A power grid economic dispatch method, system, device and medium

Abstract

The application discloses a power grid economic dispatching method, system, device and medium, and when receiving power grid corresponding power grid node data, the power grid corresponding Laplacian matrix, first auxiliary variable and second auxiliary variable are determined according to the power grid node data. Based on the Laplacian matrix, the first auxiliary variable and the second auxiliary variable are iteratively converged respectively to generate the power grid corresponding first convergence value and second convergence value. Based on the preset convergence data, the first convergence value and the second convergence value, the power grid corresponding first convergence difference value is determined. Based on the first convergence difference value and the preset load constraint condition, the power grid corresponding target dispatching instruction is determined. Through the iteration process, all information of a certain node can be collected, a fully distributed mode can be realized, and the iteration optimization time is shortened by using the limited step iteration convergence characteristic.

Description

Technical Field

[0001] This invention relates to the field of power grid economic dispatch technology, and in particular to a distributed power grid economic dispatch method, system, equipment and medium. Background Technology

[0002] With the development of the power industry and technology, traditional power grids urgently need to transition to smart grids. Economic dispatch (ED) is a crucial component of this transition and has therefore attracted widespread attention from researchers. Economic dispatch algorithms can ensure that each subgrid in the power system reliably supplies power to users at a lower generation cost. Simultaneously, these algorithms can also reduce the overall cost of the power system through interaction between subgrids.

[0003] Traditional economic dispatch algorithms are generally centralized, requiring a control center to implement them. Common methods include gradient search, iterative methods, dynamic population game theory, particle swarm optimization, and neural network algorithms. However, in large-scale power networks, the massive amounts of data and complex structures can affect the control center's fault data processing capabilities, thereby impacting system reliability.

[0004] To address the inapplicability and unreliability of centralized economic dispatch algorithms in large-scale power networks, distributed economic dispatch algorithms are employed for power grid economic dispatch. The characteristic of distributed economic dispatch algorithms is that each node in the system possesses independent communication and computing capabilities, information can be exchanged between adjacent nodes, and an optimal state is reached through iterative algorithms; that is, economic dispatch of the entire power network is achieved through limited communication between a limited number of nodes. However, existing distributed economic dispatch algorithms suffer from excessively long iterative optimization times due to the need for information exchange and iterative optimization of data among all nodes, thus reducing the performance of economic dispatch. Summary of the Invention

[0005] This invention provides a power grid economic dispatch method, system, equipment, and medium, which solves the technical problem that existing distributed economic dispatch algorithms require information exchange and iterative optimization of data between all nodes, resulting in excessively long iteration optimization time and reduced economic dispatch performance.

[0006] This invention provides a power grid economic dispatch method, comprising:

[0007] When the power grid node data corresponding to the power grid is received, the Laplace matrix, the first auxiliary variable, and the second auxiliary variable corresponding to the power grid are determined based on the power grid node data.

[0008] Based on the Laplace matrix, the first and second convergence values ​​corresponding to the power grid are generated by iterative convergence of the first auxiliary variable and the second auxiliary variable, respectively.

[0009] Based on the preset convergence data, the first convergence value, and the second convergence value, determine the first convergence difference value corresponding to the power grid;

[0010] Based on the first convergence difference and the preset load constraints, the target dispatch instruction corresponding to the power grid is determined.

[0011] Optionally, the step of determining the Laplace matrix, the first auxiliary variable, and the second auxiliary variable corresponding to the power grid based on the power grid node data when receiving the power grid node data includes:

[0012] When power grid node data corresponding to the power grid is received, the power network node corresponding to the power grid is determined based on the power grid node data;

[0013] Topology theory is used to interactively calibrate the information between the nodes of the power network, generating the Laplace matrix corresponding to the power grid;

[0014] Obtain the first auxiliary variable and the second auxiliary variable corresponding to the power grid node data.

[0015] Optionally, the step of using the finite-time iterative method based on the Laplace matrix to iteratively converge the first auxiliary variable and the second auxiliary variable to generate the first convergence value and the second convergence value corresponding to the power grid includes:

[0016] Obtain the non-zero eigenvalues ​​within the Laplacian matrix;

[0017] An auxiliary matrix is ​​constructed using the non-zero eigenvalues;

[0018] Based on the auxiliary matrix, the first auxiliary variable and the second auxiliary variable are iteratively converged using the finite-time iterative method to generate the first convergence value and the second convergence value corresponding to the power grid.

[0019] The first convergence value is:

[0020]

[0021] in, c is the first convergent value; i P is the cost function; i P represents the active power output of the i-th generator. D,i Let be the load of the i-th generator; n be the number of generators in the power grid; z be the generator data that needs to be differentiated.

[0022] The second convergence value is:

[0023]

[0024] in, c is the second convergence value. i P is the cost function; i Let be the active power output of the i-th generator; n be the number of generators in the power grid; and z be the generator data that needs to be differentiated.

[0025] Optionally, the step of determining the first convergence difference value corresponding to the power grid based on preset convergence data, the first convergence value, and the second convergence value includes:

[0026] The Lagrange multiplier difference corresponding to the first convergence value and the second convergence value is obtained by using the Newton-Raphson algorithm;

[0027] Based on the Lagrange multiplier difference and the preset convergence data, the first convergence difference corresponding to the power grid is determined.

[0028] Optionally, the step of determining the first convergence difference corresponding to the power grid based on the Lagrange multiplier difference and preset convergence data includes:

[0029] Determine whether the Lagrange multiplier differences satisfy the preset convergence data;

[0030] If so, the Lagrange multiplier difference at the current moment is taken as the first convergence difference;

[0031] The first convergence difference is:

[0032]

[0033] Where Y1 is the first convergence difference; c i P is the cost function; i P represents the active power output of the i-th generator. D,i Let be the load of the i-th generator; n be the number of generators in the power grid; z be the generator data that needs to be differentiated.

