CN102684228A - Method for optimizing configuration of intermittent distribution type power supply based on complementary - Google Patents

Abstract

The invention discloses a method for optimizing configuration of an intermittent distribution type power supply based on complementary. A target function of optimal configuration of the intermittent distribution type power supply based on the complementary is as follows: FORMULA1 (specifically being shown in the original PDF file), wherein FORMULA2 (specifically being shown in the original PDF file) is an electricity-sale gain expected value of a distributive power supply; FORMULA3 (specifically being shown in the original PDF file) is a saved grid loss cost expected value; FORMULA4 (specifically being shown in the original PDF file) is a waste exhaust gain expected value; CDG is investment cost of the distributive power supply; FORMULA5 (specifically being shown in the original PDF file) is a voltage improvement index expected value; and delta is a measurement factor for converting a voltage improvement index into comprehensive benefit. The method for optimizing the configuration of the intermittent distribution type power supply based on the complementary, disclosed by the invention, has the advantages of being capable of improving voltage level of a system, reducing grid loss, and improving utilization ratio of a renewable energy source.

Description

Distributed power source at intermittence based on complementation is distributed method rationally

Technical field

The invention belongs to technical field of power systems, relate to a kind of distributed power source at intermittence and distribute method rationally based on complementation.

Background technology

Distributed power source (distributed generation based on clean reproducible energy; DG) as distributed wind-powered electricity generation (distributed wind generation, DWG), photovoltaic cell (photovoltaic, PV) etc.; Have energy-saving and emission-reduction, alleviate environmental pollution, reduce line loss, improve advantages such as the quality of power supply and raising power supply reliability; But its power output receives the weather environmental impact very big simultaneously, has tangible intermittence, randomness and fluctuation, can influence the normal operation of electric power system; And position and the capacity of influence degree and DG are closely related; Therefore how to optimize position and the capacity of intermittent DG, making system operate in safety and most economical state becomes one of hot issue that current intelligent grid studies, and also is the important behave that advances energy-saving and emission-reduction both at home and abroad.

Existing scholar has carried out many researchs to distributing rationally of DG both at home and abroad at present; Obtained the achievement of many theory and practice aspect; The document that has is an optimization aim with the total cost minimum of electric company, has provided DG plan model under the market condition, and adopts heuristic to find the solution; The document that has to be maximizing the meritorious target function that is output as, and as constraint, the formation Mathematical Modeling utilizes linear programming method to find the solution this model then with the thermally-stabilised limit of the exerting oneself of DG, circuit etc.; The randomness that the document that has is exerted oneself to DWG and the uncertainty of load are applied to chance constrained programming in the addressing constant volume planning of DWG, adopt at random trend to judge whether programme is violated the node voltage constraint and transmitted with branch power and retrain; The document that has proposes the notion that equivalent network is decreased little gaining rate; And the allocation optimum position of adopting the DG that this notion index calculates; Can guarantee that DG inserts that the net of system decreases minimum behind the power distribution network; And voltage, the net of considering to estimate the DG benefit simultaneously decrease and 3 indexs of environmental benefit, and the problem identificatioin of DG optimizing capacity is converted into a multiobjective non linear programming problem.Though the above-mentioned method of distributing rationally has solved some relevant issues to a certain extent, but still has following limitation: adopt deterministic variable and constraint to handle the intermittent DG that power outputs such as wind power generation, photovoltaic cell have obvious uncertainty and randomness; The optimization objects type is single, and does not consider the probability distribution of intermittent DG power output in whole space-time unique, and the complementarity of probability distribution in whole space-time unique of the power output of multiple intermittent DG and system loading power.And the research of some documents shows that wind speed, the distributed constant of intensity of illumination in each period are discrepant, exists complementary.

Summary of the invention

Technical problem to be solved by this invention provides a kind of distributed power source at intermittence based on complementation and distributes method rationally, should can improve the system voltage level based on the distributed power source method of distributing rationally at intermittence of complementation, reduce the utilance that net decreases, improves regenerative resource.

The technical solution of invention is following:

A kind of distributed power source at intermittence based on complementation is distributed method rationally, based on the target function that intermittence of complementation, distributed power source was distributed rationally is:

$\max f=\max (\stackrel{\‾}{C_{\mathrm{SAL}}}+\stackrel{\‾}{C_{\mathrm{Loss}}}+\stackrel{\‾}{C_E}-C_{\mathrm{DG}}+\mathrm{\δ}\stackrel{\‾}{I_u});$

Wherein,

is distributed power source sale of electricity income desired value;

is for practicing thrift net damage expense desired value;

is toxic emission income desired value;

C _DGCost of investment for distributed power source;

improves the index desired value for voltage;

δ is for to improve the measurement factor that index converts comprehensive benefit to voltage; And have:

$\stackrel{\‾}{C_{\mathrm{SAL}}}=\underset{i=1}{\overset{T}{\mathrm{\Σ}}}{t}_i(\underset{j\∈N_{\mathrm{DWG}}}{\mathrm{\Σ}}(a_j-a_j^{\′})\stackrel{\‾}{P_{\mathrm{DWGij}}}+\underset{k\∈N_{\mathrm{PV}}}{\mathrm{\Σ}}(b_k-b_k^{\′})\stackrel{\‾}{P_{\mathrm{PVik}}});$

$\stackrel{\‾}{C_{\mathrm{Loss}}}=\underset{i=1}{\overset{T}{\mathrm{\Σ}}}{a}_{\mathrm{lossi}}\·t_i\·\stackrel{\‾}{{\mathrm{\ΔP}}_{\mathrm{lossi}}};$

$\stackrel{\‾}{C_E}=\underset{i=1}{\overset{T}{\mathrm{\Σ}}}(a_{{\mathrm{co}}_2}\stackrel{\‾}{E_{c{o}_2}^i}+a_{s{o}_2}\stackrel{\‾}{E_{s{o}_2}^i}+a_{{\mathrm{No}}_{\text{x}}}\stackrel{\‾}{E_{{\mathrm{No}}_x}^i});$

$C_{\mathrm{DG}}=C(r,m)(\underset{j\∈N_{\mathrm{DWG}}}{\mathrm{\Σ}}(c_{\mathrm{ej}}+c_{\mathrm{fj}})P_{\mathrm{DWGj}}+\underset{k\∈N_{\mathrm{PV}}}{\mathrm{\Σ}}(w_{\mathrm{ek}}+w_{\mathrm{fk}})P_{\mathrm{PVk}});$

$\stackrel{\‾}{I_u}=\frac{1}{T\·N}\·\underset{i=1}{\overset{T}{\mathrm{\Σ}}}\underset{n\∈N}{\mathrm{\Σ}}|\stackrel{\‾}{U_{\mathrm{ino}}}-U_{\mathrm{nN}}|/|\stackrel{\‾}{U_{\mathrm{in}}}-U_{\mathrm{nN}}|;$

Wherein:

Hop count when T is;

t _iBe i running time period;

N _DWGFor allowing to insert the node set of wind-powered electricity generation;

N _PVFor allowing to insert the node set of photovoltaic cell;

a _jIt is the DWG rate for incorporation into the power network of j node;

A ' _jIt is the DWG unit quantity of electricity operation and maintenance cost of j node;

b _kIt is the PV rate for incorporation into the power network of k node;

B ' _kIt is the unit quantity of electricity operation and maintenance cost of k node;

is the desired value of the photovoltaic online active power of output of k node;

a _LossiFor the i period is netted the damage electricity price;

is that the net that i period system practices thrift decreases desired value;

Be CO ₂The unit discharge fee;

Be SO ₂The unit discharge fee;

Be NO _xThe unit discharge fee;

Be CO ₂The discharge capacity desired value that reduces of i period;

Be SO ₂The discharge capacity desired value that reduces of i period;

Be NO _xThe discharge capacity desired value that reduces of i period;

C _{(r, m)}Be present value factor;

c _EjIt is the equipment investment of j node DWG unit capacity;

c _FjIt is the installation cost of j node DWG unit capacity;

P _DWGjIt is the real account constant volume of j node DWG unit capacity;

w _EkIt is the equipment investment of k node PV unit capacity;

w _FkIt is the installation cost of k node PV unit capacity;

P _PVkIt is k node PV real account constant volume;

N is the system node number except that balance node;

is for installing the preceding n node voltage of intermittent DG desired value;

is n node voltage desired value behind the intermittent DG of installation;

U _NNBe voltage rating.

