CN107169607B - Energy efficiency power plant optimization configuration and plant network coordination planning method based on cost - Google Patents

Energy efficiency power plant optimization configuration and plant network coordination planning method based on cost Download PDF

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CN107169607B
CN107169607B CN201710388777.3A CN201710388777A CN107169607B CN 107169607 B CN107169607 B CN 107169607B CN 201710388777 A CN201710388777 A CN 201710388777A CN 107169607 B CN107169607 B CN 107169607B
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范宏
左路浩
马莲
朱佩琳
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Abstract

The invention relates to a cost-based energy efficiency power plant optimal configuration and plant network coordination planning method, which comprises the following steps: 1) according to a two-layer planning theory and a plant network planning constraint condition, establishing a cost-based energy efficiency power plant optimization configuration and plant network coordination two-layer planning model which comprises an upper layer planning model and a lower layer planning model; 2) according to the reliability requirement of the planning scheme, performing reliability verification on the generated scheme to ensure that the obtained optimal scheme does not have an island net rack; 3) solving the model by adopting a hybrid algorithm of an improved genetic algorithm and an original-dual interior point method according to the characteristics of the model to obtain an optimal planning scheme, 4) dividing a planning cycle of each situation into three phases for two situations of coordination planning without considering the energy efficiency power plant and the plant network and coordination planning with considering the energy efficiency power plant and the plant network, respectively obtaining and comparing multiple indexes under each phase, and drawing a planning result graph of each scheme. Compared with the prior art, the method has the advantages of island elimination, accurate solution, clear logical structure, practicability, reasonability and the like.

Description

Energy efficiency power plant optimization configuration and plant network coordination planning method based on cost
Technical Field
The invention relates to the technical field of power transmission network planning, in particular to a cost-based energy efficiency power plant optimization configuration and plant network coordination planning method.
Background
The main task of traditional power grid planning is to determine the optimal power grid planning scheme to meet the power demand and development of the whole society by matching with power supply planning and on the basis of researching the load increase condition and power supply planning scheme during planning, so that the construction and operation cost of the whole power system is minimum. And after the power system is reformed, 'the plant network is separated', the power grid planning is independently carried out by a power grid company, although the social benefit is optimal and still is the final target to be considered by the power grid planning, the direct purpose of a power grid investor is converted from the original overall benefit maximization of power generation, transmission and distribution into the benefit maximization of power grid operation and construction, and the change is the inevitable result of the power system reformation.
With the gradual deepening of the innovation of the power system in China, the power plant and the power grid are thoroughly separated, and a power generation company and a power grid company are established. And when the conditions are mature in the future, the power transmission and supply assets of the power grid company are continuously stripped, the fields of hair, power transmission and power supply are finally formed, a free competition mechanism is introduced, and the natural monopoly property of the state is still maintained in the field of power transmission. For a power generation company, the maximum power generation economic benefit is preferably obtained with the lowest investment of a power plant and the lowest annual operation cost under a new situation; for grid companies, integrated power enterprises were usually the only investors for power grids and power plants before the power regime was reformed. After the innovation of the electric power system, a power grid company is mainly responsible for investment and construction of a power grid and power supply construction related to macroscopic development. With the large-scale construction of energy-efficient power plants, the power grid is used as a bridge between a power generation side and a user side, and the coordinated development with the construction of the energy-efficient power plants needs to be considered. Therefore, for planning departments of power sources and power grids, how to define their respective planning targets, establish new planning principles, achieve optimal configuration and sustainable development of resources, and coordinate mutual interest relationships becomes a main task.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a cost-based energy efficiency power plant optimization configuration and plant network coordination planning method which is accurate in solution, clear in logical structure, practical and reasonable and can eliminate isolated islands.
The purpose of the invention can be realized by the following technical scheme:
a cost-based energy efficiency power plant optimization configuration and plant network coordination planning method comprises the following steps:
1) according to a two-layer planning theory and a plant network planning constraint condition, establishing a cost-based energy efficiency power plant optimization configuration and plant network coordination two-layer planning model which comprises an upper layer planning model and a lower layer planning model;
2) according to the reliability requirement of the planning scheme, performing reliability verification on the generated scheme to ensure that the obtained optimal scheme does not have an island net rack;
3) and solving the model by adopting a hybrid algorithm of an improved genetic algorithm and an original-dual interior point method according to the characteristics of the model to obtain an optimal planning scheme.
