Electrical Power and Energy Systems 140 (2022) 108081

Contents lists available at ScienceDirect

International Journal of Electrical Power and Energy Systems

journal homepage: www.elsevier.com/locate/ijepes

A comprehensive review on telecommunication challenges of microgrids

secondary control
Negar Sheykhi a, Abolfazl Salami a, *, Josep M Guerrero b, Gibran D Agundis-Tinajero b,
Tayebe Faghihi c
a
Department of Electrical Engineering, Arak University of Technology, Arak, Iran
b
Center for Research on Microgrids (CROM), Department of Energy Technology, Aalborg University, Aalborg 9220, Denmark
c
Budapest University of Technology and Economics, Hungary

A R T I C L E I N F O A B S T R A C T

Keywords: The development of distributed energy resources in distribution networks has created a new concept called
Microgrid microgrids. Their control is one of the main development issues that must be addressed before any imple­
Secondary control mentation process. In this paper, a comprehensive literature review of the main hierarchical control algorithms
Centralized control
such as centralized, decentralized, and distributed, with a focus on the secondary level, with an emphasis on their
Decentralized control
main strengths and weaknesses are discussed and compared. Microgrid communication infrastructures allow the
Distributed control
use of different control schemes for the secondary control layer which is given the importance of secondary
control over the stable and reliable performance of microgrids, and the lack of comprehensive reference for
researchers. Also, provides a literature review on current key issues regarding microgrid secondary control
strategies with respect to communication network challenges. The issue of secondary control is discussed with a
focus on challenges such as time delays. Also Distributed control methods at the secondary level to reduce the use
of the communication network and subsequently reduce communication network delays are discussed.

1. Introduction hierarchical structure has three levels of control; At the first level, the
controller is responsible for maintaining voltage and frequency stability,
Over the past few decades, island power grids have been a viable which affects the stability of the MG due to the fast processes of the
solution for the supply of energy based on distributed generators (DGs). controller. The virtual impedance loop is optionally used to enhance the
The development and improvement of control systems has changed the power quality and accuracy power-sharing of first level [8]. At the pri­
way these networks are understood and designed. This change has led to mary level, controllers are implemented using local measurements, so
the introduction of a new concept called microgrid (MG) [1,2]. The they will not require a complex communication network [9,10,11]. Due
development of MG has a few challenges, which should be evaluated to the time required for information exchanges to distribute equal power
individual; The challenges include operation, control, security, and in MG units, an unrelated approach is usually adopted for initial control
stability. [12–15].
MGs can operate in islanded and connected modes [3,4,5]; In these Secondary level control methods are studied in proportion to the
operating modes, there are challenges, such as frequency maintenance, dependence on communication networks in three structures: central­
voltage stability, and accurate power sharing [6,7]; So, a proper control ized, decentralized and distributed [16]. Since communication re­
structure is necessary for a reliable MG performance. In addition, sources in the microgrid are limited, reducing dependence on the
choosing the appropriate communication network to increase system communication network is desirable. Therefore, this has led to more
reliability and security to improve bandwidth, time delay and packet attention to distributed and decentralized structures due to reduced
losses is an important challenge. The exchange of information in dependence on the communication network than the centralized struc­
microgrids and different levels of control requires a communication ture that requires a complex communication network [17,18,19].
network; Which is organized by a hierarchical control structure. The Microgrid dependence on communication networks has disadvantages

* Corresponding author.
E-mail addresses: salami@arakut.ac.ir (A. Salami), joz@et.aau.dk (J.M. Guerrero), gdat@et.aau.dk (G.D. Agundis-Tinajero).

https://doi.org/10.1016/j.ijepes.2022.108081
Received 2 April 2021; Received in revised form 1 February 2022; Accepted 18 February 2022
Available online 1 March 2022
0142-0615/© 2022 Elsevier Ltd. All rights reserved.
N. Sheykhi et al. International Journal of Electrical Power and Energy Systems 140 (2022) 108081

Table 1
Categories of secondary control types.
Controller

Secondary control of MG

Centralized Load estimation [42]

Decentralized State of charge [43]
Life optimization of energy storage units [44]
Potential function [10] Unit 1 Unit 2 Unit 3 Unit n
Adaptive PI with neural network [45]
Distributed Distributed averaging [46]
Consensus Algorithm [47,48,49]
Networked control [50,51]
Distributed averaging Proportional –integral [52] Physical Connection
Feedback linearization [53]
Multi agent system [54,55]
Fig. 1. Schematic diagram of centralized control scheme.

such as communication disturbances. For this reason, novel control shared bus by a reference value [56,57]. Since the centralized method
structures have been introduced in a way that the control objectives can requires an extensive communication system, it could be used to
always be guaranteed even with the communication disturbances monitor and control different aspects of the MG. This approach allows
[20,21]. Recent studies have discussed the development of a novel the DG to be easily imported to the MG without affecting the control
secondary control to restore voltage and frequency and accurate power program. However, it is strongly dependent on the microgrid central
sharing based on event-triggered state [22,23,24]. Due to the high cost controller (MGCC) and it could be considered as a strong limitation [22].
of communication networks, many studies have been conducted to Since all control calculations are performed at the MGCC, the failure
reduce controller dependency from the secondary level to the commu­ could be affected on the entire MG, therefore, a backup system is
nication infrastructure [25]. required to improve reliability [22]. Conventional secondary control
The purpose of this study is to provide an overview of the existing methods use a centralized structure consisting of a droop control, unit of
secondary control structure and to highlight opportunities for future computation, and a central control. These requirements reduce the
studies in this field. In this regard, some articles have provided reviews reliability and flexibility of the MG and increase its susceptibility to
on MGs [26,27] some of which focus on MG control [28,29,30] and disturbance, so that failure of a unit will cause a big problem in the MG.
some on secondary control structure [16]. Given the significant research For this reason, some distributed methods are presented [56,58,59].
conducted on the communication network of microgrid, this paper fo­ System stability is affected by delays in communication links in MG
cuses on the secondary control and the structures used at the secondary control. The existence of time delay in communication network can
level by examining the reducing dependence on the communication cause instability in microgrid [60]. This shows the importance of having
infrastructure and looking at different approaches based on the a suitable control structure to eliminate the effect of time delay on
distributed control. microgrid performance. The Multi-Agent System (MAS) based distrib­
The rest of this survey begins with the definition of the concept and uted control has provided a promising method to eliminate the time
comparison of the MG structures and the secondary control approaches delay effect [61].
are classified and compared, and the importance of this structure will be Fig. 1, shows the centralized control structure. Some of the control
highlighted by examining the approaches based on the distributed mechanisms used for centralized control are listed in Table 2; Also
structure in Section 2. In Section 3, the communication network used in Table 3 summarizes the advantages and disadvantages of centralized
the microgrid is categorized. Finally, Section 4 will present the structures.
conclusion.
2.2. Decentralized structure in microgrid
2. Secondary control in microgrid
The decentralized structure operates on the basis of local measure­
In microgrid control, if a droop structure is used at the primary level, ments. It means, unlike a centralized structure in decentralized con­
The steady state error will not be zero; Therefore, restoration for voltage trollers, each DG unit will be an independent unit [62]. Therefore, in this
and frequency deviations and further flexibility of the secondary level structure, the need for communication network is reduced and control is
structure system has been proposed [31,32]. Secondary level has tasks done locally [63]. Unlike centralized control, only local information is
such as, responsible for providing reliability and reducing frequency and used, and the system can still work even if several agents fail. This
voltage deviations to determine the primary control operating points control strategy is considered to be the most reliable, despite its limi­
and economic performance of the MG [33]. The secondary controller tations due to the absence of a communication link. The decentralized
sets the point of common coupling voltage, and the power exchange control is appropriate to reduce the complexity of communications and
between MG and main grid. The operating points of secondary control computing. It has three main branches, namely consensus-based algo­
are determined on the basis of optimization criteria for loss reduction, rithms, MASs and their combinations. In recent studies, a decentralized
power quality and result in increasing economic benefits [33,34,35]. control structure has been developed using the MAS framework.
The secondary control can be performed in three ways: centralized Decentralized control based on the MAS concept for microgrids has been
[36,37], distributed and decentralized structure [38,39,40,41]. Cate­ introduced in [64] and developed in [65]. Fig. 2 shows the decentralized
gories of secondary controller are shown in Table 1. This classification control scheme. Table 4 shows the studies performed on decentralized
shows the types of secondary control methods that have been presented controllers. However, the decentralized structure may not effectively
in various papers. It helps to understand more and get a glimpse of this manage all control objectives due to lack of communication [66]. As a
level of the controller. result, the distributed structure is developed with the advantages of both
centralized and decentralized methods. The next section describes the
features of the distributed structure.
2.1. Centralized structure in microgrid

