Power distribution network collaborative optimization system and method based on carbon flow tracking, and storage medium

Abstract

The invention provides a power distribution network collaborative optimization system and method based on carbon flow tracking and a storage medium, and belongs to the technical field of power distribution network line loss optimization. The system comprises a sensing layer which is provided with a dual-mode intelligent electric meter, a harmonic monitoring terminal and a distributed phasor measurement unit, can be deployed at nodes of a power distribution network, and collects data parameters of the nodes in real time; the computing platform is provided with a carbon energy coupling factor database, a dynamic network slice controller and a block chain evidence storage module, the computing platform can generate an optimization instruction by optimizing a target function based on the carbon energy coupling factor database, the dynamic network slice controller can distribute a slice channel for the optimization instruction, and the block chain evidence storage module can store the optimization instruction; and the execution layer is provided with a static var generator and a photovoltaic inverter interface and can execute an optimization instruction. According to the method, the carbon emission factor is embedded into the line loss optimization target, collaborative optimization of the line loss and the carbon flow is realized, and the influence of the carbon flow can be improved while the line loss optimization effect of the power distribution network is ensured.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power distribution network line loss optimization, and particularly relates to a power distribution network collaborative optimization system and method based on carbon flow tracking and a storage medium. BACKGROUND

[0002] Line loss optimization of a power distribution network is a key link for improving economic operation efficiency of a power system. Traditional power distribution networks will produce significant active power loss due to factors such as line impedance, load fluctuation and uneven distribution of reactive power, resulting in energy waste and rising power supply costs. With high proportion of distributed energy access, the power flow distribution of the power distribution network is more complex, further increasing the difficulty of line loss management.

[0003] Currently, power distribution network optimization technology mainly relies on heuristic methods such as genetic algorithm and particle swarm algorithm to reduce line loss by adjusting reactive power compensation devices, transformer taps and other equipment. However, these methods mainly focus on single power loss optimization, and simply reducing line loss may indirectly increase system carbon emissions due to the increase in high-carbon power source proportion, increasing the negative impact of carbon flow on the power distribution network. SUMMARY

[0004] The technical problem to be solved by the present application is how to improve the impact of carbon flow while ensuring the line loss optimization effect of the power distribution network. In view of the shortcomings of the prior art, the present application provides a power distribution network collaborative optimization system and method based on carbon flow tracking and a storage medium.

[0005] To solve the above technical problems, the technical solution adopted by the present application is as follows:

[0006] In a first aspect, the present application provides a power distribution network collaborative optimization system based on carbon flow tracking, comprising:

[0007] a perception layer, the perception layer comprising a dual-mode smart meter, a harmonic monitoring terminal and a distributed phasor measurement unit, and all being used for deployment at nodes of the power distribution network to collect data parameters of the nodes in real time;

[0008] a computing platform, the computing platform being in communication connection with the perception layer, the computing platform comprising a carbon-energy coupling factor database, a dynamic network slice controller and a blockchain storage module, the carbon-energy coupling factor database being configured to store carbon emission factors of power sources, the computing platform being used for: calculating a line dynamic loss value based on the data parameters, calling the carbon emission factors based on the types of the power sources to calculate carbon flow intensity, and generating an optimization instruction through an optimization objective function according to the line dynamic loss value and the carbon flow intensity, allocating a uRLLC slice channel for the optimization instruction based on the dynamic network slice controller, and writing the optimization instruction into the blockchain storage module;

[0009] an execution layer comprising a static var generator and a photovoltaic inverter interface and communicatively coupled to the computing platform to execute the optimization instructions.

