CN103413194B - A kind of District power network planning system and method containing high permeability intermittent energy source - Google Patents

Abstract

The present invention discloses a kind of offer multiple goal, many restricted selections, the District power network planning system and method containing high permeability intermittent energy source that efficiency is high. By providing, the many constraints of alternative multiple goal and multiple algorithm meet different planning needs in the present invention; The stochastic production simulation considering electrical network is resolved algorithm application in programme generative process, it is possible to solve quickly simultaneously. The present invention adopts the intermittent power supply being equivalent in the method treatment system of a conventional power unit, significantly improves the counting yield of stochastic production simulation. Meanwhile, the present invention, by adopting method of weighting process multiple goal, can also accept the feedback of subsequent evaluation decision-making link in planning process simultaneously, contribute to the continuous correction of scheme. Finally, the present invention by the disaggregation that one's respective area electric network synthetic planning method is fed back be Pareto preferably after non-bad solution, this contributes on the basis keeping disaggregation handiness, it is to increase the validity of disaggregation entirety, facilitates follow-up planning link.

Description

A kind of District power network planning system and method containing high permeability intermittent energy source

Technical field

The present invention relates to Electric Power Network Planning field, in particular to a kind of District power network planning system and method containing high permeability intermittent energy source.

Background technology

Along with the fast development of the intermittent power supply generation mode of China based on wind-powered electricity generation, the grid connection capacity of such power supply will have remarkable increase, and the operation of electrical network and planning all can be produced significant impact by its intermittent and fluctuation. So, in planning process, consider that the impact of extensive intermittent power supply has very big necessity simultaneously.

Because Electric Power Network Planning problem may face different consideration angles, relate to different targets and requirement, the expression of planning model is needed differentiated often by it, and corresponding different model and system, the performance of various method for solving is often also different, so adopting single target, constraint expression and method for solving to be difficult to adapt to the requirement of above-mentioned variation.

In the fail-safe analysis considering intermittent power supply, main employing is emulation method at present, its computation burden that may bring is relatively big, and stochastic production simulation is resolved class algorithm and can be calculated sooner, but the effect of contraction that class algorithm cannot consider network is resolved in the simulation of most of stochastic production. Simulating in analytical algorithm at stochastic production, intermittent energy source can be equivalent to M-ARY unit and be processed, but this also can bring the extra increase of computation burden.

Summary of the invention

Goal of the invention: for above-mentioned prior art Problems existing and deficiency, it is an object of the invention to provide a kind of offer multiple goal, many restricted selections, the District power network planning system and method containing high permeability intermittent energy source that efficiency is high.

Technical scheme: for achieving the above object, the present invention provides a kind of District power network planning system containing high permeability intermittent energy source, comprise data input/output module, algorithm/target/restricted selection module, intermittent energy source processing module, model and solution strategies generation module, derivation algorithm module, target and constraint and evaluate module, aided solving module, schemes ranking and processing module, wherein:

The data received, for receiving Electric Power Network Planning data, are input to other modules by described data input/output module, and export the planning result obtained;

Described algorithm/target/restricted selection module is used for the target to Electric Power Network Planning, constraint and derivation algorithm and selects, and the result of selection is input to described model and solution strategies generation module;

It is the form needed in other modules that described intermittent energy source processing module is used for the data transformations of the intermittent energy source obtained from data input/output module;

Described model and solution strategies generation module, according to the data of the target selected in algorithm/target/restricted selection module and constraint and data input/output module input, form Electric Power Network Planning solving model;

Described target and constraint are evaluated module and are evaluated for Electric Power Network Planning target and the constraint of the data that export in conjunction with data input/output module and algorithm/target/restricted selection model choice, and evaluation result inputs to derivation algorithm module;

The Electric Power Network Planning solving model that described derivation algorithm module obtains according to model and solution strategies generation module solves, and the disaggregation obtained is passed to described schemes ranking and processing module;

Described schemes ranking and processing module adopt Pareto ranking method the disaggregation that derivation algorithm module obtains to be sorted, and a result after selected and sorted is input to described data input/output module and exports.

