CN104821578A - Large-scale wind power-containing power transmission system planning method taking available transmission capacity into account - Google Patents

Abstract

Aiming at an existing high-capacity wind power generation-containing electric power system, the invention discloses a large-scale wind power-containing power transmission system planning method taking available transmission capacity into account. The technical scheme that the method adopts is that: 1) on the basis of considering a correlation between wind speed and a load random variable and failure probabilities of a line and a generator, an available transmission capacity probability model is constructed; 2) a method of combining a Latin hypercube sampling simulation method and a sensitivity analysis method is adopted to solve the available transmission capacity probability model of a system in a wind power integration environment; and 3) the minimum investment cost of a power transmission line and the maximum value of expectation of available transmission capacity serve as objective functions, an allowable line overload probability serves as a constraint condition, and a power transmission system multi-objective optimization model is established and is then converted to a single-objective optimization problem which is solved by adoption of a genetic algorithm. The planning scheme obtained by the invention can not only satisfy a safety requirement that the line is not overloaded, but also has higher robustness and flexibility.

Description

Take into account the transmission system planing method containing large-scale wind power of available transmission capacity

Technical field

The invention belongs to transmission system planning technology field, specifically one takes into account the transmission system planing method containing large-scale wind power of available transmission capacity (ATC).

Background technology

Along with increasing large-scale wind power field is connected to the grid, the intermittence of wind power output and uncertainly bring some new problems to systems organization and operation, existing document point out in target function, count overload punishment cost or using overload level as constraints to the fail safe of the reliability level and system cloud gray model that ensure programme.But, with regard to transmission system planning, to the robustness of programme and requirement on flexibility higher, to adapt to more, that degree is stronger uncertain factor.Available transmission capacity (ATC) can reflect robustness and the flexibility of system cloud gray model to a great extent.

Summary of the invention

Technical problem to be solved by this invention is for the existing electric power system containing large-scale wind power access, a kind of transmission system planing method containing large-scale wind power taking into account available transmission capacity is provided, construct a kind of Model for Multi-Objective Optimization taking into account the transmission system planning of available transmission capacity, enable transmission system planing method adapt to the uncertainty of large-scale wind power access caused by electric power system.

For this reason, the present invention adopts following technical scheme: the transmission system planing method containing large-scale wind power taking into account available transmission capacity, and its step is as follows:

1) on the basis considering correlation and circuit and generator failure probability between wind speed and load stochastic variable, structure available transmission capacity probabilistic model;

2) method adopting the Monte Carlo simulation of Latin Hypercube Sampling (LHS) and Sensitivity Analysis Method to combine, take into account correlation between wind speed and load stochastic variable and circuit and generator failure probability, solve the available transmission capacity probabilistic model of system under wind-electricity integration environment;

3) and available transmission capacity desired value minimum with transmission line cost of investment is target function to the maximum, the circuit overload probability allowed is constraints, set up transmission system Model for Multi-Objective Optimization, be converted into single-object problem afterwards and adopt genetic algorithm to solve.

Further, step 2) detailed process as follows: first, adopt the Monte Carlo simulation of Latin Hypercube Sampling to sample to the wind speed of input and load stochastic variable, then sort according to correlative relationship, obtain final sampling scene set; For each scene that Latin Hypercube Sampling obtains, adopt Sensitivity Analysis Method to calculate its available transmission capacity, obtain probability distribution and the Statistical Parameters (as desired value and variance etc.) of available transmission capacity eventually through statistical method.

Sensitivity Analysis Method calculates its available transmission capacity and can be expressed as follows:

The general type of DC flow model is:

P＝Bθ (1)

The trend p of branch road ij _ijcan be described as:

p _ij＝b _ij(θ _i-θ _j)＝b _ije _ijθ (2)

Formula (1) is substituted into formula (2) can obtain:

p _ij＝b _ije _ijB ^-1P (3)

In formula: B is system node admittance matrix; θ is node voltage phase angle vector; P is node injecting power vector; b _ijfor the susceptance value of branch road ij; e _ijfor the node associated line vector that branch road ij is corresponding, except i and j column element is respectively+1 and-1, all the other elements are 0; θ _iand θ _jbe respectively the voltage phase angle of node i and node j.

With power delivery distribution factor (PTDF) characterize send to increase unit active power between receiving end time each Branch Power Flow change:

S _ij＝b _ije _ijB ^-1β (4)

In formula: S _ijfor the PTDF that branch road ij is corresponding; β is that m ties up power Distribution of A Sequence vector (m is nodes), represents the change sending each node injecting power when to increase unit transmission power between receiving end, for power supply node β _i>0 and ∑ _iβ _i=1, for load bus β _j<0 and ∑ _jβ _j=-1.