In the step 1), the upper layer planning model takes the minimum total investment cost as an objective function, and the expression is as follows:
F＝min{F1,F2,F3,...,Fh,...,FH}
Figure GDA0002697476730000021
Figure GDA0002697476730000022
Figure GDA0002697476730000023
wherein, FhIs the upper target value of the H-th situation, H is the H newly added power node configuration situation based on the lower power planning result, T is the number of phases contained in the planning period, r is the mark rate, 1/(1+ r)(t-1)YIs the conversion factor of the fund, Y is the number of years included in the t-th phase, Ω is the set of nodes,
Figure GDA0002697476730000024
unit investment cost, L, of transmission line for nodes i to j in the t-th stageijFor the length of the transmission line between nodes i to j,
Figure GDA0002697476730000025
newly building transmission line loop number G between nodes i and j in the t stage under the h conditiontIn order to reduce the investment cost of the power plant,
Figure GDA0002697476730000026
in order to reduce the investment cost of the power transmission line,
Figure GDA0002697476730000027
the sum of the load shedding penalty fees under the N and N-1 network security of the t stage under the h situation,
Figure GDA0002697476730000028
for the investment cost of newly building a conventional power plant at the t stage,
Figure GDA0002697476730000029
for the investment cost of newly building an energy efficiency power plant in the t stage,
Figure GDA00026974767300000210
the operation and maintenance cost of the conventional power plant at the t stage is shown, I is the number of nodes, S is the number of circuit loops,
Figure GDA00026974767300000211
the load shedding amount of the node i under the normal operation state of the t stage in the h situation,
Figure GDA00026974767300000212
under the h-th condition, the load shedding amount of a node i under the N-1 running state of the s-th line disconnection at the t stage is obtained, and a and b are respectively a load shedding penalty coefficient under a normal state and a load shedding penalty coefficient under an N-1 state.
In step 1), the constraint conditions of the upper layer planning model include:
safety constraints under N conditions:
Figure GDA0002697476730000031
wherein the content of the first and second substances,
Figure GDA0002697476730000032
for the system node admittance matrix in the h-th case of the t-th stage,
Figure GDA0002697476730000033
for the nodal phase angle column vector in the h-th case of the t-th stage,
Figure GDA0002697476730000034
for the conventional power plant node output power train under the h situation of the t stageThe vector of the vector is then calculated,
Figure GDA0002697476730000035
for the node load column vector in the h-th case of the t-th stage,
Figure GDA0002697476730000036
the output column vector of the node of the energy efficiency power plant under the h-th condition in the t stage,
Figure GDA0002697476730000037
is composed of
Figure GDA0002697476730000038
The column vector of (a) is,
Figure GDA0002697476730000039
for the total power flow on the transmission line between point i and node j in the h case of the t-th stage,
Figure GDA00026974767300000310
the original number of circuit loops from node i to node j in the h-th situation of the t-th stage,
Figure GDA00026974767300000311
for the phase angle column vector of the node in the h-th case of the ith node in the t-th stage,
Figure GDA00026974767300000312
is a node phase angle column vector, x, in the h-th condition of the j-th node in the t-th stageijFor the reactance of a single transmission line between nodes i to j,
Figure GDA00026974767300000313
for a single loop capacity upper bound between node i and node j in phase t,
Figure GDA00026974767300000314
column vectors of the minimum value and the maximum value of the node output of the conventional power plant at the t stage respectively;
safety constraints under N-1 conditions:
Figure GDA00026974767300000315
wherein the content of the first and second substances,
Figure GDA00026974767300000316
the system node admittance matrix after the s line is disconnected in the h situation of the t stage,
Figure GDA00026974767300000317
for the phase angle column vector of the node after the s line of the h node is disconnected in the t stage,
Figure GDA00026974767300000318
is the output force column vector of the conventional power plant node after the s line is disconnected in the h situation of the t stage,
Figure GDA00026974767300000319
the energy efficiency power plant node output column vector is obtained after the s line is disconnected under the h situation of the t stage,
Figure GDA00026974767300000320
is composed of
Figure GDA00026974767300000321
The column vector of (a) is,
Figure GDA00026974767300000322
the total power flow on the transmission line between the node i and the node j after the s line is disconnected in the h situation of the t stage,
Figure GDA00026974767300000323
the original number of circuit loops from node i to node j after the s-th line is disconnected in the h-th situation of the t-th stage,
Figure GDA0002697476730000041
the number of circuit loops from the node i to the node j after the s line is disconnected in the h-th situation of the t-th stage,
Figure GDA0002697476730000042
is a node phase angle column vector after the s line is disconnected under the h situation of the ith node in the t stage,
Figure GDA0002697476730000043
is a node phase angle column vector after the s line is disconnected under the h situation of the j node in the t stage,
Figure GDA0002697476730000044
column vectors of the minimum value and the maximum value of the conventional power plant node output in the t stage are respectively.