The constraint equation of said target function comprises:

$\left\{\begin{array}{c}{P}_{\mathrm{is}}=U_i\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{U}_j(G_{\mathrm{ij}}\cos {\mathrm{\θ}}_{\mathrm{ij}}+B_{\mathrm{ij}}\sin {\mathrm{\θ}}_{\mathrm{ij}})\\ Q_{\mathrm{is}}=U_i\underset{j=1}{\overset{N}{\mathrm{\Σ}}}{U}_j(G_{\mathrm{ij}}\sin {\mathrm{\θ}}_{\mathrm{ij}}-B_{\mathrm{ij}}\cos {\mathrm{\θ}}_{\mathrm{ij}})\end{array}\right.$

With

$\left\{\begin{array}{cc}0\≤P_{\mathrm{DWGj}}\≤P_{\mathrm{DWGj}}^{\max }& j=\mathrm{1,2},\·\·\·,N_{\mathrm{DWG}}\\ 0\≤P_{\mathrm{PVk}}\≤P_{\mathrm{PVk}}^{\max }& k=\mathrm{1,2},\·\·\·,N_{\mathrm{PV}}\\ \underset{j\∈N_{\mathrm{DWG}}}{\mathrm{\Σ}}{P}_{\mathrm{DWGj}}+\underset{k\∈N_{\mathrm{PV}}}{\mathrm{\Σ}}{P}_{\mathrm{PVk}}=\mathrm{\ρ}{P}_L^{\max }& \\ U_i^{\min }\≤\stackrel{\‾}{U_i}\≤U_i^{\max }& i=\mathrm{1,2},\·\·\·,N\\ P\{U_i^{\min }\≤U_i\≤U_i^{\max }\}\≥{\mathrm{\λ}}_i& \\ \left|\stackrel{\‾}{P_l}\right|\≤P_l^{\max }& l=\mathrm{1,2},\·\·\·,N_l\\ P\left\{\right|P_l|\≤P_l^{\max }\}\≥{\mathrm{\ω}}_l& \end{array}\right.;$

Wherein,

P _IsInjection active power for node i;

Q _IsInjection reactive power for node i;

U _iVoltage magnitude for node i;

G _IjReal part for system's admittance matrix;

B _IjImaginary part for system's admittance matrix;

θ _IjPhase difference of voltage for node i and j;

is the DWG heap(ed) capacity that the j node allows installation;

is the PV heap(ed) capacity that the k node allows installation;

is the maximum load power of system;

ρ is for penetrating power coefficient;

is the upper voltage limit of node i;

is the lower voltage limit of node i;

P{} is the probability that incident is set up in { };

λ _iVoltage constraint confidence level for node i;

The absolute value of

expression

; is the through-put power desired value of branch road l, and its value is calculated by the probability trend; The probability trend is calculated as prior art.

is the through-put power upper limit of branch road l;

N _lBe the system branch sum;

ω _lConfidence level for the constraint of the trend of branch road l.

The probability density function of wind speed descriptive statistics is following:

$f\left(v\right)=\frac{k}{c}{\left(\frac{v}{c}\right)}^{k-1}\exp [-{\left(\frac{v}{c}\right)}^k];$

The functional relation of DWG power output and wind speed is following:

$P_{\mathrm{DWG}}=\left\{\begin{array}{cc}0& v\&le;v_{\mathrm{ci}}\\ k_{1}v+k_2& v_{\mathrm{ci}}<v\&le;v_r\\ P_r& v_r<v\&le;v_{\mathrm{co}}\\ 0& v>v_{\mathrm{co}}\end{array}\right.$

The desired value of DWG power output is following:

$E\left(P_{\mathrm{DWG}}\right)={\&Integral;}_0^{\&infin;}{P}_{\mathrm{DWG}}f\left(v\right)\mathrm{dv}$

$={\&Integral;}_{v_{\mathrm{ci}}}^{v_r}(k_{1}v+k_2)f\left(v\right)\mathrm{dv}+{\&Integral;}_{v_r}^{v_{\mathrm{co}}}{P}_{r}f\left(v\right)\mathrm{dv}$

Wherein, the DWG model that adopts in the example is the blower fan Mod-0 of U.S. NASA development, and rated capacity is 100kW, and incision wind speed, rated wind speed, cut-out wind speed are respectively 4.3,7.7,17.9m/s.

V is a wind speed;

K is the form parameter that Weibull distributes;

C is the scale parameter that Weibull distributes;

P _rBe DWG rated power;

v _CiBe the incision wind speed;

v _rBe rated wind speed;

v _CoBe cut-out wind speed;

K1 and k2 are the scale factors that wind speed is mapped as the DWG power output.

The probability density function of solar irradiation intensity is following:

$f\left(r\right)=\frac{\mathrm{\&Gamma;}(\mathrm{\&alpha;}+\mathrm{\&beta;})}{\mathrm{\&Gamma;}\left(\mathrm{\&alpha;}\right)\mathrm{\&Gamma;}\left(\mathrm{\&beta;}\right)}{\left(\frac{r}{r_{\mathrm{Max}}}\right)}^{\mathrm{\&alpha;}-1}{(1-\frac{r}{r_{\mathrm{Max}}})}^{\mathrm{\&beta;}-1};$ Γ is the expression symbol of gamma function;

The probability density function of PV square formation power output is following:

$f\left(P_{\mathrm{PV}}\right)=\frac{\mathrm{\&Gamma;}(\mathrm{\&alpha;}+\mathrm{\&beta;})}{\mathrm{\&Gamma;}\left(\mathrm{\&alpha;}\right)+\mathrm{\&Gamma;}\left(\mathrm{\&beta;}\right)}{\left(\frac{P_{\mathrm{PV}}}{R_M}\right)}^{\mathrm{\&alpha;}-1}{(1-\frac{P_{\mathrm{PV}}}{R_M})}^{\mathrm{\&beta;}-1}$

The desired value of PV power output is following:

$E\left(P_{\mathrm{PV}}\right)={\&Integral;}_0^{R_M}{P}_{\mathrm{PV}}f\left(P_{\mathrm{PV}}\right)d{P}_{\mathrm{PV}}$

Wherein,

R is an actual light intensity;

r _MaxBe largest light intensity;

α, β are the form parameter that Beta distributes;

P _PVBe PV square formation power output;

adopts the probability trend of cumulant method to carry out trend and calculates.

