Transmission section search method, device and system based on optical quantum computing

Abstract

This invention relates to the intersection of power system optimization scheduling and quantum computing technology, proposing a method, device, and system for transmission line cross-section search based on optical quantum computing. The invention first constructs a power network model; then, based on the optimization objective, it constructs a QUBO model as the cross-section search model, with the objective being to minimize the objective function of the transmission lines. The cross-section search model is solved using an optical quantum computer to obtain the power grid cross-sections. The cross-section search model is the sum of the difference between the objective function and the cross-section weights, and the sum of the total constraint difference and the relaxation variables, multiplied by the corresponding penalty coefficients. The cross-section weights are the target value for the number of lines on the transmission line cross-section. This invention utilizes the topology reconstruction characteristics of optical quantum processors to encode power grid partition constraints into qubit combinations, generating a set of candidate cross-sections that satisfy the ground-state power flow in parallel. This solves the problem of missed selection caused by combinatorial explosion in traditional algorithms, improving the comprehensiveness and efficiency of the search.

Description

Technical Field

[0001] This invention relates to the intersection of power system optimization scheduling and quantum computing technology, and proposes a method, device and system for searching transmission sections based on optical quantum computing. Background Technology

[0002] With the continuous expansion of the scale of new power systems and the deepening of the flexible interconnection architecture of multi-level power grids, transmission section search has become a core task to ensure the safe and stable operation of the power grid. In real-time monitoring and optimized scheduling, the rapid and accurate location of transmission sections is not only directly related to the transient stability and power supply reliability of the system, but also has a decisive significance for the economic allocation of cross-regional power resources. Mathematically, this problem requires efficiently selecting electrical cut sets that meet specific line number constraints from complex power grid topologies. Its core is the combinatorial optimization problem of topology-specific cut sets. However, traditional search algorithms face the severe challenge of the "curse of dimensionality" when dealing with NP-hard (Non-deterministic polynomial-time hard) problems. Dynamic changes in power grid topology cause the dimension of the feasible solution space to expand exponentially. Traditional serial algorithms are limited by the combinatorial explosion effect, making it difficult to balance global search and computational efficiency, often resulting in missed sections or local convergence problems. Summary of the Invention

[0003] To overcome the problems of high computational complexity and poor global convergence in traditional power transmission section search algorithms, this invention proposes a power transmission section search method based on optical quantum computing, which achieves a dual improvement in section search efficiency and accuracy.

[0004] This invention proposes a transmission line cross-section search method based on optical quantum computing. First, a power network model is constructed; then, a QUBO model is constructed as the cross-section search model based on the optimization objective, with the objective being to minimize the transmission line cross-section objective function; the cross-section search model is solved using an optical quantum computer to obtain the power grid cross-section.

[0005] The cross-section search model is: the difference between the objective function and the cross-section weights, the sum of the total constraint difference and the slack variables, and the sum of the two multiplied by the corresponding penalty coefficients; the cross-section weights are the target value of the number of lines on the transmission cross-section.

[0006] The preferred formula for the cross-section search model is expressed as follows:

[0007] ;

[0008] Among them, H 1 QUBO This represents the cross-section search model. M 1. M 2 represents the set penalty coefficient;n The number of nodes; x r , k For binary variables, nodes r Assigned to the first k Time in each region x r , k =1, otherwise x r , k =0; N k For the first k The minimum number of nodes corresponding to each region; S As slack variables, C For cross-sectional weights, C ( x ) is the objective function.

[0009] Preferably, the formula for calculating the slack variable S is as follows:

[0010] ;

[0011] in, b max For setting value, s is a random value for a binary number; b is an integer.

[0012] Preferably, the objective function is:

[0013] ;

[0014] in, w rj A binary number representing the connection relationships between nodes. r and j When there are edges connecting them w rj =1, otherwise w rj =0; e rj For nodes r and nodes j The connecting lines between them, where E is the set of lines. x r For nodes r Assigning a value of 0 or 1, x j For nodes j The value is assigned to 0 or 1.

