CN111539586A - Power dispatching center and decision-making assisting method - Google Patents
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Abstract
The invention provides a power dispatching center and an auxiliary decision method, which solve the problems that various functions of a traditional power grid dispatching operation control system mostly focus on the prior and middle links of power dispatching, weak links influencing the power grid operation ideality cannot be found from the overall perspective, the analysis period is short, and the analysis content is few. The method comprises the following steps: acquiring a power grid model and an operation mode in an analysis time period; establishing an ideal scheduling operation scheme model according to the power grid model and the operation mode; and solving the ideal scheduling operation scheme model to obtain an optimal scheduling scheme.
Description
Technical Field
The invention relates to the field of operation and analysis of power systems, in particular to a power dispatching center and an auxiliary decision method.
Background
The power grid dispatching mechanism is used as a power system operation command center, and how to improve the economical efficiency and the energy-saving and environment-friendly level of power grid operation on the premise of considering the safe operation of a power grid becomes an important task of current power grid dispatching work. In order to improve the system operation ideality level, the current scheduling operation manager starts from links such as mode formulation, plan arrangement, real-time control and the like in the actual scheduling production process, and researches and adopts a large number of technical means and management measures. However, such scheduling operation promotion measures mostly focus on the "prior" or "in the middle" link of scheduling production, weak links influencing the operation ideality of the power grid cannot be found from the global perspective, the analysis period is short, and the analysis content is few.
Disclosure of Invention
In view of this, the embodiment of the invention provides a power dispatching center and an auxiliary decision method, which solve the problems that various functions of the conventional power grid dispatching operation control system mostly focus on the prior and middle links of power dispatching, weak links influencing the power grid operation ideality cannot be found from the global perspective, the analysis period is short, and the analysis content is few.
An embodiment of the present invention provides a power dispatching center and an auxiliary decision method, including: acquiring a power grid model and an operation mode in an analysis time period; establishing an ideal scheduling operation scheme model according to the power grid model and the operation mode; and solving the ideal scheduling operation scheme model to obtain an optimal scheduling scheme.
In an embodiment, the obtaining of the power grid model and the operation mode in the analysis period includes: acquiring a power grid structure model and an actual operation data section; the power grid structure model and the actual operation data section comprise power grid equipment parameters, a power grid operation mode and unit power measurement at different moments.
In one embodiment, the unit output force and the section load are recorded simultaneously when the actual operation data section is obtained.
In one embodiment, the ideal scheduling run plan model is:
obj
s.t.
t＝1,2,…T
Pimin≤pi(t)+Δpi(t)≤Pimax
-ΔPi_down≤(pi(t+1)+Δpi(t+1)-pi(t)-Δpi(t))≤ΔPi_up
wherein: f1 is the objective function of the lowest total coal consumption in power generation, F2 is the objective function of the lowest power generation cost, T is the number of time periods in the analysis period, and delta pi(t) is a variable to be solved, and represents an active power adjustment strategy of the unit i at t, xi(t) continuous on-off time of unit i at t, CNi(pi(t)) is the operating coal consumption of unit i at t, CSi(xi(t-1),ui(t)) is the starting-up coal consumption from t-1 time period to t time period when the unit i has state change, xi(t)>0 denotes the continuous boot time, xi(t)<0 denotes the continuous downtime ui(t) is the state of the unit i at t, ui(t) < 1 > indicates power-on, ui(t) ═ 0 denotes shutdown, DNi(pi(t)) is the running cost, DS, of the unit i at ti(xi(t-1),ui(t)) is the starting cost from the time period t-1 to the time period t when the state of the unit i changes, Pm_tdmaxPower control limit for the d-th stable profile at time t, PiminIs the active lower limit, P, of the ith generatorimaxIs the active upper limit, Δ P, of the ith generatori_downIs the landslide capability, Δ P, of the g-th generatori_upClimbing capability of the ith generator, Sm_iThe active sensitivity of the ith generator is the mth stable section.
In one embodiment, the active sensitivity of each generator to the stable fault in the model of the ideal scheduling operation scheme is converted into the active sensitivity of the branch and the active sensitivity of the generator respectively.