In the step 1), the lower-layer planning model takes the minimum investment cost of the power plant as a target function, and the expression is as follows:
Figure GDA0002697476730000045
Figure GDA0002697476730000046
Figure GDA0002697476730000047
Figure GDA0002697476730000048
wherein G istIn order to reduce the investment cost of the power plant,
Figure GDA0002697476730000049
for the investment cost of newly building a conventional power plant at the t stage,
Figure GDA00026974767300000410
for the investment cost of newly building an energy efficiency power plant in the t stage,
Figure GDA00026974767300000411
the operation and maintenance cost of the conventional power plant at the t stage, M is the type number of the unit of the conventional power plant, CG,mInvestment cost per unit capacity of type m for newly building conventional generator set, PG,mIs the capacity of the m-type conventional unit,
Figure GDA00026974767300000412
the number of m-type conventional units is newly built in the t stage, K is the number of types of the units of the energy efficiency power plant, CE,kInvestment cost per unit capacity of type k for newly built energy efficiency power plant, PE,kFor the capacity of a k-type energy efficient power plant,
Figure GDA00026974767300000413
the number of new k-type energy-efficient power plants in the t stage is determined, Y is the number of years included in the t stage, N is the number of conventional units of the selected conventional power plant to be built in the t stage, L is the number of energy-efficient power plants of the selected energy-efficient power plant to be built in the t stage,
Figure GDA00026974767300000414
is the capacity, I, of the nth conventional power plant unit in the t-th stageOM&E,nFor the unit operating maintenance costs of the nth conventional power plant unit,
Figure GDA00026974767300000415
the number of hours of operation of the nth conventional power plant unit in the y year in the t stage,
Figure GDA00026974767300000416
capacity of the first energy-efficient power plant unit in the t stage, IOM,lFor the unit operating cost of the energy efficient power plant,
Figure GDA00026974767300000417
the number of hours of operation of the energy efficiency power plant unit in the ith year in the tth stage.
In the step 1), the constraint conditions of the lower-layer planning model are as follows:
Figure GDA00026974767300000418
Figure GDA00026974767300000419
Figure GDA0002697476730000051
Figure GDA0002697476730000052
Figure GDA0002697476730000053
Figure GDA0002697476730000054
Figure GDA0002697476730000055
wherein R is the spare capacity coefficient of the unit,
Figure GDA0002697476730000056
is the maximum load of the t-th stage,
Figure GDA0002697476730000057
load of the y year of the T phase, ThThe maximum annual operating hours, alpha is the minimum ratio of the energy efficiency power plant to the load, beta is the maximum ratio of the energy efficiency power plant to the load,
Figure GDA0002697476730000058
is the maximum load of the t-th stage,
Figure GDA0002697476730000059
the number of the m-type conventional units is newly built in the t stage,
Figure GDA00026974767300000510
the maximum number of the m-type conventional units is newly built in the t stage,
Figure GDA00026974767300000511
newly establishing the number of k-type energy efficiency power plants in the t stage,
Figure GDA00026974767300000512
and newly building the maximum number of k-type energy efficiency power plants in the t stage.
The step 2) specifically comprises the following steps:
21) performing connectivity verification on the generated scheme according to the reliability requirement;
22) for the net rack with the isolated island, the isolated island is eliminated by randomly selecting a circuit to be erected of nodes in the isolated island and the net rack, so that the net rack is communicated;
23) for the net rack which has independent small nets and is not communicated, the independent small nets are eliminated by randomly selecting the lines to be erected between the nodes in the independent small nets and the net rack nodes, the net rack communication is realized, and the net rack connectivity requirement of the optimal planning scheme is finally ensured.
The step 3) specifically comprises the following steps:
and solving by adopting a hybrid algorithm of an improved genetic algorithm and an original-dual interior point method to obtain an optimal planning scheme, wherein the upper layer adopts a self-adaptive genetic algorithm to carry out global optimization to generate an optimized grid frame to obtain line construction cost, expected values of operation maintenance cost, environment cost and investment cost are obtained through simulation operation, the lower layer calculates load shedding punishment cost by using an original-point-to-point interior point method, the load shedding punishment cost is fed back to the upper layer to obtain the total cost of an optimized target, and the optimal grid frame structure is obtained through iterative convergence.
The improved genetic algorithm is an adaptive genetic algorithm, and the cross probability P in the adaptive genetic algorithmcAnd the mutation probability PmThe calculation formula of (a) is as follows:
Figure GDA00026974767300000513
Figure GDA0002697476730000061
wherein f is the one with larger fitness value of the two individuals to be crossed, f' is the fitness value of the individual to be mutated, favgMean fitness value of the population, fmaxIs the maximum individual fitness value, P, in the populationc1、Pc2、Pm1、Pm2And A is a constant.
The method further comprises the following steps:
4) and for two situations of not considering the energy efficiency power plant and the plant network coordination planning and considering the energy efficiency power plant and the plant network coordination planning, dividing a planning period of each situation into three stages, respectively obtaining and comparing multiple indexes in each stage, and drawing a planning result graph of each scheme.
The step 4) specifically comprises the following steps:
41) for two situations of not considering the coordination planning of the energy efficiency power plant and the plant network and considering the coordination planning of the energy efficiency power plant and the plant network, the planning is divided into two categories, and the planning period of each category is divided into three stages;
42) calculating indexes of the three stages under each condition, including the number of additional units, the number of newly-built lines and the investment cost;
43) sequencing each condition according to the stage sequence, and comparing and analyzing each index;
44) and drawing a planning result graph of each scheme according to the calculation result.
