CN103500997B - Electric power system dispatching method based on hybrid multi-objective lambda iteration method and Newton method - Google Patents
Electric power system dispatching method based on hybrid multi-objective lambda iteration method and Newton method Download PDFInfo
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Abstract
The invention discloses an electric power system dispatching method based on the hybrid multi-objective lambda iteration method and the Newton method. The electric power system dispatching method comprises the following steps that 1) relevant data of each generator set of an electric power system are obtained; 2) a mathematical optimization model for the electric power system environmental economy scheduling problem is built; 3) an EED problem without regard to electric transmission line loss is solved by means of the multi-objective lambda iteration method, and Pareto optimal solutions are obtained; 4) each Pareto optimal solution is taken as an initial solution, an EDE problem with consideration to electric transmission line loss is solved by means of the Newton method, and an optimal solution set is obtained; 5) a final solution is determined in the optimal solution set by means of a multi-objective decision method; 6) the final solution is used as an instruction to be sent to a relevant power station or unit through an automatic generation control device, and unit electricity generation power is controlled through the automatic generation control device. The electric power system dispatching method based on the hybrid multi-objective lambda iteration method and the Newton method is small in calculated amount, short in calculating time, and high in convergence precision, and greatly improves the economy and efficiency of electricity generation of the electric power system.
Description
Technical Field
The invention relates to a power system scheduling method, in particular to a power system scheduling method based on a hybrid multi-target lambda iteration method and a Newton method, and belongs to the field of operation, analysis and scheduling of power systems.
Background
Economic dispatch is an important fundamental problem of power systems[1][2][3]With environmental pollution becoming a global problem in recent years, power companies that are major sources of pollutant emissions are required to reduce the pollutant emissions. Taking China as an example, the thermal power generating units account for 42.5% and 38.0% of the total emissions of SO2 and NOx in China, respectively. In order to reduce the emission, one way is to install a desulfurization and denitrification device, and the other way is to select a scheme with smaller emission in the power generation optimization scheduling. Therefore, the traditional economic scheduling becomes a multi-objective optimization problem, namely the environmental economic scheduling problem in the invention [4]This problem requires both reduction of power generation costs and pollutant emission values.
In order to solve this problem, scholars adopt a weighting method[5][6][7][8]The weighting method has the advantages that the method is simple, but the setting of the weight value is not easyThe setting needs to be performed according to experience, different weights need to be set for different problems, and different weights need to be set even if the same problem but different parameters (such as system load) exist. This deficiency reduces its utility in solving practical problems. Another approach to solve this problem is a multi-objective evolutionary algorithm[4][9][10][11][12][13][14][15][16][17]. The multi-objective evolutionary algorithm has the advantages that the multi-objective evolutionary algorithm can be used for solving various non-convex discontinuous and non-derivable complex optimization problems, but the algorithms have low efficiency and large calculation amount when the algorithm randomly searches a solution space under the condition of lacking problem specific information. With the increase of the number of the problem units, the solution space is more complex, the calculation amount consumed by the algorithms is increased sharply, and the solution precision cannot be guaranteed. The multi-objective evolutionary algorithm mostly adopts a penalty function method for the processing of constraint conditions, penalty function values are different for different systems, and the setting needs to be adjusted according to experience, so that the practicability of the multi-objective evolutionary algorithm in solving the actual problems is reduced.
The above-mentioned references are as follows:
[1]Q.H.Wu，and Y.J.Cao.Dispatching.Encyclopedia of Electrical and ElectronicsEngineering，John Wiley&Sons Inc.，edited by John G.Webster，1999.
[2]Q.H.Wu，and J.T.Ma.Power system optimal reactive power dispatch usingevolutionary programming[J].IEEE Transactions on Power Systems，1995，10(3)：1243-1249.
[3] houyun crane, corm, wu dazu, etc. power system economic load distribution based on generalized ant colony algorithm [ J ] chinese electro-mechanical engineering press, 2003, 23 (3): 59-64.
[4]C.X.Guo，J.P.Zhan，and Q.H.Wu.Dynamic economic emission dispatch based ongroup search optimizer with multiple producers[J].Electric Power Systems Research，2012，86：8-16.
