CN104538953B - A kind of TCSC Optimal Configuration Method controlled based on Probabilistic Load Flow - Google Patents

Abstract

The invention discloses a kind of TCSC Optimal Configuration Method controlled based on Probabilistic Load Flow, be applied in power system, it is characterized in that carrying out as follows: 1 obtains initial data；2 obtain uncertain factor value；3 pairs of uncertain factor values are sampled and carry out Load flow calculation；4 obtain the loading rate on each branch road；5 choose loading rate exceedes the branch road of loading rate during heavy duty as the branch road installing TCSC；6 obtain string mends branch road；7 set up the optimal load flow model improved；Uncertain factor value is sampled and carries out optimal load flow calculating by 8 again；9 optimal values obtaining TCSC reactance value；10 string mend branch road place install reactance value be the TCSC of optimal value, thus realizing TCSC Optimal Configuration Method.The present invention can consider the impact of uncertain factor in power system the trend of more efficiently control power system more comprehensively, thus advantageously in the safe operation of power system.

Description

A kind of TCSC Optimal Configuration Method controlled based on Probabilistic Load Flow

Technical field

The present invention relates to Power System Analysis field, be specifically related to a kind of TCSC Optimal Configuration Method controlled based on Probabilistic Load Flow.

Background technology

There is substantial amounts of uncertain factor in power system, these uncertain factors can affect the properly functioning of power system, and electric power system dispatching planning personnel needs the impact considering uncertain factor to be scheduling and planning.Consider that actual uncertain factor is predicted analyzing on the one hand, it is simple to dispatcher finds the weak link in power system timely revised planning scheme；On the other hand, the weak link for finding takes effective method, regulates the adverse effect that power system is caused by uncertain factor.

TCSC refers to the series capacitor (ThyristorControlledSeriesCapacitor of thyristor control, it is called for short TCSC), TCSC can the equivalent reactance of transmission line of electricity that compensates of change rapidly, continuously, there is raising power system ability to transmit electricity, strengthen stability of power system, effectively control the advantages such as transmission line of electricity trend；TCSC constantly comes into one's own, and is applied in power system more and more.

At present, TCSC distributes technology rationally and the uncertain factor in power system is considered deficiency, the configuration scheme obtained can not tackle the impact of more uncertain factor, and additionally existing TCSC Optimal Configuration Method have ignored the uncertainty impact on this optimization allocation of the new forms of energy day by day increased.

At present in the problem of the installation site and installed capacity that select TCSC, TCSC is utilized often to improve the ability to transmit electricity of power system, improve Power System Voltage Stability, reduce network loss, minimize power system risk, reduce electric power deficiency expectation etc., but have ignored the many effects for controlling electric power system tide of TCSC, more consider the TCSC control to the out-of-limit situation of POWER SYSTEM STATE, have ignored the effect of not TCSC more in limited time.

Existing TCSC distributes the TCSC computation model adopted in technology rationally, often TCSC reactance value is combined with transmission line of electricity impedance or the impact of transmission line of electricity trend is transferred to circuit two ends thus simplifying calculating by TCSC, but, there is certain defect in both approaches, if according to former approach, show that TCSC is mounted in the middle part of transmission line of electricity, do not meet practical situation, be not easy to the management to TCSC and maintenance；If calculate the power attenuation that then can ignore TCSC according to latter method, physically do not meeting reality yet.

Summary of the invention

The present invention is the weak point overcoming above-mentioned prior art to exist, a kind of TCSC Optimal Configuration Method controlled based on Probabilistic Load Flow is provided, to considering the impact of uncertain factor in power system more comprehensively, and the trend of more efficiently control power system, thus advantageously in the safe operation of power system.

The present invention solves that technical problem adopts the following technical scheme that:

A kind of TCSC Optimal Configuration Method controlled based on Probabilistic Load Flow of the present invention, it is be applied in power system, described power system is to be produced electric energy by each generating equipment and be collected on each bus, electric energy is provided to electrical equipment and to transmit electric energy extremely each transmission line of electricity by described each bus, through the transmission of described each transmission line of electricity, described electric energy is delivered to the electrical equipment on each distribution bus；Described each bus and each distribution bus are designated as node t；It is t by described node t number consecutively₁,t₂,…,t_i,…t_N；t_iRepresent i-th node；1≤i≤N；N represents the sum of described node；Described each transmission line of electricity is designated as branch road L；It is L by described branch road L number consecutively₁,L₂,…,L_k,…,L_K；L_kRepresent kth branch road；1≤k≤K；K represents the sum of described branch road；Electrical equipment on described each node t is designated as D₁,D₂,…,D_i,…D_N；D_iRepresent i-th node t_iOn electrical equipment；Described generating equipment includes conventional power generation usage unit and wind power generating set and photovoltaic power generation equipment；Conventional power generation usage unit on described each node t is designated as G₁,G₂,…,G_i,…G_N；G_iRepresent i-th node t_iOn conventional power generation usage unit；Wind power generating set on described each node t is designated as W₁,W₂,…,W_i,…W_N；W_iRepresent i-th node t_iOn wind power generating set；Photovoltaic power generation equipment on described each node t is designated as S₁,S₂,…,S_i,…S_N；S_iRepresent i-th node t_iOn photovoltaic power generation equipment；

It is characterized in: described Optimal Configuration Method is to carry out as follows:

Step 1, acquisition initial data:

Obtain from described power system by i-th node t_iOn node data and kth branch road L_kOn branch data constitute initial data；Thus the initial data on the initial data obtained on N number of node and K branch road；

Described i-th node t_iOn node data include: i-th node t_iOn voltage magnitude V_i, i-th node t_iOn voltage phase angle θ_i；I-th node t_iOn conventional power generation usage unit G_iActive powerI-th node t_iOn conventional power generation usage unit G_iReactive powerI-th node t_iOn conventional power generation usage unit G_iFault rateI-th node t_iOn wind power generating set W_iSpecified active powerI-th node t_iOn wind power generating set W_iPower-factor angleI-th node t_iOn photovoltaic power generation equipment S_iThe illumination gross areaI-th node t_iOn photovoltaic power generation equipment S_iPhotoelectric transformation efficiencyI-th node t_iOn photovoltaic power generation equipment S_iPower-factor angleI-th node t_iOn the average of active power of electrical equipmentI-th node t_iOn the variance of active power of electrical equipmentI-th node t_iOn the power-factor angle of electrical equipment

Described kth branch road L_kOn branch data include: kth branch road L_kOn impedance Z_k；Kth branch road L_kOn the maximum active-power P that passes through of permission_{max_k}；Kth branch road L_kFault rate

Step 2, employing Monte Carlo sampling approach obtain the value of uncertain factor；Described uncertain factor includes: the running status of the actual active power of conventional power generation usage unit and actual reactive power, the active power of wind power generating set and reactive power, the active power of photovoltaic power generation equipment and reactive power, the active power of electrical equipment and reactive power and branch road；

Step 3, adopt described Monte Carlo sampling approach that the value of described uncertain factor carries out Num sampling, and described power system is carried out Num Load flow calculation, it is thus achieved that the effective power flow on described K branch road Represent the effective power flow on K the branch road that n-th Load flow calculation obtains；1≤n≤Num；

Step 4, formula (1) is utilized to obtain described kth branch road L_kLoading rateThus the loading rate obtained on described K branch road:

${\mathrm{\ξ}}_{L_k}=\frac{\left|\frac{1}{Num}\underset{n=1}{\overset{Num}{\mathrm{\Σ}}}{P}_{L_k}^n\right|}{P_{\max \_k}}\×100\%---\left(1\right)$

Step 5, definitionRepresent kth branch road L_kReach loading rate during heavy duty；When meetingTime, choose kth branch road L_kAs the branch road installing TCSC；