Compared with the prior art, the invention has the following advantages:
the invention provides a method for establishing an energy efficiency power plant coordinated planning system by considering investment cost and power grid and energy efficiency power plant construction coordinated development, combining multi-objective, discrete and nonlinear power grid planning, constructing a mathematical model of energy efficiency power plant optimized configuration and power grid coordinated planning based on cost, verifying reliability of a generated scheme according to reliability requirements of a planning scheme to ensure that an island net rack does not exist in the obtained optimal scheme, effectively solving the planning model according to a self-adaptive genetic algorithm and a mixed algorithm reasonably designed by an original-dual interior point method to obtain an optimal planning scheme, drawing a planning result diagram, meeting the requirements of energy efficiency power plant optimized configuration and power grid coordinated planning based on cost, and having the advantages of clear logical structure, practicability and reasonability.
Drawings
FIG. 1 is a flow chart of the present invention.
FIG. 2 is a block diagram of an IEEE-RTS24 system configuration according to an exemplary embodiment.
FIG. 3 is a flow chart of the hybrid algorithm of the present invention.
FIG. 4 is a planning diagram of a coordinated planning phase of a plant and network without consideration of EPP according to an embodiment.
FIG. 5 is a planning diagram of a second phase of the coordinated planning of the plant and the network without considering EPP according to the embodiment.
FIG. 6 is a three-plan view of the coordinated planning stage of the plant and network without considering EPP of the embodiment.
FIG. 7 is a planning diagram of a coordinated planning stage of a plant and network considering EPP according to an embodiment.
FIG. 8 is a planning diagram of a second stage of the coordinated planning of the plant and the network considering EPP according to the embodiment.
FIG. 9 is a three-plan view of the coordinated planning stage of the plant and network considering EPP according to the embodiment.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
Examples
As shown in fig. 1, a method for energy efficiency power plant optimal configuration and plant network coordination planning based on cost includes the following steps:
s1, according to a two-layer planning theory and a plant network planning constraint condition, establishing a cost-based energy efficiency power plant optimization configuration and plant network coordination two-layer planning model, wherein the upper-layer planning model of the plant network coordination planning in the embodiment takes the minimum total investment cost as a target function, and comprises the investment cost of a power transmission line, the investment cost of power plants (conventional power plants and EPPs) and the load shedding penalty cost; the lower-layer planning model takes the minimum investment cost of the power plant as a target function, and comprises the construction cost of a newly-built conventional power plant, the construction cost of a newly-built energy efficiency power plant and the operation and maintenance cost.
The upper model is:
F＝min{F1,F2,F3,...,Fh,...,FH}
Figure GDA0002697476730000071
Figure GDA0002697476730000072
Figure GDA0002697476730000073
Figure GDA0002697476730000074
Figure GDA0002697476730000081
Figure GDA0002697476730000082
in the formula, H is the configuration situation of H kinds of newly added power nodes based on the lower power planning result; i is the number of nodes, and 24 is taken; s is the number of circuit loops; t is the number of stages contained in the planning period, and 3 is taken; fhUpper layer target value for h case; r is the sticking rate (%), 1/(1+ r)(t-1)YTaking 8% as a conversion coefficient of capital; y is the number of years included in the t stage, and 5 years are taken; Ω is a set of nodes;
Figure GDA0002697476730000083
power line list for nodes i to j in t stageSetting investment cost (ten thousand yuan/km), and taking 200 ten thousand yuan/km; l isijThe length of the transmission line between nodes i and j;
Figure GDA0002697476730000084
newly building a transmission line return number between nodes i and j in the h stage under the h situation;
Figure GDA0002697476730000085
investment cost of newly building a conventional power plant in the t stage;
Figure GDA0002697476730000086
investment cost of newly building an energy efficiency power plant in the t stage;
Figure GDA0002697476730000087
the operation and maintenance cost of the conventional power plant at the t stage is obtained;
Figure GDA0002697476730000088
the sum of the load shedding penalty cost under the N and N-1 network security of the t stage under the h situation;
Figure GDA0002697476730000089
the load shedding amount of the node i under the normal operation state of the t stage in the h situation,
Figure GDA00026974767300000810
is its column vector;
Figure GDA00026974767300000811
the load shedding amount of the node i in the N-1 running state of the s-th line disconnection in the h-th stage is obtained; a. b is a load shedding penalty coefficient under a normal state and a load shedding penalty coefficient under an N-1 state, and both the load shedding penalty coefficients are 100 ten thousand yuan/MW;
Figure GDA00026974767300000812
the node admittance matrix of the system at the t stage under the h-th situation;
Figure GDA00026974767300000813
is the t stage node phase angle column vector under the h condition;
Figure GDA00026974767300000814
the output force column vector of the conventional power plant node at the t stage in the h situation,
Figure GDA00026974767300000815
column vectors of the minimum value and the maximum value of the node output of the conventional power plant at the t stage respectively;
Figure GDA00026974767300000816
the output column vector of the energy efficiency power plant node at the t stage under the h situation;
Figure GDA00026974767300000817
the load column vector of the t stage node under the h situation;
Figure GDA00026974767300000818
the original number of circuit loops from the node i to the node j in the t stage under the h condition; x is the number ofijThe reactance of a single power transmission line between nodes i and j;
Figure GDA00026974767300000819
the total power flow of the power transmission line between the node i and the node j in the t stage under the h condition;
Figure GDA00026974767300000820
the capacity upper limit of a single loop between the node i and the node j in the t stage is set;
Figure GDA00026974767300000821
and newly establishing an upper limit of the number of the transmission lines between nodes i and j in the t stage. The symbols marked with ^ in the formula represent network parameters and corresponding trends under the condition of the line N-1.