[0034] If not, then proceed to the step of iteratively converging the first auxiliary variable and the second auxiliary variable based on the auxiliary matrix using the finite-time iterative method to generate the first convergence value and the second convergence value corresponding to the power grid, until the Lagrange multiplier difference satisfies the preset convergence data.

[0035] Optionally, the step of determining the target dispatch instruction corresponding to the power grid based on the first convergence difference and the preset load constraint conditions includes:

[0036] Based on the first convergence difference, adjust the initial active power correction value corresponding to the economic dispatch data to generate the target active power correction value corresponding to the power grid.

[0037] The target active power correction value is:

[0038]

[0039] Wherein, ΔP i c is the second convergence value. i P is the cost function; i Y1 represents the active power output of the i-th generator; Y1 represents the first convergence difference; and z represents the generator data that needs to be differentiated.

[0040] Obtain the corrected active power value corresponding to the target active power correction value;

[0041] Calculate the difference between the active power and the active power correction value, and generate the corresponding optimal active power;

[0042] Determine whether the optimal active power meets the preset load constraint conditions;

[0043] If so, the initial scheduling instruction corresponding to the optimal active power shall be used as the target scheduling instruction corresponding to the power grid;

[0044] If not, then based on the connectivity graph corresponding to the power grid node data and the preset convergence data, the target scheduling instruction corresponding to the power grid is determined.

[0045] Optionally, the step of determining the target scheduling instruction corresponding to the power grid based on the connectivity graph corresponding to the power grid node data and the preset convergence data includes:

[0046] Based on the connectivity graph corresponding to the power grid node data, determine the set of limit value indices corresponding to the power grid;

[0047] Based on the set of limit value indices, the third, fourth, and fifth auxiliary variables corresponding to the power grid are determined;

[0048] Based on the Laplace matrix, the finite-time iterative method is used to iterate and converge the third auxiliary variable, the fourth auxiliary variable, and the fifth auxiliary variable respectively, generating the third convergence value, the fourth convergence value, and the fifth convergence value corresponding to the power grid;

[0049] Calculate the sum of the third convergence value and the fifth convergence value, and calculate the ratio of the sum to the fourth convergence value to generate a second convergence difference value;

[0050] The second convergence difference is used as the first convergence difference, and the process jumps to the step of determining the target dispatch command corresponding to the power grid based on the first convergence difference and the preset load constraint conditions, until the optimal active power satisfies the preset load constraint conditions.

[0051] The present invention also provides a power grid economic dispatch system, comprising:

[0052] The Laplace matrix, first auxiliary variable, and second auxiliary variable determination module is used to determine the Laplace matrix, first auxiliary variable, and second auxiliary variable corresponding to the power grid based on the power grid node data when the power grid node data corresponding to the power grid is received.

[0053] The first convergence value and second convergence value generation module is used to generate the first convergence value and second convergence value corresponding to the power grid by iteratively converging the first auxiliary variable and the second auxiliary variable respectively based on the Laplace matrix.

[0054] The first convergence difference determination module is used to determine the first convergence difference corresponding to the power grid based on preset convergence data, the first convergence value and the second convergence value;

[0055] The target dispatch instruction determination module is used to determine the target dispatch instruction corresponding to the power grid based on the first convergence difference and preset load constraints.

[0056] The present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs steps to implement any of the above-described power grid economic dispatch methods.

[0057] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements any of the above-described power grid economic dispatch methods.

[0058] As can be seen from the above technical solutions, the present invention has the following advantages:

[0059] This invention, upon receiving grid node data, determines the corresponding Laplace matrix, first auxiliary variable, and second auxiliary variable based on the grid node data. Based on the Laplace matrix, iterative convergence is performed on the first and second auxiliary variables respectively to generate a first convergence value and a second convergence value for the grid. Based on preset convergence data, the first convergence value, and the second convergence value, a first convergence difference for the grid is determined. Based on the first convergence difference and preset load constraints, the target scheduling instruction for the grid is determined. This solves the technical problem of existing distributed economic scheduling algorithms, which require information exchange and iterative optimization between all nodes, resulting in excessively long iteration optimization time and reduced economic scheduling performance. By having a single node collect all information during the iteration process, it can be implemented in a fully distributed manner, and the finite-step iterative convergence characteristic shortens the iteration optimization time. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 This is a flowchart illustrating the steps of an economic power grid dispatching method according to Embodiment 1 of the present invention.

[0062] Figure 2 This is a flowchart illustrating the steps of a power grid economic dispatch method provided in Embodiment 2 of the present invention.

[0063] Figure 3 This is a flowchart of an economic power grid dispatch method provided in Embodiment 2 of the present invention;

[0064] Figure 4 This is a node diagram provided in Embodiment 2 of the present invention;

[0065] Figure 5 This is a structural block diagram of a power grid economic dispatch system provided in Embodiment 3 of the present invention. Detailed Implementation

[0066] This invention provides a power grid economic dispatch method, system, device, and medium to address the technical problem that existing distributed economic dispatch algorithms suffer from excessively long iteration optimization times and reduced economic dispatch performance due to the need for information exchange and iterative data optimization among all nodes.

[0067] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0068] Please see Figure 1 , Figure 1 The flowchart illustrates the steps of an economic power grid dispatching method provided in Embodiment 1 of the present invention.

[0069] This invention provides a power grid economic dispatch method, comprising:

[0070] Step 101: When the power grid node data corresponding to the power grid is received, determine the Laplace matrix, the first auxiliary variable, and the second auxiliary variable corresponding to the power grid based on the power grid node data.

