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DOE OSTI2025

Model-Free Control of Grid-Interactive Efficient Buildings Under Communication Time Delays

Grid-interactive efficient buildings (GEBs) have recently been used to enhance the reliability and stability of the electric grid through demand response (DR) programs. However, most existing DR control strategies require accurate modeling of the various building thermostatically controlled loads (TCLs) and are computationally expensive. To address these challenges, a model-free control (MFC)-based strategy has recently been introduced for coordinating and controlling GEBs. MFC is a data-enabled control strategy that is computationally efficient and does not require the analytical models of the various building equipment. In this paper, we numerically investigate the impact of communication time delays on the performance of MFC in maintaining the TCLs' temperatures within the desired comfort levels while meeting the assigned power allocation constraint.

Telsang, Bhagyashri [University of Tennessee, Knox↗

DOE OSTI2025

Reinforcement Learning-Based Secondary Control Strategy for Voltage and Frequency Regulation in Islanded Inverter-Based Microgrids

This paper presents a reinforcement learning (RL) approach for secondary voltage and frequency control in islanded inverter-based microgrids. The proposed control strategy aims to restore voltage and frequency deviations caused by the primary droop control while ensuring proper power sharing between distributed generators. The RL agent is designed to provide correction signals to the primary control, considering communication delays and system constraints. The effectiveness of the proposed control strategy is validated through simulation results in MATLAB/Simulink environment, demonstrating superior performance in maintaining voltage and frequency within the nominal values.

Rodriguez Martinez, Omar Felipe [University of Pue↗

DOE OSTI2023

Model Predictive Voltage Control of Large-Scale PV or Hybrid PV-BESS Plants

Increased penetration level of inverter-based resources (IBRs) and renewable energy in the power grid has called for more requirements from the control of IBRs. In this paper, the voltage-reactive power control of a photovoltaic (PV) power plant or hybrid PV-battery energy storage system (BESS) plant connected to a bulk power grid, whilst meeting the grid requirements, is studied. In this paper, a continuous-set model predictive control (MPC) formulation is proposed for voltage-reactive power control. For the same, an aggregated dynamic PV plant model is developed based on the recursive system equivalencing method. The formulation is then implemented in PSCAD simulation on a 125 MW PV plant or hybrid PV-BESS plant. The MPC implementation achieves 10.13% voltage improvement based on PSCAD simulation results in a balanced fault case study. Simulation results further demonstrate that MPC provides improved voltage support over the conventional proportional-integral (PI) controller post-fault occurrence.

Abu rub, Omar↗

DOE OSTI2022

Virtual Synchronous Generator Control of Multi-port Autonomous Reconfigurable Solar Plants (MARS)

Multi-port autonomous reconfigurable solar power plant (MARS) is an integrated concept for integration of solar photovoltaic (PV) and energy storage systems (ESS) to transmission alternating current (ac) grid and high-voltage direct current (HVdc) links. The integrated development incorporates advanced control methods to provide enhanced grid ancillary services such as fast frequency responses and dynamic voltage support. In this paper, the virtual synchronous generator (VSG) control algorithm of MARS is discussed. The proposed VSG-based control enables enhanced synthetic inertial response and flexible frequency response characteristics of hybrid PV and ESS power plants in accordance with grid ancillary service requirements. Multi-port power electronics interface allows controlled emergency power support from MARS to local transmission ac grid and remote grid through the HVdc link. The performance of VSG control is validated using a reduced-order model of MARS in Simulink. Methods for estimating grid ancillary service capabilities of MARS are also discussed.

Pan, Jiuping↗

DOE OSTI2025

Spike-Free Adaptive Sliding Mode Control: Application to Permanent Magnet Synchronous Motors

A new methodology for adaptive sliding mode control (ASMC) has been widely used to improve the control performance in various systems. This method exhibits several advantages, including low sliding mode control (SMC) chattering, no knowledge of the system disturbance bound, and no overestimation of the control gain. Despite its advantages, this method can be hampered by the spike phenomenon, slow control gain convergence, and difficulty in achieving optimal performance under varying disturbances. Consequently, this article proposes a spike-free ASMC method with a disturbance observer (DOB) to address these problems. Previous ASMC methods have been analyzed via simulations to verify the aforementioned problems. Here, this analysis highlights the need for disturbance compensation and improvements in the SMC gain adaptation law. Therefore, a DOB is designed to mitigate the spike phenomenon by compensating for disturbances. Subsequently, an SMC gain adaptation law based on disturbance error estimation is designed to eliminate the spike phenomenon completely. The proposed adaptation law makes the SMC gain to converge to a slightly higher value than the disturbance estimation error. Consequently, the proposed method not only eliminates the spike phenomenon, but also ensures optimal performance under varying disturbances. The performance of the proposed method is experimen tally validated through a comparative study.