At the centralized methods, it is assumed that the loads are localized

in a shared bus and the secondary control adjusts the voltage of the

2
N. Sheykhi et al. International Journal of Electrical Power and Energy Systems 140 (2022) 108081

Table 2
Control method used for centralized control.
Control method Control approach Application Performance in the presence of communication delays Relevant
reference

Model predictive -It is based on future behavior of the -Suitable for systems greatly -MPC can be effectively applied to systems as a secondary [23]
control(MPC) system and predictions. dependent on demand. control even under a severe condition where the
-It provides a feedback mechanism. -Suitable for systems greatly communication delays are unknown and complex.
dependent on renewable energy
generation.
Droop based -In this control, an offline calculation is -Suitable for microgrids with -The controller uses complex potential functions to detect [36]
control being performed, which is a cost limited distributed generation perturbations due to time delays.
effective approach. resources.
Prediction based -It is based on H∞ control and -Suitable for in time delay -The predictor-based robust controller maintained good voltage
[61]
memory control predictions. systems. regulation time-delayed systems.

[69]. Generally, three challenges exist in the distributed control to MG.

Table 3 The first is how to restore the voltage and frequency of heterogeneous
Advantages and disadvantages of centralized control. DG units to reference values simultaneously and quickly in a fully
Advantage of centralized Relevant Disadvantages of Relevant distributed method. The second, is the accurate power division ac­
control reference centralized control reference cording to the DG power capacity and the third challenge is independent
The centralized control [8] Strong dependence on [8] on the MG parameters [70,71].
scheme offers high the central controller. Distributed control provides a robust secondary control framework
controllability and designed to speed up the synchronization process and ensure consensus
observability. in a limited time [72]. The distributed control methods are summarized
Ability to add a DG to the Widespread
MG without affecting communication network
in Table 5. A review and comparison of secondary level control struc­
the microgrid central causes time delay. tures demonstrates the researchers’ interest in expanding the distributed
controller. and decentralized structure in recent articles. The reason for this can be
It’s implementation is [22] High costs generated by [56] considered the importance of reducing the dependence of the control
simple and the high-bandwidth
structure on the communication network and making the MG resilience
straightforward. communication links.
Low-cost scheme. [61] This scheme also lacks [56] to communication challenges.
flexibility and Among the distributed methods, the consensus protocol is known as
expandability. a suitable approach for the distributed structure. In reason to in many
Centralized control has the [61] Centralized structure [58] research related to distributed method they have used consensus. This
ability to collect data requires a complex
method will be discussed in apart section as follows.
from all units. communication
network.
Centralized structure is [41] 2.3.1. Consensus-based methods
more adapted to small The consensus algorithm is a distributed approach that has become
scale single user MGs.
popular over the years for microgrid control. This algorithm is able to
coordinate the distributed generator in an MG by sharing information. In
the consensus method, the definition of communication interactions and
Local Controller the rules governing information exchange is based on Multi-Agent Sys­
tem (MAS) theory [77,78,79,80]. How to design an appropriate control
with the goal that all agents can achieve a common value in the MAS
consensus algorithm is a fundamental issue. Widespread participation of
C1 C2 C3 Cn
agents makes the control and management of MAS centrally costly or
even impractical. Therefore, distributed control has been developed
using local information exchange between neighbors through shared
communication networks. Several studies on distributed consensus
control for MAS have been conducted in recent years [80,81]. The
Unit 1 Unit 2 Unit 3 Unit n application of other types of consensus methods has also been discussed
in the literature [82–96], summarized in Fig. 5.
In the conventional approach of consensus algorithm, distributed
generation control systems have access to measured data and control
Physical Connection signals through a communication network. Due to limited communica­
tion resources, saving computing resources will be important. Contin­
Fig. 2. Schematic diagram of decentralized control scheme. uous use of the communication network can intensify communication
disruptions such as long delays, increase packet loss and reduce
2.3. Distributed structure in microgrid throughput, and inevitably reduce the stability, performance and reli­
ability of the system. The event-triggered consensus control is a positive
The distributed control, in addition to reducing the use of commu­ solution for maintaining MAS control function. It also reduces the
nication network, also has the advantages of centralized control struc­ overuse of communication and computing resources [97,98].
ture, so this structure has been developed as a suitable method for The event-triggered method has been increasingly applied at the
controlling microgrids [5]. The conventional structure of distributed secondary control level of MG, as it maintains stability, reducing the
control is illustrated in Fig. 3. exchange of information between DGs. To study event-triggered control
Recent articles have attempted to eliminate the need for high-level under the consensus method, this structure can be divided into two
control and to present a fully distributed structure, as shown in Fig. 4 types. The first structure is the model-based event-triggered scheme. In

3
N. Sheykhi et al. International Journal of Electrical Power and Energy Systems 140 (2022) 108081

Table 4
Control methods used for decentralized control.
Control method Performance Control approach Application Performance in the presence of Relevant
communication delays reference

Adaptive -The strategy is based on the static droop -The control structure preserves the dynamics - Not investigated. [67]
decentralized characteristics combined with an adaptive and stability of each inverter unit at different
droop controller transient droop function. loading conditions.
Consensus -Based on state feedback. -The event-triggered consensus problem is -Not investigated. [68]
-Without requiring continuous studied for multi-agent systems with general
communication among agents. linear dynamics under a general directed
graph.
Event-Triggered - The control will detect the predefined -Systems of which there is time delay. -It is observed that the response of the [57]
event function. system become more oscillatory as the
communication delays increase.

[101]. The event triggered structure has advantages and disadvantages,

which are summarized in Table 6.
Local Controller Communication link

3. Communication network

C1 C2 C3 Cn
The study of the communication networks has been examined in two
parts, communication network modelling by graph theory and
communication protocols.

3.1. Graph theory

Unit 1 Unit 2 Unit 3 Unit n

Considering an islanded MG with multiple DGs, the communication

network among the DGs can be modelled by a graph. In a MG graph, the
Physical Connection

Fig. 3. Schematic diagram of the distributed control scheme.

