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Two cases studies of Model Predictive Control approach for hybrid Renewable Energy Systems

  • This work presents a load frequency control scheme in Renewable Energy Sources(RESs) power system by applying Model Predictive Control(MPC). The MPC is designed depending on the first model parameter and then investigate its performance on the second model to confirm its robustness and effectiveness over a wide range of operating conditions. The first model is 100% RESs system with Photovoltaic generation(PV), wind generation(WG), fuel cell, seawater electrolyzer, and storage battery. From the simulation results of the first case, it shows the control scheme is efficiency. And base on the good results of the first case study, to propose a second case using a 10-bus power system of Okinawa island, Japan, to verify the efficiency of proposed MPC control scheme again. In addition, in the second case, there also applied storage devices, demand-response technique and RESs output control to compensate the system frequency balance. Last, there have a detailed results analysis to compare the two cases simulation results, and then to Prospects for future research. All the simulations of this work are performed in Matlab®/Simulink®.

    Citation: Lei Liu, Takeyoshi Kato, Paras Mandal, Alexey Mikhaylov, Ashraf M. Hemeida, Hiroshi Takahashi, Tomonobu Senjyu. Two cases studies of Model Predictive Control approach for hybrid Renewable Energy Systems[J]. AIMS Energy, 2021, 9(6): 1241-1259. doi: 10.3934/energy.2021057

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  • This work presents a load frequency control scheme in Renewable Energy Sources(RESs) power system by applying Model Predictive Control(MPC). The MPC is designed depending on the first model parameter and then investigate its performance on the second model to confirm its robustness and effectiveness over a wide range of operating conditions. The first model is 100% RESs system with Photovoltaic generation(PV), wind generation(WG), fuel cell, seawater electrolyzer, and storage battery. From the simulation results of the first case, it shows the control scheme is efficiency. And base on the good results of the first case study, to propose a second case using a 10-bus power system of Okinawa island, Japan, to verify the efficiency of proposed MPC control scheme again. In addition, in the second case, there also applied storage devices, demand-response technique and RESs output control to compensate the system frequency balance. Last, there have a detailed results analysis to compare the two cases simulation results, and then to Prospects for future research. All the simulations of this work are performed in Matlab®/Simulink®.



    Mathematical models have been used to understand and control the dynamics of the disease (influenza, covid 19, HIV etc.). After first SIR type mathematical model constructed by Karmic and MC-Kendrick, for different kind of models (SIR, SIS, SEIR, SI etc.) have been proposed and studied by many authors (see, e.g., [1,2,3,4]). One of the important virus that whole world fighting with is the Human Immunodeficiency Virus (HIV) that is a lent virus caused HIV infection [5]. HIV virus can be transmitted in many ways like sexual intercourse, direct contact with contaminated blood products, needle, or during birth or through breastfeeding (mother to child). Since there is no recovery after HIV, SI type models are more appropriate to comprehend the dynamics of the HIV. In [3], J.J.Wang et al. have been studied mother to child transmission of HIV. The system that obtained with the constructed model in [3] extended and the numerical solutions of the system of linear parabolic equations(PEs) is studied in [6]. In the paper [7], Ashyralyev, Hincal and Kaymakamzade investigated the boundedness of solution of the initial boundary value problem for the system of PEs of observing epidemic models with general nonlinear incidence rate. This model constructed in [8] and it is well-known that (see, [9]) such and many other initial boundary value problems for system of PEs can be reduced to the initial-value problem for system of ordinary differential equations

    {dv1(t)dt+μv1(t)+Av1(t)=F(t,v1(t),v2(t)),dv2(t)dt+(α+μ)v2(t)+Av2(t)=F(t,v1(t),v2(t))G(t,v2(t)),dv3(t)dt+μv3(t)+Av3(t)=G(t,v2(t)),0<t<T,vk(0)=ψk,1k3 (1.1)

    in a Hilbert.space.H.with a self-adjoint.positive.definite.operator AδI,δ>0.

    Existence.and.uniqueness.theorems.of the.bounded.solution of linear and nonlinear systems was.established in the following theorem ([7], [9]).

    Theorem 1. Assume.the following.hypotheses hold

    1. ψn,1n3 belongs to D(A) and

    ψnD(A)=M1. (1.2)

    2. The.function.F:[0,T]×H×HH be.continuous.function, that.is

    F(t,w(t),z(t))HM2 (1.3)

    in [0,T]×H×H and Lipschitz condition holds uniformly with respect to t

    F(t,u,v)f(t,z,w)HL1[uzH+vwH]. (1.4)

    3. The.function.G:[0,T]×HH be.continuous function, that is

    G(t,v(t))HM3 (1.5)

    in [0,T]×H and Lipschitz condition holds uniformly with respect to t

    G(t,u)G(t,z)HL2uzH. (1.6)

    Then, there.exists.a unique bounded solution v(t)=(v1(t),v2(t),v3(t)) of problem (1.1).

    In applications, theorems on the bounded solutions of several systems of nonlinear. PEs were given. Moreover the first order of accuracy DS

    {v1kv1k1τ+μv1k+Av1k=F(tk,v1k,v2k),v2kv2k1τ+(α+μ)v2k+Av2k=Ff(tk,v1k,v2k)G(tk,v2k),v3kv3k1τ+μv3k+Av3k=G(tk,v2k),tk=kτ,1kN,Nτ=T,vn0=ψn,1n3 (1.7)

    for the approximate solution of problem (1.1) was studied. The existence and uniqueness of a bounded solution of DS (1.7) uniformly with.respect.to time.step.τ was.established in the following theorem.

    Theorem 2. If the assumptions (1.2)-(1.6) and μ+δ>2(L1+L2) hold, then there exists a unique solution vτ={vk}Nk=0 of DS (1.7) which is bounded uniformly with respect to τ.