[0015] The present invention proposes a device for implementing the aforementioned transmission section search method based on optical quantum computing, comprising:

[0016] The data acquisition and modeling module is used to acquire power grid operation data and establish a power network model G=(V,E), where V is the set of nodes and E is the set of lines.

[0017] The model building module is used to build cross-section search models;

[0018] The quantum computing module uses an optical quantum computer to solve a cross-section search model to obtain the power grid cross-section.

[0019] The present invention proposes a cross-section search system based on optical quantum computing, comprising a memory and a processor. The memory stores a computer program, and the processor is connected to the memory. The processor is used to execute the computer program to realize the transmission cross-section search method based on optical quantum computing.

[0020] The present invention proposes a storage medium storing a computer program, which, when executed, is used to implement the aforementioned transmission section search method based on optical quantum computing.

[0021] This invention also proposes a unit output optimization method based on optical quantum computing. By decoupling the power transmission section search and power limit calculation into a two-layer quantum model, the method significantly reduces the consumption of quantum computing resources, avoids the resource waste caused by overall optimization in traditional methods, and improves algorithm efficiency.

[0022] This invention proposes a generator output optimization method based on quantum computing. First, it selects grid cross-sections from a quantum computing-based transmission cross-section search method and constructs a DC power flow cross-section transmission limit calculation model based on the cross-section results, consisting of a cross-section power objective function and transmission area constraints. Then, it introduces binary generator adjustment variables to guide generator unit regulation strategies, transforming the DC power flow cross-section transmission limit calculation model into a QUBO model as the generator output optimization model. Finally, it injects the generator output optimization model into a quantum computer to derive the corresponding binary generator adjustment variables for each unit in the transmission area, generating the optimal generator output scheme for each unit in the transmission area. This increases the generator output in the transmission area where the binary adjustment variable is 1 and decreases the generator output in the transmission area where the binary adjustment variable is 0.

[0023] Specifically, the unit output optimization model H 2 QUBO for:

[0024] ;

[0025] in, H 3 and H 4 is a transitional term. E Ω G is the set of lines within the cross section. Ω For the collection of generators within the power transmission area,e rj For nodes r and nodes j The connection lines between them, where i is the generator serial number; P rj 0 For cross-section lines e rj The initial active power; q i Let i be the binary adjustment variable of generator i. P i max For generator i The upper limit of contribution; D i-rj For generator i For the cross section line e rj The generator power transfer distribution factor; λ This is the generator regulation coefficient, with a value range of (-1, 0); P rj max For cross-section lines e rj The line transmission power limit; M 3 represents the set penalty coefficient; P rj For cross-section lines e rj Active power transmitted upstream; S is a slack variable; min indicates taking the minimum value.

[0026] Specifically, the optimal unit output scheme is: the binary adjustment variables of the generators in the power transmission area. q i When the configuration is set to 0, the generator output is reduced to (1+ λ ) × The generator output is currently active; conversely, the generator output is adjusted to the upper limit of active power output.

[0027] Specifically, this invention calculates the total change Δ of the active power of generator units in the power transmission area based on the optimal unit output scheme of the power transmission area. P G Then, based on the law of conservation of energy, the active load increment of each load node in the power receiving area is calculated; and further combined with the classical power flow calculation program, the transmission limit power of the section is calculated, that is, the limit transmission power of each line on the transmission section.

[0028] The proposed method for optimizing generator output utilizes a two-layer QUBO (Quadratic unconstrained binary optimization) model to achieve hierarchical decoupling between cross-section search and transmission limit calculation. This significantly reduces the demand for quantum computing resources and fully leverages the dynamic programmability of optical quantum computers. Specifically, the upper-layer model uses qubits to encode grid partition constraints and combines this with the dynamic topology reconstruction capability of the optical quantum processor to generate a set of candidate cross-sections that satisfy the ground-state power flow conditions in parallel. The lower-layer model constructs a dimension-reduced optimized DC power flow QUBO model for the target cross-section, using quantum parallel computing to solve for the optimal generator output. Furthermore, it can be combined with classical power flow calculation programs to quickly calculate the transmission limit power of the cross-section.