Local Controller

Unit 1 Unit 2 Unit 3 Unit n

Physical Connection

Fig. 4. Fully distributed structure.

this structure, event trigger control is defined based on estimated errors

[99]. The second structure is an event-based sampling data that includes
methods such as event-based sampling scheme, sampled data-based
sampling scheme [100] and self-commissioned sampling scheme Fig. 5. Types of consensus methods.

Table 5
Summary of distributed control methods.
Control Method Implementation Other Features Relevant reference
Technique Complexity

Distributed Droop Easy –No dependence on communication network. [51,52,53]

Control MPC Complex -It is very useful for microgrids with a large number of resources. [54,55,56]
SMC Moderate -The closed-loop response of the system has no sensitivity to uncertainties (model [56,57,58,59,60]
parameters, perturbations, and nonlinearity).
Event- Complex -Reduce control signal updates. [61,62,63,64,65,73]
Triggered -Reduce the possibility of data loss in data transmission.
H∞ Complex -Suitable for delayed systems.
[74,75,76]

4
N. Sheykhi et al. International Journal of Electrical Power and Energy Systems 140 (2022) 108081

Table 6
Advantages and disadvantage of event triggered control scheme.
Scheme Disadvantage

High Frequency of Control Updates Requirement on Continuous Limitations of System Dynamics

Communication:

Model-based event-triggered [117,118,119,120,121,122] – –

Event-based sampling [102,103,104,105,106,107,108,109,110] [102,103,111,112,113,114,115,116] [102,107,105,106,107,108,111,112,113,114,115,116],
scheme
Sampled-data-based event- [123,124,125,126] – [123,124,125,126]
triggered
Self-triggered sampling [127,128,129] – –

Table 7 Table 8
Types of graphs for communication network modelling in microgrids. Control methods used against communication disturbances.
Graph theory Control Communication Effect of communication Related
methods disturbance disturbances on microgrid Literature
Type Advantages Disadvantages Relevant
references Noise-resilient Gaussian noise -The communication links [143]
control are subjected to uncertain
Directed • Asymmetric property • Complex [84,85,86]
noises, which can
of directed graph
significantly affect the
Undirected • Low demand for • Asymmetric [87,92]
synchronization
communication property of directed
performance of MG control.
channel graph
Distributed Additive type of -The communication links [144]
• communication
noise resilient noise are subjected to additive
equipment
communication noise,
• low resource cost
which can significantly
• high scalability
affect the Voltage and
• robustness against
frequency of MG control.
delay
Event-triggered Channel noise -The channel noise can [145]
SMC-virtual significantly affect the
leader Voltage and frequency of
nodes indicate the DG and the edge of their communications links [130]. MG control.
So the multi-agent system is presented as a graph where V = {v1 .v2 .v3 ⋯ Cooperative Measurement -Causes instability in the [146]
.vn } is the set of agents, ε ⊆ V × V, ε = {e1 .e2 ⋯en } an edge set, The control noises microgrid voltage.
[ ] Link failure - Link failure has a direct [147]
associated adjacency matrix A = aij ∈ Rn×n with aii = 0. The Lap­
effect on transient control
lacian matrix is given by L = D-A; Where D is the degree matrix and L = performance and weakens
Lij ∈ Rn×n , L is symmetric positive semi-definite [131]. In Table 7, the it.
graph structure is divided into two subsections. Fully Packet loss -Communication packet [148]
distributed loss leads to longer
cooperative transient regulating time
3.2. Communication protocol for the DGs.
Cyber-physical Packet loss -The packet loss has a direct [149]
cooperative impact on communication
For proper microgrid performance in addition to the appropriate control data. Moreover, the large
control structure, the choice of a communication protocol can have a loss rate will cause the
significant impact on microgrid performance. In other words, it is interruption of
appropriate for the communication protocol to be in line with the communication.

microgrid control objectives and not to complicate or increase the cost of

implementation. In recent studies of microgrid control, various dependence on the distributed structure on the communication network,
communication protocols have been studied with the aim of reducing microgrids are exposed to communication constraints and disturbances.
costs and accelerating the development of microgrids [132,133]. In In this regard, communication constraints, such as time delay, packet
[132], the communication structure based on IEC 61850 has been used -loss, cyber-attack, communication network failed and noise is the most
as a suitable and promising solution to maintain network security, important communication disturbances in the microgrid, which will
increasing microgrid reliability. lead to loss of synchronization of physical variables or even microgrid
The study of communication network can be divided into two types instability [130]. Therefore, designing a control structure that can
using wired and wireless [134]. Wired networks such as PLC and fiber maintain microgrid stability in the presence of disturbances caused by
optics are subject to more noise and communication disturbances due to communication networks will be important [131,137,138]. Consensus-
environmental conditions than wireless modes. The use of wired based distributed control has been considered as a suitable approach
methods can also lead to the complexity of the communication network for communication constraints, so that in [139] a consensus-based
and limit them to a specific location. Therefore, wireless networks such control is introduced considering time-varying delays and noise.
as Wi-Fi, WiMax, ZigBee, SigFox with a suitable control structure in Another approach to avoiding communication constraints is to use time-
order to prevent communication disturbances seem more cost-effective based graph theory; In [140] using communication network modelling
and appropriate [134,135,136]. Communication disturbances in wire­ by time-varying graph theory, microgrid has been significantly
less networks are studied in the next section. improved against data loss, communication network failed and time
delay.
3.3. Communication disturbances In addition to the disturbances caused by the communication
network, cyber-attacks can affect the performance of the microgrid in
It was previously stated that the distributed structure has a better such a way that it can cause the collapse of the microgrid, which has
performance compared to other control structures, however, due to the been studied in [141] by the software defined networking (SDN)

5
N. Sheykhi et al. International Journal of Electrical Power and Energy Systems 140 (2022) 108081

Table 9
The effect of secondary control methods on time delay in microgrid.
Method Type of time delay Performance of the MG in the presence of delay Delay Effect of proposed controller Reference
margin

SMC Time-varying delay Time delays will make control signals for Time- The accuracy of random [160]
reference voltage waveforms delayed, which varying delay estimation τ (t) and microgrid states estimation x
causes phase shift between reference LPG voltage (t) can be adaptively improved by SMC control.
and microgrid voltage.
Time-varying delay The estimated stochastic delays. Time- This control schemes has shown the benefits for dealing [161]
varying. with long time delays using the predictive structure
plus the robustness of the sliding mode theory.
MPC constant The voltage observer-based DMPC cannot achieve Simulation Compared to conventional control schemes, this [162]
communication link accurate voltage recovery under time delay. (0.2 s) scheme can fully take into account the constraints
time delay caused by the time delay and achieve an adjustable
balance between node voltage and power-sharing.
Variable and unknown Unstable eigenvalues. Simulation The MPC based secondary control system is [34]
communication delays (1.11 s) considerably more robust in terms of maximum delay
allowed.
Consensus Communication link The RMS currents have some oscillations before Simulation This structure shows the excellent performance of [163]
time delay the consensus is achieved. (1 s) consensus algorithms in terms of resistance to time
delay in a short time.
Communication link Increasing of communication delays lead to Simulation Compared to existing controllers, the proposed [157]
time delay higher fluctuations and slower agreement rates. (10 ms) controller gave a faster convergence rate based on the
finite-time consensus protocol.
Communication link The closed-loop system response becomes Simulation Demonstrates significant robustness against load [159]
time delay oscillatory and the convergence becomes slow (800 ms) disturbances, and successfully tolerates, small as well
under communication time delays. as large, communication time-delays.
Communication link The communication delay will Reduce the Simulation Simulation results verify the effectiveness of the [164]
time delay convergence speed of the control system. (400 ms) proposed strategy, especially, it has strong robustness
to communication delay.
Event Communication link Worsening of microgrid performance in Simulation The simulation results confirm the effectiveness of the [162]
triggered time delay maintaining stability. (0.1 s) proposed strategy against large time delays. However,
in the proposed method, the convergence speed is slow.
Communication link Worsening of microgrid performance in Simulation The proposed control is resistant to time delays of less [165]
time delay maintaining stability. (15 ms) than 15 (ms). However, with increasing delay, the
system becomes unstable.

