CN112766559B - Multi-objective optimization scheduling method for electrical thermal interconnection system based on improved NBI (network packet interconnect) method - Google Patents
Multi-objective optimization scheduling method for electrical thermal interconnection system based on improved NBI (network packet interconnect) method Download PDFInfo
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Abstract
The invention provides an improved NBI (network identifier) method-based multi-objective optimal scheduling method for an electrical and thermal interconnection system, which comprises the following steps of: 1) Constructing a multi-objective optimization model of the electrical and thermal interconnection system; 2) Solving a single-target optimization problem to determine an imaging surface, carrying out point taking correction on the boundary of the imaging surface, and equally dividing the imaging surface into subareas; 3) Determining a file mechanism; 4) The algorithm converges and judges, output pareto solution set; 5) And the decision maker selects any non-inferior solution in the pareto solution set and makes an electric thermal interconnection system scheduling plan. The method comprises the steps of obtaining an imaging surface by using an improved NBI method, taking points for correction, solving a multi-objective optimization model of the electrical thermal interconnection system through single operation to obtain the whole uniform and wide pareto front, and according to the pareto front, a decision maker can select any non-inferior solution according to decision preference and make a scheduling plan of the electrical thermal interconnection system.
Description
Technical Field
The invention relates to the field of comprehensive energy system optimal scheduling, in particular to an electrical and thermal interconnection system multi-objective optimal scheduling method based on an improved NBI method.
Background
With the continuous development of energy internet technology, an electric thermal interconnection system is attracting attention as an important physical carrier of energy internet. In order to exert the advantages of the comprehensive energy system to the maximum extent, the optimal operation of the electric heat interconnection system should consider a plurality of targets such as economy, environmental protection and the like. Aiming at a multi-objective optimization model of an electric thermal interconnection system, the traditional NBI method is low in convergence speed in a solving method, and the pareto front can be obtained through multiple times of calculation, and non-inferior solutions of the pareto front edge are frequently omitted. Therefore, the multi-objective optimization method of the electrical thermal interconnection system based on the improved NBI method is provided, firstly, a multi-objective optimization model of the electrical thermal interconnection system is built by taking the minimum system running cost and the minimum carbon emission as targets, then, an imaging surface is obtained by utilizing the improved NBI method, point taking correction is carried out, then, the imaging surface is divided into areas, further, a file registering mechanism is determined, and then, the multi-objective optimization model of the electrical thermal interconnection system is solved through single operation to obtain the whole uniform and wide pareto surface. The literature (Shengqing Wu, gu Qing, li Pengwang and Chen Hairui. Electric comprehensive energy system low-carbon economic operation of carbon-containing capture device [ J/OL ]. Electric measuring and instrument: 1-10[2020-08-16]. Http:// kns.cnki.net/kcms/detail/23.1202.TH.20200106.1622.030.html.) utilizes NBI method to solve the multi-objective optimal scheduling problem of the electric interconnection system, but NBI method solves the problem that the pareto front can miss part of non-inferior solution of the pareto front edge, resulting in insufficient universality of the pareto front edge, and NBI method can only obtain one non-inferior solution after once solution, and the pareto front edge can be obtained after multiple solutions, so that the algorithm efficiency is lower.
Disclosure of Invention
The invention provides an electrical thermal interconnection system multi-objective optimization scheduling method based on an improved NBI method. In the invention, firstly, a multi-target optimization model of the electric thermal interconnection system is constructed by taking the minimum system running cost and the minimum carbon emission as targets, and then, an imaging surface is obtained by using an improved normal boundary intersection method (NBI method) and point taking correction is carried out so as to ensure that a relatively complete pareto front edge is obtained. And carrying out regional division on the imaging surface to ensure that the obtained pareto front edge is wide and uniform, further determining a file registering mechanism, and then solving a multi-objective optimization model of the electrical and thermal interconnection system through single operation to obtain the whole uniform and wide pareto surface. After the complete and uniform pareto solution set is obtained, a decision maker can select any non-inferior solution in the pareto solution set to make an optimal scheduling plan of the electric thermal interconnection system according to self decision preference. The method has certain adaptability, and can be used for guiding a dispatcher to make a dispatching plan and solving other multi-objective optimization problems in the engineering field.