[0010] Compared with the prior art, the power distribution network collaborative optimization system based on carbon flow tracking has the following beneficial effects: the power distribution network collaborative optimization system based on carbon flow tracking is composed of a perception layer, a computing platform and an execution layer, which are sequentially communicatively connected. For the perception layer, a dual-mode smart meter, a harmonic monitoring terminal and a distributed phasor measurement unit are arranged, which can be deployed at the nodes of the power distribution network to collect data parameters of the nodes in real time. The dual-mode smart meter can collect active / reactive power and harmonic distortion rate at the same time, avoiding the problem that the traditional meter cannot perceive harmonic loss, ensuring the comprehensiveness of data collection. The harmonic monitoring terminal can track high-frequency harmonic components in real time, which is convenient for subsequent optimization to suppress additional line losses such as eddy current loss or skin effect caused by harmonics. The distributed phasor measurement unit can provide micro-level synchronous phasor data to ensure the time synchronization of dynamic power flow calculation, thereby ensuring the accuracy of data parameters in the subsequent line loss optimization process and ensuring the line loss optimization effect of the power distribution network. For the computing platform, data parameters can be received, and line loss optimization calculation is performed according to the data parameters. Meanwhile, a carbon energy coupling factor database, a dynamic network slicing controller and a blockchain storage module are arranged. The carbon emission factor database stores carbon emission factors of different power sources, which can quantify carbon flow through carbon emission factors, facilitating the improvement of carbon flow in the subsequent optimization process. In the process of line loss optimization calculation, after calculating the line dynamic loss value based on the data parameters, the computing platform can retrieve the carbon emission factor based on the type of the power source, calculate the carbon flow intensity by combining the carbon emission factor and the line dynamic loss value, and generate optimization instructions according to the line dynamic loss value and the carbon flow intensity through the optimization objective function, thereby embedding the carbon emission factor into the optimization objective and realizing the collaborative optimization of line loss and carbon flow, thereby improving the carbon flow while ensuring the line loss optimization effect of the power distribution network. For example, in the coal-dominated area, the high-carbon line loss can be reduced first, and in the photovoltaic-rich area, the loss tolerance can be relaxed to improve clean energy consumption. Finally, the computing platform can allocate a uRLLC slicing channel for the optimization instructions based on the dynamic network slicing controller to ensure the transmission speed of the optimization instructions and avoid the delay of the subsequent optimization instructions. At the same time, the optimization instructions are written into the blockchain storage module to store the optimization instructions through the hash chain to prevent data tampering and facilitate subsequent carbon auditing. For the execution layer, the optimization instructions sent by the computing platform can be received to implement the overall optimization scheduling of the power distribution network. The static var generator and the photovoltaic inverter interface are arranged. The static var generator can dynamically compensate the reactive power to suppress the power loss caused by the reactive flow, thereby ensuring the optimization effect of the line loss. The photovoltaic inverter interface can adjust the power factor of the photovoltaic system to reduce the line loss fluctuation caused by new energy fluctuation, thereby further ensuring the line loss optimization effect of the power distribution network.

[0011] Optionally, the computing platform further includes a quantum optimization engine, which is configured to map the optimization objective function to an Ising model and call a variable quantum feature solver to solve for a Pareto optimal solution set, so as to output the optimization instructions, and to perform quantum encryption on the optimization instructions written to the blockchain storage module.

[0012] Optionally, the functional expression of the Ising model is:

[0013] ,

[0014] Among them, the Let i be the spin state of node i in the distribution network, and the The Let i be the power flow coupling coefficient between node i and node j of the distribution network. The local magnetic field of node i, the This represents the global carbon flow penalty coefficient.

[0015] Optionally, the satisfy:

[0016] ,

[0017] Wherein, Q i and the Q j The reactive power of node i and node j are respectively. This represents the dynamic theoretical loss value of the line.

[0018] Optionally, the optimization objective function is:

[0019] ,

[0020] Among them, the The weighting coefficients for the line dynamic loss term are... The weighting coefficient for the voltage deviation term, the For node voltage deviation, the The carbon flow intensity, the This is the weighting coefficient for the carbon flow intensity term.

[0021] Optionally, the carbon flow intensity is calculated using the following formula:

[0022] ,

[0023] Among them, the The type of power supply, the For the first a line dynamic loss value corresponding to the type of power supply, and the is the first a carbon emission factor corresponding to the type of power supply.

[0024] Optionally, the static var generator is configured to perform reactive power compensation based on the following formula:

[0025]

[0026] wherein the is the reactive power of the static var generator, the is a proportional gain coefficient, generated by the quantum optimization engine, the is an integral gain coefficient, the is a voltage reference value, and the is a measured voltage value.

[0027] Optionally, the blockchain storage module includes a quantum key distribution unit and a regulatory node access interface, and the quantum key distribution unit is configured to generate an encryption key for encrypting the optimization instruction.