Beneficial effect:

Distributed power source at intermittence based on complementation of the present invention is distributed method rationally, can improve the system voltage level, reduce the utilance that net decreases, improves regenerative resource, and then make reaching of economic benefit and environmental benefit optimum.To distributed wind-powered electricity generation (distributed wind generation; DWG), photovoltaic cell (photovoltaic; Intermittent distributed power source (distributed generation such as PV); Otherness and the complementarity of the probability distribution of power output DG) and system loading power in space-time unique; Through dividing the period and according to the probability distribution of intermittent DG in each period and system loading power, falling comprehensive benefits such as damage, quality of voltage and reducing discharge of waste gases amount with intermittent DG investment and sale of electricity, system is target function, employing chance constrained programming method is set up the allocation optimum model of intermittent DG in the whole period.The genetic algorithm that selection is easy to handle discrete variable is carried out optimum and is found the solution.Numerical results shows; This allocation optimum model can fully take into account otherness and the complementarity of probability distribution in the time-space domain of intermittent DG power output and load power; React the integrated operation benefits such as economy, environment and quality of voltage that obtained behind the intermittent DG connecting system more all sidedly, verified model reasonability and the validity put forward.

The scientific basis of the theoretical or practice of above-mentioned target function is set:

The present invention is divided into the typical case a plurality of periods day, and DWG and PV access distribution are carried out allocation optimum.Based on each node load power and the probability distribution of relevant climate parameter in day part in the whole planning zone; Each period employing chance constrained programming is handled; And according to each node load power of system and the desired value of intermittent DG power output in day part; Take all factors into consideration intermittent DG investment and sale of electricity, net damage, quality of voltage and discharge amount of exhaust gas; With system's operation total benefit and quality of voltage COMPREHENSIVE OPTIMAL in all periods is the object of planning, adopts genetic algorithm to carry out optimum and finds the solution.Numerical results shows; Adopt the dividing time-steps that this paper put forward; Can make full use of the complementarity of probability distribution in space-time unique of intermittent DG power output; In the utilization ratio and energy-saving and emission-reduction that improve regenerative resource, make the probability of stability of system's working voltage and economy reach COMPREHENSIVE OPTIMAL.

Description of drawings

The distributed power source at intermittence based on complementation that Fig. 1 provides for the embodiment of the invention is distributed the example sketch map of model in the IEEE33 distribution system rationally;

Fig. 2 is for optimizing the out-of-limit node voltage desired value of back voltage curve chart with DG before installing to Fig. 6 situation 1DG;

Fig. 3 is in 0.9U for optimizing the out-of-limit node voltage of back voltage with DG before installing to Fig. 6 situation 1DG _NTo 1.1U _NProbability curve (chronomere be hour).

Fig. 4 is the genetic algorithm flow chart;

Fig. 5 is meritorious desired value of typical case's day day part system loading and variance curve;

The curve condition 1 that Fig. 6 is the typical case day day part DWG node to be selected wind speed probability distribution parameters c of place, k is corresponding;

The curve condition 2 that Fig. 7 is the typical case day day part DWG node to be selected wind speed probability distribution parameters c of place, k is corresponding;

Fig. 8 is the sketch map of the typical case day day part PV node to be selected intensity of illumination probability distribution parameters α of place, β and largest light intensity;

Fig. 9 is the voltage desired value curve that the DG of Fig. 6 and 7 optimizes posterior nodal point 17 and 32;

Figure 10 is that the DG optimization posterior nodal point 17 of corresponding diagram 6 and 7 and 32 voltage are in 0.9 to 1.1 probability curve.

Embodiment

Below will combine accompanying drawing and specific embodiment that the present invention is explained further details:

Embodiment 1:

A kind of distributed power source at intermittence based on complementation is distributed method rationally, it is characterized in that, based on the target function that intermittence of complementation, distributed power source was distributed rationally is:

$\max f=\max (\stackrel{\&OverBar;}{C_{\mathrm{SAL}}}+\stackrel{\&OverBar;}{C_{\mathrm{Loss}}}+\stackrel{\&OverBar;}{C_E}-C_{\mathrm{DG}}+\mathrm{\&delta;}\stackrel{\&OverBar;}{I_u});$

Wherein,

is distributed power source sale of electricity income desired value;

is for practicing thrift net damage expense desired value;

is toxic emission income desired value;

C _DGCost of investment for distributed power source;

improves the index desired value for voltage;

δ is for to improve the measurement factor that index converts comprehensive benefit to voltage; In the calculating, the δ value depends on the requirement of system to quality of voltage, when the quality of voltage of having relatively high expectations, and desirable bigger δ value, otherwise desirable less δ value.Get δ=50 among the present invention.

And have:

$\stackrel{\&OverBar;}{C_{\mathrm{SAL}}}=\underset{i=1}{\overset{T}{\mathrm{\&Sigma;}}}{t}_i(\underset{j\&Element;N_{\mathrm{DWG}}}{\mathrm{\&Sigma;}}(a_j-a_j^{\&prime;})\stackrel{\&OverBar;}{P_{\mathrm{DWGij}}}+\underset{k\&Element;N_{\mathrm{PV}}}{\mathrm{\&Sigma;}}(b_k-b_k^{\&prime;})\stackrel{\&OverBar;}{P_{\mathrm{PVik}}});$

$\stackrel{\&OverBar;}{C_{\mathrm{Loss}}}=\underset{i=1}{\overset{T}{\mathrm{\&Sigma;}}}{a}_{\mathrm{lossi}}\&CenterDot;t_i\&CenterDot;\stackrel{\&OverBar;}{{\mathrm{\&Delta;P}}_{\mathrm{lossi}}};$

$\stackrel{\&OverBar;}{C_E}=\underset{i=1}{\overset{T}{\mathrm{\&Sigma;}}}(a_{{\mathrm{co}}_2}\stackrel{\&OverBar;}{E_{c{o}_2}^i}+a_{s{o}_2}\stackrel{\&OverBar;}{E_{s{o}_2}^i}+a_{{\mathrm{No}}_{\text{x}}}\stackrel{\&OverBar;}{E_{{\mathrm{No}}_x}^i});$

$C_{\mathrm{DG}}=C(r,m)(\underset{j\&Element;N_{\mathrm{DWG}}}{\mathrm{\&Sigma;}}(c_{\mathrm{ej}}+c_{\mathrm{fj}})P_{\mathrm{DWGj}}+\underset{k\&Element;N_{\mathrm{PV}}}{\mathrm{\&Sigma;}}(w_{\mathrm{ek}}+w_{\mathrm{fk}})P_{\mathrm{PVk}});$

$\stackrel{\&OverBar;}{I_u}=\frac{1}{T\&CenterDot;N}\&CenterDot;\underset{i=1}{\overset{T}{\mathrm{\&Sigma;}}}\underset{n\&Element;N}{\mathrm{\&Sigma;}}|\stackrel{\&OverBar;}{U_{\mathrm{ino}}}-U_{\mathrm{nN}}|/|\stackrel{\&OverBar;}{U_{\mathrm{in}}}-U_{\mathrm{nN}}|;$

Wherein:

Hop count when T is, wind speed, the distributed constant of intensity of illumination in each period are discrepant, exist complementary.Get 24 periods among the present invention;

t _iBe i running time period; Hop count is confirmed during according to getting, and all is taken as 1 hour among the present invention.

