Distributed Energy Resources Based Microgrid: Review of Architecture, Control, and Reliability

Muhtadi, Abir; Pandit, Dilip; Nguyen, Nga; Mitra, Joydeep

doi:10.1109/tia.2021.3065329

“…As shown in Figure 8, the controller is taking renewable generated power from wind or scholar, hourly dispatched power set point, battery SOC, and battery voltage, as inputs. Output is a current reference ( i bess ), and within a limit of maximum charge and discharge current (i max,ch , i max disch ), as illustrated in (19). The SOC is kept within the obliged lower and upper limits (SOC LL , SOC UL ) (refer to Equation ( 18)).…”

Section: Centralized Secondary Controlmentioning

confidence: 99%

“…𝐷𝐷 𝑠𝑠𝐵𝐵𝑚𝑚 𝑑𝑑𝑑𝑑𝑠𝑠𝑑𝑑ℎ ), as illustrated in (19). The SOC is kept within the obliged lower and upper limits (𝑆𝑆𝑆𝑆𝐶𝐶 𝐿𝐿𝐿𝐿 , 𝑆𝑆𝑆𝑆𝐶𝐶 𝑈𝑈𝐿𝐿 ) (refer to Equation ( 18)).…”

Section: Centralized Secondary Controlmentioning

confidence: 99%

“…Primary Decentralized Control: The objective of this level is to regulate the load sharing of distributed energy resources and storage, via the control of their linked converters output voltage and frequency, to attain balanced and autonomous operation of these distributed systems [19,20]. The most typical strategy of this is droop control, which is responsible for implementing balanced load sharing for the distributed resources and storage, with no necessity for time-critical communication links [21].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Strategies for Controlling Microgrid Networks with Energy Storage Systems: A Review

Al-Saadi

¹

,

Al‐Greer

²

,

Short

³

2021

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Distributed Energy Storage Systems are considered key enablers in the transition from the traditional centralized power system to a smarter, autonomous, and decentralized system operating mostly on renewable energy. The control of distributed energy storage involves the coordinated management of many smaller energy storages, typically embedded within microgrids. As such, there has been much recent interest related to controlling aspects of supporting power-sharing balance and sustainability, increasing system resilience and reliability, and balancing distributed state of charge. This paper presents a comprehensive review of decentralized, centralized, multiagent, and intelligent control strategies that have been proposed to control and manage distributed energy storage. It also highlights the potential range of services that can be provided by these storages, their control complications, and proposed solutions. Specific focus on control strategies based upon multiagent communication and reinforcement learning is a main objective of this paper, reflecting recent advancements in digitalization and AI. The paper concludes with a summary of emerging areas and presents a summary of promising future directions.

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“…M ICROGRIDS are cyber-physical systems (CPS) that include multiple distributed generators (DGs), loads and a communication network. Both centralized and distributed control mechanisms have been deployed in microgrids [1]- [3]. Distributed control for microgrids provides improved reliability and scalability when compared to centralized control [4].…”

Section: Introductionmentioning

confidence: 99%

An Explainable Intelligent Framework for Anomaly Mitigation in Cyber-Physical Inverter-based Systems

Khan¹

2022

Preprint

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An explainable intelligent framework for cyber anomaly mitigation of cyber-physical inverter-based systems is presented.<div><br></div><div>Smart inverter-based microgrids essentially constitute an extensive communication layer that makes them vulnerable to cyber anomalies. The distributed cooperative controllers implemented at the secondary control level of such systems exchange information among physical nodes using the cyber layer to meet the control objectives. The cyber anomalies targeting the communication network may distort the normal operation therefore, an effective cyber anomaly mitigation technique using an artificial neural network (ANN) is proposed in this paper. The intelligent anomaly mitigation control is modeled using adynamic recurrent neural network that employs a nonlinear autoregressive network with exogenous inputs. The effects of false data injection to the distributed cooperative controller at the secondary control level are considered. The training data for designing the neural network are generated by multiple simulations of the designed microgrid under various operating conditions using MATLAB/Simulink. The neural network is trained offline and tested online in the simulated microgrid. The proposed technique is applied as secondary voltage and frequency control of distributed cooperative control-based microgrid to regulate the voltage under various operating conditions. The performance of the proposed control technique is verified by injecting various types of false data injection-based cyber anomalies. The proposed ANN-based secondary controller maintained the normal operation of microgrid in the presence of cyber anomalies as demonstrated by real-time simulations on a real-time digital simulator OPAL-RT.<br></div>

show abstract

“…M ICROGRIDS are cyber-physical systems (CPS) that include multiple distributed generators (DGs), loads and a communication network. Both centralized and distributed control mechanisms have been deployed in microgrids [1]- [3]. Distributed control for microgrids provides improved reliability and scalability when compared to centralized control [4].…”

Section: Introductionmentioning

confidence: 99%

An Explainable Intelligent Framework for Anomaly Mitigation in Cyber-Physical Inverter-based Systems

Khan¹,

Beg²,

Jin³

et al. 2022

Preprint

1

0

View full text Add to dashboard Cite

An explainable intelligent framework for cyber anomaly mitigation of cyber-physical inverter-based systems is presented.<div><br></div><div>Smart inverter-based microgrids essentially constitute an extensive communication layer that makes them vulnerable to cyber anomalies. The distributed cooperative controllers implemented at the secondary control level of such systems exchange information among physical nodes using the cyber layer to meet the control objectives. The cyber anomalies targeting the communication network may distort the normal operation therefore, an effective cyber anomaly mitigation technique using an artificial neural network (ANN) is proposed in this paper. The intelligent anomaly mitigation control is modeled using adynamic recurrent neural network that employs a nonlinear autoregressive network with exogenous inputs. The effects of false data injection to the distributed cooperative controller at the secondary control level are considered. The training data for designing the neural network are generated by multiple simulations of the designed microgrid under various operating conditions using MATLAB/Simulink. The neural network is trained offline and tested online in the simulated microgrid. The proposed technique is applied as secondary voltage and frequency control of distributed cooperative control-based microgrid to regulate the voltage under various operating conditions. The performance of the proposed control technique is verified by injecting various types of false data injection-based cyber anomalies. The proposed ANN-based secondary controller maintained the normal operation of microgrid in the presence of cyber anomalies as demonstrated by real-time simulations on a real-time digital simulator OPAL-RT.<br></div>

show abstract

Distributed Energy Resources Based Microgrid: Review of Architecture, Control, and Reliability

Cited by 167 publications

References 106 publications

Strategies for Controlling Microgrid Networks with Energy Storage Systems: A Review

Strategies for Controlling Microgrid Networks with Energy Storage Systems: A Review

An Explainable Intelligent Framework for Anomaly Mitigation in Cyber-Physical Inverter-based Systems

An Explainable Intelligent Framework for Anomaly Mitigation in Cyber-Physical Inverter-based Systems

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