42 ENGINEERING↗

DOE OSTI2022

Multiport Control with Partial Power Processing in Solid-State Transformer for PV, Storage, and Fast-Charging Electric Vehicle Integration

This article proposes a multiport control method to enable partial power processing (PPP) in a medium-voltage (MV) multiport solid-state transformer (SST). MV multiport SSTs are promising in integrating low-voltage DC sources or loads such as solar photovoltaic, energy storage, and electric vehicles into smart grids without bulky line-frequency transformers. Compared to voltage-source SST, current-source (CS) SST features single-stage isolated bidirectional AC/AC, AC/DC, or DC/DC conversion using an inductive DC link. For a multiport CS SST, it is revealed in this article that the PPP capability can be enabled through the proposed control without extra hardware, different from the case of voltage-source converters where special hardware architecture is required for the PPP. With the PPP, most power exchange between LV ports is processed by only a fraction of the entire conversion stage, leading to reduced DC-link current, volume, loss, and improved efficiency. The proposed multiport PPP control scheme is analyzed to verify the advantages across a wide voltage and power range against conventional full power processing (FPP) multiport control, using the soft-switching solid-state transformer (S4T) with reduced conduction loss as an example. Comparative experimental results based on a SiC three-port S4T prototype verify the effectiveness of the proposed PPP scheme against the FPP scheme under both steady state and dynamic conditions. Here, the DC-link current reduction is measured to be more than 36%. Significantly, the proposed multiport PPP control scheme is generic and applicable to any hard-switching or soft-switching CS SSTs without extra hardware.

14 SOLAR ENERGY↗

DOE OSTI2020

Automated Control of Transactive HVACs in Energy Distribution Systems

Heating, Ventilation, and Air Conditioning (HVAC) systems contribute significantly to a building’s energy consumption. In the recent years, there is an increased interest in developing transactive approaches which could enable automated and flexible scheduling of HVAC systems based on the customer demand and the electricity prices decided by the suppliers. Flexible and automated scheduling of the HVAC systems make it a prime source for participation in residential demand response or transactive energy systems. Therefore, it is of significant interest to identify an optimal strategy to control the HVAC systems. Here, reducing the energy cost while keeping the comfort level acceptable to the users, we argue that such a control strategy should consider both the energy cost and user comfort simultaneously. Accordingly, we develop the control strategy through the solution of an optimization problem that balances between the energy cost and consumer’s dissatisfaction. This optimization enables us to solve a decision-making problem through first price prediction and then choosing HVAC temperature settings throughout the day based on the predicted price, history of the price and HVAC settings, and outside temperature. More specifically, we formulate the control design as a Markov decision process (MDP) using deep neural networks and use Deep Deterministic Policy Gradients (DDPG)-based deep reinforcement learning algorithm to find the optimal control strategy for HVAC systems that balances between electricity cost and user comfort.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Data-Driven Multi-agent Deep Reinforcement Learning for Distribution System Decentralized Voltage Control with High Penetration of PVs

This paper proposes a novel model-free/data-driven centralized training and decentralized execution multi-agent deep reinforcement learning (MADRL) framework for distribution system voltage control with high penetration of PVs. The proposed MADRL can coordinate both the real and reactive power control of PVs with existing static var compensators and battery storage systems. Unlike the existing DRL-based voltage control methods, our proposed method does not rely on a system model during both the training and execution stages. This is achieved by developing a new interaction scheme between the surrogate modeling of the original system and the multi-agent soft actor critic (MASAC) MADRL algorithm. In particular, the sparse pseudo-Gaussian process with a few-shots of measurements is utilized to construct the surrogate model of the original environment, i.e., power flow model. This is a data-driven process and no model parameters are needed. Furthermore, the MASAC enabled MADRL allows to achieve better scalability by dividing the original system into different voltage control regions with the aid of real and reactive power sensitivities to voltage, where each region is treated as an agent. This also serves as the foundation for the centralized training and decentralized execution, thus significantly reducing the communication requirements as only local measurements are required for control. Comparative results with other alternatives on the IEEE 123-nodes and 342-nodes systems demonstrate the superiority of the proposed method.