In one embodiment, a method for determining the active sensitivity of a branch includes: converting the shorthand direct current power flow equation into a branch power flow equation; writing the branch power flow equation into a matrix form; substituting a branch node incidence matrix of a power grid to be analyzed into a matrix form of the branch tide equation to obtain a sensitivity matrix of the branch active power to node injection; and solving the active sensitivity of the branch according to the active-to-node injection sensitivity matrix of the branch.
In one embodiment, a method for determining a reactive sensitivity of a generator includes: and obtaining the active sensitivity of the generator according to the active-to-node injection sensitivity matrix of the branch.
In an embodiment, the solving the model of the ideal scheduling operation scheme to obtain the optimal scheduling scheme includes: performing single-objective optimization solution on the objective function with the lowest total coal consumption for power generation and the objective function with the lowest power generation cost separately; selecting one of the objective function of the total coal consumption for power generation and the objective function of the lowest power generation cost as a secondary optimization objective; degrading the secondary optimization target into a constraint equation; and solving the secondary optimization target under the constraint form of the constraint equation to obtain the ideal scheduling operation scheme.
In one embodiment, the constraint equation is: f1< ═ S1 per1, where S1 is the minimum of the objective function found for the single target of F1, and per1 represents the proportionality coefficient for the secondary optimization target degradation.
An electric power dispatching center adopts an auxiliary decision method to solve an ideal dispatching operation adjusting scheme obtained by an ideal dispatching operation scheme model to dispatch a power grid.
According to the power dispatching center, the auxiliary decision method and the power dispatching center, an optimal dispatching scheme is obtained by obtaining a power grid model and an operation mode in an analysis time period, establishing an ideal dispatching operation scheme model according to the power grid model and the operation mode and solving the ideal dispatching operation scheme model, the previous actual operation mode of power grid dispatching is deeply analyzed from the 'after' perspective, the ideal dispatching operation scheme model is further established according to the power grid model and the operation mode which are obtained from the after, and the optimal dispatching scheme is obtained by solving. The invention analyzes the effect of the previous power grid dispatching actual operation scheme from the 'after-the-fact' perspective and carries out auxiliary decision, and the power dispatching operation personnel can find the defects in the daily dispatching process through the given after-the-fact regulation suggestion, and the power dispatching operation personnel can improve the defects in the subsequent dispatching production process by referring to the auxiliary decision suggestion.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic flow chart illustrating an assistant decision method for improving scheduling performance of a power scheduling center according to an embodiment of the present invention.
Fig. 2 is a schematic flow chart illustrating an assistant decision method for improving scheduling performance of a power scheduling center according to another embodiment of the present invention.
Fig. 3 is a flowchart illustrating an assistant decision method for improving scheduling performance of a power scheduling center according to another embodiment of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The following detailed description is exemplary in nature and is intended to provide further details of the invention. Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow chart illustrating an assistant decision method for improving scheduling performance of a power scheduling center according to an embodiment of the present invention.
As shown in fig. 1, the method for assisting decision making of scheduling performance improvement in a power scheduling center includes:
step 01: acquiring a power grid model and an operation mode in an analysis time period;
step 02: establishing an ideal scheduling operation scheme model according to the power grid model and the operation mode;
step 03: and solving the ideal scheduling operation scheme model according to the power grid model and the operation mode.
And then, scheduling the power grid according to the ideal scheduling operation adjustment scheme obtained by solving the ideal scheduling operation scheme model.
The method deeply analyzes the previous actual operation scheme of power grid dispatching from the 'after' perspective, optimizes and dispatches the power grid according to the actual operation data of the power grid acquired after the 'after', and solves the optimal adjustment strategy. The optimized rescheduling objective function integrates the goals of energy conservation, economy, fairness and the like, obtains an optimal solution through a sequence optimization solving method, and assists scheduling operators to find out the defects or the cognitive error zones in the scheduling production process. The effect of the previous actual operation scheme of power grid dispatching is analyzed and assisted in decision making from the 'after' perspective, and the power dispatching operation personnel can find the defects in the daily dispatching process through the given after adjustment suggestion, and the power dispatching operation personnel can improve the defects in the subsequent dispatching production process by referring to the assisted decision suggestion.