[0028] In a second aspect, the present application further provides a power distribution network collaborative optimization method based on carbon flow tracking, comprising:

[0029] S1, collecting data parameters of the nodes in real time through a perception layer of a power distribution network collaborative optimization system based on carbon flow tracking;

[0030] S2, calculating a line dynamic loss value based on the data parameters through a computing platform of the power distribution network collaborative optimization system based on carbon flow tracking;

[0031] S3, based on the type of power supply, retrieving a carbon emission factor from a carbon energy coupling factor database of the computing platform, and calculating a carbon flow intensity based on the carbon emission factor and the line dynamic loss value through the computing platform;

[0032] S4, generating an optimization instruction based on an optimization objective function through the computing platform according to the line dynamic loss value and the carbon flow intensity;

[0033] S5, allocating a uRLLC slice channel for the optimization instruction through a dynamic network slice controller of the computing platform, and writing the optimization instruction into a blockchain storage module of the computing platform;

[0034] S6, executing the optimization instruction through an execution layer of the power distribution network collaborative optimization system based on carbon flow tracking.

[0035] ​Compared with the prior art, the multi-resource coordinated voltage stability control method for distribution networks of the present invention has the same beneficial effects as the multi-resource coordinated voltage stability control system for distribution networks described above, and will not be repeated here.

[0036] Thirdly, the present invention also provides a computer storage medium storing a computer program thereon, wherein when the computer program is executed by a processor, the multi-resource coordinated voltage stability control method for power distribution networks described above is implemented.

[0037] Compared to existing technologies, the beneficial effects of the computer storage medium of the present invention are the same as those of the multi-resource collaborative voltage stability control method for power distribution networks described above, and will not be repeated here. Attached Figure Description

[0038] The present invention will now be described in further detail with reference to the accompanying drawings.

[0039] Figure 1 : A schematic diagram of the structure of the distribution network collaborative optimization system based on carbon flow tracing in this embodiment of the invention;

[0040] Figure 2 : A schematic diagram of the process structure of the distribution network collaborative optimization method based on carbon flow tracing in this embodiment of the invention.

[0041] Among them, 1-Perception layer, 11-Dual-mode smart meter, 12-Harmonic monitoring terminal, 23-Distributed phasor measurement unit, 2-Computing platform, 21-Carbon energy coupling factor database, 22-Dynamic network slicing controller, 23-Blockchain evidence storage module, 231-Quantum key distribution unit, 232-Supervisory node access interface, 24-Quantum optimization engine, 3-Execution layer, 31-Static var generator, 32-Photovoltaic inverter interface. Detailed Implementation

[0042] To better understand the present invention, the following embodiments further illustrate the content of the invention, but the scope of protection of the present invention is not limited to the following embodiments. Numerous specific details are set forth in the following description to provide a more thorough understanding of the invention. However, it will be apparent to those skilled in the art that the present invention can be practiced without one or more of these details.

[0043] It should be noted that the Z-axis in the drawings represents the vertical direction, that is, the up-down position, and the positive direction of the Z-axis represents the upper side, and the negative direction of the Z-axis represents the lower side; the Y-axis in the drawings represents the horizontal direction, and is designated as the front-rear position, and the positive direction of the Y-axis represents the front side, and the negative direction of the Y-axis represents the rear side; and the X-axis in the drawings represents the left-right position, and the positive direction of the X-axis represents the right side, and the negative direction of the X-axis represents the left side. It should also be noted that the above-mentioned meanings of the Z-axis, the Y-axis and the X-axis are only for the convenience of describing the present application and simplifying the description, and are not intended to indicate or imply that the devices or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present application.

[0044] The term "comprising" and variations thereof as used herein are open-ended, that is "including, but not limited to"; the term "based on" is, at least in part, based on; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optional" means "optional in at least some embodiments". Related definitions are given throughout the detailed description. It should be noted that the concepts mentioned in the present application are only used to distinguish different devices, modules or units, and are not intended to limit the functions performed by these devices, modules or units or the mutual dependency therebetween.

[0045] It should be noted that the modification of "one" or "multiple" mentioned in the present application is illustrative but not restrictive, and those skilled in the art should understand that unless the context clearly indicates otherwise, it should be understood as "one or more".