14 SOLAR ENERGY↗

DOE OSTI2024

Singular Perturbation-Based Large-Signal Order Reduction of Microgrids for Stability and Accuracy Synthesis With Control

The increasing penetration of distributed energy resources (DERs) highlights the growing importance of microgrids (MGs) in enhancing power system reliability. Employing electromagnetic transient (EMT) analysis in MGs becomes crucial for controlling the rapid transients. However, this requires an accurate but high-order model of power electronics and their underlying control loops, complexifying the stability analysis from the viewpoint of a higher control level. To overcome these challenges, this paper proposes a large-signal order reduction (LSOR) method for MGs with considerations of external control inputs and the detailed dynamics of underlying control levels based on singular perturbation theory (SPT). Specially, we innovatively proposed and strictly proved a general stability and accuracy assessment theorem that allows us to analyze the dynamic stability of a full-order nonlinear system by only leveraging our derived reduced-order model (ROM) and boundary layer model (BLM). Furthermore, this theorem furnishes a set of conditions that determine the accuracy of the developed ROM. Lastly, by embedding such a theorem into the SPT, we propose a novel LSOR approach with guaranteed accuracy and stability analysis equivalence. Case studies are conducted on MG systems to show the effectiveness of the proposed approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Microgrid Service Restoration Incorporating Unmonitored Automatic Voltage Controllers and Net Metered Loads

Islanded microgrids may experience voltage and frequency instability due to uncontrolled state changes of voltage regulation devices and inaccurate demand forecasts. Uncontrolled state changes can occur if optimal microgrid restoration and dispatch algorithms, used for generating control commands for distributed energy resources, do not incorporate the behavior of automatic controllers of voltage regulation devices. Inaccurate demand forecasts may be encountered since post-outage demand of behind-the-meter net metered (NM) loads can vary significantly from their historical NM profiles. Here, this paper proposes an optimization formulation which allows optimal control of voltage regulators and capacitor banks without remote control and communication capabilities. A generalized demand model for NM loads is proposed which incorporates the cold load pickup phenomenon and their time varying post-outage demand in accordance with the IEEE 1547 standard. The time dependent optimal control formulation and the NM demand model are integrated in a sequential microgrid restoration algorithm by linearization of the involved logic propositions. A detailed case study on the unbalanced IEEE 123-node test system in OpenDSS validates the effectiveness of the proposed approach.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2023

Transcriptional Control of hgcAB by an ArsR-Like Regulator in Pseudodesulfovibrio mercurii ND132

The hgcAB gene pair encodes mercury (Hg) methylation capability in a diverse group of microorganisms, but its evolution and transcriptional regulation remain unknown. Working from the possibility that the evolutionary function of HgcAB may not be Hg methylation, we test a possible link to arsenic resistance. Using model Hg methylator Pseudodesulfovibrio mercurii ND132, we evaluated transcriptional control of hgcAB by a putative ArsR encoded upstream and cotranscribed with hgcAB. This regulator shares homology with ArsR repressors of arsenic resistance and S-adenosylhomocysteine (SAH)- responsive regulators of methionine biosynthesis but is distinct from other ArsR/SahR proteins in P. mercurii. Using quantitative PCR (qPCR) and RNA sequencing (RNA-seq) transcriptome analyses, we confirmed this ArsR regulates hgcAB transcription and is responsive to arsenic and SAH. Additionally, RNA-seq indicated a possible link between hgcAB activity and arsenic transformations, with significant upregulation of other ArsRregulated arsenic resistance operons alongside hgcAB. Interestingly, wild-type ND132 was less sensitive to As(V) (but not As(III)) than an hgcAB knockout strain, supporting the idea that hgcAB may be linked to arsenic resistance. Arsenic significantly impacted rates of Hg methylation by ND132; however, responses varied with culture conditions. Differences in growth and metabolic activity did not account for arsenic impacts on methylation. While arsenic significantly increased hgcAB expression, hgcAB gene and transcript abundance was not a good predictor of Hg methylation rates. Taken together, these results support the idea that Hg and As cycling are linked in P. mercurii ND132. Our results may hold clues to the evolution of hgcAB and the controls on Hg methylation in nature. This work reveals a link between microbial mercury methylation and arsenic resistance and may hold clues to the evolution of mercury methylation genes (hgcAB). Microbes with hgcAB produce methylmercury, a strong neurotoxin that readily accumulates in the food web. This study addresses a critical gap in our understanding about the environmental factors that control hgcAB expression. We show that hgcAB expression is controlled by an ArsR-like regulator responsive to both arsenic and S-adenosylhomocysteine in our model organism, Pseudodesulfovibrio mercurii ND132. Exposure to arsenic also significantly impacted Pseudodesulfovibrio mercurii ND132 mercury methylation rates. However, expression of hgcAB was not always a good predictor of Hg methylation rates, highlighting the roles of Hg bioavailability and other biochemical mechanisms in methylmercury production. This study improves our understanding of the controls on hgcAB expression, which is needed to better predict environmental methylmercury production