[0029] The advantages of this invention are:

[0030] (1) The present invention proposes a power transmission section search method based on optical quantum computing. By utilizing the topology reconstruction characteristics of optical quantum processors, the power grid partition constraints are encoded into quantum bit combinations, and a set of candidate sections that satisfy the ground state power flow is generated in parallel. This solves the problem of missed selection caused by combinatorial explosion in traditional algorithms and improves the comprehensiveness and efficiency of the search.

[0031] (2) This invention constructs an optimal DC power flow QUBO model for a selected section, quickly solves the optimal unit output through quantum parallel computing, and combines it with a classical power flow calculation module to balance quantum acceleration and classical accuracy, thus breaking through the accuracy bottleneck of traditional quantum optimization in power limit calculation.

[0032] (3) Based on the optimization of unit output, this invention proposes a method for optimizing unit output and calculating the limit power of cross sections based on optical quantum computing, in response to the trend of increasing grid scale and complexity in new power systems. Through hierarchical decoupling of the two-layer QUBO model, the demand for quantum bit resources is effectively reduced. At the same time, it leverages the parallel computing advantages of optical quantum computers in dynamic topology reconstruction and continuous optimization, which can quickly and effectively search for grid transmission sections and quickly calculate the transmission limit power of sections. It can also dynamically adapt to changes in grid operation mode, providing a new paradigm of quantum enhancement for the safe and stable analysis and real-time control of smart grids, improving the real-time monitoring and optimized scheduling capabilities of power systems, and ensuring the safe and stable operation of future grids. Attached Figure Description

[0033] Figure 1 This is a flowchart of the unit output optimization method based on optical quantum computing of the present invention;

[0034] Figure 2 This is the network topology diagram of the IEEE 39-node system of this invention, showing the ground-state power flow direction.

[0035] Figure 3 This is a partition diagram of the selected section in the implementation example of the present invention. Detailed Implementation

[0036] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] Example 1: A method for searching power transmission sections based on optical quantum computing.

[0038] In this embodiment, a power network model G=(V,E) is first constructed, where V is the set of nodes and E is the set of lines. Then, a QUBO model is constructed based on the optimization objective as a cross-section search model. The cross-section search model is solved by an optical quantum computer to obtain the power grid cross-section.

[0039] In this implementation, the objective function is minimized. C ( x To optimize the process, a topology cut set is constructed to divide the nodes in the power network model into set A and set B.

[0040] objective function C ( x As shown in equation (1):

[0041] (1);

[0042] In the formula: w rj To represent nodes r and nodes j The binary number of connections between nodes; when nodes r and j If there is an edge connecting them, then w rj Take 1; when node r and j If there are no edges connecting them, then w rj Set to 0; n For the number of nodes, x r For nodes r The assignment, x j For nodes j The node is assigned a value of 0 or 1, and is a binary variable; e rj For nodes r and nodes jThe connection lines between them; after each node is assigned a value, nodes with a value of 0 are assigned to the first region A, and nodes with a value of 1 are assigned to the second region B, thus obtaining sets A and B. C is the cross-sectional weight, that is, the target value of the number of lines on the transmission cross-section. To find the cut value of a specific transmission cross-section, C uses a set positive integer value.

[0043] Secondly, considering that the number of nodes in the region is greater than or equal to a certain threshold, the cross-sectional constraints are constructed as shown in equation (2):

[0044] (2);

[0045] In the formula, x r , k For binary variables, if the node r Assigned to the first k Within each region, x r , k =1, otherwise x r , k =0; N k For the first k The minimum number of nodes corresponding to each region. Formula (2) is the constraint on the number of nodes in set A and set B.

[0046] Finally, equations (1) and (2) are transformed into a QUBO model that can run on a quantum computer as the cross-section search model H. 1 QUBO Introducing penalty coefficients and slack variables S Implementing constraints. Slack variables. S The expression is shown in (3), and the specific H 1 QUBO The derivation process is shown in equation (4):

[0047] (3);

[0048] (4);

[0049] In the formula: b max For slack variables S The number of qubits used b max b is a set value, which can take values ​​in the interval [1, 5]; b is an integer. s It is a binary number. s Randomly set to 0 or 1; M 1. M 2 is the penalty coefficient, which is a set positive integer value;H 1 and H 2 is a transitional term.

