CN102841965B - The modeling method of receiving end grid security domain optimal load flow model - Google Patents

Abstract

The invention discloses a kind of modeling method of receiving end grid security domain optimal load flow model, concrete steps are: the first step: carry out constructing system security domain by namely running criterion to the stability of static system voltage, phase angle, oscillation frequency and N-1 to the stability analysis of power flow equation, N number of load of system can be transformed to by given power generation dispatching criterion M the different sets that load direction forms, and generates critical load matrix and carries out approximate processing to security domain: second step: build dynamic security constrained optimum tide model; 3rd step: set up Adaptive Neuro-fuzzy Inference; 4th step: train adaptive nuero-fuzzy inference system system and set up optimal load flow model.Optimal load flow model of the present invention illustrates the operation criterion of current electric power system dispatching well, can apply to the optimizing scheduling behind layering and zoning between receiving end Grid based on the optimal load flow security domain approximation technique of adaptive nuero-fuzzy inference system system.

Description

The modeling method of receiving end grid security domain optimal load flow model

Technical field

The present invention relates to a kind of modeling method of optimal load flow model, particularly relate to a kind of modeling method of receiving end grid security domain optimal load flow model.

Background technology

In electric system long-run development process, it is complementary that operation of power networks planning and system load flow calculate.On the one hand, for ensureing the safety and stability of whole electrical network, the formation of Electric Power Network Planning need be based upon system load flow and calculate on basis; On the other hand, system optimal Load flow calculation can improve stability and the economy of Electric Power Network Planning.Optimal load flow is meeting under specific system cloud gray model and security constraints, by utilizing control device in adjustment System, can realize the system stable operation state of intended target optimum.Therefore, in system safety operation problem under increasingly sharp-pointed and complicated situation, optimal load flow calculates nature becomes the system stability analysis aid indispensable with optimization.

After the access of extra-high voltage grid, the Voltage Instability that during system jam, power shifts initiation on a large scale will become one of subject matter of receiving end electricity net safety stable.Especially, after extra-high voltage backbone network builds up substantially, system short-circuit levels of current will be increased.Due to the importance of 500kV partition power grid, it will inevitably exist with the form of looped network, and this just weakens the benefit that layering and zoning can be brought.What the fault rate of electric system was the highest is single-line to ground fault.Receiving-end system is comparatively weak, and load center lacks forceful electric power source and supports, and especially the support of 500kV electrical network is weak, cause 220kV electrical network too intensive, short-circuit current exceeds standard, and after extra-high voltage access large receiving-end grid, power system delamination and subarea operates to power grid security and stability and brings stern challenge.

Summary of the invention

Object of the present invention is exactly that provide a kind of computing method of receiving end grid security domain optimal load flow, it has the advantage of the operation criterion illustrating current electric power system dispatching well in order to solve the problem.

To achieve these goals, the present invention adopts following technical scheme:

A modeling method for receiving end grid security domain optimal load flow model, concrete steps are:

The first step: by namely carrying out constructing system security domain to the stability of static system voltage, phase angle, oscillation frequency and N-1 criterion to the stability analysis of power flow equation.For obtaining discrete security domain expression formula, N number of load of system can be transformed to by given power generation dispatching criterion M the different sets that load direction forms, N, M be more than or equal to 1 for natural number, N is load quantity in system, given system has fixing load quantity, generates following critical load matrix and carries out approximate processing to security domain:

Second step: build dynamic security constrained optimum tide model as follows:

Objective function: $S_b=-(C_d^T{P}_d-C_s^T{P}_s)---\left(2\right)$

Constraint condition: F _pF(δ, V, Q _g, P _s, P _d)=0 (3)

$0\≤P_s\≤P_{s_{\max }}---\left(4\right)$

$0\≤P_d\≤P_{d_{\mathrm{mac}}}---\left(5\right)$

i, j be more than or equal to 1 natural number (6)

$Q_{g_{\min }}\≤Q_g\≤Q_{g_{\max }}---\left(7\right)$

V _min≤V≤V _max（8）

$f_{{\mathrm{NR}}_0}-f_{\mathrm{NR}}(V,\mathrm{\δ})\≤0---\left(9\right)$

Wherein, C _sand C _dbe the bid of electric power supply and demand respectively, unit is $/MWh; System supply and demand power is respectively P _sand P _d, unit is MW; F _pF() is system load flow equation; V and δ is node voltage respectively and intersects; I _ijthe electric current by transmission line of electricity ij, this constraint definition thermally-stabilised limit of system; Q _gfor generator reactive power; f _nR() for representing security of system territory, for the critical value that it is suitable.