    Bounded solutions of several systems of nonlinear PEs and DSs for the approximate solution of these systems were constituted. Numerical results were provided.

    In general, .it is not possible to get exact solution of nonlinear problems. Therefore, we are interested in constructing a uniformly bounded high order of accuracy DSs with respect to time step size for the approximate solutions initial value problem (1.1).

    In this work, for the approximate solution.of problem (1.1), the second.order of accuracy.Crank-Nicholson DS is investigated. The existence and uniqueness theorems of bounded solution of Crank-Nicholson DS uniformly with respect to time step τ is proved. In practice, theoretical results are presented on four nonlinear systems of parabolic equations to explain how it works on one and multidimensional problems. Numerical results are provided.

    In this section, it will be considered the second order of accuracy Crank-Nicholson DS

    {v1kv1k1τ+μv1k+v1k12+Av1k+v1k12=F(tkτ2,v1k+v1k12,v2k+v2k12),v2kv2k1τ+(α+μ)v2k+v2k12+Av2k+v2k12=F(tkτ2,v1k+v1k12,v2k+v2k12)G(tkτ2,v2k+v2k12),v3kv3k1τ+μv2k+v2k12+Av2k+v2k12=G(tkτ2,v2k+v2k12),tk=kτ,1kN,Nτ=T,vn0=ψn,1n3 (2.1)

    for the approximate solution.of the initial.value.problem (1.1).

    It is interested to studied the existence and uniqueness of a bounded solution of Crank- Nicholson DS (2.1) uniformly with respect to time step τ under the assumptions of Theorem 2.

    Unfortunately, we are not able to establish the Theorem 3 for the solution of Crank-Nicholson DS (2.1) under the assumption μ+δ>2(L1+L2) without restriction to T. Nevertheless, it could be established the such result under assumption 1+1/2τ(μ+δ)>2(L1+L2)T. It is more strong than μ+δ>2(L1+L2) that means it is under assumption with restriction for T. Thus such result we can proved under assumption with restriction for T. It is based on reducing this DS to an equivalent system of nonlinear equations and is used as an operator method to prove the main theorem on the existence and uniqueness of a bounded solution of DS (2.1) uniformly with respect to τ.

    An equivalent system of nonlinear equations for the DS (2.1) is

    {v1k=Bkψ1km=1BkmRF(tmτ2,v1m+v1m12,v2m+v2m12)τ,v2k=Bk1ψ2+km=1Bkm1R1[F(tmτ2,v1m+v1m12,v2m+v2m12)G(tmτ2v2m+v2m12)]τ,v3k=Bkψ3+km=1BkmRG(tmτ2v2m+v2m12)τ,1kN (2.2)

    in Cτ(H)×Cτ(H)×Cτ(H) and the using of successive approximations. Here.and.in.future B=(Iτ(μI+A)2)R,R=(I+τ(μI+A)2)1,B1=(Iτ((μ+α)I+A)2)R1,R1=(I+τ((μ+α)I+A)2)1 and Cτ(H)=C([0,T]τ,H) stands for the Banach space of the mesh functions vτ={vl}Nl=0 defined on [0,T]τ with values in H,equipped with the norm

    vτCτ(H)=max0lNvlH.

    For the solution of DS (2.1), the recursive formula is

    {jv1kjv1k1τ+μjv1k+jv1k12+Ajv1k+jv1k12=F(tkτ2,(j1)v1k+(j1)v1k12,(j1)v2k+(j1)v2k12),jv2kjv2k1τ+(α+μ)jv2k+jv2k12+Ajv2k+jv2k12=F(tkτ2,(j1)v1k+(j1)v1k12,(j1)v2k+(j1)v2k12)G(tkτ2,(j1)v2k+(j1)v2k12),jv3kjv3k1τ+μjv2k+jv2k12+Ajv2k+jv2k12=G(tkτ2,(j1)v2k+(j1)v2k12),tk=kτ,1kN,Nτ=T,jvn0=ψn,1n3,j=1,2,...,0vnk=Bkψn,n=1,3,0v2k=Bkψ2,0kN. (2.3)

    From (2.2) and (2.3) it follows

    {jv1k=Bkψ1km=1BkmRF(tkτ2,(j1)v1k+(j1)v1k12,(j1)v2k+(j1)v2k12)τ,jv2k=Bk1ψ2+km=1Bkm1R1F(tkτ2,(j1)v1k+(j1)v1k12,(j1)v2k+(j1)v2k12)τkm=1Bkm1R1G(tkτ2,(j1)v2k+(j1)v2k12)τ,jv3k=Bkψ3+km=1BkmRG(tkτ2,(j1)v2k+(j1)v2k12),(j1)v2k)τ,1kN,j=1,2,...,0vmk=Bkψm,m=1,3,0v2k=Bkψ2,0kN. (2.4)

    Theorem 3. Let the assumptions (1.2)-(1.6) be satisfied and 2(L1+L2)T<1+τ(μ+δ)2. Then, there exists a unique solution vτ={vk}Nk=0 of DS (2.1) that is bounded in Cτ(H)×Cτ(H)×Cτ(H) of uniformly wrt. τ.