[0071] Grid node data includes the generator operation data of each node in the grid, such as the active power output of each generator, cost factor, generator load, number of generators, generator data that needs to be differentiated, and the total load of the power system.

[0072] In this embodiment of the invention, the power network nodes corresponding to the power grid are determined based on power grid node data. Topology theory is used to interactively calibrate the information between power network nodes, generating a Laplace matrix corresponding to the power grid. First and second auxiliary variables corresponding to the power grid node data are obtained.

[0073] Step 102: Based on the Laplace matrix, iterative convergence is performed on the first auxiliary variable and the second auxiliary variable respectively to generate the first convergence value and the second convergence value corresponding to the power grid.

[0074] In this embodiment of the invention, non-zero eigenvalues ​​within the Laplace matrix are obtained, and an auxiliary matrix is ​​constructed using these non-zero eigenvalues. Based on the auxiliary matrix, a finite-time iterative method is used to iteratively converge the first and second auxiliary variables, respectively, generating the first and second convergent values ​​corresponding to the power grid.

[0075] Step 103: Determine the first convergence difference value corresponding to the power grid based on the preset convergence data, the first convergence value, and the second convergence value.

[0076] In this embodiment of the invention, the Newton-Raphson algorithm is used to obtain the Lagrange multiplier difference corresponding to the first convergence value and the second convergence value. Based on the Lagrange multiplier difference and the preset convergence data, the first convergence difference corresponding to the power grid is determined.

[0077] Step 104: Determine the target dispatch command corresponding to the power grid based on the first convergence difference and the preset load constraints.

[0078] In this embodiment of the invention, the initial active power correction value corresponding to the economic dispatch data is adjusted based on the first convergence difference to generate the target active power correction value for the power grid. The corrected active power corresponding to the target active power correction value is obtained, and the difference between the active power and the active power correction value is calculated to generate the corresponding optimal active power. It is determined whether the optimal active power satisfies the preset load constraint conditions; if so, the initial dispatch instruction corresponding to the optimal active power is used as the target dispatch instruction for the power grid; if not, the target dispatch instruction for the power grid is determined based on the connectivity graph corresponding to the power grid node data and the preset convergence data.

[0079] In this embodiment of the invention, upon receiving grid node data corresponding to the power grid, the Laplace matrix, first auxiliary variable, and second auxiliary variable corresponding to the power grid are determined based on the grid node data. Based on the Laplace matrix, iterative convergence is performed on the first and second auxiliary variables respectively to generate a first convergence value and a second convergence value corresponding to the power grid. Based on preset convergence data, the first convergence value, and the second convergence value, a first convergence difference value corresponding to the power grid is determined. Based on the first convergence difference value and preset load constraints, the target scheduling instruction corresponding to the power grid is determined. This solves the technical problem of existing distributed economic scheduling algorithms, where the need for information exchange and iterative optimization of data among all nodes leads to excessively long iterative optimization times, thus reducing the performance of economic scheduling. By having a single node collect all information during the iteration process, a fully distributed approach can be implemented, and the finite-step iterative convergence characteristic shortens the iterative optimization time.

[0080] Please see Figure 2 , Figure 2 The flowchart illustrates the steps of a power grid economic dispatch method provided in Embodiment 2 of the present invention.

[0081] Another power grid economic dispatch method provided by the present invention includes:

[0082] Step 201: When the power grid node data corresponding to the power grid is received, determine the power network node corresponding to the power grid based on the power grid node data.

[0083] In this embodiment of the invention, when power grid node data corresponding to the power grid is received, the power grid network node information included in the power grid node data is obtained, and based on the power grid network node information, it is determined which power network nodes are included in the power grid and the connection relationship between the power network nodes.

[0084] Step 202: Use topology theory to interactively calibrate the information between power network nodes and generate the Laplace matrix corresponding to the power grid.

[0085] In this embodiment of the invention, topology theory is used to interactively identify the information between power network nodes, that is, to obtain the information of each node in the power network through limited information interaction operations:

[0086]

[0087] in, The state of the i-th node and its neighboring nodes during iteration r; m ii and m ij Indicates the update gain; This represents the state value of the iterating node i.

[0088] Assuming the power grid is an undirected connected graph G, then let,

[0089]

[0090]

[0091] in, G represents the state value of the iterating node i. t (r) is the state matrix of the entire network during iteration r, G t Let (r+1) be the state matrix of the entire network at iteration r+1, and M(r) be the matrix for updating the profit. Then, according to the iteration method, we can obtain...

[0092] G t (r+1)=M(r)G t (r)

[0093] Before iteration, the information of each node needs to be initialized, and each node has its own unique identifier.

[0094]

[0095] ID i and ID j Its adjacent nodes, The weights of adjacent boundaries, when k≥1, represent the identifier information at the (k+1)th step of information exchange. Updated to:

[0096]

[0097] if This indicates that the information exchange is complete.

[0098] Step 203: Obtain the first auxiliary variable and the second auxiliary variable corresponding to the power grid node data.

[0099] In this embodiment of the invention, in order to obtain the first convergence difference within a finite number of steps using a distributed computing method, a first auxiliary variable x corresponding to the power grid is first defined. i (0) and the second auxiliary variable s i (0) is as follows:

[0100]

[0101] Among them, c i P is the cost function; i P represents the active power output of the i-th generator. D,i Let be the load of the i-th generator; n be the number of generators in the power grid; and z be the generator data that needs to be differentiated.

[0102] Step 204: Based on the Laplace matrix, iterative convergence is performed on the first auxiliary variable and the second auxiliary variable respectively to generate the first convergence value and the second convergence value corresponding to the power grid.

[0103] Furthermore, step 204 may include the following sub-steps S11-S13:

[0104] S11. Obtain the non-zero eigenvalues ​​within the Laplacian matrix.