[0046] In a first aspect, an embodiment of the present application provides a power distribution network collaborative optimization system based on carbon flow tracking, comprising: a perception layer 1, the perception layer 1 comprising a dual-mode smart meter 11, a harmonic monitoring terminal 12 and a distributed phasor measurement unit 13, and all being used to be deployed at nodes of a power distribution network to collect data parameters of the nodes in real time; a computing platform 2, the computing platform 2 being in communication connection with the perception layer 1, the computing platform 2 comprising a carbon-energy coupling factor database 21, a dynamic network slicing controller 22 and a blockchain storage module 23, the carbon-energy coupling factor database 21 being configured to store carbon emission factors of power sources, the computing platform 2 being used to: calculate a line dynamic loss value based on the data parameters, retrieve the carbon emission factors based on the types of the power sources to calculate carbon flow intensity, and generate optimization instructions through an optimization objective function according to the line dynamic loss value and the carbon flow intensity, allocate uRLLC slice channels for the optimization instructions based on the dynamic network slicing controller 22, and write the optimization instructions into the blockchain storage module 23; and an execution layer 3, the execution layer 3 comprising a static var generator 31 and a photovoltaic inverter interface 32, and being in communication connection with the computing platform 2 to execute the optimization instructions.

[0047] Specifically, the carbon emission factor of the power source is different according to the type of the power source, for example, the carbon emission factor of coal power is 0.8 kgCO2 / kWh, and the carbon emission factor of photovoltaic power is 0.05 kgCO2 / kWh.

[0048] In this embodiment, as Figure 1As shown, the carbon flow tracking-based power distribution network collaborative optimization system is composed of a perception layer 1, a computing platform 2 and an execution layer 3, which are sequentially connected in communication. For the perception layer 1, a dual-mode smart meter 11, a harmonic monitoring terminal 12 and a distributed phasor measurement unit 13 are arranged, which can be deployed at the nodes of the power distribution network to collect data parameters of the nodes in real time. The dual-mode smart meter 11 can collect active / reactive power and harmonic distortion rate at the same time, avoiding the problem that the traditional meter cannot perceive harmonic loss, ensuring the comprehensiveness of data collection. The harmonic monitoring terminal 12 can track high-frequency harmonic components in real time, facilitating the subsequent optimization of suppressing additional line losses such as eddy current loss or skin effect caused by harmonics. The distributed phasor measurement unit 13 can provide micro-level synchronous phasor data, ensuring the time synchronization of dynamic power flow calculation, and then ensuring the accuracy of data parameters in the subsequent line loss optimization process, and ensuring the line loss optimization effect of the power distribution network. For the computing platform 2, data parameters can be received, and line loss optimization calculation is performed according to the data parameters. Meanwhile, a carbon energy coupling factor database 21, a dynamic network slicing controller 22 and a blockchain storage module 23 are arranged. The carbon energy coupling factor database 21 stores carbon emission factors of different power sources, which can quantify carbon flow through carbon emission factors, facilitating the improvement of carbon flow in the subsequent optimization process. In the process of line loss optimization calculation, after calculating the line dynamic loss value based on the data parameters, the computing platform 2 can retrieve the carbon emission factor based on the type of power source, calculate the carbon flow intensity by combining the carbon emission factor and the line dynamic loss value, and then generate optimization instructions according to the line dynamic loss value and the carbon flow intensity through the optimization objective function, so as to embed the carbon emission factor into the optimization objective, realize the collaborative optimization of line loss and carbon flow, and then improve the carbon flow while ensuring the line loss optimization effect of the power distribution network. For example, in the coal-dominated area, the high-carbon line loss can be reduced first, and in the photovoltaic-rich area, the loss tolerance can be relaxed to improve clean energy consumption. Finally, the computing platform 2 can allocate a uRLLC slicing channel for the optimization instructions based on the dynamic network slicing controller 22 to ensure the transmission speed of the optimization instructions and avoid the delay of the subsequent optimization instruction execution. At the same time, the optimization instructions are written into the blockchain storage module 23, and the optimization instructions are stored through the hash chain to prevent data tampering and facilitate subsequent carbon auditing needs. For the execution layer 3, the optimization instructions sent by the computing platform 2 can be received to implement the overall optimization scheduling of the power distribution network. The static var generator 31 and the photovoltaic inverter interface 32 are arranged. The static var generator 31 can dynamically compensate the reactive power to suppress the power loss caused by the reactive flow, thereby ensuring the optimization effect of the line loss. The photovoltaic inverter interface 32 can adjust the power factor of the photovoltaic system to reduce the line loss fluctuation caused by new energy fluctuation, thereby further ensuring the line loss optimization effect of the power distribution network.