In order to Electric Power Network Planning result can be obtained sooner, more accurately, the present invention also comprises aided solving module, and described aided solving module is evaluated module according to the data that data input/output module and intermittent energy source processing module export to target and constraint and provided aided solving.

In order to the needs of more Electric Power Network Planning and be applicable to solving of different electric network model, containing clonal selection algorithm, genetic algorithm, particle cluster algorithm and shuffled frog leaping algorithm in derivation algorithm module.

In order to make the power network planning scheme of acquisition better, described target and constraint are evaluated module and are comprised electrical network connectivity constraint evaluation module, module is evaluated in the constraint of maximum load running status line effective power flow, module is evaluated in chance constraint line effective power flow constraint, reliability loss assessment module, module is evaluated in power supply reliability constraint, wherein, described in conjunction with electrical network connectivity constraint evaluation module, module is evaluated in the constraint of described maximum load running status line effective power flow, module is evaluated in the constraint line effective power flow constraint of described chance, described reliability loss assessment module, the data that described power supply reliability constraint evaluation module exports in conjunction with data input/output module respectively are evaluated with the constraint that algorithm/target/Electric Power Network Planning of restricted selection model choice is corresponding, and respectively evaluation result is inputed to derivation algorithm module.

In order to effectively reduce the calculated amount of whole system, improve the working efficiency of the present invention, the constraint of described power supply reliability is evaluated module employing and containing intermittent energy source and is considered that the reliability index of electrical network and each load node is evaluated by the stochastic production simulation analytical algorithm of web influence, comprises the steps:

Step 301: the intermittent power supply exported by data input/output module go out force characteristic or history goes out force data, the part throttle characteristics of system or historical load data, the capacity of conventional power generation usage unit, forced outage rate data, the year peak load LOLP curve A and B being obtained respectively by emulation method not comprising with comprising intermittent power supply;

Step 302: corresponding to a certain LOLP level, obtains curve A, the upper corresponding horizontal W of year peak load of B₁��W₂, system features slope M, calculating useful load capacity is C_ef=W₂-W₁;

Step 303: application character used in proper names and in rendering some foreign names Fu Er formula $C_{\mathrm{ef}}=C_n-M1n[(1-p_{\mathrm{eq}})+p_{\mathrm{eq}}\×e^{C_n/M}],$ Equivalent unit installed capacity C according to setting_nCalculate equivalent unit forced outage rate p_eq;

Step 304: use capacity C_n, forced outage rate p_eqConventional power unit replace intermittent power supply to carry out the calculating of subsequent production;

Step 305: the load obtaining each load point by part throttle characteristics or historical load data continues curve;

Step 306: form system significant condition collection, that reflects the operability of generator, these elements of circuit, wherein each state is corresponding, and a generator, circuit can be with combinations of states and state probability:

$p_S^{\mathrm{Ng},j}=\underset{n\∈G_0}{\mathrm{\Π}}{p}_n\underset{n\∈G_1}{\mathrm{\Π}}{q}_n\underset{n\∈l_0}{\mathrm{\Π}}{p}_l\underset{n\∈l_1}{\mathrm{\Π}}{q}_l$

In formula, N_gFor generator number, j is state number, p_n��p_lFor the probability of fault occurs generator n and circuit l, q_n=1-p_n, q_l=1-p_l, G₀��G₁Available set and the fault set being respectively the corresponding generator of system state j, l₀��l₁It is respectively available set and the fault set of system state j corresponding line;