[0050] Thus, the cross-section search model H is ultimately derived from the cross-section search. 1 QUBO Information is injected into a quantum computer, and through quantum solving, a set of candidate cross sections can be obtained. Based on the result of any cross section, the power grid node can be divided into two electrical partitions (set A and set B). The two electrical partitions satisfy the following conditions: "the electrical partitions are interconnected" and "the tie lines between the electrical partitions have the same power flow direction".

[0051] Example 2: A Unit Output Optimization Method Based on Optical Quantum Computing

[0052] This embodiment specifically includes the following steps:

[0053] First, obtain the grid section results and construct a DC power flow section transmission limit calculation model consisting of the section power objective function and the power transmission area constraints. Then, introduce the generator binary adjustment variables that guide the generator unit regulation strategy and transform the DC power flow section transmission limit calculation model into a QUBO model as the unit output optimization model. Inject the unit output optimization model into a quantum computer to derive the generator binary adjustment variables corresponding to each unit in the power transmission area and generate the optimal unit output scheme for each unit in the power transmission area.

[0054] The grid cross-section results in this embodiment can be obtained using any existing method. Alternatively, under the ground state power flow condition, the candidate quantum cross-section results obtained in Example 1 can be verified for grid partition properties. Any cross-section result that satisfies "internal connectivity within the electrical partition" and "inter-electrical partition interconnection lines have consistent power flow direction" can be used as the data basis for unit output optimization in this embodiment.

[0055] Based on the power flow direction of the tie lines between electrical zones (sets A and B), the power grid is divided into a power transmission zone and a power receiving zone. The area where the power flow flows out is called the power transmission zone, and the area where the power flow flows in is called the power receiving zone. The purpose of this embodiment is to optimize the unit output in the power transmission zone and maximize the active power transmitted across the cross-section.

[0056] This embodiment takes maximizing the active power transmitted across the cross section as the optimization objective, and minimizing the negative inverse of the optimization objective as the cross section power objective function F, as shown in equation (5). Considering conditions such as cross section transmission capacity constraints and generator active power constraints, the power transmission area constraint conditions are constructed as shown in equation (6). Thus, ignoring the influence of node voltage and phase angle changes, a DC power flow cross section transmission limit calculation model composed of equations (5) and (6) is constructed:

[0057] (5);

[0058] (6);

[0059] In the formula: E Ω The set of lines within the cross-section; P rj For cross-section lines e rj Active power transmitted upstream, e rj For nodes r and nodes j Connection lines between; P rj 0 For cross-section lines e rj The initial active power; G Ω For the collection of generators within the power transmission area; Δ P i For generator i The increase in active power output; D i-rj For generator i For the cross section line e rj The generator power transfer distribution factor is determined by network parameters; P rj max For cross-section lines e rj The line transmission power limit; P i min For generator i The lower limit of meritorious service P i 0 For generator i The initial value of the work output, P i max For generator i The upper limit of contribution.

[0060] When the cross-sectional transmission power is at its maximum, the active power output of the generator units in the power transmission area must reach its upper limit. Therefore, a binary adjustment variable for the generator is introduced. q i ; q i When =0, it indicates a generator. i Reduce the amount of effort required; q i When =1, it indicates a generator. i The active power output is adjusted to the upper limit. Finally, equations (5) and (6) are transformed into a QUBO model that can run on a quantum computer as the unit output optimization model H.2 QUBO Unit output optimization model H 2 QUBO The derivation process is shown in equation (7):

[0061] (7);

[0062] In the formula: λ This is the generator regulation coefficient, with a value range of (-1, 0), which represents the degree to which the generator reduces its output. M 3 is the penalty coefficient, which is a set integer value; S These are slack variables; H 3 and H 4 is a transitional item.

[0063] Thus, the unit output optimization model H is ultimately achieved. 2 QUBO Information is injected into a quantum computer to solve for the optimal power output scheme of the power transmission area.