The lower model is:
Figure GDA0002697476730000091
Figure GDA0002697476730000092
Figure GDA0002697476730000093
Figure GDA0002697476730000094
Figure GDA0002697476730000095
Figure GDA0002697476730000096
Figure GDA0002697476730000097
Figure GDA0002697476730000098
Figure GDA0002697476730000099
Figure GDA00026974767300000910
Figure GDA00026974767300000911
in the formula, M is the type number of the conventional power plant unit; k is the type number of the energy efficiency power plant unit; n is the t-th orderThe section comprises the number of the selected conventional units of the conventional power plant to be built; l is the number of energy efficiency power plants including the selected energy efficiency power plant to be built in the t stage; cG,mInvestment cost of unit capacity of type m for newly building a conventional generator set; pG,mThe capacity of an m-type conventional unit;
Figure GDA00026974767300000912
newly building the number of m-type conventional units in the t stage; cE,kInvestment cost of unit capacity of type k for newly building an energy efficiency power plant; pE,kThe capacity of a k-type energy efficiency power plant;
Figure GDA00026974767300000913
newly building the number of k-type energy efficiency power plants in the t stage;
Figure GDA00026974767300000914
the capacity of the nth conventional power plant unit in the t stage; i isOM&E,nThe unit operation maintenance cost of the nth conventional power plant unit comprises the fuel cost, the environmental cost and the maintenance cost of the conventional power plant;
Figure GDA00026974767300000915
the number of hours of operation of the nth conventional power plant unit in the y year in the t stage;
Figure GDA00026974767300000916
the capacity of the first energy efficiency power plant unit in the t stage; i isOM,lThe unit operating cost of the first energy efficiency power plant;
Figure GDA00026974767300000917
the number of operating hours of the energy efficiency power plant unit in the ith year in the tth stage; t ishThe maximum number of hours of operation per year; r is the spare capacity coefficient of the unit, and is generally 0.2;
Figure GDA00026974767300000918
is the maximum load of the t stage; alpha is the minimum ratio of the energy efficiency power plant to the load; beta is the maximum ratio of the energy efficiency power plant to the load;
Figure GDA00026974767300000919
the load of the y year in the t stage;
Figure GDA00026974767300000920
newly building the maximum number of m-type conventional units in the t stage;
Figure GDA00026974767300000921
newly building the maximum number of k-type energy efficiency power plants in the t stage;
s2, according to the reliability requirement of the planning scheme, the reliability of the generated scheme is verified to ensure that the obtained optimal scheme has no island net rack, and the method specifically comprises the following steps:
step S21: performing connectivity verification on the generated scheme according to the reliability requirement;
step S22: for the net rack with the isolated island, the isolated island is eliminated by randomly selecting a circuit to be erected of nodes in the isolated island and the net rack, so that the net rack is communicated;
step S23: for the net rack which has an independent small net and is not communicated, the independent small net is eliminated by randomly selecting a line to be erected between a node in the independent small net and a net rack node, so that the net rack communication is realized, and the net rack connectivity requirement of an optimal planning scheme is finally ensured;
the embodiment is an IEEE-RTS system, the system has 24 existing nodes, the basic total load is 5700MW, the system network structure diagram is shown in fig. 3, in the diagram, the solid line is the existing power transmission line, and the dotted line is the power transmission line to be selected. Connectivity verification is carried out, and an island net rack does not exist in the embodiment and meets the reliability requirement;
s3, solving the model by adopting a hybrid algorithm of an improved genetic algorithm and an original-dual interior point method according to the characteristics of the model to obtain an optimal planning scheme, wherein the upper layer adopts a self-adaptive genetic algorithm to perform global optimization to generate an optimized grid frame, the expected values of operation maintenance cost, environment cost, investment cost and the like are obtained through simulation operation when the line construction cost is reached, the lower layer calculates the load shedding punishment cost by utilizing the original-dual-point interior point method and feeds back the load shedding punishment cost to the upper layer to obtain the total cost of an optimized target, and the optimal grid frame structure is obtained through iterative convergence.