Step 307: each load point that calculating electrical network significant condition concentrates each state corresponding is maximum reaches performance number AP:

min1-�ˡ�

$\left\{\begin{array}{cc}{P}_n^G\≤\stackrel{\‾}{P_n^G}& n\∈1~N_g\\ \underset{n=1}{\overset{N_g}{\mathrm{\Σ}}}{T}_{n,l}^G{P}_n^G+\underset{i=1}{\overset{\mathrm{NL}}{\mathrm{\Σ}}}{T}_{i,l}^L{\mathrm{AP}}_i=P_l^{\mathrm{line}}& l=1~N_l\\ \underset{n=1}{\overset{N_g}{\mathrm{\Σ}}}{P}_n^G=\underset{i=1}{\overset{\mathrm{NL}}{\mathrm{\Σ}}}{\mathrm{AP}}_i& \\ -\stackrel{\‾}{P_l^{\mathrm{line}}}\≤P_l^{\mathrm{line}}\≤\stackrel{\‾}{P_l^{\mathrm{line}}}& l=1~N_l\\ {\mathrm{\λ}}^{\′}{L}_{\mathrm{pi}}-{\mathrm{AP}}_i\≤0& i=1~\mathrm{NL}\end{array}\right.$

Wherein,For exerting oneself of generator n,For the upper limit of exerting oneself of generator n,For the wattful power that circuit l transmits,For circuit l transmits the wattful power upper limit, For generator n and load i is to the relation conefficient of circuit l wattful power, N_lFor circuit number, NL is load number, N_gFor generator number, AP_iMaximum for load point i reaches performance number; L_piFor the load peak value of load point i; �� ' is non-negative real number;

Step 308: form the equivalent M-ARY unit being connected to each generator with the maximum performance number that reaches of each load point that system significant condition collection is corresponding;

Step 309: continue curve by the load of above-mentioned equivalence M-ARY unit and each load point, calculate the reliability index LOLP of each load point_i��EENS_i, and then calculate the LOLP of system entirety_sys��EENS_sys��

Present invention also offers a kind of District power network planning method containing high permeability intermittent energy source, comprise the following steps:

Step 1: data input/output module obtains rack data, comprises the power data of intermittent power supply, system operation data;

Step 2: target, the constraint solving algorithm of Electric Power Network Planning are selected, and set correlation parameter by algorithm/target/restricted selection module;

Step 3: according to the target of algorithm/target/restricted selection model choice and constraint, and the associated weight of setting, model and solution strategies generation module generation power network planning scheme are optimized solving model and are automatically selected suitable solution strategies;

Step 4: the Electric Power Network Planning solving model formed in step 3 is solved by the optimization algorithm selected in invocation step 2, obtains disaggregation;

Step 5: according to each sub-goal selected in step 2, the disaggregation obtained in step 4 is carried out Pareto sequence, is exported the optimum solution after sequence by data input/output module.

Useful effect: the present invention compared with prior art provides alternative multiple goal and many constraints, provides multiple alternative derivation algorithm, it is possible to meet different planning needs simultaneously. The stochastic production simulation considering electrical network is resolved algorithm application in programme generative process, compared to emulation method, it is possible to solve quickly. Further, the application of stochastic production simulation can obtain system and even the reliability index of single load node, can adapt to the needs of dissimilar load. The present invention adopts the intermittent power supply being equivalent in the method treatment system of a conventional power unit, relative to adopting more M-ARY unit equivalent method, it is possible to significantly improve the counting yield of stochastic production simulation. The present invention is by adopting method of weighting process multiple goal, and its weights can be determined by multiple method in advance, can also accept the feedback of subsequent evaluation decision-making link in planning process simultaneously, contribute to the continuous correction of scheme. Finally, the present invention by the disaggregation that one's respective area electric network synthetic planning method is fed back be Pareto preferably after non-bad solution, this contributes on the basis keeping disaggregation handiness, it is to increase the validity of disaggregation entirety, facilitates follow-up planning link.

Accompanying drawing explanation

Fig. 1 is function structure chart of the present invention;

Fig. 2 is schematic flow sheet of the present invention;

Fig. 3 is the useful load capacity schematic diagram solving intermittent power supply based on year peak load-LOLP curve.