Step 6, assume and described kth branch road L_kTwo node respectively t that two ends are connected_aAnd t_b；At described kth branch road L_kOn, branch impedance Z_kIt is in described node t_aWith node t_bBetween, at described branch impedance Z_kWith described node t_aBetween or described branch impedance Z_kWith described node t_bBetween described TCSC is installed, thus at described branch impedance Z_kAnd form string between described TCSC and mend node t_τ；Node t is mended at described string_τWith described node t_aBetween or described string mend node t_τWith described node t_bBetween formed string mend branch road L_u；

The optimal load flow model that step 7, foundation improve；

Step 8, optimal load flow model according to described improvement, adopt described Monte Carlo sampling approach that described uncertain factor value carries out Num sampling again, and described power system carry out Num suboptimum Load flow calculation, it is thus achieved that the reactance value x={x of described TCSC¹,x²,…,xⁿ,…,x^Num}；

Step 9, formula (2) is utilized to obtain the optimal value of reactance value x of described TCSC

$\stackrel{\‾}{x}=\frac{1}{Num}\underset{n=1}{\overset{Num}{\mathrm{\Σ}}}{x}^n---\left(2\right)$

Step 10, at described kth branch road L_kBranch impedance Z_kWith described node t_aBetween install reactance value beTCSC, thus realizing TCSC Optimal Configuration Method.

The TCSC Optimal Configuration Method controlled based on Probabilistic Load Flow of the present invention, its feature lies also in,

Described step 2 is to carry out as follows:

Step 2.1, acquisition i-th node t_iOn conventional power generation usage unit G_iActual active powerWith i-th node t_iOn conventional power generation usage unit G_iActual reactive powerThus the actual active power of the conventional power generation usage unit obtained on N number of node and actual reactive power:

Step 2.1.1, generate equally distributed random number between N number of obedience 0～1, be designated as ε₁,ε₂,…,ε_i,…ε_N；ε_iRepresent i-th random number；

Step 2.1.2, formula (3) is utilized to obtain described i-th node t_iOn conventional power generation usage unit G_iActual active powerWith i-th node t_iOn conventional power generation usage unit G_iActual reactive powerThus the actual active power of the conventional power generation usage unit obtained on N number of node and actual reactive power:

$\[P_{G_i}^{\′},Q_{G_i}^{\′}\]=\left\{\begin{array}{c}\[P_{G_i},Q_{G_i}\],{\mathrm{\ϵ}}_i>{\mathrm{\λ}}_{G_i}\\ \[0,0\],{\mathrm{\ϵ}}_i\≤{\mathrm{\λ}}_{G_i}\end{array}\right.---\left(3\right)$

Step 2.2, acquisition i-th node t_iOn wind power generating set W_iActive powerWith i-th node t_iOn wind power generating set W_iReactive powerThus the active power of the wind power generating set obtained on N number of node and reactive power:

Step 2.2.1, generate N number of obedience independent standard normal distribution random number；

Step 2.2.2, the method for orthogonal transformation and semiempirical formula is adopted to process the random number of described N number of obedience independent standard normal distribution, it is thus achieved that N number of obedience Weibull distribution and have the stochastic variable of dependency；

Step 2.2.3, make i-th node t_iOn wind power generating set W_iWind speed v_iEqual to described N number of obedience Weibull distribution and the i-th variable during there is the stochastic variable of dependency；Thus the wind speed of the wind power generating set obtained on N number of node；

Step 2.2.4, formula (4) is utilized to obtain described i-th node t_iOn wind power generating set W_iActive powerThus the active power of the wind power generating set obtained on N number of node:

$P_{W_i}=\left\{\begin{array}{cc}0& v_i<v_{c\_i},v_i>v_{o\_i}\\ \frac{v_i^{m_i}-v_{c\_i}^{m_i}}{v_{r\_i}^{m_i}-v_{c\_i}^{m_i}}{P}_{r\_W_i}& v_{c\_i}\&le;v_i<v_{r\_i}\\ P_{r\_W_i}& v_{r\_i}\&le;v_i\&le;v_{o\_i}\end{array}\right.---\left(4\right)$

In formula (4), v_{c_i}Represent i-th node t_iOn wind power generating set W_iIncision wind speed, v_{o_i}Represent i-th node t_iOn wind power generating set W_iCut-out wind speed, v_{r_i}Represent i-th node t_iOn wind power generating set W_iRated wind speed, m_iRepresent i-th node t_iOn wind power generating set W_iThe coefficient being asked for active power by wind speed；

Step 2.2.5, formula (5) is utilized to obtain described i-th node t_iOn wind power generating set W_iReactive powerThus the reactive power of the wind power generating set obtained on N number of node:

Step 2.3, acquisition i-th node t_iOn photovoltaic power generation equipment S_iActive powerWith i-th node t_iOn photovoltaic power generation equipment S_iReactive powerThus the active power of the photovoltaic power generation equipment obtained on N number of node and reactive power:

Step 2.3.1, generate N number of obedience beta distribution random number；

Step 2.3.2, make i-th node t_iOn photovoltaic power generation equipment S_iIntensity of illuminationEqual to the i-th random number in the random number of described N number of obedience beta distribution；Thus the intensity of illumination of the photovoltaic power generation equipment obtained on N number of node；

Step 2.3.3, formula (6) is utilized to obtain described i-th node t_iOn photovoltaic power generation equipment S_iActive powerWith i-th node t_iOn photovoltaic power generation equipment S_iReactive powerThus the active power of the photovoltaic power generation equipment obtained on N number of node and reactive power:

Step 2.4, acquisition i-th node t_iOn the active power of electrical equipmentWith i-th node t_iOn the reactive power of electrical equipmentThus the active power of the electrical equipment obtained on N number of node and reactive power:

Step 2.4.1, again generate the random number that N number of obedience separate standards is just being distributed very much；

Step 2.4.2, adopt the method for orthogonal transformation process described in the random number that is just being distributed very much of N number of obedience separate standards of again generating, obtain N number of normal random variable with dependency, in described N number of normal random variable with dependency, the average of i-th stochastic variable isAnd i-th variance of a random variable is

Step 2.4.3, make i-th node t_iOn the active power of electrical equipmentI-th stochastic variable equal to the N number of stochastic variable in described step 2.4.2；Thus the active power of the electrical equipment obtained on N number of node；

Step 2.4.4, utilize formula (7) obtain i-th node t_iOn the reactive power of electrical equipmentThus the reactive power of the electrical equipment obtained on N number of node:

Step 2.5, acquisition kth branch road L_kRunning status；Thus obtaining the running status of K branch road, described running status is normal condition or malfunction:

Step 2.5.1, again generation K obey equally distributed random number between 0～1；

The kth random number that K again generated described in step 2.5.2, comparison obeys between 0～1 in equally distributed random number and described kth branch road L_kFault rateSize, if described again generate K the kth random number obeyed between 0～1 in equally distributed random number is more than described kth branch road L_kFault rateThen kth branch road L_kRunning status be normal condition；If described K the kth random number obeyed between 0～1 in equally distributed random number again generated is less than or equal to described kth branch road L_kFault rateThen kth branch road L_kRunning status be malfunction；Thus obtaining kth branch road L_kRunning status；And then obtain the running status of K branch road.