In this embodiment, the lower layer shear load constraint adopts an original-dual interior point method, the upper layer adopts an adaptive genetic algorithm, fig. 2 is a flow chart of the hybrid algorithm of this embodiment, and the specific steps are as follows:
the first step is as follows: inputting original parameters such as node load data, existing unit data, unit data to be built, EPP data to be built and the like, and setting a stage counter t to be 1;
the second step is that: calculating the maximum load at the end of the phase and the load at the end of each year;
the third step: randomly generating an initial population, wherein a genetic algebra counter gen is 1;
the fourth step: judging whether an individual meets the spare capacity constraint and the EPP capacity constraint, if so, calculating construction cost of a conventional power plant and the EPP, taking the number of hours of operation of each unit as a variable, calculating operation maintenance cost of the unit by using an interior point method, setting the sum of the construction cost and the operation maintenance cost as a lower-layer objective function value, and if not, setting the individual lower-layer objective function value to be 0;
the fifth step: calculating a target function value and a fitness value of the lower-layer initial population, and performing selection, crossing and variation operations according to the fitness value to generate a new individual;
and a sixth step: taking the filial generation as a new original population, and calculating an objective function value and a fitness value of the filial generation;
the seventh step: judging whether the genetic algebra gen reaches the maximum value, if so, outputting an optimal solution and a node configuration set of the newly-built unit, if not, setting a genetic algebra counter gen as gen +1, and returning to the fourth step;
eighth step: numbering the node configuration sets, taking the node configuration condition h as 1, and entering an upper-layer power grid planning stage;
the ninth step: randomly generating an initial population, wherein a genetic algebra counter gen is 1;
the tenth step: carrying out connectivity verification and correction on the individuals, calculating the line construction cost, and calculating the load shedding penalty cost by using an interior point method;
the eleventh step: calculating a target function value and a fitness value of the initial population of the upper layer, and performing selection, intersection and variation operations according to the fitness value to generate a new individual;
the twelfth step: taking the filial generation as a new original population, and calculating an objective function value and a fitness value of the filial generation;
the thirteenth step: judging whether the genetic algebra gen reaches the maximum value, if so, outputting an optimal solution and storing, if not, returning to the tenth step, wherein a genetic algebra counter gen is gen + 1;
the fourteenth step is that: judging whether h reaches the maximum value, if so, outputting the optimal solution and the optimal planning scheme, and if not, returning to the ninth step, wherein h is h + 1;
the fifteenth step: and judging whether t reaches the maximum value, if so, ending the operation, and if not, returning to the second step, wherein t is t + 1.
The improved genetic algorithm is an adaptive genetic algorithm, and the cross probability and the mutation probability of the improved genetic algorithm can be automatically changed according to the fitness. Cross probability P in adaptive genetic algorithmscAnd the mutation probability PmThe calculation formula of (a) is as follows:
Figure GDA0002697476730000111
Figure GDA0002697476730000112
in the formula, f is the one with larger fitness value in the two individuals to be crossed; f' is the fitness value of the individual to be mutated, favgMean fitness value of the population, fmaxThe maximum individual fitness value in the population. Pc1、Pc2、Pm1、Pm2A is a constant, wherein A is 9.903438.
As can be seen from the above formula, when the fitness value of an individual is higher than the population average fitness value, the population is increasedThe smaller probability is crossed and mutated, and when the fitness value of the individual is lower than the average fitness value of the population, the population has a larger probability Pc1Proceed to cross, Pm1Performing mutation, and when the fitness value of the individual is close to or equal to the maximum fitness value, the cross probability is Pc2The mutation probability is Pm2And local optimal solution of the evolution area is avoided. Here take Pc1＝0.5，Pc2＝0.9，Pm1＝0.02，Pm2＝0.05；
S4 analyzes and considers two conditions of energy efficiency power plant and plant network coordination planning and energy efficiency power plant and plant network coordination planning, divides the planning period of each condition into three stages, respectively calculates the number of newly added machine sets, the number of newly built lines, static and dynamic investment cost and the discharge amount of various pollutants in each stage, contrasts and analyzes each index, and draws a planning result chart of each scheme, and the specific steps are as follows:
step S41: aiming at two situations of not considering the coordination planning of an energy efficiency power plant and a plant network and considering the coordination planning of the energy efficiency power plant and the plant network, the planning is divided into two categories, and the planning period of each category is divided into three stages, wherein each stage is 5 years;
step S42: calculating the number of additional units, the number of newly-built lines, the investment cost and each index of each condition at each stage;
step S43: sequencing each condition according to the stage sequence, and comparing and analyzing each index;
step S44: and drawing a planning result graph of each scheme according to the calculation result.
In the embodiment, the simulated power system adopts an IEEE-RTS24 system, and the planning diagrams of each stage of the plant network coordination planning without considering EPP are shown in FIGS. 4-6; the planning diagrams of the various stages of the plant network coordination planning considering the EPP are shown in FIGS. 7-9.