Embodiment

Below in conjunction with accompanying drawing, the invention will be further described:

As shown in Figure 1, a kind of District power network planning system containing high permeability intermittent energy source, comprise data input/output module, algorithm/target/restricted selection module, intermittent energy source processing module, model and solution strategies generation module, derivation algorithm module, target and constraint and evaluate module, aided solving module, schemes ranking and processing module, wherein: the data of acquisition are input to intermittent energy source processing module, target and constraint and evaluate module and aided solving module by data input/output module respectively; The result of selection is input to model and solution strategies generation module and intermittent energy source processing module by algorithm/target/restricted selection module respectively; Data after process are input to target and evaluate module and aided solving module with constraint by intermittent energy source processing module respectively; The result of aided solving is input to target and evaluates module with constraint by aided solving module; The model that model and solution strategies generation module generate and corresponding solution strategies are input to derivation algorithm module and calculate, derivation algorithm module invocation target and constraint are evaluated module and are carried out evaluation calculation, and last disaggregation being input to schemes ranking and processing module sorts, final data input/output module exports optimum solution.

As shown in Figure 2, a kind of District power network planning method containing high permeability intermittent energy source, comprises the steps:

Step 1: data input/output module obtains rack data, comprises the power data of intermittent power supply, system operation data; Wherein rack data be each bar line corridor list loop line in electrical network erection expense, the whole story node, circuit return several upper limit, existing circuit return number, line impedance value, circuit forced outage rate.

The probability that system operation data each node load level that to be each node load level of electrical network under maximum operation scheme corresponding with generator output level, scene collection and generator output level, scene concentrate each scene corresponding or lasting time.

The power data comprising intermittent power supply is exert oneself bound and the forced outage rate of each conventional power generation usage machine, the priority of conventional power generation usage machine, and the installed capacity of intermittent power supply, probability distribution of exerting oneself, history go out force data and other relevant data.

Step 2: the target of Electric Power Network Planning, constraint and derivation algorithm are selected by algorithm/target/restricted selection module, and set correlation parameter, setting multiple goal weight.

The planning target of selectable electrical network comprises: the enlarging expense of rack, system cloud gray model expense, annual electrical network damage the cutting load expected shortfall under expense and scene collection.

Wherein, the enlarging expense of rack:

In formula, k₁For recovery of the capital coefficient, k₁=rc (1+rc)ⁿ/[(1+rc)ⁿ-1], rc is rate of discount (%), n is engineering economics working life, c_jFor line corridor j expands the investment cost (ten thousand yuan) of a newly-built circuit, x_jFor the line loop number after newly-built in line corridor j,For line loop number initial in line corridor j, �� is line corridor set.

Working cost:In formula, k₂For the fixing working cost rate (%) of engineering.

Annual electrical network damages expense:

In formula, k₃For annual electrical network damages coefficient, k₃=C_ost����/u², wherein C_ostFor network loss electricity price (unit/kWh), �� is maximum load loss time (h), and u is system nominal voltage (kV), r_jFor the resistance of line corridor j; P_jFor the wattful power (MW) that line corridor j when maximum load is run under normal circumstances carries.

Cutting load expected shortfall under scene collection:

$f_4=\underset{s\∈S}{\mathrm{\Σ}}{T}_s\underset{i=1}{\overset{\mathrm{NL}}{\mathrm{\Σ}}}{C}_{{\mathrm{LS}}_i}\×{\mathrm{EENS}}_{i,s},$

${\mathrm{EENS}}_{i,s}=\underset{q\∈S_F}{\mathrm{\Σ}}{L}_{q,s}\underset{j\∈S_{\mathrm{hq}}}{\mathrm{\Π}}{p}_{\mathrm{qj}}\underset{k\∈S_{\mathrm{Hq}}}{\mathrm{\Π}}(1-p_{\mathrm{qk}})$

In formula, S is scene collection, T_sFor the time length (h) of scene s a certain in S, NL is load node number,It is the cutting load cost of i-th load node, EENS_i,sIt is the expected loss of energy of i-th load node under the s scene, S_FFor fault set, S_hqFor there is malfunction equipment set during fault q, S_HqFor there is normal device set during fault q, p_qj��p_qkFor the emergency shut-down coefficient of equipment j, k during generation fault q, L_q,sFor there is cutting load amount during fault q under scene s.

Selectable power constraint comprises: the constraint of electrical network connectivity constraint, maximum load running status line effective power flow, chance constraint line effective power flow retrain, corridor circuit extends back number upper limit constraint, electrical network is overall and the power supply reliability constraint of each load node.

