
The advancement in communication technology and the availability of intelligent electronic devices (IEDs) have impacted positively on the penetration of renewable energy sources (RES) into the main electricity grid. High penetration of RES also come along with greater demand for more effective control approaches, congestion management techniques, and microgrids optimal dispatch. Most of the secondary control methods of microgrid systems in the autonomous mode require communication links between the distributed generators (DGs) for sharing power information and data for control purposes. This article gives ample review on the communication induced impairments in islanded microgrids. In the review, attention is given to communication induced delay, data packet loss, and cyber-attack that degrades optimal operations of islanded microgrids. The review also considered impairments modelling, the impact of impairments on microgrids operation and management, and the control methods employed in mitigating some of their negative impacts. The paper revealed that innovative control solutions for impairment mitigation rather than the development of new high-speed communication infrastructure should be implemented for microgrid control. It was also pointed out that a sparse communication graph is the basis for communication topology design for distributed secondary control in the microgrid.
Citation: Olayanju Sunday Akinwale, Dahunsi Folasade Mojisola, Ponnle Akinlolu Adediran. Mitigation strategies for communication networks induced impairments in autonomous microgrids control: A review[J]. AIMS Electronics and Electrical Engineering, 2021, 5(4): 342-375. doi: 10.3934/electreng.2021018
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The advancement in communication technology and the availability of intelligent electronic devices (IEDs) have impacted positively on the penetration of renewable energy sources (RES) into the main electricity grid. High penetration of RES also come along with greater demand for more effective control approaches, congestion management techniques, and microgrids optimal dispatch. Most of the secondary control methods of microgrid systems in the autonomous mode require communication links between the distributed generators (DGs) for sharing power information and data for control purposes. This article gives ample review on the communication induced impairments in islanded microgrids. In the review, attention is given to communication induced delay, data packet loss, and cyber-attack that degrades optimal operations of islanded microgrids. The review also considered impairments modelling, the impact of impairments on microgrids operation and management, and the control methods employed in mitigating some of their negative impacts. The paper revealed that innovative control solutions for impairment mitigation rather than the development of new high-speed communication infrastructure should be implemented for microgrid control. It was also pointed out that a sparse communication graph is the basis for communication topology design for distributed secondary control in the microgrid.
Mathematical forecasts are yet to gain its improvement in fighting the COVID-19 pandemic. The meteoric spread, the course of actions, and the methods concerned in the containment of the infection demand the soonest and quickest understanding in perceiving solutions in line along with the biological, physiological, environmental, and continual aspects of life with much better predictions by mathematical computerized modeling. Almost all the countries are still suffering from the infection and its destructiveness is ascending gradually. The disease has spread, and is spreading amongst different countries or regions mainly through air-travel mode [1]. The infection doesn't happen on its own and the present occurrence is possibly because of our ignorance or casual thinking and way of living [2]. As a matter of fact, there are many looming queries about COVID-19 spread, such as "number of individuals that will be infected in coming days", "time of appearance of rate of inflexion point of infection", "infections during the pinnacle period and number of individuals that are infected at that time", "effectiveness of present action to control the spread", etc. Usually, in analysis of epidemiology, virus causing diseases grow exponentially maintaining a determined rate of reproduction [3].
One of the most economical and efficient means to prevent and control the infection spread is mass vaccination. The world is now getting leaky vaccinated to reduce the infection as the vaccines obstruct signs and symptoms of lethal infections but do not put a stop to contamination or onward spread [4,5,6,7,8,9,10,11]. It is seen in India that the government is facing a huge challenge to attain preferable vaccination coverage in order for group immunity to be in effect [12]. Hysteresis loops can surface in social replica of dynamical behaviour of vaccination [13]. The impact of varying vaccine efficacies has been investigated in response to control the spread of disease [14,15,16]. The vaccine is effective (infection can be prevented) for η=1 (here we denote η as vaccine efficacy), whilst the value η=0 means the vaccine is ineffective to prevent the virus infection. In the bio-mathematics regime, fractional calculus is now one of the vital studies to elaborate precise cellular behavior, and many human disease's mathematical models have been described by various types of fractional-order derivative operators [17,18,19,20].
FDEs (short for Fractional Differential Equation) are used to include the memory of the phenomenon in the system's dynamics. As per literature [21], there are multiple kinds of fractional derivative operators, amongst which we present the study of the model by fractional differential equation by CF (short for Caputo-Fabrizio derivative) and ABC (short for Atangana-Baleanu-Caputo derivative) sense. ODEs (short for Ordinary Differential Equation) are particular cases of FDEs. FDEs provide descriptions of the courses of endemics, epidemics, pandemics, and some other natural processes in a better way than the ODEs [22]. In reference to deterministic models of the contagiousness of any disease, ODE models are well-structured yet sometimes researchers use models using FDE [23,24]. Some recent research papers focusing on FDE for various real-life problems which are modelled mathematically to understand the problems and their solutions has been studied [25,26,27,28,29,30,31,32,33,34,35,36].
As per the author's knowledge, the remark and application for vaccine efficacy, and the study and experimentation has not yet looked into comparison between the mentioned fractional derivative operators for the SEVR COVID-19 model with the memory effect. The quantitative analysis makes the two operators propitious choices in modeling the dynamics of the disease mathematically. In the upcoming segments, the model is formed with fractional differential equation by CF and ABC sense. This technique is used to find the course of action of the infection whilst vaccination program is on the go. Using Next Generation Matrix Method the expression for the basic reproduction number, Rn, is found and by dint of that local stability are investigated for the equilibrium points. The existence and uniqueness for the models are done using the Banach fixed-point theorem. Considering the course of events for beginning of the transmission to clinical recuperation and seriousness of illness, the appropriateness of utilizing the two operators, joined by relative similitudes, are examined just as their disparities. Lastly, the article is wrapped-up by scrutinizing the seriousness of the spread of infection in terms of CF and ABC, and depict their upcoming involvement for the growth of infection control policy at worldwide level to limit the spread amongst humans.
The follow-up sections deal with the essence of the manuscript. In Section 2, some preliminary requisite knowings of CF and ABC non-singular kernel fractional derivatives is done to get acquainted with customary notations. This section also has model formulation with a box diagram to have a glimpse on the transmission dynamics. The parameters are broadly described with proper justification. The equilibrium points are evaluated and also Rn is calculated. The stability analysis for the infection-free equilibrium has been done in regard to Rn and Jacobian matrix. In Section 3, the solutions of the CF and ABC systems are shown to exist and unique using Banach fixed-point theory and Lipschitz condition. Section 4 deals with discussion and numerical simulation of both the systems in terms of graphs, order of fractional derivative and Rn. At last, in Section 5, we draw the conclusions of the entire manuscript.
Here, the preliminary and primary definitions in terms of properties of the mentioned two fractional derivative operators with exponential and Mittag-Leffler kernels are presented.
Definition 1. [37] Consider f∈H1(c1,c2), c2>c1, α∈]0,1[ then the CF fractional derivative is defined as:
CFc1Dαt[f(t)]=N(α)1−α∫tc1f′(x)e[−αt−x1−α]dx, |
where N(α) is a normalization function satisfying N(0)=N(1)=1 if f∉H1(c1,c2) then
Dαt(f(t))=αN(α)1−α∫tc1(f(t)−f(x))e[−αt−x1−α]dx. |
Definition 2. [37,38] Let us assume that 0<α<1, then the integral of the fractional order α for a function f(t) is defined as:
Iαt(f(t))=2(1−α)(2−α)N(α)f(t)+2α(2−α)N(α)∫t0f(y)dy, |
t≥0.
Definition 3. [39] Consider f∈H1(c1,c2), c2>c1, α∈]0,1[ then the ABC is defined as:
ABCc1Dαt[f(t)]=N(α)1−α∫tc1f′(x)Mα[−α(t−x)α1−α]dx, |
where Mα represent the Mittag-Leffler function and N(α) is a normalization function satisfying N(0)=N(1)=1 and N(α)=1−α+αΓ(α).
Definition 4. [39] Let us assume that 0<α<1, then the integral of the ABC, fractional order α for a function f(t) is defined as:
ABCc1Iαt[f(t)]=1−αN(α)f(t)+αN(α)Γ(α)×∫tc1(t−y)α−1f(y)dy, |
t≥0.
It is important to identify and then study the dynamical behaviour of unwanted results of COVID-19 disease on the populace growth and to predict what may occur. Keeping track of the infection has been a steadily tiring job and vaccination has been a usual approach for lessening the infection, day-to-day vaccination is currently supplied in all nations. It has been believed that the immune system will generate antibody against the virus after the vaccine doses. Even so, it may be not in a fully protective level. The memory effect is an important element in several biological phenomenons, which is included in the models. Hence, it is practicable to use fractional alternation, as fractional derivatives essentially involve the effect of memory. One of the best ways to tackle such issue is to put the fractional derivatives of order α∈(0,1) for the state variables. The next step is to have some modification in the biological parameters such that for a FDE system the both sides of an equation must have dimension 1(time)α. This can be done by raising each of the parameters by α [40]. Motivated by these details, the SEV model presented Liu et al. [42] and Abuasad et al. [41], the updated SEVR model with vaccination by CF and ABC non-singular kernel fractional derivative operators are as follows:
CF0DαtS(t)=λα−γαSE−(dα+να)S, | (2.1) |
CF0DαtE(t)=γαSE+(1−η)γαVE−(dα+βα)E, | (2.2) |
CF0DαtV(t)=ναS−dαV−(1−η)γαVE, | (2.3) |
CF0DαtR(t)=βαE−dαR. | (2.4) |
and
ABC0DαtS(t)=λα−γαSE−(dα+να)S, | (2.5) |
ABC0DαtE(t)=γαSE+(1−η)γαVE−(dα+βα)E, | (2.6) |
ABC0DαtV(t)=ναS−dαV−(1−η)γαVE, | (2.7) |
ABC0DαtR(t)=βαE−dαR. | (2.8) |
The initial conditions for both the systems are S(0)=S0>0, E(0)=E0≥0, V(0)=V0≥0, and R(0)=R0≥0. The virus spread method is considered amongst humans. In the models (2.1)–(2.4) and (2.5)–(2.8), S(t) denotes the fractions of susceptible individuals, E(t) denotes the fractions of virus effected individuals, V(t) denotes fractions of vaccinated individuals, and R(t) denotes fractions of individuals who have recovered from the disease, at any time t. The explanation for the parameters (non-state variables) is as following:
i) The parameter values involved in the aforementioned systems are considered to be non-negative, and λ>0.
ii) λ: rate at which individuals are considered to be born susceptible.
iii) d: death rate of all the state variables.
iv) ν: rate at which susceptibles are vaccinated.
v) γ: rate at which susceptibles are effected by the virus.
vi) (1−η)γ: rate at which vaccinated individuals are affected, which can be possible due to findings present in [43].
v) β: recovery rate of affected individuals but are open to infection again if proper medical and government guidelines are not followed.