[0064] In practical implementation, the generator binary adjustment variable is used. q i A generator configured to 0 can have its output reduced to (1+ λ ) × Current active power output; binary adjustment variables for generators q i A generator configured as 1 can have its output adjusted to the upper limit of its active power output.

[0065] This invention leverages the advantages of hierarchical modeling and quantum parallel computing to decompose the calculation of the power limit of a power grid section into two levels: section search and limit power calculation. The upper level transforms the grid partition constraints into a quantum-solvable QUBO model under the ground-state power flow conditions, and uses the ability of optical quantum computers to dynamically reconstruct the topology to quickly generate a set of candidate quantum sections. The lower level randomly selects the target section and constructs an optimal DC power flow QUBO model with the goal of maximizing the active power transmitted through the section. It then uses quantum computing to solve for the optimal unit output scheme in parallel and can further combine classical power flow calculation programs to calculate the limit power transmitted through the section.

[0066] Example 3

[0067] Based on Example 2, this embodiment calculates the total change Δ of the active power of the generator units in the power transmission area according to the optimal unit output scheme of the power transmission area. P G Then, based on the law of conservation of energy, the active load increment of each load node in the power receiving area is calculated.

[0068] Total initial active load of load nodes in the power receiving area P LAs shown in equation (8). Ignoring network loss changes, based on the law of conservation of energy, the total change in active power at the load nodes in the power receiving area is Δ P L Equal to the total change in active power of generator units in the power transmission area Δ P G As shown in equation (9), when the total active power in the power receiving area increases, the change law of active power at the load node is shown in equation (10):

[0069] (8);

[0070] (9);

[0071] (10);

[0072] In the formula: P L This represents the initial total active load of the load nodes in the power receiving area; L Ω This refers to the set of load nodes in the power receiving area. P La For the load nodes of the power receiving area a The initial active load; Δ P L Δ represents the total change in active power at the load nodes in the power receiving area. P G Δ represents the total change in active power of generator units in the power transmission area. P La For the load node of the power receiving area a The increase in active load.

[0073] Based on the optimal generator output scheme obtained in Example 2, the change in active power output of the generator set is calculated. The corresponding active power changes of the load nodes in the power receiving area are carried out through Equations (8) to (10). Combined with the classic power flow calculation program, the transmission limit power of the section is calculated, specifically including the limit transmission power of each line on the transmission section.

[0074] The present invention will be described below with reference to specific embodiments.

[0075] This embodiment uses an IEEE 39-node system for verification. The network topology diagram of the IEEE 39-node system with ground-state power flow direction is shown below. Figure 2 As shown, it includes 39 nodes and 46 lines; the nodes include generator nodes and load nodes, and the lines include transmission lines and transformer branches.

[0076] In this embodiment, the cross-sectional weight is set... C =3, meaning the number of lines on the cross-section is 3; minimum number of nodes in the region. N k=10, slack variable S Number of qubits used b max =3, adjustment coefficient λ The penalty coefficient is -0.5. M 1=200, penalty coefficient M 2=300, penalty coefficient M 3 = 200.

[0077] In this embodiment, the transmission section search method based on quantum computing provided in Example 1 is used to obtain the transmission section, and the results are shown in Table 1. Then, a section result that satisfies "internal connectivity within the electrical zone" and "consistent power flow direction between electrical zones" is selected as the data basis, and the unit output optimization method based on quantum computing provided in Example 2 is executed to obtain the output optimization scheme for each generator in the power transmission area. The section search model H in this embodiment is... 1 QUBO And unit output optimization model H 2 QUBO This constitutes a two-layer QUBO model.

[0078] Table 1. Specific cross-sectional results and power grid zoning of the IEEE 39-bus system.

[0079] ;

[0080] In Table 1, l r-j Represents a node r and nodes j The connecting lines between zones. The lines between zones are the lines on the cross-section, and the two nodes they connect are located in the power transmission zone and the power receiving zone, respectively.

