A dynamic partitioning method, device, electronic device and storage medium for distribution network

Abstract

The present invention discloses a method, device, electronic device, and storage medium for dynamic partitioning of a distribution network. The method includes obtaining a typical day's dispatch plan and inputting it into a distribution network partition model to generate a first partitioning scheme for each dispatch moment. It determines whether the voltage of each node at the first dispatch moment exceeds a preset range. If so, the indicator weight is adjusted to update the distribution network partition model, generating a second partitioning scheme for the first dispatch moment. The actual partitioning scheme for the first dispatch moment is determined by comparing the score difference between the two partitioning schemes. For subsequent dispatch moments, the actual partitioning scheme for the current dispatch moment is determined based on the difference between the actual partitioning scores at the previous moment. The implementation of the present invention can achieve dynamic partitioning of the distribution network and improve the operating efficiency and reliability of the distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network partitioning, and in particular to a method, device, electronic equipment and storage medium for dynamic partitioning of a distribution network. Background Art

[0002] Proper zoning of distribution networks is crucial for improving the operational efficiency and stability of power systems. Scientific zoning not only optimizes the distribution of electricity but also effectively reduces network losses, improves power quality, and enhances the system's responsiveness to load fluctuations and the integration of renewable energy. Through proper zoning, distribution networks can efficiently allocate resources across regions, ensuring a balanced supply and demand for electricity, thereby improving the overall economic efficiency and reliability of the power grid.

[0003] However, existing distribution network zoning technologies often rely on typical grid operating scenarios or statically partitioning using data from typical time periods. These static zoning methods often struggle to effectively address the complex and volatile nature of distribution networks in real-world operations. The operational state of distribution networks is influenced by numerous factors, including load characteristics, climate change, and fluctuations in renewable energy generation. These combined factors render traditional zoning structures rigid and lack sufficient flexibility.

[0004] With the rapid development of renewable energy sources such as photovoltaic power generation, distribution networks are facing frequent fluctuations in load and power generation. This volatility results in significant operational deficiencies in traditional zoning structures, making them difficult to adjust in real time. This inflexible structure often proves inadequate for responding to emergencies and diverse operating scenarios, unable to adapt to rapidly changing power demand and supply conditions. Furthermore, static zoning can cause some areas to be overloaded while others remain idle, which not only impacts the grid's reliability but also restricts the economic operation of the power system, increasing system operating costs. Summary of the Invention

[0005] Embodiments of the present invention provide a method, device, electronic device, and storage medium for dynamic partitioning of a distribution network. By implementing the present invention, a distribution network partitioning model can be constructed to generate an optimal partitioning scheme for dynamically adjusting the partitioning results of various areas of the distribution network, thereby improving the overall management efficiency and stability of the power grid.

[0006] An embodiment of the present invention provides a method for dynamically partitioning a distribution network, comprising:

[0007] Obtain the dispatch plan for a typical day of the distribution network;

[0008] Inputting the dispatch plan into a distribution network partition model to generate a first partition scheme and a first partition score at each dispatching moment when the partition scheme score is the largest;

[0009] Obtain the first voltage of each node at the first scheduling moment during the operation day of the distribution network;

[0010] Determine whether the first voltage of each node exceeds a preset voltage range; if there is a node that exceeds the preset voltage range, adjust the weight of each evaluation indicator in the distribution network partition model to obtain an updated distribution network partition model, input the scheduling plan into the updated distribution network partition model, and generate a second partition scheme and a second partition score at the first scheduling time when the partition scheme score is the maximum; wherein, under the second partition scheme, the first voltage of each node does not exceed the preset voltage range;

[0011] Calculate the difference between the first partition score and the second partition score at the first scheduling time. If the difference exceeds the set threshold, the corresponding second partition scheme is used as the actual partition scheme for the first scheduling time. If the difference does not exceed the set threshold, the corresponding first partition scheme is used as the actual partition scheme for the first scheduling time.

[0012] If there is no node exceeding the preset voltage range, the corresponding first partitioning scheme is used as the actual partitioning scheme at the first scheduling moment;

[0013] For other scheduling moments except the first scheduling moment, the difference between the first partition score and the actual partition score of the previous scheduling moment is calculated. If the difference exceeds the set threshold, the first partition scheme at the current moment is used as the actual partition scheme; if the difference does not exceed the set threshold, the actual partition scheme at the previous scheduling moment is used as the actual partition scheme of the distribution network at the current moment.