In an embodiment of the present invention, acquiring a power grid model and an operation mode in an analysis period includes: acquiring a power grid structure model and an actual operation data section from an energy management system of a dispatching center; preferably, the power grid structure model and the actual operation data section include power grid equipment parameters, a power grid operation mode and unit power measurement at different moments. And acquiring a power grid structure model and an actual operation data section from an EMS (energy management system) of a dispatching center, wherein the actual operation data section comprises power grid equipment parameters, a power grid operation mode, unit power measurement at different moments and the like. The SCADA and the state estimation module in the EMS system both store the operation modes of the system at all times, and in order to reduce the influence of phenomena such as measuring burrs on subsequent calculation, the invention adopts the power grid operation mode stored by state estimation as an analysis basis. The existing power system optimization algorithm basically adopts a linearization mode to calculate the relationship between the section active power and the unit output power. Such a linearized processing simplifies the computational model, but introduces additional errors. Aiming at the characteristics of the load size and the known section load of the later scheduling and evaluation, in order to improve the calculation precision,
in one embodiment of the invention, when the section of the actual operation data is obtained, the output of the unit and the section load are recorded at the same time. The existing power system optimization algorithm basically adopts a linearization mode to calculate the relationship between the section active power and the unit output power, the linearization processing mode simplifies a calculation model, but introduces extra errors, and aims at the characteristics of the load size and the known section load of the later scheduling evaluation, in order to improve the calculation precision, the invention takes the generator output offset as a solving variable, converts the offset of the stable section power according to the generator output offset, and reduces the errors caused by full linearization, so when the power grid operation data in an analysis period is obtained, the unit output power and the section load need to be recorded simultaneously. Recording the number of the units of the system as I, the number of the total stable sections as M, and recording the actual active power measurement value of the unit I at t as pi(t), the actual power measurement value of the d-th stable cross section at the time t is Pm_td. Because the post optimization process does not need to consider the change of the load, and the influence of the load on the section load is reflected in Pm_tdTherefore, the load size in the calculation model can be ignored, and the calculation amount is reduced.
In an embodiment of the present invention, establishing an ideal scheduling operation scheme model includes: the active sensitivity of the stable section to the active sensitivity of each generator is converted into the active sensitivity of the branch and the active sensitivity of the generator. Because the stable section is the set of a group of branches, the sensitivity of the active power of the stable section to the active power of each generator can be converted into the sensitivity of the active power of the branches and the active power of the generators.
Fig. 2 is a schematic flow chart illustrating an assistant decision method for improving scheduling performance of a power scheduling center according to another embodiment of the present invention.
As shown in fig. 2, the method for converting the sensitivity of the active power of the stable cross section to the active power of each generator into the sensitivity of the active power of the branch circuit includes:
step 021: converting the shorthand direct current power flow equation into a branch power flow equation;
step 022: writing a branch flow equation into a matrix form;
step 023: substituting the branch node incidence matrix of the power grid to be analyzed into a matrix form of a branch tide equation to obtain a sensitivity matrix of branch active power to node injection;
and 024: and solving the active sensitivity of the branch according to the active sensitivity matrix of the branch to the node injection.
Firstly, a direct current power flow equation is briefly recorded:
P＝Bθ (1)
in the formula (1), P is a node injection power vector, theta is a point voltage phase angle vector, and B is an imaginary part of a node admittance matrix. Similarly, according to the simplified conditions of the P-Q decomposition method, the branch power flow equation can be obtained as follows:
in the formula (2), the subscript i is the number of the head end of the branch, j is the number of the tail end of the branch, PijFor active power flow at the beginning of the branch, BijFor branch admittance, θijIs the phase angle difference between the head and the tail of the branch, thetaiIs the phase angle of the head end, thetajIs the terminal phase angle, xijIs the branch reactance. The branch tide equation is written in a matrix form as follows:
Pl＝BlΦ (3)
in the formula (3), PlVector formed for active power flow of each branch, BlAnd phi is a phase angle difference vector at two ends of each branch.