3rd step: set up Adaptive Neuro-fuzzy Inference.

4th step: train adaptive nuero-fuzzy inference system system and set up optimal load flow model, security domain constrained optimum tide model is:

Objective function: $S_b=C_s^T{P}_s-C_d^T\mathrm{\Δ}{P}_d---\left(10\right)$

Constraint condition: F _pF(δ, V, Q _g, P _s, P _d, Q _d)=0 (11)

0≤P _s≤P _smax(12)

Q _smin≤Q _s≤Q _smax(13)

V _min≤V≤V _max(14)

${\mathrm{\λ}}_l-\frac{\underset{i}{\mathrm{\Σ}}{w}_{i_m}{f}_{i_m}}{\underset{i}{\mathrm{\Σ}}{w}_{i_m}}\≤0,\∀m=1,...,G---\left(15\right)$

Δ P _dj≤ 0 j be more than or equal to 1 natural number (16)

j be more than or equal to 1 natural number (17)

Wherein: Q _dsystem requirements reactive powers, Q _ssystem supply reactive power, for the changing load amount of a jth node, P _dj0for the initial load of a jth node, scalar ce>=0 represents loading coefficient, α ₀for initial load coefficient, d _j0represent that initial load increases vector, d _jrepresent the load growth vector of all loads under i-th load growth rate: d _j=[d _j1, d _j2... d _jN] ^t.

j be more than or equal to 1 natural number (18)

0≤d _j≤ 1j be more than or equal to 1 natural number (19)

$\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{d}_j=1---\left(20\right)$

α≥0 (21)

Wherein, Δ P _dfor changing load amount; M is m security of system territory of all G scheduling scheme.Constraint condition (11) makes Δ P by force _dbe 0 or negative.If Δ P _dbe 0, then optimal load flow model has solution; Otherwise, if Δ P _dfor negative, then represent that optimal load flow model is without solution.Therefore, this optimal load flow model illustrates the operation criterion of current electric power system dispatching well, be merit angle, active power, reactive power and applied power composition right-angle triangle, applied power is hypotenuse, and the angle between meritorious and applied power is at merit angle, and generally represent power factor with its cosine value, G is an imaginary number, and m is the number in 1 ~ G.

The concrete steps of the described first step are:

(1) setting up electric system can micro-algebraic equation be: $\begin{array}{c}\stackrel{\·}{x}=h(x,y,\mathrm{\ρ},\mathrm{\λ})\\ 0=g(x,y,\mathrm{\ρ},\mathrm{\λ})\end{array}---\left(22\right)$ Wherein, x is system state variables, and common have generator speed and corner; Y represents algebraic variable, as load side voltage etc.; ρ represents system controllable variable, as generator voltage grade; λ is one group of uncontrollable parameter, and common have load to gain merit and reactive power.

(2) load direction d is determined _i=[d _i1d _i2... d _iN] ^twhen load increases along a certain specific direction, electric system will reach operational limit, by carrying out constructing system security domain to the stability analysis of power flow equation, namely run criterion carry out constructing system security domain, if λ according to carrying out stability and N-1 to static system voltage, phase angle, oscillation frequency _i=[λ _i1λ _i2... λ _iN] ^tfor the rate of growth of i-th load in N number of load, i be more than or equal to 1 natural number, λ is expressed as

λ _i1＝αd _i1

λ _i2＝αd _i2（23）

λ _iN＝αd _iN

Wherein, scalar ce>=0 represents loading coefficient, d _ijrepresent the load growth direction of load j under i-th load growth rate, i and j is the natural number being more than or equal to 1, and load direction meets following condition:

$0\≤d_{\mathrm{ij}}\≤1\∀j$

$\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{d}_{\mathrm{ij}}=1---\left(24\right)$

(3) system can be made to reach safety and stability border gradually by increasing loading coefficient α, and then determine stability boundaris ultimate value n number of load of system can be transformed to by given power generation dispatching criterion M the different sets that load direction forms, and generates following critical load matrix and is similar to security domain:

In described 3rd step, Adaptive Neuro-fuzzy Inference is divided into six layers: X ₁, X ₂be the input of system, y inference system exports; Each node of network same layer has similar function, uses O _1i, O _2irepresent that i-th node exports, i be more than or equal to 1 natural number;

Ground floor: input data are carried out Fuzzy Processing:

O _1i＝μ _Ai(x ₁),O _2i＝μ _Bi(x ₂),i＝1,2 （25）

Wherein, A _ior B _iit is fuzzy set; μ _ai(x ₁), μ _bi(x ₂) be the membership function of fuzzy set.