[5]A.K.Basu，A.Bhattacharya，S.Chowdhury，and S.P.Chowdhury.PlannedScheduling for Economic Power Sharing in a CHP-Based Micro-Grid[J].IEEE Transactionson PowerSystems，2012，27(1)：30-38.
[6]L.Bayon，J.Grau，M.Ruiz，and P.Suarez.The exact solution of theenvironmental/economic dispatch problem[J].IEEE Transactions on Power Systems，2012，27(2)：723-731.
[7]P.Venkatesh，R.Gnanadass，and N.Padhy.Comparison and application ofevolutionary programming techniques to combined economic emission dispatch with lineflow constraints[J].IEEE Transactions on Power Systems，2003，18(2)：688-697.
[8]C.Palanichamy and N.S.Babu.Analytical solution for combined economic andemissions dispatch[J].Electric Power Systems Research，2008，78(7)：1129-1137.
[9]M.Abido.Environmental/economic power dispatch using multiobjectiveevolutionary algorithms[J].IEEE Transactions on Power Systems，2003，18(4)：1529-1537.
[10]L.H.Wu，Y.N.Wang，X.F.Yuan，and S.W.Zhou.Environmental/economicpower dispatch problem using multi-objective differential evolution algorithm[J].ElectricPower Systems Research，2010，80(9)：1171-1181.
[11]Q.H.Wu，Z.Lu，M.S.Li，and T.Y.Ji.Optimal placement of facts devices by agroup search optimizer with multiple producer.in Evolutionary Computation.CEC 2008.IEEE Congress on，Jun.2008，pp.1033-1039.
[12]S.He，Q.H.Wu，and J.R.Saunders.Group search optimizer：An optimizationalgorithm inspired by animal searching behavior[J].IEEE Transactions on EvolutionaryComputation，2009，13(5)：973-990.
[13]X.Xia and A.M.Elaiw.Optimal dynamic economic dispatch of generation：Areview[J].Electric Power Systems Research，2010，80(8)：975-986.
[14]M.Basu.Dynamic economic emission dispatch using nondominted sortinggenetic algprthim-II[J].Electric Power Energy System，2008，30(2)：140-149.
[15]L.H.Wu，Y.N.Wang，X.F.Yuan，and S.W.Zhou.Environmental/economicpower dispatch problem using multi-objective differential evolution algorithm[J].ElectricPower Systems Research，2010，80(9)：1171-1181.
[16]J.-B.Park，Y.-W.Jeong，J.-R.Shin，and K.Lee.An improved particle swarmoptimization for nonconvex economic dispatch problems[J].IEEE Transactions on PowerSystems，2010，25(1)：156-166.
[17]L.Wang and C.Singh.Balancing risk and cost in fuzzy economic dispatchincluding wind power penetration based on particle swarm optimization[J].Electric PowerSystems Research，2008，78(8)：1361-1368.
Disclosure of Invention
The invention aims to solve the defects that in the prior art, weighted values among different objective functions need to be set, penalty function values corresponding to constraint conditions are different for different systems, the setting needs to be adjusted according to experience, the solving precision cannot be guaranteed, and the consumed computing time is long, and provides the electric power system scheduling method based on the hybrid multi-objective lambda iteration method and the Newton method, which is suitable for solving the problem of a large-scale electric power system and can improve the economic efficiency.
The purpose of the invention can be achieved by adopting the following technical scheme:
the power system scheduling method based on the hybrid multi-target lambda iteration method and the Newton method is characterized by comprising the following steps of:
1) acquiring output upper limit and lower limit data, output-fuel cost function coefficient data, output-emission function coefficient data, transmission line loss B coefficient data and system total load data of each unit in an electric power system with a plurality of generator sets;
2) establishing a mathematical optimization model of the environmental economic dispatching problem of the power system according to the data obtained in the step 1);
3) solving the power system environmental economic dispatching problem without considering the transmission line loss by adopting a multi-target lambda iteration method (M lambda I) based on the multi-target Kuntake optimal condition according to the model established in the step 2) to obtain a group of pareto optimal solutions;
4) taking each pareto optimal solution obtained in the step 3) as an initial solution, and solving the power system environmental economic scheduling problem considering the loss of the power transmission line by adopting a Newton method to obtain an optimal solution set;
5) determining a final solution in the optimal solution set obtained in the step 4) by adopting a multi-objective decision method;
6) and (3) sending the final solution determined in the step 5) as an instruction to a relevant power plant or unit through an automatic power generation control device, and realizing the control of the power generation power of the unit through an automatic control adjusting device of the power plant or unit.