[0049] Optionally, the computing platform 2 further comprises a quantum optimization engine 24 configured to map the optimization objective function into an Ising model and call a variational quantum eigensolver to solve a Pareto optimal solution set to output the optimization instruction, and for quantum encryption of the optimization instruction written into the blockchain storage module 23.

[0050] In this optional embodiment, in order to ensure the optimization calculation effect of the computing platform 2, as shown in Figure 1 , a quantum optimization engine 24 is further provided, and the quantum optimization engine 24 is configured to map the optimization objective function into an Ising model and call a variational quantum eigensolver to solve a Pareto optimal solution set to generate the optimization instruction, wherein the Ising model mapping can convert the discrete optimization problem into a spin system Hamiltonian, improve the local optimal limit of the classical algorithm, and ensure the global optimization effect of the optimization instruction, and the variational quantum eigensolver (VQE) can solve the Pareto optimal solution set on the NISQ (Noise Intermediate-Scale Quantum) device, effectively improve the processing speed of the multi-node problem, that is, the classical heuristic algorithm such as the particle swarm optimization algorithm can be avoided to fall into the curse of dimensionality on a network of more than 100 nodes, the quantum engine can search in parallel through the quantum superposition state, reduce the time complexity, ensure the optimality of the solution can be verified, and effectively ensure the optimization calculation effect of the computing platform 2; on this basis, the quantum optimization engine 24 can also perform quantum encryption on the optimization instruction written into the blockchain storage module 23 to ensure the storage stability of the optimization instruction.

[0051] Optionally, the function expression of the Ising model is:

[0052] (1.1),

[0053] wherein is the spin state of node i of the power distribution network, and , is the power flow coupling coefficient between node i and node j of the power distribution network, is the local magnetic field of node i, is the global carbon flow penalty coefficient.

[0054] In this optional embodiment, as shown in formula (1.1), the Ising model can map the power distribution network topology structure into a spin glass model, wherein not only the coordinated regulation and control of line loss optimization can be realized through , and , but also the global carbon flow penalty coefficient is added, and for each increase of 1 unit of carbon intensity, the objective function can deteriorate value, and through the λ coefficient, the carbon cost is forcibly converted into a physical optimization constraint, so that the quantum annealing process naturally converges to a low-carbon solution, drives low-carbon optimization, and ensures the coordinated optimization of subsequent line loss and carbon flow.

[0055] Optionally, satisfies:

[0056] (2.1),

[0057] wherein Q i and Q j are the reactive power of node i and node j respectively, is the line dynamic theoretical loss value.

[0058] In this optional embodiment, As the power flow coupling coefficient between node i and j, it can reflect the synergistic effect of reactive power compensation. According to equation (2.1), the second derivative of is derived from the Hessian matrix, wherein, > 0 indicates that the reactive power compensation behavior of node i and j is mutually inhibited, such as adjacent node compensation leading to voltage rise, weakening the compensation effect of the node, which needs to be synergistically scheduled, and < 0 indicates that the synergistic compensation can be superimposed to reduce loss, thereby ensuring the synergistic optimization effect of line loss and carbon flow.

[0059] Optionally, the optimization objective function is:

[0060] (3.1),

[0061] wherein, is the weight coefficient of the line dynamic loss term, is the weight coefficient of the voltage deviation term, is the node voltage deviation, is the carbon flow intensity, is the weight coefficient of the carbon flow intensity term.

[0062] In this optional embodiment, in order to ensure the synergistic optimization effect of line loss and carbon flow, as shown in (3.1), is the total dynamic line loss of the whole network, that is, the core optimization objective, is the voltage deviation penalty term, and is the total carbon flow intensity, and through the strengthening minimization optimization setting of the total dynamic line loss of the whole network, the voltage deviation penalty term and the total carbon flow intensity, the line loss optimization effect is effectively guaranteed while the carbon flow influence is improved, realizing the synergistic optimization of line loss and carbon flow; on this basis, in the process of generating optimization instructions through the optimization objective function, , and can be dynamically adjusted according to the running scene, wherein can be increased to preferentially reduce loss at high load period, and Weight, improve voltage deviation, can be adjusted when carbon quota is tight , trigger low-carbon mode, realize the coordinated optimization of line loss and carbon flow.