The invention is realized at least by one of the following technical schemes.
The multi-objective optimization scheduling method for the electrical thermal interconnection system based on the improved NBI method comprises the following steps:
1) Constructing a multi-objective optimization model of the electrical thermal interconnection system, and setting an optimization objective;
2) Solving a single-target optimization problem to determine an imaging plane, and carrying out point-taking correction on the boundary of the imaging plane to obtain a pareto solution plane;
3) Determining a file mechanism;
4) Convergence determination, outputting pareto solution set
5) And selecting any non-inferior solution in the pareto solution set, and optimizing the scheduling plan of the electrical and thermal interconnection system.
Preferably, the multi-objective optimization model of the electrical and thermal interconnection system comprises the following models:
and (3) a power grid model:
wherein: omega shape EN For the collection of grid nodes, A G 、A P2G 、A CHP Respectively a node-unit association matrix, a node-P2G association matrix and a node-CHP association matrix; p (P) D,t The node load matrix is adopted; b is the imaginary part, theta, of the node admittance matrix t Is a node voltage phase angle vector;
x ij the maximum transmission power and reactance of the straight path ij are respectively; p (P) G,t The active output vector is the unit active output vector; p (P) P2G,t Consuming active power vectors for the P2G device; p (P) CHP,t Active input power vector for CHP device; a, a d 、a u The upper limit vector and the lower limit vector are respectively used for restraining the climbing rate of the unit; />
Is a balanced node phase angle; θ i,t 、θ j,t The voltage phase angle at the moment of the i node t and the voltage phase angle at the moment of the j node t in the straight path ij are respectively; />
Respectively the minimum value and the maximum value of the active output force of the unit at the moment t;
and (3) air network model:
wherein:
the gas pressure and the gas flow at the position of the ij pipeline d at the moment t are respectively; m is M 1 、M 2 A pipeline transmission constant; Δt is the time step; omega shape Gp Is a natural gas pipeline set; />
Respectively pressurizing the front and rear air pressures of the pressurizing station;
the lower limit and the upper limit of the boosting ratio of the pressurizing station are respectively; f (f) i t Is a pipeline air flow>
Is the upper limit of the air flow of the pipeline; omega shape g 、Ω GB Respectively collecting gas sources and natural gas nodes; />
The air pressure of the i node at the moment t; fg, j and t are the j air source output at the moment t; omega s is a gas storage tank set; />
The natural gas input quantity and the natural gas output quantity of the gas storage tank n at the moment t are respectively; />
The inflation and deflation efficiencies of the air storage tank n at the moment t are respectively; />
Rated gas storage capacity for the gas storage tank n; b (B) g 、B P2G 、B S 、B CHP 、B GT 、A g The node and gas source, node and P2G, node and gas storage tank, node and CHP, node and gas unit and node and pipeline are respectively associated matrixes; f (f) g,t 、f P2G,t 、f CHP,t 、f GT,t 、f D,t The system comprises an air source output vector, a P2G natural gas injection vector, a CHP natural gas injection vector, a gas unit airflow injection vector and a natural gas load vector; Δx ij Is the position step length; />
The air flow after the end of the pipeline scheduling period is scheduled for ij at the moment t; s is S n,t The air storage capacity of the air storage tank n at the moment t; />
The air inflow vector of the air storage tank at the moment t;
and (3) a heat supply network model:
wherein:
the water inflow temperature and the outflow temperature of the pipeline k at the moment t are respectively; deltaτ k Time for water to flow through the k pipe; mu (mu) k Is a heat loss factor; c (C) w Is the specific heat capacity of water; ρ w Is the density of water; r is R k Is the radius of the pipeline; />
The temperature of the external environment at the moment t of the k pipeline; l (L) k K pipe lengths; m is M k K is the water flow of the pipeline; />
For the electric heating ratio of the unit, the common CHP working mode is to use heat to fix electricity, taking +.>
0.8; />
The power is generated for the CHP unit; />
Is water flow; />
The water supply temperature; />
Is the return water temperature; c is the specific heat capacity of water; />
Is the heat load of the heat exchange station; omega shape pipe,out,g 、Ω pipe,in,g Respectively a pipeline set taking a node g as an outflow point and an inflow point; t (T) mix,g Mixing temperature for node g; />
Respectively the upper and lower limits of the water temperature of the node g;
the power output of the CHP unit is at the time t; />
The water inlet temperature and the water return temperature of the heat exchange station are respectively; />
Is the water flow of the heat exchange station; />
The water flow of the pipeline b at the moment t; />
The temperature of the water entering the pipeline b at the moment t.