Claims (2)

1. A cost-based energy efficiency power plant optimization configuration and plant network coordination planning method is characterized by comprising the following steps:
1) according to a two-layer planning theory and a plant network planning constraint condition, a cost-based energy efficiency power plant optimization configuration and plant network coordination two-layer planning model is established, and comprises an upper layer planning model and a lower layer planning model, wherein the upper layer planning model takes the minimum total investment cost as a target function, and the expression is as follows:
F＝min{F1,F2,F3,...,Fh,...,FH}
Figure FDA0002697476720000011
Figure FDA0002697476720000012
Figure FDA0002697476720000013
wherein, FhIs the upper target value of the H-th situation, H is the H newly added power node configuration situation based on the lower power planning result, T is the number of phases contained in the planning period, r is the mark rate, 1/(1+ r)(t-1)YIs the conversion factor of the fund, Y is the number of years included in the t-th phase, Ω is the set of nodes,
Figure FDA0002697476720000014
unit investment cost, L, of transmission line for nodes i to j in the t-th stageijFor the length of the transmission line between nodes i to j,
Figure FDA0002697476720000015
newly building transmission line loop number G between nodes i and j in the t stage under the h conditiontIn order to reduce the investment cost of the power plant,
Figure FDA0002697476720000016
in order to reduce the investment cost of the power transmission line,
Figure FDA0002697476720000017
the sum of the load shedding penalty fees under the N and N-1 network security of the t stage under the h situation,
Figure FDA0002697476720000018
for the investment cost of newly building a conventional power plant at the t stage,
Figure FDA0002697476720000019
for the investment cost of newly building an energy efficiency power plant in the t stage,
Figure FDA00026974767200000110
the operation and maintenance cost of the conventional power plant at the t stage is shown, I is the number of nodes, S is the number of circuit loops,
Figure FDA00026974767200000111
the load shedding amount of the node i under the normal operation state of the t stage in the h situation,
Figure FDA00026974767200000112
under the h-th condition, the load shedding amount of a node i under the N-1 running state of the s-th line disconnection at the t stage is obtained, and a and b are respectively a load shedding penalty coefficient under a normal state and a load shedding penalty coefficient under an N-1 state;
the constraints of the upper layer planning model include:
safety constraints under N conditions:
Figure FDA0002697476720000021
wherein the content of the first and second substances,
Figure FDA0002697476720000022
for the system node admittance matrix in the h-th case of the t-th stage,
Figure FDA0002697476720000023
is the node in the h-th situation of the t-th stageThe phase angle column vector is then calculated,
Figure FDA0002697476720000024
the output force column vector of the conventional power plant node in the h situation of the t stage,
Figure FDA0002697476720000025
for the node load column vector in the h-th case of the t-th stage,
Figure FDA0002697476720000026
the output column vector of the node of the energy efficiency power plant under the h-th condition in the t stage,
Figure FDA0002697476720000027
is composed of
Figure FDA0002697476720000028
The column vector of (a) is,
Figure FDA0002697476720000029
for the total power flow on the transmission line between point i and node j in the h case of the t-th stage,
Figure FDA00026974767200000210
the original number of circuit loops from node i to node j in the h-th situation of the t-th stage,
Figure FDA00026974767200000211
for the phase angle column vector of the node in the h-th case of the ith node in the t-th stage,
Figure FDA00026974767200000212
is a node phase angle column vector, x, in the h-th condition of the j-th node in the t-th stageijFor the reactance of a single transmission line between nodes i to j,
Figure FDA00026974767200000213
for single bar between node i and node j in the t stageThe upper limit of the capacity of the loop,
Figure FDA00026974767200000214
column vectors of the minimum value and the maximum value of the node output of the conventional power plant at the t stage respectively;
safety constraints under N-1 conditions:
Figure FDA00026974767200000215
wherein the content of the first and second substances,
Figure FDA00026974767200000216
the system node admittance matrix after the s line is disconnected in the h situation of the t stage,
Figure FDA00026974767200000217
for the phase angle column vector of the node after the s line of the h node is disconnected in the t stage,
Figure FDA00026974767200000218
is the output force column vector of the conventional power plant node after the s line is disconnected in the h situation of the t stage,
Figure FDA00026974767200000219
the energy efficiency power plant node output column vector is obtained after the s line is disconnected under the h situation of the t stage,
Figure FDA00026974767200000220
is composed of
Figure FDA00026974767200000221
The column vector of (a) is,
Figure FDA00026974767200000222
the total power flow on the transmission line between the node i and the node j after the s line is disconnected in the h situation of the t stage,
Figure FDA00026974767200000223
the original number of circuit loops from node i to node j after the s-th line is disconnected in the h-th situation of the t-th stage,
Figure FDA0002697476720000031
the number of circuit loops from the node i to the node j after the s line is disconnected in the h-th situation of the t-th stage,
Figure FDA0002697476720000032
is a node phase angle column vector after the s line is disconnected under the h situation of the ith node in the t stage,
Figure FDA0002697476720000033
is a node phase angle column vector after the s line is disconnected under the h situation of the j node in the t stage,
Figure FDA0002697476720000034
column vectors of the minimum value and the maximum value of the node output of the conventional power plant at the t stage respectively;
the lower-layer planning model takes the minimum investment cost of the power plant as an objective function, and the expression of the lower-layer planning model is as follows:
Figure FDA0002697476720000035
Figure FDA0002697476720000036
Figure FDA0002697476720000037
Figure FDA0002697476720000038
wherein G istIn order to reduce the investment cost of the power plant,
Figure FDA0002697476720000039
for the investment cost of newly building a conventional power plant at the t stage,
Figure FDA00026974767200000310
for the investment cost of newly building an energy efficiency power plant in the t stage,