Specifically, the number of the units in the power system in the step 1) is ngAnd 2) establishing a mathematical optimization model of the power system environmental economic dispatching problem in the step 2), specifically as follows:
2.1) the output-fuel cost function for unit i is given by:
wherein i is more than or equal to 1 and less than or equal to ng，ffuel(xi) As a function of output-fuel cost for unit i, xiIs the active power output of the unit, ai、biAnd ciThe output-fuel cost coefficient of the unit i is obtained;
2.2) the output-emission function of unit i is given by:
wherein f isemi(xi) As a function of the discharge of the unit i, alphai、βi、γi、iAnd xiiThe output-emission function coefficient of the unit i is obtained;
2.3) the upper and lower limits of the output of the unit i are constrained as follows:
wherein,andrespectively representing the upper limit and the lower limit of the output of the unit i;
2.4) the load balancing constraint considering the transmission line loss is shown as follows:
wherein x isDFor the total load of the system, xLFor transmission line losses, xLThe B coefficient is calculated by the following formula:
wherein, Bij、Bi0And B00The B coefficient is the loss of the transmission line;
2.5) the mathematical optimization model of the power system environmental economic dispatching problem considering the power transmission line loss is as follows:
s.t. (3)(4)(5)
wherein f isfuel(xi) And femi(xi) Are represented by formula (1) and formula (2), respectively.
Specifically, in the step 3), the multi-target lambda iteration method is adopted to solve the power system environmental economic dispatching problem without considering the loss of the power transmission line, so as to obtain an optimal pareto solution, which is specifically as follows:
3.1) giving a force output value of the fixed force output unit;
3.2) for a very large lambda value, obtaining the corresponding active output of each unit, and calculating the unbalance amount of the power generation load;
3.3) for a very small lambda value, obtaining the active output corresponding to each unit, and calculating the unbalance amount of the power generation load;
3.4) if the amount of the generated load unbalance calculated in the step 3.2) and the step 3.3) is the same, the potential pareto optimal solution does not exist, and the step 3.1) is returned; and if the number of the generated load unbalance calculated in the step 3.2) and the number of the generated load unbalance calculated in the step 3.3) are different, modifying the lambda value by adopting a dichotomy until one lambda value is found, the active output of each unit corresponding to the lambda value meets the load balance, storing the lambda value and the active output of each unit, and obtaining the active output of each unit as the pareto optimal solution.
Specifically, the amount of the generated load unbalance is a difference between the total generated power of each unit and the total load of the system.
Specifically, the active power output corresponding to each unit enables every two units to meet the equal-difference-ratio micro-increment rate rule.
Specifically, the multi-target decision method in step 5) adopts a sorting method approaching to an ideal value, which is specifically as follows:
5.1) first, a per-unit weighted decision matrix v is calculatedij：
Wherein, <math>
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</math> fijthe ith objective function value, N, for the jth solution in the optimal solution setobjThe number of targets in the multi-target optimization is J, and the number of solutions in the optimal solution set is J;
5.2) calculating the optimal points A respectively+And the least ideal point A-：
Wherein,
5.3) respectively calculating the distance D from each optimal solution to the optimal point+And a distance D to the least ideal point-：
Wherein J is 1, 2, 3, … J;
5.4) calculating the distance ratio R of each optimal solutionj：
Wherein J is 1, 2, 3, … J;
5.5) reacting RjAnd selecting the maximum optimal solution as a final unit overhaul and output scheme.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the method, the solution obtained is a global optimal solution rather than an approximate optimal solution according to the multi-target Countak condition; the calculated amount and the number of the units are in a linear relation, and the method is suitable for solving the problem of a large-scale power system.
2. The method is suitable for the large-scale power system with a large number of units, has the advantages of small calculated amount, short calculating time and high convergence precision, has obvious advantages in the aspects of precision and calculated amount in the large-scale power system with a large number of units, and can greatly improve the economy and the efficiency of power generation of the power system.
Drawings
Fig. 1 is a schematic flow chart of a power system scheduling method according to the method of the present invention.
FIG. 2 is a pareto frontier curve diagram obtained by the method and MOPSO algorithm of the present invention on the 6-unit EED problem.