PreferablyThe optimization target is to set the optimization target f with the minimum operation cost and the minimum carbon emission of the electric heat interconnection system 1 、f 2 The method specifically comprises the following steps:
wherein: t is a scheduling period; omega shape G The method is a coal-fired unit set; omega shape g Is an air source set; omega shape W The method comprises the steps of collecting wind turbines; omega shape S Is a gas storage tank set omega h Is a heat source set; p (P) G,i,t I output, a of coal-fired unit at t moment i 、b i 、c i Respectively the secondary and primary cost coefficients and the constant cost coefficients of the unit; f (f) g,j,t For the air output of the air source j at the moment t, xi j Is the cost coefficient of purchasing gas; p (P) W,k,max For the upper output limit of the wind turbine, P W,i,t For the i output, delta of the wind turbine at the moment t k Punishment coefficients for the wind curtailment;
for the gas outlet quantity of the gas storage tank n at the moment t, C S,n,t Is the cost coefficient of the air storage tank; h h,m,t For the heat source m output at the moment t, C h,m,t The cost is for purchasing heat; alpha i 、β i 、γ i The secondary and primary and constant carbon emission coefficients of the coal-fired unit are respectively; τ g The carbon emission conversion coefficient of the natural gas is calculated; />
And the natural gas output of the gas storage tank n at the moment t respectively.
Preferably, the multi-objective optimization model of the electrical and thermal interconnection system is as follows:
min F＝(f 1 ,f 2 ,…,f l )
s.t.H(x)＝0
G(x)≤0
wherein: f is a target set; x is a variable vector; h is a set of equality constraints; g is the inequality constraint set, f l Is the first target; s.t. means that the constraint is satisfied.
Preferably, the mapping surface is used for respectively and independently optimizing each target to obtain each target maximum value f 1max 、f 2max And minimum value f 1min 、f 2min In which the object F is 1 Is the horizontal axis, another object F 2 Establishing a coordinate system for the vertical axis, and recording the point A as (f 1max ,f 2min ) Point B is (f 1min ,f 2max ) Connecting the points A and B, the line segment AB is a shadow line or a plane.
Preferably, the correction process is: taking a line segment AB as an image ray, taking a point from the line segment AB, and taking a line segment normal vector of the line segment AB as a projection direction to project to obtain the pareto front, wherein the method omits a solution on a part of the pareto front boundary, so that the image ray boundary taking point is corrected: and (3) recording the length of the line segment AB as l, and enabling the line segment AB to extend by 0.1l along the two end points respectively to obtain a line segment CD, wherein the line segment CD is the corrected shadow ray.
Preferably, in order to obtain a uniform pareto solution plane, the line segments CD are divided, and the number of dividing regions is set to be N q The midpoint of each sub-area is called a sub-area projection center, and the distance d from the rest points in the sub-area to the sub-area projection center is called a deviation distance for representing the aggregation degree of the sub-area projection points to the sub-area projection center; the output priority of the solution obtained by taking the points in the same subarea is as follows: the points with small offset distances are preferred over the points with large offset distances, so that the obtained pareto solutions are distributed as evenly as possible.