Recording a branch node incidence matrix of a power grid to be analyzed as A, wherein A is an m-row n-column matrix, m is the number of branches, and n is the number of nodes. In the row element corresponding to the branch ij, the ith column is 1, the jth column is-1, and other elements are all 0. Then there are:
Pl＝BlΦ＝BlAθ＝BlAB-1P (4)
from formula (4):
ΔPl＝BlAB-1ΔP (5)
as shown in formula (5), if F is equal to BlAB-1Then there is Δ PlF Δ P, i.e. the matrix F is the sensitivity matrix of branch active power injection to the node.
In an embodiment of the present invention, converting sensitivity of active power of a stable cross section to active power of each generator into sensitivity of active power of the generator includes: and obtaining the active sensitivity of the generator according to the sensitivity matrix of the branch active power to the node injection. After the sensitivity matrix of the branch active power pair node injection is obtained, the branch power flow of the unit sensitivity of the stable section can be conveniently obtained. If the stable section m consists of the j-th branch and the k-th branch, the sensitivity of the stable section m to the active injection of the node i is as follows:
Sm_i＝Fji+Fki(6)
wherein, FjiIs the element of the jth row and ith column of the matrix F, FkiIs the element of the kth row, the ith column of the matrix F.
In one embodiment of the invention, the ideal operation scheme can synthesize environmental protection and economic targets according to the actual operation requirements of the system, and establish a model of an optimal scheduling scheme. The ideal scheduling operating scheme model may be:
obj
s.t.
t＝1,2,…T
Pimin≤pi(t)+Δpi(t)≤Pimax
-ΔPi_down≤(pi(t+1)+Δpi(t+1)-pi(t)-Δpi(t))≤ΔPi_up
wherein: f1 is the objective function of the lowest total coal consumption in power generation, F2 is the objective function of the lowest power generation cost, T is the number of time periods in the analysis period, and delta pi(t) is a variable to be solved, and represents an active power adjustment strategy of the unit i at t, xi(t) continuous on-off time of unit i at t, CNi(pi(t)) is the operating coal consumption of unit i at t, CSi(xi(t-1),ui(t)) is the starting-up coal consumption from t-1 time period to t time period when the unit i has state change, xi(t)>0 denotes the continuous boot time, xi(t)<0 denotes the continuous downtime ui(t) is the state of the unit i at t, ui(t) < 1 > indicates power-on, ui(t) ═ 0 denotes shutdown, DNi(pi(t)) is the running cost, DS, of the unit i at ti(xi(t-1),ui(t)) is the starting cost from the time period t-1 to the time period t when the state of the unit i changes, Pm_tdmaxPower control limit for the d-th stable profile at time t, PiminIs the active lower limit, P, of the ith generatorimaxIs the active upper limit, Δ P, of the ith generatori_downIs the landslide capability, Δ P, of the g-th generatori_upClimbing capability of the ith generator, Sm_iThe active sensitivity of the ith generator is set for the mth stable section; the specific calculation method is as follows:
because the stable section is the set of a group of branches, the sensitivity of the active power of the stable section to the active power of each generator can be converted into the sensitivity of the active power of the branches and the active power of the generators. The method for solving the branch active power and the generator active power sensitivity comprises the following steps:
firstly, a direct current power flow equation is briefly recorded:
P＝Bθ (7)
in the formula (1), P is a node injection power vector, theta is a point voltage phase angle vector, and B is an imaginary part of a node admittance matrix. Similarly, according to the simplified conditions of the P-Q decomposition method, the branch power flow equation can be obtained as follows:
in the formula (2), the subscript i is the number of the head end of the branch, j is the number of the tail end of the branch, PijFor active power flow at the beginning of the branch, BijFor branch admittance, θijIs the phase angle difference between the head and the tail of the branch, thetaiIs the phase angle of the head end, thetajIs the terminal phase angle, xijIs the branch reactance. The branch tide equation is written in a matrix form as follows:
Pl＝BlΦ (9)
in the formula (3), PlVector formed for active power flow of each branch, BlAnd phi is a phase angle difference vector at two ends of each branch.