FIG. 3 is a pareto frontier curve diagram obtained by the method and MOPSO algorithm of the present invention on the 14 unit EED problem.
FIG. 4 is a pareto frontier curve graph obtained by the method and MOPSO algorithm of the present invention on the 140 unit EED problem.
Detailed Description
Example 1:
as shown in fig. 1, the power system scheduling method based on the hybrid multi-target λ iterative method and the newton method of the embodiment includes the following steps:
1) obtaining a compound having ngOutput upper limit and lower limit data of each set in power system of setAndcoefficient data a of output-fuel cost functioni，bi，ciCoefficient data alpha of output-emission functioni，βi，γi，i，ξiB coefficient data B of power transmission line lossij，Bi0，B00And total system load data xD；
2) Establishing a mathematical optimization model, namely an EED model, of the environmental economic dispatching problem of the power system according to the data obtained in the step 1) (the environmental economic dispatching problem and the environmental economic dispatching model are written as the EED problem and the EED model respectively by the following expression), specifically as follows:
2.1) the output-fuel cost function for unit i is represented by a quadratic polynomial of the following formula:
wherein i is more than or equal to 1 and less than or equal to ng，ffuel(xi) As a function of output-fuel cost for unit i, xiIs the active power output of the unit, ai、biAnd ciThe output-fuel cost coefficient of the unit i is obtained;
2.2) the output-emission function of the unit i is represented by the sum of a quadratic polynomial and an exponential power of the following formula:
wherein f isemi(xi) As a function of the discharge of the unit i, alphai、βi、γi、iAnd xiiThe output-emission function coefficient of the unit i is obtained;
2.3) the upper and lower limits of the output of the unit i are constrained as follows:
wherein,andrespectively representing the upper limit and the lower limit of the output of the unit i;
2.4) the load balancing constraint considering the transmission line loss is shown as follows:
wherein x isDFor the total load of the system, xLFor transmission line losses, xLThe B coefficient is calculated by the following formula:
wherein, Bij、Bi0And B00The B coefficient is the loss of the transmission line;
2.5) EED model considering transmission line loss is as follows:
s.t. (3)(4)(5)
wherein f isfue1(xi) And femi(xi) Represented by formula (1) and formula (2), respectively;
3) solving the EED problem without considering the loss of the power transmission line by adopting a multi-target lambda iteration method (M lambda I) based on the multi-target Kunstak optimal condition according to the EED model established in the step 2) to obtain a pareto optimal solution, which is as follows:
3.1) giving a force output value of the fixed force output unit;
3.2) for a very large lambda value (the value is set to be-0.00001), obtaining the corresponding active output of each unit, and calculating the unbalance amount of the generating load;
3.3) for a very small lambda value (the lambda value is set to be-6000), obtaining the corresponding active output of each unit, and calculating the unbalance amount of the power generation load;
3.4) if the amount of the generated load unbalance calculated in the step 3.2) and the step 3.3) is the same, the potential pareto optimal solution does not exist, and the step 3.1) is returned; and if the number of the generated load unbalance calculated in the step 3.2) and the number of the generated load unbalance calculated in the step 3.3) are different, modifying the lambda value by adopting a dichotomy until one lambda value is found, the active output of each unit corresponding to the lambda value meets the load balance, storing the lambda value and the active output of each unit, and obtaining the active output of each unit as the pareto optimal solution.