The second layer: be multiplied by each input data membership function, as this layer of regular relevance grade w _i:

w _i＝μ _Ai(x ₁)μ _Bi(x ₂),i＝1,2 （26）

Third layer: the w calculating the i-th rule _iand all relevance grade sum w ₁+ w ₂, and the normalization of each rule relevance grade is completed by both ratios:

$w_i^{\′}=\frac{w_i}{w_1+w_2},i=\mathrm{1,2}---\left(27\right)$

4th layer: for calculating the output of each rule:

$O_{4i}^{\′}=w_i^{\′}{f}_i=w_i^{\′}(p_i{x}_1+q_i{x}_2+r_i),i=\mathrm{1,2}---\left(28\right)$

Wherein, f _ifor the consequent conclusion output function of fuzzy system, p _i, q _ibe the system weighting coefficient under the i-th rule, r _ibe the constant under the i-th rules and regulations, when this output function is linear function, be called " first-order system "; If constant, be called " zero order system ".

Layer 5: the total output for computing system:

$y=\underset{i}{\mathrm{\Σ}}{w}_i^{\′}{f}_i=\frac{\underset{i}{\mathrm{\Σ}}{w}_i{f}_i}{\underset{i}{\mathrm{\Σ}}{w}_i},i=\mathrm{1,2}---\left(29\right)$

Layer 6, carries out defuzzification process by method of weighted mean by this Output rusults, and makes the error between constrained input minimum by back propagation and least square method.

Concrete steps in described 4th step are:

(1) adaptive nuero-fuzzy inference system system is trained; Make M ultimate value of N-1 load the border formed as the input of adaptive nuero-fuzzy inference system system, N be greater than 1 natural number, M be more than or equal to 1 natural number, definition i-th load bus safe edge dividing value be expressed as:

${\mathrm{\λ}}_{\mathrm{il}}^c\≈f({\mathrm{\λ}}_{i1},{\mathrm{\λ}}_{i2},...,{\mathrm{\λ}}_{\mathrm{il}-1},{\mathrm{\λ}}_{\mathrm{il}+1},...,{\mathrm{\λ}}_{\mathrm{iN}})\≈f\left({\widehat{\mathrm{\λ}}}_i\right)$ I be more than or equal to 1 natural number: (30) by formula (9) and formula (25) the mapping function of load growth rate is:

${\mathrm{\λ}}_l^c=\frac{\underset{i}{\mathrm{\Σ}}{w}_i{f}_i}{\underset{i}{\mathrm{\Σ}}{w}_i}---\left(31\right)$

(2) formula (26) is about intrafascicular for the security domain of optimal load flow equation, forming security domain constrained optimum tide model is:

Objective function: $S_b=C_s^T{P}_s-C_d^T\mathrm{\Δ}{P}_d---\left(10\right)$

Constraint condition: F _pF(δ, V, Q _g, P _s, P _d, Q _d)=0 (11)

0≤P _s≤P _smax(12)

Q _smin≤Q _s≤Q _smax(13)

V _min≤V≤V _max(14)

${\mathrm{\λ}}_l-\frac{\underset{i}{\mathrm{\Σ}}{w}_{i_m}{f}_{i_m}}{\underset{i}{\mathrm{\Σ}}{w}_{i_m}}\≤0,\∀m=1,...,G---\left(15\right)$

$\mathrm{\Δ}{P}_{\mathrm{dj}}\≤0,\∀j=1,...,N---\left(16\right)$

$\mathrm{\Δ}{P}_{d_j}=({\mathrm{\αd}}_j-{\mathrm{\α}}_0{d}_{j0})P_{\mathrm{dj}0},\∀j=1,...,N---\left(17\right)$

$0\≤d_j\≤1,\∀j=1,...,N---\left(19\right)$

$\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{d}_j=1---\left(20\right)$

α≥0 (21)

Beneficial effect of the present invention: the present invention is based on adaptive nuero-fuzzy inference system system certainty annuity security domain.Adopt IEEE 118 node standard test system to carry out simulation calculation and checking to the feasibility of adaptive nuero-fuzzy inference system system and efficiency, and in the theoretical foundation of layering and zoning, verify the feasibility of the method to receiving end electrical network.Result shows, can apply to the optimizing scheduling behind layering and zoning between receiving end Grid based on the optimal load flow security domain approximation technique of adaptive nuero-fuzzy inference system system.