Preferably, the determining the profile mechanism comprises setting up a regular profile DA 1 Archive DA 1 Is set to N 1 When the calculated non-inferior number is less than or equal to N 1 When in use, pareto is recorded in the file DA 1 The method comprises the steps of carrying out a first treatment on the surface of the When the obtained non-inferior solution is greater than N 1 When the method is used, firstly, the solution with small deviation distance is recorded, the residual space is randomly filled by other non-inferior solutions, and the file is used for recording the pareto solution set finally output.
Preferably, the step 4) algorithm convergence determination is performed, the pareto solution set is output, the initial point-taking mode is that h points are randomly extracted from each sub-area to be used as an initial population P, solutions corresponding to the points in the P are calculated, and the non-inferior solutions are recorded in the record DA 1 If the file DA 1 The number of the intermediate solutions reaches N 1 The algorithm converges if it is less than N 1 And randomly taking points from each sub-area again as a new population to continue solving until the algorithm converges.
Preferably, step 5) selects any non-inferior solution in the pareto solution set, makes a scheduling plan of the electrical thermal interconnection system, selects any non-inferior solution according to the pareto solution set, and optimizes the scheduling plan of the electrical thermal interconnection system according to the coal-fired unit output information, the wind-powered unit output information, the air source output information and the heat source output information corresponding to the non-inferior solution.
Compared with the prior art, the invention has the following advantages and effects:
(1) The point-taking correction strategy for improving the NBI method overcomes the defect that the traditional NBI cannot obtain the boundary solution of the real pareto front, and ensures the integrity of the obtained pareto front. The edge non-inferior solution missed by the traditional NBI method can be obtained, so that the obtained pareto front edge is wider and more complete; by utilizing the regional division and the archive registering mechanism, the pareto front can be obtained through single operation, the efficiency is high, and the pareto front can be used as a theoretical basis for guiding the establishment of an actual scheduling plan.
(2) The improved NBI method provided by the invention can output a wide and uniform pareto front by only needing few times of calculation when solving the multi-objective optimization problem of the electrical and thermal interconnection system, and overcomes the defect that the traditional NBI can only solve one non-inferior solution by one-time calculation.
(3) The improved NBI method ensures the preferential output of the non-inferior solution close to the center of the region and ensures the uniformity of the front edge of the pareto through the regional division and the archival mechanism.
Drawings
FIG. 1 is a flow chart of a multi-objective optimized scheduling method of an electrical thermal interconnect system based on an improved NBI method of the present invention;
FIG. 2 is a schematic diagram of an electrical and thermal integrated energy system;
FIG. 3 is a pareto front of a multi-objective optimization model of an electrical thermal interconnect system based on an improved NBI method.
Detailed Description
The present invention will be described in further detail below with reference to the accompanying drawings and examples in order to make the objects, technical solutions and advantages of the present invention more clear and obvious.
As shown in fig. 1, 2 and 3, the multi-objective optimization scheduling method of the electrical thermal interconnection system based on the improved NBI method comprises the following steps:
step S110, constructing a multi-objective optimization model of the electrical and thermal interconnection system:
(1) Constructing a multi-objective optimization model of the electrical and thermal interconnection system, wherein the multi-objective optimization model comprises the following models:
and (3) a power grid model:
wherein: omega shape EN For the collection of grid nodes, A G 、A P2G 、A CHP Respectively a node-unit association matrix, a node-P2G association matrix and a node-CHP association matrix; p (P) D,t The node load matrix is adopted; b is the imaginary part, theta, of the node admittance matrix t Is a node voltage phase angle vector;
x ij the maximum transmission power and reactance of the straight path ij are respectively; p (P) G,t The active output vector is the unit active output vector; p (P) P2G,t Consuming active power vectors for the P2G device; p (P) CHP,t Active input power vector for CHP device; a, a d 、a u The upper limit vector and the lower limit vector are respectively used for restraining the climbing rate of the unit; />