[0014] Furthermore, obtaining a typical day's dispatch plan for the distribution network includes:

[0015] Obtain load data, distributed photovoltaic data, and distribution network operation data for a typical day of the distribution network;

[0016] The load data includes: the active power of the node load; the distributed photovoltaic data includes: the predicted power of the distributed photovoltaic at the distributed power node and the capacity of the distributed photovoltaic at the distributed power node; the distribution network operation data includes: the topology information of the distribution network, the impedance of the branch, the current upper limit of the branch, the voltage upper limit of the node, the voltage lower limit of the node, the active power upper limit of the node, and the active power lower limit of the node;

[0017] constructing an optimal power flow model for the distribution network based on the distribution network operation data of the typical day;

[0018] Based on the load data of the typical day and the distributed photovoltaic forecast data, the optimal power flow model of the distribution network is solved to generate a scheduling plan for the typical day; wherein the scheduling plan includes: node voltage, node active power, branch current, branch active power, distributed photovoltaic active power and distributed photovoltaic reactive power.

[0019] Furthermore, the distribution network optimal power flow model includes an objective function and constraints;

[0020] The objective function includes:

[0021]

[0022] Where L is the network loss power of the distribution network; I ij，t is the current of branch ij at the dispatching time t; r ij is the resistance of branch ij; T is the scheduling time set; ε is the branch set;

[0023] The constraints include: node power balance constraint, Ohm's law constraint, branch head end power constraint, node voltage amplitude constraint, node injection power constraint, branch current amplitude constraint and distributed photovoltaic constraint.

[0024] Furthermore, the node power balance constraint is:

[0025]

[0026]

[0027] Among them, S jk，t is the active power of branch jk at the dispatching time t; S ij，t is the active power of branch ij at the dispatching time t; S j，t is the active power of node j at scheduling time t; z ij is the impedance of branch ij; I ij，t is the current of branch ij at the dispatching time t; is the active power of the distributed photovoltaic connected to node j at the scheduling time t; is the load active power of node j at scheduling time t;

[0028] The Ohm's law constraints are:

[0029] V i，t -V j，t =z ij I ij，t

[0030] Among them, V i，t is the voltage of node i at the scheduling time t; V j，tis the voltage of node j at the scheduling time t; z ij is the impedance of branch ij; I ij，t is the current of branch ij at the dispatching time t;

[0031] The power constraint at the head end of the branch is:

[0032]

[0033] Among them, S ij，t is the active power of branch ij at the dispatching time t; V i，t is the voltage of node i at scheduling time t; is the conjugate of the current of branch ij at the dispatching time t;

[0034] The node voltage amplitude constraint is:

[0035]

[0036] in, V i is the lower limit of the voltage at node i; is the voltage upper limit of node i; V i，t is the voltage of node i at scheduling time t;

[0037] The node injection power constraint is:

[0038]

[0039] in, S i is the upper limit of active power of node i; The lower limit of active power of node i; S i，t is the active power of node i at scheduling time t;

[0040] The branch current amplitude constraint is:

[0041]

[0042] Among them, I ij，t is the current of branch ij at the dispatching time t; is the upper limit of the current of branch ij;

[0043] The distributed photovoltaic constraints are:

[0044]

[0045] in, is the active power of the distributed photovoltaic connected to node j at the scheduling time t; is the predicted power of the distributed photovoltaic connected to node j at the scheduling time t.

[0046] Furthermore, the distribution network partition model includes an objective function;

[0047]

[0048] Among them, G is the partition scheme score; is the partition structure at the scheduling time t; I1 is the regional modularity index; ω1 is the weight of the regional modularity index; I2 is the load rate construction index; ω2 is the weight of the load rate construction index; I3 is the average load difference index; ω3 is the weight of the average load difference index; I4 is the average peak-to-valley difference rate index; ω4 is the weight of the average peak-to-valley difference rate index; I5 is the voltage regulation capability index; ω5 is the weight of the voltage regulation capability index; I6 is the power regulation capability index; ω6 is the weight of the power regulation capability index.