Described step 7 is to carry out as follows:

Assume at described kth branch road L_kBranch impedance Z_kWith described node t_aBetween described TCSC is installed；Formula (8)-Shi (14) is utilized to obtain the optimal load flow model improved:

Object function:

Equality constraint:

$P_{set\_L_u}-\frac{V_a{V}_{\mathrm{\&tau;}}{\mathrm{sin\&theta;}}_{a\mathrm{\&tau;}}}{x}=0---\left(10\right)$

Inequality constraints: χ_{min_j}≤χ_j≤χ_{max_j}(11)

$-P_{max\_l}\&le;P_{L_l}\&le;P_{max\_l},l\&Element;\{1,2,...,K\}---\left(12\right)$

x_min≤x≤x_max(13)

$\max \{{\mathrm{\&eta;}}_{\min }{P}_{\max \_k},\frac{V_a{V}_{\mathrm{\&tau;}}{\mathrm{sin\&theta;}}_{a\mathrm{\&tau;}}}{x_{\max }}\}\&le;P_{set\_L_u}\&le;\min \{{\mathrm{\&eta;}}_{\max }{P}_{\max \_k},\frac{V_a{V}_{\mathrm{\&tau;}}{\mathrm{sin\&theta;}}_{\mathrm{\&alpha;}\mathrm{\&tau;}}}{x_{\min }}\}---\left(14\right)$

Formula (8) represents the operating cost and the TCSC cost of investment that minimize power system；Represent i-th node t respectively_iOn conventional power generation usage unit G_iOperating cost coefficient, α₀、α₁、α₂Representing the cost coefficient of described TCSC respectively, s represents the reactive power value flowing through described TCSC, and I represents the current amplitude flowing through described TCSC, and x represents the reactance value of described TCSC；

Formula (9) represents jth node t_jPower balance equation, Δ P_jRepresent described jth node t_jMeritorious amount of unbalance；ΔQ_jRepresent described jth node t_jIdle amount of unbalance；P_jRepresent described jth node t_jNode active power, Q_jRepresent described jth node t_jNode reactive power；

Formula (10) represents mends branch road L by string_uEffective power flowControl isRepresent that described string mends branch road L_uEffective power flow expected value, θ_aτRepresent a node t_aVoltage phase angle and the τ node t_τPhase difference of voltage；

Formula (11) represents jth node t_jVariable χ_jSpan, and haveχ_{min_j}And χ_{max_j}Respectively variable χ_jMinima and maximum, and have V_{max_j}And V_{min_j}Represent jth node t respectively_jMaximum voltage amplitude and minimum voltage amplitude, θ_{max_j}And θ_{min_j}Represent jth node t respectively_jMaximum voltage phase angle and minimum voltage phase angle,WithRepresent jth node t respectively_jOn conventional power generation usage unit G_jMaximum active power and minimum active power,WithRepresent jth node t respectively_jOn conventional power generation usage unit G_jMaximum reactive power and minimum reactive power；

Formula (12) represents the l branch road L_lOn effective power flowNot can exceed that the maximum effective power flow P of permission_{max_l}；

Formula (13) represents the span of the reactance value x of described TCSC；x_maxAnd x_minRepresent the maximum reactance value of described TCSC and minimum reactance value respectively；

Formula (14) represents that described string mends branch road L_uEffective power flow expected valueSpan；η_maxAnd η_minRepresent that described string mends branch road L respectively_uEffective power flow expected valueWith described kth branch road L_kThe maximum effective power flow P allowed_{max_k}Between the maximum of ratio and minima.

Compared with the prior art, beneficial effects of the present invention is embodied in:

1, the present invention controls the angle of electric power system tide from TCSC, consider the multiformity of uncertain factor in power system, the object function of the cost of investment of TCSC has been taken into account while utilizing TCSC load disturbance, practical situation can be more fully reflected with this TCSC configuration scheme selected, TCSC can be utilized to improve the adverse effect that system load flow is caused by uncertain factor, electric power system tide is controlled in safer scope；Result according to Load flow calculation installs TCSC, installs TCSC in time, be beneficial to power system and run more safely before power system is had a negative impact by uncertain factor；The formulation of the scheduling planning scheme of power system is also had actual reference by the method for the present invention.

2, the present invention considers the multiformity of uncertain factor in power system, consider the random fault of conventional generator in the power system containing generation of electricity by new energy, the randomness of the wind speed that Wind turbines obtains, the uncertainty of the intensity of illumination that photovoltaic generation obtains, the random fluctuation of electrical equipment power and the random fault of transmission line of electricity, overcome existing TCSC and distribute the shortcoming that uncertain factor in power system is considered deficiency by technology rationally, it is thus possible to react the impact of actual uncertain factor more comprehensively, the needs of reality " uncertain " more can be met with this TCSC prioritization scheme selected, can better suitable in power system.

3, the present invention considers the wind speed of Wind turbines and has a dependency and electrical equipment power also has the feature of dependency, take into full account that the wind speed of reality will not be completely independent and actual power load can interactive situation, overcome existing TCSC and distribute the problem lacking this dependency of consideration in technology rationally, so that the technology of the present invention more meets reality.

4, the present invention is from the angle Selection TCSC installation site of power flowcontrol, utilize heavily loaded branch road that the numerous uncertain impact of method choice of Probabilistic Load Flow causes as the installation site of TCSC, overcome existing selection TCSC installation site technology and have ignored the TCSC shortcoming to concrete Branch Power Flow control action, more take into full account the flexible load disturbance ability of TCSC, selected to consider that numerous uncertain factors makes the selection more reasonability of installation site during installation site simultaneously.

5, the present invention adopts the mode that TCSC is arranged on branch road one end, increase node and branch road, overcome existing TCSC and distribute the defect adopting mode TCSC reactance value being combined or the impact of transmission line of electricity trend is transferred to by TCSC circuit two ends with transmission line of electricity impedance in technology rationally, it is contemplated that the power attenuation of TCSC, physical arrangement more meets reality, according to the mode of the present invention, TCSC is arranged on the side of transmission line of electricity, the be more convenient for management to TCSC and maintenance.

6, on the present invention previously determined TCSC addressing basis, adopt probability optimal load flow model, take into account the power flowcontrol effect of TCSC and optimize the cost of investment of TCSC, the installed capacity of TCSC is optimized, overcome in existing selection TCSC installed capacity technology and lack consideration TCSC to concrete Branch Power Flow control action, electric power system tide can be controlled in safer scope, optimization of investment cost simultaneously, selects to consider during installed capacity that numerous uncertain factors makes the installed capacity selected more correspond to actual needs simultaneously.

7, the present invention optimizes the angle of TCSC value from controlling brancher trend in safety range simultaneously, selects effective power flow expected valueSpan, and define corresponding inequality constraints, overcome and prior art lacks the weak point that TCSC controls goal setting, it is possible to branch road power flow control is optimized in safety range the value of TCSC simultaneously.

Accompanying drawing explanation

Fig. 1 is the flow chart of the present invention；

Fig. 2 is transmission line of electricity Π shape Equivalent Model in prior art；

Fig. 3 is the TCSC series arm illustraton of model adopted in the present invention；

Fig. 4 is the structure chart of TCSC in prior art.

Detailed description of the invention

In the present embodiment, a kind of TCSC Optimal Configuration Method controlled based on Probabilistic Load Flow, it is be applied in power system, power system is to be produced electric energy by each generating equipment and be collected on each bus, electric energy is provided to electrical equipment and to transmit electric energy extremely each transmission line of electricity by each bus, through the transmission of each transmission line of electricity, electric energy is delivered to the electrical equipment on each distribution bus；Each bus and each distribution bus are designated as node t；It is t by node t number consecutively₁,t₂,…,t_i,…t_N；t_iRepresent i-th node；1≤i≤N；N represents the sum of node；Each transmission line of electricity is designated as branch road L；It is L by branch road L number consecutively₁,L₂,…,L_k,…,L_K；L_kRepresent kth branch road；1≤k≤K；K represents the sum of branch road；Electrical equipment on each node t is designated as D₁,D₂,…,D_i,…D_N；D_iRepresent i-th node t_iOn electrical equipment；Generating equipment includes conventional power generation usage unit and wind power generating set and photovoltaic power generation equipment；Conventional power generation usage unit on each node t is designated as G₁,G₂,…,G_i,…G_N；G_iRepresent i-th node t_iOn conventional power generation usage unit；Wind power generating set on each node t is designated as W₁,W₂,…,W_i,…W_N；W_iRepresent i-th node t_iOn wind power generating set；Photovoltaic power generation equipment on each node t is designated as S₁,S₂,…,S_i,…S_N；S_iRepresent i-th node t_iOn photovoltaic power generation equipment；