System connectivity constraint under electrical network connectivity constraint, normal operation and disconnection one loop line line state, wherein a loop line road refers to single line, that is: do not form isolated island under normal operating condition; Isolated island is not formed under disconnecting a line fault conditions. Network power flow equation is formed based on DC power flow, needs to be formed according to the difference that normal operation and N-1 calculate; Power balance is constrained to the balance of total load and gross generation, ignores the impact of network loss.

Normal operation and disconnection one loop line road effective power flow under maximum running status retrain:

$\left|P_j\right|\≤x_j\×\stackrel{\‾}{P_j},j\∈\mathrm{\Ω};$

$\left|P_j^{N-1}\right|\≤x_j^{N-1}\×\stackrel{\‾}{P_j},j\∈\mathrm{\Ω}$

In formula,For single loop line meritorious transmission upper limit of line corridor j,The wattful power being off under a line condition maximum load when running, line corridor j carrying,The circuit being off a line condition line corridor j returns number.

The circuit effective power flow constraint of chance constraint type, adopts probabilistic loadflow mode to solve;

$\mathrm{Pro}\left(\right|\widetilde{P_j}|\≤x_j\×\stackrel{\‾}{P_j})\≥\mathrm{\α},j\∈\mathrm{\Ω};$

In formula,For the wattful power random value that line corridor j carries under normal circumstances,The wattful power random value being off a line condition line corridor j conveying, ��, �� are the confidence bottom valve value of setting.

Corridor circuit extends back number upper limit constraint

$0\≤x_j\≤\stackrel{\‾}{x_j},j\∈\mathrm{\Ω}.$

Electrical network power supply reliability that is overall and each load node retrains

${\mathrm{LOLP}}_{\mathrm{sys}}\≤\stackrel{\‾}{{\mathrm{LOLP}}_{\mathrm{sys}}};$

${\mathrm{EENS}}_{\mathrm{sys}}\≤\stackrel{\‾}{{\mathrm{EENS}}_{\mathrm{sys}}};$

${\mathrm{LOLP}}_i\≤\stackrel{\‾}{{\mathrm{LOLP}}_i},i=1~\mathrm{NL};$

${\mathrm{EENS}}_i\≤\stackrel{\‾}{{\mathrm{EENS}}_i},i=1~\mathrm{NL}.$

In formula, LOLP_sysAnd EENS_sysIt is respectively loss of load probability and the expected loss of energy of electrical network; It is respectively the loss of load probability upper limit and the expected loss of energy upper limit of electrical network; LOLP_iAnd EENS_iIt is respectively loss of load probability and the expected loss of energy of i-th load node;WithIt is respectively the loss of load probability upper limit and the expected loss of energy upper limit of i-th load node.

Selectable optimization algorithm comprises: particle cluster algorithm, genetic algorithm, shuffled frog leaping algorithm, clonal selection algorithm.

Elected set pattern draws target, the constraint condition of consideration, and after derivation algorithm, is arranged by correlation parameter, comprising correlation parameter required in selected target, constraint computation process, and the correlation parameter of derivation algorithm.

Step 3: according to the target of algorithm/target/restricted selection model choice and constraint, and the associated weight of setting, model and solution strategies generation module generation power network planning scheme are optimized solving model and are automatically selected suitable solution strategies. Multiple goal weight is corresponding to each selected target, and it can derive from the feedback that assessing network analyzes module or evaluate alternatives optimization software, it is also possible to artificially sets.

Described target and constraint are evaluated module and are comprised: the normal system connectivity constraint run and disconnect under a loop line line state is evaluated, normal operation under maximum load running status and disconnect that a loop line road effective power flow constraint is evaluated, normal operation under chance constraint type and disconnect that a loop line road effective power flow constraint is evaluated, corridor circuit extend back that number upper limit constraint is evaluated, reliability of powering retrains evaluation, economic goal evaluation, reliability loss assessment, wherein:

The node data that the existing network of the electrical network by data input/output module input is evaluated in the normal system connectivity constraint run and disconnect under a loop line line state carries out searching for the connection judging also electrical network, then can be repaired by the power network planning scheme of non-interconnected and repair result feedback to derivation algorithm module.