N _DWGFor allowing to insert the node set of wind-powered electricity generation; DWG node to be selected is 13,15,29,30 in the example;

N _PVFor allowing to insert the node set of photovoltaic cell; PV node to be selected is 17,21,32 in the example;

The DWG model that adopts in the example is the blower fan Mod-0 of U.S. NASA development, and rated capacity is 100kW, and incision wind speed, rated wind speed, cut-out wind speed are respectively 4.3,7.7,17.9m/s.The PV assembly is selected the PILKINGTONSFM144Hx250wp type, and each assembly area is 2.16m ², photoelectric conversion efficiency is that the number of components of 13.44%, one PV square formation is 400.But each node unit capacity equipment investment, installation cost installed capacity, rate for incorporation into the power network and unit quantity of electricity maintenance cost are seen appendix Table A 1.

a _jIt is the DWG rate for incorporation into the power network of j node; Confirm that to the electricity price of electrical network sale electric energy the example value is seen appendix Table A 1 when runing according to actual wind power supply;

A ' _jIt is the DWG unit quantity of electricity operation and maintenance cost of j node; Maintenance cost conversion when runing according to actual wind power supply obtains, and the example value is seen appendix Table A 1;

b _kIt is the PV rate for incorporation into the power network of k node; Confirm that to the electricity price of electrical network sale electric energy the example value is seen appendix Table A 1 when runing according to actual photo-voltaic power supply;

B ' _kIt is the unit quantity of electricity operation and maintenance cost of k node; Maintenance cost conversion when runing according to actual photo-voltaic power supply obtains, and the example value is seen appendix Table A 1.

is the desired value of the photovoltaic online active power of output of k node; At first confirm the probability density function of illumination patterns by the intensity of illumination probability distribution parameters of this node; Confirm the probability density function of photovoltaic cell power output then by the functional relation of intensity of illumination and photovoltaic cell power output; Calculate its desired value according to photovoltaic cell power output probability density function at last, the value of PV node intensity of illumination probability distribution parameters to be selected is seen accompanying drawing 8.

The desired value of the wind-powered electricity generation active power of output of

j node; At first confirm the probability density function of wind speed profile by the wind speed probability distribution parameters of this node; Confirmed the probability density function of wind-powered electricity generation power output then by the functional relation of wind speed and wind-powered electricity generation power output, the power output probability density function according to wind-powered electricity generation calculates its desired value at last.The value of DWG node wind speed probability distribution parameters to be selected is seen accompanying drawing 6 and Fig. 7.

a _LossiFor the i period is netted the damage electricity price; Consider actual conditions, the system losses electricity price is 0.4 yuan/kW.h in the example.

The net of practicing thrift for i period system decreases desired value; The probability trend of at first calculating system when not containing DG-C can get the system losses desired value P of this moment _Loss1i, the probability trend of calculating system when containing DG-C then can get system losses desired value P this moment _Loss2i, it is P that the damage desired value falls in final system _Loss1i-P _Loss2iPromptly

P _Loss1iAnd P _Loss2iBe calculated as prior art.

Ignore the dusty gas that DWG and PV discharge, and think the balance node injecting power 65% for thermal power plant provides, waste gas and discharge fee that its unit energy output produces are seen appendix Table A 2.

Be CO ₂The unit discharge fee; Consider actual conditions, see appendix Table A 2 in the example;

Be SO ₂The unit discharge fee; Consider actual conditions, see appendix Table A 2 in the example;

Be NO _xThe unit discharge fee; Consider actual conditions, see appendix Table A 2 in the example;

Be CO ₂The discharge capacity desired value that reduces of i period; At first the system of calculating falls damage desired value----and falls the net damage desired value that the damage desired value is exactly saving:

Provide CO when obtaining thermal power generation then and sending unit quantity of electricity above the computational methods ₂Discharge amount of exhaust gas, then system falls and decreases desired value and multiply by CO ₂The waste gas unit discharge can obtain CO ₂The reduction of discharging desired value.See appendix Table A 2 in the example]

Be SO ₂The discharge capacity desired value that reduces of i period; At first the damage desired value falls in the system of calculating, and falls and decreases the net damage desired value that desired value is exactly saving:

Provide SO when obtaining thermal power generation then and sending unit quantity of electricity above the computational methods ₂Discharge amount of exhaust gas, then system falls and decreases desired value and multiply by SO ₂The waste gas unit discharge can obtain SO ₂The reduction of discharging desired value.See appendix Table A 2 in the example.

Be NO _xThe discharge capacity desired value that reduces of i period; At first the damage desired value falls in the system of calculating, and falls and decreases the net damage desired value that desired value is exactly saving:

Provided NO when sending unit quantity of electricity according to thermal power generation then above the computational methods _XDischarge amount of exhaust gas, then system falls and decreases desired value and multiply by NO _XThe waste gas unit discharge can obtain NO _XThe reduction of discharging desired value.See appendix Table A 2 in the example]

C _{(r, m)}Be present value factor; The following formula of CALCULATION OF PARAMETERS foundation:

C(r,m)=(r(1+r) ^m)/((1+r) ^m-1)。Relevant with discount rate r with DG m in useful life; Be 20 years the useful life of DWG, PV in the example, and discount rate is 0.08.

c _EjIt is the equipment investment of j node DWG unit capacity; According to making the required expense conversion of wind power supply in the actual engineering.See appendix Table A 1 in the example;

c _FjIt is the installation cost of j node DWG unit capacity; According to the required expense conversion of wind power supply is installed in the actual engineering, see appendix Table A 1 in the example;

P _DWGjIt is the real account constant volume of j node DWG unit capacity;

The desired value that refers to the real account constant volume of j node DWG unit capacity.According to the complex optimum allocation models that this patent proposes, find the solution the wind power supply capacity of confirming afterwards through optimized Algorithm in this node installation based on complementation.The DWG capacity that each of trying to achieve in instance DWG node to be selected is installed is referring to table 1, and 2, the method for distributing rationally of intermittent distributed power source in power distribution network based on complementarity of utilizing this paper to propose can be tried to achieve.

w _EkIt is the equipment investment of k node PV unit capacity; According to making the required expense conversion of photovoltaic cell power supply in the actual engineering.See appendix Table A 1 in the example;

w _FkIt is the installation cost of k node PV unit capacity; According to the required expense conversion of photovoltaic cell is installed in the actual engineering, see appendix Table A 1 in the example;

P _PVkIt is k node PV real account constant volume; According to the complex optimum allocation models that this patent proposes, find the solution the photovoltaic cell capacity of confirming afterwards through optimized Algorithm in this node installation based on complementation.The PV capacity that each of trying to achieve in instance PV node to be selected is installed is referring to table 1, and 2, the method for distributing rationally of intermittent distributed power source in power distribution network based on complementarity of utilizing this paper to propose can be tried to achieve.

The installable intermittent DG range of capacity of system is the centrifugal pump that DG model and actual geographical position are limited.The genetic algorithm of select tape elitism strategy of the present invention is optimized calculating, and calculation procedure is following:

1) confirm the node of DWG to be installed and PV, confirm chromosomal gene number p according to the number of DG node to be installed, and p=p _DWG+ p _PV, p wherein _DWG, p _PVThe node number of representing DWG to be installed and PV respectively.

2) according to the installed capacity centrifugal pump of each DWG to be selected and PV node; In its corresponding gene, adopt not homoimerous base character; And but the element that each node installed capacity centrifugal pump to be selected and its base character are concentrated is corresponding one by one, thereby has reduced chromosomal length, improves operation efficiency.

3) calculate each individual pairing target function value in the population, adopt the linear ordering method to calculate each individual fitness, be about to each individual corresponding target function value and arrange from small to large, select pressure reduction sp, be calculated as follows each individual fitness:

FitnV(Pos)＝2－sp+2×(sp－1)×(Pos－1)/(Nind－1)

Wherein Nind representes individual number, and Pos representes the position at place, the individual ordering of functional value according to target back, and FitnV (Pos) representes the fitness that it is corresponding, and the fitness that can be got optimum individual by following formula is sp, and the poorest ideal adaptation degree is 0.

4) select, intersection, mutation operation, replace in the father population with new population individual, according to the elitism strategy principle, with the optimum individual in each generation keep and heredity in the next generation.Fig. 4 is the algorithm flow that the present invention taked.