[0105] S12. Construct an auxiliary matrix using non-zero eigenvalues.

[0106] S13. Based on the auxiliary matrix, the finite-time iterative method is used to iterate and converge the first auxiliary variable and the second auxiliary variable respectively, generating the first convergence value and the second convergence value corresponding to the power grid.

[0107] The first convergence value is:

[0108]

[0109] in, c is the first convergent value; i P is the cost function; i P represents the active power output of the i-th generator. D,i Let be the load of the i-th generator; n be the number of generators in the power grid; and z be the generator data that needs to be differentiated.

[0110] The second convergence value is:

[0111]

[0112] in, c is the second convergence value. i P is the cost function;i Let be the active power output of the i-th generator; n be the number of generators in the power grid; and z be the generator data that needs to be differentiated.

[0113] In this embodiment of the invention, the first auxiliary variable x = (x1, x2, ..., x) is obtained by using a finite-time iteration method based on the auxiliary matrix. n ) T Second auxiliary variable s = (s1, s2, ..., s n ) T The following values ​​can be obtained:

[0114] x t (r+1)=H(r)x t (r)

[0115] s t (r+1)=H(r)s t (r)

[0116] In the above formula, x t (r+1) and s t (r+1) represents the calculated consensus average of the auxiliary variables at iteration r+1, and H(r) is the auxiliary matrix composed of the non-zero eigenvalues ​​generated by the Laplace matrix L. This indicates that, through simple calculation, the auxiliary variables converge to a specific convergence value. and The first and second convergence values ​​corresponding to the power grid are obtained nearby:

[0117]

[0118] in, c is the first convergent value; i P is the cost function; i P represents the active power output of the i-th generator. D,i Let be the load of the i-th generator; n be the number of generators in the power grid; and z be the generator data that needs to be differentiated.

[0119]

[0120] in, c is the second convergence value. i P is the cost function; i Let be the active power output of the i-th generator; n be the number of generators in the power grid; and z be the generator data that needs to be differentiated.

[0121] Step 205: Determine the first convergence difference value corresponding to the power grid based on the preset convergence data, the first convergence value, and the second convergence value.

[0122] Further, step 205 may include the following sub-steps S21-S22:

[0123] S21. Use the Newton-Raphson algorithm to obtain the Lagrange multiplier difference corresponding to the first convergence value and the second convergence value.

[0124] S22. Determine the first convergence difference corresponding to the power grid based on the Lagrange multiplier difference and the preset convergence data.

[0125] Furthermore, step S22 may include the following sub-steps S221-S223:

[0126] S221. Determine whether the Lagrange multiplier differences meet the preset convergence data.

[0127] S222. If so, then the Lagrange multiplier difference at the current time is taken as the first convergence difference.

[0128] The first convergence difference is:

[0129]

[0130] Where Y1 is the first convergence difference; c i P is the cost function; i P represents the active power output of the i-th generator. D,i Let be the load of the i-th generator; n be the number of generators in the power grid; and z be the generator data that needs to be differentiated.

[0131] S223. If not, then proceed to the step of using the finite-time iterative method based on the auxiliary matrix to iterate and converge the first auxiliary variable and the second auxiliary variable respectively, generating the first convergence value and the second convergence value corresponding to the power grid, until the difference of the Lagrange multipliers satisfies the preset convergence data.

[0132] When there are no load constraints, the ED problem becomes as follows:

[0133]

[0134] Among them, c i P is the cost function; i Let be the active power output of the i-th generator; φ be the Lagrange multiplier; Δφ be the equilibrium point Lagrange multiplier; φ-Δφ be the convergence difference generated during the iteration of the Newton-Raphson algorithm; ΔP i The target active power correction value; P D The load of the generator; P D,i Let be the load of the i-th generator; z is a set of generator information, which in the formula means which information needs to be differentiated.

[0135] Simplifying the above equation, we get:

[0136]

[0137] After at most N-1 steps, the first and second auxiliary variables will converge to near the above values. At this point, the first convergence difference is:

[0138]

[0139] In this embodiment of the invention, the preset convergence data refers to the critical values ​​set in advance based on the actual situation of the power grid. When determining the first and second convergence values ​​using a finite-step iterative method, it is necessary to specifically divide the problem into two cases: one with load constraints and one without load constraints, as with the traditional ED problem. In this embodiment, only the case without load constraints needs to be considered when determining the first and second convergence values.

[0140] Step 206: Determine the target dispatch command corresponding to the power grid based on the first convergence difference and the preset load constraints.

[0141] Furthermore, step 206 may include the following sub-steps S31-S36:

[0142] S31. Adjust the initial active power correction value corresponding to the economic dispatch data based on the first convergence difference, and generate the target active power correction value corresponding to the power grid.

[0143] The target active power correction value is:

[0144]

[0145] Wherein, ΔP i c is the second convergence value. i P is the cost function; i Y1 represents the active power output of the i-th generator; Y1 represents the first convergence difference; and z represents the generator data that needs to be differentiated.

[0146] S32. Obtain the corrected active power value corresponding to the target active power correction value.

[0147] S33. Calculate the difference between the active power and the active power correction value, and generate the corresponding optimal active power.

[0148] S34. Determine whether the optimal active power meets the preset load constraint conditions.

[0149] S35. If so, the initial dispatch instruction corresponding to the optimal active power shall be used as the target dispatch instruction corresponding to the power grid.

[0150] S36. If not, then determine the target scheduling instruction corresponding to the power grid based on the connectivity graph corresponding to the power grid node data and the preset convergence data.

[0151] The preset load constraint condition refers to the critical value that the optimal active power output of the generator in the power grid should meet.