The vaccine efficacy can be given a numerical value [44], and as per our knowledge the vaccine efficacy has not yet been modelled to any mathematical COVID-19 model. So for the solutions of system (2.1)–(2.4) and (2.5)–(2.8) we take two values of η in order to compare the solution trajectories. Next, one of the values for death rate of state variables, d, is taken as 0.002 to have a view at the infection in terms of the basic reproduction number. The values of fractional order α are taken arbitrarily. This section is followed by two other subsections dealing with finding the equilibrium points and the basic reproduction number along with stability analysis of the equilibrium points.
Here, only two equilibrium points of the models given by the system (2.1)–(2.4) and (2.5)–(2.8) are presented with biological clarification. The points are as following:
i) X0(λαdα+να,0,λαναdα(dα+να),0), which is named as "Infection-free equilibrium (IFE) point". This is the case where affected individuals are zero and individuals are in infection free state. The vaccination for this point is fruitful for both short and long run as the infection can be contained.
ii) X∗(S∗,E∗,V∗,R∗), which is named as "Co-existing equilibrium (CoE) point". The point X∗ satisfies the below mentioned system of the equations.
λα−γαS∗E∗−(dα+να)S∗=0,γαS∗E∗+(1−η)γαV∗E∗−(dα+βα)E∗=0,ναS∗−dαV∗−(1−η)γαV∗E∗=0,βαE∗−dαR∗=0. |
In this case, the affected individuals co-exist with susceptible individuals and this can be an alarming issue for the infection spread. The vaccination cannot be applied to the affected compartment for this situation as it will wreck havoc to the immune system, and also rate of positive infection will rise leading to indefinite death toll.
It is important information in the mathematical regime of infectious disease models as it helps in finding whether a pandemic will happen or not. The number Rn resembles the expected cases of infections caused by an infectious individual inside susceptible populace. The next-generation matrix method [45] is used to find Rn of the model presented in this article.
The Jacobian matrices F and V with the rate of appearance of new infections and rate of transfer of individuals into and out of compartments, respectively, at X0 are given by
F=(00000γαλα{dα+(1−η)να}dα(dα+να)00να0000βα00),V=(dα+ναγαλαdα+να000dα+βα000(1−η)γαλαναdα(dα+να)dα0000dα). |
Proceeding further, we get
FV−1=diag(0,Rn,0,0). |
The eigenvalues for the diagonal matrix FV−1 are the diagonal elements itself, and amongst them the dominant eigenvalue is Rn=γαλα{dα+(1−η)να}dα(dα+βα)(dα+να), which is the reproduction number for the proposed models.
The infection will self-annihilate if Rn<1, otherwise for Rn>1 the infection will conquer and begin to a pandemic or an epidemic until and unless the medical as well as government guidelines are not implemented amongst the mass.
The discussion will be for the case Rn<1, then the system (2.1)–(2.4) and (2.5)–(2.8) has IFE point X0 and it is globally asymptotically stable in an invariant region [41,42]
Γ={(S,E,V,R):S>0,E≥0,V≥0,R≥0,0<S+E+V+R≤1}. |
Now, to check local stablity the Jacobian of the models (2.1)–(2.4) or (2.5)–(2.8) at X0 is evaluated and it is given by the matrix
J(X0)=F(X0)−V(X0)=(−(dα+να)−γαλαdα+να000(dα+βα)(Rn−1)00να−(1−η)γαλαναdα(dα+να)−dα00βα0−dα). |
The characteristic equation for the matrix J(X0) in variable ξ is given by
4∏i=1(aii−ξ)=0, |
where a11=−(dα+να), a22=(dα+βα)(Rn−1), a33=−dα, and a44=−dα are the eigenvalues. Therefore, the IFE point X0 of the models (2.1)–(2.4) and (2.5)–(2.8) is locally asymptotically stable for a22<0, implies that Rn<1. So, it can be stated in a theorem as following:
Theorem 1. For Rn<1, the IFE point X0 is locally asymptotically stable for the COVID-19 models (2.1)–(2.4) and (2.5)–(2.8).
The follow-up section is about two of the most essential parts of any system of equations viz, existence and uniqueness of solutions.
Here, in this section, necessary theorems are provided and discussed thoroughly to support the existence and uniqueness of solutions of models (2.1)–(2.4) and (2.5)–(2.8) together with the initial conditions, by applying the Banach fixed-point theory.
The findings for the system of equations of COVID-19 CF model using the fractional integral are as follows [38,46]:
S(t)=CF0Iαt{λα−γαSE−(dα+να)S}+S0,E(t)=CF0Iαt{γαSE+(1−η)γαVE−(dα+βα)E}+E0,V(t)=CF0Iαt{ναS−dαV−(1−η)γαVE}+V0,R(t)=CF0Iαt{βαE−dαR}+R0. |
The symbols, after arranged in the view of the problem, gives the following results:
S(t)=S0+2(1−α)(2−α)N(α)[λα−γαS(t)E(t)−(dα+να)S(t)]+2α(2−α)N(α)∫t0[λα−γαS(y)E(y)−(dα+να)S(y)]dy,E(t)=E0+2(1−α)(2−α)N(α)[γαS(t)E(t)+(1−η)γαV(t)E(t)−(dα+βα)E(t)]+2α(2−α)N(α)∫t0[γαS(y)E(y)+(1−η)γαV(y)E(y)−(dα+βα)E(y)]dy,V(t)=V0+2(1−α)(2−α)N(α)[ναS(t)−dαV(t)−(1−η)γαV(t)E(t)]+2α(2−α)N(α)∫t0[ναS(y)−dαV(y)−(1−η)γαV(y)E(y)]dy,R(t)=R0+2(1−α)(2−α)N(α)[βαE(t)−dαR(t)]+2α(2−α)N(α)∫t0[βαE(y)−dαR(y)]dy. |
For the sake of simplicity, we consider
H1(S,t)=[λα−γαS(t)E(t)−(dα+να)S(t)], | (3.1) |
H2(E,t)=[γαS(t)E(t)+(1−η)γαV(t)E(t)−(dα+βα)E(t)], | (3.2) |
H3(V,t)=[ναS(t)−dαV(t)−(1−η)γαV(t)E(t)], | (3.3) |
H4(R,t)=[βαE(t)−dαR(t)]. | (3.4) |
Theorem 2. The Lipschitz condition and contraction are justified by the kernel
i) H1 if 0≤(γαω2+dα+να)<1.
ii) H2 if 0≤(γαω1+(1−η)γαω3+dα+βα)<1.
iii) H3 if 0≤(dα+(1−η)γαω2)<1.
iv) H4 if 0≤dα<1.
Proof. Let us assume that the state variables present in the COVID-19 models are bounded, and therefore it can be said that ∥S(t)∥≤ω1, ∥E(t)∥≤ω2, ∥V(t)∥≤ω3, and ∥R(t)∥≤ω4 [46].
Let us consider S(t) and S′(t) be the two functions, and therefore
∥H1(S,t)−H1(S′,t)∥=∥−γα(S(t)−S′(t))E(t)−(dα+να)(S(t)−S′(t))∥≤∥γαE(t)(S(t)−S′(t))∥+∥(dα+να)(S(t)−S′(t))∥≤[γα∥E(t)∥+(dα+να)]∥S(t)−S′(t)∥≤[γαω2+dα+να]∥S(t)−S′(t)∥. |
So, it can be mentioned now that
∥H1(S,t)−H1(S′,t)∥≤ϕ1∥S(t)−S′(t)∥, |
where ϕ1=(γαω2+dα+να). Thus, the Lipschitz condition is achieved for kernel H1 and 0≤(γαω2+dα+να)<1 [46] gives H1, and also contraction is justified.
Like wise, contraction and the Lipschitz condition can be done and written for the kernels H2, H3, and H4 as mentioned below;
∥H2(E,t)−H2(E′,t)∥≤ϕ2∥E(t)−E′(t)∥,∥H3(V,t)−H3(V′,t)∥≤ϕ3∥V(t)−V′(t)∥,∥H4(R,t)−H4(R′,t)∥≤ϕ4∥R(t)−R′(t)∥, |
where ϕ2=(γαω1+(1−η)γαω3+dα+βα), ϕ3=(dα+(1−η)γαω2), and ϕ4=dα.