approach. Failure in the communication network is one of the distur­ shown that each has different requirements in terms of communication
bances affecting the performance of the microgrid controller, which is performance. In this study, it was found that the primary level is a time-
studied in [142] a proposed approach for the reconstruction of sensitive mechanism that ensures voltage and instantaneous frequency
communication lines. Table 8 summarizes the control methods used in control, so communication-less control methods are adopted. The sec­
the presence of communication disturbances. Among the communica­ ondary level, unlike the primary level, is more dependent on the
tion disturbances studied, time delay has been studied by researchers in communication network. Depending on the communication network,
recent studies due to its importance in maintaining stability. Therefore, this level was divided into three structures; centralized, decentralized
it has been studied in Section 3.3.1. and distributed. Comparison of control structures showed that distrib­
uted control has many advantages over a centralized design (e.g., higher
3.3.1. Communication delays on secondary control reliability and resistance to unit failure), it may require more complex
Data transmission by communication networks such as WiFi, WiMax, data transmission through communication lines. Dependence on the
Internet, Ethernet and ZigBee in microgrid is associated with time delay communication network in the distributed control structure requires the
[150,151]. Time delay in communication networks at the worst case it study of the controller behavior in the presence of communication dis­
can cause poor and unstable performance of microgrids. To investigate turbances. Thus, “how to stabilize data transmission with communica­
the time delay in communication networks, it can be divided into two tion disturbances in the limited network resources of an MG” is one of
groups; input delay and communication delay [152]. the challenges in controlling MG. Finally, this study provided a
The distributed control structure is an effective method for microgrid comprehensive overview of the secondary level with respect to the
control in the presence of time delay [153]. However, this structure has communication network in MG and the behavior of controllers in the
limited resistance to time delays. So, finding the delay margin so that the presence of communication disturbance. It is recommended that com­
microgrid performs well is a challenging issue. Taylor series [154], munications attacks and their impact on the secondary level control
Linear matrix inequality [155,156], Simulation-based [157,158], structure and MG performance be investigated in future work.
Experiment/HIL [159] are well-known methods for determining the
delay margin in microgrids. In addition, in [159] an extensive study has
been conducted on methods for determining the time delay margin in Declaration of Competing Interest
microgrids. Table 9 summarizes the four control structures in order to
study the types of delays and how to calculate the delay margin, as well The authors declare that they have no known competing financial
as the effect of the control method on time delay. interests or personal relationships that could have appeared to influence
the work reported in this paper.
4. Conclusion
Acknowledgement
One of the challenges in microgrids is the proper control system. In
this paper, the structure of secondary control with the definition of three The third and fourth author would like to acknowledge the supported
levels of primary, secondary and tertiary was examined and it was by VILLUM FONDEN under the VILLUM Investigator Grant (no. 25920):
Center for Research on Microgrids (CROM); www.crom.et.aau.dk.

6
N. Sheykhi et al. International Journal of Electrical Power and Energy Systems 140 (2022) 108081

References [33] Vandoorn TL, De Kooning JDM, Meersman B, Vandevelde L. Communication-