[0152] In this embodiment of the invention, the initial active power correction value corresponding to the economic dispatch data is updated using the first convergence difference to generate the target active power correction value corresponding to the power grid. The corrected active power value corresponding to the target active power correction value is obtained. The active power P is calculated. i With active power correction value ΔP i The difference is used to generate the corresponding optimal active power P. i * That is: P i * =P i -ΔP i .

[0153] The system determines whether the optimal active power meets the preset load constraints. If the optimal active power meets the preset load constraints, the initial dispatch command corresponding to the optimal active power is used as the target dispatch command for the power grid. Otherwise, the target dispatch command for the power grid is determined based on the connectivity graph corresponding to the power grid node data and the preset convergence data.

[0154] Further, step S36 may include the following sub-steps S361-S365:

[0155] S361. Based on the connectivity graph corresponding to the power grid node data, determine the set of limit value indices corresponding to the power grid.

[0156] S362. Based on the limit value index set, determine the third, fourth, and fifth auxiliary variables corresponding to the power grid.

[0157] S363. Based on the Laplace matrix, the finite-time iterative method is used to iterate and converge the third, fourth, and fifth auxiliary variables respectively, generating the third, fourth, and fifth convergence values ​​corresponding to the power grid.

[0158] S364. Calculate the sum of the third and fifth convergence values, and calculate the ratio of the sum to the fourth convergence value to generate the second convergence difference.

[0159] S365. Take the second convergence difference as the first convergence difference, and jump to execute the step of determining the target dispatch command corresponding to the power grid based on the first convergence difference and the preset load constraint conditions, until the optimal active power meets the preset load constraint conditions.

[0160] In this embodiment of the invention, there are a total of n generators in the power grid, P iLet C represent the active power output of the i-th generator, and let C be the cost function of the power grid expressed as a quadratic function. i (P i )for:

[0161] C i (P i )=δ i +η i P i +ξ i P i 2

[0162] Where, δ i η i and ξ i These are the cost coefficients associated with the i-th generator; C i (P i P is the cost function; i P represents the active power output of the i-th generator. D,i Let P be the load of the i-th generator, then the total load P of the entire power system is... D for:

[0163]

[0164] Therefore, the traditional definition of the ED problem is:

[0165]

[0166] In the formula P i min and P i max C represents the upper and lower limits of the active power output of the i-th generator, respectively; i (P i P is the cost function; i P represents the active power output of the i-th generator; D This is the load on the generator.

[0167] When the load constraint is not considered, the Lagrange multiplier φ, which is related to the supply-demand equality constraint, is introduced to obtain the Lagrange function Y:

[0168]

[0169] Among them, C i (P i ) represents the cost function; φ represents the Lagrange multiplier; P i P represents the active power output of the i-th generator; D This is the load on the generator.

[0170] At this point, the ED problem simplifies to the following:

[0171]

[0172] The optimal solution to the ED problem when considering load constraints It can be represented in the following form. Where, To consider the Lagrange multipliers related to the supply-demand equality constraint under the load constraint:

[0173]

[0174]

[0175] in, The optimal solution for economic scheduling; P i min and P i max ξ represents the upper and lower limits of the active power output of the i-th generator, respectively; i For the purpose of spending C i (P i Related cost coefficients; To consider the Lagrange multipliers related to the supply-demand equality constraint under load constraints; η i For the purpose of spending C i (P i Related cost coefficients.

[0176] When the optimal active power does not meet the preset load constraints, there is a set Ω at index i in the connected graph G. Then, the set Ω can be divided into the following three cases:

[0177] Case 1: For any i belonging to set Ω, and P i ≥P i max If set Ω is not empty, then... therefore, Based on the constraints considering load constraints in step 202, for any i belonging to set Ω, we have:

[0178] Case 2: For any i belonging to set Ω, and P i ≤P i min If set Ω is not empty, then... therefore, Based on the constraints considering load constraints in step 202, for any i belonging to set Ω, we have:

[0179] Case 3: For any i belonging to set Ω1, and P i ≥P i max For any i belonging to set Ω2, and P i ≤P i min Both of the above conditions must be satisfied simultaneously, i.e., Ω = Ω1∪Ω2.

[0180] set up Let the set of limit value indices represent the three scenarios described above. Then, for... The information that may be generated will not participate in the subsequent scheduling process, but will instead affect the generator data. We can obtain:

[0181]

[0182]

[0183] therefore,

[0184]

[0185] Among them, c i P is the cost function; i P represents the active power output of the i-th generator; φ is the Lagrange multiplier; Δφ is the equilibrium point Lagrange multiplier; φ-Δφ is the convergence difference generated during the iteration of the Newton-Raphson algorithm; P D The load of the generator; P D,i Let be the load of the i-th generator.

[0186] Set the third auxiliary variable A corresponding to the power grid i Fourth auxiliary variable B i and the fifth auxiliary variable C i for:

[0187]

[0188]

[0189]

[0190] Based on the Laplace matrix, a finite-time iterative method is used to iterate and converge the third, fourth, and fifth auxiliary variables respectively. That is, after calculating the auxiliary variable A = (A1, A2, ..., A...), the results are obtained. n ) T B = (B1, B2, ..., B n ) T and C = (C1, C2, ..., C n ) TThe following values ​​can be obtained:

[0191]

[0192]

[0193]

[0194] h ii These are the non-zero eigenvalues ​​generated by the corresponding Laplace matrix L. Through simple calculations, it can be determined that the auxiliary variable converges to a value near which the third convergent value corresponding to the generated power grid is obtained. Fourth convergence value and the fifth convergence value for:

[0195]

[0196]

[0197]

[0198] Among them, c i P is the cost function; i P represents the active power output of the i-th generator. D The load of the generator; P D,i Let be the load of the i-th generator. Calculate the sum of the third and fifth convergence values, and calculate the ratio of this sum to the fourth convergence value. Generate the second convergence difference value Y2 as follows:

[0199]

[0200] Finally, the second convergence difference is used as the first convergence difference, and the process jumps to the step of determining the target dispatch command corresponding to the power grid based on the first convergence difference and preset load constraints. For example... Figure 3 The given node graph shows that for nodes 2 and 3, k = 2 steps are needed to discover the entire topology, while for nodes 1, 4, and 5, k = 3 steps are needed. Therefore, it can be ensured that the variables converge to the vicinity of the above values ​​within no more than N-1 steps. Table 1 shows the information interaction process of the corresponding nodes in the node graph.