Taking the kernels H1, H2, H3, and H4 into consideration the following equations are obtained, which are as follows:
S(t)=S0+2(2−α)N(α)[(1−α)H1(S,t)+α∫t0H1(S,z)dz], | (3.5) |
E(t)=E0+2(2−α)N(α)[(1−α)H2(E,t)+α∫t0H2(E,z)dz], | (3.6) |
V(t)=V0+2(2−α)N(α)[(1−α)H3(V,t)+α∫t0H3(V,z)dz], | (3.7) |
R(t)=R0+2(2−α)N(α)[(1−α)H4(R,t)+α∫t0H4(R,z)dz]. | (3.8) |
The following iterative formulae are now seen closely, which are as follows:
Sn(t)=S0+2(2−α)N(α)[(1−α)H1(Sn−1,t)+α∫t0H1(Sn−1,z)dz], | (3.9) |
En(t)=E0+2(2−α)N(α)[(1−α)H2(En−1,t)+α∫t0H2(En−1,z)dz], | (3.10) |
Vn(t)=V0+2(2−α)N(α)[(1−α)H3(Vn−1,t)+α∫t0H3(Vn−1,z)dz], | (3.11) |
Rn(t)=R0+2(2−α)N(α)[(1−α)H4(Rn−1,t)+α∫t0H4(Rn−1,z)dz], | (3.12) |
with the initial conditions of the COVID-19 model given in the system of Eqs (2.1)–(2.4). The upcoming expressions for the difference of sequential terms are written as:
ρn(t)=Sn(t)−Sn−1(t)=2(1−α)(2−α)N(α)[(H1(Sn−1,t)−H1(Sn−2,t))]+2α(2−α)N(α)∫t0(H1(Sn−1,z)−H1(Sn−2,z))dz,σn(t)=En(t)−En−1(t)=2(1−α)(2−α)N(α)[(H2(En−1,t)−H2(En−2,t))]+2α(2−α)N(α)∫t0(H2(En−1,z)−H2(En−2,z))dz,δn(t)=Vn(t)−Vn−1(t)=2(1−α)(2−α)N(α)[(H3(Vn−1,t)−H3(Vn−2,t))]+2α(2−α)N(α)∫t0(H3(Vn−1,z)−H3(Vn−2,z))dz,πn(t)=Rn(t)−Rn−1(t)=2(1−α)(2−α)N(α)[(H4(Rn−1,t)−H4(Rn−2,t))]+2α(2−α)N(α)∫t0(H4(Rn−1,z)−H4(Rn−2,z))dz. |
It is evident that
Sn(t)=n∑m=1ρm(t),En(t)=n∑m=1σm(t),Vn(t)=n∑m=1δm(t),Rn(t)=n∑m=1πm(t). |
Involving norm to ρn(t) then using triangle inequality, we get
∥ρn(t)∥=∥Sn(t)−Sn−1(t)∥≤2(1−α)(2−α)N(α)∥(H1(Sn−1,t)−H1(Sn−2,t))∥+2α(2−α)N(α)∥∫t0(H1(Sn−1,z)−H1(Sn−2,z))dz∥. |
It has been shown that the the Lipschitz condition is satisfied by the kernel H1, therefore
∥Sn(t)−Sn−1(t)∥≤2ϕ1(2−α)N(α)[(1−α)∥Sn−1−Sn−2∥+α∫t0∥Sn−1−Sn−2∥dz]. |
Thus, it can be deduced that
∥ρn(t)∥≤2ϕ1(2−α)N(α)[(1−α)∥ρn−1(t)∥+α∫t0∥ρn−1(z)∥dz]. | (3.13) |
In the same way, other results are followed as;
∥σn(t)∥≤2ϕ2(2−α)N(α)[(1−α)∥σn−1(t)∥+α∫t0∥σn−1(z)∥dz], | (3.14) |
∥δn(t)∥≤2ϕ3(2−α)N(α)[(1−α)∥δn−1(t)∥+α∫t0∥δn−1(z)∥dz], | (3.15) |
∥πn(t)∥≤2ϕ4(2−α)N(α)[(1−α)∥πn−1(t)∥+α∫t0∥πn−1(z)∥dz]. | (3.16) |
By taking the inequalities (3.13)–(3.16) into consideration the following theorem can be stated, which is:
Theorem 3. The system (2.1)–(2.4) has a solution if there exist a real number τ0, satisfying
2ϕi(2−α)N(α)[1+α(τ0−1)]<1, |
for all i=1,2,3,4.
Proof. Inequations (3.13)–(3.16) are considered along with the fact that the state variables are bounded and the kernels Hi (i=1,2,3,4) satisfy the Lipschitz condition. The following mentioned relations employ the iterative method [46]:
∥ρn(t)∥≤∥Sn(0)∥{2ϕ1(2−α)N(α)[1+α(τ−1)]}n, | (3.17) |
∥σn(t)∥≤∥En(0)∥{2ϕ2(2−α)N(α)[1+α(τ−1)]}n, | (3.18) |
∥δn(t)∥≤∥Vn(0)∥{2ϕ3(2−α)N(α)[1+α(τ−1)]}n, | (3.19) |
∥πn(t)∥≤∥Rn(0)∥{2ϕ4(2−α)N(α)[1+α(τ−1)]}n. | (3.20) |
Thus, solutions of the system (2.1)–(2.4) exist, which are given by the functions (or inequalities) (3.17)–(3.20), and are also continuous. Let us now consider that
S(t)−S0=Sn(t)−Sn(t),E(t)−E0=En(t)−En(t),V(t)−V0=Vn(t)−Vn(t),R(t)−R0=Rn(t)−Rn(t). |
Taking Sn(t) into consideration, we now achieve
∥Sn(t)∥=2(2−α)N(α)∥{(1−α)(H1(S,t)−H1(Sn−1,t))}+α∫t0(H1(S,z)−H1(Sn−1,z))dz∥≤ϕ1∥S−Sn−1∥{2(1−α)(2−α)N(α)[1+α(τ−1)]}. |
If this process is iterated then at τ=τ0, we get
∥Sn(t)∥≤{2ϕ1(2−α)N(α)[1+α(τ0−1)]}n+1ω1. |
Now, it is clear that limn→∞∥Sn(t)∥=0. In the same manner it can be put that En(t)→0, Vn(t)→0, and Rn(t)→0 whenever n→∞.
To clarify the uniqueness for the solutions of the system (2.1)–(2.4), we now assume that S′(t), E′(t), V′(t), and R′(t) are a distinct set of solutions for the said system, then
S(t)−S′(t)=2(1−α)(2−α)N(α)(H1(S,t)−H1(S′,t))+2α(2−α)N(α)∫t0(H1(S,z)−H1(S′,z))dz. |
Let us consider that kernel H1 justify the Lipschitz condition and taking the norm on the above equation, we get the inequality given below;
∥S(t)−S′(t)∥{1−2ϕ1(2−α)N(α)[1+α(τ−1)]}≤0. | (3.21) |
Similarly,
∥E(t)−E′(t)∥{1−2ϕ2(2−α)N(α)[1+α(τ−1)]}≤0, | (3.22) |
∥V(t)−V′(t)∥{1−2ϕ3(2−α)N(α)[1+α(τ−1)]}≤0, | (3.23) |
∥R(t)−R′(t)∥{1−2ϕ4(2−α)N(α)[1+α(τ−1)]}≤0. | (3.24) |
Theorem 4. For the system (2.1)–(2.4), a unique solution exists if the following inequalities holds good
{1−2ϕi(2−α)N(α)[1+α(τ−1)]}>0, |
for all i = 1, 2, 3, 4.
Proof. If we consider the assumption made, then from the inequalities given by (3.21)–(3.24), we get
∥S(t)−S′(t)∥=0,∥E(t)−E′(t)∥=0,∥V(t)−V′(t)∥=0,∥R(t)−R′(t)∥=0. |
This implies that S(t)=S′(t), E(t)=E′(t), V(t)=V′(t), and R(t)=R′(t). And this justifies the proof of COVID-19 model (2.1)–(2.4).
The system of equations of COVID-19 ABC model using the fractional integral [38,46], the findings involve the following:
S(t)=ABC0Iαt{λα−γαSE−(dα+να)S}+S0,E(t)=ABC0Iαt{γαSE+(1−η)γαVE−(dα+βα)E}+E0,V(t)=ABC0Iαt{ναS−dαV−(1−η)γαVE}+V0,R(t)=ABC0Iαt{βαE−dαR}+R0. |
The symbols, after arranged in view of the problem, give the following results;
S(t)=S0+(1−α)N(α)[λα−γαS(t)E(t)−(dα+να)S(t)]+αN(α)Γ(α)∫t0(t−y)α−1[λα−γαS(y)E(y)−(dα+να)S(y)]dy,E(t)=E0+(1−α)N(α)[γαS(t)E(t)+(1−η)γαV(t)E(t)−(dα+βα)E(t)]+αN(α)Γ(α)∫t0(t−y)α−1[γαS(y)E(y)+(1−η)γαV(y)E(y)−(dα+βα)E(y)]dy,V(t)=V0+(1−α)N(α)[ναS(t)−dαV(t)−(1−η)γαV(t)E(t)]+αN(α)Γ(α)∫t0(t−y)α−1[ναS(y)−dαV(y)−(1−η)γαV(y)E(y)]dy,R(t)=R0+(1−α)N(α)[βαE(t)−dαR(t)]+αN(α)Γ(α)∫t0[βαE(y)−dαR(y)]dy. |
For the sake of simplicity, we consider
ˉH1(S,t)=[λα−γαS(t)E(t)−(dα+να)S(t)], | (3.25) |
ˉH2(E,t)=[γαS(t)E(t)+(1−η)γαV(t)E(t)−(dα+βα)E(t)], | (3.26) |
ˉH3(V,t)=[ναS(t)−dαV(t)−(1−η)γαV(t)E(t)], | (3.27) |
ˉH4(R,t)=[βαE(t)−dαR(t)]. | (3.28) |
Theorem 5. The Lipschitz condition and contraction are justified by the kernel
i) ˉH1 if 0≤(γαˉω2+dα+να)<1.
ii) ˉH2 if 0≤(γαˉω1+(1−η)γαˉω3+dα+βα)<1.
iii) ˉH3 if 0≤(dα+(1−η)γαˉω2)<1.
iv) ˉH4 if 0≤dα<1.
Proof. Let us assume that the state variables present in the COVID-19 models are bounded, and therefore it can be said that ∥S(t)∥≤ˉω1, ∥E(t)∥≤ˉω2, ∥V(t)∥≤ˉω3, and ∥R(t)∥≤ˉω4 [46].
Let us consider S(t) and S′(t) be the two functions, and therefore
∥ˉH1(S,t)−ˉH1(S′,t)∥=∥−γα(S(t)−S′(t))E(t)−(dα+να)(S(t)−S′(t))∥≤∥γαE(t)(S(t)−S′(t))∥+∥(dα+να)(S(t)−S′(t))∥≤[γα∥E(t)∥+(dα+να)]∥S(t)−S′(t)∥≤[γαˉω2+dα+να]∥S(t)−S′(t)∥. |
So, it can be mentioned now that
∥ˉH1(S,t)−ˉH1(S′,t)∥≤ˉϕ1∥S(t)−S′(t)∥, |
where ˉϕ1=(γαˉω2+dα+να). Thus, the Lipschitz condition is achieved for kernel ˉH1 and 0≤(γαˉω2+dα+να)<1 [46] gives ˉH1, and also contraction is justified.
Like wise, the Lipschitz condition and contraction can be performed and written for the kernels ˉH2, ˉH3, and ˉH4 as mentioned below;
∥ˉH2(E,t)−ˉH2(E′,t)∥≤ˉϕ2∥E(t)−E′(t)∥,∥ˉH3(V,t)−ˉH3(V′,t)∥≤ˉϕ3∥V(t)−V′(t)∥,∥ˉH4(R,t)−ˉH4(R′,t)∥≤ϕ4∥R(t)−R′(t)∥, |
where ˉϕ2=(γαˉω1+(1−η)γαˉω3+dα+βα), ˉϕ3=(dα+(1−η)γαˉω2), and ϕ4=dα.
