A generator output carbon emission optimization method based on an optimal power flow model

Abstract

The application relates to a generator output carbon emission optimization method based on an optimal power flow model. First, generator processing data, including node data, generator data and branch data, is collected; then the collected data is input into an optimal power flow model with the minimum carbon emission of each generator as a target function, and after iterative correction and solution based on a power flow constraint condition, optimal optimization control parameters are output. Compared with a conventional optimal power flow model with minimum operation cost, minimum network loss or minimum load shedding as a target function, the generator output carbon emission optimization method based on the optimal power flow model can effectively reduce carbon emission.

Description

Technical Field

[0001] This invention belongs to the field of carbon reduction and decarbonization in power systems. Specifically, it relates to a generator output carbon emission optimization method based on an optimal power flow model. The method uses the minimum carbon emissions of generators as the objective function of the optimal power flow, thereby minimizing the overall carbon emissions of the power grid and achieving the goal of carbon reduction and decarbonization. Background Technology

[0002] Currently, with the increasing depletion of traditional energy sources such as coal, oil, and natural gas, the power system, as a key energy-intensive industry reliant on these traditional energy sources, has attracted significant attention from scientific and industrial communities worldwide. In particular, with the rapid development of smart grid technology, countries around the world have invested considerable effort in researching energy-saving dispatching technologies and increasing the integration of new energy sources into the grid. Therefore, the rational allocation of various resources within the power system, including conventional and new energy sources (such as wind power), and the pursuit of optimized power system dispatching, is of great practical significance for energy conservation and emission reduction.

[0003] Classical optimal power flow, given a fixed grid structure and load, aims to achieve predetermined objectives by satisfying power flow constraints, line constraints, and generator constraints, thereby realizing power flow control under different operating requirements. Existing optimal power flow control methods only pursue objectives such as minimum operating costs, minimum grid losses, or minimum load shedding, but lack optimal power flow calculation methods that prioritize minimizing carbon emissions from power generation.

[0004] Therefore, this invention will take into account the carbon emissions caused by generators generating electricity, and propose an optimal power flow calculation method that balances the above factors and aims to minimize the carbon emissions of generators, so as to ensure that the power grid operates reliably at a low carbon level. Summary of the Invention

[0005] The above-mentioned technical problems of the present invention are mainly solved by the following technical solutions:

[0006] A method for optimizing generator output carbon emissions based on an optimal power flow model, characterized by including:

[0007] Collect and process generator data, including node data, generator data, and branch data;

[0008] The collected data is input into the objective function that minimizes the carbon emissions of each generator as the optimal power flow. Based on the power flow constraints, the optimal control parameters are output after iterative correction and solution.

[0009] In the above method, the network structure calculation data required by the present invention is divided into node data, generator data, and branch data, as detailed below:

[0010] (1) Node data includes: active load, reactive load, and upper and lower limits of voltage amplitude for each node in the network structure;

[0011] (2) Generator data includes: carbon emission intensity, active and reactive power output upper and lower limits of each generator in the grid;

[0012] (3) Branch data include: voltage phase angle difference range, conductance, and susceptance of each branch in the grid.

[0013] After collecting relevant data using the above method, this invention proposes an objective function for optimal power flow, as shown in equation (1), which minimizes the carbon emissions of each generator. f ( x ),

[0014] (1)

[0015] In the formula: N G Indicates the number of generators; P Gi For the first i The power output of each generator; CEF i Indicates the first i The carbon emission factor of each generator.

[0016] In the above method, when solving for the optimal power flow, the present invention transforms equation (1) as follows:

[0017] (2)

[0018] In the formula, m For obstacle parameters, r denoted as the number of inequality constraints.

[0019] The above method satisfies the following constraints:

[0020] The equality constraint is the power flow constraint. Each node in the network structure has two power flow constraints. The power flow constraint for the load node is shown in equation (3):

[0021] (3)

[0022] In the formula, P Di 、Q Di They are nodes i Active and reactive loads, V i , V j They are nodes i With nodes j voltage amplitude, i ij For nodes i With nodes j voltage phase angle difference, G ij , B ij Branch roads ij The conductivity and susceptance, N D This indicates the number of nodes in the network structure.

[0023] The power flow constraints for the generator node are shown in equation (4):

[0024] (4)

[0025] In the formula, k∈i Indicates the first k A generator is connected to the node. i superior, P Gk , Q Rk These refer to the active power output and reactive power output of the generator, respectively.

[0026] In the above method, the inequality constraints are shown in equation (5), which are the active power output constraints of the generator ( g 1) Generator reactive power output constraint ( g 2) Node voltage constraints ( g 3) and line power flow constraints ( g 4).

[0027] (5)

[0028] In the formula, , , , These represent the upper and lower limits of active and passive output, respectively. , These are the upper and lower limits of the node voltage amplitude, respectively. This represents the upper limit of the power flow along the line. Among them, .

[0029] In the above method, the present invention transforms equations (2)-(5) to obtain the model shown in equation (6).

[0030] (6)

[0031] In the formula, y , z , w All are Lagrange multipliers, with dual gaps. .

[0032] Based on the above method and the above parameters, this invention proposes the iterative equation shown in equation (7):

[0033] (7)

[0034] After solving the iterative equation (7), we can obtain the first... k The corrected variable after the next iteration.