N is the system node number except that balance node; Actual node number according to carrying out based on complementary complex optimum system configured is confirmed, is IEEE33 node example here, and N is 32.

is for installing the preceding n node voltage of intermittent DG desired value; Application is carried out the probability trend to distribution system and is calculated based on the probability tidal current computing method of cumulant method under distribution-free formula power supply prerequisite, can draw the voltage desired value of each node.The probability trend is calculated as prior art.

is n node voltage desired value behind the intermittent DG of installation; Application is carried out the probability trend to the distribution system that distributed power source has been installed and is calculated based on the probability tidal current computing method of cumulant method, can draw the voltage desired value of each node.

U _NNBe voltage rating.Confirm that by concrete system getting reference voltage in the example is 12.66kV.The constraint equation of said target function comprises:

$\left\{\begin{array}{c}{P}_{\mathrm{is}}=U_i\underset{j=1}{\overset{N}{\mathrm{\&Sigma;}}}{U}_j(G_{\mathrm{ij}}\cos {\mathrm{\&theta;}}_{\mathrm{ij}}+B_{\mathrm{ij}}\sin {\mathrm{\&theta;}}_{\mathrm{ij}})\\ Q_{\mathrm{is}}=U_i\underset{j=1}{\overset{N}{\mathrm{\&Sigma;}}}{U}_j(G_{\mathrm{ij}}\sin {\mathrm{\&theta;}}_{\mathrm{ij}}-B_{\mathrm{ij}}\cos {\mathrm{\&theta;}}_{\mathrm{ij}})\end{array}\right.$

With

$\left\{\begin{array}{cc}0\&le;P_{\mathrm{DWGj}}\&le;P_{\mathrm{DWGj}}^{\max }& j=\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;,N_{\mathrm{DWG}}\\ 0\&le;P_{\mathrm{PVk}}\&le;P_{\mathrm{PVk}}^{\max }& k=\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;,N_{\mathrm{PV}}\\ \underset{j\&Element;N_{\mathrm{DWG}}}{\mathrm{\&Sigma;}}{P}_{\mathrm{DWGj}}+\underset{k\&Element;N_{\mathrm{PV}}}{\mathrm{\&Sigma;}}{P}_{\mathrm{PVk}}=\mathrm{\&rho;}{P}_L^{\max }& \\ U_i^{\min }\&le;\stackrel{\&OverBar;}{U_i}\&le;U_i^{\max }& i=\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;,N\\ P\{U_i^{\min }\&le;U_i\&le;U_i^{\max }\}\&GreaterEqual;{\mathrm{\&lambda;}}_i& \\ \left|\stackrel{\&OverBar;}{P_l}\right|\&le;P_l^{\max }& l=\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;,N_l\\ P\left\{\right|P_l|\&le;P_l^{\max }\}\&GreaterEqual;{\mathrm{\&omega;}}_l& \end{array}\right.;$

Wherein,

P _IsInjection active power for node i;

Q _IsInjection reactive power for node i;

U _iVoltage magnitude for node i;

G _IjReal part for system's admittance matrix; The admittance matrix that can confirm system according to the network configuration and the network parameter of optimised system, and then can confirm the real part of its admittance matrix by the admittance matrix of system.--confirm that admittance matrix is a prior art.

B _IjImaginary part for system's admittance matrix;

θ _IjPhase difference of voltage for node i and j; The electric power system of distributing rationally is carried out can confirming each node voltage phase angle difference after the trend calculating.

is the DWG heap(ed) capacity that the j node allows installation; The installed DWG heap(ed) capacity of confirming each node to be selected according to network configuration, network parameter and electric pressure and the load level of system to be optimized.Wind-powered electricity generation (distributed wind generation, DWG), (photovoltaic PV) sees appendix Table A 1 to photovoltaic cell in the example;

is the PV heap(ed) capacity that the k node allows installation; According to the installed PV heap(ed) capacity that network configuration, network parameter and electric pressure and the load level of system to be optimized are confirmed each node to be selected, see appendix Table A 1 in the example;

is the maximum load power of system; Can confirm the maximum load power that system can carry according to network configuration, network parameter and the load level of system to be optimized.

ρ is for penetrating power coefficient; Confirm that by network configuration value 60% in the example;

Upper voltage limit for node i; Value is 1.1U in this example _N, U _NBe system's rated voltage;

Lower voltage limit for node i; Value is 0.9U in this example _N, U _NBe system's rated voltage;

P{} is the probability that incident is set up in { };

λ _iVoltage constraint confidence level for node i; λ in the example _iGet 0.9;

The absolute value of

expression

;

is the through-put power desired value of branch road l, and its value is calculated by the probability trend;

is the through-put power upper limit of branch road l; Can confirm the through-put power upper limit of each branch road according to the network configuration of system to be optimized, network parameter and to the requirement of each node voltage.

N _lBe the system branch sum; Network configuration according to system to be optimized is confirmed.

ω _lConfidence level for the constraint of the trend of branch road l.According to the parameter of circuit and the electric pressure of system; Transmission line has maximum transmission capacity restriction; Surpass the safe operation of the maximum transmission capacity upper limit entail dangers to system of circuit; The confidence level of circuit trend constraint is set, is guaranteeing that the circuit trend in the scope of constraint, can guarantee the security of operation of system effectively.ω _lValue can determine that value is 0.95 in the instance by experience.Be system after distributing rationally, each branch road trend is 0.95 smaller or equal to the probability of the branch road transmission capacity upper limit.

It is that calculating for the trend of this type of power system network all has this constraint because need to consider the data reasonability that this constraints is set.

The probability density function of wind speed descriptive statistics is following: $f\left(v\right)=\frac{k}{c}{\left(\frac{v}{c}\right)}^{k-1}\mathrm{Exp}[-{\left(\frac{v}{c}\right)}^k];$ (this function is used for confirming respectively to install the wind speed statistical law of DWG node, thereby confirms the power output of DWG, for carrying out the calculating of probability trend parameter is provided.This is that Optimization Model is found the solution required initial data.）

The functional relation of DWG power output and wind speed is following:

$P_{\mathrm{DWG}}=\left\{\begin{array}{cc}0& v\&le;v_{\mathrm{ci}}\\ k_{1}v+k_2& v_{\mathrm{ci}}<v\&le;v_r\\ P_r& v_r<v\&le;v_{\mathrm{co}}\\ 0& v>v_{\mathrm{co}}\end{array}\right.$

The desired value of DWG power output is following:

$E\left(P_{\mathrm{DWG}}\right)={\&Integral;}_0^{\&infin;}{P}_{\mathrm{DWG}}f\left(v\right)\mathrm{dv}$

$={\&Integral;}_{v_{\mathrm{ci}}}^{v_r}(k_{1}v+k_2)f\left(v\right)\mathrm{dv}+{\&Integral;}_{v_r}^{v_{\mathrm{co}}}{P}_{r}f\left(v\right)\mathrm{dv}$

Wherein, the DWG model that adopts in the example is the blower fan Mod-0 of U.S. NASA development, and rated capacity is 100kW, and incision wind speed, rated wind speed, cut-out wind speed are respectively 4.3,7.7,17.9m/s.

V is a wind speed, refers to specifically install the wind speed in DWG place.

K is the form parameter that Weibull distributes; Wind conditions according to concrete installation DWG place is confirmed.At first obtain the historical wind speed situation in the place that DWG has been installed; Promptly pass by the wind speed size of identical historical juncture at this point; The function expression match that distributes according to Weibull then obtains the value of k parameter, and in fact the k parameter is that value is seen accompanying drawing 6 or Fig. 7 in this example that is obtained by forecasting wind speed;

C is the scale parameter that Weibull distributes; Wind conditions according to the concrete DWG of installation place is confirmed.Principle is with the k parameter.Value is seen Fig. 6 or Fig. 7 in this example.