[0049] Furthermore, the regional modularity index is determined by the following formula:

[0050]

[0051] e ij =1-L ij / max((L)

[0052]

[0053]

[0054]

[0055] Among them, I1 is the regional modularity index; e ij is the electrical distance between node i and node j; m is the half value of the electrical distance between all nodes in the planning area; k i is the sum of the weights of node i; k j is the sum of the weights of node i and node j; δ(i, j) is a 0-1 variable indicating whether nodes i and j are in the same region; L ij is the degree of association between node i and node j; max(L) is the maximum degree of association; d in is the influence degree of node n on node i; d jn is the influence degree of node n on node j; d ij is the influence degree of node j on node i; S VP，jj is the sensitivity in the sensitivity matrix associated with the voltage and active power of node j; S VQ，jj is the sensitivity in the sensitivity matrix associated with the voltage and reactive power of node j; S VP，ij is the sensitivity in the sensitivity matrix associated with the voltage and active power of node i and node j; S VQ，ijis the sensitivity in the sensitivity matrix associated with the voltage and reactive power of node i and node j;

[0056] The load factor construction index is determined by the following formula:

[0057]

[0058]

[0059] Where I2 is the load factor structure index; N is the number of regions; s is the region index; f s is the load rate of each area; f av is the average load factor of each area;

[0060] The average load difference index is determined by the following formula:

[0061]

[0062] Among them, I3 is the average load difference index; N is the number of regions; s is the region index; P max is the maximum value among the maximum loads in each area; P s max is the maximum load of the sth area;

[0063] The average peak-to-valley rate indicator is determined by the following formula:

[0064]

[0065] Among them, I4 is the average peak-to-valley difference rate index; N is the number of regions; s is the region index; f sp is the peak-to-valley difference rate of each region;

[0066] The voltage regulation capability index is determined by the following formula:

[0067]

[0068]

[0069] Where I5 ​​is the voltage regulation capability index; N is the number of regions; s is the region index; is the voltage regulation capability of the region; ΔV i is the voltage regulation amount of node i in region s; is the maximum voltage regulation amount of node i in region s;

[0070] The power continuous regulation capability index is determined by the following formula:

[0071]

[0072]

[0073]

[0074] Where, I6 is the power continuous regulation capability indicator; N is the number of regions; s is the region index; is the power regulation capability of region s; is the adjustable power of area s; is the net load of area s at dispatch time t; is the adjustable power of distributed photovoltaic in area s at scheduling time t; is the adjustable power of the flexible load in area s at the scheduling time t.

[0075] Based on the above method embodiments, the present invention provides corresponding device embodiments.

[0076] An embodiment of the present invention provides a dynamic partitioning device for a distribution network, comprising: a scheduling plan acquisition module, a first partitioning scheme generation module, a second partitioning scheme generation module, and an actual partitioning scheme confirmation module.

[0077] The dispatch plan acquisition module is used to obtain the dispatch plan of the distribution network on a typical day;

[0078] The first partitioning scheme generating module is configured to input the scheduling plan into the distribution network partitioning model, and generate a first partitioning scheme and a first partitioning score at each scheduling moment when the partitioning scheme score is maximized;

[0079] The second partitioning scheme generating module is configured to determine whether the first voltage of each node exceeds a preset voltage range. If there is a node that exceeds the preset voltage range, an updated distribution network partitioning model is obtained by adjusting the weights of the evaluation indicators in the distribution network partitioning model, and the scheduling plan is input into the updated distribution network partitioning model to generate a second partitioning scheme and a second partitioning score at the first scheduling moment when the partitioning scheme score is the largest. Under the second partitioning scheme, the first voltage of each node does not exceed the preset voltage range.

[0080] The actual partitioning scheme confirmation module is used to calculate the difference between the first partitioning score and the second partitioning score at the first scheduling moment. If the difference exceeds the set threshold, the corresponding second partitioning scheme is used as the actual partitioning scheme at the first scheduling moment; if the difference does not exceed the set threshold, the corresponding first partitioning scheme is used as the actual partitioning scheme at the first scheduling moment; if there is no node exceeding the preset voltage range, the corresponding first partitioning scheme is used as the actual partitioning scheme at the first scheduling moment; for other scheduling moments except the first scheduling moment, the difference between the first partitioning score and the actual partitioning score at the previous scheduling moment is calculated. If the difference exceeds the set threshold, the first partitioning scheme at the current moment is used as the actual partitioning scheme; if the difference does not exceed the set threshold, the actual partitioning scheme at the previous scheduling moment is used as the actual partitioning scheme of the distribution network at the current moment.

