Consensus Control for Reactive Power Sharing Using an Adaptive Virtual Impedance Approach

Alsafran, Ahmed S.; Daniels, Malcolm

doi:10.3390/en13082026

Cited by 12 publications

(13 citation statements)

References 40 publications

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“…VI has been used for many applications recently, such as reactive power sharing by ensuring a consistent and equivalent output impedance for all parallel DGs in the autonomous MGs [2,[25][26][27]. This VI can be adjusted adaptively in order to calculate the total impedance and then the voltage reference.…”

Section: Adaptive Virtual Impedancementioning

confidence: 99%

Enhanced Reactive Power Sharing and Voltage Restoration Based on Adaptive Virtual Impedance and Consensus Algorithm

Keddar

Doumbia

Belmokhtar³

et al. 2022

Energies

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In this paper, power-sharing management control on an AC islanded microgrid is investigated to achieve accurate reactive power sharing. The droop control method is primarily used to manage the active and reactive power sharing among the DGs in the microgrid. However, the line impedance mismatch causes unbalanced reactive power sharing. As a solution a consensus-based adaptive virtual impedance controller is proposed, where the consensus algorithm is used to set the reactive power mismatch; then a virtual impedance correction term is generated through a proportional-integral controller to eliminate the line impedance mismatch. Thus, reactive power sharing is achieved without knowledge of the line impedances or using a central controller. Moreover, the consensus algorithm is used to restore the AC bus voltage to the nominal value by estimating the DGs average voltage using neighbor communication to compensate for the decreased magnitude of the voltage reference. Matlab/Simulink is used to validate the accuracy of reactive power sharing and voltage restauration achievement of the proposed solution through simulation of different scenarios. In addition, a dSPACE DS1104 is used within a developed experimental testbench based on two parallel DGs to validate the effectiveness of the proposed solution in the real world.

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Section: Adaptive Virtual Impedancementioning

confidence: 99%

Enhanced Reactive Power Sharing and Voltage Restoration Based on Adaptive Virtual Impedance and Consensus Algorithm

Keddar

Doumbia

Belmokhtar³

et al. 2022

Energies

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“…Most of these references use only dispatchable resources, as sources and storage units, modeled as voltage sources. [16][17][18][19][20][21] As for the choice and specification of the agents, in References 16-21, there is an agent for each dispatchable DER, differently from Reference 22, which presents an agent for each system bus. On the other hand, in Reference 16, there are three centralized agents and in Reference 23, each agent is related to a set of energy resource and load.…”

Section: Literature Reviewmentioning

confidence: 99%

“…27 Some references use MAS in order to improve the reactive power sharing. The improvement occurs through the interaction between the primary and the secondary control by using an additional variable in the consensus, 17 an adaptive virtual impedance, 18 and an adaptive droop gain event-triggered. 19 Demand-side management is an important feature in the operation of a microgrid.…”

Section: Literature Reviewmentioning

confidence: 99%

Microgrid distributed secondary control and energy management using multi‐agent system