Every bar branch road ij has a maximum power transfer ability T _ij, T _ijnamely minimum branch road is the bottleneck branch road affecting ATC, the T that this branch road is corresponding _ijbe the ATC of system under current state:

$T_{\mathrm{ij}}=\left\{\begin{array}{cc}(\stackrel{\&OverBar;}{p_{\mathrm{ij}}}-p_{\mathrm{ij}})/S_{\mathrm{ij}}& S_{\mathrm{ij}}>0\\ (\underset{\&OverBar;}{p_{\mathrm{ij}}}-p_{\mathrm{ij}})/S_{\mathrm{ij}}& S_{\mathrm{ij}}<0\end{array}\right.---\left(5\right)$

$\mathrm{ATC}=\underset{\mathrm{ij}\&Element;N_B}{\min }\left\{T_{\mathrm{ij}}\right\}---\left(6\right)$

In formula: for the transmission power higher limit of branch road ij, p _ij for the transmission power lower limit of branch road ij; N _bfor the branch road collection in system.

Afterwards, by sampling generating set state, transmission line status, output of wind electric field and load, scene x is tried to achieve _iunder ATC, after obtaining the ATC under N number of scene, statistical analysis technique can be adopted to obtain the probability distribution of ATC, also can obtain the probability distribution of circuit overload simultaneously.

Calculate the desired value E of ATC under N number of scene _aTC:

$E_{\mathrm{ATC}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}\mathrm{ATC}\left(x_i\right)---\left(7\right)$

Further, step 3) detailed process as follows: to minimize Transmission Investment cost and to maximize available transmission capacity desired value as two optimization aim:

$f_1:\min C=\underset{l\&Element;N_B}{\mathrm{\&Sigma;}}{C}_l{Z}_l---\left(8\right)$

f ₂:max W＝E _ATC(9)

In formula: C _lfor the single back line cost of branch road l, ten thousand yuan/km; Z _lfor the enlarging circuit number of branch road l; W is ATC desired value corresponding to programme.

To target function f ₁and f ₂carry out the merging treatment shown in formula (10), form the single-object problem of transmission system planning:

max W/C (10)

s.t. Bθ＝P _W+P _G-P _L(11)

$\underset{\&OverBar;}{p_{\mathrm{Gi}}}\&le;p_{\mathrm{Gi}}\&le;\stackrel{\&OverBar;}{p_{\mathrm{Gi}}}---\left(12\right)$

$\underset{\&OverBar;}{p_{\mathrm{Lj}}}\&le;p_{\mathrm{Lj}}\&le;\stackrel{\&OverBar;}{p_{\mathrm{Lj}}}---\left(13\right)$

$Z_l\&le;\stackrel{\&OverBar;}{Z_l}---\left(14\right)$

$r_l\&GreaterEqual;\underset{\&OverBar;}{r_l},r_l=p_r\left(\right|p_l|\&le;\stackrel{\&OverBar;}{p_l})---\left(15\right)$

Above-mentioned various in: P _gand P _lbe respectively the meritorious vector sum load power vector of exerting oneself of conventional generator; P _wfor wind energy turbine set is gained merit force vector; p _gifor the generated power of node i is exerted oneself, with p _gi be respectively its upper limit value and lower limit value; p _ljfor the load power of node j, with p _lj be respectively its upper limit value and lower limit value; for branch road l can extend number of lines; r _lwith r _l be respectively the not out-of-limit probability of trend of branch road l and given lower limit; p _r() is probable value; p _lwith be respectively trend and the higher limit thereof of branch road l.

The available transmission capacity of the present invention's with due regard to transmission system in transmission system planning, using maximum for available transmission capacity desired value and Transmission Investment cost minimization as target, establish transmission system Model for Multi-Objective Optimization, reasonable balance is carried out between economy and operation risk, make the programme obtained not only can meet the not overladen security requirement of circuit, also there is higher robustness and flexibility, the uncertainty that wind-powered electricity generation brings can better be adapted to, and adapt with the trend of the power industry marketization.

Accompanying drawing explanation

Fig. 1 is 18 node example system diagrams (in figure, solid line represents existing circuit, and dotted line represents alternative route).

Fig. 2 is 46 node example system diagrams (in figure, solid line represents existing circuit, and dotted line represents alternative route).

Embodiment

Below in conjunction with specification drawings and specific embodiments, the invention will be further described.