Is a balanced node phase angle; θ i,t 、θ j,t The voltage phase angle at the moment of the i node t and the voltage phase angle at the moment of the j node t in the straight path ij are respectively; />
Respectively the minimum value and the maximum value of the active output force of the unit at the moment t;
and (3) air network model:
wherein:
the gas pressure and the gas flow at the position of the ij pipeline d at the moment t are respectively; m is M 1 、M 2 A pipeline transmission constant; Δt is the time step; omega shape Gp Is a natural gas pipeline set; />
Respectively pressurizing the front and rear air pressures of the pressurizing station;
the lower limit and the upper limit of the boosting ratio of the pressurizing station are respectively; f (f) i t Is a pipeline air flow>
Is the upper limit of the air flow of the pipeline; omega shape g 、Ω GB Respectively collecting gas sources and natural gas nodes; />
The air pressure of the i node at the moment t; fg, j and t are the j air source output at the moment t; omega s is a gas storage tank set; />
Natural gas in gas tanks n at time t respectivelyInput and output; />
The inflation and deflation efficiencies of the air storage tank n at the moment t are respectively; />
Rated gas storage capacity for the gas storage tank n; b (B) g 、B P2G 、B S 、B CHP 、B GT 、A g The node and gas source, node and P2G, node and gas storage tank, node and CHP, node and gas unit and node and pipeline are respectively associated matrixes; f (f) g,t 、f P2G,t 、f CHP,t 、f GT,t 、f D,t The system comprises an air source output vector, a P2G natural gas injection vector, a CHP natural gas injection vector, a gas unit airflow injection vector and a natural gas load vector; Δx ij Is the position step length; />
The air flow after the end of the pipeline scheduling period is scheduled for ij at the moment t; s is S n,t The air storage capacity of the air storage tank n at the moment t; />
The air inflow vector of the air storage tank at the moment t;
and (3) a heat supply network model:
wherein:
the water inflow temperature and the outflow temperature of the pipeline k at the moment t are respectively; deltaτ k Time for water to flow through the k pipe; mu (mu) k Is a heat loss factor; c (C) w Is the specific heat capacity of water; ρ w Is the density of water; r is R k Is the radius of the pipeline; />
The temperature of the external environment at the moment t of the k pipeline; l (L) k K pipe lengths; m is M k K is the water flow of the pipeline; />
For the electric heating ratio of the unit, the common CHP working mode is to use heat to fix electricity, taking +.>
0.8; />
The power is generated for the CHP unit; />
Is water flow; />
The water supply temperature; />
Is the return water temperature; c is the specific heat capacity of water; />
Is the heat load of the heat exchange station; omega shape pipe,out,g 、Ω pipe,in,g Respectively a pipeline set taking a node g as an outflow point and an inflow point; t (T) mix,g Mixing temperature for node g; />
Respectively the upper and lower limits of the water temperature of the node g;
the power output of the CHP unit is at the time t; />
The water inlet temperature and the water return temperature of the heat exchange station are respectively; />
Is the water flow of the heat exchange station; />
The water flow of the pipeline b at the moment t; />
The temperature of the water entering the pipeline b at the moment t.
(2) Setting an optimization target f by minimum system operation cost and minimum carbon emission 1 、f 2 The method specifically comprises the following steps:
wherein: t is a scheduling period; omega shape G The method is a coal-fired unit set; omega shape g Is an air source set; omega shape W The method comprises the steps of collecting wind turbines; omega shape S Is a gas storage tank set omega h Is a heat source set; p (P) G,i,t I output, a of coal-fired unit at t moment i 、b i 、c i Respectively the secondary and primary cost coefficients and the constant cost coefficients of the unit; f (f) g,j,t For the air output of the air source j at the moment t, xi j Is the cost coefficient of purchasing gas; p (P) W,k,max For the upper output limit of the wind turbine, P W,i,t For the i output, delta of the wind turbine at the moment t k Punishment coefficients for the wind curtailment;
for the gas outlet quantity of the gas storage tank n at the moment t, C S,n,t Is the cost coefficient of the air storage tank; h h,m,t For the heat source m output at the moment t, C h,m,t The cost is for purchasing heat; alpha i 、β i 、γ i The secondary and primary and constant carbon emission coefficients of the coal-fired unit are respectively; τ g The carbon emission conversion coefficient of the natural gas is calculated; />
The natural gas output of the gas storage tank n at the moment t respectively;
the more generalized multi-objective optimization model is represented as follows:
min F＝(f 1 ,f 2 ,…,f all )
s.t.H(x)＝0
G(x)≤0
wherein: f is a target set; x is a variable vector; h is a set of equality constraints; g is the inequality constraint set, f l Is the first target; s.t. means that the constraint is satisfied.
