CN104239967A - Multi-target economic dispatch method for power system with wind farm - Google Patents

Abstract

The invention provides a multi-target economic dispatch method for a power system with a wind farm. The method comprises the steps that first, possible wind power output scenes in a dispatch cycle are generated, and the number of the scenes is reduced to S; second, a mathematical model of the method is built, target functions include total electricity generation cost, power balance levels of all the moments of all the scenes and spinning reserve levels of all the moments of all the scenes, and constraint conditions include output upper limit and output lower limit constraints of thermal power units, climbing rate constraints and the minimum start and shut down time constraints; third, fuzzification processing is carried out by building membership functions of all the target functions; fourth, the minimum values of all the membership functions are obtained to serve as global satisfaction degree indexes; fifth, an economic dispatch result meeting the constraint conditions in the second step and enabling the global satisfaction degree indexes in the fourth step to be maximum is solved. The economic dispatch method is suitable for solving the problems about economic dispatch of the power system with the wind farm and the thermal power units, and a plurality of optimization targets are balanced in a dispatch result.

Description

A kind of electric system multiple goal economic load dispatching method containing wind energy turbine set

Technical field

The present invention relates to dispatching automation of electric power systems technical field, be specifically related to a kind of electric system multiple goal economic load dispatching method containing wind energy turbine set.

Background technology

Because wind-powered electricity generation has undulatory property and intermittent feature, the large-scale grid connection of wind-powered electricity generation brings huge challenge to Economic Dispatch.Economic Dispatch Problem containing wind energy turbine set relates generally to fired power generating unit and wind energy turbine set, because prior art is difficult to output of wind electric field Accurate Prediction, therefore, needing when carrying out economic load dispatching to ensure that scheduling result can tackle the various possibilities of exerting oneself of wind-powered electricity generation in dispatching cycle well, ensureing that scheduling result has good economy simultaneously.

Obscurity model building method is one of important method addressed this problem, the basic thought adopting obscurity model building to solve this problem is that decision maker thinks that wind power output precision of prediction is not high, therefore its undulatory property is considered when carrying out scheduling and arranging, by setting up the membership function of each optimization aim thus making the expectation to meet decision maker such as the expense that always generates electricity, system power balance and spinning reserve level, realize multiple-objection optimization object.But, because fluctuation range is difficult to determine, therefore the accuracy of scheduling result depends on that whether the selection of membership function is reasonable to a great extent, and the parameter of membership function determines the method not having a set of relative maturity, once membership function Selecting parameter is improper, be difficult to the fluctuation in a big way adapting to wind power output by causing scheduling result.

Scene method solves the another kind of common method containing wind energy turbine set Economic Dispatch Problem, scene method is mainly according to historical data or predicted data, wind-powered electricity generation in dispatching cycle may the situation of exerting oneself be sampled into some typical scenes, can simulate may exerting oneself of wind-powered electricity generation in a big way, therefore scheduling result is stronger for the various possible adaptability of exerting oneself of wind-powered electricity generation.When use scenes method, scene scale is crossed conference and is made calculated amount too large and be difficult to solve and maybe cannot solve, and therefore simplifies calculating usually through scene reduction.But while scene reduction, the precise decreasing of scheduling result can be caused, and the typical case that the scene after reduction can only represent in dispatching cycle exerts oneself, and can not simulate various possible situation of exerting oneself completely.

Summary of the invention

For solving with above-mentioned the deficiencies in the prior art, the invention provides a kind of electric system multiple goal economic load dispatching method containing wind energy turbine set, dispatching method of the present invention combines the advantage of obscurity model building method and scene method, first exert oneself that to carry out typical wind power output in the operation simulation period possible for scene by generating the typical case of wind energy turbine set, then to be exerted oneself combination meeting the exert oneself optimum of finding each fired power generating unit under restriction and the restriction of climbing rate and the constraint condition such as fired power generating unit startup-shutdown time restriction of fired power generating unit by obscurity model building method, make the expense that always generates electricity, the satisfaction of the power-balance under each scene and spinning reserve level is the highest, both the balance of scheduling result to multiple target had been achieved, in turn ensure that the adaptation that scheduling result is possible to various wind power output.