[0081] Furthermore, obtaining a typical day's dispatch plan for the distribution network includes:

[0082] Obtain load data, distributed photovoltaic data, and distribution network operation data for a typical day of the distribution network;

[0083] The load data includes: the active power of the node load; the distributed photovoltaic data includes: the predicted power of the distributed photovoltaic at the distributed power node and the capacity of the distributed photovoltaic at the distributed power node; the distribution network operation data includes: the topology information of the distribution network, the impedance of the branch, the current upper limit of the branch, the voltage upper limit of the node, the voltage lower limit of the node, the active power upper limit of the node, and the active power lower limit of the node;

[0084] constructing an optimal power flow model for the distribution network based on the distribution network operation data of the typical day;

[0085] Based on the load data of the typical day and the distributed photovoltaic forecast data, the optimal power flow model of the distribution network is solved to generate a scheduling plan for the typical day; wherein the scheduling plan includes: node voltage, node active power, branch current, branch active power, distributed photovoltaic active power and distributed photovoltaic reactive power.

[0086] Based on the above method embodiment, the present invention provides a corresponding electronic device embodiment.

[0087] An embodiment of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for dynamically partitioning a distribution network as described in any one of the above-mentioned method embodiments can be implemented.

[0088] Based on the above method embodiment, the present invention provides a corresponding storage medium embodiment.

[0089] An embodiment of the present invention provides a storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for dynamically partitioning a distribution network as described in any one of the above method embodiments can be implemented.

[0090] Compared with the prior art, the present invention has the following beneficial effects:

[0091] Embodiments of the present invention provide a method, device, electronic device, and storage medium for dynamic partitioning of a distribution network. The method constructs a highly self-balancing distribution network partitioning model using a regional modularity index, a regional load rate index, an average load difference index, an average peak-to-valley difference index, a voltage regulation capability index, a power regulation capability index, and the corresponding indicator weights of all indicators. A first partitioning scheme and a first partitioning score are calculated for each dispatching moment based on the dispatch plan of a typical distribution network day. The first partitioning scheme enhances internal adaptability within the partition and effectively reduces mutual interference between regions. At the first dispatching moment of the operating day, a determination is made as to whether the voltage exceeds the limit. If the voltage exceeds the limit, the indicator weights of the distribution network partitioning model are adjusted, and a second partitioning scheme and a second partitioning score are recalculated. Based on the difference between the first and second partitioning scores, it is determined whether the second partitioning scheme should be used as the actual partitioning scheme for the distribution network at the first dispatching moment. This ensures that the partitioning structure can promptly respond to voltage fluctuations, quickly eliminate the risk of exceeding the limit, and thus achieve precise regulation of the distribution network. If the voltage exceeds the limit but the difference between the first and second partitioning scores does not exceed a preset threshold, the first partitioning scheme is still selected instead of the second partitioning scheme after weight adjustment, indicating that the need for partitioning adjustment is not strong. Even if the voltage exceeds the limit, the original scheme has little impact on the overall stability and dispatching effect of the system. This selection, based on a difference threshold, ensures that the system doesn't frequently adjust partitions due to minor optimization effects, thus reducing unnecessary partition switching. For other scheduling moments, the partitioning scheme is only changed when the difference between the first partition score at the current scheduling moment and the actual score at the previous scheduling moment exceeds a preset threshold. This avoids frequent partition switching while ensuring the efficiency and consistency of the actual running partitioning scheme across different time periods, preventing instability caused by frequent switching and rationally controlling the frequency of partition switching, making operation and maintenance easier and reducing system scheduling complexity. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] Figure 1 It is a flowchart of a dynamic partitioning method for a distribution network provided by one embodiment of the present invention.

[0093] Figure 2 It is a structural diagram of a dynamic partitioning device for a distribution network provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0094] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0095] like Figure 1 As shown, an embodiment of the present invention provides a method for dynamic partitioning of a distribution network, which includes at least the following steps:

[0096] Step S1: Obtain a typical day dispatch plan for the distribution network;

[0097] Specifically, in a preferred embodiment, obtaining a typical day's dispatch plan for the distribution network includes:

[0098] Obtain load data, distributed photovoltaic data, and distribution network operation data for a typical day of the distribution network;

[0099] The load data includes: the active power of the node load; the distributed photovoltaic data includes: the predicted power of the distributed photovoltaic at the distributed power node and the capacity of the distributed photovoltaic at the distributed power node; the distribution network operation data includes: the topology information of the distribution network, the impedance of the branch, the current upper limit of the branch, the voltage upper limit of the node, the voltage lower limit of the node, the active power upper limit of the node, and the active power lower limit of the node;

[0100] constructing an optimal power flow model for the distribution network based on the distribution network operation data of the typical day;

[0101] Based on the load data of the typical day and the distributed photovoltaic forecast data, the optimal power flow model of the distribution network is solved to generate a scheduling plan for the typical day; wherein the scheduling plan includes: node voltage, node active power, branch current, branch active power, distributed photovoltaic active power and distributed photovoltaic reactive power.