Taking the kernels ˉH1, ˉH2, ˉH3, and ˉH4 into consideration the following equations are obtained:
S(t)=S0+1N(α)Γ(α)[Γ(α)(1−α)ˉH1(S,t)+α∫t0(t−z)α−1ˉH1(S,z)dz], | (3.29) |
E(t)=E0+1N(α)Γ(α)[Γ(α)(1−α)ˉH2(E,t)+α∫t0(t−z)α−1ˉH2(E,z)dz], | (3.30) |
V(t)=V0+1N(α)Γ(α)[Γ(α)(1−α)ˉH3(V,t)+α∫t0(t−z)α−1ˉH3(V,z)dz], | (3.31) |
R(t)=R0+1N(α)Γ(α)[Γ(α)(1−α)ˉH4(R,t)+α∫t0(t−z)α−1ˉH4(R,z)dz]. | (3.32) |
The following iterative formulae with the initial conditions of the COVID-19 model given in the system of Eqs (2.5)–(2.8) are now seen closely, which are as follows:
S(t)=S0+1N(α)Γ(α)[Γ(α)(1−α)ˉH1(Sn−1,t)+α∫t0(t−z)α−1ˉH1(Sn−1,z)dz], | (3.33) |
E(t)=E0+1N(α)Γ(α)[Γ(α)(1−α)ˉH2(En−1,t)+α∫t0(t−z)α−1ˉH2(En−1,z)dz], | (3.34) |
V(t)=V0+1N(α)Γ(α)[Γ(α)(1−α)ˉH3(Vn−1,t)+α∫t0(t−z)α−1ˉH3(Vn−1,z)dz], | (3.35) |
R(t)=R0+1N(α)Γ(α)[Γ(α)(1−α)ˉH4(Rn−1,t)+α∫t0(t−z)α−1ˉH4(Rn−1,z)dz]. | (3.36) |
The upcoming expressions for the difference of sequential terms are written as follows:
ˉρn(t)=Sn(t)−Sn−1(t)=(1−α)N(1−α)(ˉH1(Sn−1,t)−ˉH1(Sn−2,t))+αN(α)Γ(α)∫t0(t−z)α−1(ˉH1(Sn−1,z)−ˉH1(Sn−2,z))dz,ˉσn(t)=En(t)−En−1(t)=(1−α)N(1−α)(ˉH2(En−1,t)−ˉH2(En−2,t))+αN(α)Γ(α)∫t0(t−z)α−1(ˉH2(En−1,z)−ˉH2(En−2,z))dz, |
ˉδn(t)=Vn(t)−Vn−1(t)=(1−α)N(1−α)(ˉH3(Vn−1,t)−ˉH3(Vn−2,t))+αN(α)Γ(α)∫t0(t−z)α−1(ˉH3(Vn−1,z)−ˉH3(Vn−2,z))dz,ˉπn(t)=Rn(t)−Rn−1(t)=(1−α)N(1−α)(ˉH4(Rn−1,t)−ˉH4(Rn−2,t))+αN(α)Γ(α)∫t0(t−z)α−1(ˉH4(Rn−1,z)−ˉH4(Rn−2,z))dz. |
It is evident that
Sn(t)=n∑m=1ˉρm(t),En(t)=n∑m=1ˉσm(t),Vn(t)=n∑m=1ˉδm(t),Rn(t)=n∑m=1ˉπm(t). |
Involving norm to ˉρn(t) then using triangle inequality, we get
∥ˉρn(t)∥≤(1−α)N(α)∥(ˉH1(Sn−1,t)−ˉH1(Sn−2,t))∥+αN(α)Γ(α)∥∫t0(t−z)α−1(H1(Sn−1,z)−H1(Sn−2,z))dz∥. |
It has been shown that the Lipschitz condition is satisfied by the kernel ˉH1, therefore
∥Sn(t)−Sn−1(t)∥≤(1−α)ˉϕ1N(α)∥Sn−1−Sn−2∥+αˉϕ1N(α)Γ(α)[∫t0(t−z)α−1∥Sn−1−Sn−2∥dz]. |
Thus, it can be deduced that
∥ˉρn(t)∥≤ˉϕ1N(α)Γ(α)[Γ(α)∥ˉρn−1(t)∥+α∫t0(t−z)α−1∥ˉρn−1(z)∥dz]. | (3.37) |
In the same way, other results are followed as:
∥ˉσn(t)∥≤ˉϕ2N(α)Γ(α)[Γ(α)∥ˉσn−1(t)∥+α∫t0(t−z)α−1∥ˉσn−1(z)∥dz], | (3.38) |
∥ˉδn(t)∥≤ˉϕ3N(α)Γ(α)[Γ(α)∥ˉδn−1(t)∥+α∫t0(t−z)α−1∥ˉδn−1(z)∥dz], | (3.39) |
∥ˉπn(t)∥≤ˉϕ4N(α)Γ(α)[Γ(α)∥ˉπn−1(t)∥+α∫t0(t−z)α−1∥ˉπn−1(z)∥dz]. | (3.40) |
Taking the inequalities (3.37)–(3.40) into consideration, the following theorem can be put forward:
Theorem 6. The system (2.5)–(2.8) has a solution if there exist a real number τ0, satisfying
ˉϕiN(α)Γ(α)[Γ(α)(1−α)+τα0]<1, |
for all i=1,2,3,4.
Proof. The Inequations (3.37)–(3.40) are considered along with the fact that the state variables are bounded and the kernels ˉHi (i=1,2,3,4) satisfy the Lipschitz condition. The following mentioned relations employ the iterative method [46]:
∥ˉρn(t)∥≤∥Sn(0)∥{ˉϕ1N(α)Γ(α)[Γ(α)(1−α)+τα0]}n, | (3.41) |
∥ˉσn(t)∥≤∥En(0)∥{ˉϕ2N(α)Γ(α)[Γ(α)(1−α)+τα0]}n, | (3.42) |
∥ˉδn(t)∥≤∥Vn(0)∥{ˉϕ3N(α)Γ(α)[Γ(α)(1−α)+τα0]}n, | (3.43) |
∥ˉπn(t)∥≤∥Rn(0)∥{ˉϕ4N(α)Γ(α)[Γ(α)(1−α)+τα0]}n. | (3.44) |
Thus, solutions of the system (2.5)–(2.8) exist, which are given by the functions (or inequalities) (3.41)–(3.44), and are also continuous. Let us now consider that
S(t)−S0=Sn(t)−ˉSn(t),E(t)−E0=En(t)−ˉEn(t),V(t)−V0=Vn(t)−ˉVn(t),R(t)−R0=Rn(t)−ˉRn(t). |
Taking ˉSn(t) into consideration, we now achieve
∥ˉSn(t)∥=1N(α)Γ(α)∥{(1−α)(ˉH1(S,t)−ˉH1(Sn−1,t))}+α∫t0(t−z)α−1(ˉH1(S,t)−ˉH1(Sn−1,t))dz∥≤ˉϕ1∥Sn−Sn−1∥{1N(α)Γ(α)[Γ(α)(1−α)+τα]}. |
If this process is iterated then at τ=τ0, we get
∥ˉSn(t)∥≤{1N(α)Γ(α)[Γ(α)(1−α)+τα0]}ˉω1. |
Now, it is clear that limn→∞∥ˉSn(t)∥=0. In the same manner it can be put that ˉEn(t)→0, ˉVn(t)→0, and ˉRn(t)→0 whenever n→∞.
To clarify the uniqueness for the solutions of the system (2.5)–(2.8), we now assume that S′(t), E′(t), V′(t), and R′(t) are a distinct set of solutions for the said system, then
S(t)−S′(t)=(1−α)N(α)(ˉH1(S,t)−ˉH1(S′,t))+αN(α)Γ(α)∫t0(t−z)α−1(ˉH1(S,z)−ˉH1(S′,z))dz. |
Let us consider that kernel ˉH1 justify the Lipschitz condition and taking norm on above equation, we get the following inequality given below as:
∥S(t)−S′(t)∥{1−ˉϕ1N(α)Γ(α)[Γ(α)(1−α)+τα]}≤0. | (3.45) |
Similarly,
∥E(t)−E′(t)∥{1−ˉϕ2N(α)Γ(α)[Γ(α)(1−α)+τα]}≤0, | (3.46) |
∥V(t)−V′(t)∥{1−ˉϕ3N(α)Γ(α)[Γ(α)(1−α)+τα]}≤0, | (3.47) |
∥R(t)−R′(t)∥{1−ˉϕ4N(α)Γ(α)[Γ(α)(1−α)+τα]}≤0. | (3.48) |
Theorem 7. For the system (2.5)–(2.8), a unique solution exists if the following inequalities hold good
{1−ˉϕiN(α)Γ(α)[Γ(α)(1−α)+τα]}>0 |
for all i=1,2,3,4.
Proof. If we consider the assumption made, then from the inequalities given by (3.45)–(3.48),
∥S(t)−S′(t)∥=0,∥E(t)−E′(t)∥=0,∥V(t)−V′(t)∥=0,∥R(t)−R′(t)∥=0. |
This implies that S(t)=S′(t), E(t)=E′(t), V(t)=V′(t), and R(t)=R′(t). And this justifies the proof for the uniqueness of the solutions for COVID-19 model (2.5)–(2.8).
The numerical simulation is done using Mathematica for both CF and ABC approach models (2.1)–(2.4) and (2.5)–(2.8), respectively. Next, the comparison and discussion of the following results are done. The dynamics of the novel COVID-19 infection-vaccination model are given by the system (2.1)–(2.4) and (2.5)–(2.8), respectively, for various values of α, randomly selected, are plotted in Figures 2–5. The parametric plot amongst the state variables of the SEVR model are plotted in Figures 6 and 7. The initial values of the state variables are taken as: S0=0.1, E0=0.05, V0=0.5, and R0=0 [41,46].
Due to lack of clinical evidence and support, it is not sure the precise death rate of individuals due to COVID-19 infection, and so we manipulate the value of d to understand the death dynamics. The numerical simulations' comparison of Figures 2(a)–(d), 3(a)–(d), 4(a)–(d), 5(a)–(d) and Figures 2(e)–(h), 3(e)–(h), 4(e)–(h), 5(e)–(h) for both the CF- and ABC approach, respectively, show that for same non-natural values of α, parameter values given in Table 1 and same initial conditions the SEVR model shows different trajectories. It is observed that susceptible populace decrease at a slower rate with the ABC approach as compared to the CF approach for the values of α in Table 1, which can be clearly seen in Figure 2(e), (f) and 2(a), (b). But Figure 2(c), (d) and 2(g), (h) show that both CF- and ABC approach provides precisely same result. It is deduced from Figures 3(a), (b) vs. 3(e), (f) and 4(a), (b) vs. 4(e), (f) about comparisons that the ABC approach gives much variation in effected and vaccinated populaces than the CF approach, for d=0.002 and η=0.5, and 0.8. One more prominent aspect suggests that the effected and vaccinated populaces provide a sharp increase because of high spread of COVID-19 infection presented by Hellewell et al.[47] and Gupta et al. [48]. But Figures 3(c), (d) vs. 3(g), (h) and 4(c), (d) vs. 4(g), (h) explain that both the CF- and ABC approach yields almost same outcome.