For achieving the above object, object of the present invention can be achieved through the following technical solutions:

Containing an electric system multiple goal economic load dispatching method for wind energy turbine set, described method specifically comprises the following steps:

Step S1, according to the probability density characteristics of wind power prediction data and wind power prediction error in dispatching cycle, generates the scene of exerting oneself that in dispatching cycle, wind-powered electricity generation is possible, and scene is reduced to S.

Step S2; set up the mathematical model of the described electric system multiple goal economic load dispatching method containing wind energy turbine set; model comprises objective function and constraint condition; wherein; objective function comprises the spinning reserve level in each moment under the power-balance level in each moment under total generating expense, each scene and each scene, and constraint condition comprises fired power generating unit and to exert oneself bound constraint, the constraint of fired power generating unit climbing rate, the minimum startup-shutdown time-constrain of fired power generating unit.

Step S3, carries out obfuscation to each objective function, sets up the membership function μ of total generating expense respectively _tC, the power-balance membership function μ in each moment under each scene _{lD, s}the spinning reserve membership function μ in each moment under (t) and each scene _{sR, s}(t).

Step S4, by the global satisfying degree index λ setting up described economic load dispatching method mathematical model, multi-objective optimization question is converted into single-object problem, global satisfying degree index is the minimum value of each membership function, that is:

λ=min [μ _tC, μ _{lD, s}(t), μ _{sR, s}(t)], wherein, moment t=1,2 ..., T, scene number s=1,2 ..., S, then the target optimized just becomes and makes global satisfying degree index λ maximum.

Step S5, solves and meets constraint condition described in step S2 and the economic load dispatching result making global satisfying degree index λ described in step S4 maximum.

Described fired power generating unit bound of exerting oneself is constrained to: wherein, P _it () is fired power generating unit i exerting oneself at moment t, with be respectively minimum load and the maximum output of fired power generating unit i.

Described fired power generating unit climbing rate is constrained to: P _i(t-1)-DR _i≤ P _i(t)≤P _i(t-1)+UR _i, wherein, DR _iand UR _ibe respectively the downward climbing rate restriction of fired power generating unit i and ratio of slope restriction of climbing.

The minimum startup-shutdown time-constrain of described fired power generating unit is: $\left\{\begin{array}{c}{T}_i^{\mathrm{on}}\≤X_i^{\mathrm{on}}\left(t\right)\\ T_i^{\mathrm{off}}\≤X_i^{\mathrm{off}}\left(t\right)\end{array}\right.,$ Wherein, with be respectively fired power generating unit i in continuous on time of moment t and continuous stop time, with be respectively the minimum continuous on time of fired power generating unit i and minimum continuous stop time.

The membership function μ of total generating expense _tCbe expressed as:

${\mathrm{\&mu;}}_{\mathrm{TC}}=\left\{\begin{array}{cc}1& \mathrm{TC}\&le;{\mathrm{TC}}_0\\ \frac{{\mathrm{TC}}^{\max }-\mathrm{TC}}{{\mathrm{TC}}^{\max }-{\mathrm{TC}}_0}& {\mathrm{TC}}_0<C\&le;{\mathrm{TC}}^{\max }\\ 0& \mathrm{TC}>{\mathrm{TC}}^{\max }\end{array}\right.$

Wherein, μ _tCrepresent the total generating expense degree of membership corresponding to total generating expense TC in this moment, TC ₀for desirable total generating cost value, TC ^maxfor maximum acceptable total generating cost value, desirable total generating cost value and maximum acceptable total generating cost value obtain according to operating experience usually.

Total generating expense TC, comprise operating cost and payment for initiation two parts of fired power generating unit, it is expressed as:

$\mathrm{TC}=\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{U}_i\left(t\right)\&times;{\mathrm{OC}}_i\left[P_i\left(t\right)\right]+\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{SC}}_i\left(t\right)\&times;U_i\left(t\right)[1-U_i(t-1)]$

Wherein, T is the time hop count in dispatching cycle, and N is fired power generating unit quantity in system, π _srepresent the probability that scene s occurs; U _it (), for fired power generating unit i is in the start and stop state of moment t, " 1 " represents startup, " 0 " represents shutdown; OC _i[P _i(t)] represent the operating cost of fired power generating unit i at moment t; SC _i(t) represent fired power generating unit i time t switching cost.

Operating cost OC _ithe fuel cost that main finger consumes in power generation process, conventional quadratic function carries out matching at present:

${\mathrm{OC}}_i\left[P_i\left(t\right)\right]=a_i+b_i{P}_i\left(t\right)+c_i{P}_i^2\left(t\right)$

Wherein, a _i, b _iand c _ibe fuel cost coefficient.