As it is shown in figure 1, TCSC Optimal Configuration Method is to carry out as follows:

Step 1, acquisition initial data:

Obtain from power system by i-th node t_iOn node data and kth branch road L_kOn branch data constitute initial data；Thus the initial data on the initial data obtained on N number of node and K branch road；

I-th node t_iOn node data include: i-th node t_iOn voltage magnitude V_i, i-th node t_iOn voltage phase angle θ_i；I-th node t_iOn conventional power generation usage unit G_iActive powerI-th node t_iOn conventional power generation usage unit G_iReactive powerI-th node t_iOn conventional power generation usage unit G_iFault rateI-th node t_iOn wind power generating set W_iSpecified active powerI-th node t_iOn wind power generating set W_iPower-factor angleI-th node t_iOn photovoltaic power generation equipment S_iThe illumination gross areaI-th node t_iOn photovoltaic power generation equipment S_iPhotoelectric transformation efficiencyI-th node t_iOn photovoltaic power generation equipment S_iPower-factor angleI-th node t_iOn the average of active power of electrical equipmentI-th node t_iOn the variance of active power of electrical equipmentI-th node t_iOn the power-factor angle of electrical equipment

Power system is not each node is connected to generating equipment: conventional power generation usage unit, wind power generating set, photovoltaic power generation equipment and electrical equipment, node for being connected to these equipment then obtains corresponding device data on the voltage magnitude of this node, voltage phase angle and this node, and the node for not being connected to these equipment then obtains the voltage magnitude of this node, voltage phase angle and corresponding device data is set to zero.So it is easy on the whole data be integrated and process；

Kth branch road L_kOn branch data include: kth branch road L_kOn impedance Z_k；Kth branch road L_kOn the maximum active-power P that passes through of permission_{max_k}；Kth branch road L_kFault rate

Transmission line of electricity is by the impact of self-bearing capacity, environmental factors and Operation of Electric Systems situation so that be restricted by the active power of transmission line of electricity, and it allows the maximum active power passed through to be designated as P_{max_k}；

Step 2, adopt Monte Carlo sampling approach to obtain the value of uncertain factor: uncertain factor refers to the factor with randomness existed in practical power systems, for instance the random fault of conventional power generation usage unit, the randomness of wind speed, the uncertainty of intensity of illumination, the power random fluctuation of electrical equipment, transmission line of electricity random fault；In the present embodiment, uncertain factor includes: the running status of the actual active power of conventional power generation usage unit and actual reactive power, the active power of wind power generating set and reactive power, the active power of photovoltaic power generation equipment and reactive power, the active power of electrical equipment and reactive power and branch road；

DSMC is prior art, also known as stochastic sampling technology or statistical test method, it it is a kind of Method of Stochastic, based on probability and theory of statistics, the problem solved is associated with certain probabilistic model, solve computational problem with random number, be obtained in that the value of the substantial amounts of uncertain factor with randomness with DSMC sampling, it is possible to describe the actual features of the things with random nature；

Step 2.1, acquisition i-th node t_iOn conventional power generation usage unit G_iActual active powerWith i-th node t_iOn conventional power generation usage unit G_iActual reactive powerThus the actual active power of the conventional power generation usage unit obtained on N number of node and actual reactive power:

Step 2.1.1, generate equally distributed random number between N number of obedience 0～1, be designated as ε₁,ε₂,…,ε_i,…ε_N；ε_iRepresent i-th random number；

Step 2.1.2, make i-th node t_iOn conventional power generation usage unit G_iRunning status judge that number is the i-th random number ε between N number of obedience 0～1 in equally distributed random number_i；Thus the running status of the conventional power generation usage unit obtained on N number of node judges number；

Step 2.1.3, utilize formula (1) obtain i-th node t_iOn conventional power generation usage unit G_iActual active powerWith i-th node t_iOn conventional power generation usage unit G_iActual reactive powerThus the actual active power of the conventional power generation usage unit obtained on N number of node and actual reactive power:

$\&lsqb;P_{G_i}^{\&prime;},Q_{G_i}^{\&prime;}\&rsqb;=\left\{\begin{array}{c}\&lsqb;P_{G_i},Q_{G_i}\&rsqb;,{\mathrm{\&epsiv;}}_i>{\mathrm{\&lambda;}}_{G_i}\\ \&lsqb;0,0\&rsqb;,{\mathrm{\&epsiv;}}_i\&le;{\mathrm{\&lambda;}}_{G_i}\end{array}\right.---\left(1\right)$

The random number that formula (1) expression produces judges the running status of conventional power generation usage unit, obtains active power and the reactive power of conventional power generation usage unit: as i-th node t simultaneously_iOn conventional power generation usage unit G_iRunning status judge number ε_iMore than i-th node t_iOn conventional power generation usage unit G_iFault rateTime, it was shown that i-th node t_iOn conventional power generation usage unit G_iProperly functioning；Work as ε_iLess than or equal toTime show i-th node t_iOn conventional power generation usage unit G_iFault, its active power and reactive power are zero；

Step 2.2, acquisition i-th node t_iOn wind power generating set W_iActive powerWith i-th node t_iOn wind power generating set W_iReactive powerThus the active power of the wind power generating set obtained on N number of node and reactive power:

Step 2.2.1, generate N number of obedience independent standard normal distribution random number；

Step 2.2.2, the method for orthogonal transformation and semiempirical formula is adopted to process the random number of N number of obedience independent standard normal distribution, it is thus achieved that N number of obedience Weibull distribution and have the stochastic variable of dependency；

Orthogonal transformation and semiempirical formula are the mathematical method of employing, and change of variable independent mutually can be become the variable that there is certain association each other by orthogonal transformation, and semiempirical formula gives the transformational relation between different distributions function；

Step 2.2.3, make i-th node t_iOn wind power generating set W_iWind speed v_iEqual to N number of obedience Weibull distribution and the i-th variable during there is the stochastic variable of dependency；Thus the wind speed of the wind power generating set obtained on N number of node；

It is theory of probability and mathematical statistics as a result, it is possible to describe the regularity of distribution of actual wind speed that wind speed obeys Weibull distribution；There is certain contact between the wind speed of the different wind energy turbine set that actual location is closed on, on the different node of this contact, the dependency of wind speed describes；

Step 2.2.4, utilize formula (2) obtain i-th node t_iOn wind power generating set W_iActive powerThus the active power of the wind power generating set obtained on N number of node:

$P_{W_i}=\left\{\begin{array}{cc}0& v_i<v_{c\_i},v_i>v_{o\_i}\\ \frac{v_i^{m_i}-v_{c\_i}^{m_i}}{v_{r\_i}^{m_i}-v_{c\_i}^{m_i}}{P}_{r\_W_i}& v_{c\_i}\&le;v_i<v_{r\_i}\\ P_{r\_W_i}& v_{r\_i}\&le;v_i\&le;v_{o\_i}\end{array}\right.---\left(2\right)$

Formula (2) represents the relation between output and the wind speed of wind-driven generator；

In formula (2), v_{c_i}Represent i-th node t_iOn wind power generating set W_iIncision wind speed, v_{o_i}Represent i-th node t_iOn wind power generating set W_iCut-out wind speed, v_{r_i}Represent i-th node t_iOn wind power generating set W_iRated wind speed, m_iRepresent i-th node t_iOn wind power generating set W_iThe coefficient being asked for active power by wind speed；

Step 2.2.5, utilize formula (3) obtain i-th node t_iOn wind power generating set W_iReactive powerThus the reactive power of the wind power generating set obtained on N number of node:

Step 2.3, acquisition i-th node t_iOn photovoltaic power generation equipment S_iActive powerWith i-th node t_iOn photovoltaic power generation equipment S_iReactive powerThus the active power of the photovoltaic power generation equipment obtained on N number of node and reactive power:

Step 2.3.1, generate N number of obedience beta distribution random number；

Step 2.3.2, make i-th node t_iOn photovoltaic power generation equipment S_iIntensity of illuminationEqual to the i-th random number in the random number of N number of obedience beta distribution；Thus the intensity of illumination of the photovoltaic power generation equipment obtained on N number of node；

It is theory of probability and mathematical statistics as a result, it is possible to describe the regularity of distribution of actual intensity of illumination that intensity of illumination obeys beta distribution；

Step 2.3.3, utilize formula (4) obtain i-th node t_iOn photovoltaic power generation equipment S_iActive powerWith i-th node t_iOn photovoltaic power generation equipment S_iReactive powerThus the active power of the photovoltaic power generation equipment obtained on N number of node and reactive power:

The core of photovoltaic power generation equipment is solar panel, and solar panel obtains illumination and is converted into electric energy, but energy exists certain loss, it is impossible to luminous energy is completely converted into electric energy, so there is photoelectric transformation efficiency

Step 2.4, acquisition i-th node t_iOn the active power of electrical equipmentWith i-th node t_iOn the reactive power of electrical equipmentThus the active power of the electrical equipment obtained on N number of node and reactive power:

Step 2.4.1, again generate the random number that N number of obedience separate standards is just being distributed very much；

Step 2.4.2, the method for orthogonal transformation is adopted to process the random number that N number of obedience separate standards of again generating just is being distributed very much, obtain N number of normal random variable with dependency, in N number of normal random variable with dependency, the average of i-th stochastic variable isAnd i-th variance of a random variable is

Step 2.4.3, make i-th node t_iOn the active power of electrical equipmentThe i-th stochastic variable of N number of stochastic variable equal in step 2.4.2；Thus the active power of the electrical equipment obtained on N number of node；

Step 2.4.4, utilize formula (5) obtain i-th node t_iOn the reactive power of electrical equipmentThus the reactive power of the electrical equipment obtained on N number of node:

Actual power load power Normal Distribution is the result of theory of probability and mathematical statistics, there is reciprocal influence between different power loads, describes with the dependency of electrical equipment power on different nodes；

Step 2.5, acquisition kth branch road L_kRunning status；Thus obtaining the running status of K branch road, running status is normal condition or malfunction:

Step 2.5.1, again generation K obey equally distributed random number between 0～1；

Kth random number in equally distributed random number and kth branch road L between step 2.5.2, K more again generated obedience 0～1_kFault rateSize, if again generate K the kth random number obeyed between 0～1 in equally distributed random number is more than kth branch road L_kFault rateThen kth branch road L_kRunning status be normal condition；If K again the generated kth random number obeyed between 0～1 in equally distributed random number is less than or equal to kth branch road L_kFault rateThen kth branch road L_kRunning status be malfunction；Thus obtaining kth branch road L_kRunning status；And then obtain the running status of K branch road.

Step 3, adopt the Monte Carlo sampling approach of step 2 to carry out probabilistic load flow, namely the value of the uncertain factor in step 2 is carried out Num time and samples, and power system is carried out Num Load flow calculation, it is thus achieved that the effective power flow on K branch road Represent the effective power flow on K the branch road that n-th Load flow calculation obtains；1≤n≤Num；

It is research that electric power system tide calculatesPower system mesomeric stateThe a kind of basic of ruuning situation electrically calculates, and calculates, according to given service condition and network structure, the running status obtaining whole system: the voltage (amplitude and phase angle) on each bus, the power of each branch road and power attenuation etc..The result that electric power system tide calculates is the basis of Model for Stability Calculation of Power System and accident analysis；

Probabilistic load flow is to uncertain factor multiple sampling and carries out repeatedly Load flow calculation；Probabilistic Load Flow is just suggested as far back as 1974, probabilistic load flow can take into account the impact on electric power system tide of the random disturbance in Operation of Electric Systems process or uncertain factor, the result calculated is no longer that some determines value, but the value of a series of change, the situation of change of actual trend (node voltage, Branch Power Flow and loss etc.) can be described, it is possible to try to achieve the expectation of result, variance, probability density and distribution function etc.；

Frequency in sampling Num is more big, it is thus achieved that result of calculation more good, more meet reality；

Step 4, utilize formula (6) obtain kth branch road L_kLoading rateThus the loading rate obtained on K branch road:

${\mathrm{\&xi;}}_{L_k}=\frac{\left|\frac{1}{Num}\underset{n=1}{\overset{Num}{\mathrm{\&Sigma;}}}{P}_{L_k}^n\right|}{P_{\max \_k}}\&times;100\%---\left(6\right)$

Due to the impact of uncertain factor, branch road effective power flow is no longer a value determined, the expected value of the branch road effective power flow result obtained with probabilistic load flow characterizes the actual effective power flow of branch road；And the absolute value of the maximum effective power flow ratio that the actual effective power flow of branch road loading rate branch road allows with this branch road describes, i.e. formula (6)；

Step 5, definitionRepresent kth branch road L_kReach loading rate during heavy duty；When meetingTime, choose kth branch road L_kAs the branch road installing TCSC；

Branch road heavy duty refers to that branch circuit load allows upper loading limit not less than it but load is very big, is generally obtained branch road heavy duty scope by practical application, and system safety operation is often had adverse influence by branch road heavy duty；

Whether heavily loaded describing branch road by loading rate size, loading rate value is called overload more than 1, it was shown that load exceedes it and allows upper loading limit, and loading rate exceedesAnd show as heavy duty less than 1；

When simulating actual uncertain factor, large change can be there is in calculated Branch Power Flow, select heavy duty even transship branch road be in practical power systems running occur heavy duty or overload maximum probability branch road, select to install TCSC on this branch road, it is possible to utilize TCSC effectively to be controlled when power system is had a negative impact by uncertain factor.

Loading rate during branch road heavy dutyBy the restriction (heat restriction, pulling force restriction, sag restriction) of transmission line of electricity physical condition, relevant to the physical state of transmission line of electricity and weather environment；On the other hand by the restriction of the restriction of node voltage level and static system angle stability, it is subject to the restriction of service condition (sending receiving end grid strength, voltage support ability etc.), is subject to the restriction of transmission cross-section delivering power ability.

Step 6, assume and kth branch road L_kTwo node respectively t that two ends are connected_aAnd t_b；As in figure 2 it is shown, kth branch road L_kAdopt the Π shape mathematical model of transmission line of electricity, Y_kRepresent kth branch road L_kOn admittance over the ground,Represent kth branch road L_kOn admittance Y over the ground_kDimidiation is parallel to kth branch road L respectively_kBoth sides, at kth branch road L_kOn, branch impedance Z_kIt is in node t_aWith node t_bBetween；

At branch impedance Z_kWith node t_aBetween or branch impedance Z_kWith node t_bBetween TCSC is installed, thus at branch impedance Z_kAnd form string between TCSC and mend node t_τ；Node t is mended at string_τWith node t_aBetween or string mend node t_τWith node t_bBetween formed string mend branch road L_u；

TCSC is arranged on the branch road side near node, it is possible to the bus of TCSC Yu branch road side is placed in same electric substation, it is simple to management and the maintenance to TCSC；Increasing node and branch road, physical arrangement more meets reality, and will not neglect the power attenuation of TCSC；

Step 7, as shown in Figure 3, it is assumed that at kth branch road L_kBranch impedance Z_kWith node t_aBetween install TCSC；Arrow on TCSC figure represents that the reactance value of TCSC can regulate change；Formula (7)-Shi (13) is utilized to obtain the optimal load flow model improved:

Optimal load flow refers to when the structural parameters of power system and load condition etc. are all to timing, regulate available control variable (such as generated output power etc.) to find and can meet all operation constraints, and make the electric power system tide distribution that a certain performance indications (such as cost of electricity-generating or via net loss) of power system reach under optimal value；