Figure FDA00026974767200000311
the operation and maintenance cost of the conventional power plant at the t stage, M is the type number of the unit of the conventional power plant, CG,mInvestment cost per unit capacity of type m for newly building conventional generator set, PG,mIs the capacity of the m-type conventional unit,
Figure FDA00026974767200000312
the number of m-type conventional units is newly built in the t stage, K is the number of types of the units of the energy efficiency power plant, CE,kInvestment cost per unit capacity of type k for newly built energy efficiency power plant, PE,kFor the capacity of a k-type energy efficient power plant,
Figure FDA00026974767200000313
the number of new k-type energy-efficient power plants in the t stage is determined, Y is the number of years included in the t stage, N is the number of conventional units of the selected conventional power plant to be built in the t stage, L is the number of energy-efficient power plants of the selected energy-efficient power plant to be built in the t stage,
Figure FDA00026974767200000314
is the capacity, I, of the nth conventional power plant unit in the t-th stageOM&E,nFor the unit operating maintenance costs of the nth conventional power plant unit,
Figure FDA00026974767200000315
the number of hours of operation of the nth conventional power plant unit in the y year in the t stage,
Figure FDA00026974767200000316
capacity of the first energy-efficient power plant unit in the t stage, IOM,lFor the unit operating cost of the energy efficient power plant,
Figure FDA00026974767200000317
the number of operating hours of the energy efficiency power plant unit in the ith year in the tth stage;
the constraint conditions of the lower-layer planning model are as follows:
Figure FDA00026974767200000318
Figure FDA00026974767200000319
Figure FDA00026974767200000320
Figure FDA0002697476720000041
Figure FDA0002697476720000042
Figure FDA0002697476720000043
Figure FDA0002697476720000044
wherein R is the reserve of the unitThe coefficient of the capacity is that,
Figure FDA0002697476720000045
is the maximum load of the t-th stage,
Figure FDA0002697476720000046
load of the y year of the T phase, ThThe maximum annual operating hours, alpha is the minimum ratio of the energy efficiency power plant to the load, beta is the maximum ratio of the energy efficiency power plant to the load,
Figure FDA0002697476720000047
is the maximum load of the t-th stage,
Figure FDA0002697476720000048
the number of the m-type conventional units is newly built in the t stage,
Figure FDA0002697476720000049
the maximum number of the m-type conventional units is newly built in the t stage,
Figure FDA00026974767200000410
newly establishing the number of k-type energy efficiency power plants in the t stage,
Figure FDA00026974767200000411
newly building the maximum number of k-type energy efficiency power plants in the t stage;
2) according to the reliability requirement of the planning scheme, performing reliability verification on the generated scheme to ensure that the obtained optimal scheme does not have an island net rack;
3) solving the model by adopting a hybrid algorithm of an improved genetic algorithm and an original-dual interior point method according to the characteristics of the model to obtain an optimal planning scheme, which specifically comprises the following steps:
solving by adopting a hybrid algorithm of an improved genetic algorithm and an original-dual interior point method to obtain an optimal planning scheme, wherein the upper layer adopts a self-adaptive genetic algorithm to carry out global optimization to generate an optimized net rack to obtain the line construction cost, and then the simulation operation is carried outCalculating expected values of operation maintenance cost, environment cost and investment cost, calculating load shedding penalty cost by using an original-point-to-point internal couple method at the lower layer, feeding back to the upper layer to obtain total cost of an optimization target, and obtaining an optimal grid structure through iterative convergence, wherein the improved genetic algorithm is an adaptive genetic algorithm, and the cross probability P in the adaptive genetic algorithmcAnd the mutation probability PmThe calculation formula of (a) is as follows:
Figure FDA00026974767200000412
Figure FDA00026974767200000413
wherein f is the one with larger fitness value of the two individuals to be crossed, f' is the fitness value of the individual to be mutated, favgMean fitness value of the population, fmaxIs the maximum individual fitness value, P, in the populationc1、Pc2、Pm1、Pm2A is a constant;
4) the method specifically comprises the following steps of for two situations of not considering energy efficiency power plant and plant network coordination planning and considering energy efficiency power plant and plant network coordination planning, dividing a planning cycle of each situation into three stages, respectively obtaining and comparing multiple indexes in each stage, and drawing a planning result graph of each scheme:
41) for two situations of not considering the coordination planning of the energy efficiency power plant and the plant network and considering the coordination planning of the energy efficiency power plant and the plant network, the planning is divided into two categories, and the planning period of each category is divided into three stages;
42) calculating indexes of the three stages under each condition, including the number of additional units, the number of newly-built lines and the investment cost;
43) sequencing each condition according to the stage sequence, and comparing and analyzing each index;
44) and drawing a planning result graph of each scheme according to the calculation result.
2. The method for cost-based energy efficiency power plant optimal configuration and plant network coordination planning according to claim 1, wherein the step 2) specifically comprises the following steps:
21) performing connectivity verification on the generated scheme according to the reliability requirement;
22) for the net rack with the isolated island, the isolated island is eliminated by randomly selecting a circuit to be erected of nodes in the isolated island and the net rack, so that the net rack is communicated;
23) for the net rack which has independent small nets and is not communicated, the independent small nets are eliminated by randomly selecting the lines to be erected between the nodes in the independent small nets and the net rack nodes, the net rack communication is realized, and the net rack connectivity requirement of the optimal planning scheme is finally ensured.
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