Pr is a DWG rated power; Model by actual installation DWG confirms that rated capacity is 100kW in the example]

v _CiBe the incision wind speed; Refer to the minimum wind speed that wind turbine generator begins to generate electricity by way of merging two or more grid systems, confirm by the model of actual installation DWG.In the example 4.3m/s;

v _rBe rated wind speed; Model by actual installation DWG is confirmed.In the example 7.7m/s;

v _CoBe cut-out wind speed.Finger wind energy conversion system shearing device is had an effect, and the wind speed when impeller is stopped the rotation is confirmed by the model of actual installation DWG.In the example 17.9m/s;

K1 and k2 are the scale factors that wind speed is mapped as the DWG power output.The rated power P of the value of k1 and k2 and DWG _r, incision wind speed v _CiWith rated wind speed v _rCommon definite, the physical relationship formula is as follows: k1=P _r/ (v _r-v _Ci), k2=-(P _r* v _Ci)/(v _r-v _Ci).

The probability density function of solar irradiation intensity;

As follows:

$f\left(r\right)=\frac{\mathrm{\&Gamma;}(\mathrm{\&alpha;}+\mathrm{\&beta;})}{\mathrm{\&Gamma;}\left(\mathrm{\&alpha;}\right)\mathrm{\&Gamma;}\left(\mathrm{\&beta;}\right)}{\left(\frac{r}{r_{\mathrm{Max}}}\right)}^{\mathrm{\&alpha;}-1}{(1-\frac{r}{r_{\mathrm{Max}}})}^{\mathrm{\&beta;}-1};$ Γ is the expression symbol of gamma function.

This function is used for confirming respectively to install the intensity of illumination statistical law of PV node, thereby confirms the power output of PV, for carrying out the calculating of probability trend parameter is provided.This is that Optimization Model is found the solution required initial data.

The PV assembly is selected PILKINGTON SFM144Hx250wp type, and each assembly area is 2.16m ², photoelectric conversion efficiency is that the number of components of 13.44%, one PV square formation is 400.

The probability density function of PV square formation power output is following:

$f\left(P_{\mathrm{PV}}\right)=\frac{\mathrm{\&Gamma;}(\mathrm{\&alpha;}+\mathrm{\&beta;})}{\mathrm{\&Gamma;}\left(\mathrm{\&alpha;}\right)+\mathrm{\&Gamma;}\left(\mathrm{\&beta;}\right)}{\left(\frac{P_{\mathrm{PV}}}{R_M}\right)}^{\mathrm{\&alpha;}-1}{(1-\frac{P_{\mathrm{PV}}}{R_M})}^{\mathrm{\&beta;}-1}$

The desired value of PV power output is following:

$E\left(P_{\mathrm{PV}}\right)={\&Integral;}_0^{R_M}{P}_{\mathrm{PV}}f\left(P_{\mathrm{PV}}\right)d{P}_{\mathrm{PV}}$

Wherein,

R is an actual light intensity; Finger is respectively installed the concrete illumination in the place of PV node;

r _MaxBe largest light intensity; Confirm according to each concrete light conditions that the place of PV node is installed.

α, β are the form parameter that Beta distributes; Confirm according to each concrete light conditions that the place of PV node is installed.K parameter and c parameter that the principle of value distributes with wind speed Weibull.Value is seen accompanying drawing 8 in this example.

P _PVBe PV square formation power output; According to the probability density function of PV square formation power output, utilize calculus methods to calculate and confirm.P _PVBe a name variable, the power output of expression photovoltaic cell can only be calculated P here _PVDesired value, and variable P _PVProbability density function be:

$f\left(P_{\mathrm{PV}}\right)=\frac{\mathrm{\&Gamma;}(\mathrm{\&alpha;}+\mathrm{\&beta;})}{\mathrm{\&Gamma;}\left(\mathrm{\&alpha;}\right)+\mathrm{\&Gamma;}\left(\mathrm{\&beta;}\right)}{\left(\frac{P_{\mathrm{PV}}}{R_M}\right)}^{\mathrm{\&alpha;}-1}{(1-\frac{P_{\mathrm{PV}}}{R_M})}^{\mathrm{\&beta;}-1},$ So $E\left(P_{\mathrm{PV}}\right)={\&Integral;}_0^{R_M}{P}_{\mathrm{PV}}f\left(P_{\mathrm{PV}}\right)d{P}_{\mathrm{PV}},$ RM=A η r _MaxBe known, i.e. the upper limit of integration, and under be limited to 0, according to the operation rule of definite integral, P _PVDesired value E (P _PV) can try to achieve.r _MaxBe largest light intensity; Confirm according to each concrete light conditions that the place of PV node is installed.

R _M=A η _RmaxBe the square formation peak power output.A representes the monolithic photocell assembly area of (being used for luminous energy is transformed into the parts of electric energy), is set in the example: A=2.16; η representes luminous energy is transformed into the efficient of electric energy, is set in the example: 13.44%.

adopts the probability trend of cumulant method to carry out trend and calculates.

In carrying out trend computational process, probability trend hypothesis is only considered the uncertain factor of injecting power, ignore that network topology structure changes and the uncertain factor of the stoppage in transit of intermittent DG.

In carrying out trend computational process, separate between each intermittent DG, each node injecting power.

Table 1DG optimal case and system optimization result

It is example that the present invention selects IEEE33 node distribution system, and system wiring is as shown in Figure 1.The optimization plan model and the algorithm flow that adopt the present invention to propose, it is as shown in table 1 with relevant Optimization result to solve DG The optimum layout scheme.

Keep the average statistics distribution characters of meteorologic factor in the whole period such as wind speed, intensity of illumination constant; Change its probability distribution in day part; Can cause each node load power of system, DWG and probability distribution otherness and the complementarity of PV power output in space-time unique to change, thereby influence the allocation optimum scheme of DG.Suppose that other condition is constant; And replace data shown in Figure 6 with wind speed probability distribution parameters shown in Figure 7; Check probability distribution otherness and complementary influence to DG allocation optimum result with this, allocation optimum scheme that solves and Optimization result contrast are as shown in table 2.From table 2, can know, data corresponding shown in Figure 6, optimal case needs at node 15,17 DWG of 300kW and the PV of 100kW to be installed respectively; When adopting data shown in Figure 7, the allocation optimum scheme becomes to be needed at node 15,17 DWG of 200kW and the PV of 300kW to be installed respectively.Verified that thus probability distribution otherness and complementary change can cause the allocation optimum scheme of DG that bigger variation takes place.

Table 2 Fig. 6 and the contrast of 7 DG Optimization result

The optimal case of corresponding diagram 7 has reached 1.551 though voltage is expected the improvement rate in addition, and the minimum voltage desired value of system appears at the node 32 of 12 periods, and has only 0.9003, and its probability that is in 0.9 to 1.1 is 0.5146 simultaneously.When Fig. 9, Figure 10 are wind speed parameter shown in respective figure 6 and 7, its DG allocation optimum scheme corresponding nodes 17 and 32 voltage desired value curve chart, and voltage is in 0.9 to 1.1 probability curve.From figure, can see; Because system loading power, DWG and PV power output probability distribution otherness and the complementary change in space-time unique; The allocation optimum scheme of respective figure 7; Though its voltage expectation improvement rate is better than the The optimum layout scheme of corresponding accompanying drawing 6; But in system's heavy load period, the output of the power of DG has lost complementarity with system loading, causes the allocation optimum scheme of the voltage desired value of this moment and the probability respective figure 6 that voltage fluctuates in normal range (NR) all low.Especially node 32, and when being in 11-14 during the period, the probability difference that its voltage fluctuates in normal range (NR) is very big.Therefore only consider that the average statistics characteristic of DG power output in whole time domain can not satisfy planning requirement; Must fully study and utilize system loading power and DG power output probability distribution otherness and the complementarity in space-time unique; And other adjusting pressure measure of reasonable combination in view of the above, further improve its voltage at the coefficient of stabilization of heavy load in the period.