    Proof. Since v3k does not appear in equations for vnkvnk1τ,n=1,2, it is sufficient to analyze the behaviors of solutions v1k and v2k of (2.1). According to the method of recursive approximation (2.4), we get

    vnk=0vnk+i=0[(i+1)vnkivnk],n=1,2, (2.5)

    where

    0vnk={Bkψn,n=1,3,Bk1ψ2,n=2. (2.6)

    Applying formula (2.6), estimates

    BHH1,B1HH1, (2.7)

    we get

    0vnkHψnHM1. (2.8)

    Applying formula (2.4), estimates (2.7) and

    RHH11+τ(μ+δ)2,R1HH11+τ(μ+δ+α)2, (2.9)

    we get

    1v1k0v1kHkm=1BkmRHHf(tmτ2,0v1m+0v1m12,0v2m+0v2m12)Hτ
    M2km=1τ1+τ(μ+δ)2M2T1+τ(μ+δ)2,
    1v2k0v2kHkm=1Bkm1R1HH[F(tmτ2,0v1m+0v1m12,0v2m+0v2m12)H
    +G(tmτ2,0v2m+0v2m12)H]τ
    (M2+M3)km=1τ1+τ(μ+δ+α)2(M2+M3)T1+τ(μ+δ+α)2

    for any k=1,,N. With using triangle inequality, is is obtained that

    1v1kHM1+(M2+M3)T1+τ(μ+δ)2,
    1v2kHM1+(M2+M3)T1+τ(μ+δ)2

    for any.k=1,,N. Applying.formula (2.4), and estimates (2.7), (2.9), (1.4), (1.2) and (1.3), we get

    2v1k1v1kHτkm=1BkmRHH
    ×F(tmτ2,1v1m+1v1m12,1v2m+1v2m12)F(tmτ2,0v1m+0v1m12,0v2m+0v2m12)H
    km=1L1τ1+τ(μ+δ)2[1v1m+1v1m120v1m+0v1m12H+1v2m+1v2m120v2m+0v2m12H]
    2L1(M2+M3)T1+τ(μ+δ)2km=1τ1+τ(μ+δ)22(L1+L2)(M2+M3)T2(1+τ(μ+δ)2)2,
    2v2k1v2kHτkm=1Bkm1R1HH
    ×F(tmτ2,1v1m+1v1m12,1v2m+1v2m12)F(tmτ2,0v1m+0v1m12,0v2m+0v2m12)H
    +τkm=1Bkm1R1HHG(tmτ2,1v2m+1v2m12)G(tmτ2,0v2m+0v2m12)H
    L1km=1τ1+τ(μ+δ+α)2
    ×[1v1m+1v1m120v1m+0v1m12H+1v2m+1v2m120v2m+0v2m12H]
    +L2km=1τ1+τ(μ+δ+α)21v2m+1v2m120v2m+0v2m12H
    (2L1+L2)(M2+M3)T1+τ(μ+δ)2km=1τ1+τ(μ+δ+α)22(L1+L2)(M2+M3)T2(1+τ(μ+δ)2)2

    for any k=1,,N. Then

    2vnkHM1+(M2+M3)T1+τ(μ+δ)2+2(L1+L2)(M2+M3)T2(1+τ(μ+δ)2)2,n=1,2

    for any k=1,,N. Let

    jvnk(j1)vnkH2j1(L1+L2)j1(M2+M3)Tj(1+τ(μ+δ)2)j,n=1,2.

    Applying formula (2.4), estimates (2.7), (1.4), (1.2) and (1.3), we get

    (j+1)v1kjv1kHτkm=1BkmRHH
    ×f(tmτ2,jv1m+jv1m12,jv2m+jv2m12)
    f(tmτ2,(j1)v1m+(j1)v1m12,(j1)v2m+(j1)v2m12)H
    km=1L1τ1+τ(μ+δ)2
    ×[jv1m+jv1m12(j1)v1m+(j1)v1m12H
    +jv2m+jv2m12(j1)v2m+(j1)v2m12H]
    2L12j1(L1+L2)j1(M2+M3)Tj(1+τ(μ+δ)2)jkm=1τ1+τ(μ+δ)2(2(L1+L2))j(M2+M3)Tj+1(1+τ(μ+δ)2)j+1,
    (j+1)v2kjv2kHτkm=1Bkm1R1HH
    ×F(tmτ2,jv1m+jv1m12,jv2m+jv2m12)
    F(tmτ2,(j1)v1m+(j1)v1m12,(j1)v2m+(j1)v2m12)H
    +τkm=1Bkm1R1HH
    ×G(tmτ2,jv2m+jv2m12)F(tmτ2,(j1)v2m+(j1)v2m12)H
    km=1L1τ1+τ(μ+δ+a)2
    ×[jv1m+jv1m12(j1)v1m+(j1)v1m12H
    +jv2m+jv2m12(j1)v2m+(j1)v2m12H]
    +km=1L2τ1+τ(μ+δ+a)2jv2m+jv2m12(j1)v2m+(j1)v2m12H
    (2L1+L2)2j1(L1+L2)j1(M2+M3)Tj(1+τ(μ+δ)2)jkm=1τ1+τ(μ+δ)2(2(L1+L2))j(M2+M3)Tj+1(1+τ(μ+δ)2)j+1

    for any k=1,,N. Then

    (j+1)vnkHM1+(M2+M3)T1+τ(μ+δ)2
    +2(L1+L2)(M2+M3)T2(1+τ(μ+δ)2)2++(2(L1+L2))j(M2+M3)Tj+1(1+τ(μ+δ)2)j+1,n=1,2

    for any k=1,,N. Therefore, for any j,j1, we have that

    (j+1)vnkjvnkH(2(L1+L2))j(M2+M3)Tj+1(1+τ(μ+δ)2)j+1,n=1,2,

    and

    (j+1)vnkHM1+(M2+M3)T1+τ(μ+δ)2
    +2(L1+L2)(M2+M3)T2(1+τ(μ+δ)2)2++(2(L1+L2))j(M2+M3)Tj+1(1+τ(μ+δ)2)j+1,n=1,2

    by mathematical.induction. From that and formula (2.5) it is obtained

    vnkH0vnkH+i=0(i+1)vnkivnkH
    M1+(M2+M3)T1+τ(μ+δ)2i=02i(L1+L2)iTi(1+τ(μ+δ)2)i,n=1,2

    that proves the existence of a bounded solution of DS (2.1) which is bounded.in Cτ(H)×Cτ(H)×Cτ(H) of uniformly wrt. τ. Theorem 3 is proved.