Accompanying drawing explanation

Fig. 1 is typical adaptive nuero-fuzzy inference system system architecture;

Fig. 2 is IEEE tri-district system security domain.

Embodiment

Below in conjunction with accompanying drawing and embodiment, the invention will be further described.

A modeling method for receiving end grid security domain optimal load flow model, concrete steps are:

The first step: by namely carrying out constructing system security domain to the stability of static system voltage, phase angle, oscillation frequency and N-1 criterion to the stability analysis of power flow equation, for obtaining discrete security domain expression formula, N number of load of system can be transformed to by given power generation dispatching criterion M the different sets that load direction forms, N is load quantity in system, given system has fixing load quantity, generates following critical load matrix and carries out approximate processing to security domain:

Second step: build dynamic security constrained optimum tide model as follows:

Objective function: $S_b=-(C_d^T{P}_d-C_s^T{P}_s)---\left(2\right)$

Constraint condition: F _pF(δ, V, Q _g, P _s, P _d)=0(3)

$0\≤P_s\≤P_{s_{\max }}---\left(4\right)$

$0\≤P_d\≤P_{d_{\max }}---\left(5\right)$

i, j be more than or equal to 1 natural number (6)

$Q_{g_{\min }}\≤Q_g\≤Q_{g_{\max }}---\left(7\right)$

V _min≤V≤V _max（8）

$f_{{\mathrm{NR}}_0}-f_{\mathrm{NR}}(V,\mathrm{\δ})\≤0---\left(9\right)$

Wherein, C _sand C _dbe the bid of electric power supply and demand respectively, unit is $/MWh; System supply and demand power is respectively P _sand P _d, unit is MW; F _pF() is system load flow equation; V and δ is node voltage respectively and intersects; I _ijthe electric current by transmission line of electricity ij, this constraint definition thermally-stabilised limit of system; Q _gfor generator reactive power; f _nR() for representing security of system territory, for the critical value that it is suitable.

3rd step: set up Adaptive Neuro-fuzzy Inference.

4th step: train adaptive nuero-fuzzy inference system system and set up optimal load flow model, security domain constrained optimum tide model is:

Objective function: $S_b=C_s^T{P}_s-C_d^T\mathrm{\Δ}{P}_d---\left(10\right)$

Constraint condition: F _pF(δ, V, Q _g, P _s, P _d, Q _d)=0 (11)

0≤P _s≤P _smax(12)

Q _smin≤Q _s≤Q _smax(13)

V _min≤V≤V _max(14)

${\mathrm{\λ}}_l-\frac{\underset{i}{\mathrm{\Σ}}{w}_{i_m}{f}_{i_m}}{\underset{i}{\mathrm{\Σ}}{w}_{i_m}}\≤0,\∀m=1,...,G---\left(15\right)$

Δ P _di≤ 0j be more than or equal to 1 natural number (16)

j be more than or equal to 1 natural number (17)

j be more than or equal to 1 natural number (18)

0≤d _j≤ 1j be more than or equal to 1 natural number (19)

$\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{d}_j=1---\left(20\right)$

α≥0(21)

Wherein, Δ P _dfor changing load amount; M is m security of system territory of all G scheduling scheme.Constraint condition (11) makes Δ P by force _dbe 0 or negative.If Δ P _dbe 0, then optimal load flow model has solution; Otherwise, if Δ P _dfor negative, then represent that optimal load flow model is without solution.Therefore, this optimal load flow model illustrates the operation criterion of current electric power system dispatching well, be merit angle, active power, reactive power and applied power composition right-angle triangle, applied power is hypotenuse, and the angle between meritorious and applied power is at merit angle, and generally represent power factor with its cosine value, G is an imaginary number, and m is the number in 1 ~ G.

The concrete steps of the described first step are:

(1) setting up electric system can micro-algebraic equation be: $\begin{array}{c}\stackrel{\·}{x}=h(x,y,\mathrm{\ρ},\mathrm{\λ})\\ 0=g(x,y,\mathrm{\ρ},\mathrm{\λ})\end{array}---\left(22\right)$ Wherein, x is system state variables, and common have generator speed and corner; Y represents algebraic variable, as load side voltage etc.; ρ represents system controllable variable, as generator voltage grade; λ is one group of uncontrollable parameter, and common have load to gain merit and reactive power.