The form of optimal load flow model mathematical model describes each and runs constraints and the performance indications to reach；

The optimal load flow model improved, describes to minimize Operation of Electric Systems cost and TCSC cost of investment and control string and mends branch road L_uEffective power flow less than heavy duty purpose, describe the restriction of each constraints simultaneously:

Object function:

Equality constraint:

$P_{set\_L_u}-\frac{V_a{V}_{\mathrm{\&tau;}}{\mathrm{sin\&theta;}}_{a\mathrm{\&tau;}}}{x}=0---\left(9\right)$

Inequality constraints: χ_{min_j}≤χ_j≤χ_{max_j}(10)

$-P_{max\_l}\&le;P_{L_l}\&le;P_{max\_l},l\&Element;\{1,2,...,K\}---\left(11\right)$

x_min≤x≤x_max(12)

$\max \{{\mathrm{\&eta;}}_{\min }{P}_{\max \_k},\frac{V_a{V}_{\mathrm{\&tau;}}{\mathrm{sin\&theta;}}_{a\mathrm{\&tau;}}}{x_{\max }}\}\&le;P_{set\_L_u}\&le;\min \{{\mathrm{\&eta;}}_{\max }{P}_{\max \_k},\frac{V_a{V}_{\mathrm{\&tau;}}{\mathrm{sin\&theta;}}_{\mathrm{\&alpha;}\mathrm{\&tau;}}}{x_{\min }}\}---\left(13\right)$

Formula (7) represents the operating cost and the TCSC cost of investment that minimize power system；Represent i-th node t respectively_iOn conventional power generation usage unit G_iOperating cost coefficient, α₀、α₁、α₂Representing the cost coefficient of TCSC respectively, s represents the reactive power value flowing through TCSC, and I represents the current amplitude flowing through TCSC, and x represents the reactance value of TCSC；

Formula (8) represents jth node t_jPower balance equation, Δ P_jRepresent jth node t_jMeritorious amount of unbalance；ΔQ_jRepresent jth node t_jIdle amount of unbalance；P_jRepresent jth node t_jNode active power, Q_jRepresent jth node t_jNode reactive power；Jth node t_jNode active-power P_jRepresent by jth node t_jFlow to and jth node t_jThe active power sum of the node being connected, jth node t_jNode reactive power Q_jRepresent by jth node t_jFlow to and jth node t_jThe reactive power sum of the node being connected；

Formula (9) represents mends branch road L by string_uEffective power flowControl isThis formula realizes the TCSC control action to branch road effective power flow；Represent that string mends branch road L_uEffective power flow expected value, θ_aτRepresent a node t_aVoltage phase angle and the τ node t_τPhase difference of voltage；

Formula (10) represents jth node t_jVariable χ_jSpan, and haveχ_{min_j}And χ_{max_j}Respectively variable χ_jMinima and maximum, and have V_{max_j}And V_{min_j}Represent jth node t respectively_jMaximum voltage amplitude and minimum voltage amplitude, θ_{max_j}And θ_{min_j}Represent jth node t respectively_jMaximum voltage phase angle and minimum voltage phase angle,WithRepresent jth node t respectively_jOn conventional power generation usage unit G_jMaximum active power and minimum active power,WithRepresent jth node t respectively_jOn conventional power generation usage unit G_jMaximum reactive power and minimum reactive power；

Formula (11) represents the l branch road L_lOn effective power flowNot can exceed that the maximum effective power flow P of permission_{max_l}；

Formula (12) represents the span of the reactance value x of TCSC；x_maxAnd x_minRepresent the maximum reactance value of TCSC and minimum reactance value respectively；

As shown in Figure 4, TCSC is made up of bypass breaker, spark gap, electric capacity, resistance, reactance, IGCT, the maximum reactance value of TCSC and minimum reactance value x_maxAnd x_minValue run the impact of constraint by the restriction of pressure, the flow-resistant capacity of the element (bypass breaker, spark gap, electric capacity, resistance, reactance, IGCT) of TCSC and protection device and system, its string benefit scope is also affected by simultaneously expensive for TCSC cost to some extent；

Formula (13) represents that string mends branch road L_uEffective power flow expected valueSpan；η_maxAnd η_minRepresent that string mends branch road L respectively_uEffective power flow expected valueWith kth branch road L_kThe maximum effective power flow P allowed_{max_k}Between the maximum of ratio and minima；η_maxAnd η_minString can be limited and mend branch road L_uEffective power flow expected valueIt is unlikely to heavy duty, η_maxAnd η_minSize be subject to transmission line of electricity self-condition and environment and the impact of Operation of Electric Systems condition；

Formula (13) considers string simultaneously and mends branch road L_uEffective power flow expected valueValue also taken into account the impact of span of TCSC lower than heavy duty simultaneously:

Consider that restriction string mends branch road L_uEffective power flow expected valueIt is unlikely to heavy duty, has formula (14):

${\mathrm{\&eta;}}_{min}{P}_{\max \_k}\&le;P_{set\_L_u}\&le;{\mathrm{\&eta;}}_{max}{P}_{\max \_k}---\left(14\right)$

Consideration formula (9) and formula (12), it is possible to obtain string and mend branch road L_uEffective power flow expected valueWith the maximum reactance value of TCSC and minimum reactance value x_maxAnd x_minRelation such as formula (15):

$\frac{V_a{V}_{\mathrm{\&tau;}}{\mathrm{sin\&theta;}}_{a\mathrm{\&tau;}}}{x_{max}}\&le;P_{set\_L_u}\&le;\frac{V_a{V}_{\mathrm{\&tau;}}{\mathrm{sin\&theta;}}_{a\mathrm{\&tau;}}}{x_{\min }}---\left(15\right)$

Consider that string mends branch road L_uEffective power flow expected valueValue lower than heavy duty, take into account the impact of TCSC span, association type (14) and formula (15) simultaneously, obtain string mend branch road L_uEffective power flow expected valueFinal span, i.e. formula (13)；

Step 8, according to improve optimal load flow model, adopt probability optimal load flow method, namely adopt the Monte Carlo sampling approach of step 2 that the uncertain factor value in step 2 carries out Num sampling again, and power system is carried out Num suboptimum Load flow calculation, it is thus achieved that the reactance value x={x of TCSC¹,x²,…,xⁿ,…,x^Num}；

The calculating of probability optimal load flow is to uncertain factor multiple sampling and carries out repeatedly optimal load flow calculating；Probability optimal load flow refers on optimal load flow basis, consider the uncertain factor in power system, when the structural parameters of power system and load condition etc. are all no longer a certain set-point, find electric power system tide distribution when making Power System Performance index optimum；Obviously the trend of now power system is no longer some value；

Carry out Num suboptimum Load flow calculation according to the optimal load flow model improved and refer under meeting Operation of Electric Systems constraints, find reactance value and the trend distribution of TCSC, to reach to control to go here and there to mend branch road L_uEffective power flow beWhile minimize Operation of Electric Systems cost and the purpose of TCSC cost of investment；

Step 9, formula (16) is utilized to obtain the optimal value of reactance value x of TCSC

$\stackrel{\&OverBar;}{x}=\frac{1}{Num}\underset{n=1}{\overset{Num}{\mathrm{\&Sigma;}}}{x}^n---\left(16\right)$

Impact due to uncertain factor, it is thus achieved that the reactance value x={x of TCSC¹,x²,…,xⁿ,…,x^NumIt it is no longer a value determined, optimal load flow calculated TCSC reactance value is all the optimal result of satisfied constraint and the target obtained when this uncertain factor value each time, and actual uncertain factor has randomness, any result of calculation can not be adopted to determine the final value of TCSC reactance value, and the final value of TCSC reactance value will by representativeness, therefore the expected value asking for repeatedly result of calculation characterizes the final value of the TCSC reactance value being actually needed, i.e. formula (16)；

Step 10, at kth branch road L_kBranch impedance Z_kWith node t_aBetween install reactance value beTCSC, thus realizing TCSC Optimal Configuration Method.