Payment for initiation SC _ithe fuel cost that main finger stoppage in transit fired power generating unit consumes when starting, start-up cost can be divided into cold start-up expense and warm start expense according to fired power generating unit idle time length, the payment for initiation of fired power generating unit can be expressed as:

${\mathrm{SC}}_i\left(t\right)=\left\{\begin{array}{cc}{\mathrm{SC}}_i^H& T_i^{\mathrm{off}}\&le;X_i^{\mathrm{off}}\&le;X_i^{\mathrm{off}}\left(t\right)\&le;H_i^{\mathrm{off}}\\ {\mathrm{SC}}_i^C& X_i^{\mathrm{off}}\left(t\right)>H_i^{\mathrm{off}}\end{array}\right.$

Wherein, for the cold start-up time of fired power generating unit i, for the warm start expense of fired power generating unit i, represent the cold start-up expense of fired power generating unit i.

The power-balance membership function μ in each moment under each scene _{lD, s}t () is expressed as:

${\mathrm{\&mu;}}_{\mathrm{LD},s}\left(t\right)=\left\{\begin{array}{cc}0& P_D\left(t\right)\&le;D^{\min }\left(t\right)\\ \frac{P_D\left(t\right)-D^{\min }\left(t\right)}{D\left(t\right)-D^{\min }\left(t\right)}& D^{\min }\left(t\right)<P_D\left(t\right)\&le;D\left(t\right)\\ \frac{D^{\max }\left(t\right)-P_D\left(t\right)}{D^{\max }\left(t\right)-D\left(t\right)}& D\left(t\right)<P_D\left(t\right)\&le;D^{\max }\left(t\right)\\ 0& P_D\left(t\right)>D^{\max }\left(t\right)\end{array}\right.$

Wherein, μ _{lD, s}t () to represent under scene s in t corresponding to the P that exerts oneself _dthe spinning reserve degree of membership of (t), P _dt summation of exerting oneself that () is all fired power generating unit and wind energy turbine set, namely d (t) is current actual load demand, D ^min(t) and D ^maxt () is acceptable peak load fluctuation lower limit and the upper limit.

The spinning reserve membership function μ in each moment under each scene _{sR, s}t () is expressed as:

${\mathrm{\&mu;}}_{\mathrm{SR},s}\left(t\right)=\left\{\begin{array}{cc}0& R\left(t\right)\&le;R^{\min }\left(t\right)\\ \frac{R\left(t\right)-R^{\min }\left(t\right)}{R_{0,s}\left(t\right)-R^{\min }\left(t\right)}& R^{\min }\left(t\right)<R\left(t\right)<R_{0,s}\left(t\right)\\ 1& R\left(t\right)\&GreaterEqual;R_{0,s}\left(t\right)\end{array}\right.$

Wherein, μ _{sR, s}t correspond to the spinning reserve degree of membership of spinning reserve capacity R (t) under () expression scene s in t, spinning reserve capacity R (t) can basis determine; R ^min(t) under scene s in the acceptable minimum spinning reserve value of t, can according to R ^mint ()=Δ D (t) is determined; The desirable a certain proportion of current loads value of Δ D (t), gets 10%, i.e. Δ D (t)=0.1 × D (t) usually; R _{0, s}t () is the desirable spinning reserve value under scene s, can according to R _{0, s}(t)=Δ D (t)+W _j,st () is determined.

The available derivation algorithm of described dispatching method mathematical model comprises: mixed integer programming approach, genetic algorithm, particle cluster algorithm etc.

The present invention has following remarkable advantage and beneficial effect:

(1) the present invention is directed to the Economic Dispatch problem containing grid connected wind power, propose a kind of fuzzy Modeling Method based on scene collection, the method is on the basis of the typical wind power output scene of simulation, set up the power-balance under the membership function of total generating expense, each scene and the spinning reserve membership function under each scene respectively, and by global satisfying degree index, multi-objective optimization question is transformed in order to single-object problem.

(2) the economic load dispatching method that the present invention proposes ensure that the adaptability of optimum results to wind power output various in dispatching cycle by scenario simulation, achieved the balance of internal power balance dispatching cycle, margin capacity level and total multiple target of generating expense by obscurity model building method, balance security and the economy of scheduling result.