based secondary control in microgrids with voltage-based droop control. In: PES
T&D 2012, Orlando, FL, USA; 2012. p. 1–6.
[1] Xie H, Zheng S, Ni M. Microgrid Development in China: A method for renewable
[34] Ahumada C, Cárdenas R, Sáez D, Guerrero JM. Secondary Control Strategies for
energy and energy storage capacity configuration in a megawatt-level isolated
Frequency Restoration in Islanded Microgrids With Consideration of
microgrid. IEEE Electrif Mag 2017;5(2):28–35.
Communication Delays. IEEE Trans Smart Grid 2016;7(3):1430–41.
[2] Gu W, Lou G, Tan W, Yuan X. A Nonlinear State Estimator-Based Decentralized
[35] Li Z, Duan Z, Chen G, Huang L. Consensus of Multiagent Systems and
Secondary Voltage Control Scheme for Autonomous Microgrids. IEEE Trans
Synchronization of Complex Networks: A Viewpoint. IEEE Unified Transactions on
Power Syst 2017;32(6):4794–804.
Circuits and Systems I: Regular Papers 2010;57(1):213–24.
[3] Oliveira TR, Gonçalves Silva WWA, Donoso-Garcia PF. Distributed Secondary
[36] Yao G, et al. A central control strategy of parallel inverters in AC microgrid.
Level Control for Energy Storage Management in DC Microgrids. IEEE Trans
IECON 2013–39th Annual Conference of the IEEE Industrial Electronics Society.
Smart Grid 2017;8(6):2597–607.
2013.
[4] Che L, Khodayar M, Shahidehpour M. Only Connect: Microgrids for Distribution
[37] Bidram A, Davoudi A. Hierarchical Structure of Microgrids Control System. IEEE
System Restoration. IEEE Power Energy Mag 2014;12(1):70–81.
Trans Smart Grid 2012;3(4):1963–76.
[5] Chmiel Z, Bhattacharyya SC. Analysis of off-grid electricity system at Isle of Eigg
[38] Palizban O, Kauhaniemi K. Secondary control in AC microgrids challenges and
(Scotland): Lessons for developing countries. Renew Energy 2015;81:578–88.
solutions. In: 2015 International Conference on Smart Cities and Green ICT
https://doi.org/10.1016/j.renene.2015.03.061.
Systems (SMARTGREENS), Lisbon, Portugal; 2015. p. 1–6.
[6] Sahoo SK, Sinha AK, Kishore NK. Control Techniques in AC, DC, and Hybrid
[39] Bidram A, Davoudi A, Lewis FL, Qu Z. Secondary control of microgrids based on
AC–DC Microgrid: A Review. IEEE J Emerg Sel Top Power Electron 2018;6(2):
distributed cooperative control of multi-agent systems. IET Gener Transm Distrib
738–59.
2013;7(8):822–31.
[7] Cagnano A, De Tuglie E, Mancarella P. Microgrids: Overview and guidelines for
[40] Shafiee Q, Guerrero JM, Vasquez JC. Distributed Secondary Control for Islanded
practical implementations and operation. Appl Energy 2020;258:114039.
Microgrids—A Novel Approach. IEEE Trans Power Electron 2014;29(2):1018–31.
[8] Rey JM, Martí P, Velasco M, Miret J, Castilla M. Secondary Switched Control
[41] Rajesh KS, Dash SS, Rajagopal Ragam, Sridhar R. A review on control of ac
With no Communications for Islanded Microgrids. IEEE Trans Ind Electron 2017;
microgrid. Renewable and sustainable energy reviews. Renew Sustainable Energy
64(11):8534–45.
Rev 2017;71:814–9.
[9] Peyghami S, Mokhtari H, Blaabjerg F. Decentralized Load Sharing in a Low-
[42] Wu T, Liu J, Liu Z, Wang S, Liu B. Load power estimation based secondary control
Voltage Direct Current Microgrid With an Adaptive Droop Approach Based on a
for microgrids. In: 2015 9th International Conference on Power Electronics and
Superimposed Frequency. IEEE J Emerg Sel Top Power Electron 2017;5(3):
ECCE Asia (ICPE-ECCE Asia), Seoul, Korea (South); 2015. p. 722–7.
1205–15.
[43] Lu X, Guerrero JM, Sun K, Vasquez JC. An Improved Droop Control Method for
[10] Mehrizi-Sani A, Iravani R. Potential-Function Based Control of a Microgrid in
DC Microgrids Based on Low Bandwidth Communication With DC Bus Voltage
Islanded and Grid-Connected Modes. IEEE Trans Power Syst 2010;25(4):1883–91.
Restoration and Enhanced Current Sharing Accuracy. IEEE Trans Power Electron
[11] Dragičević T. Model Predictive Control of Power Converters for Robust and Fast
2014;29(4):1800–12.
Operation of AC Microgrids. IEEE Trans Power Electron 2018;33(7):6304–17.
[44] Li J, Su J, Shi Y, Mao M, Yang X, Liu N, et al. International Power Electronics and
[12] Serban I, Céspedes S, Marinescu C, Azurdia-Meza CA, Gómez JS, Hueichapan DS.
Application Conference and Exposition. Shanghai, China 2014;2014:1143–7.
Communication Requirements in Microgrids: A Practical Survey. IEEE Access
[45] Mahmoud MS, Hussain SA. Adaptive PI secondary control for smart autonomous
2020;8:47694–712.
microgrid systems. In International Journal of Adaptive Control and Signal
[13] Yazdanian M, Mehrizi-Sani A. Distributed Control Techniques in Microgrids. IEEE
Processing 2015;29(11):1442–58.
Trans Smart Grid 2014;5(6):2901–9.
[46] Simpson-Porco JW, Shafiee Q, Dörfler F, Vasquez JC, Guerrero JM, Bullo F.
[14] Olivares DE, et al. Trends in Microgrid Control. IEEE Trans Smart Grid 2014;5(4):
Secondary Frequency and Voltage Control of Islanded Microgrids via Distributed
1905–19.
Averaging. IEEE Trans Ind Electron 2015;62(11):7025–38.
[15] Vasquez JC, Guerrero JM, Miret J, Castilla M, de Vicuña LG. Hierarchical Control
[47] Wu D, Dragicevic T, Vasquez JC, Guerrero JM, Guan Y. Secondary coordinated
of Intelligent Microgrids. IEEE Ind Electron Mag 2010;4(4):23–9.
control of islanded microgrids based on consensus algorithms. In: 2014 IEEE
[16] Khayat Y, et al. On the Secondary Control Architectures of AC Microgrids: An
Energy Conversion Congress and Exposition (ECCE), Pittsburgh, PA, USA; 2014.
Overview. IEEE Trans Power Electron 2020;35(6):6482–500.