The data acquisition normal operating condition line effective power flow solution inputted based on DC power flow method is evaluated in normal operation and the effective power flow constraint of disconnection one loop line road under maximum load running status according to data input/output module, then this trend solution is obtained the distribution of N-1 state line effective power flow as benchmark value by the correction under network transmission line fault, meet situation according to circuit effective power flow distribution constraint IF;

Under chance constraint type, the probability distribution adopting the probabilistic loadflow under DC power flow to calculate circuit trend is evaluated in the normal circuit effective power flow constraint running and disconnecting a loop line road, random factor grid nodes injects meritorious expression, adopts Cumulants method process.

Reliability loss assessment adopts the cutting load expected shortfall under scene collection to calculate reliability loss, first the out-of-limit situation of circuit trend under each scene is judged, if there is out-of-limit, then calculate optimum cutting load amount, and obtained the cutting load expected shortfall under scene collection by scene probability/time weight; According to the optimum cutting load model under scene formation DC power flow, then obtain corresponding optimum cutting load amount by solving this model.

Power supply reliability constraint evaluation test submodule block adopts containing intermittent energy source and considers that the stochastic production simulation analytical algorithm reliability index that is system is overall and each load node of web influence is evaluated, the method continues curve method (being called for short CMELDC method) based on the complicated electric power system workload under DC power flow, adopting equivalence volumetry intermittent energy source to be processed, concrete steps are:

Step 301: by intermittent power supply go out force characteristic or history goes out force data, the part throttle characteristics of system or historical load data, the capacity of conventional power generation usage unit, forced outage rate data, as shown in Figure 3, when not considering network, the year peak load-LOLP curve A and B being obtained respectively by emulation method not comprising with comprising intermittent power supply;

Step 302: corresponding to a certain LOLP level, obtains curve A, the upper corresponding horizontal W of year peak load of B₁��W₂, system features slope M, calculating useful load capacity is C_ef=W₂-W₁;

Step 303: application character used in proper names and in rendering some foreign names Fu Er formula $C_{\mathrm{ef}}=C_n-M1n[(1-p_{\mathrm{eq}})+p_{\mathrm{eq}}\×e^{C_n/M}],$ Equivalent unit installed capacity C according to setting_nCalculate equivalent unit forced outage rate p_eq;

Step 304: use capacity C_n, forced outage rate p_eqConventional power unit replace intermittent power supply to carry out the calculating of subsequent production;

Step 305: the load obtaining each load point by part throttle characteristics or historical load data continues curve;

Step 306: form system significant condition collection, that reflects the operability of generator, these elements of circuit, wherein each state is corresponding, and a generator, circuit can be with combinations of states and state probability:

$p_S^{\mathrm{Ng},j}=\underset{n\∈G_0}{\mathrm{\Π}}{p}_n\underset{n\∈G_1}{\mathrm{\Π}}{q}_n\underset{n\∈l_0}{\mathrm{\Π}}{p}_l\underset{n\∈l_1}{\mathrm{\Π}}{q}_l$

In formula, N_gFor generator number, j is state number, p_n��p_lFor the probability of fault occurs generator n and circuit l, q_n=1-p_n, q_l=1-p_l, G₀��G₁Available set and the fault set being respectively the corresponding generator of system state j, l₀��l₁It is respectively available set and the fault set of system state j corresponding line;