Accompanying drawing explanation

Fig. 1 is the algorithm flow chart of a kind of multi objective fuzzy method for solving containing integrated wind plant Economic Dispatch problem of the present invention.

Fig. 2 is the membership function figure of total generating expense of the present invention.

Fig. 3 is the membership function figure of power-balance of the present invention.

Fig. 4 is the membership function figure of spinning reserve of the present invention.

Embodiment

Containing an electric system multiple goal economic load dispatching method for wind energy turbine set, specifically descend step:

Step S1, according to the probability density characteristics of wind power prediction data and wind power prediction error in dispatching cycle, generates the scene of exerting oneself that in dispatching cycle, wind energy turbine set is possible, and scene is reduced to S.Its Scene generates can adopt the Monte Carlo methods of sampling or Latin Hypercube Sampling method, and scene reduction can adopt the scene reduction method based on probability metrics.

Step S2; set up the mathematical model of the described electric system multiple goal economic load dispatching method containing wind energy turbine set; model comprises objective function and constraint condition; wherein; objective function comprises the spinning reserve level in each moment under the power-balance level in each moment under total generating expense, each scene and each scene, and constraint condition comprises fired power generating unit and to exert oneself bound constraint, the constraint of fired power generating unit climbing rate, the minimum startup-shutdown time-constrain of fired power generating unit.

Step S3, carries out obfuscation to each objective function, sets up the membership function μ of total generating expense respectively _tC, the power-balance membership function μ in each moment under each scene _{lD, s}the spinning reserve membership function μ in each moment under (t) and each scene _{sR, s}(t).

Step S4, is converted into single-object problem by the global satisfying degree index setting up described Economic Dispatch method by multi-objective optimization question: λ=min [μ _tC, μ _{lD, s}(t), μ _{sR, s}(t)], wherein, moment t=1,2 ..., T, scene number s=1,2 ..., S.

Step S5, solves and meets constraint condition described in step S2 and the fired power generating unit start and stop arrangement making global satisfying degree described in step S4 maximum and load distribution result.

Retrain below scheduling result demand fulfillment:

Fired power generating unit bound of exerting oneself is constrained to: wherein, P _it () is fired power generating unit i exerting oneself at moment t, with be respectively minimum load and the maximum output of fired power generating unit i.

Fired power generating unit climbing rate is constrained to:

P _i(t-1)-DR _i≤ P _i(t)≤P _i(t-1)+UR _i, wherein, DR _iand UR _ibe respectively the downward climbing rate restriction of fired power generating unit i and ratio of slope restriction of climbing.

The minimum startup-shutdown time-constrain of described fired power generating unit is: $\left\{\begin{array}{c}{T}_i^{\mathrm{on}}\&le;X_i^{\mathrm{on}}\left(t\right)\\ T_i^{\mathrm{off}}\&le;X_i^{\mathrm{off}}\left(t\right)\end{array}\right.$

Wherein, with be respectively fired power generating unit i in continuous on time of moment t and continuous stop time, with be respectively the minimum continuous on time of fired power generating unit i and minimum continuous stop time.

The membership function μ of total generating expense _tCbe expressed as:

${\mathrm{\&mu;}}_{\mathrm{TC}}=\left\{\begin{array}{cc}1& \mathrm{TC}\&le;{\mathrm{TC}}_0\\ \frac{{\mathrm{TC}}^{\max }-\mathrm{TC}}{{\mathrm{TC}}^{\max }-{\mathrm{TC}}_0}& {\mathrm{TC}}_0<C\&le;{\mathrm{TC}}^{\max }\\ 0& \mathrm{TC}>{\mathrm{TC}}^{\max }\end{array}\right.$

Wherein, μ _tCrepresent the total generating expense degree of membership corresponding to total generating expense TC in this moment, TC ₀for desirable total generating cost value, TC ^maxfor maximum acceptable total generating cost value, desirable total generating cost value and maximum acceptable total generating cost value obtain according to operating experience usually.