The electric power system accessed for there being large-scale wind power, the present invention constructs a kind of Stochastic Optimization Model taking into account the transmission system planning of ATC.First, on the basis considering correlation and circuit and generator failure probability between wind speed and load stochastic variable, structure available transmission capacity probabilistic model; Secondly, adopt the method that the Monte Carlo simulation of Latin Hypercube Sampling (LHS) and Sensitivity Analysis Method combine, take into account correlation between wind speed and load stochastic variable and circuit and generator failure probability, solve the available transmission capacity probabilistic model of system under wind-electricity integration environment; Finally, target function is to the maximum with available transmission capacity desired value so that transmission line cost of investment is minimum, the circuit overload probability allowed is constraints, sets up transmission system Model for Multi-Objective Optimization, is converted into single-object problem afterwards and adopts genetic algorithm to solve.

Show (to refer to table 1-6 by the analysis of the multiple optimization planning schemes to 18 nodes and 46 nodes, two example systems, table 1 is 18 node system node parameters, table 2 is 46 node system node parameters, table 3 is 18 node system, three optimization planning project plan comparison, table 4 is the programme of 18 node systems gained when wind speed correlation is different, the 18 node system programmes of table 5 for obtaining under different wind energy turbine set access waies, table 6 is that 46 node system, three programmes compare):

The correlation of wind speed and the access way of wind energy turbine set can have an impact to Transmission Investment cost and ATC, therefore need when Optimal Transmission Expansion Planning decision-making to trade off between Transmission Investment economy and system cloud gray model risk, make the comprehensive benefit of Optimal Transmission Expansion Planning scheme best.

Table 1 18 node system node parameter

Node number	Generator rating power (MW)	Load power (MW)	Node number	Generator rating power (MW)	Load power (MW)
						1	0	550	10	9750	940
2	9360	840	11	7020	7000
						3	0	1540	12	0	1900
4	0	380	13	0	1100
						5	9880	6390	14	7020	320
6	0	1990	15	0	2000
						7	0	2130	16	6435	1320
8	0	880	17	0	4000
						9	0	2590	18	1846	0

Table 2 46 node system node parameter

Node number	Generator rating power (MW)	Load power (MW)	Node number	Generator rating power (MW)	Load power (MW)
						1	0	0	24	0	478.2
2	0	443.1	25	0	0
						3	0	0	26	0	231.9
4	0	300.7	27	70.2	0
						5	0	238	28	949	0
6	0	0	29	0	0
						7	0	0	30	0	0
8	0	72.2	31	403	0
						9	0	0	32	585	0
10	0	0	33	0	229.1
						11	0	0	34	287.3	0
12	0	511.9	35	0	216
						13	0	185.8	36	0	90.1
14	1227.2	0	37	275.6	0
						15	0	0	38	0	216
16	1775.8	0	39	287.3	0
						17	1500	0	40	0	262.1
18	0	0	41	0	0
						19	1004.9	0	42	0	1607.9
20	0	1091.2	43	0	0
						21	0	0	44	0	79.1
22	0	81.9	45	0	86.7
						23	0	458.1	46	778.7	0

Table 3 18 node system three optimization planning project plan comparison

Table 4 18 node system is gained programme when wind speed correlation is different

The 18 node system programmes that table 5 obtains under different wind energy turbine set access waies

Table 6 46 node system three programmes compare

Claims (4)

1. take into account the transmission system planing method containing large-scale wind power of available transmission capacity, its step is as follows:

1) on the basis considering correlation and circuit and generator failure probability between wind speed and load stochastic variable, structure available transmission capacity probabilistic model;

2) method adopting the Monte Carlo simulation of Latin Hypercube Sampling (LHS) and Sensitivity Analysis Method to combine, take into account correlation between wind speed and load stochastic variable and circuit and generator failure probability, solve the available transmission capacity probabilistic model of system under wind-electricity integration environment;

3) and available transmission capacity desired value minimum with transmission line cost of investment is target function to the maximum, the circuit overload probability allowed is constraints, set up transmission system Model for Multi-Objective Optimization, be converted into single-object problem afterwards and adopt genetic algorithm to solve.

2. transmission system planing method according to claim 1, it is characterized in that, step 2) detailed process as follows: first, the Monte Carlo simulation of Latin Hypercube Sampling is adopted to sample to the wind speed of input and load stochastic variable, then sort according to correlative relationship, obtain final sampling scene set; For each scene that Latin Hypercube Sampling obtains, adopt Sensitivity Analysis Method to calculate its available transmission capacity, obtain probability distribution and the Statistical Parameters of available transmission capacity eventually through statistical method.