Step 307: each load point that calculating electrical network significant condition concentrates each state corresponding is maximum reaches performance number AP:

min1-�ˡ�

$\left\{\begin{array}{cc}{P}_n^G\≤\stackrel{\‾}{P_n^G}& n\∈1~N_g\\ \underset{n=1}{\overset{N_g}{\mathrm{\Σ}}}{T}_{n,l}^G{P}_n^G+\underset{i=1}{\overset{\mathrm{NL}}{\mathrm{\Σ}}}{T}_{i,l}^L{\mathrm{AP}}_i=P_l^{\mathrm{line}}& l=1~N_l\\ \underset{n=1}{\overset{N_g}{\mathrm{\Σ}}}{P}_n^G=\underset{i=1}{\overset{\mathrm{NL}}{\mathrm{\Σ}}}{\mathrm{AP}}_i& \\ -\stackrel{\‾}{P_l^{\mathrm{line}}}\≤P_l^{\mathrm{line}}\≤\stackrel{\‾}{P_l^{\mathrm{line}}}& l=1~N_l\\ {\mathrm{\λ}}^{\′}{L}_{\mathrm{pi}}-{\mathrm{AP}}_i\≤0& i=1~\mathrm{NL}\end{array}\right.$

Wherein,For exerting oneself of generator n,For the upper limit of exerting oneself of generator n,For the wattful power that circuit l transmits,For circuit l transmits the wattful power upper limit, For generator n and load i is to the relation conefficient of circuit l wattful power, N_lFor circuit number, NL is load number, N_gFor generator number, AP_iMaximum for load point i reaches performance number; L_piFor the load peak value of load point i; �� ' is non-negative real number;

Step 308: form the equivalent M-ARY unit being connected to each generator with the maximum performance number that reaches of each load point that system significant condition collection is corresponding;

Step 309: continue curve by the load of above-mentioned equivalence M-ARY unit and each load point, calculate the reliability index LOLP of each load point_i��EENS_i, and then calculate the LOLP of system entirety_sys��EENS_sys��

Step 4: the Electric Power Network Planning solving model formed in step 3 is solved by the optimization algorithm selected in invocation step 2, obtains disaggregation.

Step 5: according to each sub-goal selected in step 2, the disaggregation obtained in step 4 is carried out Pareto sequence, is exported the optimum solution after sequence by data input/output module.

Claims (6)

1. one kind contains the District power network planning system of high permeability intermittent energy source, it is characterized in that: comprise data input/output module, algorithm/target/restricted selection module, intermittent energy source processing module, model and solution strategies generation module, derivation algorithm module, target and constraint and evaluate module, aided solving module, schemes ranking and processing module, wherein:

The data received, for receiving Electric Power Network Planning data, are input to other modules by described data input/output module, and export the planning result obtained;

Described algorithm/target/restricted selection module is used for the target to Electric Power Network Planning, constraint and derivation algorithm and selects, and the result of selection is input to described model and solution strategies generation module;

It is the form needed in other modules that described intermittent energy source processing module is used for the data transformations of the intermittent energy source obtained from data input/output module;

Described model and solution strategies generation module, according to the data of the target selected in algorithm/target/restricted selection module and constraint and data input/output module input, form Electric Power Network Planning solving model;

Described target and constraint are evaluated module and are evaluated for Electric Power Network Planning target and the constraint of the data that export in conjunction with data input/output module and algorithm/target/restricted selection model choice, and evaluation result inputs to derivation algorithm module;

The Electric Power Network Planning solving model that described derivation algorithm module obtains according to model and solution strategies generation module solves, and the disaggregation obtained is passed to described schemes ranking and processing module;

Described schemes ranking and processing module adopt Pareto ranking method the disaggregation that derivation algorithm module obtains to be sorted, and a result after selected and sorted is input to described data input/output module and exports.

2. the District power network planning system containing high permeability intermittent energy source according to claim 1, it is characterized in that: also comprise aided solving module, described aided solving module is evaluated module according to the data that data input/output module and intermittent energy source processing module export to target and constraint and is provided aided solving.

3. the District power network planning system containing high permeability intermittent energy source according to claim 1, it is characterised in that: containing clonal selection algorithm, genetic algorithm, particle cluster algorithm and shuffled frog leaping algorithm in described derivation algorithm module.