    A study of discretization, over time only, of the initial value problem also permits one to include general DSs in applications, if the differential operator A is replaced by the difference operator Ah that act in the Hilbert spaces and are uniformly self-adjoint positive definite in h for 0<hh0.

    In this section it will be given considered some nonlinear partial differential equations(PDEs).

    First, we consider the initial-boundary.value problem for one.dimensional system.of nonlinear PDEs.

    {v1(t,x)t(a(x)v1x(t,x))x+(δ+μ)v1(t,x)=F(t,x;v1(t,x),v2(t,x)),v2(t,x)t(a(x)v2x(t,x))x+(δ+μ+α)v2(t,x)=F(t,x;v1(t,x),v2(t,x))G(t,x;v2(t,x)),v3(t,x)t(a(x)v3x(t,x))x+(δ+μ)v3(t,x)=G(t,x;v2(t,x)),0<t<T,0<x<l,vn(0,x)=ψn(x),ψn(0)=ψn(l),φmx(0)=ψnx(l),x[0,l],n=1,2,3,vn(t,0)=vn(t,l),vnx(t,0)=vnx(t,l),0tT,n=1,2,3, (3.1)

    where a(x),ψ(x) are given sufficiently smooth functions and δ>0 is the sufficiently large number. We will assume that a(x)a>0 and a(l)=a(0).

    Assume the following.hypotheses hold

    1. ψn,n=1,2,3 belongs to W22[0,l] and

    ψnW22[0,l]M1. (3.2)

    2. The function f:[0,T]×[0,l]×L2[0,l]×L2[0,l]L2[0,l] be continuous function in t, that is

    F(t,,u(t,),v(t,))L2[0,l]M2 (3.3)

    in [0,T]×[0,l]×L2[0,l]×L2[0,l] and Lipschitz condition holds uniformly with respect to t

    G(t,,u,v)G(t,,z,w)L2[0,l]L1[uzL2[0,l]+vwL2[0,l]]. (3.4)

    3. The function g:[0,T]×[0,l]×L2[0,l]L2[0,l] be continuous function in t, that is

    G(t,,u(t,))L2[0,l]M3 (3.5)

    in [0,T]×[0,l]×L2[0,l] and Lipschitz condition holds uniformly with respect to t

    G(t,,u)G(t,,z)L2[0,l]L2uzL2[0,l]. (3.6)

    Here and in future, Lm,m=1,2,Mm,m=1,2,3 are positive constants.

    The discretization of problem (3.1) is carried out in two steps. In the first step, let us define the grid space

    [0,l]h={x:xr=rh,0rK,Kh=l}.

    We introduce the Hilbert spaces L2h=L2([0,l]h) and W22h=W22([0,l]h)\ of the grid functions ψh(x)={ψr}K0 defined on [0,l]h, equipped with the norms

    ψhL2h=(x[0,l]h|ψh(x)|2h)1/2

    and

    ψhW22h=ψhL2h+(x[0,l]h|(ψh)x¯x,j|2h)1/2

    respectively. To the differential operator A generated by problem (3.1), we assign the difference operator Axh by the formula

    Axhψh(x)={(a(x)ψ¯x)x,r+δψr}K11, (3.7)

    acting in the space of grid functions ψh(x)={ψr}K0 satisfying the conditions ψ0=ψK, ψ1ψ0=ψKψK1. With the help of Axh, we arrive at the initial value problem

    {dv1h(t,x)dt+μv1h(t,x)+Axhv1h(t,x)=Fh(t,x;v1h(t,x),v2h(t,x)),dv2h(t,x)dt+(μ+α)v2h(t,x)+Axhv2h(t,x)=Fh(t,x;v1h(t,x),v2h(t,x))Gh(t,x;v2h(t,x)),dv3h(t,x)dt+μv3h(t,x)+Axhv3h(t,x)=Gh(t,x;v2h(t,x)),0<t<T,x[0,l]h,vnh(0,x)=ψn(x),n=1,2,3,x[0,l]h (3.8)

    for an infinite system of nonlinear ordinary differential equations (ODEs). In the second step, we replace problem (3.8) by DS (2.1)

    {v1kv1k1τ+μv1k+v1k12+Axhv1k+v1k12=Fh(tkτ2,x,v1k+v1k12,v2k+v2k12),v2kv2k1τ+(α+μ)v2k+v2k12+Axhv2k+v2k12=Fh(tkτ2,x,v1k+v1k12,v2k+v2k12)Gh(tkτ2,x,v2k+v2k12),v3kv3k1τ+μv3k+v3k12+Axhv3k+v3k12=Gh(tkτ2,x,v2k+v2k12),tk=kτ,1kN,Nτ=T,x[0,l]h,vn0=ψn,n=1,2,3. (3.9)

    Theorem 4. Let the assumptions (3.2)-(3.6) be satisfied and 2(L1+L2)T<1+τ(μ+δ)2. Then, there exists a unique solution vτ={vk}Nk=0 of DS (3.9) which is bounded in Cτ(L2h)×Cτ(L2h)×Cτ(L2h) of uniformly with respect to τ and h.

    The proof of Theorem 4 is based on the abstract Theorem 3 and symmetry properties of the difference operator Axh defined by formula (3.7)[11].