For a multi-objective optimization model containing equality and inequality constraints, it can be expressed as equation (7):
Minimize：f(x)
s.t. g(x)≤0 (7)
h(x)＝0
if it isFor a pareto optimal solution of equation (7), there is a set of (λ, u, v) that satisfies equation (8):
wherein ifThen u isi0, whereinλ is more than or equal to 0, λ ≠ 0 means that each element in λ is more than or equal to 0, but each element cannot be equal to 0 at the same time;
the corresponding f, g and h of the EED model can be expressed as formula (9):
wherein, I0＝{1，2，…，ng}；
According to the multi-objective Coulter condition, ifIs a pareto optimal solution, then:
wherein, lambda is more than or equal to 0, lambda is not equal to 0, and u is more than or equal to 0;
equation (10) can be discussed in terms of three cases of equations (11) to (13):
if λ10 and λ2If > 0, then:
if λ20 and λ1If > 0, then:
if λ20 and λ1If > 0, then:
wherein, <math>
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according to x1And xiWhether the output is located at the boundary between the upper limit and the lower limit of the output can be classified into 4 cases by equations (11) to (13):
a. if it is <math>
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b. If ( <math>
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c. If it isAnd isThen B is1＝1，B2＝1；
d. If it isAnd isThen B is1＝1，B2＝0；
The solution of equation (10), i.e., equations (11) to (13), can be divided into two steps:
in a first step, equations (11) and (12) are solved:
solving equation (11): the method can be simply applied to a lambda iteration method, giving an initial lambda valueAnd determining the output of each unit satisfying the formula (11). If it isThen λ is increased0A value of, ifThen decrease λ0Up to the values of formulae (11) andare simultaneously satisfied, the solution obtained at this time being such that the objective function f2(x) Minimized solution according to equations (11) andif for a given lambda, is a monotonically increasing characteristic of0Is provided withThen orderIf for a given lambda0Is provided withThen order
Solving equation (12): can be simpleUsing the iterative method of lambda alone, giving an initial lambda valueAnd determining the output of each unit satisfying the formula (12). If it isThen λ is increased0A value of, ifThen decrease λ0Up to the values of formulae (12) andare simultaneously satisfied, the solution obtained at this time being such that the objective function f1(x) Minimized solution according to equations (12) andif for a given lambda, is a monotonically increasing characteristic of0Is provided withThen orderIf for a given lambda0Is provided withThen order
In a second step, equation (13) is solved: the program pseudo code for solving the EED problem without considering the transmission line loss is shown in the following tables 1 and 2 (λ in Table 1)lIs set to-0.00001, lambdasSet to-6000), table 2 is a subroutine pseudo code table in table 1.
TABLE 1 Multi-target lambda iterative method program pseudo code table
TABLE 2 pseudo code table for unit output program obtained according to lambda
Two points are illustrated in table 2: first, how to judgeAndwhether or not the formula (13) is satisfied is determined firstThe above B classes are taken as examples for explanation, namely B1＝0，B2If 1, thenAnd isThe second row in equation (13) is satisfied; if it is <math>
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</math> The first row in equation (13) is satisfied; similarly, it can be judged similarlyWhether or not the formula (13) is satisfied.
Second, how to determine x by equation (13)iThe value of (c). There are generally two approaches, one is direct solution; the other is obtained by looking up a table for a fixed x1Value of each xiCorresponding toThe value of (a) is pre-stored in a table, and in the process of solving M lambda I, the table is directly looked up to obtain xiThe value is obtained. Because the table lookup method is simple and fast, the table lookup method is adopted in the calculation process of the embodiment.
4) In the step (3), since the power transmission line loss is not considered, in this step, each pareto optimal solution obtained in the step 3) is used as an initial solution, and an EED problem considering the power transmission line loss is solved by using a newton method to obtain an optimal solution set, which is specifically as follows:
with Taylor expansion, equation (8) can be represented as equation (14):
equation (14) can be written as a compact form of equation (15):
A×Δy＝b (15)
wherein
Δy＝[Δx Δλ Δμ Δv]T (16)
In formula (17):
the pseudo code of the program for solving the EED problem considering the transmission line loss by using the newton method is listed in table 3 below.
TABLE 3 program pseudo code table for solving EED problem by Newton method
5) Determining a final solution in the optimal solution set obtained in the step 4) by adopting a multi-objective decision method; the multi-target decision adopts a sorting method (TOPSIS) of approaching to an ideal value. TOPSIS comprises the following steps:
5.1) first, a per-unit weighted decision matrix v is calculatedij：
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5.2) calculating the optimal points A respectively+And the least ideal point A-：
Wherein,
5.3) respectively calculating the distance D from each optimal solution to the optimal point+And a distance D to the least ideal point-：
Wherein J is 1, 2, 3, … J;
5.4) calculating the distance ratio R of each optimal solutionj：
Wherein J is 1, 2, 3, … J;
5.5) reacting RjSelecting the maximum optimal solution as a final unit maintenance and output scheme;
6) and (3) sending the final solution determined in the step 5) as an instruction to a relevant power plant or unit through an automatic power generation control device, and realizing the control of the power generation power of the unit through an automatic control adjusting device of the power plant or unit.