(2) load direction d is determined _i=[d _i1d _i2... d _iN] ^twhen load increases along a certain specific direction, electric system will reach operational limit, by carrying out constructing system security domain to the stability analysis of power flow equation, namely run criterion carry out constructing system security domain, if λ according to carrying out stability and N-1 to static system voltage, phase angle, oscillation frequency _i=[λ _i1λ _i2... λ _iN] ^tfor the rate of growth of i-th load in N number of load, i be more than or equal to 1 natural number, λ is expressed as

λ _i1＝αd _i1

λ _i2＝αd _i2（23）

λ _i＝αd _iN

Wherein, scalar ce>=0 represents loading coefficient, d _ijrepresent the load growth direction of load j under i-th load growth rate, i and j is the natural number being more than or equal to 1, and load direction meets following condition:

$0\≤d_{\mathrm{ij}}\≤1\∀j$

$\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{d}_{\mathrm{ij}}=1---\left(24\right)$

(3) system can be made to reach safety and stability border gradually by increasing loading coefficient α, and then determine stability boundaris ultimate value n number of load of system can be transformed to by given power generation dispatching criterion M the different sets that load direction forms, and generates following critical load matrix and is similar to security domain:

In described 3rd step, Adaptive Neuro-fuzzy Inference is divided into six layers: X ₁, X ₂be the input of system, y inference system exports; Each node of network same layer has similar function, uses O _1irepresent that i-th node exports, i be more than or equal to 1 natural number;

Ground floor: input data are carried out Fuzzy Processing:

O _1i=μ _ai(x ₁), O _2i=μ _bi(x ₂), i=1,2(25) wherein, A _ior B _iit is fuzzy set; μ _ai(x ₁) be the membership function of fuzzy set.

The second layer: be multiplied by each input data membership function, as this layer of regular relevance grade w _i:

w _i＝μ _Ai(x ₁)μ _Bi(x ₂),i＝1,2（26）

Third layer: the w calculating the i-th rule _iand all relevance grade sum w ₁+ w ₂, and the normalization of each rule relevance grade is completed by both ratios:

$w_i^{\′}=\frac{w_i}{w_1+w_2},i=\mathrm{1,2}---\left(27\right)$

4th layer: for calculating the output of each rule:

$O_{4i}^{\′}=w_i^{\′}{f}_i=w_i^{\′}(p_i{x}_1+q_i{x}_2+r_i),i=\mathrm{1,2}---\left(28\right)$

Wherein, f _ifor the consequent conclusion output function of fuzzy system, p _i, q _ibe the system weighting coefficient under the i-th rule, r _ibe the constant under the i-th rules and regulations, when this output function is linear function, be called " first-order system "; If constant, be called " zero order system ".

Layer 5: the total output for computing system:

$y=\underset{i}{\mathrm{\Σ}}{w}_i^{\′}{f}_i=\frac{\underset{i}{\mathrm{\Σ}}{w}_i{f}_i}{\underset{i}{\mathrm{\Σ}}{w}_i}---\left(29\right)$

Layer 6, carries out defuzzification process by method of weighted mean by this Output rusults, and makes the error between constrained input minimum by back propagation and least square method.

Concrete steps in described 4th step are:

(1) adaptive nuero-fuzzy inference system system is trained; Make M ultimate value of N-1 load the border formed, as the input of adaptive nuero-fuzzy inference system system, defines the safe edge dividing value of i-th load bus be expressed as:

${\mathrm{\λ}}_{\mathrm{il}}^c\≈f({\mathrm{\λ}}_{i1},{\mathrm{\λ}}_{i2},...,{\mathrm{\λ}}_{\mathrm{il}-1},{\mathrm{\λ}}_{\mathrm{il}+1},...,{\mathrm{\λ}}_{\mathrm{iN}})\≈f\left({\widehat{\mathrm{\λ}}}_i\right)$ I be more than or equal to 1 natural number: (30)

The mapping function being obtained load growth rate by formula (9) and formula (25) is:

${\mathrm{\λ}}_l^c=\frac{\underset{i}{\mathrm{\Σ}}{w}_i{f}_i}{\underset{i}{\mathrm{\Σ}}{w}_i}---\left(31\right)$

(2) formula (26) is about intrafascicular for the security domain of optimal load flow equation, forming security domain constrained optimum tide model is:

Objective function: $S_b=C_s^T{P}_s-C_d^T\mathrm{\Δ}{P}_d---\left(10\right)$

Constraint condition: F _pF(δ, V, Q _g, P _s, P _d, Q _d)=0 (11)

0≤P _s≤P _smax(12)

Q _smin≤Q _s≤Q _smax(13)

V _min≤V≤V _max(14)

${\mathrm{\λ}}_l-\frac{\underset{i}{\mathrm{\Σ}}{w}_{i_m}{f}_{i_m}}{\underset{i}{\mathrm{\Σ}}{w}_{i_m}}\≤0,\∀m=1,...,G---\left(15\right)$