    Second, we consider the initial-boundary value problem for one dimensional system of nonlinear PDs with involution

    {v1(t,x)t(a(x)v1x(t,x))xβ(a(x)vx(t,x))x+(δ+μ)v1(t,x)=F(t,x;v1(t,x),v2(t,x)),v2(t,x)t(a(x)v2x(t,x))xβ(a(x)vx(t,x))x+(δ+μ+α)v2(t,x)=F(t,x;v1(t,x),v2(t,x))G(t,x;v2(t,x)),v3(t,x)t(a(x)v3x(t,x))xβ(a(x)vx(t,x))x+(δ+μ)v3(t,x)=G(t,x;v2(t,x)),0<t<T,l<x<l,vn(0,x)=ψn(x),ψn(l)=ψn(l)=0,x[l,l],n=1,2,3,vn(t,l)=vn(t,l)=0,0tT,n=1,2,3, (3.10)

    where a(x),ψ(x) are given sufficiently smooth functions and δ>0 is the sufficiently large number. We will assume that aa(x)=a(x)δ>0,δa|β|0.

    Assume the following hypotheses:

    1. ψn,n=1,2,3 belongs to W22[l,l] and

    ψnW22[l,l]M1. (3.11)

    2. The function F:[0,T]×[l,l]×L2[l,l]×L2[l,l]L2[l,l] be continuous function in t, that is

    F(t,,u(t,),v(t,))L2[l,l]M2 (3.12)

    in [0,T]×[l,l]×L2[l,l]×L2[l,l] and Lipschitz condition holds uniformly with respect to t

    F(t,,u,v)F(t,,z,w)L2[l,l]L1[uzL2[l,l]+vwL2[l,l]]. (3.13)

    3. The function G:[0,T]×[l,l]×L2[l,l]L2[l,l] be continuous function in t, that is

    G(t,,u(t,))L2[0,l]M3 (3.14)

    in [0,T]×[l,l]×L2[l,l] and Lipschitz condition holds uniformly with respect to t

    G(t,,u)G(t,,z)L2[l,l]L2uzL2[l,l]. (3.15)

    The discretization of problem (3.10) is carried out in two steps. In the first step, let us define the grid space

    [l,l]h={x:xr=rh,KrK,Kh=l}.

    We introduce the Hilbert spaces L2h=L2([l,l]h) and W22h=W22([l,l]h)\ of the grid functions ψh(x)={ψr}KK defined on [l,l]h, equipped with the norms

    ψhL2h=(x[l,l]h|ψh(x)|2h)1/2

    and

    ψhW22h=ψhL2h+(x[l,l]h|(ψh)x¯x,j|2h)1/2

    respectively. To the differential operator A generated by problem (3.10), we assign the difference operator Axh by the formula

    Axhψh(x)={(a(x)ψ¯x(x))x,rβ(a(x)ψ¯x(x))x,r+δψr}K1K+1, (3.16)

    acting in the space of grid functions ψh(x)={ψr}KK satisfying the conditions ψK=ψK=0. With the help of Axh, we arrive at the initial value problem

    {dv1h(t,x)dt+μv1h(t,x)+Axhv1h(t,x)=Fh(t,x;v1h(t,x),v2h(t,x)),dv2h(t,x)dt+(μ+α)v2h(t,x)+Axhv2h(t,x)=Fh(t,x;v1h(t,x),v2h(t,x))Gh(t,x;v2h(t,x)),dv3h(t,x)dt+μv3h(t,x)+Axhv3h(t,x)=Gh(t,x;v2h(t,x)),0<t<T,x[l,l]h,vnh(0,x)=ψn(x),n=1,2,3,x[l,l]h (3.17)

    for an infinite system of nonlinear ODEs. In the second step, we replace problem (3.17) by DS (2.1)

    {v1kv1k1τ+μv1k+v1k12+Axhv1k+v1k12=Fh(tkτ2,x,v1k+v1k12,v2k+v2k12),v2kv2k1τ+(α+μ)v2k+v2k12+Axhv2k+v2k12=Fh(tkτ2,x,v1k+v1k12,v2k+v2k12)Gh(tkτ2,x,v2k+v2k12),v3kv3k1τ+μv3k+v3k12+Axhv3k+v3k12=Gh(tkτ2,x,v2k+v2k12),tk=kτ,1kN,Nτ=T,x[l,l]h,vn0=ψn,n=1,2,3. (3.18)

    Theorem 5. Let the assumptions (3.11)-(3.15) be satisfied and 2(L1+L2)T<1+τ(μ+δ)2. Then, there exists a unique solution vτ={vk}Nk=0 of DS (3.18) which is bounded in Cτ(L2h)×Cτ(L2h)×Cτ(L2h) of uniformly with respect to τ and h.

    The proof of Theorem 5 is based on the abstract Theorem 3 and symmetry properties of the difference operator Axh defined by formula (3.16)[12].

    Third, let ΩRn be a bounded open domain with smooth boundary S, ¯Ω=ΩS. In [0,T]×Ω we consider the initial-boundary value problem for multidimensional system of nonlinear PDEs

    {v1(t,x)tnr=1(ar(x)v1xr)xr+(δ+μ)v1(t,x)=F(t,x;v1(t,x),v2(t,x)),v2(t,x)tnr=1(ar(x)v2xr)xr+(δ+μ+α)v2(t,x)=F(t,x;v1(t,x),v2(t,x))G(t,x;v2(t,x)),v3(t,x)tnr=1(ar(x)v3xr)xr+(δ+μ)v3(t,x)=G(t,x;v2(t,x)),0<t<T,x=(x1,...,xn)Ω,vm(0,x)=ψm(x),x¯Ω,m=1,2,3,vm(t,x)=0,0tT,xS,m=1,2,3, (3.19)

    where ar(x) and ψm(x) are given sufficiently smooth functions and δ>0 is the sufficiently large number and ar(x)>0.