Recording a branch node incidence matrix of a power grid to be analyzed as A, wherein A is an m-row n-column matrix, m is the number of branches, and n is the number of nodes. In the row element corresponding to the branch ij, the ith column is 1, the jth column is-1, and other elements are all 0. Then there are:
Pl＝BlΦ＝BlAθ＝BlAB-1P (10)
from formula (4):
ΔPl＝BlAB-1ΔP (11)
as shown in formula (5), if F is equal to BlAB-1Then there is Δ PlF Δ P, i.e. the matrix F is the sensitivity matrix of branch active power injection to the node.
After the sensitivity matrix of the branch active power to the node injection is obtained, the sensitivity of the stable section to the unit can be conveniently obtained. If the stable section m consists of the j-th branch and the k-th branch, the sensitivity of the stable section m to the active injection of the node i is as follows:
Sm_i＝Fji+Fki(12)
Fjiis the element of the jth row and ith column of the matrix F, FkiIs the k-th matrix FRow, element of column i.
Fig. 3 is a schematic flow chart illustrating a process of solving an ideal scheduling operation scenario model according to another embodiment of the present invention.
As shown in fig. 3, solving the model of the ideal scheduling operating scenario includes:
step 031: performing single-objective optimization solution on the objective function with the lowest total coal consumption for power generation and the objective function with the lowest power generation cost;
step 032: selecting one of the objective function of the lowest total coal consumption in power generation and the objective function of the lowest power generation cost as a primary optimization objective, and the other objective function of the lowest total coal consumption in power generation as a secondary optimization objective;
step 033: degrading the secondary optimization target to form a constraint equation; and
step 034: and solving the secondary optimization target under the constraint form of the constraint equation to obtain an ideal scheduling operation scheme.
Firstly, single-objective optimization solution is carried out on F1 and F2, and then one most concerned optimization objective is selected as a main optimization objective. If F2 is selected as the primary optimization goal, then F1 is selected as the secondary optimization goal. The secondary optimization objective F1 is degenerated into new constraints, the constraint equation is as follows:
F1<＝S1*per1 (13)
wherein, S1 is the minimum value of the objective function obtained for the F1 single target; per1 represents the scaling factor for the secondary optimization objective degradation.
Under the condition of newly adding constraints, the F2 is solved again, and an ideal scheduling operation adjustment scheme, namely a specific after-event assistant decision strategy, can be obtained.
In an embodiment of the present invention, an electric power dispatching center adopts any one of the above-described assistant decision methods to improve dispatching effectiveness of the electric power dispatching center. The auxiliary decision method comprises the steps of establishing an ideal scheduling operation scheme model and solving the ideal scheduling operation scheme model by obtaining a power grid model and an operation mode in an analysis time period, deeply analyzing a previous power grid scheduling actual operation scheme from the 'future' perspective, optimizing and re-scheduling according to power grid actual operation data obtained from the future, and solving an optimal adjustment strategy. The optimized rescheduling objective function integrates the goals of energy conservation, economy, fairness and the like, and an optimal solution is obtained through a sequence optimization solving method. The invention analyzes the effect of the previous power grid dispatching actual operation scheme from the 'after-the-fact' perspective and carries out auxiliary decision, and the power dispatching operation personnel can find the defects in the daily dispatching process through the given after-the-fact regulation suggestion, and the power dispatching operation personnel can improve the defects in the subsequent dispatching production process by referring to the auxiliary decision suggestion.
Each functional unit in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
In an embodiment of the present invention, an electric power dispatching center suitable for an electric power system includes: a processor and a memory coupled to the processor, the memory storing a computer program which, when executed by the processor, performs the method steps of the method for assisting decision-making in a power dispatch center.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program check codes, such as a U disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk.
It will be appreciated by those skilled in the art that the invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The embodiments disclosed above are therefore to be considered in all respects as illustrative and not restrictive. All changes which come within the scope of or equivalence to the invention are intended to be embraced therein.