But the unit capacity cost installed capacity of Table A 1 DG node to be installed, rate for incorporation into the power network and current potential electric weight maintenance cost

Table A 2 unit thermoelectricity discharge amount of exhaust gas and discharge fees

Through above Fig. 2,3 and table 1 analyze and can know; The complex optimum plan model that proposes according to the present invention; Intermittent DG allocation optimum scheme has not only been improved the system voltage level; Reduce net and decreased, and improved the utilance of regenerative resource, obtained the COMPREHENSIVE OPTIMAL of economic benefit and environmental benefit.Can know by Fig. 2; The desired value of each node day part voltage of system is all less than 1 (perunit value); This moment, the desired value of voltage was high more; Explain that voltage that this node should the period is more near rated value (perunit value of rated value is 1); And the various device that constitutes distribution system moves the runnability (this is the basic general knowledge of system's operation) that can obtain optimal running status or optimum under rated voltage, so be no more than under the prerequisite of rated value in the voltage desired value, the high more description effect of voltage desired value is good more.

With the IEEE33 distribution system shown in accompanying drawing 1 is example; Adopting the distributed power source at intermittence based on complementation provided by the invention to distribute model rationally calculates; It is as shown in Figure 2 before its DG installs with the out-of-limit node voltage desired value of DG optimization back voltage; Optimizing the out-of-limit node voltage of back voltage with DG before its DG installs, to be in the probability of 0.9Un to 1.1Un as shown in Figure 3; The arrangement that the distributed power source at intermittence based on complementation that the embodiment of the invention provides is distributed the optimum of model rationally is, is 8 * 100kW at 13 nodes dress DWG capacity, is 3 * 100kW at 15 nodes dress DWG capacity; At 29 nodes dress DWG capacity is 10 * 100kW, is 1 * 100kW at 7 nodes dress PV capacity.When the arrangement of distributing the optimum of model rationally at the distributed power source at intermittence based on complementation that the embodiment of the invention is provided calculates; Employing is carried out trend based on the probability trend of cumulant method and is calculated; Obtain

wherein; In carrying out trend computational process; Probability trend hypothesis is only considered the uncertainty of injecting power, do not consider that network topology structure changes and the uncertain factors such as stoppage in transit of intermittent DG; And, separate between each intermittent DG, each node injecting power.Further obtain conclusion as shown in table 1.

The implication of representing about first hurdle of table 1; Such as 13 (8) expressions, 13 nodes 800kWDWG is installed; The 13 expression node numbers that this wants associative list A1 to see, (8) expression installed capacity is from table 1; Can find out; Use the distributed power source at intermittence based on complementation provided by the invention and distribute model rationally the distributed power source at intermittence based on complementation is optimized configuration, can improve the system voltage level, reduce net and decrease, improve the renewable energy utilization rate, and then make the configuration that is optimized of economic benefit and environmental benefit.

Above-described embodiment; The object of the invention, technical scheme and beneficial effect have been carried out further explain, and institute it should be understood that the above is merely embodiment of the present invention; Be not limited to the present invention; All within spirit of the present invention and principle, any modification of being made, be equal to replacement, improvement etc., all should be included within protection scope of the present invention.

Claims (5)

1. the distributed power source at intermittence based on complementation is distributed method rationally, it is characterized in that, based on the target function that intermittence of complementation, distributed power source was distributed rationally is:

$\max f=\max (\stackrel{\&OverBar;}{C_{\mathrm{SAL}}}+\stackrel{\&OverBar;}{C_{\mathrm{Loss}}}+\stackrel{\&OverBar;}{C_E}-C_{\mathrm{DG}}+\mathrm{\&delta;}\stackrel{\&OverBar;}{I_u});$

Wherein,

is distributed power source sale of electricity income desired value;

is for practicing thrift net damage expense desired value;

is toxic emission income desired value;

C _DGCost of investment for distributed power source;

improves the index desired value for voltage;

δ is for to improve the measurement factor that index converts comprehensive benefit to voltage; And have:

$\stackrel{\&OverBar;}{C_{\mathrm{SAL}}}=\underset{i=1}{\overset{T}{\mathrm{\&Sigma;}}}{t}_i(\underset{j\&Element;N_{\mathrm{DWG}}}{\mathrm{\&Sigma;}}(a_j-a_j^{\&prime;})\stackrel{\&OverBar;}{P_{\mathrm{DWGij}}}+\underset{k\&Element;N_{\mathrm{PV}}}{\mathrm{\&Sigma;}}(b_k-b_k^{\&prime;})\stackrel{\&OverBar;}{P_{\mathrm{PVik}}});$

$\stackrel{\&OverBar;}{C_{\mathrm{Loss}}}=\underset{i=1}{\overset{T}{\mathrm{\&Sigma;}}}{a}_{\mathrm{lossi}}\&CenterDot;t_i\&CenterDot;\stackrel{\&OverBar;}{{\mathrm{\&Delta;P}}_{\mathrm{lossi}}};$

$\stackrel{\&OverBar;}{C_E}=\underset{i=1}{\overset{T}{\mathrm{\&Sigma;}}}(a_{{\mathrm{co}}_2}\stackrel{\&OverBar;}{E_{c{o}_2}^i}+a_{s{o}_2}\stackrel{\&OverBar;}{E_{s{o}_2}^i}+a_{{\mathrm{No}}_{\text{x}}}\stackrel{\&OverBar;}{E_{{\mathrm{No}}_x}^i});$

$C_{\mathrm{DG}}=C(r,m)(\underset{j\&Element;N_{\mathrm{DWG}}}{\mathrm{\&Sigma;}}(c_{\mathrm{ej}}+c_{\mathrm{fj}})P_{\mathrm{DWGj}}+\underset{k\&Element;N_{\mathrm{PV}}}{\mathrm{\&Sigma;}}(w_{\mathrm{ek}}+w_{\mathrm{fk}})P_{\mathrm{PVk}});$

$\stackrel{\&OverBar;}{I_u}=\frac{1}{T\&CenterDot;N}\&CenterDot;\underset{i=1}{\overset{T}{\mathrm{\&Sigma;}}}\underset{n\&Element;N}{\mathrm{\&Sigma;}}|\stackrel{\&OverBar;}{U_{\mathrm{ino}}}-U_{\mathrm{nN}}|/|\stackrel{\&OverBar;}{U_{\mathrm{in}}}-U_{\mathrm{nN}}|;$

Wherein:

Hop count when T is;

t _iBe i running time period;

N _DWGFor allowing to insert the node set of wind-powered electricity generation;

N _PVFor allowing to insert the node set of photovoltaic cell;

a _jIt is the DWG rate for incorporation into the power network of j node;

A ' _jIt is the DWG unit quantity of electricity operation and maintenance cost of j node;

b _kIt is the PV rate for incorporation into the power network of k node;

B ' _kIt is the unit quantity of electricity operation and maintenance cost of k node;

is the desired value of the photovoltaic online active power of output of k node;

a _LossiFor the i period is netted the damage electricity price;

is that the net that i period system practices thrift decreases desired value;

Be CO ₂The unit discharge fee;

Be SO ₂The unit discharge fee;

Be NO _xThe unit discharge fee;

Be CO ₂The discharge capacity desired value that reduces of i period;

Be SO ₂The discharge capacity desired value that reduces of i period;

Be NO _xThe discharge capacity desired value that reduces of i period;

C _{(r, m)}Be present value factor;

c _EjIt is the equipment investment of j node DWG unit capacity;

c _FjIt is the installation cost of j node DWG unit capacity;

P _DWGjIt is the real account constant volume of j node DWG unit capacity;

w _EkIt is the equipment investment of k node PV unit capacity;

w _FkIt is the installation cost of k node PV unit capacity;

P _PVkIt is k node PV real account constant volume;

N is the system node number except that balance node;

is for installing the preceding n node voltage of intermittent DG desired value;

is n node voltage desired value behind the intermittent DG of installation;

U _NNBe voltage rating.