    Assume the following hypotheses:

    1. ψm,m=1,2,3 belongs to L2(¯Ω) and

    ψmW22(¯Ω)M1. (3.20)

    2. The function f:[0,T]×[0,l]×L2(¯Ω)×L2(¯Ω)L2(¯Ω) be continuous function in t, that is

    F(t,,u(t,),v(t,))L2(¯Ω)M2 (3.21)

    in [0,T]×[0,l]×L2(¯Ω)×L2(¯Ω) and Lipschitz condition holds uniformly with respect to t

    F(t,,u,v)F(t,,z,w)L2(¯Ω)L1[uzL2(¯Ω)+vwL2(¯Ω)]. (3.22)

    3. The function G:[0,T]×[0,l]×L2(¯Ω)L2(¯Ω) be continuous function in t, that is

    G(t,,u(t,))L2(¯Ω)M3 (3.23)

    in [0,T]×[0,l]×L2(¯Ω) and Lipschitz condition holds uniformly with respect to t

    G(t,,u)G(t,,z)L2(¯Ω)L2uzL2(¯Ω). (3.24)

    The discretization of problem (3.19) is also carried out in two steps. In the first step, let us define the grid sets

    ¯Ωh={x=xr=(h1r1,...,hmrm),r=(r1,...,rm),
     0rjNj,hjNj=1,j=1,...,m},
    Ωh=¯ΩhΩ,Sh=¯ΩhS.

    We introduce the Banach spaces L2h=L2(¯Ωh) and W22h=W22(¯Ωh)\ of the grid functions φh(x)={ψ(h1r1,...,hmrm)} defined on ¯Ωh, equipped with the norms

    ψhL2h=(x¯Ωh|ψh(x)|2h1hm)1/2

    and

    ψhW2h=ψhL2h+(x¯Ωhmr=1|(ψh)xr¯xr,jr|2h1hm)1/2

    respectively. To the differential operator A generated by problem (3.19), we assign the difference operator Axh by the formula

    Axhvhx=mr=1(ar(x)vh¯xr)xr,jr (3.25)

    acting in the space of grid functions uh(x), satisfying the conditions vh(x)=0 for all xSh. It is known that Axh is a self-adjoint positive definite operator in L2(¯Ωh). With the help of Axh, we arrive at the initial value problem

    {dv1h(t,x)dt+μv1h(t,x)+Axhv1h(t,x)=Fh(t,x;v1h(t,x),v2h(t,x)),dv2h(t,x)dt+(μ+α)v2h(t,x)+Axhv2h(t,x)=Fh(t,x;v1h(t,x),v2h(t,x))Gh(t,x;v2h(t,x)),dv3h(t,x)dt+μv3h(t,x)+Axhv3h(t,x)=Gh(t,x;v2h(t,x)),0<t<T,x¯Ωh,vmh(0,x)=ψm(x),m=1,2,3,x¯Ωh (3.26)

    for an infinite system of nonlinear ODEs. In the second step, we replace problem (3.26) by DS (2.1)

    {v1kv1k1τ+μv1k+v1k12+Axhv1k+v1k12=Fh(tkτ2,x,v1k+v1k12,v2k+v2k12),v2kv2k1τ+(α+μ)v2k+v2k12+Axhv2k+v2k12=Fh(tkτ2,x,v1k+v1k12,v2k+v2k12)Gh(tkτ2,x,v2k+v2k12),v3kv3k1τ+μV3k+v3k12+Axhv3k+v3k12=Gh(tkτ2,x,v2k+v2k12),tk=kτ,1kN,Nτ=T,x¯Ωh,vm0=ψm,m=1,2,3. (3.27)

    Theorem 6. Let the assumptions (3.20)-(3.24) be satisfied and 2(L1+L2)T<1+τ(μ+δ)2. Then, there exists a unique solution vτ={vk}Nk=0 of DS (3.27) which is bounded in Cτ(L2h)×Cτ(L2h)×Cτ(L2h) of uniformly with respect to τ and h.

    The proof of Theorem 6 is based on the abstract Theorem 4 and symmetry properties of the difference operator Axh defined by formula (3.25) and the following theorem on coercivity inequality for the solution of the elliptic problem in L2h.

    Theorem 7. For the solutions of the elliptic difference problem

    {Axhvh(x)=gh(x), xΩh,vh(x)=0, xSh

    the following coercivity inequality

    mr=1vhxr¯xr,jrL2hMghL2h.

    holds (see [13]).

    Fourth, in [0,T]×Ω we consider the initial-boundary value problem for multidimensional system of nonlinear PDEs

    {v1(t,x)tnr=1(ar(x)v1xr)xr+(δ+μ)v1(t,x)=F(t,x;v1(t,x),v2(t,x)),v2(t,x)tnr=1(ar(x)v2xr)xr+(δ+μ+α)v2(t,x)=f(t,x;v1(t,x),v2(t,x))G(t,x;v2(t,x)),v3(t,x)tnr=1(ar(x)v3xr)xr+(δ+μ)v3(t,x)=G(t,x;v2(t,x)),0<t<T,x=(x1,...,xn)Ω,vm(0,x)=ψm(x),x¯Ω,m=1,2,3,vp(t,x)=0,0tT,xS,m=1,2,3, (3.28)

    where ar(x) and ψm(x) are given sufficiently smooth functions and δ>0 is the sufficiently large number and ar(x)>0. Here, p is the normal vector to Ω.

