulation

The increasing adoption of distributed energy resources (DERs) over the last decade warrants a reconsideration of control of generation resources. This paper proposes a distributed Automatic Generation Control (AGC) using transmission-and-distribution (T&D) dynamic co-simulation framework for the efficient DPV frequency regulation services. The co-simulation framework allows AGC units to exchange the information for distributed AGC, based on their adopted communication network topology. As a result, a cost-effective automatic generation control is achieved with DPV and conventional generators. The proposed distributed AGC is based on the gossip algorithm in which the neighboring AGC units share the relevant local information with each other and updates their share of AGC regulation signal. Distributed photovoltaics (DPV) unit contribute to AGC response based on their headroom capacity via DER aggregators. The algorithm is tested on IEEE-14 bus transmission system under conditions of generation failure and random load variation to observe effective frequency regulations service offered by DPVs and other AGC units. The study shows that DPV can effectively participate in AGC with the proposed distributed control framework.

automatic generation control↗

DOE OSTI2025

Reversible control over the distribution of chemical inhomogeneities in multiferroic BiFeO3

Abstract Despite the appeal of flawless order, semiconductor technology has demonstrated that implanting inhomogeneities into single-crystalline materials is pivotal for modern electronics. However, the influence of the local arrangement of chemical inhomogeneities on the material’s functionalities is underexplored. In this work, we control the distribution of chemical inhomogeneities in La 3+ -substituted ferroelectric BiFeO 3 thin films. By means of a stress- and composition-driven phase transition, we trigger the formation of a lattice of La 3+ -rich and La 3+ -poor layers. This ordering correlates with the emergence of an antipolar phase. An electric field restores the original ferroelectric phase and re-randomizes the distribution of the La 3+ inhomogeneities. Leveraging these insights, we tune the polar/antipolar phase coexistence to set the net polarization of La 0.15 Bi 0.85 FeO 3 to any desired value between its saturation limits. Finally, we control the net polarization response in device-compliant capacitor heterostructures to show that inhomogeneity-distribution control is a valuable tool in the design of functional oxide electronics.

Science & Technology - Other Topics↗

DOE OSTI2024

Trajectory Shaper: A Solution for Disrupted Cooperative Adaptive Cruise Control

Cooperative adaptive cruise control (CACC) can effectively reduce energy consumption, alleviate traffic congestion, and enhance safety. However, communication-related constraints and uncooperative vehicle users can disrupt CACC during real-world operations, significantly undermining the putative benefits of CACC. To alleviate the negative impacts of disrupted CACC, this study develops the trajectory shaper (TS) methods as backup solutions for two scenarios: (i) communication between vehicles is infeasible, and vehicles execute adaptive cruise control (ACC) using local sensor measurements; (ii) follower vehicles reject forming a cooperative platoon and execute their local distributed controllers using the information attained via communication. When communication is infeasible, a distributed TS is devised on each vehicle to modify the sensor measurements, enabling safe and efficient ACC operations. When communication is available but uncooperative agents are involved, the lead vehicle of the platoon executes a centralized TS to modify the information shared with uncooperative agents, achieving optimal platoon-level performance. The centralized and distributed TSs are implemented based on the model predictive control algorithms to yield optimal modifications on input information. Robustness is also factored to tackle model uncertainties during TS operations to ensure safety and efficiency. Numerical experiments validate the control performance of the proposed TSs.

Zhou, Anye [ORNL] (ORCID:0000000301455579)↗