[0035] In the above method, the present invention proposes the method shown in formula (8). k The method for correcting the variable in +1 iterations.

[0036] (8)

[0037] The value of equation (7) after iteration is corrected according to equation (8) until Gap < e Finally, the optimal solution is obtained.

[0038] Compared to conventional optimal power flow models with objective functions such as minimum operating cost, minimum network loss, or minimum load shedding, the generator output carbon emission optimization method proposed in this invention based on the optimal power flow model can effectively reduce carbon emissions. Detailed Implementation

[0039] The technical solution of the present invention will be further described in detail below through embodiments.

[0040] Example:

[0041] The present invention proposes an optimal power flow model that minimizes generator carbon emissions, and the implementation steps are as follows:

[0042] First, data collection is performed, and the collected data are as follows: node data (active load, reactive load, and upper and lower limits of voltage amplitude for each node in the grid); generator data (carbon emission intensity, upper and lower limits of active and reactive power output for each generator in the grid); branch data (voltage phase angle difference range, conductance, and susceptance for each branch in the grid).

[0043] The collected data is then substituted into the computational model for calculation. The calculation steps are as follows:

[0044] Step 1: With the goal of minimizing the carbon emissions of the generator, construct a mathematical model as shown in equation (1) and initialize the model;

[0045] Step 2: Perform the transformation shown in equation (2) on equation (1);

[0046] Step 3: Construct the constraints as shown in equations (3), (4), and (5);

[0047] Step 4: Transform equations (2) and (3)-(5) to obtain equation (6);

[0048] Step 5: Select the average of the upper and lower limits as the initial point for the operation. , , , Set up Lagrange multipliers and calculate accuracy e ;

[0049] Step 6: Calculate the disturbance factor for equation (6). m ;

[0050] Step 7: Calculate the relevant constraints and their first and second derivatives at the point... , , , The specific value to be taken at;

[0051] Step 8: Substitute the result obtained in Step 7 into the correction equation to calculate the relevant correction amount Δ. y Δ CEF Δ P G Δ V， Δ z Δ w ;

[0052] Step 9: Update the iteration variables according to equation (8) based on the results obtained in step 8;

[0053] Step 10: Calculate the complementary gap Gap;

[0054] Step 11: If Gap > ε, return to step 6; if Gap < ε, return to step 6. e Then the optimal solution will be output.

[0055] The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

Claims

1. A method for optimizing generator output carbon emissions based on an optimal power flow model, characterized in that, include Collect and process generator data, including node data, generator data, and branch data; The collected data is input into the objective function of minimizing the carbon emissions of each generator as the optimal power flow. Based on the power flow constraints, the optimal control parameters are output after iterative correction and solution. Equation (1) shows that the objective function for optimal power flow is to minimize the carbon emissions of each generator. f ( x ), (1) In the formula: N G Indicates the number of generators; P Gi For the first i The power output of each generator; CEF i Indicates the first i The carbon emission factor of each generator; When solving for the optimal power flow, equation (1) is transformed as follows: (2) In the formula, μ For obstacle parameters, r The number of inequality constraints; Based on the iterative equation shown in equation (7): (7) After solving the iterative equation (7), we can obtain the first... k The correction variable after the next iteration; Based on the first as shown in equation (8) k The method for correcting the variable in +1 iterations. (8) The value of equation (7) after iteration is corrected according to equation (8) until Gap < ε Finally, the optimal solution is obtained.

2. The method for optimizing generator output carbon emissions based on an optimal power flow model according to claim 1, characterized in that, Node data includes: active load, reactive load, and upper and lower limits of voltage amplitude for each node in the network structure; The generator data includes: carbon emission intensity, active and reactive power output upper and lower limits of each generator in the grid; Branch data includes: voltage phase angle difference range, conductance, and susceptance of each branch in the grid.

3. The method for optimizing generator output carbon emissions based on an optimal power flow model according to claim 2, characterized in that, Constraints include equality constraints and inequality constraints, among which The equality constraint is the power flow constraint. Each node in the network structure has two power flow constraints. The power flow constraint for the load node is shown in equation (3): (3) In the formula, P Di 、Q Di They are nodes i Active and reactive loads, V i , V j They are nodes i With nodes j voltage amplitude, θ ij For nodes i With nodes j voltage phase angle difference, G ij , B ij Branch roads ij The conductivity and susceptance, N D Indicates the number of nodes in the network structure; The power flow constraints for the generator node are shown in equation (4): (4) In the formula, k∈i Indicates the first k A generator is connected to the node. i superior, P Gk , Q Rk These refer to the active power output and reactive power output of the generator, respectively.

4. The method for optimizing generator output carbon emissions based on an optimal power flow model according to claim 2, characterized in that, The inequality constraints are shown in equation (5), which are the active power output constraints of the generator ( g 1) Generator reactive power output constraint ( g 2) Node voltage constraints ( g 3) and line power flow constraints ( g 4); (5) In the formula, , , , These represent the upper and lower limits of active and passive output, respectively. , These are the upper and lower limits of the node voltage amplitude, respectively. This is the upper limit of the power flow of the line; among which, .

5. The method for optimizing generator output carbon emissions based on an optimal power flow model according to claim 2, characterized in that, By transforming equations (2)-(5), we obtain the model shown in equation (6). (6) In the formula, y , z , w All are Lagrange multipliers, with dual gaps. .

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