ν | α | η | d | β | γ | λ |
0.01 | 0.8, 0.7, 0.6 | 0.5, 0.8 | 0.002, 0.05 | 0.2 | 0.7 | 0.01 |
[41] | estimated | estimated | estimated, [41] | [41] | [41] | [41] |
Moreover, on minute speculation of comparisons shown in Figure 3(a), (b), (e), (f) with Figure 5(a), (b), (e), (f) we see a difference between the peak of effected and recovered populaces, when d=0.002. Be that as it may, it is noted that in Figure 3(c), (d), (g), (h) with Figure 5(c), (d), (g), (h) the difference between the decreasing peak points is approximately 5 days, calculated at d=0.05. The gradient of curves for different values of α share similarities with the findings. This shows that affected individuals are recovering very quickly by the ABC approach, whereas the CF approach shows almost a delay in the transfer of affected individuals to recover compartments.
In all this, we have come across that for almost any efficacy of vaccine, and for arbitrary chosen α if the death rate d=0.002, then affected individuals exhibits virulent characteristics. This may result in tending towards the co-existing equilibrium point X∗. From the Table 2, it can be deduced mathematically that for any vaccine efficacy ranging between 50% to 80% effectiveness can bring stability to the susceptible individuals to fight against the virus. It would be interesting to see how some state variables of the SEVR model are responding to each other for the cases when Rn>1. Therefore, Figures 6 and 7 provide parametric plot between the state variables and it is noticeable that they experience a chaotic behaviour.
η | d | α | Rn | X0 Remark |
0.8 | 5.852 | Unstable | ||
0.002 | 0.7 | 4.434 | Unstable | |
0.6 | 3.341 | Unstable | ||
0.5 | 0.8 | 0.504 | Stable | |
0.05 | 0.7 | 0.495 | Stable | |
0.6 | 0.484 | Stable | ||
0.8 | 3.594 | Unstable | ||
0.002 | 0.7 | 2.821 | Unstable | |
0.6 | 2.206 | Unstable | ||
0.8 | 0.8 | 0.467 | Stable | |
0.05 | 0.7 | 0.454 | Stable | |
0.6 | 0.436 | Stable |
The virtue of the models (2.1)–(2.4) and (2.5)–(2.8), when contrasted, found remarkable differences under the parameter values given in Table 1. The reason is memory properties of the kernel in the fractional derivatives' definitions. The CF and ABC derivative has exponential kernel and generalized Mittag-Leffler kernel, respectively. The ABC type shows a power-law memory, and also partial exponential decay memory [49]. It is clear that the model lies upon the fractional order notably, for various values of α it displays a clear difference and suggested models (2.1)–(2.4) and (2.5)–(2.8) explores new aspects at the fractional values of α.
Infection-related contact ascends with a growth in the size of population. Thus, COVID-19 infection spread during vaccination program for non-integer order model is considered, using the CF and ABC sense. Numerical simulation output for the proposed fractional order models are shown with distinct fractional order values α∈]0,1[ and briefly compared, discussed and investigated. The vaccine efficacy and the death rate for state variables plays a significant role in escaping the infection. Since COVID-19 virus biology is changing rapidly, so the precise estimation of death rate, d, can unfold the appropriate time for vaccine administration and how effective it should be. The graphical results demonstrate the ABC approach provides better suitability for mild cases. Whereas, the CF approach provides superior and more flexible results for critical cases. The CF approach provided a guarantee for use in lenient cases, whereas the ABC approach has a notable impact on the dynamics of COVID-19 infection and has given new insight for its use as a device to further research in dynamics of the spread of the disease in critical cases. These results show that CF and ABC approach implementation in real life situations are both plausible and doable as per the severity of illness for patient management. The findings also underline that even incomplete vaccination can have delay positive repercussions in minding the symptoms and spread of the Coronavirus COVID-19. It is found from the analysis that proposed fractional order mathematical models yield better results than that of the classical ones. The entire manuscript deals with the solution of a dynamical system of COVID-19 model for various fractional order α ranging in the interval [0,1].
For future research work the analysis of COVID-19 infection spread for different geographical areas can be achieved by examining the models with relevant parameter values as per data trends of the region, and also including quarantine and number of dosages of vaccines compartments. It is anticipated that this research will provide significance and will thus strengthen the research relevant to COVID-19 transmission dynamics, so that progressive disease control policies are formulated to provide patients with better medical care to all in need.
The researchers would like to thank the Deanship of Scientific Research, Qassim University for funding the publication of this project. We are also thankful to the editor and anonymous reviewers for their fruitful comments and suggestions that help to improve this manuscript.
The authors declare there is no conflict of interest as well as no known competing financial interests.
[1] |
Kahrobaeian A, Mohamed YARI (2015) Networked-based hybrid distributed power sharing and control for islanded microgrid systems. IEEE T Power Electr 30: 603-617. doi: 10.1109/TPEL.2014.2312425. doi: 10.1109/TPEL.2014.2312425
![]() |
[2] | Bidram A, Nasirian V, Davoudi A, et al. (2017) Cooperative Synchronization in Distributed Microgrid Control. Advances in Industrial Control, vol. 1. |
[3] |
Alzahrani A, Ferdowsi M, Shamsi P, et al. (2017) Modeling and Simulation of Microgrid. Procedia Comput Sci 114: 392-400. doi: 10.1016/j.procs.2017.09.053. doi: 10.1016/j.procs.2017.09.053
![]() |
[4] |
Hossain MA, Pota HR, Issa W, et al. (2017) Overview of AC microgrid controls with inverter-interfaced generations. Energies 10: 1-27. doi: 10.3390/en10091300. doi: 10.3390/en10091300
![]() |
[5] |
Hossain E, Kabalci E, Bayindir R, et al. (2014) A comprehensive study on microgrid technology. Int J Renew Energy Res 4: 1094-1104. doi: 10.20508/ijrer.20561. doi: 10.20508/ijrer.20561
![]() |
[6] |
Porsinger T, Janik P, Leonowicz Z, et al. (2017) Modelling and optimization in microgrids. Energies 10: 1-22. doi: 10.3390/en10040523. doi: 10.3390/en10040523
![]() |
[7] |
Siddique AB, Munsi S, Sarkar SK, et al. (2019) Model reference modified adaptive PID controller design for voltage and current control of islanded microgrid. 4th Int Conf Electr Eng Inf Commun Technol iCEEiCT 2018, 130-135. doi: 10.1109/CEEICT.2018.8628074. doi: 10.1109/CEEICT.2018.8628074
![]() |
[8] | Shayeghi H, Sobhany B, Moradzadeh M (2017) Management of Autonomous Microgrids Using Multi-Agent Based Online Optimized NF-PID Controller. J Energy Manag Technol 1: 79-87. |
[9] |
Rana MM, Li L, Su SW (2017) Distributed State Estimation of Smart Grids with Packet Losses. Asian J Control 19: 1306-1315. doi: 10.1002/asjc.1578. doi: 10.1002/asjc.1578
![]() |
[10] |
Parisio A, Wiezorek C, Kyntäjä T, et al. (2017) Cooperative MPC-Based Energy Management for Networked Microgrids. IEEE T Smart Grid 8: 3066-3074. doi: 10.1109/TSG.2017.2726941. doi: 10.1109/TSG.2017.2726941
![]() |
[11] | Sun Y, Hu J, Zhang Y, et al. (2018) Distributed Secondary Voltage Control of Microgrids with Nonuniform Time-Varying Delays. in Chinese Control Conference, CCC, 2018, 8809-8814. doi: 10.23919/ChiCC.2018.8483983. |
[12] |
Sarkar SK, Roni MHK, Datta D, et al. (2019) Improved Design of High-Performance Controller for Voltage Control of Islanded Microgrid. IEEE Syst J 13: 1786-1795. doi: 10.1109/JSYST.2018.2830504. doi: 10.1109/JSYST.2018.2830504
![]() |
[13] |
Aghaee F, Dehkordi NM, Bayati N, et al. (2019) Distributed control methods and impact of communication failure in AC microgrids: A comparative review. Electronics 8: 1265. doi: 10.3390/electronics8111265. doi: 10.3390/electronics8111265
![]() |
[14] |
Lou G, Gu W, Lu X, et al. (2020) Distributed Secondary Voltage Control in Islanded Microgrids with Consideration of Communication Network and Time Delays. IEEE T Smart Grid 11: 3702-3715. doi: 10.1109/TSG.2020.2979503. doi: 10.1109/TSG.2020.2979503
![]() |
[15] |
Ci S, Qian J, Wu D, et al. (2012) Impact of wireless communication delay on load sharing among distributed generation systems through smart microgrids. IEEE Wirel Commun 19: 24-29. doi: 10.1109/MWC.2012.6231156. doi: 10.1109/MWC.2012.6231156
![]() |
[16] |
Xu Y, Wang W (2013) Wireless mesh network in smart grid: Modeling and analysis for time critical communications. IEEE T Wirel Commun 12: 3360-3371. doi: 10.1109/TWC.2013.061713.121545. doi: 10.1109/TWC.2013.061713.121545