3. transmission system planing method according to claim 2, is characterized in that, Sensitivity Analysis Method calculates its available transmission capacity and can be expressed as follows:

The general type of DC flow model is:

P＝Bθ (1)，

The trend p of branch road ij _ijcan be described as:

p _ij＝b _ij(θ _i-θ _j)＝b _ije _ijθ (2)，

Formula (1) is substituted into formula (2) can obtain:

p _ij＝b _ije _ijB ^-1P (3)，

In formula: B is system node admittance matrix; θ is node voltage phase angle vector; P is node injecting power vector; b _ijfor the susceptance value of branch road ij; p _ijfor the active power of branch road ij; e _ijfor the node associated line vector that branch road ij is corresponding, except i and j column element is respectively+1 and-1, all the other elements are 0; θ _iand θ _jbe respectively the voltage phase angle of node i and node j;

With power delivery distribution factor (PTDF) characterize send to increase unit active power between receiving end time each Branch Power Flow change:

S _ij＝b _ije _ijB ^-1β (4)，

In formula: S _ijfor the PTDF that branch road ij is corresponding; β is that m ties up power Distribution of A Sequence vector (m is nodes), represents the change sending each node injecting power when to increase unit transmission power between receiving end, for power supply node β _i>0 and ∑ _iβ _i=1, for load bus β _j<0 and ∑ _jβ _j=-1;

Every bar branch road ij has a maximum power transfer ability T _ij, T _ijnamely minimum branch road is the bottleneck branch road affecting ATC, the T that this branch road is corresponding _ijbe the ATC of system under current state:

$T_{\mathrm{ij}}=\left\{\begin{array}{cc}(\stackrel{\&OverBar;}{p_{\mathrm{ij}}}-p_{\mathrm{ij}})/S_{\mathrm{ij}}& S_{\mathrm{ij}}>0\\ \left(\underset{\&OverBar;}{p_{\mathrm{ij}}}{-p}_{\mathrm{ij}}\right)/S_{\mathrm{ij}}& S_{\mathrm{ij}}<0\end{array}\right.---\left(5\right),$

$\mathrm{ATC}=\underset{\mathrm{ij}\&Element;N_B}{\min }\left\{T_{\mathrm{ij}}\right\}---\left(6\right),$

In formula: for the transmission power higher limit of branch road ij, p _ij for the transmission power lower limit of branch road ij; N _bfor the branch road collection in system;

Afterwards, by sampling generating set state, transmission line status, output of wind electric field and load, scene x is tried to achieve _iunder ATC, after obtaining the ATC under N number of scene, adopt statistical analysis technique to obtain the probability distribution of ATC, also can obtain the probability distribution of circuit overload simultaneously;

Calculate the desired value E of ATC under N number of scene _aTC:

$E_{\mathrm{ATC}}=\frac{1}{N}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}\mathrm{ATC}\left(x_i\right)---\left(7\right).$

4. transmission system planing method according to claim 3, is characterized in that, step 3) detailed process as follows: to minimize Transmission Investment cost and to maximize available transmission capacity desired value as two optimization aim:

$f_1:\min C=\underset{l\&Element;N_B}{\mathrm{\&Sigma;}}{C}_l{Z}_l---\left(8\right),$

f ₂:max W＝E _ATC(9)，

In formula: C _lfor the single back line cost of branch road l, ten thousand yuan/km; Z _lfor the enlarging circuit number of branch road l; W is ATC desired value corresponding to programme;

To target function f ₁and f ₂carry out the merging treatment shown in formula (10), form the single-object problem of transmission system planning:

max W/C (10)，

s.t. Bθ＝P _W+P _G-P _L(11)，

$\underset{\&OverBar;}{p_{\mathrm{Gi}}}\&le;p_{\mathrm{Gi}}\&le;\stackrel{\&OverBar;}{p_{\mathrm{Gi}}}---\left(12\right),$

$\underset{\&OverBar;}{p_{\mathrm{Lj}}}\&le;p_{\mathrm{Lj}}\&le;\stackrel{\&OverBar;}{p_{\mathrm{Lj}}}---\left(13\right),$

$Z_l\&le;\stackrel{\&OverBar;}{Z_l}---\left(14\right),$

$r_l\&GreaterEqual;\underset{\&OverBar;}{r_l},r_l=p_r\left(\right|p_l|\&le;\stackrel{\&OverBar;}{p_l})---\left(15\right),$

Above-mentioned various in: P _gand P _lbe respectively the meritorious vector sum load power vector of exerting oneself of conventional generator; P _wfor wind energy turbine set is gained merit force vector; p _gifor the generated power of node i is exerted oneself, with p _gi be respectively its upper limit value and lower limit value; p _ljfor the load power of node j, with p _lj be respectively its upper limit value and lower limit value; for branch road l can extend number of lines; r _lwith r _l be respectively the not out-of-limit probability of trend of branch road l and given lower limit; p _r() is probable value; p _lwith be respectively trend and the higher limit thereof of branch road l.