[0081] In this embodiment, section number 39-6 is selected, which includes l 2-1 , l 3-4 , l 16-15 Three lines. The cross-sectional zoning diagram is as follows: Figure 3 As shown, the area above the red dashed line is the power transmission zone, and the area below the red dashed line is the power receiving zone. Black dots without arrows are pure connection points, such as substation busbars. Circles with arrows (G-shaped) represent generators under load; black dots with arrows represent load nodes; circles without arrows (G-shaped) represent generators. The generator node numbers in the power transmission zone are 30, 33, 34, 35, 36, 37, and 38, and the load node numbers in the power receiving zone are 1, 4, 7, 8, 9, 12, 15, 31, and 39. Generator number 31 is designated as the balancing unit. The unit output optimization model H... 2 QUBOThe optimal generator output scheme is obtained by injecting optical quantum computers, calculating the changes in active power output of generator units and the corresponding active power changes of the load nodes in the power receiving area. Combined with classical power flow calculation programs, the transmission limit power of the cross section is calculated. The optimal adjustment amount of the generator units in the power transmission area and the calculated values ​​of the transmission limit power of the cross section are shown in Table 2.

[0082] Table 2 Calculation results of optimal generator adjustment and ultimate transmission power

[0083] ;

[0084] Table 2 shows that the optimal generator set combination is [0, 1, 0, 1, 1, 1, 1], meaning that generators 30 and 34 reduce their output by half, while generators 33, 35, 36, 37, and 38 increase their output to the maximum limit. The actual generator output adjustment scheme and the optimal generator set output scheme yield the same results, but the adjusted generator active power output differs somewhat from the theoretical output. This is because the classical power flow calculation program considers changes in voltage amplitude and phase angle, which corrects the theoretical generator output.

[0085] Of course, those skilled in the art will recognize that the present invention is not limited to the details of the exemplary embodiments described above, but also includes the same or similar structures that can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0086] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

[0087] The technologies, shapes, and structures not described in detail in this invention are all known technologies.

Claims

1. A method for searching power transmission sections based on optical quantum computing, characterized in that, First, a power network model is constructed; then, a QUBO model is constructed as a cross-section search model based on the optimization objective, with the objective being to minimize the transmission cross-section; the cross-section search model is solved using an optical quantum computer to obtain the power grid cross-section. The cross-section search model is: the difference between the objective function and the cross-section weights, the sum of the total constraint difference and the slack variables, and the sum of the two multiplied by the corresponding penalty coefficients; the cross-section weights are the target value of the number of lines on the transmission cross-section. The cross-section search model formula is expressed as: Among them, H 1 QUBO This represents the cross-section search model, where M1 and M2 are the set penalty coefficients; n is the number of nodes; x r,k As a binary variable, x represents the value of node r when it is assigned to the k-th region. r,k =1, otherwise x r,k =0; N k S represents the minimum number of nodes corresponding to the k-th region; S is the slack variable, C is the section weight, and C(x) is the objective function. The objective function is: Among them, w rj w is a binary number representing the connections between nodes. When there is an edge connecting nodes r and j... rj =1, otherwise w rj =0; e rj Let x be the connection line between node r and node j, E be the set of lines, and x be the connection line between node r and node j. r Assign a value of 0 or 1 to node r, x j Assign a value of 0 or 1 to node j.

2. The transmission section search method based on optical quantum computing as described in claim 1, characterized in that, The formula for calculating the slack variable S is as follows: Among them, b max Here, s is a set value, and b is a random value of a binary number.

3. An apparatus for implementing the transmission section search method based on optical quantum computing as described in claim 1 or 2, characterized in that, include: The data acquisition and modeling module is used to acquire power grid operation data and establish a power network model G = (V, E), where V is the set of nodes and E is the set of lines. The model building module is used to build cross-section search models; The quantum computing module uses an optical quantum computer to solve a cross-section search model to obtain the power grid cross-section.

4. A cross-section search system based on optical quantum computing, characterized in that, It includes a memory and a processor. The memory stores a computer program, and the processor is connected to the memory. The processor is used to execute the computer program to implement the power transmission section search method based on optical quantum computing as described in claim 1 or 2.

5. A storage medium, characterized in that, The system contains a computer program that, when executed, is used to implement the transmission section search method based on optical quantum computing as described in claim 1 or 2.

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