![]() |
[17] |
Lai J, Zhou H, Hu W, et al. (2015) Synchronization of Hybrid Microgrids with Communication Latency. Math Probl Eng 2015: 1-10. doi: 10.1155/2015/586260. doi: 10.1155/2015/586260
![]() |
[18] |
Alfergani A, Khalil A (2017) Modeling and control of master-slave microgrid with communication delay. 2017 8th Int Renew Energy Congr IREC, 1-6. doi: 10.1109/IREC.2017.7926049. doi: 10.1109/IREC.2017.7926049
![]() |
[19] |
Chen G, Guo Z (2019) Distributed secondary and optimal active power sharing control for islanded microgrids with communication delays. IEEE T Smart Grid 10: 2002-2014. doi: 10.1109/TSG.2017.2785811. doi: 10.1109/TSG.2017.2785811
![]() |
[20] |
Nasirian V, Davoudi A, Lewis FL, et al. (2014) Distributed adaptive droop control for DC distribution systems. IEEE T Energy Conver 29: 944-956. doi: 10.1109/TEC.2014.2350458. doi: 10.1109/TEC.2014.2350458
![]() |
[21] |
Nasirian V, Moayedi S, Davoudi A, et al. (2015) Distributed cooperative control of dc microgrids. IEEE T Power Electr 30: 2288-2303. doi: 10.1109/TPEL.2014.2324579. doi: 10.1109/TPEL.2014.2324579
![]() |
[22] |
Nasirian V, Shafiee Q, Guerrero JM, et al. (2016) Droop-Free Distributed Control for AC Microgrids. IEEE T Power Electr 31: 1600-1617. doi: 10.1109/TPEL.2015.2414457. doi: 10.1109/TPEL.2015.2414457
![]() |
[23] |
Shafiee Q, Stefanovic C, Dragicevic T, et al. (2014) Robust networked control scheme for distributed secondary control of islanded microgrids. IEEE T Ind Electron 61: 5363-5374. doi: 10.1109/TIE.2013.2293711. doi: 10.1109/TIE.2013.2293711
![]() |
[24] |
Sun Y, Zhong C, Hou X, et al. (2017) Distributed cooperative synchronization strategy for multi-bus microgrids. Int J Electr Power 86: 18-28. doi: 10.1016/j.ijepes.2016.09.002. doi: 10.1016/j.ijepes.2016.09.002
![]() |
[25] |
Serban I, Cespedes S, Marinescu C, et al. (2020) Communication requirements in microgrids: A practical survey. IEEE Access 8: 47694-47712. doi: 10.1109/ACCESS.2020.2977928. doi: 10.1109/ACCESS.2020.2977928
![]() |
[26] |
Han, Y, Li, H, Shen P, et al. (2017) Review of Active and Reactive Power Sharing Strategies in Hierarchical Controlled Microgrids. IEEE T Power Electr 32: 2427-2451. doi: 10.1109/TPEL.2016.2569597 doi: 10.1109/TPEL.2016.2569597
![]() |
[27] |
Dragicevic T, Lu X, Vasquez JC, et al. (2016) DC Microgrids - Part Ⅰ: A Review of Control Strategies and Stabilization Techniques. IEEE T Power Electron 31: 4876-4891. doi: 10.1109/TPEL.2015.2478859. doi: 10.1109/TPEL.2015.2478859
![]() |
[28] |
Vandoorn TL, De Kooning JDM, Meersman B, et al. (2013) Review of primary control strategies for islanded microgrids with power-electronic interfaces. Renewable and Sustainable Energy Reviews 19: 613-628. doi: 10.1016/j.rser.2012.11.062. doi: 10.1016/j.rser.2012.11.062
![]() |
[29] |
Ekanayake UN, Navaratne US (2020) A Survey on Microgrid Control Techniques in Islanded Mode. Electr Comput Eng 2020: 1-8. doi: 10.1155/2020/6275460. doi: 10.1155/2020/6275460
![]() |
[30] |
Rajesh KS, Dash SS, Rajagopal R, et al. (2016) A review on control of ac microgrid. Renew Sustain Energy Rev 71: 814-819. doi: 10.1016/j.rser.2016.12.106. doi: 10.1016/j.rser.2016.12.106
![]() |
[31] |
Han Y, Ning X, Yang P, et al. (2019) Review of Power Sharing, Voltage Restoration and Stabilization Techniques in Hierarchical Controlled DC Microgrids. IEEE Access 7: 149202-149223. doi: 10.1109/ACCESS.2019.2946706. doi: 10.1109/ACCESS.2019.2946706
![]() |
[32] |
Meng L, Shafiee Q, Trecate GF, et al. (2017) Review on Control of DC Microgrids and Multiple Microgrid Clusters. IEEE J Emerg Sel Top Power Electron 5: 928-948. doi: 10.1109/JESTPE.2017.2690219. doi: 10.1109/JESTPE.2017.2690219
![]() |
[33] |
Arbab-Zavar B, Palacios-Garcia EJ, Vasquez JC, et al. (2019) Smart inverters for microgrid applications: A review. Energies 12: 840. doi: 10.3390/en12050840. doi: 10.3390/en12050840
![]() |
[34] |
Nejabatkhah F, Li YW, Liang H, et al. (2021) Cyber-security of smart microgrids: A survey. Energies 14: 27. doi: 10.3390/en14010027. doi: 10.3390/en14010027
![]() |
[35] |
Habib HF, Lashway CR, Mohammed OA (2017) On the adaptive protection of microgrids: A review on how to mitigate cyber attacks and communication failures. 2017 IEEE Ind Appl Soc Annu Meet IAS, 1-8. doi: 10.1109/IAS.2017.8101886. doi: 10.1109/IAS.2017.8101886
![]() |
[36] |
Dahunsi F, Olayanju S, Ponle A, et al. (2021) Communication Network Simulation for Smart Metering Applications: A Review. J Innov Sci Eng 5: 101-128. doi: 10.38088/jise.835725. doi: 10.38088/jise.835725
![]() |
[37] |
O'Raw J, Laverty DM, Morrow DJ (2016) Software defined networking as a mitigation strategy for data communications in power systems critical infrastructure. IEEE Power and Energy Society General Meeting, 1-5. doi: 10.1109/PESGM.2016.7741417. doi: 10.1109/PESGM.2016.7741417
![]() |
[38] |
Sivaneasan B, So PL, Gooi HB, et al. (2013) Performance measurement and analysis of WiMAX-LAN communication operating at 5.8 GHz. IEEE T Ind Inform 9: 1497-1506. doi: 10.1109/TⅡ.2013.2258163. doi: 10.1109/TⅡ.2013.2258163
![]() |
[39] |
Sevilla AP, Ortega EI, Hincapie R (2015) FiWi network planning for smart metering based on multistage stochastic programming. IEEE Lat Am Trans 13: 3838-3843. doi: 10.1109/TLA.2015.7404917. doi: 10.1109/TLA.2015.7404917
![]() |
[40] |
Llaria A, Terrasson G, Curea O, et al. (2016) Application of wireless sensor and actuator networks to achieve intelligent microgrids: A promising approach towards a global smart grid deployment. Appl Sci 6: 61. doi: 10.3390/app6030061. doi: 10.3390/app6030061
![]() |
[41] |
Siow LK, So PL, Gooi HB, et al. (2009) Wi-Fi based server in microgrid energy management system. IEEE Reg 10 Annu Int Conf Proceedings/TENCON, 1-5. doi: 10.1109/TENCON.2009.5395995. doi: 10.1109/TENCON.2009.5395995
![]() |
[42] |
Setiawan MA, Shahnia F, Rajakaruna S, et al. (2015) ZigBee-Based Communication System for Data Transfer Within Future Microgrids. IEEE T Smart Grid 6: 2343-2355. doi: 10.1109/TSG.2015.2402678. doi: 10.1109/TSG.2015.2402678
![]() |
[43] |
Sharma D, Dubey A, Mishra S, et al. (2019) A Frequency Control Strategy Using Power Line Communication in a Smart Microgrid. IEEE Access 7: 21712-21721. doi: 10.1109/ACCESS.2019.2897051. doi: 10.1109/ACCESS.2019.2897051
![]() |
[44] |
Jeong DK, Kim HS, Baek JW, et al. (2018) Autonomous control strategy of DC microgrid for islanding mode using power line communication. Energies 11: 1-22. doi: 10.3390/en11040924. doi: 10.3390/en11040924
![]() |
[45] |
Ustun TS, Khan RH (2015) Multiterminal Hybrid Protection of Microgrids over Wireless Communications Network. IEEE T Smart Grid 6: 2493-2500. doi: 10.1109/TSG.2015.2406886. doi: 10.1109/TSG.2015.2406886
![]() |
[46] |
Ndukwe C, Iqbal MT, Liang X, et al. (2020) LoRa-based communication system for data transfer in microgrids. AIMS Electron Electr Eng 4: 303-325. doi: 10.3934/ElectrEng.2020.3.303. doi: 10.3934/ElectrEng.2020.3.303
![]() |
[47] |
Khatua PK, Ramachandaramurthy VK, Kasinathan P, et al. (2020) Application and assessment of internet of things toward the sustainability of energy systems: Challenges and issues. Sustain Cities Soc 53: 101957. doi: 10.1016/j.scs.2019.101957. doi: 10.1016/j.scs.2019.101957
![]() |
[48] | Nojavanzadeh D, Lotfifard S, Liu Z, et al. (2021) Scale-free Distributed Cooperative Voltage Control of Inverter-based Microgrids with General Time-varying Communication Graphs. IEEE T Power Syst, 1-8. |
[49] |
Cardwell N, Savage S, Anderson T (2000) Modeling TCP latency. Proc IEEE INFOCOM 3: 1742-1751. doi: 10.1109/infcom.2000.832574. doi: 10.1109/infcom.2000.832574
![]() |
[50] | Jacobsson K, Hjalmarsson H, Möller N, et al. (2004) Round trip time estimation in communication networks using adpative Kalman filtering. Regl Conf. |
[51] |
Jiang L, Yao W, Wu QH, et al. (2012) Delay-dependent stability for load frequency control with constant and time-varying delays. IEEE T Power Syst 27: 932-941. doi: 10.1109/TPWRS.2011.2172821. doi: 10.1109/TPWRS.2011.2172821
![]() |
[52] |
Cheng L, Hou ZG, Tan M (2014) A mean square consensus protocol for linear multi-agent systems with communication noises and fixed topologies. IEEE T Automat Contr 59: 261-267. doi: 10.1109/TAC.2013.2270873. doi: 10.1109/TAC.2013.2270873
![]() |
[53] |
Wang Y, Cheng L, Hou ZG, et al. (2015) Consensus seeking in a network of discrete-time linear agents with communication noises. Int J Syst Sci 46: 1874-1888. doi: 10.3182/20140824-6-za-1003.00344. doi: 10.3182/20140824-6-za-1003.00344
![]() |
[54] |
Morita R, Wada T, Masubuchi I, et al. (2016) Multiagent consensus with noisy communication: Stopping rules based on network graphs. IEEE T Control Netw 3: 358-365. doi: 10.1109/TCNS.2015.2481119. doi: 10.1109/TCNS.2015.2481119
![]() |
[55] |