Almada

Leão

Almeida

et al. 2021

Int Trans Electr Energ Syst

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This paper is concerned with the two-stage hierarchical control of microgrids (MG) that can operate in both grid-connected and off-grid modes. The procedure consists in the segmentation of the control scheme in the primary and secondary levels, thus enabling the application of faster and slower dynamics according to the process. A decentralized droop control-based method is used in the primary level, while a distributed multi-agent coordination scheme is applied in the secondary one. The multi-agent system is run on Python Agent DEvelopment (PADE) environment, a FIPA-compliant agent framework. The key contributions this paper provides are the control strategies of reactive power output of nondispatchable sources, dynamic sharing of energy storage systems, and the management of loads of microgrids, all of them based on multi-agent system. Cosimulations using PSCAD TM and PADE are performed considering several operating scenarios of the MG resources under different load conditions. Simulation results validate the accurate performance of the proposed control strategies. K E Y W O R D Sdistributed energy resources, distributed generation, droop control, energy storage system, hierarchical control, microgrid, multi-agent systems List of abbreviations: ω*, frequency for P*; V*, voltage amplitude for Q*; ω, measured frequency; V, measured voltage amplitude; P*, active power reference; Q*, reactive power reference; P, measured active power; Q, measured reactive power; G P (s), active power controller; G Q (s), reactive power controller; m p , proportional gain of active power; m d , derivative gain of active power; m i , integral gain of active power; n p , proportional gain of reactive power; n i , integral gain of reactive power; MOP, MG status; α, coeficiente modifica a potência BT com seu SoC; Δδ, angular difference between the MG and the main network voltages; k, gain for achieve synchronism; f nom and ω nom , nominal frequency [Hz and rad/s]; S nom , rated power; V nom , nominal voltage; Z cable , cable impedance; E max , maximum energy of battery bank; P MPP , optimal instantaneous active power of the nondispatchable source; β, adjustment factor for reactive power; Q max , maximum active power generated by intermittent sources; Q L , reactive power requested by the load; Q int *, reference reactive power for intermittent resources; Q disp *, reference reactive power for dispatchable resources; CX x , the availability of the resource (0 = unavailable, 1 = in operation); nd, number of dispatchable DERs; ni, number of intermittent DERs; ACL, agent communication language; AgDERdisp, agent of dispatchable DER; AgDispG, agent of dispatchable generation; AgESS, agent of ESS; AgIG, agent of intermittent generation; AgIGB, agent of intermittent generation balance; AgL, agent of load; AgPCCandB, agent of pcc and balance; AgTL, agent of total load;

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“…sendo Q i a potência reativa fornecida e Q * i a potência reativa nominal da i-ésima GD (∀ i = 1 : k). Como os coeficientes droop são escolhidos com base na potência nominal da GD, pode-se reescrever (12) como (Eskandari et al, 2019;Alsafran e Daniels, 2020):…”

Section: Compartilhamento Baseado Em Consensounclassified

Controlador secundário distribuído fuzzy consenso aplicado em uma microrrede CA ilhada

Silva

Neves

Oliveira

et al. 2021

Procedings Do XV Simpósio Brasileiro De Automação Inteligente

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To correct the frequency and voltage deviations in the buses of an islanded AC microgrid (MG), generated by the droop control, and to achieve precision in the reactive power sharing between the distributed generation (DG) units, this work proposes a distributed fuzzy consensus secondary controller (FCSC), composed by a fuzzy controller (FC) in association with a consensus-based strategy (CBS). The FC is used in each unit to correct deviations in frequency and voltage, using local measurements. The CBS is composed by two algorithms that aim for precision in reactive power sharing. Through a narrowband communication channel, the units exchange reactive power information with neighboring. Simulation results are presented to validate the strategy in a MG composed by four units. The proposed FCSC reduces the frequency and voltage deviations and achieves the accuracy in the reactive power sharing for connection and disconnection of loads on the MG buses. Resumo: Para corrigir os desvios de frequência e tensão nos barramentos de uma microrrede (MR) CA ilhada, gerados pelo controle em droop, e atingir precisão no compartilhamento de potência reativa entre as unidades de geração distribuída (GD), este trabalho propõe um controlador secundário distribuído fuzzy consenso (CSFC), composto por um controlador fuzzy (CF) em associação à uma estratégia baseada em consenso (EBC). O CF é utilizado em cada unidade para correção dos desvios de frequência e tensão, fazendo uso de medições locais. A EBC é composta por dois algoritmos que visam a precisão no compartilhamento de potência reativa. Através de um canal de comunicação de banda estreita, as unidades trocam informação de potência reativa com as vizinhas. Resultados de simulação são apresentados para validar a estratégia em uma MR composta por quatro unidades. O CSFC proposto reduz os desvios de frequência e tensão e alcança a precisão no compartilhamento de potência reativa para eventos de conexão e desconexão de cargas nos barramentos da MR.

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Consensus Control for Reactive Power Sharing Using an Adaptive Virtual Impedance Approach

Cited by 12 publications

References 40 publications

Enhanced Reactive Power Sharing and Voltage Restoration Based on Adaptive Virtual Impedance and Consensus Algorithm

Enhanced Reactive Power Sharing and Voltage Restoration Based on Adaptive Virtual Impedance and Consensus Algorithm

Microgrid distributed secondary control and energy management using multi‐agent system

Controlador secundário distribuído fuzzy consenso aplicado em uma microrrede CA ilhada

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