Power sharing through interlinking converters in adaptive droop controlled multiple microgrid system

Sinha, Shweta; Ghosh, Sounavo; Bajpai, Prabodh

doi:10.1016/j.ijepes.2020.106649

Cited by 26 publications

(4 citation statements)

References 21 publications

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“…These characteristics of the control loop ensure robust operation within the MG system. However, droop control limits frequency deviations; moreover, it suffers from disadvantages such as poor transient performance or instability issues, unsuitability for distribution networks, poor performance in terms of load dynamics, and inability for black starts after a collapse [40][41] [42][43] [44]. Accordingly, non-droop methods have become increasingly popular for the primary control of MGs.…”

Section: A Controlmentioning

confidence: 99%

Energy Management in Microgrid and Multi-microgrid

Xing¹,

Jia²

2023

Preprint

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<p>Considered as basic structures of next-generation energy system, environment-friendly and flexible microgrid (MG) systems are potential solutions to deal with stochastic renewable energy sources (RESs). The essence of management is to maximize the benefits of available resources through coordination and optimization, which also applies to energy systems. Adaptable energy management approaches provide the possibility to construct effective and various energy interaction. The purpose of this paper is to present a systematic review of energy management in MG systems, and further to propose an energy management model. Therefore, this paper first comprehensively reviews recent research studies on MG, particularly in the interconnected mode (called as multi-microgrid, MMG). Based on the analysis of relative literatures, a STEER (stability, touch, efficiency, evenness, and resilience) strategy is proposed and formulated from the perspective of energy utilization for energy management. Then, critical technologies in energy management are reviewed and summarized from the aspects of control, communication, optimization, prediction, and evaluation. Furthermore, energy management system is illustrated from the perspectives of system function, management framework and operation logic, based on a four-layer hierarchical architecture. Eight main prospects on the future trend of energy management in MG and MMG are also presented.</p>

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Section: A Controlmentioning

confidence: 99%

Energy Management in Microgrid and Multi-microgrid

Xing¹,

Jia²

2023

Preprint

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“…The control techniques became more complex as we went to the upper levels. Adaptive droop controllers were used at the system level to balance the shared power among the distributed resources while maintaining the system stability [38].…”

Section: Introductionmentioning

confidence: 99%

Hardware Implementation of a Resilient Energy Management System for Networked Microgrids

Hussein,

Rafin,

Abdelrahman

et al. 2024

WEVJ

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A networked microgrid is composed of multiple nearby microgrids linked together to gain additional flexibility for resilient operations. Networked microgrids collaborate to prevent power shortages in microgrid clusters by sharing critical renewable and energy storage resources. However, controlling the local resources of each microgrid, including the energy storage systems’ charging and discharging, maintaining the DC bus voltage, and even overseeing the power shared by multiple microgrids, is challenging. Therefore, a microgrid control technique and distributed energy management are used cooperatively in this study to handle the shared power between a system of networked microgrids incorporating photovoltaics and battery energy storage systems. Numerical simulation results from a networked microgrid system verify the accuracy and soundness of the suggested distributed energy management under several operating conditions, including renewable uncertainties and sequential load variations in different zones. The applicability of the suggested technique is confirmed by hardware implementation, and several operational scenarios further evaluate the proposed system on a practical two-microgrid system located in the Florida International University (FIU) testbed.

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“…In the literature, several approaches have been suggested to improve the dynamic response speed of the APL. A number of methods have been suggested for frequency droop control [31][32][33]. If the frequency droop control is used directly in synchronverters, it induces the adverse coupling effect between the reactive power loop (RPL) and the APL.…”

Section: Introductionmentioning

confidence: 99%

Microgrid Stability Improvement Using a Deep Neural Network Controller Based VSG

Mohammadreza

Tavakoli

Samanfar

2022

International Transactions on Electrical Energy Systems

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In order to support the inertia of a microgrid, virtual synchronous generator control is a suitable control method. However, the use of the virtual synchronous generator control leads to unacceptable transient active power sharing, active power oscillations, and the inverter output power oscillation in the event of a disturbance. This study aims to propose a deep neural network controller which combines the features of a restricted Boltzmann machine and a multilayer neural network. To initialize a multilayer neural network in the unsupervised pretraining method, the restricted Boltzmann machine is applied as a very important part of the deep learning controller. The Lyapunov stability method is used to update the weight of the deep neural network controller. The proposed method performs power oscillation damping and frequency stabilization. The experimental and simulation results are presented to assess the usefulness of the suggested method in damping oscillations and frequency stabilization.

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Power sharing through interlinking converters in adaptive droop controlled multiple microgrid system

Cited by 26 publications

References 21 publications

Energy Management in Microgrid and Multi-microgrid

Energy Management in Microgrid and Multi-microgrid

Hardware Implementation of a Resilient Energy Management System for Networked Microgrids

Microgrid Stability Improvement Using a Deep Neural Network Controller Based VSG

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