The Evolution of Research in Microgrids Control

Vasilakis, Athanasios; Zafeiratou, Igyso; Lagos, Dimitris T.; Hatziargyriou, Nikos D.

doi:10.1109/oajpe.2020.3030348

Cited by 62 publications

(20 citation statements)

References 114 publications

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“…Simulation and experimental results in [9,10] show that the MPC-based methods represent higher performance compared to the conventional linear controllers. MPC methods applied to converters can be classified into two categories: Continuouscontrol-set MPC (CCS-MPC) and finite-control-set MPC (FCS-MPC) [11]. In CCS-MPC continuous signals are generated for the PWM regulator, while FCS-MPC is based on the discrete nature of the converter.…”

Section: Literature Reviewmentioning

confidence: 99%

Model-Free Neural Network-Based Predictive Control for Robust Operation of Power Converters

et al. 2021

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An accurate definition of a system model significantly affects the performance of model-based control strategies, for example, model predictive control (MPC). In this paper, a model-free predictive control strategy is presented to mitigate all ramifications of the model’s uncertainties and parameter mismatch between the plant and controller for the control of power electronic converters in applications such as microgrids. A specific recurrent neural network structure called state-space neural network (ssNN) is proposed as a model-free current predictive control for a three-phase power converter. In this approach, NN weights are updated through particle swarm optimization (PSO) for faster convergence. After the training process, the proposed ssNN-PSO combined with the predictive controller using a performance criterion overcomes parameter variations in the physical system. A comparison has been carried out between the conventional MPC and the proposed model-free predictive control in different scenarios. The simulation results of the proposed control scheme exhibit more robustness compared to the conventional finite-control-set MPC.

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Section: Literature Reviewmentioning

confidence: 99%

Model-Free Neural Network-Based Predictive Control for Robust Operation of Power Converters

et al. 2021

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“…Distributed intelligence and controls enable the flexibility needed to facilitate safe, reliable, and secure integration of inverter based DERs, independent of the asset ownership structure. A hierarchical control structure for microgrids with primary, secondary, and tertiary control functions has been standardized and studied [8], [20]- [23]. Primary (fast, local device) control provides inverter-level current and voltage references, and also regulates power sharing through techniques such as droop control.…”

Section: B Distributed Intelligence and Interoperability At The Grid-edge To Increase Operational Flexibilitymentioning

confidence: 99%

A Multi-Site Networked Hardware-in-the-Loop Platform for Evaluation of Interoperability and Distributed Intelligence at Grid-Edge

Essakiappan

Chowdhury

Schneider

et al. 2021

IEEE Open J. Power Energy

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Electric power systems have experienced large increases in the number of intelligent, connected and controllable devices being deployed, leading to a high degree of distributed intelligence at the grid-edge. These devices, both utility-owned and consumer-owned, include renewable generation, energy storage, remote switches, voltage regulators, and smart controllable loads. These new devices provide significant potential for increased operational flexibility that can be leveraged to achieve system reconfiguration, resiliency improvements, power quality improvements, and distribution system automation. However, two significant challenges must be addressed before these assets can be leveraged for operations: interoperability and system level validation prior to deployment. Because of the complexity of distributed control systems, and their interactions with legacy centralized controls, a purely simulations-based approach for predeployment validation is not sufficient. It requires hardware-inthe-loop (HIL) testing to emulate hardware devices and evaluate their performance. Additionally, securely integrating multiple test facilities at utility operators and vendors can enable the rapid scale-up of evaluation platforms and remove the need for multiple expensive standalone installations. Presented in this paper, is the development of a multi-site evaluation platform that employs Advanced Distribution Management Systems (ADMS), distributed control devices, real-time HIL assets, secure communication links, and protocol adapters. The testbed has been developed at three sites, with one site hosting the ADMS and the other two hosting HIL capabilities. This platform uses standardsbased approaches and open-source tools, and hence can serve as a template for other researchers and institutions to implement their multi-site evaluation frameworks for pre-deployment testing.

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“…MGs have slowly started to transform into cyber-physical MGs incorporating decentralized and distributed multi-agent frameworks and integrating advanced power electronics as well as a growing number of embedded devices for voltage and power factor control [1]. All these agents are connected through the physical layer representation of the system (e.g., power lines), and on top of the physical nodes and links, the cyber information and communication technology (ICT) network connects the different cyber-physical components.…”

Section: Introductionmentioning

confidence: 99%

Detection of Malicious Attacks in Autonomous Cyber-Physical Inverter-Based Microgrids

Zografopoulos

Konstantinou

2022

IEEE Trans. Ind. Inf.

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The distributed generation capabilities of microgrids (MGs) arise as essential assets in enhancing grid resilience. The integration of distributed energy sources (DERs), controllable loads, and prosumers necessitates the deployment of potent control and communication synergies. While those synergies transform MGs into cyber-physical systems through information technologies able to sense, control, and actuate local resources and loads, they inadvertently expose MGs to cyber-attack threats. Increasing the security of critical communication and control systems against 'black swan' events, i.e., high-impact lowprobability cyber-physical incidents, is a major priority for MGs operations. Such incidents, if left unabated, can intensify and elicit system dynamics instability, eventually causing outages and system failures. In this paper, we develop an integrated approach for multi-agent MG systems able to perform the detection of malicious cyber-physical attacks based on subspace methods. We employ the small signal model of an autonomous/islanded MG and consider different attack models targeting the MG's secondary frequency control. The attack detector is constructed via identifying the stable kernel representation of the autonomous cyber-physical MG in the attack-free case. We illustrate the impact of the attack models as well as the feasibility of the developed detection method in simulation models of the Canadian urban benchmark distribution system.

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The Evolution of Research in Microgrids Control

Cited by 62 publications

References 114 publications

Model-Free Neural Network-Based Predictive Control for Robust Operation of Power Converters

Model-Free Neural Network-Based Predictive Control for Robust Operation of Power Converters

A Multi-Site Networked Hardware-in-the-Loop Platform for Evaluation of Interoperability and Distributed Intelligence at Grid-Edge

Detection of Malicious Attacks in Autonomous Cyber-Physical Inverter-Based Microgrids

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