Step S120, solving a single-target optimization problem to determine an imaging plane, correcting the boundary point of the imaging plane, and equally dividing the imaging plane into sub-areas:
firstly, respectively and independently optimizing each target to obtain each target maximum value f 1max ，f 2max And minimum value f 1min ，f 2min By F 1 Is the transverse axis, F 2 A coordinate system is established for the vertical axis. Point A is (f) 1max ,f 2min ) Point B is (f 1min ,f 2max ). Connecting points A and B, the line segment AB is the shadow line (plane).
The conventional NBI method takes AB as a shadow ray, takes a point from AB, takes the normal vector of an AB line segment as a projection direction to project to obtain the pareto front, but the method ignores the solution on the boundary of part of the pareto front. Therefore, correction is performed on the shadow ray boundary fetch point: the length of AB is recorded as l, and the length of AB is prolonged by 0.1l along the two end points respectively, so as to obtain a line segment CD. CD is the corrected ray. To obtain a uniform pareto solution plane, the CD needs to be partitioned. The number of the divided subareas depends on the number of solutions needed by a decision maker, and the number of the divided subareas is N q . After the projection surface divides the subareas, the midpoint of each subarea is called a subarea projection center, and the distance d from the rest points in the subarea to the subarea projection center is called a deviation distance for representing the aggregation degree from the subarea projection point to the subarea projection center. In the algorithm, the output priority of the solution obtained by taking the points in the same subarea is as follows: points with small offset distances have precedence over points with large offset distances. The step can make the pareto solution as uniform as possible
Step S130, determining a file mechanism:
setting up a conventional archive DA 1 。DA 1 Is fixed, set to N 1 When the calculated non-inferior number is less than or equal to N 1 When all the solutions are recorded in DA 1 The method comprises the steps of carrying out a first treatment on the surface of the When the obtained non-inferior solution is greater than N 1 When the method is used, firstly, the solution with small deviation distance is recorded, the residual space is randomly filled by other non-inferior solutions, and the file is used for recording the pareto solution set finally output.
Step S140, algorithm convergence judgment is carried out, and a pareto solution set is output:
the initial point taking mode is to randomly extract 1 point from each sub-area as an initial population P, calculate the solutions corresponding to each point in P, record the non-inferior solutions to DA 1 If DA 1 The number of medium solutions reaches N 1 The algorithm converges if it is less than N 1 And randomly taking points from each sub-area again as a new population to continue solving until the algorithm converges.
And step S150, a decision maker selects any non-inferior solution in the pareto solution set and makes an electric thermal interconnection system scheduling plan. And selecting any non-inferior solution according to the obtained pareto solution set, and making an electric thermal interconnection system dispatching plan according to the coal-fired unit output information, the wind-power unit output information, the air source output information and the heat source output information corresponding to the non-inferior solution.
The above examples merely represent a few embodiments of the present invention, which are described in more detail and are not to be construed as limiting the scope of the present invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of the invention should be assessed as that of the appended claims.