Total generating expense TC, is characterized in that, comprise thermal power unit operation expense and payment for initiation two parts, it is expressed as:

$\mathrm{TC}=\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{U}_i\left(t\right)\&times;{\mathrm{OC}}_i\left[P_i\left(t\right)\right]+\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{SC}}_i\left(t\right)\&times;U_i\left(t\right)[1-U_i(t-1)]$

Wherein, T is the time hop count in dispatching cycle, and N is fired power generating unit quantity in system, π _srepresent the probability that scene s occurs; U _it (), for fired power generating unit i is in the start and stop state of moment t, " 1 " represents startup, " 0 " represents shutdown; OC _i[P _i(t)] represent the operating cost of fired power generating unit i at moment t; SC _i(t) represent fired power generating unit i time t switching cost.

Operating cost OC _ithe fuel cost that main finger consumes in power generation process, conventional quadratic function carries out matching at present:

${\mathrm{OC}}_i\left[P_i\left(t\right)\right]=a_i+b_i{P}_i\left(t\right)+c_i{P}_i^2\left(t\right)$

Wherein, a _i, b _iand c _ibe fuel cost coefficient.

The payment for initiation SC of fired power generating unit _ithe fuel cost that main finger stoppage in transit fired power generating unit consumes when starting, start-up cost can be divided into cold start-up expense and warm start expense according to fired power generating unit idle time length, the payment for initiation of fired power generating unit can be expressed as:

${\mathrm{SC}}_i\left(t\right)=\left\{\begin{array}{cc}{\mathrm{SC}}_i^H& T_i^{\mathrm{off}}\&le;X_i^{\mathrm{off}}\&le;X_i^{\mathrm{off}}\left(t\right)\&le;H_i^{\mathrm{off}}\\ {\mathrm{SC}}_i^C& X_i^{\mathrm{off}}\left(t\right)>H_i^{\mathrm{off}}\end{array}\right.$

Wherein, for the cold start-up time of fired power generating unit i, for the warm start expense of fired power generating unit i, represent the cold start-up expense of fired power generating unit i.

The power-balance membership function μ in each moment under each scene _{lD, s}t () is expressed as:

${\mathrm{\&mu;}}_{\mathrm{LD},s}\left(t\right)=\left\{\begin{array}{cc}0& P_D\left(t\right)\&le;D^{\min }\left(t\right)\\ \frac{P_D\left(t\right)-D^{\min }\left(t\right)}{D\left(t\right)-D^{\min }\left(t\right)}& D^{\min }\left(t\right)<P_D\left(t\right)\&le;D\left(t\right)\\ \frac{D^{\max }\left(t\right)-P_D\left(t\right)}{D^{\max }\left(t\right)-D\left(t\right)}& D\left(t\right)<P_D\left(t\right)\&le;D^{\max }\left(t\right)\\ 0& P_D\left(t\right)>D^{\max }\left(t\right)\end{array}\right.$

Wherein, μ _{lD, s}t () to represent under scene s in t corresponding to the P that exerts oneself _dthe spinning reserve degree of membership of (t), P _dt summation of exerting oneself that () is all fired power generating unit and wind energy turbine set, namely d (t) is current actual load demand, with for acceptable peak load fluctuation lower limit and the upper limit.

The spinning reserve membership function μ in each moment under each scene _{sR, s}t () is expressed as:

${\mathrm{\&mu;}}_{\mathrm{SR},s}\left(t\right)=\left\{\begin{array}{cc}0& R\left(t\right)\&le;R^{\min }\left(t\right)\\ \frac{R\left(t\right)-R^{\min }\left(t\right)}{R_{0,s}\left(t\right)-R^{\min }\left(t\right)}& R^{\min }\left(t\right)<R\left(t\right)<R_{0,s}\left(t\right)\\ 1& R\left(t\right)\&GreaterEqual;R_{0,s}\left(t\right)\end{array}\right.$

Wherein, μ _{sR, s}the spinning reserve degree of membership of spinning reserve capacity R (t) is corresponded in t, R under (t) expression scene s ^min(t) under scene s in the acceptable minimum spinning reserve value of t, can according to R ^mint ()=Δ D (t) is determined; The desirable a certain proportion of current loads value of Δ D (t), gets 10%, i.e. Δ D (t)=0.1 × D (t) usually; R _{0, s}t () is the desirable spinning reserve value under scene s, can according to R _{0, s}(t)=Δ D (t)+W _j,st () is determined.