[0201] Based primarily on the topological method given in step 1 (K=1), only information about adjacent neighbors can be discovered each time. The actual connection graph is as follows: Figure 3As shown, taking node 1 as an example, when K=0, the neighboring node identifiers ID1 and ID2 are determined. When K=1, the information ID1(0) and ID2(0) are obtained. When K=2, the neighboring information of nodes 1 and 2 is obtained, ID1(0), ID2(0), ID3(0), and ID5(0). When K=3, the neighboring information of nodes 1, 2, 3, and 5 is obtained, ID1(0), ID2(0), ID3(0), ID4(0), and ID5(0).

[0202] Table 1 shows the information interaction process of corresponding nodes in the node diagram.

[0203]

[0204] In embodiments of the present invention, such as Figure 4 As shown, when power grid node data is received, topology theory is used to interactively calibrate the information between power network nodes to obtain the Laplace matrix corresponding to the power grid. The first and second auxiliary variables corresponding to the power grid node data are then obtained. Based on the auxiliary matrix, a finite-time iterative method is used to iteratively converge the first and second auxiliary variables to determine the first convergence value corresponding to the power grid. Second convergence value Based on the Lagrange multiplier difference and preset convergence data, the first convergence difference value corresponding to the power grid is determined. The initial active power correction value corresponding to the economic dispatch data is adjusted based on the first convergence difference to generate the target active power correction value for the power grid. The corrected active power value corresponding to the target active power correction value is obtained, and the difference between the active power and the active power correction value is calculated to generate the corresponding optimal active power P. i * If yes, then the initial scheduling instruction corresponding to the optimal active power is taken as the target scheduling instruction for the power grid. If not, then determine whether the set Ω corresponding to the connected graph based on the power grid node data belongs to one of the following three cases:

[0205] Case 1:

[0206] Case 2:

[0207] Case 3: and

[0208] The third auxiliary variable A corresponding to the initialization of the power grid i (0), Fourth auxiliary variable B i (0) and the fifth auxiliary variable C i(0), based on the Laplace matrix, the finite-time iterative method is used to iterate and converge the third auxiliary variable, the fourth auxiliary variable, and the fifth auxiliary variable respectively, to generate the third convergent value corresponding to the power grid. Fourth convergence value and the fifth convergence value The sum of the third and fifth convergence values ​​is calculated, and the ratio of this sum to the fourth convergence value is calculated to generate the second convergence difference. This second convergence difference is used as the first convergence difference, and the process jumps to the step of determining the target dispatch command for the power grid based on the first convergence difference and preset load constraints, until the optimal active power meets the preset load constraints. This distributed iterative approach solves the problems of poor applicability and scalability of centralized algorithms. The finite-step iterative convergence characteristic achieves fast-convergence ED dispatch, enabling a high-efficiency, highly scalable, and highly flexible power network.

[0209] Please see Figure 5 , Figure 5 This is a structural block diagram of a power grid economic dispatch system provided in Embodiment 3 of the present invention.

[0210] This invention provides a power grid economic dispatch system, comprising:

[0211] The Laplace matrix, first auxiliary variable, and second auxiliary variable determination module 501 is used to determine the Laplace matrix, first auxiliary variable, and second auxiliary variable corresponding to the power grid based on the power grid node data when the power grid node data corresponding to the power grid is received.

[0212] The first convergence value and second convergence value generation module 502 is used to generate the first convergence value and second convergence value corresponding to the power grid by iteratively converging the first auxiliary variable and the second auxiliary variable respectively based on the Laplace matrix.

[0213] The first convergence difference determination module 503 is used to determine the first convergence difference corresponding to the power grid based on preset convergence data, the first convergence value, and the second convergence value.

[0214] The target dispatch instruction determination module 504 is used to determine the target dispatch instruction corresponding to the power grid based on the first convergence difference and the preset load constraint conditions.

[0215] Optionally, the Laplace matrix, the first auxiliary variable, and the second auxiliary variable determination module 501 include:

[0216] The power network node determination module is used to determine the corresponding power network node based on the power grid node data when the power grid node data is received.

[0217] The Laplace matrix generation module is used to interactively calibrate the information between nodes in the power network using topology theory, and generate the Laplace matrix corresponding to the power grid.

[0218] The module for obtaining the first auxiliary variable and the second auxiliary variable is used to obtain the first auxiliary variable and the second auxiliary variable corresponding to the power grid node data.

[0219] Optionally, the first convergence value and second convergence value generation module 502 includes:

[0220] The non-zero eigenvalue acquisition module is used to obtain the non-zero eigenvalues ​​within the Laplacian matrix.

[0221] The auxiliary matrix construction module is used to construct auxiliary matrices using non-zero eigenvalues.

[0222] The first convergence value and second convergence value generation submodule is used to perform iterative convergence of the first auxiliary variable and the second auxiliary variable based on the auxiliary matrix using the finite-time iterative method, respectively, to generate the first convergence value and the second convergence value corresponding to the power grid.

[0223] The first convergence value is:

[0224]

[0225] in, c is the first convergent value; i P is the cost function; i P represents the active power output of the i-th generator. D,i Let be the load of the i-th generator; n be the number of generators in the power grid; and z be the generator data that needs to be differentiated.