    The discretization of problem (3.28) is also carried out in two steps. In the first step, to the differential operator A generated by problem (3.28), we assign the difference operator Axh by the formula

    Axhvhx=mr=1(ar(x)vh¯xr)xr,jr+δvh(x) (3.29)

    acting in the space of grid functions vh(x), satisfying the conditions Dhvh(x)=0 for all xSh. Here Dh is the approximation of operator p. It is known that Axh is a self-adjoint positive definite operator in L2(¯Ωh). With the help of Axh, we arrive at the initial value problem (3.26) for an infinite system of nonlinear ODEs. In the second step, we replace problem (3.26) by DS (2.1), we get DS (3.27).

    Theorem 8. Let the assumptions (3.20)-(3.24) be satisfied and 2(L1+L2)T<1+τ(μ+δ)2. Then, there exists a unique solution vτ={vk}Nk=0 of DS (3.27) which is bounded in Cτ(L2h)×Cτ(L2h)×Cτ(L2h) of uniformly with respect to τ and h.

    The proof of Theorem 8 is based on the abstract Theorem 3 and symmetry properties of the difference operator Axh defined by formula (3.29) and the following theorem on coercivity inequality for the solution of the elliptic problem in L2h.

    Theorem 9. For the solutions of the elliptic difference problem

    {Axhvh(x)=gh(x), xΩh,Dhvh(x)=0, xSh

    the following coercivity inequality holds (see [13]):

    mr=1vhxr¯xr,jrL2hMghL2h.

    In present section, we consider the initial-boundary value problem

    {v1(t,x)tλ+μv1(t,x)β2v1(t,x)x2=λ+(1+μ+β)etsinxsin(v1(t,x)v2(t,x)e2tsin2x),v2(t,x)t+(μ+α)v2(t,x)d2v2(t,x)x2=(1+μ+α+d)etsinx+sin(v1(t,x)v2(t,x)e2tsin2x)cos(v2(t,x)etsinx),v3(t,x)t+μv1(t,x)γ2v1(t,x)x2=(1+μ+γ)etsinx+cos(v2(t,x)etsinx),0<t<1,0<x<π,vm(0,x)=sin(x),0xπ,m=1,2,3,vm(t,0)=um(t,π)=0,0t1,m=1,2,3 (4.1)

    for the system of nonlinear PDEs. The spatial factor, x, can be spatially discrete or spatially continuous. In either case, the spatial factor is used to describe the mobility of the population. This mobility can be due to travel and migration, and it could be between cities, towns or even countries, depending on the studied case. The exact solution of problem (4.1) is vm(t,x)=etsinx,m=1,2,3.

    Numerical solutions of system (4.1) will be given for first and second order of DS. Firstly, we consider the first order of accuracy iterative DS

    {jv1,knjv1,k1nτ+μjv1,knβjv1,kn+12(jv1,kn)+jv1,kn1h2=(1+μ+β)etksinxnsin((j1)v1,kn(j1)v2,kne2tksin2xn),jv2,knjv2,k1nτ+(α+μ)jv2,kndjv2,kn+12(jv2,kn)+jv2,kn1h2=(1+μ+α+d)etksinxn+sin((j1)v1,kn(j1)v2,kne2tksin2xn)cos((j1)v2,knetksinxn),jv3,knjv3,k1nτ+μjv3,knγjv3,kn+12(jv3,kn)+jv3,kn1h2=(1+μ+γ)etksinxn+cos((j1)v2,knetksinxn),tk=kτ,1kN,Nτ=1,xn=nh,1nM1,Mh=π,jvm,0n=ψm(xn),jvm,k0=jum,kM=0,0kN,m=1,2,3,j=1,2,,0vm,kn,0kN,0nM,m=1,2,3 is given (4.2)