Finally should be noted that: above embodiment is only in order to illustrate that technical scheme of the present invention is not intended to limit, although with reference to above-described embodiment to invention has been detailed description, those of ordinary skill in the field are to be understood that: still can modify to the specific embodiment of the present invention or equivalent replacement, and not departing from any amendment of spirit and scope of the invention or equivalent replacement, it all should be encompassed in the middle of right of the present invention.

Claims (7)

1., containing an electric system multiple goal economic load dispatching method for wind energy turbine set, it is characterized in that, said method comprising the steps of:

Step S1, according to the probability density characteristics of wind power prediction data and wind power prediction error in dispatching cycle, generates the scene of exerting oneself that in dispatching cycle, wind energy turbine set is possible, and scene is reduced to S.

Step S2; set up the mathematical model of the described electric system multiple goal economic load dispatching method containing wind energy turbine set; model comprises objective function and constraint condition; wherein; objective function comprises the spinning reserve level in each moment under the power-balance level in each moment under total generating expense, each scene and each scene, and constraint condition comprises fired power generating unit and to exert oneself bound constraint, the constraint of fired power generating unit climbing rate, the minimum startup-shutdown time-constrain of fired power generating unit.

Step S3, carries out obfuscation to each objective function, sets up the membership function μ of total generating expense respectively _tC, the power-balance membership function μ in each moment under each scene _{lD, s}the spinning reserve membership function μ in each moment under (t) and each scene _{sR, s}(t).

Step S4, is converted into single-object problem by the global satisfying degree index λ setting up described economic load dispatching method mathematical model by multi-objective optimization question: λ=min [μ _tC, μ _{lD, s}(t), μ _{sR, s}(t)], wherein, moment t=1,2 ..., T, scene number s=1,2 ..., S.

Step S5, solves and meets constraint condition described in step S2 and the economic load dispatching result making global satisfying degree index λ described in step S4 maximum.

2. the method for claim 1, is characterized in that, the constraint condition described in step S2 is expressed as:

Described fired power generating unit bound of exerting oneself is constrained to:

$P_i^{\min }\&le;P_i\left(t\right)\&le;P_i^{\max }$

Wherein, P _it () is fired power generating unit i exerting oneself at moment t, with be respectively minimum load and the maximum output of fired power generating unit i.

Described fired power generating unit climbing rate is constrained to:

P _i(t-1)-DR _i≤P _i(t)≤P _i(t-1)+UR _i

Wherein, DR _iand UR _ibe respectively the downward climbing rate restriction of fired power generating unit i and ratio of slope restriction of climbing.

The minimum startup-shutdown time-constrain of described fired power generating unit is:

$\left\{\begin{array}{c}{T}_i^{\mathrm{on}}\&le;X_i^{\mathrm{on}}\left(t\right)\\ T_i^{\mathrm{off}}\&le;X_i^{\mathrm{off}}\left(t\right)\end{array}\right.$

Wherein, with be respectively fired power generating unit i in continuous on time of moment t and continuous stop time, with be respectively the minimum continuous on time of fired power generating unit i and minimum continuous stop time.

3. the method for claim 1, is characterized in that, the membership function μ of the total generating expense described in step S3 _tCbe expressed as:

${\mathrm{\&mu;}}_{\mathrm{TC}}=\left\{\begin{array}{cc}1& \mathrm{TC}\&le;{\mathrm{TC}}_0\\ \frac{{\mathrm{TC}}^{\max }-\mathrm{TC}}{{\mathrm{TC}}^{\max }-{\mathrm{TC}}_0}& {\mathrm{TC}}_0<C\&le;{\mathrm{TC}}^{\max }\\ 0& \mathrm{TC}>{\mathrm{TC}}^{\max }\end{array}\right.$

Wherein, μ _tCrepresent the total generating expense degree of membership corresponding to total generating expense TC in this moment, TC ₀for desirable total generating cost value, TC ^maxfor maximum acceptable total generating cost value, desirable total generating cost value and maximum acceptable total generating cost value obtain according to operating experience usually.