[0226] The second convergence value is:

[0227]

[0228] in, c is the second convergence value. i P is the cost function; i Let be the active power output of the i-th generator; n be the number of generators in the power grid; and z be the generator data that needs to be differentiated.

[0229] Optionally, the first convergence difference determination module 503 includes:

[0230] The Lagrange multiplier difference acquisition module is used to obtain the Lagrange multiplier difference corresponding to the first convergence value and the second convergence value using the Newton-Raphson algorithm.

[0231] The first convergence difference determination submodule is used to determine the first convergence difference corresponding to the power grid based on the Lagrange multiplier difference and preset convergence data.

[0232] Optionally, the first convergence difference determination submodule may perform the following steps:

[0233] Determine whether the Lagrange multiplier differences meet the preset convergence data;

[0234] If so, then the Lagrange multiplier difference at the current moment is taken as the first convergent difference;

[0235] The first convergence difference is:

[0236]

[0237] Where Y1 is the first convergence difference; c i P is the cost function; i P represents the active power output of the i-th generator. D,i Let be the load of the i-th generator; n be the number of generators in the power grid; z be the generator data that needs to be differentiated.

[0238] If not, then proceed to the step of iteratively converging the first auxiliary variable and the second auxiliary variable using the finite-time iterative method based on the auxiliary matrix to generate the first convergence value and the second convergence value corresponding to the power grid, until the difference of the Lagrange multipliers satisfies the preset convergence data.

[0239] Optionally, the target scheduling instruction determination module 504 includes:

[0240] The target active power correction value generation module is used to adjust the initial active power correction value corresponding to the economic dispatch data based on the first convergence difference, and generate the target active power correction value corresponding to the power grid.

[0241] The target active power correction value is:

[0242]

[0243] Wherein, ΔP i c is the second convergence value. i P is the cost function; i Y1 represents the active power output of the i-th generator; Y1 represents the first convergence difference; and z represents the generator data that needs to be differentiated.

[0244] The corrected active power acquisition module is used to acquire the corrected active power corresponding to the target active power correction value.

[0245] The optimal active power generation module is used to calculate the difference between the active power and the active power correction value, and generate the corresponding optimal active power.

[0246] The optimal active power determination module is used to determine whether the optimal active power meets the preset load constraint conditions.

[0247] The first submodule for determining the target scheduling instruction is used to, if so, take the initial scheduling instruction corresponding to the optimal active power as the target scheduling instruction for the power grid.

[0248] The second submodule for determining the target scheduling instruction is used to determine the target scheduling instruction corresponding to the power grid based on the connectivity graph corresponding to the power grid node data and the preset convergence data if no.

[0249] Optionally, the target scheduling instruction determines that the second submodule can perform the following steps:

[0250] Based on the connectivity graph corresponding to the power grid node data, determine the set of limit value indices corresponding to the power grid.

[0251] Based on the limit value index set, the third, fourth, and fifth auxiliary variables corresponding to the power grid are determined;

[0252] Based on the Laplace matrix, the finite-time iterative method is used to iterate and converge the third, fourth, and fifth auxiliary variables respectively, generating the third, fourth, and fifth convergence values ​​corresponding to the power grid.

[0253] Calculate the sum of the third and fifth convergence values, and then calculate the ratio of this sum to the fourth convergence value to generate the second convergence difference.

[0254] The second convergence difference is used as the first convergence difference, and the process jumps to the step of determining the target dispatch command corresponding to the power grid based on the first convergence difference and the preset load constraints, until the optimal active power meets the preset load constraints.

[0255] This invention also provides an electronic device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor performs the power grid economic dispatch method as described in any of the above embodiments.

[0256] The memory can be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read-Only Memory), EPROM, hard disk, or ROM. The memory has storage space for program code used to perform any of the method steps described above. For example, the storage space for program code may include individual program codes for implementing the various steps in the methods described above. This program code can be read from or written to one or more computer program products. These computer program products include program code carriers such as hard disks, compact discs (CDs), memory cards, or floppy disks. The program code may be compressed, for example, in a suitable form. When run by a computing processing device, this code causes the computing processing device to perform the various steps in the power grid economic dispatch method described above.

[0257] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the power grid economic dispatch method as described in any of the above embodiments.

[0258] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0259] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0260] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0261] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0262] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0263] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A power grid economic dispatch method, characterized in that, include: When the power grid node data corresponding to the power grid is received, the Laplace matrix, the first auxiliary variable, and the second auxiliary variable corresponding to the power grid are determined based on the power grid node data. Based on the Laplace matrix, the first auxiliary variable and the second auxiliary variable are iteratively converged using the finite-time iterative method to generate the first convergence value and the second convergence value corresponding to the power grid. Based on the preset convergence data, the first convergence value, and the second convergence value, determine the first convergence difference value corresponding to the power grid; Based on the first convergence difference and the preset load constraints, the target dispatch instruction corresponding to the power grid is determined; The step of using the finite-time iterative method based on the Laplace matrix to iteratively converge the first auxiliary variable and the second auxiliary variable to generate the first convergence value and the second convergence value corresponding to the power grid includes: Obtain the non-zero eigenvalues ​​within the Laplacian matrix; An auxiliary matrix is ​​constructed using the non-zero eigenvalues; Based on the auxiliary matrix, the first auxiliary variable and the second auxiliary variable are iteratively converged using the finite-time iterative method to generate the first convergence value and the second convergence value corresponding to the power grid. The first convergence value is: ； in, This is the first convergent value; It is the cost function; Let be the active power output of the i-th generator; Let be the load of the i-th generator; n be the number of generators in the power grid; z be the generator data that needs to be differentiated. The second convergence value is: ； in, This is the second convergence value; It is the cost function; Let be the active power output of the i-th generator; n be the number of generators in the power grid; and z be the generator data that needs to be differentiated.