Claims (1)
1. The multi-objective optimal scheduling method for the electrical thermal interconnection system based on the improved NBI method is characterized by comprising the following steps of:
1) Constructing a multi-objective optimization model of the electrical and thermal interconnection system;
2) Solving a single-target optimization problem to determine an imaging surface, carrying out point taking correction on the boundary of the imaging surface, and equally dividing the imaging surface into subareas;
3) Determining a archive mechanism: setting up a conventional archive DA 1 Archive DA 1 Is fixed, set to N 1 When the calculated non-inferior number is less than or equal to N 1 When all the solutions are recorded in the archive DA 1 The method comprises the steps of carrying out a first treatment on the surface of the When the obtained non-inferior solution is greater than N 1 When the global solution set distribution is considered to be as uniform as possible, solutions with small deviation distances are preferentially recorded, the residual space is randomly filled by other non-inferior solutions, and the archive is used for recording the pareto solution set finally output;
4) And (3) carrying out algorithm convergence judgment, and outputting a pareto solution set: the initial point-taking mode is to randomly extract 1 point from each sub-area as an initial population P, calculate the solutions corresponding to each point in P, record the non-inferior solutions into the file DA 1 If the file DA 1 The number of medium solutions reaches N 1 The algorithm converges if it is less than N 1 Then, randomly taking points from each sub-area as a new population again to continue solving until the algorithm converges;
5) The decision maker selects any non-inferior solution in the pareto solution set and makes an electric thermal interconnection system scheduling plan: according to the obtained pareto solution set, a decision maker can select any non-inferior solution according to self decision preference, and a scheduling plan of the electric heat interconnection system is formulated according to coal-fired unit output information, wind power unit output information, air source output information and heat source output information corresponding to the non-inferior solution;
the multi-objective optimization model of the electrical and thermal interconnection system is constructed as follows:
(1) Constructing a multi-objective optimization model of the electrical and thermal interconnection system:
the grid model is as follows:
wherein: omega shape EN For the collection of grid nodes, A G 、A P2G 、A CHP Respectively a node-unit association matrix, a node-P2G association matrix and a node-CHP association matrix; p (P) D,t The node load matrix is the node load matrix at the moment t; b is the imaginary part, theta, of the node admittance matrix t The node voltage phase angle vector is at the time t;
x ij the maximum transmission power and reactance of the straight path ij are respectively; p (P) G,t The active output vector of the unit at the moment t; p (P) P2G,t Active power vectors are consumed for the P2G device at the time t; p (P) CHP,t The active input power vector of the CHP device at the time t; a, a d 、a u The upper limit vector and the lower limit vector are respectively used for restraining the climbing rate of the unit; />
The phase angle of the balance node at the moment t; θ i,t 、θ j,t The voltage phase angle at the moment of the i node t and the voltage phase angle at the moment of the j node t in the straight path ij are respectively; />
Respectively the minimum value and the maximum value of the active output force of the unit at the moment t;
the air network model is as follows:
wherein:
the gas pressure and the gas flow at the position of the ij pipeline d at the moment t are respectively; m is M 1 、M 2 A pipeline transmission constant; Δt is the time step; omega shape Gp Is a natural gas pipeA set of tracks; />
The air pressure after the pressurization of the pressurization station; />
The lower limit and the upper limit of the boosting ratio of the pressurizing station are respectively; f (f) i t Is a pipeline air flow>
Is the upper limit of the air flow of the pipeline; omega shape g 、Ω GB Respectively collecting gas sources and natural gas nodes; />
The air pressure of the i node at the moment t; fg, j and t are the j air source output at the moment t; omega s is a gas storage tank set; />
The natural gas input quantity and the natural gas output quantity of the gas storage tank n at the moment t are respectively; />
The inflation and deflation efficiencies of the air storage tank n at the moment t are respectively; />
Rated gas storage capacity for the gas storage tank n; b (B) g 、B P2G 、B S 、B CHP 、B GT 、A g The node and gas source, node and P2G, node and gas storage tank, node and CHP, node and gas unit and node and pipeline are respectively associated matrixes; f (f) g,t 、f P2G,t 、f CHP,t 、f GT,t 、f D,t The system comprises an air source output vector, a P2G natural gas injection vector, a CHP natural gas injection vector, a gas unit airflow injection vector and a natural gas load vector; Δx ij Is the position step length; />