[0063] Optionally, the carbon flow intensity is calculated by the following formula:

[0064] (4.1),

[0065] wherein, is the type of power supply, is the line dynamic loss value corresponding to the power supply of the first type, is the line dynamic loss value corresponding to the power supply of the second type, is the carbon emission factor corresponding to the power supply of the first type, is the carbon emission factor corresponding to the power supply of the second type.

[0066] In this optional embodiment, as shown in formula (4.1), according to the difference of the type of power supply, the line dynamic loss value and the carbon emission factor corresponding to different power supplies are selected to calculate the carbon flow intensity of the corresponding power supply, so that the carbon responsibility of loss can be accurately located, and when optimization is performed according to the optimization instruction in the subsequent process, the line loss of the power supply with higher carbon emission factor can be preferentially reduced, realizing the reduction of loss and carbon, and ensuring the coordinated optimization of line loss and carbon flow.

[0067] Optionally, the static var generator 31 is configured to perform reactive power compensation based on the following formula:

[0068] (5.1),

[0069] wherein, is the reactive power of the static var generator 31, is a proportional gain coefficient, generated by the quantum optimization engine 24, is an integral gain coefficient, is a voltage reference value, is a measured voltage value.

[0070] In this optional embodiment, when the layer 3 executes the optimization instruction, the static var generator 31 performs reactive power compensation through formula (5.1), wherein, can be adaptively adjusted based on the quantum optimization engine 24 with the change of network topology, so as to realize optimal setting, such as increasing by 50% at the end of the feeder, accelerating voltage recovery, and reducing at the harmonic resonance point to avoid oscillation, so as to ensure the voltage stability of the power distribution network during reactive power compensation, and at the same time, can eliminate steady-state error, further ensuring the stability in the process of line loss optimization.

[0071] Optionally, the blockchain storage module 23 comprises a quantum key distribution unit 231 and a regulatory node access interface 232, the quantum key distribution unit 231 is configured to generate an encryption key for encryption of the optimization instruction.

[0072] In this optional embodiment, as shown in Figure 1 The blockchain storage module 23 is provided with a quantum key distribution unit 231 and a regulatory node access interface 232, wherein the quantum key distribution unit 231 can generate an encryption key for encryption of the optimization instruction based on the BB84 protocol, the encryption key is a random key, which can effectively resist quantum computing attacks and ensure storage stability, and the regulatory node access interface 232 can open a read-only interface to the power grid regulatory agency, support real-time audit of carbon flow optimization data, and facilitate subsequent audit convenience.

[0073] In a second aspect, an embodiment of the present application provides a power distribution network collaborative optimization method based on carbon flow tracking, comprising: S1, collecting data parameters of nodes in real time through a perception layer 1 of a power distribution network collaborative optimization system based on carbon flow tracking; S2, calculating a line dynamic loss value through a calculation platform 2 of the power distribution network collaborative optimization system based on carbon flow tracking based on the data parameters; S3, based on the type of power source, calling a carbon emission factor of a carbon energy coupling factor database 21 of the calculation platform 2, and calculating a carbon flow intensity based on the carbon emission factor and the line dynamic loss value through the calculation platform 2; S4, generating an optimization instruction based on an optimization objective function through the calculation platform 2 according to the line dynamic loss value and the carbon flow intensity; S5, allocating a uRLLC slice channel for the optimization instruction through a dynamic network slice controller 22 of the calculation platform 2, and writing the optimization instruction into a blockchain storage module 23 of the calculation platform 2; and S6, executing the optimization instruction through an execution layer 3 of the power distribution network collaborative optimization system based on carbon flow tracking.

[0074] As shown in Figure 2 The technical effects of the power distribution network multi-resource collaborative voltage stability control method in this embodiment are similar to those of the power distribution network multi-resource collaborative voltage stability control system described above, and will not be repeated here.

[0075] In a third aspect, an embodiment of the present application provides a computer storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the power distribution network multi-resource collaborative voltage stability control method described above.

[0076] The technical effects of the computer storage medium in this embodiment are similar to those of the power distribution network multi-resource collaborative voltage stability control method described above, and will not be repeated here.