2. The power grid economic dispatch method according to claim 1, characterized in that, The step of determining the Laplace matrix, the first auxiliary variable, and the second auxiliary variable corresponding to the power grid based on the power grid node data received when the power grid node data is received includes: When power grid node data corresponding to the power grid is received, the power network node corresponding to the power grid is determined based on the power grid node data; Topology theory is used to interactively calibrate the information between the nodes of the power network, generating the Laplace matrix corresponding to the power grid; Obtain the first auxiliary variable and the second auxiliary variable corresponding to the power grid node data.

3. The power grid economic dispatch method according to claim 1, characterized in that, The step of determining the first convergence difference value corresponding to the power grid based on preset convergence data, the first convergence value, and the second convergence value includes: The Lagrange multiplier difference corresponding to the first convergence value and the second convergence value is obtained by using the Newton-Raphson algorithm; Based on the Lagrange multiplier difference and the preset convergence data, the first convergence difference corresponding to the power grid is determined.

4. The power grid economic dispatch method according to claim 3, characterized in that, The step of determining the first convergence difference corresponding to the power grid based on the Lagrange multiplier difference and preset convergence data includes: Determine whether the Lagrange multiplier differences satisfy the preset convergence data; If so, the Lagrange multiplier difference at the current moment is taken as the first convergence difference; The first convergence difference is: ； in, This is the first convergence difference; It is the cost function; Let be the active power output of the i-th generator; Let be the load of the i-th generator; n be the number of generators in the power grid; z be the generator data that needs to be differentiated. If not, then proceed to the step of iteratively converging the first auxiliary variable and the second auxiliary variable based on the auxiliary matrix using the finite-time iterative method to generate the first convergence value and the second convergence value corresponding to the power grid, until the Lagrange multiplier difference satisfies the preset convergence data.

5. The power grid economic dispatch method according to claim 1, characterized in that, The step of determining the target dispatch instruction corresponding to the power grid based on the first convergence difference and the preset load constraints includes: Based on the first convergence difference, adjust the initial active power correction value corresponding to the economic dispatch data to generate the target active power correction value corresponding to the power grid. The target active power correction value is: ； in, The target active power correction value; It is the cost function; Let be the active power output of the i-th generator; is the first convergence difference; z is the generator data that needs to be differentiated; Obtain the corrected active power value corresponding to the target active power correction value; Calculate active power P i The difference between the active power correction value and the target active power correction value is used to generate the corresponding optimal active power. Determine whether the optimal active power meets the preset load constraint conditions; If so, the initial scheduling instruction corresponding to the optimal active power shall be used as the target scheduling instruction corresponding to the power grid; If not, then based on the connectivity graph corresponding to the power grid node data and the preset convergence data, the target scheduling instruction corresponding to the power grid is determined.

6. The power grid economic dispatch method according to claim 5, characterized in that, The step of determining the target scheduling instruction corresponding to the power grid based on the connectivity graph corresponding to the power grid node data and the preset convergence data includes: Based on the connectivity graph corresponding to the power grid node data, determine the set of limit value indices corresponding to the power grid; Based on the set of limit value indices, the third, fourth, and fifth auxiliary variables corresponding to the power grid are determined; Based on the Laplace matrix, the finite-time iterative method is used to iterate and converge the third auxiliary variable, the fourth auxiliary variable, and the fifth auxiliary variable respectively, generating the third convergence value, the fourth convergence value, and the fifth convergence value corresponding to the power grid; Calculate the sum of the third convergence value and the fifth convergence value, and calculate the ratio of the sum to the fourth convergence value to generate a second convergence difference value; The second convergence difference is used as the first convergence difference, and the process jumps to the step of determining the target dispatch command corresponding to the power grid based on the first convergence difference and the preset load constraint conditions, until the optimal active power satisfies the preset load constraint conditions.

7. A power grid economic dispatch system, characterized in that, include: The Laplace matrix, first auxiliary variable, and second auxiliary variable determination module is used to determine the Laplace matrix, first auxiliary variable, and second auxiliary variable corresponding to the power grid based on the power grid node data when the power grid node data corresponding to the power grid is received. The first convergence value and second convergence value generation module is used to generate the first convergence value and second convergence value corresponding to the power grid by iteratively converging the first auxiliary variable and the second auxiliary variable respectively based on the Laplace matrix. The first convergence difference determination module is used to determine the first convergence difference corresponding to the power grid based on preset convergence data, the first convergence value and the second convergence value; The target dispatch instruction determination module is used to determine the target dispatch instruction corresponding to the power grid based on the first convergence difference and the preset load constraint conditions. The first convergence value and second convergence value generation module includes: A non-zero eigenvalue acquisition module is used to acquire non-zero eigenvalues ​​within the Laplacian matrix; An auxiliary matrix construction module is used to construct an auxiliary matrix using the non-zero eigenvalues; The first convergence value and second convergence value generation submodule is used to perform iterative convergence of the first auxiliary variable and the second auxiliary variable respectively using the finite-time iterative method based on the auxiliary matrix, and generate the first convergence value and the second convergence value corresponding to the power grid. The first convergence value is: ； in, This is the first convergent value; It is the cost function; Let be the active power output of the i-th generator; Let be the load of the i-th generator; n be the number of generators in the power grid; z be the generator data that needs to be differentiated. The second convergence value is: ； in, This is the second convergence value; It is the cost function; Let be the active power output of the i-th generator; n be the number of generators in the power grid; and z be the generator data that needs to be differentiated.

8. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor causes the processor to perform the steps of the power grid economic dispatch method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed, it implements the power grid economic dispatch method as described in any one of claims 1-6.

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