4. the District power network planning system containing high permeability intermittent energy source according to claim 1, it is characterized in that: described target and constraint are evaluated module and comprised electrical network connectivity constraint evaluation module, module is evaluated in the constraint of maximum load running status line effective power flow, module is evaluated in chance constraint line effective power flow constraint, reliability loss assessment module, module is evaluated in power supply reliability constraint, wherein, described in conjunction with electrical network connectivity constraint evaluation module, module is evaluated in the constraint of described maximum load running status line effective power flow, module is evaluated in the constraint line effective power flow constraint of described chance, described reliability loss assessment module, the data that described power supply reliability constraint evaluation module exports in conjunction with data input/output module respectively are evaluated with the constraint that algorithm/target/Electric Power Network Planning of restricted selection model choice is corresponding, and respectively evaluation result is inputed to derivation algorithm module.

5. consideration according to claim 4 is containing the regional power grid unified plan system of high permeability intermittent energy source, it is characterized by, the constraint of described power supply reliability is evaluated module employing and containing intermittent energy source and is considered that the reliability index of electrical network and each load node is evaluated by the stochastic production simulation analytical algorithm of web influence, comprises the steps:

Step 301: the intermittent power supply exported by data input/output module go out force characteristic or history goes out force data, the part throttle characteristics of system or historical load data, the capacity of conventional power generation usage unit, forced outage rate data, the year peak load LOLP curve A and B being obtained respectively by emulation method not comprising with comprising intermittent power supply;

Step 302: corresponding to a certain LOLP level, obtains curve A, the upper corresponding horizontal W of year peak load of B₁��W₂, system features slope M, calculating useful load capacity is C_ef=W₂-W₁;

Step 303: application character used in proper names and in rendering some foreign names Fu Er formulaEquivalent unit installed capacity C according to setting_nCalculate equivalent unit forced outage rate p_eq;

Step 304: use capacity C_n, forced outage rate p_eqConventional power unit replace intermittent power supply to carry out the calculating of subsequent production;

Step 305: the load obtaining each load point by part throttle characteristics or historical load data continues curve;

Step 306: form system significant condition collection, that reflects the operability of generator, these elements of circuit, wherein each state is corresponding, and a generator, circuit can be with combinations of states and state probability:

In formula, N_gFor generator number, j is state number, p_n��p_lFor the probability of fault occurs generator n and circuit l, q_n=1-p_n, q_l=1-p_l, G₀��G₁Available set and the fault set being respectively the corresponding generator of system state j, l₀��l₁It is respectively available set and the fault set of system state j corresponding line;

Step 307: each load point that calculating electrical network significant condition concentrates each state corresponding is maximum reaches performance number AP:

min1-�ˡ�

Wherein,For exerting oneself of generator n,For the upper limit of exerting oneself of generator n, P_l ^lineFor the wattful power that circuit l transmits,For circuit l transmits the wattful power upper limit,For generator n and load i is to the relation conefficient of circuit l wattful power, N_lFor circuit number, NL is load number, N_gFor generator number, AP_iMaximum for load point i reaches performance number; L_piFor the load peak value of load point i; �� ' is non-negative real number;

Step 308: form the equivalent M-ARY unit being connected to each generator with the maximum performance number that reaches of each load point that system significant condition collection is corresponding;

Step 309: continue curve by the load of above-mentioned equivalence M-ARY unit and each load point, calculate the reliability index LOLP of each load point_i��EENS_i, and then calculate the LOLP of system entirety_sys��EENS_sys��

6. based on the District power network planning method containing high permeability intermittent energy source of system according to claim 1, it is characterised in that: comprise the following steps:

Step 1: data input/output module obtains rack data, comprises the power data of intermittent power supply, system operation data;

Step 2: the target of Electric Power Network Planning, constraint, derivation algorithm are selected, and set correlation parameter by algorithm/target/restricted selection module;

Step 3: according to the target of algorithm/target/restricted selection model choice and constraint, and the weight of setting, model and solution strategies generation module generation power network planning scheme are optimized solving model and are automatically selected suitable solution strategies;

Step 4: the Electric Power Network Planning solving model formed in step 3 is solved by the optimization algorithm selected in invocation step 2, obtains disaggregation;

Step 5: according to each sub-goal selected in step 2, the disaggregation obtained in step 4 is carried out Pareto sequence, is exported the optimum solution after sequence by data input/output module.