p. 4290–7.
[17] Khayat Y, et al. Decentralized Optimal Frequency Control in Autonomous
[48] Danzi P, Stefanovic C, Meng L, Guerrero JM, Popovski P. On the Impact of
Microgrids. IEEE Trans Power Syst 2019;34(3):2345–53.
Wireless Jamming on the Distributed Secondary Microgrid Control. In: 2016 IEEE
[18] Castilla M, Camacho A, Martí P, Velasco M, Ghahderijani MM. Impact of Clock
Globecom Workshops (GC Wkshps), Washington, DC; 2016. p. 1–6.
Drifts on Communication-Free Secondary Control Schemes for Inverter-Based
[49] Coelho EA, et al. Small-Signal Analysis of the Microgrid Secondary Control
Islanded Microgrids. IEEE Trans Ind Electron 2018;65(6):4739–49.
Considering a Communication Time Delay. IEEE Trans Ind Electron 2016;63(10):
[19] Martí P, Torres-Martínez J, Rosero CX, Velasco M, Miret J, Castilla M. Analysis of
6257–69.
the Effect of Clock Drifts on Frequency Regulation and Power Sharing in Inverter-
[50] Shafiee Q, Stefanović Č, Dragičević T, Popovski P, Vasquez JC, Guerrero JM.
Based Islanded Microgrids. IEEE Trans Power Electron 2018;33(12):10363–79.
Robust Networked Control Scheme for Distributed Secondary Control of Islanded
[20] Lou G, Gu W, Xu Y, Jin W, Du X. Stability Robustness for Secondary Voltage
Microgrids. IEEE Trans Ind Electron 2014;61(10):5363–74.
Control in Autonomous Microgrids With Consideration of Communication Delays.
[51] Kahrobaeian A, Ibrahim Mohamed YA. Networked-Based Hybrid Distributed
IEEE Trans Power Syst 2018;33(4):4164–78.
Power Sharing and Control for Islanded Microgrid Systems. IEEE Transactions on
[21] Chen M, Xiao X. Secondary voltage control in islanded microgrids using event-
Power Electronics, Feb. 2015;30(2):603–17.
triggered control. IET Gener Transmiss Distrib 2018;12(8):1872–8.
[52] Simpson-Porco JW, Dörfler F, Bullo F, Shafiee Q, Guerrero JM. Stability, power
[22] Sahoo S, Mishra S. An Adaptive Event-Triggered Communication-Based
sharing, & distributed secondary control in droop-controlled microgrids. In: 2013
Distributed Secondary Control for DC Microgrids. IEEE Trans Smart Grid 2018;9
IEEE International Conference on Smart Grid Communications
(6):6674–83.
(SmartGridComm), Vancouver, BC, Canada; 2013. p. 672–7.
[23] Tan KT, Peng XY, So PL, Chu YC, Chen MZQ. Centralized Control for Parallel
[53] Bidram A, Davoudi A, Lewis FL, Guerrero JM. Distributed Cooperative Secondary
Operation of Distributed Generation Inverters in Microgrids. IEEE Trans Smart
Control of Microgrids Using Feedback Linearization. IEEE Trans Power Syst 2013;
Grid 2012;3(4):1977–87.
28(3):3462–70.
[24] Zhang Y, Shotorbani AM, Wang L, Mohammadi-Ivatloo B. Distributed Secondary
[54] Wang Zhao, Sun Hongbo, Nikovski D. Distributed secondary voltage controller for
Control of a Microgrid With A Generalized PI Finite-Time Controller. IEEE Open
droop-controlled microgrids to improve power quality in power distribution
Access Journal of Power and Energy 2021;8:57–67.
networks. In: 2016 IEEE Power and Energy Society General Meeting (PESGM),
[25] Li Q, Peng C, Wang M, Chen M, Guerrero JM, Abbott D. Distributed Secondary
Boston, MA, USA; 2016. p. 1–5.
Control and Management of Islanded Microgrids via Dynamic Weights. IEEE
[55] Yu Z, et al. A novel secondary control for microgrid based on synergetic control of
Trans Smart Grid 2019;10(2):2196–207.
multi-agent syste. In Energies 2016;Vol. 9(4):243.
[26] Memon AA, Kauhaniemi K. A critical review of AC Microgrid protection issues
[56] Lu X, Yu X, Lai J, Guerrero JM, Zhou H. Distributed Secondary Voltage and
and available solutions. Electr Power Syst Res 2015;129:23–31.
Frequency Control for Islanded Microgrids With Uncertain Communication Links.
[27] Stadler M, et al. Value streams in microgrids: A literature review. Appl Energy
IEEE Trans Ind Inf 2017;13(2):448–60.
2016;162:980–9.
[57] Chen M, Xiao X, Guerrero JM. Secondary Restoration Control of Islanded
[28] Han Y, Li H, Shen P, Coelho EAA, Guerrero JM. Review of Active and Reactive
Microgrids With a Decentralized Event-Triggered Strategy. IEEE Trans Ind Inf
Power Sharing Strategies in Hierarchical Controlled Microgrids. IEEE Trans
2018;14(9):3870–80.
Power Electron 2017;32(3):2427–51.
[58] Dehkordi NM, Sadati N, Hamzeh M. Distributed Robust Finite-Time Secondary
[29] Unamuno E, Barrena JA. Hybrid ac/dc microgrids—Part II: Review and
Voltage and Frequency Control of Islanded Microgrids. IEEE Trans Power Syst
classification of control strategies. Renew Sustain Energy Rev 2015;52:1123–34.
2017;32(5):3648–59.
[30] Mahmoud MS, Azher Hussain S, Abido MA. “Modeling and control of microgrid:
[59] Dehkordi NM, Sadati N, Hamzeh M. Fully Distributed Cooperative Secondary
An overview”. In. J Franklin Inst 2014;351(5):2822–59.
Frequency and Voltage Control of Islanded Microgrids. IEEE Trans Energy
[31] Anand S, Fernandes BG, Guerrero J. Distributed Control to Ensure Proportional
Convers 2017;32(2):675–85.
Load Sharing and Improve Voltage Regulation in Low-Voltage DC Microgrids.
[60] Asheibi A, et al. Stability analysis of PV-based DC microgrid with communication
IEEE Trans Power Electron 2013;28(4):1900–13.
delay. In: 2018 9th International Renewable Energy Congress (IREC),
[32] Nutkani IU, Peng W, Chiang LP, Blaabjerg F. Secondary Droop for Frequency and
Hammamet, Tunisia; 2018. p. 1–6.
Voltage Restoration in Microgrids. In: 2015 17th European Conference on Power
Electronics and Applications (EPE’15 ECCE-Europe), Geneva, Switzerland; 2015.
p. 1–7.

