Secure Scalable Microgrid Test Bed at Sandia National Laboratories

Glover, Steven F.; Neely, Jason C.; Lentine, Anthony L.; Finn, Jason; White, F.E.; Foster, Phillip R.; Wasynczuk, Oleg; Pekarek, Steven; Loop, Benjamin

doi:10.1109/cyber.2012.6392520

Cited by 18 publications

(5 citation statements)

References 7 publications

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“…The SSM testbed was developed to validate controls for networked microgrids [38] and has also been configured to represent an all-electric ship power system with multiple busses (or zones) [39], [40]. The SMM is shown in To aid in control design and analyze more varied microgrid systems, a Simulink library replicating the components of the SSM was developed.…”

Section: A Simulation Modelmentioning

confidence: 99%

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Evaluation of an Autonomous Control Scheme for Interconnected DC Microgrids Using a Power Hardware-in-the-Loop Platform

Gonzalez-Candelario,

Darbali-Zamora,

Aponte-Bezares

et al. 2023

IEEE Access

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DC microgrids (DCMGs), typically envisioned with high bandwidth communications, may well expand their application range by considering additional autonomous control strategies, especially to increase resiliency in contingency situation. These strategies are based on the droop control methods, enabling voltage regulation and proportional load sharing based only on local measurements. Control challenges arise when coordinating the output of multiple DCMGs, each composed of several distributed energy resources through bus-to-bus transactional converters. This paper proposes an autonomous control strategy for transactional converters when multiple DCMGs are connected through a common bus. This control strategy seeks to match the external bus voltage with the internal bus voltage, balancing power. Three case scenarios are considered: standalone operation of each DCMG, excess generation on one DCMG, and generation deficit in one DCMG. Results using Sandia National Laboratories' Secure Scalable Microgrid Simulink library, and models developed in MATLAB are compared. These results demonstrated that the proposed control strategy supported the advantage of seamless operation with and without local generation-load mismatches and expanded the range of autonomous operation between DCMGs.

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Section: A Simulation Modelmentioning

confidence: 99%

“…An Arbitrary Response Energy Storage Emulator (ARESE) was used to model DER energy assets [38]. The ARESE contains a two-quadrant chopper in series with a two-stage LC output filter, as shown in Fig.…”

Section: A Simulation Modelmentioning

confidence: 99%

Evaluation of an Autonomous Control Scheme for Interconnected DC Microgrids Using a Power Hardware-in-the-Loop Platform

Gonzalez-Candelario,

Darbali-Zamora,

Aponte-Bezares

et al. 2023

IEEE Access

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“…Although precautions should be taken in securing reporting devices in an MG, it is wise to consider the scenario where devices may be compromised and report falsified data to control algorithms. Experimentation with MG attacks and defenses using testbeds can be used to this end without causing any negative real-world effects [18], [19].…”

Section: Background and Related Workmentioning

confidence: 99%

Reinforcement Learning Based Penetration Testing of a Microgrid Control Algorithm

Neal¹,

Dagdougui²,

Lodi³

et al. 2020

Preprint

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Microgrids (MGs) are small-scale power systems which interconnect distributed energy resources and loads within clearly defined regions. However, the digital infrastructure used in an MG to relay sensory information and perform control commands can potentially be compromised due to a cyberattack from a capable adversary. An MG operator is interested in knowing the inherent vulnerabilities in their system and should regularly perform Penetration Testing (PT) activities to prepare for such an event. PT generally involves looking for defensive coverage blindspots in software and hardware infrastructure, however the logic in control algorithms which act upon sensory information should also be considered in PT activities. This paper demonstrates a case study of PT for an MG control algorithm by using Reinforcement Learning (RL) to uncover malicious input which compromises the effectiveness of the controller. Through trial-and-error episodic interactions with a simulated MG, we train an RL agent to find malicious input which reduces the effectiveness of the MG controller.

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“…A comprehensive microgrid testbed has been constructed which includes a reconfigurable bus, two mechanical source emulators capable of emulating a wind turbine, diesel engine, water turbine, or a wave energy device, a high-power digital resistor rated to 6.7 kW at 400V bus voltage, an energy storage emulator capable of sourcing or sinking 5kW of power, and master control console that scripts the experiments [11], [12], [13], [14]. The system under consideration for this paper has two wind turbine generators, three energy storage systems and a resistive load; see Figs.…”

Section: Microgrid System and Reduced Order Modelmentioning

confidence: 99%

Hamiltonian control design for DC microgrids with stochastic sources and loads with applications

Wilson

Neely

et al. 2014

2014 International Symposium on Power Electronics, Electrical Drives, Automation and Motion

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To achieve high performance operation of microgrids that contain stochastic sources and loads is a challenge that will impact cost and complexity. Developing alternative methods for controlling and analyzing these systems will provide insight into tradeoffs that can be made during the design phase. This paper presents a design methodology, based on Hamiltonian Surface Shaping and Power Flow Control (HSSPFC) [1] for a hierarchical control scheme that regulates renewable energy sources and energy storage in a DC microgrid. Recent literature has indicated that there exists a trade-off in information and power flow and that intelligent, coordinated control of power flow in a microgrid system can modify energy storage hardware requirements. Two scenarios are considered; i) simple two stochastic source with variable load renewable DC Microgrid example and ii) a three zone electric ship with DC Microgrid and varying pulse load profiles.

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Secure Scalable Microgrid Test Bed at Sandia National Laboratories

Cited by 18 publications

References 7 publications

Evaluation of an Autonomous Control Scheme for Interconnected DC Microgrids Using a Power Hardware-in-the-Loop Platform

Evaluation of an Autonomous Control Scheme for Interconnected DC Microgrids Using a Power Hardware-in-the-Loop Platform

Reinforcement Learning Based Penetration Testing of a Microgrid Control Algorithm

Hamiltonian control design for DC microgrids with stochastic sources and loads with applications

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