RNA-seq↗

DOE OSTI2025

A molecular conveyor belt-associated protein controls the rotational direction of the bacterial type 9 secretion system

ABSTRACT Many bacteria utilize the type 9 secretion system (T9SS) for gliding motility, surface colonization, and pathogenesis. This dual-function motor supports both gliding motility and protein secretion, where rotation of the T9SS plays a central role. Fueled by the energy of the stored proton motive force and transmitted through the torque of membrane-anchored stator units, the rotary T9SS propels an adhesin-coated conveyor belt along the bacterial outer membrane like a molecular snowmobile, thereby enabling gliding motion. However, the mechanisms controlling the rotational direction and gliding motility of T9SS remain elusive. Shedding light on this mechanism, we find that in the gliding bacteriumFlavobacterium johnsoniae, deletion of the C-terminus of the conveyor belt-associated protein GldJ controls and, in fact, reverses the rotational direction of T9SS from counterclockwise (CCW) to clockwise (CW). This suggests that the interface between the conveyor belt-associated protein GldJ and the T9SS ring protein GldK plays an important role in controlling the directionality of T9SS, potentially by modulating its interaction with the stator complex GldLM, which drives motor rotation. Combined with MD simulation of the T9SS stator units GldLM, we suggest a “tri-component gearset” model where GldJ controls the rotational direction of its driver, the T9SS, thus providing adaptive sensory feedback to influence the motility of the gliding bacterium. IMPORTANCE The type 9 secretion system (T9SS) is fundamental to bacterial gliding motility, pathogenesis, and surface colonization. Our findings reveal that the C-terminal region of the conveyor belt-associated protein GldJ functions as a molecular switch which is capable of reversing the rotational direction of T9SS. Through the coordinated actions of the T9SS stator units (akin to a driving motor), the GldK ring (the gear that converts rotational energy into linear movement), and GldJ, this machinery forms a smart conveyor belt system reminiscent of flexible or cognitive mechanical conveyors. Such advanced conveyors can alter their direction to adapt to shifting demands. Here, we show that the bacterial T9SS similarly adjusts its rotational bias based on feedback from the conveyor belt-associated protein GldJ. This dual-role feedback mechanism underscores an evolved, controllable biological snowmobile, offering new avenues for studying how bacteria fine-tune motility in dynamic environments.

Microbiology↗

DOE OSTI2025

Enabling Command-and-Control in Advanced In Situ Workflows

Scientific discovery is progressing towards autonomous science with the combination of scientific instruments, high-performance computing, and artificial intelligence in complex workflows. This evolution introduces new requirements for managing scientific workflows, including feedback loops, near real-time constraints, and the ability to dynamically control workflow execution. In situ workflows that analyze and visualize data as it is generated are well-suited to satisfy stringent time constraints and their iterative nature offers greater opportunities for command-and-control. However, only a few of the many workflow management systems available have been specifically designed to manage in situ workflows and often lack support for automated feedback loops that allow analysis and visualization components to interact with the main scientific data producer. To address this need, we present in this paper how to add command-and-control capabilities to a workflow management system. We identify the functional design requirements of such a command-and-control system, detail its architecture, interface, and core mechanisms, and illustrate how advanced in situ workflows can leverage command-and-control in three use cases: graceful termination with checkpoint, dynamic and adaptive data reduction, and event-triggered analysis.