7
N. Sheykhi et al. International Journal of Electrical Power and Energy Systems 140 (2022) 108081

[61] Guo F, Xu Q, Wen C, Wang L, Wang P. Distributed Secondary Control for Power [93] Li Z, Duan Z, Chen G, Huang L. ‘Consensus of multiagent systems and
Allocation and Voltage Restoration in Islanded DC Microgrids. IEEE Trans synchronization of complex networks: A unified viewpoint’. IEEE Trans. Circuits
Sustainable Energy 2018;9(4):1857–69. Syst. I, Reg. Papers, Jan. 2010;57(1):213–24.
[62] Mehdi M, Kim C, Saad M. Robust Centralized Control for DC Islanded Microgrid [94] Zheng Y, Zhu Y, Wang L. ‘Consensus of heterogeneous multiagent system’. IET
Considering Communication Network Delay. IEEE Access 2020;8:77765–78. Control Theory Appl 2011;5(16):1881–8.
[63] Bharath K, Choutapalli H, Kanakasabapathy P. Control of bidirectional DC-DC [95] Kim JM, Choi YH, Park JB. ‘Leaderless and leader-following consensus for
converter in renewable based DC microgrid with improved voltage stability. heterogeneous multi-agent systems with random link failures’. IET Control
International Journal of Renewable Energy Research (IJRER) 2018;8(2):871–7. Theory Appl 2014;8(1):51–60.
[64] Rokrok E, Golshan MEH. Adaptive volatge droop scheme for volatge source [96] Hui Q, Haddad WM, Bhat SP. ‘On robust control algorithms for nonlinear network
converters in an islanded multibus microgrid. IET Gener Transm Distrib 2010;4 consensus protocols’. Int J Robust Nonlinear Control 2010;20(3):269–84.
(5):562–78. [97] Dimarogonas DV, Frazzoli E, Johansson KH. ‘Distributed eventtriggered control
[65] Hajimiragha A, Zadeh MRD. Research and development of a microgrid control for multi-agent systems’. IEEE Trans Autom Control 2012;57(5):1291–7.
and monitoring system for the remote commu- nity of Bella Coola: Challenges, [98] Ge X, Han Q-L. ‘Distributed formation control of networked multiagent systems
solutions achievements and lessons learned. In: Proceedings of the IEEE using a dynamic event-triggered communication mechanism’. IEEE Trans Ind
International Conference on Smart Energy Grid Engineering (SEGE’13). 2013. Electron 2017;64(10):8118–27.
[66] Lou G, Gu W, Wang J, Sheng W, Sun L. Optimal Design for Distributed Secondary [99] Yin X, Yue D, Hu S, Peng C, Xue Y. Model-based eventtriggered predictive control
Voltage Control in Islanded Microgrids: Communication Topology and Controller. for networked systems with data dropout. SIAM J Control Optim 2016;54(2):
IEEE Trans Power Syst 2019;34(2):968–81. 567–86.
[67] Mohamed Yasser Abdel-Rady Ibrahim, El-Saadany Ehab F. Adaptive [100] Zhang X-M, Han Q-L. Event-triggered dynamic output feedback control for
decentralized droop controller to preserve power sharing stability of paralleled networked control systems. IET Control Theory Appl 2014;8(4):226–34.
inverters in distributed generation microgrids. IEEE Trans Power Electron 2008; [101] Heemels WPMH, Johansson KH, Tabuada P. An introduction to event-triggered
23(6):2806–16. and self-triggered control. In: Proc. 51st IEEE Annu. Conf. Decis. Control; 2012.
[68] Yang D, et al. Decentralized event-triggered consensus for linear multi-agent p. 3270–85.
systems under general directed graphs. Automatica 2016;69:242–9. [102] Dimarogonas DV, Frazzoli E, Johansson KH. Distributed event-triggered control
[69] Anand S, Fernandes BG, Guerrero J. “Distributed control to ensure proportional for multi-agent systems. IEEE Trans Autom Control 2012;57(5):1291–7.
load sharing and improve voltage regulation in low voltage DC microgrids”, IEEE. [103] Zhang Z, Hao F, Zhang L, Wang L. Consensus of linear multi-agent systems via
Trans. Power Electron. 2013;28(4):1900–13. event-triggered control. Int J Control 2014;87(6):1243–51.
[70] Guo F, Wang L, Wen C, Zhang D, Xu Q. Distributed voltage restoration and [104] Nowzari C, Cortés J. Distributed event-triggered coordination for average
current sharing control in islanded DC microgrid systems without continuous consensus on weight-balanced digraphs. Automatica 2016;68:237–44.
communication. IEEE Trans Ind Electron 2020;67(4):3043–53. [105] Garcia E, Cao Y, Yu H, Antsaklis P, Casbeer D. Decentralised event-triggered
[71] Fan B, Peng J, Duan J, Yang Q, Liu W. Distributed control of multiple- cooperative control with limited communication. Int J Control 2013;86(9):
busmicrogrid with paralleled distributed generators. IEEE/CAA J. Automatica 1479–88.
Sinica 2019;6(3):676–84. [106] Kia SS, Cortés J, Martínez S. Distributed event-triggered communication for
[72] Gao F, Bozhko S, Asher G, Wheeler P, Patel C. An improved voltage compensation dynamic average consensus in networked systems. Automatica 2015;59:112–9.
approach in a droop-controlled DC power system for the more electric aircraft. [107] Yu H, Antsaklis PJ. Output synchronization of networked passive systems with
IEEE Trans Power Electron 2015;31(10):7369–83. event-driven communication. IEEE Trans Autom Control 2014;59(3):750–6.
[73] Mohammadi F, et al. A bidirectional power charging control strategy for plug-in [108] Yin X, Yue D, Hu S. Adaptive periodic event-triggered consensus for multi-agent
hybrid electric vehicles. Sustainability 2019;11(16). systems subject to input saturation. Int J Control 2016;89(4):653–67.
[74] Guo F, Wen C, Mao J, Chen J, Song Y. Distributed Cooperative Secondary Control [109] Seyboth GS, Dimarogonas DV, Johansson KH. Event-based broadcasting for multi-
for Voltage Unbalance Compensation in an Islanded Microgrid. IEEE Trans Ind Inf agent average consensus. Automatica 2013;49(1):245–52.
2015;11(5):1078–88. [110] Xing L, Wen C, Guo F, Liu Z, Su H. Event-based consensus for linear multiagent
[75] Ferreira RAF, Braga HAC, Ferreira AA, Barbosa PG. Analysis of voltage droop systems without continuous communication. IEEE Trans Cybern 2017;47(8):
control method for dc microgrids with Simulink: Modelling and simulation. In: 2132–42.
2012 10th IEEE/IAS International Conference on Industry Applications, [111] Fan Y, Feng G, Wang Y, Song C. Distributed event-triggered control of multi-agent
Fortaleza, Brazil; 2012. p. 1–6. systems with combinational measurements. Automatica 2013;49(2):671–5.
[76] Gholami S, et al. Robust multiobjective control method for power sharing among [112] Zhu W, Jiang Z-P, Feng G. Event-based consensus of multi-agent systems with
distributed energy resources in islanded microgrids with unbalanced and general linear models. Automatica 2014;50(2):552–8.
nonlinear loads. Int J Electr Power Energy Syst 2018;94. [113] Li H, Liao X, Huang T, Zhu W. Event-triggering sampling based leader-following
[77] Shamma J. Coperative Control of Distributed Multi-Agent Systems. Hoboken, NJ, consensus in second-order multi-agent systems. IEEE Trans Autom Control 2015;
USA: Wiley; 2007. 60(7):1998–2003.
[78] Lewis FL, Zhang H, Hengster-Movric K, Das A. Cooperative Control of Multi- [114] Cheng Y, Ugrinovskii V. Event-triggered leader-following tracking control for
Agent Systems (Communications and Control Engineering). London, U.K.: multivariable multi-agent systems. Automatica 2016;70:204–10.
Springer; 2014. [115] Zhu W, Jiang Z-P. Event-based leader-following consensus of multi-agent systems
[79] Saber RO, Murray RM. Consensus protocols for networks of dynamic agents. In: with input time delay. IEEE Trans Autom Control 2015;60(5):1362–7.
Proc. Amer. Control Conf., vol. 2; 2003. p. 951–6. [116] Hu W, Liu L, Feng G. Output consensus of heterogeneous linear multi-agent
[80] Bidram A, Davoudi A, Lewis FL. Cooperative Synchronization in Distributed systems by distributed event-triggered/self-triggered strategy. IEEE Trans Cybern
Microgrid Control. Cham, Switzerland: Springer; 2017. 2017;47(8):1914–24.
[81] Antoniadou-Plytaria KE, Kouveliotis-Lysikatos IN, Georgilakis PS, [117] Garcia E, Cao Y, Casbeer DW. Decentralized event-triggered consensus with
Hatziargyriou ND. ‘Distributed and decentralized voltage control of smart general linear dynamics. Automatica 2014;50(10):2633–40.
distribution networks’. IEEE Trans Smart Grid 2017;8(6):2999–3008. [118] Yang D, Ren W, Liu X, Chen W. Decentralized eventtriggered consensus for linear
[82] Münz U, Papachristodoulou A, Allgöwer F. ‘Delay robustness in consensus multi-agent systems under general directed graphs. Automatica 2016;69:242–9.
problems’. Automatica 2010;46(8):1252–65. [119] Zhang H, Feng G, Yan H, Chen Q. Observer-based output feedback event-triggered
[83] Nowzari C, Garcia E, Cortés J. ‘Event-triggered communication and control of control for consensus of multi-agent systems. IEEE Trans Ind Electron 2014;61(9):
networked systems for multi-agent consensus’. Automatica 2019;105:1–27. 4885–94.
[84] Qin J, Zheng WX, Gao H. ‘Convergence analysis for multiple agents with double- [120] Zhang Z, Zhang L, Hao F, Wang L. Leader-following consensus for linear and
integrator dynamics in a sampled-data setting’. IET Control Theory Appl 2011;5 Lipschitz nonlinear multiagent systems with quantized communication. IEEE
(18):2089–97. Trans Cybern 2017;47(8):1970–82.
[85] Qin J, Xing Zheng W, Gao H. ‘Coordination of multiple agents with double- [121] Adaldo A, et al. Event-triggered pinning control of switching networks. IEEE
integrator dynamics under generalized interaction topologies’. IEEE Trans. Syst. Trans. Control Netw. Syst. 2015;2(2):204–13.
Man, Cybern. B, Cybern., Feb. 2012;42(1):44–57. [122] Liuzza D, Dimarogonas DV, Di Bernardo M, Johansson KH. Distributed model
[86] Yu W, DeLellis P, Chen G, di Bernardo M, Kurths J. ‘Distributed adaptive control based event-triggered control for synchronization of multi-agent systems.
of synchronization in complex networks’. IEEE Trans Autom Control 2012;57(8): Automatica 2016;73:1–7.
2153–8. [123] Zhao Y, Ding L, Guo G, Yang G. Event-triggered average consensus for mobile
[87] Li Z, Ren W, Liu X, Xie L. ‘Distributed consensus of linear multiagent systems with sensor networks under a given energy budget. J. Frankl. Inst. 2015;352(12):
adaptive dynamic protocols’. Automatica 2013;49(7):1986–95. 5646–60.
[88] Nedic A, Ozdaglar A, Parrilo PA. ‘Constrained consensus and optimization in [124] Meng X, Chen T. Event based agreement protocols for multi-agent networks.
multi-agent networks’. IEEE Trans Autom Control 2010;55(4):922–38. Automatica 2013;49(7):2125–32.
[89] Lin P, Ren W. ‘Constrained consensus in unbalanced networks with [125] Xiao F, Meng X, Chen T. Sampled-data consensus in switching networks of
communication delays’. IEEE Trans Autom Control 2014;59(3):775–81. integrators based on edge events. Int J Control 2015;88(2):391–402.
[90] Weiss L. ‘Converse theorems for finite time stability’. SIAM J Appl Math 1968;16 [126] Garcia E, Cao Y, Casbeer DW. Periodic event-triggered synchronization of linear
(6):1319–24. multi-agent systems with communication delays. IEEE Trans Autom Control
[91] Wang L, Xiao F. ‘Finite-time consensus problems for networks of dynamic agents’. 2017;62(1):366–71.
IEEE Trans Autom Control 2010;55(4):950–5. [127] Fan Y, Liu L, Feng G, Wang Y. Self-triggered consensus for multi-agent systems
[92] Spanos DP, Olfati-Saber R, Murray RM. Dynamic consensus on mobile networks. with Zeno-free triggers. IEEE Trans Autom Control 2015;60(10):2779–84.
In: Proc. 16th IFAC World Congr.; 2005. p. 1–6. [128] De Persis C, Frasca P. Robust self-triggered coordination with ternary controllers.
IEEE Trans Autom Control 2013;58(12):3024–38.