2. the distributed power source at intermittence based on complementation according to claim 1 is distributed method rationally, it is characterized in that the constraint equation of said target function comprises:

$\left\{\begin{array}{c}{P}_{\mathrm{is}}=U_i\underset{j=1}{\overset{N}{\mathrm{\&Sigma;}}}{U}_j(G_{\mathrm{ij}}\cos {\mathrm{\&theta;}}_{\mathrm{ij}}+B_{\mathrm{ij}}\sin {\mathrm{\&theta;}}_{\mathrm{ij}})\\ Q_{\mathrm{is}}=U_i\underset{j=1}{\overset{N}{\mathrm{\&Sigma;}}}{U}_j(G_{\mathrm{ij}}\sin {\mathrm{\&theta;}}_{\mathrm{ij}}-B_{\mathrm{ij}}\cos {\mathrm{\&theta;}}_{\mathrm{ij}})\end{array}\right.$

With

$\left\{\begin{array}{cc}0\&le;P_{\mathrm{DWGj}}\&le;P_{\mathrm{DWGj}}^{\max }& j=\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;,N_{\mathrm{DWG}}\\ 0\&le;P_{\mathrm{PVk}}\&le;P_{\mathrm{PVk}}^{\max }& k=\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;,N_{\mathrm{PV}}\\ \underset{j\&Element;N_{\mathrm{DWG}}}{\mathrm{\&Sigma;}}{P}_{\mathrm{DWGj}}+\underset{k\&Element;N_{\mathrm{PV}}}{\mathrm{\&Sigma;}}{P}_{\mathrm{PVk}}=\mathrm{\&rho;}{P}_L^{\max }& \\ U_i^{\min }\&le;\stackrel{\&OverBar;}{U_i}\&le;U_i^{\max }& i=\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;,N\\ P\{U_i^{\min }\&le;U_i\&le;U_i^{\max }\}\&GreaterEqual;{\mathrm{\&lambda;}}_i& \\ \left|\stackrel{\&OverBar;}{P_l}\right|\&le;P_l^{\max }& l=\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;,N_l\\ P\left\{\right|P_l|\&le;P_l^{\max }\}\&GreaterEqual;{\mathrm{\&omega;}}_l& \end{array}\right.;$

Wherein,

P _IsInjection active power for node i;

Q _IsInjection reactive power for node i;

U _iVoltage magnitude for node i;

G _IjReal part for system's admittance matrix;

B _IjImaginary part for system's admittance matrix;

θ _IjPhase difference of voltage for node i and j;

is the DWG heap(ed) capacity that the j node allows installation;

is the PV heap(ed) capacity that the k node allows installation;

is the maximum load power of system;

ρ is for penetrating power coefficient;

is the upper voltage limit of node i;

is the lower voltage limit of node i;

P{} is the probability that incident is set up in { };

λ _iVoltage constraint confidence level for node i;

The absolute value of

expression

;

is the through-put power desired value of branch road l, and its value is calculated by the probability trend;

is the through-put power upper limit of branch road l;

N _lBe the system branch sum;

ω _lConfidence level for the constraint of the trend of branch road l.

3. the distributed power source at intermittence based on complementation according to claim 1 is distributed method rationally, it is characterized in that,

The probability density function of wind speed descriptive statistics is following:

$f\left(v\right)=\frac{k}{c}{\left(\frac{v}{c}\right)}^{k-1}\exp [-{\left(\frac{v}{c}\right)}^k];$

The functional relation of DWG power output and wind speed is following:

$P_{\mathrm{DWG}}=\left\{\begin{array}{cc}0& v\&le;v_{\mathrm{ci}}\\ k_{1}v+k_2& v_{\mathrm{ci}}<v\&le;v_r\\ P_r& v_r<v\&le;v_{\mathrm{co}}\\ 0& v>v_{\mathrm{co}}\end{array}\right.$

The desired value of DWG power output is following:

$E\left(P_{\mathrm{DWG}}\right)={\&Integral;}_0^{\&infin;}{P}_{\mathrm{DWG}}f\left(v\right)\mathrm{dv}$

$={\&Integral;}_{v_{\mathrm{ci}}}^{v_r}(k_{1}v+k_2)f\left(v\right)\mathrm{dv}+{\&Integral;}_{v_r}^{v_{\mathrm{co}}}{P}_{r}f\left(v\right)\mathrm{dv}$

Wherein, the DWG model that adopts in the example is the blower fan Mod-0 of U.S. NASA development, and rated capacity is 100kW, and incision wind speed, rated wind speed, cut-out wind speed are respectively 4.3,7.7,17.9m/s.

V is a wind speed;

K is the form parameter that Weibull distributes;

C is the scale parameter that Weibull distributes;

P _rBe DWG rated power;

v _CiBe the incision wind speed;

v _rBe rated wind speed;

v _CoBe cut-out wind speed;

K1 and k2 are the scale factors that wind speed is mapped as the DWG power output.

4. the distributed power source at intermittence based on complementation according to claim 2 is distributed method rationally, it is characterized in that the probability density function of solar irradiation intensity is following:

$f\left(r\right)=\frac{\mathrm{\&Gamma;}(\mathrm{\&alpha;}+\mathrm{\&beta;})}{\mathrm{\&Gamma;}\left(\mathrm{\&alpha;}\right)\mathrm{\&Gamma;}\left(\mathrm{\&beta;}\right)}{\left(\frac{r}{r_{\mathrm{Max}}}\right)}^{\mathrm{\&alpha;}-1}{(1-\frac{r}{r_{\mathrm{Max}}})}^{\mathrm{\&beta;}-1};$ Γ is the expression symbol of gamma function;

The probability density function of PV square formation power output is following:

$f\left(P_{\mathrm{PV}}\right)=\frac{\mathrm{\&Gamma;}(\mathrm{\&alpha;}+\mathrm{\&beta;})}{\mathrm{\&Gamma;}\left(\mathrm{\&alpha;}\right)+\mathrm{\&Gamma;}\left(\mathrm{\&beta;}\right)}{\left(\frac{P_{\mathrm{PV}}}{R_M}\right)}^{\mathrm{\&alpha;}-1}{(1-\frac{P_{\mathrm{PV}}}{R_M})}^{\mathrm{\&beta;}-1}$

The desired value of PV power output is following:

$E\left(P_{\mathrm{PV}}\right)={\&Integral;}_0^{R_M}{P}_{\mathrm{PV}}f\left(P_{\mathrm{PV}}\right)d{P}_{\mathrm{PV}}$

Wherein,

R is an actual light intensity;

r _MaxBe largest light intensity;

α, β are the form parameter that Beta distributes;

P _PVBe PV square formation power output;

5. the distributed power source at intermittence based on complementation according to claim 1 is distributed method rationally; It is characterized in that,

adopt the probability trend of cumulant method to carry out trend to calculate.

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