An adaptive droop control method for balancing the SoC of distributed batteries in AC microgrids

Gkavanoudis, Spyros I.; Oureilidis, Konstantinos O.

doi:10.1109/compel.2016.7556698

Cited by 17 publications

(11 citation statements)

References 14 publications

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“…• P G-SECi : active power setpoint for the converter output, for secondary control (W) • Q G-SECi : reactive power setpoint for the converter output, for secondary control (VAr) • m p , m q , k p , k q : droop coefficients Droop coefficients control P or Q variations with voltage and frequency. They are usually obtained, starting from extreme values of proper ranges of voltage, frequency, and active and reactive power, although some authors propose their adjustment as a function of battery SoC [23,24].…”

Section: Element Modelling For Power Flow Algorithmmentioning

confidence: 99%

Secondary Control for Storage Power Converters in Isolated Nanogrids to Allow Peer-to-Peer Power Sharing

et al. 2020

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It is usual in literature that power sharing among grid-forming sources of an isolated microgrid obeys their energy rating, instead of economic agreements between stakeholders, and circulating energy among them is usually avoided. However, these energy interchanges make strong sense and classical power sharing methods must be reformulated in the context of prosumer-based microgrids. This paper proposes a secondary control method for a prosumer-based low-voltage nanogrid that allows for energy interchange between prosumers, where storage systems, together with PV generators, are the controllable grid-forming sources. A power flow technique adapted to islanded microgrids is used for secondary control algorithm and the whole hierarchical control strategy for the prosumer converter is simulated and validated. This hierarchical control consists of three stages: tertiary control plans the energy interchange among prosumers, secondary obtains different voltage and power setpoints for each of the grid-forming sources, and, finally, primary control guarantees stable voltage and frequency values within the nanogrid with droop rules. Inner control loops for the power converter are also defined to track setpoints and assure stable performance. Simulation tests are carried out, which prove the stability of the proposed methods and the accuracy of the setpoint tracking.

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Section: Element Modelling For Power Flow Algorithmmentioning

confidence: 99%

Secondary Control for Storage Power Converters in Isolated Nanogrids to Allow Peer-to-Peer Power Sharing

et al. 2020

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“…Battery-based microgrids require energy balancing, and consequently several adaptive droop control strategies based on SoC level have been proposed, [25,[35][36][37][38][39][40][41]. Overall, these adaptive droop controllers are found on the same principle: ESSs higher SoC should inject more active power compared with others and in the charging mode, less charged ESSs should absorb more power.…”

Section: Secondary Controlmentioning

confidence: 99%

“…The objective of the proposed primary controller is to achieve proportional power sharing among the DERs based on their real-time state, meaning the available power they can provide, and not their rated power [25,42,43]. However, unlike the previously mentioned approaches [40,41], one of the innovations of the proposed primary controller is the analytical calculation of droop and reversed-droop coefficients while taking into consideration the control mode of each DER (master or slave).…”

Section: Battery Controlmentioning

confidence: 99%

“…Apart from conventional and VIL‐enhanced droop controllers, more advanced schemes have been proposed, aiming toward changing the droop coefficients dynamically based on various strategies. Such strategies are implemented either locally (decentralised), as in [25], or using communication in a centralised [26] or distributed way [27]. These controllers are analysed in the next section as they belong to second control objectives.…”

Section: Related Workmentioning

confidence: 99%

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Hybrid multi‐agent‐based adaptive control scheme for AC microgrids with increased fault‐tolerance needs

Bintoudi

Zyglakis

Tsolakis

et al. 2019

IET Renewable Power Generation

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This paper presents a fault-tolerant secondary and adaptive primary microgrid control scheme using a hybrid multiagent system (MAS), capable of operating either in a semi-centralised or distributed manner. The proposed scheme includes a droop-based primary level that considers the microgrid energy reserves in production and storage. The secondary level is responsible for: a) the microgrid units' coordination, b) voltage and frequency restoration and c) calculation of the droop/ reversed-droop coefficients. The suggested architecture is arranged upon a group of dedicated asset agents that collect local measurements, take decisions independently and, collaborate in order to achieve more complex control objectives. Additionally, a supervising agent is added to fulfill secondary level objectives. The hybrid MAS can operate either with or without the supervising agent operational, manifesting fast redistribution of the supervising agent tasks. The proposed hybrid scheme is tested in simulation upon two separate physical microgrids using three scenarios. Additionally, a comparison with conventional control methodologies is performed in order to illustrate further the operation of a hybrid approach. Overall, results show that the proposed control framework exhibits unique characteristics regarding reconfigurability and fault-tolerance, while power quality and improved load sharing are ensured even in case of critical component failure. PF min minimum power factor (-) m f-P droop coefficient (Hz/W) n V-Q droop coefficient (Hz/VAr) v ocd compensating d-axis voltage by virtual impedance (V) v ocq compensating q-axis voltage by virtual impedance (V) i od inverter output inductor current on the d-axis (A) i oq inverter output inductor current on the q-axis (A) R v resistive part of the virtual impedance (Ω) L v inductive part of the virtual impedance (H) M on exchanged agent messages during one operation cycle-MGCC operational (-) M off exchanged agent messages during one operation cycle-MGCC inactive (-) N ESS total number of ESSs in a microgrid (-) N PV total number of PVs in a microgrid (-) N Load total number of load buses in a microgrid (-) E nom Nominal Battery Capacity (Wh)

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“…5. and islanded operation [4], [29]. The droop control scheme considered in this paper is shown in Fig.…”

Section: Application To Droop-controlled Convertersmentioning

confidence: 99%

Analysis of an Online Stability Monitoring Approach for DC Microgrid Power Converters

Khodamoradi

Liu

Mattavelli

et al. 2019

IEEE Trans. Power Electron.

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An on-line approach to evaluate and monitor the stability margins of dc microgrid power converters is presented in this paper. The discussed online stability monitoring technique (MT) is based on the Middlebrook's loop-gain measurement technique, adapted to the digitally controlled power converters. In this approach, a perturbation is injected into a specific digital control loop of the converter and after measuring the loop gain, its crossover frequency and phase margin are continuously evaluated and monitored. The complete analytical derivation of the model, as well as detailed design aspects, are reported. In addition, the presence of multiple power converters connected to the same dc bus, all having the stability monitoring unit, is also investigated. An experimental microgrid prototype is implemented and considered to validate the theoretical analysis and simulation results, and to evaluate the effectiveness of the digital implementation of the technique for different control loops. The obtained results confirm the expected performance of the stability monitoring tool in steady-state and transient operating conditions. The proposed method can be extended to generic control loops in power converters operating in dc microgrids.

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An adaptive droop control method for balancing the SoC of distributed batteries in AC microgrids

Cited by 17 publications

References 14 publications

Secondary Control for Storage Power Converters in Isolated Nanogrids to Allow Peer-to-Peer Power Sharing

Secondary Control for Storage Power Converters in Isolated Nanogrids to Allow Peer-to-Peer Power Sharing

Hybrid multi‐agent‐based adaptive control scheme for AC microgrids with increased fault‐tolerance needs

Analysis of an Online Stability Monitoring Approach for DC Microgrid Power Converters

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