$\mathrm{\Δ}{P}_{\mathrm{dj}}\≤0,\∀j=1,...,N---\left(16\right)$

$\mathrm{\Δ}{P}_{d_j}=({\mathrm{\αd}}_j-{\mathrm{\α}}_0{d}_{j0})P_{\mathrm{dj}0},\∀j=1,...,N---\left(17\right)$

$0\≤d_j\≤1,\∀j=1,...,N---\left(19\right)$

$\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{d}_j=1---\left(20\right)$

α≥0 (21)

Utilize PST, PSAT and UWPFLOW software emulates system.PSAT for calculating electric system conventional Load Flow parameter, and using the input of this flow data as UWPFLOW and PST, obtains voltage stability boundary and the concussion stability boundaris of system respectively; Again by the Output rusults of PST and UWPFLOW gained as the input in PSAT, calculate remaining voltage-regulation coefficient with this, thus determine whole system safety and stability territory.Generate closed differentiable function by adaptive nuero-fuzzy inference system system software by gained safety and stability territory, and by this function embedded system optimal load flow model, calculate optimal load flow model respectively by Newton method and interior point method.

Select IEEE 118 node standard test system to verify put forward the practicality of optimal load flow model, to simplify the analysis and do not lose ubiquity, optimal load flow model safety territory is by typical deployments pattern acquiring, such as, in formula 31 G=1, IEEE 118 node standard test system is made up of 53 generators and 91 loads, and table 1 is genset bid data.

Table 1 genset bid data

By layering and zoning principle and electricity market related notion, this test macro is divided into three and four operation areas respectively, namely corresponding security domain represents the restriction of each interregional power delivery, using 631 different load flows obtaining to as adaptive nuero-fuzzy inference system systematic training data.

Embodiment one:

As shown in Figure 2, former 118 node standard test system are divided into three pieces of regions, and wherein region 1 and region 2 respectively comprise 31 loads, and region 3 comprises 29 loads.To a station symbol semicomputer (Duo 2 double-core 2.2Ghz processor, 2G internal memory), within remaining on 10-5 for making the error between constrained input, within 156 seconds consuming time, obtain security of system territory altogether.

Put forward by test the validity of optimal load flow model, select P by table 2 _dA1, P _dA2and P _dA3force system running state to jump out security domain, mark three in Fig. 2 respectively and jump out a little, wherein suppose that the bid of changing load amount is C _dA1=200 $/MWh, C _dA2=400 $/MWh and C _dA3=600 $/MWh, corresponding optimal load flow solution to model is as shown in table 3; Analyze known, in the ordinary course of things, bid the highest load P _dA1reduction Δ Pd _a1minimum, get back in security domain to make system running state.

Table 2 system testing scheme

Table 3 system loading knots modification

Embodiment two:

Former 118 node standard test system are divided into four pieces of regions, and wherein region 1, region 2 respectively comprise 22 loads with region 3, and region 4 comprises 25 loads.10 are remained on for making the error between constrained input ^-5within, within 225 seconds consuming time, obtain security of system territory altogether.Known to three region sample calculation analysis, selected P _dA1, P _dA2, P _dA3and P _dA4system running state is forced to jump out security domain, as table 4; Suppose that the bid of changing load amount is C _dA1=800 $/MWh, C _dA2=100 $/MWh, C _dA3=300 $/MWh and C _dA4=600 $/MWh;

Table 5-4 system testing scheme

Table 5-5 system loading knots modification

Corresponding optimal load flow solution to model is as shown in table 5; In four region examples, bid the highest load P _dA1the load P relatively low with bid _dA2and P _dA3reduction is all close to 0, and the region four load reduction Δ Pd larger on security of system impact _a4maximum, analyze known, in the ordinary course of things, measure valency compared to changing load, security of system is larger on the impact of load reduction.

By reference to the accompanying drawings the specific embodiment of the present invention is described although above-mentioned; but not limiting the scope of the invention; one of ordinary skill in the art should be understood that; on the basis of technical scheme of the present invention, those skilled in the art do not need to pay various amendment or distortion that creative work can make still within protection scope of the present invention.