Liu J, Ming P, Li S (2016) Consensus gain conditions of stochastic multi-agent system with communication noise. Int J Control Autom 14: 1223-1230. doi: 10.1007/s12555-014-0360-5. doi: 10.1007/s12555-014-0360-5
![]() |
[56] |
Chaudhuri B, Majumder R, Pal BC (2004) Wide-area measurement-based stabilizing control of power system considering signal transmission delay. IEEE T Power Syst 19: 1971-1979. doi: 10.1109/TPWRS.2004.835669. doi: 10.1109/TPWRS.2004.835669
![]() |
[57] |
Rana MM (2017) Least mean square fourth based microgrid state estimation algorithm using the internet of things technology. PLoS One 12: 1-13. doi: 10.1371/journal.pone.0176099. doi: 10.1371/journal.pone.0176099
![]() |
[58] |
Setiawan MA, Abu-Siada A, Shahnia F (2018) A New Technique for Simultaneous Load Current Sharing and Voltage Regulation in DC Microgrids. IEEE T Ind Inform 14: 1403-1414. doi: 10.1109/TⅡ.2017.2761914. doi: 10.1109/TⅡ.2017.2761914
![]() |
[59] |
Ullah S, Khan L, Sami I, et al. (2021) Consensus-Based Delay-Tolerant Distributed Secondary Control Strategy for Droop Controlled AC Microgrids. IEEE Access 9: 6033-6049. doi: 10.1109/ACCESS.2020.3048723. doi: 10.1109/ACCESS.2020.3048723
![]() |
[60] |
Shuai Z, Huang W, Shen X, et al. (2019) A Maximum Power Loading Factor (MPLF) Control Strategy for Distributed Secondary Frequency Regulation of Islanded Microgrid. IEEE T Power Electron 34: 2275-2291. doi: 10.1109/TPEL.2018.2837125. doi: 10.1109/TPEL.2018.2837125
![]() |
[61] |
Jin D, Li Z, Hannon C, et al. (2017) Toward a Cyber Resilient and Secure Microgrid Using Software-Defined Networking. IEEE T Smart Grid 8: 2494-2504. doi: 10.1109/TSG.2017.2703911. doi: 10.1109/TSG.2017.2703911
![]() |
[62] |
Liu X, Li Z (2017) False Data Attacks Against AC State Estimation with Incomplete Network Information. IEEE T Smart Grid 8: 2239-2248. doi: 10.1109/TSG.2016.2521178. doi: 10.1109/TSG.2016.2521178
![]() |
[63] |
Saha S, Roy TK, Mahmud MA, et al. (2018) Electrical Power and Energy Systems Sensor fault and cyber attack resilient operation of DC microgrids. Int J Elec Power 99: 540-554. doi: 10.1016/j.ijepes.2018.01.007. doi: 10.1016/j.ijepes.2018.01.007
![]() |
[64] | Wu D, Member S, Ci S, et al. (2010) Application-Centric Routing for Video Streaming Over MultiHop Wireless Networks. IEEE T Circ Syst Vid 20: 1721-1734. |
[65] |
Rana MM, Li L, Su SW (2016) Distributed condition monitoring of renewable microgrids using adaptive-then-combine algorith. IEEE Power and Energy Society General Meeting, 1-6. doi: 10.1109/PESGM.2016.7741544. doi: 10.1109/PESGM.2016.7741544
![]() |
[66] |
Zheng L, Lu N, Cai L (2013) Reliable wireless communication networks for demand response control. IEEE T Smart Grid 4: 133-140. doi: 10.1109/TSG.2012.2224892. doi: 10.1109/TSG.2012.2224892
![]() |
[67] |
Zhang R, Hredzak B (2019) Distributed finite-time multiagent control for DC microgrids with time delays. IEEE T Smart Grid 10: 2692-2701. doi: 10.1109/TSG.2018.2808467. doi: 10.1109/TSG.2018.2808467
![]() |
[68] |
Zhou J, Tsai MJ, Cheng PT (2020) Consensus-Based Cooperative Droop Control for Accurate Reactive Power Sharing in Islanded AC Microgrid. IEEE J Em Sel Top P 8: 1108-1116. doi: 10.1109/JESTPE.2019.2946658. doi: 10.1109/JESTPE.2019.2946658
![]() |
[69] | Jacobsson K, Hjalmarsson H, Möller N, et al. (2004) Round trip time estimation in communication networks using adpative Kalman filtering. Reglermöte Conference, 1-5. |
[70] |
Das A, Shukla A, Shyam AB, et al. (2021) A Distributed-Controlled Harmonic Virtual Impedance Loop for AC Microgrids. IEEE T Ind Electron 68: 3949-3961. doi: 10.1109/TIE.2020.2987290. doi: 10.1109/TIE.2020.2987290
![]() |
[71] |
Da Silva WWAG, Oliveira TR, Donoso-Garcia PF (2020) Hybrid Distributed and Decentralized Secondary Control Strategy to Attain Accurate Power Sharing and Improved Voltage Restoration in DC Microgrids. IEEE T Power Electron 35: 6458-6469. doi: 10.1109/TPEL.2019.2951012. doi: 10.1109/TPEL.2019.2951012
![]() |
[72] |
Sharma D, Mishra S (2020) Disturbance-Observer-Based Frequency Regulation Scheme for Low-Inertia Microgrid Systems. IEEE Syst J 14: 782-792. doi: 10.1109/JSYST.2019.2901749. doi: 10.1109/JSYST.2019.2901749
![]() |
[73] |
Prabhakaran P, Goyal Y, Agarwal V (2019) A novel communication-based average voltage regulation scheme for a droop controlled DC microgrid. IEEE T Smart Grid 10: 1250-1258. doi: 10.1109/TSG.2017.2761864. doi: 10.1109/TSG.2017.2761864
![]() |
[74] |
Liu J, Du Y, Yim S, et al. (2020) Steady-State Analysis of Microgrid Distributed Control under Denial of Service Attacks. IEEE J Em Sel Top P 9: 5311-5325. doi: 10.1109/JESTPE.2020.2990879. doi: 10.1109/JESTPE.2020.2990879
![]() |
[75] |
Fu R, Huang X, Sun J, et al. (2017) Stability analysis of the cyber physical microgrid system under the intermittent DoS attacks. Energies 10: 1-15. doi: 10.3390/en10050680. doi: 10.3390/en10050680
![]() |
[76] |
Danzi P, Angjelichinoski M, Stefanovic C, et al. (2018) Software-Defined Microgrid Control for Resilience Against Denial-of-Service Attacks. IEEE T Smart Grid 10: 5258-5268. doi: 10.1109/TSG.2018.2879727. doi: 10.1109/TSG.2018.2879727
![]() |
[77] |
Ding L, Han QL, Ning B, et al. (2020) Distributed Resilient Finite-Time Secondary Control for Heterogeneous Battery Energy Storage Systems under Denial-of-Service Attacks. IEEE T Ind Inform 16: 4909-4919. doi: 10.1109/TII.2019.2955739. doi: 10.1109/TII.2019.2955739
![]() |
[78] |
Mustafa A, Poudel B, Bidram A, et al. (2020) Detection and Mitigation of Data Manipulation Attacks in AC Microgrids. IEEE T Smart Grid 11: 2588-2603. doi: 10.1109/TSG.2019.2958014. doi: 10.1109/TSG.2019.2958014
![]() |
[79] |
Poudel BP, Mustafa A, Bidram A, et al. (2020) Detection and mitigation of cyber-threats in the DC microgrid distributed control system. Int J Elec Power 120: 105968. doi: 10.1016/j.ijepes.2020.105968. doi: 10.1016/j.ijepes.2020.105968
![]() |
[80] |
Ghiasi M, Dehghani M, Niknam T, et al. (2021) Cyber-Attack Detection and Cyber-Security Enhancement in Smart DC-Microgrid Based on Blockchain Technology and Hilbert Huang Transform. IEEE Access 9: 29429-29440. doi: 10.1109/ACCESS.2021.3059042. doi: 10.1109/ACCESS.2021.3059042
![]() |
[81] |
Islam SN, Mahmud MA, Oo AMT (2018) Impact of optimal false data injection attacks on local energy trading in a residential microgrid. ICT Express 4: 30-34. doi: 10.1016/j.icte.2018.01.015. doi: 10.1016/j.icte.2018.01.015
![]() |
[82] |
Beg OA, Nguyen LV, Johnson TT, et al. (2019) Signal Temporal Logic-Based Attack Detection in DC Microgrids. IEEE T Smart Grid 10: 3585-3595. doi: 10.1109/TSG.2018.2832544. doi: 10.1109/TSG.2018.2832544
![]() |
[83] |
Yassaie N, Hallajiyan M, Sharifi I, et al. (2021) Resilient control of multi-microgrids against false data injection attack. ISA T 110: 238-246. doi: 10.1016/j.isatra.2020.10.030. doi: 10.1016/j.isatra.2020.10.030
![]() |
[84] |
Mahmood H, Mahmood D, Shaheen Q, et al. (2021) S-DPs: An SDN-based DDoS protection system for smart grids. Secur Commun Netw. doi: 10.1155/2021/6629098. doi: 10.1155/2021/6629098
![]() |
[85] |
Rokrok E, Shafie-khah M, Catalão JPS (2018) Review of primary voltage and frequency control methods for inverter-based islanded microgrids with distributed generation. Renewable and Sustainable Energy Reviews 82: 3225-3235. doi: 10.1016/j.rser.2017.10.022. doi: 10.1016/j.rser.2017.10.022
![]() |
[86] |
Han H, Liu Y, Sun Y, et al. (2015) An Improved Droop Control Strategy for Reactive Power Sharing in Islanded Microgrid. IEEE T Power Electr 30: 3133-3141. doi: 10.1109/TPEL.2014.2332181. doi: 10.1109/TPEL.2014.2332181
![]() |
[87] |
Ahumada C, Cárdenas R, Sáez D, et al. (2016) Secondary Control Strategies for Frequency Restoration in Islanded Microgrids With Consideration of Communication Delays. IEEE T Smart Grid 7: 1430-1441. doi: 10.1109/TSG.2015.2461190. doi: 10.1109/TSG.2015.2461190
![]() |
[88] |
Sheng W, Hong Y, Wu M, et al. (2020) A cooperative control scheme for AC/DC hybrid autonomous microgrids. Processes 8: 1-15. doi: 10.3390/pr8030311. doi: 10.3390/pr8030311
![]() |
[89] |
Ma J, Wang X, Liu J, et al. (2019) An improved droop control method for voltage-source inverter parallel systems considering line impedance differences. Energies 12: 1158. doi: 10.3390/en12061158. doi: 10.3390/en12061158
![]() |
[90] |
Sreekumar P, Khadkikar V (2015) A New Virtual Harmonic Impedance Scheme for Harmonic Power Sharing in an Islanded Microgrid. IEEE T Power Deliver 31: 936-945. doi: 10.1109/TPWRD.2015.2402434. doi: 10.1109/TPWRD.2015.2402434
![]() |
[91] |
Mahmood H, Michaelson D, Jiang J (2015) Reactive Power Sharing in Islanded Microgrids Using Adaptive Voltage Droop Control. IEEE T Smart Grid 6: 3052-3060. doi: 10.1109/TSG.2015.2399232. doi: 10.1109/TSG.2015.2399232
![]() |
[92] |