Example 2:
to verify the effectiveness of the power system scheduling method based on the hybrid multi-objective λ iterative method and the newton method in the above embodiment 1, this embodiment takes systems of one 6-unit, one 14-unit, and one 140-unit as examples, and the data of the three systems can be obtained from the above documents [15] and [16] in the background art, respectively, where the total load of the 6-unit system is 283.4MW, the total load of the 14-unit system is 950MW, and the total load of the 140-unit system is 49342 MW.
For comparison, the EED problem is solved by using the MOPSO algorithm. The population number and maximum iteration number settings in MOPSO are shown in table 4. When solving the EED problem with MOPSO, a penalty function method is used to handle the constraint, i.e. the absolute value of the unbalance amount of the equality constraint represented by equation (4) is multiplied by a penalty factor, which is also listed in table 4 below, and added to the corresponding objective function.
| System for controlling a power supply | Number of groups | Maximum number of iterations | f1Corresponding penalty factor | f2Corresponding penalty factor |
| 6 machine set | 50 | 100 | 5000 | 3 |
| 14 units | 400 | 400 | 2000 | 4 |
| 140 units | 800 | 600 | 20000 | 40 |
Table 4 parameters and penalty factor settings for MOPSO algorithm in 3 systems
Fig. 2 shows a solution obtained by solving the 6-unit system by using the method of embodiment 1 of the present invention, and also shows a result obtained by solving the problem by using the MOPSO algorithm, and fig. 3 shows a solution obtained by solving the 14-unit system by using the method of embodiment 1 of the present invention, and also shows a result obtained by solving the problem by using the MOPSO algorithm.
It can be seen that, compared with the result obtained by adopting the MOPSO algorithm, the solution with a smaller generation cost value and a smaller transmission line loss value can be obtained by adopting the extended lambda iterative solution method, and the convergence precision is high.
In order to verify the effectiveness of the method in embodiment 1 of the present invention in solving a large-scale power system, the method is further used for solving an EED problem in a power system including 140 units, and results obtained by solving the problem by using an MOPSO algorithm are also listed, as shown in fig. 4.
The method of embodiment 1 of the invention is implemented by one processorCoreTMThe results of solving the 6 unit system, the 14 unit system and the 140 unit system are shown IN the following table 5, and the delta indexes and the time spent on solving the C lambda IN and the MOPSO are shown IN the table 5 (note: the power transmission line loss is considered when the 6 unit system and the 14 unit system are solved, but the power transmission line loss is not considered when the 140 unit system is solved due to the lack of data, the difference between M lambda I and C lambda IN IN the tables 5, 2, 3 and 4 is that M lambda I represents a multi-target lambda iteration method, namely only the step 3 is included, but not the step 4 is included, and C lambda IN represents a mixed multi-target lambda iteration method and a Newton method, namely both the step 3 and the step 4 are included).
In conclusion, the invention provides a simple and efficient multi-target scheduling method for the power system, which has the advantages of small calculated amount, short calculation time and high convergence precision, has obvious advantages in the aspects of precision and calculated amount in a large-scale power system containing a large number of units, and greatly improves the economy and efficiency of power generation of the power system.
TABLE 5 Delta index of results and time spent to solve EED problem in 3 systems for three different methods
The above description is only for the preferred embodiments of the present invention, but the protection scope of the present invention is not limited thereto, and any person skilled in the art can substitute or change the technical solution and the inventive concept of the present invention within the scope of the present invention.
Claims (6)
1. The power system scheduling method based on the hybrid multi-target lambda iteration method and the Newton method is characterized by comprising the following steps of:
1) acquiring output upper limit and lower limit data, output-fuel cost function coefficient data, output-emission function coefficient data, transmission line loss B coefficient data and system total load data of each unit in an electric power system with a plurality of generator sets;
2) establishing a mathematical optimization model of the environmental economic dispatching problem of the power system according to the data obtained in the step 1);
3) solving the environmental economic dispatching problem of the power system without considering the loss of the power transmission line by adopting a multi-target lambda iteration method based on the multi-target Coueta gram optimal condition according to the model established in the step 2) to obtain a group of pareto optimal solutions;
4) taking each pareto optimal solution obtained in the step 3) as an initial solution, and solving the power system environmental economic scheduling problem considering the loss of the power transmission line by adopting a Newton method to obtain an optimal solution set;
5) determining a final solution in the optimal solution set obtained in the step 4) by adopting a multi-objective decision method;
6) and (3) sending the final solution determined in the step 5) as an instruction to a relevant power plant or unit through an automatic power generation control device, and realizing the control of the power generation power of the unit through an automatic control adjusting device of the power plant or unit.