Claims (1)

1. a modeling method for receiving end grid security domain optimal load flow model, is characterized in that, concrete steps are:

The first step: by namely carrying out constructing system security domain to the stability of static system voltage, phase angle, oscillation frequency and N-1 criterion to the stability analysis of power flow equation, for obtaining discrete security domain expression formula, N number of load of system is transformed to by given power generation dispatching criterion M the different sets that load direction forms, N, M be more than or equal to 1 natural number, N is load quantity in system, given system has fixing load quantity, generates critical load matrix and carries out approximate processing to security domain;

Second step: build dynamic security constrained optimum tide model;

3rd step: set up Adaptive Neuro-fuzzy Inference;

4th step: train adaptive nuero-fuzzy inference system system and set up optimal load flow model;

The concrete steps of the described first step are:

(1) setting up electric system can micro-algebraic equation be: $\begin{array}{c}\stackrel{\·}{x}=h(x,y,\mathrm{\ρ},\mathrm{\λ})\\ 0=g(x,y,\mathrm{\ρ},\mathrm{\λ})\end{array}---\left(22\right)$ Wherein, x is system state variables, and y represents algebraic variable, and ρ represents system controllable variable; λ is one group of uncontrollable parameter;

(2) load direction d is determined _i=[d _i1d _i2... d _iN] ^twhen load increases along a certain specific direction, electric system will reach operational limit, by carrying out constructing system security domain to the stability analysis of power flow equation, namely run criterion carry out constructing system security domain according to carrying out stability and N-1 to static system voltage, phase angle, oscillation frequency merit angle, active power, reactive power and applied power composition right-angle triangle, applied power is hypotenuse, and the angle between meritorious and applied power is at merit angle, and generally represent power factor with its cosine value, G is an imaginary number, and m is the number in 1 ~ G;

If λ _i=[λ _i1λ _i2... λ _iN] ^tfor the rate of growth of i-th load in N number of load, i be more than or equal to 1 natural number, λ is expressed as:

$\begin{array}{c}{\mathrm{\λ}}_{i1}=\mathrm{\α}{d}_{i1}\\ {\mathrm{\λ}}_{i2}=\mathrm{\α}{d}_{i2}\\ \·\·\·\\ {\mathrm{\λ}}_{\mathrm{iN}}=\mathrm{\α}{d}_{\mathrm{iN}}\end{array}---\left(23\right)$

Wherein, scalar ce>=0 represents loading coefficient, d _ijrepresent the load growth direction of load j under i-th load growth rate, i and j is the natural number being more than or equal to 1, and load direction meets following condition:

$\begin{array}{cc}0\≤d_{\mathrm{ij}}\≤1& \∀j\\ \underset{j=1}{\overset{N}{\mathrm{\Σ}}}{d}_{\mathrm{ij}}=1& \end{array}---\left(24\right)$

(3) system can be made to reach safety and stability border gradually by increasing loading coefficient α, and then determine stability boundaris ultimate value n number of load of system can be transformed to by given power generation dispatching criterion M the different sets that load direction forms, and generates following critical load matrix and is similar to security domain:

The optimal load flow model of described second step is as follows:

Objective function: $S_b=-(C_d^T{P}_d-C_s^T{P}_s)---\left(2\right)$

Constraint condition: F _pF(δ, V, Q _g, P _s, P _d)=0 (3)

$0\≤P_s\≤P_{s_{\max }}---\left(4\right)$

$0\≤P_d\≤P_{d_{\max }}---\left(5\right)$

$I_{\mathrm{ij}}(\mathrm{\δ},V)\≤I_{{\mathrm{ij}}_{\max }},\∀i,j,i\≠j$ I, j be more than or equal to 1 natural number (6)

Q _gmin≤Q _g≤Q _gmax(7)

V _min≤V≤V _max(8)

f _NR0-f _NR(V,δ)≤0 (9)

Wherein, C _sand C _dbe the bid of electric power supply and demand respectively, unit is $/MWh; System supply and demand power is respectively P _sand P _d, unit is MW; F _pF() is system load flow equation; V and δ is node voltage respectively and intersects; I _ijthe electric current by transmission line of electricity ij, this constraint definition thermally-stabilised limit of system; Q _gfor generator reactive power; f _nR() for representing security of system territory, for the critical value that it is suitable;

In described 3rd step, Adaptive Neuro-fuzzy Inference is divided into six layers: X ₁, X ₂be the input of system, y is that inference system exports; Each node of network same layer has similar function, uses O _1irepresent that i-th node exports, i be more than or equal to 1 natural number;

Ground floor: input data are carried out Fuzzy Processing:

O _1i＝μ _Ai(x ₁),O _2i＝μ _Bi(x ₂),i＝1,2 (25)

Wherein, A _ior B _iit is fuzzy set; μ _ai(x ₁) be the membership function of fuzzy set;