Claims (10)
1. An assistant decision-making method of a power dispatching center is characterized in that,
acquiring a power grid model and an operation mode in an analysis time period;
establishing an ideal scheduling operation scheme model according to the power grid model and the operation mode; and
and solving the model of the ideal scheduling operation scheme to obtain an optimal scheduling scheme.
2. The aid decision method according to claim 1, wherein the obtaining of the grid model and the operation mode during the analysis period comprises: acquiring a power grid structure model and an actual operation data section; the power grid structure model and the actual operation data section comprise power grid equipment parameters, a power grid operation mode and unit power measurement at different moments.
3. The aid-decision method according to claim 2, wherein the unit output and section load are recorded simultaneously when the actual operational data section is obtained.
4. An aid decision method according to claim 1, characterized in that the ideal scheduling run plan model is:
Pimin≤pi(t)+Δpi(t)≤Pimax-ΔPi_down≤(pi(t+1)+Δpi(t+1)-pi(t)-Δpi(t))≤ΔPi_up
wherein: f1 is the objective function of the lowest total coal consumption in power generation, F2 is the objective function of the lowest power generation cost, T is the number of time periods in the analysis period, and delta pi(t) is a variable to be solved, and represents an active power adjustment strategy of the unit i at t, xi(t) continuous on-off time of unit i at t, CNi(pi(t)) is the operating coal consumption of unit i at t, CSi(xi(t-1),ui(t)) is the starting-up coal consumption from t-1 time period to t time period when the unit i has state change, xi(t)>0 denotes the continuous boot time, xi(t)<0 denotes the continuous downtime ui(t) is the state of the unit i at t, ui(t) < 1 > indicates power-on, ui(t) ═ 0 denotes shutdown, DNi(pi(t)) is the running cost, DS, of the unit i at ti(xi(t-1),ui(t)) is the starting cost from the time period t-1 to the time period t when the state of the unit i changes, Pm_tdmaxPower control limit for the d-th stable profile at time t, Pimin is the active lower limit of the ith generator, PimaxIs the active upper limit, Δ P, of the ith generatori_downIs the landslide capability, Δ P, of the g-th generatori_upClimbing capability of the ith generator, Sm_iThe active sensitivity of the ith generator is the mth stable section.
5. The aid-decision method according to claim 4, characterized in that the active sensitivities of each generator in the ideal scheduling operation scheme model for the stable fault are converted into the active sensitivity of the branch and the active sensitivity of the generator, respectively.
6. An aid decision method according to claim 5, characterized in that the branch-active sensitivity finding method comprises:
converting the shorthand direct current power flow equation into a branch power flow equation;
writing the branch power flow equation into a matrix form;
substituting a branch node incidence matrix of a power grid to be analyzed into a matrix form of the branch tide equation to obtain a sensitivity matrix of the branch active power to node injection; and
and solving the active sensitivity of the branch according to the active-to-node injection sensitivity matrix of the branch.
7. An aid decision method according to claim 6, characterised in that the method of determining the sensitivity of the generator to work comprises: and obtaining the active sensitivity of the generator according to the active-to-node injection sensitivity matrix of the branch.
8. The aid-decision method according to claim 4, wherein the solving of the model of the ideal scheduling operation scheme to obtain the optimal scheduling scheme comprises:
performing single-objective optimization solution on the objective function with the lowest total coal consumption for power generation and the objective function with the lowest power generation cost separately;
selecting one of the objective function of the total coal consumption for power generation and the objective function of the lowest power generation cost as a secondary optimization objective;
degrading the secondary optimization target into a constraint equation; and
and under the constraint form of the constraint equation, solving the secondary optimization target to obtain the ideal scheduling operation scheme.
9. An aid decision method according to claim 8, characterized in that the constraint equation is: f1< ═ S1 per1, where S1 is the minimum of the objective function found for the single target of F1, and per1 represents the proportionality coefficient for the secondary optimization target degradation.
10. An electric power dispatching center, characterized in that, the ideal dispatching operation adjustment scheme obtained by solving the ideal dispatching operation scheme model by the aid of the aid decision method according to any one of claims 1 to 9 is adopted to dispatch the power grid.
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