    and secondly, the second order of accuracy iterative Crank-Nicholson DS

    \begin{equation} \left\{ \begin{array}{l} \frac{jv_{n}^{1,k}-jv_{n}^{1,k-1}}{\tau }+\mu \frac{ jv_{n}^{^{_{1,k}}}+jv_{n}^{^{_{1,k-1}}}}{2}-\beta \frac{_{j}v_{n+1}^{1,k}-2 \left( jv_{n}^{1,k}\right) +jv_{n-1}^{1,k}}{2h^{2}}-\beta \frac{ _{j}v_{n+1}^{1,k-1}-2\left( jv_{n}^{1,k-1}\right) +jv_{n-1}^{1,k-1}}{2h^{2}} \\ \\ = \left( -1+\mu +\beta \right) e^{-\left( t_{k}-\frac{\tau }{2}\right) }\sin x_{n} \\ \\ -\sin \left( \frac{\left( j-1\right) v_{n}^{^{_{1,k}}}+\left( j-1\right) v_{n}^{^{_{1,k-1}}}}{2}\frac{\left( j-1\right) v_{n}^{^{_{2,k}}}+\left( j-1\right) v_{n}^{^{_{2,k-1}}}}{2}-e^{-2\left( t_{k}-\frac{\tau }{2}\right) }\sin ^{2}x_{n}\right) , \\ \\ \frac{jv_{n}^{2,k}-jv_{n}^{2,k-1}}{\tau }+\left( \alpha +\mu \right) \frac{ jv_{n}^{^{_{2,k}}}+jv_{n}^{^{_{2,k-1}}}}{2}-d\frac{_{j}v_{n+1}^{2,k}-2\left( jv_{n}^{2,k}\right) +jv_{n-1}^{2,k}}{2h^{2}}-d\frac{_{j}v_{n+1}^{2,k-1}-2 \left( jv_{n}^{2,k-1}\right) +jv_{n-1}^{2,k-1}}{2h^{2}} \\ \\ = \left( -1+\mu +\alpha +d\right) e^{-\left( t_{k}-\frac{\tau }{2}\right) }\sin x_{n} \\ \\ +\sin \left( \frac{\left( j-1\right) v_{n}^{^{_{1,k}}}+\left( j-1\right) v_{n}^{^{_{1,k-1}}}}{2}\frac{\left( j-1\right) v_{n}^{^{_{2,k}}}+\left( j-1\right) v_{n}^{^{_{2,k-1}}}}{2}-e^{-2\left( t_{k}-\frac{\tau }{2}\right) }\sin ^{2}x_{n}\right) \\ \\ -\cos \left( \frac{\left( j-1\right) v_{n}^{^{_{2,k}}}+\left( j-1\right) v_{n}^{^{_{2,k-1}}}}{2}-e^{-\left( t_{k}-\frac{\tau }{2}\right) }\sin x_{n}\right) , \\ \\ \frac{jv_{n}^{3,k}-jv_{n}^{3,k-1}}{\tau }+\mu \frac{ jv_{n}^{^{_{3,k}}}+jv_{n}^{^{_{3,k-1}}}}{2}-\gamma \frac{_{j}v_{n+1}^{3,k}-2 \left( jv_{n}^{3,k}\right) +jv_{n-1}^{3,k}}{2h^{2}}-\gamma \frac{ _{j}v_{n+1}^{3,k-1}-2\left( jv_{n}^{3,k-1}\right) +jv_{n-1}^{3,k-1}}{2h^{2}} \\ \\ = \left( -1+\mu +\gamma \right) e^{-\left( t_{k}-\frac{\tau }{2}\right) }\sin x_{n}+\cos \left( \frac{\left( j-1\right) v_{n}^{^{_{2,k}}}+\left( j-1\right) v_{n}^{^{_{2,k-1}}}}{2}-e^{-\left( t_{k}-\frac{\tau }{2}\right) }\sin x_{n}\right) , \\ \\ t_{k} = k\tau ,1\leq k\leq N,N\tau = 1,x_{n} = nh,1\leq n\leq M-1,Mh = \pi , \\ \\ jv_{n}^{m,0} = \psi ^{m}(x_{n}),jv_{0}^{m,k} = jv_{M}^{m,k} = 0,0\leq k\leq N,m = 1,2,3,j = 1,2,\cdot \cdot \cdot , \\ \\ 0v_{n}^{m,k},0\leq k\leq N,0\leq n\leq M,m = 1,2,3\text{ is given} \end{array} \right. \end{equation} (4.3)

    for the.approximate solution of the.initial-boundary.value.problem (4.1) for the system.of nonlinear. PEs. Here and in future j denotes the iteration.index and an.initial guess. _{0}u_{n}^{k}, k\geq 1, 0\leq n\leq M is to be made. For solving DS (4.3), the numerical.steps are.given.below. For 0\leq k < N, 0\leq n\leq M the algorithm.is as follows.[10] :

    1. j = 1 .

    2. _{j-1}v_{n}^{k} is.known.

    3. _{j}v_{n}^{k} is.calculated.

    4. If the.max absolute.error between _{j-1}v_{n}^{k} and _{j}v_{n}^{k} is greater.than the given.tolerance value \varepsilon = 10^{-8} , take j = j+1 and go.to.step 2. Otherwise, terminate.the iteration.process and take _{j}v_{n}^{k} as the result of the given problem. The errors are computed by

    \left( jE^{m}\right) _{M}^{N} = \max\limits_{1\leq k\leq N, 1\leq n\leq M-1}\left\vert v^{m}(t_{k},x_{n})-\left( jv^{m}\right) _{n}^{k}\right\vert, m = 1,2,3 (4.4)

    of the numerical solutions, where v^{m}(t_{k}, x_{n}) \;, m = 1, 2, 3 represents the exact solutions and \left(jv^{m}\right) _{n}^{k} \;, m = 1, 2, 3 represents.the numerical.solutions at (t_{k}, x_{n}) and the results of the first and second order of DS are given.in Table 1 and Table 2 respectively.

    Table 1.  First order DS.
    \left(jE^{m}\right) _{M}^{N} N=M=20 N=M=40 N=M=80
    m=1 0.0068, j=6 0.0032, j=6 0.0016, j=6
    m=2 0.0071, j=6 0.0033, j=6 0.0016, j=6
    m=3 0.0073, j=6 0.0034, j=6 0.0017, j=6

     | Show Table
    DownLoad: CSV
    Table 2.  Second order DS.
    \left(jE^{m}\right) _{M}^{N} N=M=20 N=M=40 N=M=80
    m=1 5.5516e-5, j=7 1.3882e-5, j=7 3.4708e-6, j=7
    m=2 8.7420e-5, j=7 2.1857e-5, j=7 5.4645e-6, j=7
    m=3 1.1120e-4, j=7 2.7803e-5, j=7 , 6.9510e-6j=7

     | Show Table
    DownLoad: CSV

    According to Table 1 and Table 2, if N and M are doubled, the value of errors in the first order of accuracy DS decrease by a factor of 1/2, the errors in the second order of accuracy DS (4.3) decrease approximately by a factor of 1/4 . The errors presented in the tables indicate the stability of the DS and the accuracy of the results. Thus, the second order of accuracy DS increases faster than the first order of accuracy DS.

    In the present paper, the.initial boundary value problem for the nonlinear system of PEs observing epidemic models with general nonlinear incidence rate is investigated. The main theorem on the existence and uniqueness of a bounded solution of Crank-Nicholson DS uniformly with respect to time step \tau is established. Applications of the theoretical results are presented for the four systems of one and multidimensional problems with different boundary conditions. Numerical results are given.

    The publication has been prepared with the support of the "RUDN University Program 5-100" and published under target program BR05236656 of the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan.

    The author declares that there are no conflicts of interest.



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