The air flow after the end of the pipeline scheduling period is scheduled for ij at the moment t; s is S n,t The air storage capacity of the air storage tank n at the moment t; />
The air inflow vector of the air storage tank at the moment t;
the heat supply network model is as follows:
wherein:
the water inflow temperature and the outflow temperature of the pipeline k at the moment t are respectively; deltaτ k Time for water to flow through the k pipe; mu (mu) k Is a heat loss factor; c w Is the specific heat capacity of water; ρ w Is the density of water; r is R k Is the radius of the pipeline; />
The temperature of the external environment at the moment t of the k pipeline; l (L) k K pipe lengths; m is M k K is the water flow of the pipeline; the common CHP working mode is to use heat to fix electricity; />
The power is generated for the CHP unit; />
Is water flow; />
The water supply temperature; />
For backwaterA temperature; c is the specific heat capacity of water; />
Is the heat load of the heat exchange station; omega shape pipe,out,g 、Ω pipe,in,g Respectively a pipeline set taking a node g as an outflow point and an inflow point; t (T) mix,g Mixing temperature for node g; />
Respectively the upper and lower limits of the water temperature of the node g; />
The power output of the CHP unit is at the time t; />
The water inlet temperature and the water return temperature of the heat exchange station are respectively; />
Is the water flow of the heat exchange station; />
The water flow of the pipeline b at the moment t; />
The water inlet temperature of the pipeline b at the moment t;
setting an optimization target f by using minimum operation cost and minimum carbon emission of an electric thermal interconnection system 1 、f 2 The method specifically comprises the following steps:
wherein: t is a scheduling period; omega shape G The method is a coal-fired unit set; omega shape g Is an air source set; omega shape W The method comprises the steps of collecting wind turbines; omega shape S Is a gas storage tank set omega h Is a heat source set; p (P) G,i,t I output, a of coal-fired unit at t moment i 、b i 、c i Respectively the secondary and primary cost coefficients and the constant cost coefficients of the unit; f (f) g,j,t For the air output of the air source j at the moment t, xi j Is the cost coefficient of purchasing gas; p (P) W,k,max For the upper output limit of the wind turbine, P W,i,t For the i output, delta of the wind turbine at the moment t k Punishment coefficients for the wind curtailment;
for the gas outlet quantity of the gas storage tank n at the moment t, C S,n,t Is the cost coefficient of the air storage tank; h h,m,t For the heat source m output at the moment t, C h,m,t The cost is for purchasing heat; alpha i 、β i 、γ i The secondary and primary and constant carbon emission coefficients of the coal-fired unit are respectively; τ g The carbon emission conversion coefficient of the natural gas is calculated; />
The natural gas output of the gas storage tank n at the moment t respectively;
the more generalized multi-objective optimization model is represented as follows:
minF＝(f 1 ,f 2 ,…,f l )
s.t.H(x)＝0
G(x)≤0
wherein: f is a target set; x is a variable vector; h is a set of equality constraints; g is the inequality constraint set, f l Is the first target; s.t. represents meeting the constraint;
solving a single-target optimization problem to determine an imaging surface, carrying out point taking correction on the boundary of the imaging surface, and dividing the imaging surface into sub-areas specifically:
firstly, respectively and independently optimizing each target to obtain each target maximum value f 1max 、f 2max And minimum value f 1min 、f 2min By F 1 Is the transverse axis, F 2 Establishing a coordinate system for a longitudinal axis, and recording a point A as%f 1max ,f 2min ) Point B is (f 1min ,f 2max ) Connecting the points A and B, and the line segment AB is an imaging line or a plane; correcting the shadow ray boundary sampling points: recording the length of AB as l, enabling the AB to extend by 0.1l along the two end points respectively to obtain a line segment CD, wherein the line segment CD is the corrected shadow ray, and the corrected shadow ray covers the end points of the line segment of the AB shadow ray by the traditional NBI method; in order to obtain a uniform pareto solution plane, the CD needs to be divided, the number of dividing subareas depends on the number of solutions needed by a decision maker, and the number of dividing areas is N q The method comprises the steps of carrying out a first treatment on the surface of the After the projection surface divides the subareas, the midpoint of each subarea is called a subarea projection center, and the distance d from the rest points in the subarea to the subarea projection center is called a deviation distance for representing the aggregation degree from the subarea projection point to the subarea projection center; in the algorithm, the output priority of the solution obtained by taking the points in the same subarea is as follows: the points with small offset distances are preferred over the points with large offset distances, and the step ensures that the pareto solutions are distributed as evenly as possible.
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