2. The power system scheduling method based on the hybrid multi-objective lambda iterative method and the Newton method according to claim 1, wherein: step 1) the number of the units in the power system is ngAnd 2) establishing a mathematical optimization model of the power system environmental economic dispatching problem in the step 2), specifically as follows:
2.1) the output-fuel cost function for unit i is given by:
wherein i is more than or equal to 1 and less than or equal to ng，ffuel(xi) As a function of output-fuel cost for unit i, xiIs the active power output of the unit, ai、biAnd ciThe output-fuel cost coefficient of the unit i is obtained;
2.2) the output-emission function of unit i is given by:
wherein f isemi(xi) As a function of the discharge of the unit i, alphai、βi、γi、∈iAnd xiiThe output-emission function coefficient of the unit i is obtained;
2.3) the upper and lower limits of the output of the unit i are constrained as follows:
wherein,andrespectively representing the upper limit and the lower limit of the output of the unit i;
2.4) the load balancing constraint considering the transmission line loss is shown as follows:
wherein x isDFor the total load of the system, xLFor transmission line losses, xLThe B coefficient is calculated by the following formula:
wherein, Bij、Bi0And B00The B coefficient is the loss of the transmission line;
2.5) the mathematical optimization model of the power system environmental economic dispatching problem considering the power transmission line loss is as follows:
s.t. (3)(4)(5)
wherein f isfuel(xi) And femi(xi) Are represented by formula (1) and formula (2), respectively.
3. The power system scheduling method based on the hybrid multi-objective lambda iterative method and the Newton method according to claim 1 or 2, wherein: step 3), solving the power system environmental economic dispatching problem without considering the loss of the power transmission line by adopting a multi-target lambda iteration method to obtain a pareto optimal solution, which is as follows:
3.1) giving a force output value of the fixed force output unit;
3.2) for the lambda value with the size of-0.00001, obtaining the active output corresponding to each unit, and calculating the unbalance amount of the power generation load;
3.3) for the lambda value of-6000, calculating the corresponding active output of each unit, and calculating the unbalance amount of the power generation load;
3.4) if the amount of the generated load unbalance calculated in the step 3.2) and the step 3.3) is the same, the potential pareto optimal solution does not exist, and the step 3.1) is returned; and if the number of the generated load unbalance calculated in the step 3.2) and the number of the generated load unbalance calculated in the step 3.3) are different, modifying the lambda value by adopting a dichotomy until one lambda value is found, the active output of each unit corresponding to the lambda value meets the load balance, storing the lambda value and the active output of each unit, and obtaining the active output of each unit as the pareto optimal solution.
4. The power system scheduling method based on the hybrid multi-objective lambda iterative method and the Newton method according to claim 3, wherein: the generating load unbalance is the difference between the total generating power of each unit and the total load of the system.
5. The power system scheduling method based on the hybrid multi-objective lambda iterative method and the Newton method according to claim 3, wherein: the active power output corresponding to each unit enables every two units to meet the equal difference ratio micro-increment rate rule.
6. The power system scheduling method based on the hybrid multi-objective lambda iterative method and the Newton method according to claim 1, wherein: step 5) the multi-target decision method adopts a sequencing method approaching to an ideal value, and the method specifically comprises the following steps:
5.1) first, a per-unit weighted decision matrix v is calculatedij：
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</math> fijthe ith objective function value, N, for the jth solution in the optimal solution setobjThe number of targets in the multi-target optimization is J, and the number of solutions in the optimal solution set is J;
5.2) calculating the optimal points A respectively+And the least ideal point A-：
Wherein,
5.3) respectively calculating the distance D from each optimal solution to the optimal point+And a distance D to the least ideal point-：
Wherein J is 1, 2, 3, … J;
5.4) calculating the distance ratio R of each optimal solutionj：
Wherein J is 1, 2, 3, … J;
5.5) reacting RjAnd selecting the maximum optimal solution as a final unit overhaul and output scheme.
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