8
N. Sheykhi et al. International Journal of Electrical Power and Energy Systems 140 (2022) 108081

[129] Nowzari C, Cortés J. Team-triggered coordination for real-time control of [149] Peng C, Sun H, Yang M, Wang Y-L. A Survey on Security Communication and
networked cyber-physical systems. IEEE Trans Autom Control 2016;61(1):34–47. Control for Smart Grids Under Malicious Cyber Attacks. IEEE Transactions on
[130] Lu X, Lai J. Communication constraints for distributed secondary control of Systems, Man, and Cybernetics: Systems 2019;49(8):1554–69.
heterogenous microgrids: A survey. IEEE Trans Ind Appl 2021. [150] Pérez-Guzmán Ricardo Enrique, et al. Modelling Communication Network for
[131] Zhang Z, et al. An Event-Triggered Secondary Control Strategy With Network Intelligent Applications in Microgrids-Part II. 2018 IEEE International Conference
Delay in Islanded Microgrids. IEEE Syst J 2019;13(2):1851–60. on Automation/XXIII Congress of the Chilean Association of Automatic Control
[132] Ali Ikbal, Suhail Hussain SM. Communication design for energy management (ICA-ACCA). IEEE; 2018.
automation in microgrid. IEEE Trans. Smart Grid 2016;9(3):2055–64. [151] Aghaee F, et al. Distributed control methods and impact of communication failure
[133] Bayindir R, et al. A comprehensive study on microgrid technology. Int J Renew in AC microgrids: A comparative review. Electronics 2019;8(11):1265.
Energy Res (IJRER) 2014;4(4):1094–107. [152] Wang D, Wang Z, Chen M, Wang W. Distributed optimization for multi-agent
[134] Yadav M, Pal N, Saini DK. “Microgrid control, storage, and communication systems with constraints set and communication time-delay over a directed
strategies to enhance resiliency for survival of critical load.” IEEE. Access 2020;8: graph. Inf Sci 2018;438:1–4.
169047–69. [153] Dong C, et al. Dc microgrid stability analysis considering time delay in the
[135] Gabert SG, et al. Data Acquisition and Control of Microgrid Using ZigBee–A senior distributed control. Energy Procedia 2017;142:2126–31.
design project. 2013 North Midwest Section Meeting 2021. [154] Khalil AF, Wang J. A new stability and time-delay tolerance analysis approach for
[136] Ghosh S, Chanda CK, Das JK. A Comprehensive Survey on Communication networked control systems. In: Proc. 49th IEEE Conf. Decis. Control, Dec. 2010;
Technologies for a Grid Connected Microgrid System. International Conference on 2010. p. 4753–8.
Artificial Intelligence and Smart Systems (ICAIS) 2021;2021:1525–8. [155] Dou C, Yue D, Guerrero JM, Xie X, Hu S. Multiagent systembased distributed
[137] Yan H, et al. Adaptive event-triggered predictive control for finite time microgrid. coordinated control for radial dc microgrid considering transmission time delays.
IEEE Trans Circuits Syst I Regul Pap 2020;67(3):1035–44. IEEE Trans Smart Grid 2017;8(5):2370–81.
[138] Feng, Yiwei, Ma, Jing, Wang Xin. Robust H∞ Control of Networked System In [156] Dong M, Li L, Nie Y, Song D, Yang J. Stability analysis of a novel distributed
Microgrid Under Random Packet Loss; 2021. secondary control considering communication delay in dc microgrids. IEEE Trans
[139] Dehkordi NM, et al. Voltage and frequency consensusability of autonomous Smart Grid 2019;10(6):6690–700.
microgrids over fading channels. IEEE Trans Energy Convers 2020;36(1):149–58. [157] Wang Z, Wu W, Zhang B. A distributed control method with minimum generation
[140] Nojavanzadeh D, et al. Scale-free Cooperative Control of Inverter-based cost for dc microgrids. IEEE Trans Energy Convers 2016;31(4):1462–70.
Microgrids with General Time-varying Communication Graphs. IEEE Trans Power [158] Chen X, Shi M, Sun H, Li Y, He H. Distributed cooperative control and stability
Syst 2021. analysis of multiple dc electric springs in a dc microgrid. IEEE Trans Ind Electron
[141] Jin D, et al. Toward a cyber-resilient and secure microgrid using software-defined 2018;65(7):5611–22.
networking. IEEE Trans Smart Grid 2017;8(5):2494–504. [159] Hu Y, Wang X, Peng Y, Xiang J, Wei W. Distributed finite-time secondary control
[142] Dou CX, Zhang B, Yue D, Zhang ZQ. Cyber-physical cooperative control strategy for dc microgrids with virtual impedance arrangement. IEEE Access 2019;7:
for islanded micro-grid considering communication interruption. Int. Trans. Elect. 57060–8.
Energy Syst. 2019;29(1):1–28. [160] Yan H, et al. A novel sliding mode estimation for microgrid control with
[143] Shrivastava S, Subudhi B, Das S. Noise-resilient voltage and frequency communication time delays. IEEE Trans Smart Grid 2017;10(2):1509–20.
synchronisation of an autonomous microgrid. IET Gener Transm Distrib 2019;13 [161] Niu Y, Ho DWC, Lam J. Robust integral sliding mode control for uncertain
(2):189–200. stochastic systems with time-varying delay. Automatica 2005;41(5):873–80.
[144] Dehkordi NM, et al. Distributed noise-resilient secondary voltage and frequency [162] Yang Q, Zhou J, Chen X, Wen J. Distributed MPC-Based Secondary Control for
control for islanded microgrids. IEEE Trans Smart Grid 2018;10(4):3780–90. Energy Storage Systems in a DC Microgrid. IEEE Trans Power Syst 2021;36(6):
[145] Dou C, et al. A novel hierarchical control strategy combined with sliding mode 5633–44.
control and consensus control for islanded micro-grid. IET Renew Power Gener [163] Burgos-Mellado C, et al. Single-Phase Consensus-Based Control for Regulating
2018;12(9):1012–24. Voltage and Sharing Unbalanced Currents in 3-Wire Isolated AC Microgrids. IEEE
[146] Afshari A, et al. Resilient cooperative control of AC microgrids considering Access 2020;8:164882–98.
relative state-dependent noises and communication time-delays. IET Renew [164] Cai P, Wen C, Song C. Consensus-Based Secondary Frequency Control for Islanded
Power Gener 2020;14(8):1321–31. Microgrid with Communication Delays. In: 2018 International Conference on
[147] Lu X, et al. A novel distributed secondary coordination control approach for Control, Automation and Information Sciences (ICCAIS); 2018. p. 107–12.
islanded microgrids. IEEE Trans Smart Grid 2016;9(4):2726–40. [165] Xing L, et al. Distributed Secondary Control for DC Microgrid with Event-
[148] Zhao C, et al. Distributed cooperative secondary networked optimal control with triggered Signal Transmissions. IEEE Trans Sustainable Energy 2021.
packet loss for islanded microgrid. IET Gener Transm Distrib 2019;13(20):
4733–40.