TCSC Optimal Configuration Method with the present invention, the TCSC installation site selected and TCSC installed capacity (i.e. the reactance value of TCSC), it can be considered that the multiformity of uncertain factor in power system, while utilizing TCSC load disturbance, take into account the cost of investment of TCSC；TCSC Optimal Configuration Method with the present invention, it is simple to install TCSC before power system is had a negative impact by uncertain factor in time, be beneficial to power system and run more safely, also has actual reference to the formulation of the scheduling planning scheme of power system.

Claims (3)

1. the TCSC Optimal Configuration Method controlled based on Probabilistic Load Flow, it is be applied in power system, described power system is to be produced electric energy by each generating equipment and be collected on each bus, electric energy is provided to electrical equipment and to transmit electric energy extremely each transmission line of electricity by described each bus, through the transmission of described each transmission line of electricity, described electric energy is delivered to the electrical equipment on each distribution bus；Described each bus and each distribution bus are designated as node t；It is t by described node t number consecutively₁,t₂,…,t_i,…t_N；t_iRepresent i-th node；1≤i≤N；N represents the sum of described node；Described each transmission line of electricity is designated as branch road L；It is L by described branch road L number consecutively₁,L₂,…,L_k,…,L_K；L_kRepresent kth branch road；1≤k≤K；K represents the sum of described branch road；Electrical equipment on described each node t is designated as D₁,D₂,…,D_i,…D_N；D_iRepresent i-th node t_iOn electrical equipment；Described generating equipment includes conventional power generation usage unit and wind power generating set and photovoltaic power generation equipment；Conventional power generation usage unit on described each node t is designated as G₁,G₂,…,G_i,…G_N；G_iRepresent i-th node t_iOn conventional power generation usage unit；Wind power generating set on described each node t is designated as W₁,W₂,…,W_i,…W_N；W_iRepresent i-th node t_iOn wind power generating set；Photovoltaic power generation equipment on described each node t is designated as S₁,S₂,…,S_i,…S_N；S_iRepresent i-th node t_iOn photovoltaic power generation equipment；

It is characterized in that: described Optimal Configuration Method is to carry out as follows:

Step 1, acquisition initial data:

Obtain from described power system by i-th node t_iOn node data and kth branch road L_kOn branch data constitute initial data；Thus the initial data on the initial data obtained on N number of node and K branch road；

Described i-th node t_iOn node data include: i-th node t_iOn voltage magnitude V_i, i-th node t_iOn voltage phase angle θ_i；I-th node t_iOn conventional power generation usage unit G_iActive powerI-th node t_iOn conventional power generation usage unit G_iReactive powerI-th node t_iOn conventional power generation usage unit G_iFault rateI-th node t_iOn wind power generating set W_iSpecified active powerI-th node t_iOn wind power generating set W_iPower-factor angleI-th node t_iOn photovoltaic power generation equipment S_iThe illumination gross areaI-th node t_iOn photovoltaic power generation equipment S_iPhotoelectric transformation efficiencyI-th node t_iOn photovoltaic power generation equipment S_iPower-factor angleI-th node t_iOn the average of active power of electrical equipmentI-th node t_iOn the variance of active power of electrical equipmentI-th node t_iOn the power-factor angle of electrical equipment

Described kth branch road L_kOn branch data include: kth branch road L_kOn impedance Z_k；Kth branch road L_kOn the maximum active-power P that passes through of permission_{max_k}；Kth branch road L_kFault rate

Step 2, employing Monte Carlo sampling approach obtain the value of uncertain factor；Described uncertain factor includes: the running status of the actual active power of conventional power generation usage unit and actual reactive power, the active power of wind power generating set and reactive power, the active power of photovoltaic power generation equipment and reactive power, the active power of electrical equipment and reactive power and branch road；

Step 3, adopt described Monte Carlo sampling approach that the value of described uncertain factor carries out Num sampling, and described power system is carried out Num Load flow calculation, it is thus achieved that the effective power flow on described K branch road Represent the effective power flow on K the branch road that n-th Load flow calculation obtains；1≤n≤Num；

Step 4, formula (1) is utilized to obtain described kth branch road L_kLoading rateThus the loading rate obtained on described K branch road:

${\mathrm{\&xi;}}_{L_k}=\frac{\left|\frac{1}{Num}\underset{n=1}{\overset{Num}{\mathrm{\&Sigma;}}}{P}_{L_k}^n\right|}{P_{\max \_k}}\&times;100\%---\left(1\right)$

Step 5, definitionRepresent kth branch road L_kReach loading rate during heavy duty；When meetingTime, choose kth branch road L_kAs the branch road installing TCSC；

Step 6, assume and described kth branch road L_kTwo node respectively t that two ends are connected_aAnd t_b；At described kth branch road L_kOn, branch impedance Z_kIt is in described node t_aWith node t_bBetween, at described branch impedance Z_kWith described node t_aBetween or described branch impedance Z_kWith described node t_bBetween described TCSC is installed, thus at described branch impedance Z_kAnd form string between described TCSC and mend node t_τ；Node t is mended at described string_τWith described node t_aBetween or described string mend node t_τWith described node t_bBetween formed string mend branch road L_u；

The optimal load flow model that step 7, foundation improve；

Step 8, optimal load flow model according to described improvement, adopt described Monte Carlo sampling approach that described uncertain factor value carries out Num sampling again, and described power system carry out Num suboptimum Load flow calculation, it is thus achieved that the reactance value x={x of described TCSC¹,x²,…,xⁿ,…,x^Num}；

Step 9, formula (2) is utilized to obtain the optimal value of reactance value x of described TCSC

$\stackrel{\&OverBar;}{x}=\frac{1}{Num}\underset{n=1}{\overset{Num}{\mathrm{\&Sigma;}}}{x}^n---\left(2\right)$

Step 10, at described kth branch road L_kBranch impedance Z_kWith described node t_aBetween install reactance value beTCSC, thus realizing TCSC Optimal Configuration Method.

2. the TCSC Optimal Configuration Method controlled based on Probabilistic Load Flow according to claim 1, is characterized in that, described step 2 is to carry out as follows:

Step 2.1, acquisition i-th node t_iOn conventional power generation usage unit G_iActual active powerWith i-th node t_iOn conventional power generation usage unit G_iActual reactive powerThus the actual active power of the conventional power generation usage unit obtained on N number of node and actual reactive power:

Step 2.1.1, generate equally distributed random number between N number of obedience 0～1, be designated as ε₁,ε₂,…,ε_i,…ε_N；ε_iRepresent i-th random number；

Step 2.1.2, formula (3) is utilized to obtain described i-th node t_iOn conventional power generation usage unit G_iActual active powerWith i-th node t_iOn conventional power generation usage unit G_iActual reactive powerThus the actual active power of the conventional power generation usage unit obtained on N number of node and actual reactive power:

$\&lsqb;P_{G_i}^{\&prime;},Q_{G_i}^{\&prime;}\&rsqb;=\left\{\begin{array}{c}\&lsqb;P_{G_i},Q_{G_i}\&rsqb;,{\mathrm{\&epsiv;}}_i>{\mathrm{\&lambda;}}_{G_i}\\ \&lsqb;0,0\&rsqb;,{\mathrm{\&epsiv;}}_i\&le;{\mathrm{\&lambda;}}_{G_i}\end{array}\right.---\left(3\right)$

Step 2.2, acquisition i-th node t_iOn wind power generating set W_iActive powerWith i-th node t_iOn wind power generating set W_iReactive powerThus the active power of the wind power generating set obtained on N number of node and reactive power:

Step 2.2.1, generate N number of obedience independent standard normal distribution random number；