[0077] The above merely describes the preferred embodiments of the present application, and is not used to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A distribution network collaborative optimization system based on carbon flow tracing, characterized in that, include: The sensing layer (1) includes a dual-mode smart meter (11), a harmonic monitoring terminal (12), and a distributed phasor measurement unit (13), all of which are deployed at nodes in the distribution network to collect data parameters of the nodes in real time. The computing platform (2) is communicatively connected to the perception layer (1). The computing platform (2) includes a carbon energy coupling factor database (21), a dynamic network slice controller (22), and a blockchain storage module (23). The carbon energy coupling factor database (21) is configured to store the carbon emission factors of the power source. The computing platform (2) is used to: calculate the dynamic loss value of the line based on the data parameters; retrieve the carbon emission factors based on the type of the power source to calculate the carbon flow intensity; generate optimization instructions by optimizing the objective function based on the dynamic loss value of the line and the carbon flow intensity; allocate uRLLC slice channels for the optimization instructions based on the dynamic network slice controller (22); and write the optimization instructions into the blockchain storage module (23). The execution layer (3) includes a static var generator (31) and a photovoltaic inverter interface (32), and is communicatively connected to the computing platform (2) to execute the optimization instructions.

2. The distribution network collaborative optimization system based on carbon flow tracing as described in claim 1, characterized in that, The computing platform (2) also includes a quantum optimization engine (24), which is configured to map the optimization objective function to the Ising model and call the variable quantum feature solver to solve the Pareto optimal solution set, so as to output the optimization instructions, and to perform quantum encryption on the optimization instructions written to the blockchain storage module (23).

3. The distribution network collaborative optimization system based on carbon flow tracing as described in claim 2, characterized in that, The functional expression of the Ising model is: ， Among them, the Let i be the spin state of node i in the distribution network, and the The Let i be the power flow coupling coefficient between node i and node j of the distribution network. The local magnetic field of node i, the This represents the global carbon flow penalty coefficient.

4. The distribution network collaborative optimization system based on carbon flow tracing as described in claim 3, characterized in that, The satisfy: ， Wherein, Q i and the Q j The reactive power of node i and node j are respectively. This represents the dynamic theoretical loss value of the line.

5. The distribution network collaborative optimization system based on carbon flow tracing as described in claim 4, characterized in that, The optimization objective function is: ， Among them, the The weighting coefficients for the line dynamic loss term are... The weighting coefficient for the voltage deviation term, the For node voltage deviation, the The carbon flow intensity, the This is the weighting coefficient for the carbon flow intensity term.

6. The distribution network collaborative optimization system based on carbon flow tracing as described in any one of claims 1 to 5, characterized in that, The carbon flux intensity is calculated using the following formula: ， Among them, the The type of power supply, the For the first The dynamic loss value of the line corresponding to the type of power supply, the For the first The carbon emission factor corresponding to the type of power supply.

7. The distribution network collaborative optimization system based on carbon flow tracing as described in any one of claims 2 to 5, characterized in that, The static var generator (31) is configured to perform reactive power compensation based on the following formula: ， Among them, the The reactive power of the static var generator (31) is... The proportional gain coefficient is generated by the quantum optimization engine (24). The integral gain coefficient, the The voltage reference value, the This is the measured voltage value.

8. The distribution network collaborative optimization system based on carbon flow tracing as described in any one of claims 2 to 5, characterized in that, The blockchain evidence storage module (23) includes a quantum key distribution unit (231) and a supervisory node access interface (232). The quantum key distribution unit (231) is used to generate an encryption key for the optimization instructions.

9. A method for coordinated optimization of distribution networks based on carbon flow tracing, characterized in that, include: S1. The data parameters of the nodes are collected in real time through the sensing layer (1) of the distribution network collaborative optimization system based on carbon flow tracing; S2. Based on the data parameters, calculate the dynamic loss value of the line through the calculation platform (2) of the distribution network collaborative optimization system based on carbon flow tracing; S3. Based on the type of power supply, retrieve the carbon emission factor from the carbon energy coupling factor database (21) of the computing platform (2), and calculate the carbon flow intensity based on the carbon emission factor and the line dynamic loss value through the computing platform (2). S4. Based on the line dynamic loss value and the carbon flow intensity, generate optimization instructions by optimizing the objective function based on the computing platform (2); S5. The dynamic network slice controller (22) of the computing platform (2) allocates uRLLC slice channels for the optimization instructions and writes the optimization instructions into the blockchain storage module (23) of the computing platform (2). S6. The optimization instructions are executed through the execution layer (3) of the distribution network collaborative optimization system based on carbon flow tracing.

10. A computer storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the multi-resource collaborative voltage stability control method for power distribution networks as described in claim 9.

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