The second layer: be multiplied by each input data membership function, as this layer of regular relevance grade w _i:

w _i＝μ _Ai(x ₁)μ _Bi(x ₂),i＝1,2 (26)

Third layer: the w calculating the i-th rule _iand all relevance grade sum w ₁+ w ₂, and the normalization of each rule relevance grade is completed by both ratios:

$w_i^{\′}=\frac{w_i}{w_1+w_2},i=\mathrm{1,2}---\left(27\right)$

4th layer: for calculating the output of each rule:

$O_{4i}^{\′}=w_i^{\′}{f}_i=w_i^{\′}(p_i{x}_1+q_i{x}_2+r_i),i=\mathrm{1,2}---\left(28\right)$

Wherein, f _ifor the consequent conclusion output function of fuzzy system, p _i, q _ibe the system weighting coefficient under the i-th rule, r _ibe the constant under the i-th rules and regulations, when this output function is linear function, be called " first-order system "; If constant, be called " zero order system ";

Layer 5: the total output for computing system:

$y=\underset{i}{\mathrm{\Σ}}{w}_i^{\′}{f}_i=\frac{\underset{i}{\mathrm{\Σ}}{w}_i{f}_i}{\underset{i}{\mathrm{\Σ}}{w}_i}---\left(29\right)$

Layer 6, carries out defuzzification process by method of weighted mean by this Output rusults, and makes the error between constrained input minimum by back propagation and least square method;

Concrete steps in described 4th step are:

(1) adaptive nuero-fuzzy inference system system is trained; Make M ultimate value of N-1 load the border formed is as the input of adaptive nuero-fuzzy inference system system, and N is load quantity in system, and given system has fixing load quantity, and M is natural number, the safe edge dividing value of definition i-th load bus be expressed as:

${\mathrm{\λ}}_{\mathrm{il}}^c\≈f({\mathrm{\λ}}_{i1},{\mathrm{\λ}}_{i2},...,{\mathrm{\λ}}_{\mathrm{il}-1},{\mathrm{\λ}}_{\mathrm{il}+1},...,{\mathrm{\λ}}_{\mathrm{iN}})\≈f\left({\widehat{\mathrm{\λ}}}_i\right)$ I be more than or equal to 1 natural number: (30) by formula (9) and formula (25) the mapping function of load growth rate is:

${\mathrm{\λ}}_l^c=\frac{\underset{i}{\mathrm{\Σ}}{w}_i{f}_i}{\underset{i}{\mathrm{\Σ}}{w}_i}---\left(31\right)$

(2) formula (26) is about intrafascicular for the security domain of optimal load flow equation, forming security domain constrained optimum tide model is:

Objective function: $S_b=C_s^T{P}_s-C_d^T\mathrm{\Δ}{P}_d---\left(10\right)$

Constraint condition: F _pF(δ, V, Q _g, P _s, P _d, Q _d)=0 (11)

0≤P _s≤P _smax(12)

Q _smin≤Q _s≤Q _smax(13)

V _min≤V≤V _max(14)

${\mathrm{\λ}}_l-\frac{\underset{i}{\mathrm{\Σ}{w}_{i_m}}{f}_{i_m}}{\underset{i}{\mathrm{\Σ}}{w}_{i_m}}\≤0,\∀m=1,...,G---\left(15\right)$

${\mathrm{\ΔP}}_{\mathrm{dj}}\≤0,\∀j=1,...,N---\left(16\right)$

${\mathrm{\ΔP}}_{d_j}=(\mathrm{\α}{d}_j-{\mathrm{\α}}_0{d}_{j0})P_{\mathrm{dj}0},\∀j=1,...,N---\left(17\right)$

$0\≤d_j\≤1,\∀j=1,...,N---\left(19\right)$

$\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{d}_j=1---\left(20\right)$

α≥0 (21)；

Wherein, P _dj0for the initial load of a jth node, P _djfor the load of a jth node, Δ P _dfor changing load amount; M is m security of system territory of all G scheduling scheme, and constraint condition (11) makes Δ P by force _dbe 0 or negative; If Δ P _dbe 0, then optimal load flow model has solution; Otherwise, if Δ P _dfor negative, then represent that optimal load flow model is without solution, therefore, this optimal load flow model illustrates the operation criterion of current electric power system dispatching well, be merit angle, active power, reactive power and applied power composition right-angle triangle, applied power is hypotenuse, and the angle between meritorious and applied power is at merit angle, and generally represent power factor with its cosine value, G is an imaginary number, and m is the number in 1 ~ G.