Step 2.2.2, the method for orthogonal transformation and semiempirical formula is adopted to process the random number of described N number of obedience independent standard normal distribution, it is thus achieved that N number of obedience Weibull distribution and have the stochastic variable of dependency；

Step 2.2.3, make i-th node t_iOn wind power generating set W_iWind speed v_iEqual to described N number of obedience Weibull distribution and the i-th variable during there is the stochastic variable of dependency；Thus the wind speed of the wind power generating set obtained on N number of node；

Step 2.2.4, formula (4) is utilized to obtain described i-th node t_iOn wind power generating set W_iActive powerThus the active power of the wind power generating set obtained on N number of node:

$P_{W_i}=\left\{\begin{array}{cc}0& v_i<v_{c\_i},v_i>v_{o\_i}\\ \frac{v_i^{m_i}-v_{c\_i}^{m_i}}{v_{r\_i}^{m_i}-v_{c\_i}^{m_i}}{P}_{r\_W_i}& v_{c\_i}\&le;v_i<v_{r\_i}\\ P_{r\_W_i}& v_{r\_i}\&le;v_i\&le;v_{o\_i}\end{array}\right.---\left(4\right)$

In formula (4), v_{c_i}Represent i-th node t_iOn wind power generating set W_iIncision wind speed, v_{o_i}Represent i-th node t_iOn wind power generating set W_iCut-out wind speed, v_{r_i}Represent i-th node t_iOn wind power generating set W_iRated wind speed, m_iRepresent i-th node t_iOn wind power generating set W_iThe coefficient being asked for active power by wind speed；

Step 2.2.5, formula (5) is utilized to obtain described i-th node t_iOn wind power generating set W_iReactive powerThus the reactive power of the wind power generating set obtained on N number of node:

Step 2.3, acquisition i-th node t_iOn photovoltaic power generation equipment S_iActive powerWith i-th node t_iOn photovoltaic power generation equipment S_iReactive powerThus the active power of the photovoltaic power generation equipment obtained on N number of node and reactive power:

Step 2.3.1, generate N number of obedience beta distribution random number；

Step 2.3.2, make i-th node t_iOn photovoltaic power generation equipment S_iIntensity of illuminationEqual to the i-th random number in the random number of described N number of obedience beta distribution；Thus the intensity of illumination of the photovoltaic power generation equipment obtained on N number of node；

Step 2.3.3, formula (6) is utilized to obtain described i-th node t_iOn photovoltaic power generation equipment S_iActive powerWith i-th node t_iOn photovoltaic power generation equipment S_iReactive powerThus the active power of the photovoltaic power generation equipment obtained on N number of node and reactive power:

Step 2.4, acquisition i-th node t_iOn the active power of electrical equipmentWith i-th node t_iOn the reactive power of electrical equipmentThus the active power of the electrical equipment obtained on N number of node and reactive power:

Step 2.4.1, again generate the random number that N number of obedience separate standards is just being distributed very much；

Step 2.4.2, adopt the method for orthogonal transformation process described in the random number that is just being distributed very much of N number of obedience separate standards of again generating, obtain N number of normal random variable with dependency, in described N number of normal random variable with dependency, the average of i-th stochastic variable isAnd i-th variance of a random variable is

Step 2.4.3, make i-th node t_iOn the active power of electrical equipmentI-th stochastic variable equal to the N number of stochastic variable in described step 2.4.2；Thus the active power of the electrical equipment obtained on N number of node；

Step 2.4.4, utilize formula (7) obtain i-th node t_iOn the reactive power of electrical equipmentThus the reactive power of the electrical equipment obtained on N number of node:

Step 2.5, acquisition kth branch road L_kRunning status；Thus obtaining the running status of K branch road, described running status is normal condition or malfunction:

Step 2.5.1, again generation K obey equally distributed random number between 0～1；

The kth random number that K again generated described in step 2.5.2, comparison obeys between 0～1 in equally distributed random number and described kth branch road L_kFault rateSize, if described again generate K the kth random number obeyed between 0～1 in equally distributed random number is more than described kth branch road L_kFault rateThen kth branch road L_kRunning status be normal condition；If described K the kth random number obeyed between 0～1 in equally distributed random number again generated is less than or equal to described kth branch road L_kFault rateThen kth branch road L_kRunning status be malfunction；Thus obtaining kth branch road L_kRunning status；And then obtain the running status of K branch road.

3. the TCSC Optimal Configuration Method controlled based on Probabilistic Load Flow according to claim 2, is characterized in that, described step 7 is to carry out as follows:

Assume at described kth branch road L_kBranch impedance Z_kWith described node t_aBetween described TCSC is installed；Formula (8)-Shi (14) is utilized to obtain the optimal load flow model improved:

Object function:

Equality constraint:

$P_{set\_L_u}-\frac{V_a{V}_{\mathrm{\&tau;}}{\mathrm{sin\&theta;}}_{a\mathrm{\&tau;}}}{x}=0---\left(10\right)$

Inequality constraints: χ_{min_j}≤χ_j≤χ_{max_j}(11)

$-P_{max\_l}\&le;P_{L_l}\&le;P_{max\_l},l\&Element;\{1,2,...,K\}---\left(12\right)$

x_min≤x≤x_max(13)

$max\{{\mathrm{\&eta;}}_{min}{P}_{\max \_k},\frac{V_a{V}_{\mathrm{\&tau;}}{\mathrm{sin\&theta;}}_{a\mathrm{\&tau;}}}{x_{\max }}\}\&le;P_{set\_L_u}\&le;min\{{\mathrm{\&eta;}}_{max}{P}_{\max \_k},\frac{V_a{V}_{\mathrm{\&tau;}}{\mathrm{sin\&theta;}}_{a\mathrm{\&tau;}}}{x_{min}}\}---\left(14\right)$

Formula (8) represents the operating cost and the TCSC cost of investment that minimize power system；Represent i-th node t respectively_iOn conventional power generation usage unit G_iOperating cost coefficient, α₀、α₁、α₂Representing the cost coefficient of described TCSC respectively, s represents the reactive power value flowing through described TCSC, and I represents the current amplitude flowing through described TCSC, and x represents the reactance value of described TCSC；

Formula (9) represents jth node t_jPower balance equation, Δ P_jRepresent described jth node t_jMeritorious amount of unbalance；ΔQ_jRepresent described jth node t_jIdle amount of unbalance；P_jRepresent described jth node t_jNode active power, Q_jRepresent described jth node t_jNode reactive power；

Formula (10) represents mends branch road L by string_uEffective power flowControl isRepresent that described string mends branch road L_uEffective power flow expected value, θ_aτRepresent a node t_aVoltage phase angle and the τ node t_τPhase difference of voltage；

Formula (11) represents jth node t_jVariable χ_jSpan, and haveχ_{min_j}And χ_{max_j}Respectively variable χ_jMinima and maximum, and have V_{max_j}And V_{min_j}Represent jth node t respectively_jMaximum voltage amplitude and minimum voltage amplitude, θ_{max_j}And θ_{min_j}Represent jth node t respectively_jMaximum voltage phase angle and minimum voltage phase angle,WithRepresent jth node t respectively_jOn conventional power generation usage unit G_jMaximum active power and minimum active power,WithRepresent jth node t respectively_jOn conventional power generation usage unit G_jMaximum reactive power and minimum reactive power；

Formula (12) represents the l branch road L_lOn effective power flowNot can exceed that the maximum effective power flow P of permission_{max_l}；

Formula (13) represents the span of the reactance value x of described TCSC；x_maxAnd x_minRepresent the maximum reactance value of described TCSC and minimum reactance value respectively；

Formula (14) represents that described string mends branch road L_uEffective power flow expected valueSpan；η_maxAnd η_minRepresent that described string mends branch road L respectively_uEffective power flow expected valueWith described kth branch road L_kThe maximum effective power flow P allowed_{max_k}Between the maximum of ratio and minima.