Mehta, Kshitij [ORNL] (ORCID:0000000297149981)↗

DOE OSTI2022

A Right Transfer Access Control Model of Internet of Things Based on Smart Contract

Sensor nodes play a crucial role in the promotion of development of Internet of Things (IoT). Through this transaction, RO defines access control policies in script form based on ABAC's access control model to grant access right. The identity of all users in the model is identified by address. This paper builds a more flexible right transfer access control model by means of combining the Attribute-Based Access Control model (ABAC) and blockchain technology. Owing to the characteristics of ABAC’s attributes and right association, the massive problems of some sensor nodes can be solved. At the same time, for the sake of addressing the dynamic problems such as node access and right transfer, right transfer contract (TS) and access control contract (CS) are employed on the chain to ensure efficient and safe transmission of rights. To solve on-chain storage problems and ensure transparency of the operation, the idea of Rollup in Ethereum expansion is used to upload the final state of protocol policy and right exchange to the chain. Any user can know the policy and current right transfer status at any time. Finally, comparative and security analysis show that the model presented here can solve IoT devices’ massive and dynamic problems more effectively and it is more secure than the traditional models.

Wang, Jiuru↗

DOE OSTI2021

Development of Distributed Microgrid Controllers Using HELICS [SWR-21-94]

Development of Distributed Microgrid Controllers Using HELICS uses HELICS framework develop distributed microgrid controllers (DMCs) for coordinated grid service. Development of Distributed Microgrid Controllers Using HELICS includes thirteen agents, including OpenDSS agent, data manager agent, and eleven DMC agents. Development of Distributed Microgrid Controllers Using HELICS can be used as a standard framework to develop fully distributed control solutions that dispatches simulated DERs in simulation platform.

Wang, Jing↗

DOE OSTI2023

Autonomous Controls For Reactor Technologies (acorn)

ACORN (Autonomous Controls fOr Reactor techNologies) software utilizes data, obtained from an experimental test bed and/or simulation, to implement a control command for microreactor operation. Command examples include a change to the temperature profile, power profiles, heat fluxes, etc. The control command recommended by the code is derived based on future predicted states of a microreactor, allowing proactive optimal and autonomous microreactor operation. The software is written in Python languages. The current software supports autonomous temperature controls of heat pipe simulator and autonomous heat flux controls of a 37 heat pipe non-nuclear testbed simulator (or its surrogate models).

Lin, Linyu [Idaho National Laboratory (INL), Idaho↗

DOE OSTI2023

Demand Flexibility Controls Library using Semantics (DFLEXLIBS) v0.1

DFLEXLIBS is a library/repository of HVAC-based demand flexibility control applications developed using Python. The library is based on portable control applications that exclusively contain control logic and are abstract to building details, such as point names and communication protocols. The library leverages semantic models and control platform-oriented interfaces to configure and run the controls in specific buildings. To date, the library contains two applications and two interfaces (for BOPTEST and VOLTTRON) and has been demonstrated in five heterogeneous buildings.

Paul, Lazlo↗

DOE OSTI2025

EVSE DERMS Controls [SWR-26-010]

An MQTT (Message Queuing Telemetry Transport) and OCPP (Open Charge Point Protocol) based remote smart charging controller framework for AC Electric Vehicle Supply Equipments (EVSEs). The code in this repo allows for the National Laboratory of the Rockies (NLR) controls to interface with the real Distributed Energy Resource Management System (DERMS) and EVSEs in NLR's ESIF Optimization and Control Laboratory (OCL). Different charge management algorithms can be tested to determine which power allocation method is most effective with the overall goal of demonstrating clear and well documented test results as well as providing functional control algorithms which could be utilized to provide effective smart charge management (SCM) at EV charging stations. Different power allocation methods are programmed in lab_demo_controller.py and include allocation based on first come first served, equal sharing, state of charge (SOC), priority factors, and behind the meter control methods.

Panossian, Nadia [National Laboratory of the Rocki↗