4. the method for claim 1, is characterized in that, the power-balance membership function μ in each moment under each scene described in step S3 _{lD, s}t () is expressed as:

${\mathrm{\&mu;}}_{\mathrm{LD},s}\left(t\right)=\left\{\begin{array}{cc}0& P_D\left(t\right)\&le;D^{\min }\left(t\right)\\ \frac{P_D\left(t\right)-D^{\min }\left(t\right)}{D\left(t\right)-D^{\min }\left(t\right)}& D^{\min }\left(t\right)<P_D\left(t\right)\&le;D\left(t\right)\\ \frac{D^{\max }\left(t\right)-P_D\left(t\right)}{D^{\max }\left(t\right)-D\left(t\right)}& D\left(t\right)<P_D\left(t\right)\&le;D^{\max }\left(t\right)\\ 0& P_D\left(t\right)>D^{\max }\left(t\right)\end{array}\right.$

Wherein, μ _{lD, s}t () corresponds to gross capability P in t under representing scene s _dthe spinning reserve degree of membership of (t), P _dt summation of exerting oneself that () is all fired power generating unit and wind energy turbine set, namely d (t) is current actual load demand, D ^min(t) and D ^maxt () is acceptable peak load fluctuation lower limit and the upper limit.

5. the method for claim 1, is characterized in that, the spinning reserve membership function μ in each moment under each scene described in step S3 _{sR, s}t () is expressed as:

${\mathrm{\&mu;}}_{\mathrm{SR},s}\left(t\right)=\left\{\begin{array}{cc}0& R\left(t\right)\&le;R^{\min }\left(t\right)\\ \frac{R\left(t\right)-R^{\min }\left(t\right)}{R_{0,s}\left(t\right)-R^{\min }\left(t\right)}& R^{\min }\left(t\right)<R\left(t\right)<R_{0,s}\left(t\right)\\ 1& R\left(t\right)\&GreaterEqual;R_{0,s}\left(t\right)\end{array}\right.$

Wherein, μ _{sR, s}the spinning reserve degree of membership of spinning reserve capacity R (t) is corresponded in t, R under (t) expression scene s ^min(t) under scene s in the acceptable minimum spinning reserve value of t, can according to R ^mint ()=Δ D (t) is determined; The desirable a certain proportion of current loads value of Δ D (t), such as, can get 10%, i.e. Δ D (t)=0.1 × D (t) usually; R _{0, s}t () is the desirable spinning reserve value under scene s, can according to R _{0, s}(t)=Δ D (t)+W _j,st () is determined.

6. total generating expense TC as claimed in claim 3, it is characterized in that, comprise thermal power unit operation expense and payment for initiation two parts, it is expressed as:

$\mathrm{TC}=\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{U}_i\left(t\right)\&times;{\mathrm{OC}}_i\left[P_i\left(t\right)\right]+\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{SC}}_i\left(t\right)\&times;U_i\left(t\right)[1-U_i(t-1)]$

Wherein, T is the time hop count in dispatching cycle, and N is fired power generating unit quantity in system, π _srepresent the probability that scene s occurs; U _it (), for fired power generating unit i is in the start and stop state of moment t, " 1 " represents startup, " 0 " represents shutdown; OC _i[P _i(t)] represent the operating cost of fired power generating unit i at moment t; SC _i(t) represent fired power generating unit i time t switching cost.

Operating cost OC _ithe fuel cost that main finger consumes in power generation process, conventional quadratic function carries out matching at present:

${\mathrm{OC}}_i\left[P_i\left(t\right)\right]=a_i+b_i{P}_i\left(t\right)+c_i{P}_i^2\left(t\right)$

Wherein, a _i, b _iand c _ibe fuel cost coefficient.

Payment for initiation SC _ithe fuel cost that main finger stoppage in transit unit consumes when starting, start-up cost can be divided into cold start-up expense and warm start expense according to unit outage time length, payment for initiation can be expressed as:

${\mathrm{SC}}_i\left(t\right)=\left\{\begin{array}{cc}{\mathrm{SC}}_i^H& T_i^{\mathrm{off}}\&le;X_i^{\mathrm{off}}\&le;X_i^{\mathrm{off}}\left(t\right)\&le;H_i^{\mathrm{off}}\\ {\mathrm{SC}}_i^C& X_i^{\mathrm{off}}\left(t\right)>H_i^{\mathrm{off}}\end{array}\right.$

Wherein, for the cold start-up time of fired power generating unit i, for the warm start expense of fired power generating unit i, represent the cold start-up expense of fired power generating unit i.

7. spinning reserve capacity R (t) as claimed in claim 5, it is characterized in that, spinning reserve capacity R (t) is expressed as:

$R\left(t\right)=\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{U}_i\left(t\right)\min [P_i^{\max }-P_i\left(t\right),{\mathrm{UR}}_i]$