Khan MRB, Jidin R, Pasupuleti J (2016) Multi-agent based distributed control architecture for microgrid energy management and optimization. Energy Convers Manag 112: 288-307. doi: 10.1016/j.enconman.2016.01.011. doi: 10.1016/j.enconman.2016.01.011
![]() |
[93] |
Li Q, Chen F, Chen M, et al. (2016) Agent-Based Decentralized Control Method for Islanded Microgrids. IEEE T Smart Grid 7: 637-649. doi: 10.1109/TSG.2015.2422732. doi: 10.1109/TSG.2015.2422732
![]() |
[94] |
Zhang H, Kim S, Sun Q, et al. (2017) Distributed Adaptive Virtual Impedance Control for Accurate Reactive Power Sharing Based on Consensus Control in Microgrids. IEEE T Smart Grid 8: 1749-1761. doi: 10.1109/TSG.2015.2506760. doi: 10.1109/TSG.2015.2506760
![]() |
[95] | Olfati-Saber R, Murray RM (2004) Consensus problems in networks of agents with switching topology and time-delays. IEEE T Automat Contr 49: 1520-1533. |
[96] |
Sugie T, Anderson BDO, Sun Z, et al. (2018) On a hierarchical control strategy for multi-agent formation without reflection. Proceedings of the IEEE Conference on Decision and Control, 2023-2028. doi: 10.1109/CDC.2018.8619404. doi: 10.1109/CDC.2018.8619404
![]() |
[97] |
Ajorlou A, Aghdam AG (2013) Connectivity preservation in nonholonomic multi-agent systems: A bounded distributed control strategy. IEEE T Automat Contr 58: 2366-2371. doi: 10.1109/TAC.2013.2251792. doi: 10.1109/TAC.2013.2251792
![]() |
[98] |
Dou CX, Liu B (2013) Multi-agent based hierarchical hybrid control for smart microgrid. IEEE T Smart Grid 4: 771-778. doi: 10.1109/TSG.2012.2230197. doi: 10.1109/TSG.2012.2230197
![]() |
[99] |
Liu W, Gu W, Sheng W, et al. (2014) Decentralized multi-agent system-based cooperative frequency control for autonomous microgrids with communication constraints. IEEE T Sustain Energy 5: 446-456. doi: 10.1109/TSTE.2013.2293148. doi: 10.1109/TSTE.2013.2293148
![]() |
[100] |
Nguyen TL, Tran QT, Caire R, et al. (2017) Agent based distributed control of islanded microgrid-Real-time cyber-physical implementation. 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2017 - Proceedings, 1-6. doi: 10.1109/ISGTEurope.2017.8260275. doi: 10.1109/ISGTEurope.2017.8260275
![]() |
[101] | Yao J, Yang S, Wang K, et al. (2014) Framework for Future Smart Grid Operation and Control with Source-Grid-Load Interaction. IFAC Proceedings Volumes 47: 2788-2793. |
[102] |
Han R, Meng L, Ferrari-Trecate G, et al. (2017) Containment and Consensus-Based Distributed Coordination Control to Achieve Bounded Voltage and Precise Reactive Power Sharing in Islanded AC Microgrids. IEEE T Ind Appl 53: 5187-5199. doi: 10.1109/TIA.2017.2733457. doi: 10.1109/TIA.2017.2733457
![]() |
[103] |
Dehkordi NM, Baghaee HR, Sadati N, et al. (2019) Distributed Noise-Resilient Secondary Voltage and Frequency Control for Islanded Microgrids. IEEE T Smart Grid 10: 3780-3790. doi: 10.1109/TSG.2018.2834951. doi: 10.1109/TSG.2018.2834951
![]() |
[104] |
Badal FR, Das P, Sarker SK, et al. (2019) A survey on control issues in renewable energy integration and microgrid. Prot Control Mod Power Syst 4: 1-27. doi: 10.1186/s41601-019-0122-8. doi: 10.1186/s41601-019-0122-8
![]() |
[105] |
Nair UR (2020) A Model Predictive Control-Based Energy Management Scheme for Hybrid Storage System in Islanded Microgrids. IEEE Access 8: 97809-97822. doi: 10.1109/ACCESS.2020.2996434. doi: 10.1109/ACCESS.2020.2996434
![]() |
[106] |
Parisio A, Rikos E, Tzamalis G, et al. (2014) Use of model predictive control for experimental microgrid optimization. Appl Energy 115: 37-46. doi: 10.1016/j.apenergy.2013.10.027. doi: 10.1016/j.apenergy.2013.10.027
![]() |
[107] |
Verma AK, Gooi HB, Ukil A, et al. (2017) Microgrid frequency stabilization using model predictive controller. 2016 IEEE PES Transm Distrib Conf Expo Am PES T D-LA, 1-6. doi: 10.1109/TDC-LA.2016.7805637. doi: 10.1109/TDC-LA.2016.7805637
![]() |
[108] |
Sarkar SK, Badal FR, Das SK, et al. (2017) Discrete time model predictive controller design for voltage control of an islanded microgrid. 3rd Int Conf Electr Inf Commun Technol EICT, 1-6. doi: 10.1109/EICT.2017.8275162. doi: 10.1109/EICT.2017.8275162
![]() |
[109] |
Lou G, Gu W, Sheng W, et al. (2018) Distributed model predictive secondary voltage control of islanded microgrids with feedback linearization. IEEE Access 6: 50169-50178. doi: 10.1109/ACCESS.2018.2869280. doi: 10.1109/ACCESS.2018.2869280
![]() |
[110] |
Sarker SK, Badal FR, Das P, et al. (2019) Multivariable integral linear quadratic Gaussian robust control of islanded microgrid to mitigate voltage oscillation for improving transient response. Asian J Control 21: 2114-2125. doi: 10.1002/asjc.2215. doi: 10.1002/asjc.2215
![]() |
[111] | Vandoorn T, Renders B, Degroote L, et al. (2010) Voltage control in islanded microgrids by means of a linear-quadratic regulator. Proc IEEE Benelux Young Researchers Symposium in Electrical Power Engineering (YRS10). |
[112] |
Rahman M, Sarkar SK, Das SK, et al. (2018) A comparative study of LQR, LQG, and integral LQG controller for frequency control of interconnected smart grid. 3rd Int Conf Electr Inf Commun Technol EICT, 1-6. doi: 10.1109/EICT.2017.8275216. doi: 10.1109/EICT.2017.8275216
![]() |
[113] |
Sedhom BE, Hatata AY, El-Saadawi MM, et al. (2019) Robust adaptive H-infinity based controller for islanded microgrid supplying non-linear and unbalanced loads. IET Smart Grid 2: 420-435. doi: 10.1049/iet-stg.2019.0024. doi: 10.1049/iet-stg.2019.0024
![]() |
[114] |
Vandoorn TL, Ionescu CM, De Kooning JDM, et al. (2013) Theoretical analysis and experimental validation of single-phase direct versus cascade voltage control in islanded microgrids. IEEE T Ind Electron 60: 789-798. doi: 10.1109/TIE.2012.2205362. doi: 10.1109/TIE.2012.2205362
![]() |
[115] |
Singh A, Suhag S (2020) Frequency regulation in an AC microgrid interconnected with thermal system employing multiverse-optimised fractional order-PID controller. Int J Sustain Energy 39: 250-262. doi: 10.1080/14786451.2019.1684286. doi: 10.1080/14786451.2019.1684286
![]() |
[116] |
Singh A, Suhag S (2019) Frequency Regulation in AC Microgrid with and without Electric Vehicle Using Multiverse-Optimized Fractional Order- PID controller. Int J Comput Digit Syst 8: 375-385. doi: 10.12785/ijcds/080406. doi: 10.12785/ijcds/080406
![]() |
[117] |
Sarkar SK, Badal FR, Das SK (2018) A comparative study of high performance robust PID controller for grid voltage control of islanded microgrid. Int J Dyn Control 6: 1207-1217. doi: 10.1007/s40435-017-0364-0. doi: 10.1007/s40435-017-0364-0
![]() |
[118] |
Mongkoltanatas J, Riu D, Lepivert X (2013) H infinity controller design for primary frequency control of energy storage in islanding MicroGrid. 2013 15th Eur Conf Power Electron Appl (EPE), 1-11. doi: 10.1109/EPE.2013.6634714. doi: 10.1109/EPE.2013.6634714
![]() |
[119] |
Bevrani H, Feizi MR, Ataee S (2016) Robust Frequency Control in an Islanded Microgrid: H∞ and μ-Synthesis Approaches. IEEE T Smart Grid 7: 706-717. doi: 10.1109/TSG.2015.2446984. doi: 10.1109/TSG.2015.2446984
![]() |
[120] |
Kerdphol T, Rahman FS, Mitani Y, et al. (2018) Robust Virtual Inertia Control of an Islanded Microgrid Considering High Penetration of Renewable Energy. IEEE Access 6: 625-636. doi: 10.1109/ACCESS.2017.2773486. doi: 10.1109/ACCESS.2017.2773486
![]() |
[121] |
Krishna Metihalli B, Narayana Sabhahit J (2021) Disturbance Observer Based Distributed Consensus Control Strategy of Multi-Agent System with External Disturbance in a Standalone DC Microgrid. Asian J Control 23: 920-936. doi: 10.1002/asjc.2287. doi: 10.1002/asjc.2287
![]() |
[122] |
Keshta HE, Ali AA, Saied EM, et al. (2019) Real-time operation of multi-micro-grids using a multi-agent system. Energy 174: 576-590. doi: 10.1016/j.energy.2019.02.145. doi: 10.1016/j.energy.2019.02.145
![]() |
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η | d | α | Rn | X0 Remark |
0.8 | 5.852 | Unstable | ||
0.002 | 0.7 | 4.434 | Unstable | |
0.6 | 3.341 | Unstable | ||
0.5 | 0.8 | 0.504 | Stable | |
0.05 | 0.7 | 0.495 | Stable | |
0.6 | 0.484 | Stable | ||
0.8 | 3.594 | Unstable | ||
0.002 | 0.7 | 2.821 | Unstable | |
0.6 | 2.206 | Unstable | ||
0.8 | 0.8 | 0.467 | Stable | |
0.05 | 0.7 | 0.454 | Stable | |
0.6 | 0.436 | Stable |
ν | α | η | d | β | γ | λ |
0.01 | 0.8, 0.7, 0.6 | 0.5, 0.8 | 0.002, 0.05 | 0.2 | 0.7 | 0.01 |
[41] | estimated | estimated | estimated, [41] | [41] | [41] | [41] |
η | d | α | Rn | X0 Remark |
0.8 | 5.852 | Unstable | ||
0.002 | 0.7 | 4.434 | Unstable | |
0.6 | 3.341 | Unstable | ||
0.5 | 0.8 | 0.504 | Stable | |
0.05 | 0.7 | 0.495 | Stable | |
0.6 | 0.484 | Stable | ||
0.8 | 3.594 | Unstable | ||
0.002 | 0.7 | 2.821 | Unstable | |
0.6 | 2.206 | Unstable | ||
0.8 | 0.8 | 0.467 | Stable | |
0.05 | 0.7 | 0.454 | Stable | |
0.6 | 0.436 | Stable |