[0102] In an optional embodiment, the distribution network optimal power flow model includes an objective function and constraints;

[0103] The objective function includes:

[0104]

[0105] Where L is the network loss power of the distribution network; I ij，t is the current of branch ij at the dispatching time t; r ij is the resistance of branch ij; T is the scheduling time set; ε is the branch set;

[0106] The constraints include: node power balance constraint, Ohm's law constraint, branch head end power constraint, node voltage amplitude constraint, node injection power constraint, branch current amplitude constraint and distributed photovoltaic constraint.

[0107] Preferably, the node power balance constraint is:

[0108]

[0109]

[0110] Among them, S jk，t is the active power of branch jk at the dispatching time t; S ij，t is the active power of branch ij at the dispatching time t; S j，t is the active power of node j at scheduling time t; z ij is the impedance of branch ij; I ij，t is the current of branch ij at the dispatching time t; is the active power of the distributed photovoltaic connected to node j at the scheduling time t; is the load active power of node j at scheduling time t;

[0111] The Ohm's law constraints are:

[0112] V i，t -V j，t =z ij I ij，t

[0113] Among them, V i，t is the voltage of node i at the scheduling time t; V j，t is the voltage of node j at the scheduling time t; z ij is the impedance of branch ij; II j，t is the current of branch ij at the dispatching time t;

[0114] The power constraint at the head end of the branch is:

[0115]

[0116] Among them, S ij，t is the active power of branch ij at the dispatching time t; V i，t is the voltage of node i at scheduling time t; is the conjugate of the current of branch ij at the dispatching time t;

[0117] The node voltage amplitude constraint is:

[0118]

[0119] in,V i is the lower limit of the voltage at node i; is the voltage upper limit of node i; V i，t is the voltage of node i at scheduling time t;

[0120] The node injection power constraint is:

[0121]

[0122] in, S i is the upper limit of active power of node i; The lower limit of active power of node i; S i，t is the active power of node i at scheduling time t;

[0123] The branch current amplitude constraint is:

[0124]

[0125] Among them, I ij，t is the current of branch ij at the dispatching time t; is the upper limit of the current of branch ij;

[0126] The distributed photovoltaic constraints are:

[0127]

[0128] in, is the active power of the distributed photovoltaic connected to node j at the scheduling time t; is the predicted power of the distributed photovoltaic connected to node j at the scheduling time t.

[0129] It should be noted that a typical day of the distribution network refers to a representative day selected in the distribution network operation analysis, which is used to simulate and study the operating characteristics of the distribution network in a specific time period.

[0130] In the specific implementation, to facilitate and effectively solve the distribution network optimal power flow model, second-order cone relaxation and the Big-M method were used to transform it into a mixed-integer second-order cone model. This transformation significantly reduces the solution complexity and improves solution efficiency while retaining the model's key characteristics. Furthermore, using the commercial solver Gurobi for the solution fully leverages its efficient algorithms and powerful optimization capabilities to quickly and accurately obtain a solution to the distribution network optimal power flow model. This not only ensures the efficiency of the solution process but also provides strong support for subsequent distribution network optimization scheduling and management.

[0131] Step S2: inputting the dispatch plan into the distribution network partition model to generate the first partition scheme and the first partition score at each dispatching moment when the partition scheme score is the largest;

[0132] Specifically, in a preferred embodiment, the distribution network partition model includes an objective function;

[0133]

[0134] Among them, G is the partition scheme score; is the partition structure at the scheduling time t; I1 is the regional modularity index; ω1 is the weight of the regional modularity index; I2 is the load rate construction index; ω2 is the weight of the load rate construction index; I3 is the average load difference index; ω3 is the weight of the average load difference index; I4 is the average peak-to-valley difference rate index; ω4 is the weight of the average peak-to-valley difference rate index; I5 is the voltage regulation capability index; ω5 is the weight of the voltage regulation capability index; I6 is the power regulation capability index; ω6 is the weight of the power regulation capability index.

[0135] It can be understood that by inputting the dispatch plan into the distribution network partitioning model and generating the first partitioning scheme and the partitioning scheme with the highest score at each dispatch time, the management efficiency and dispatch optimization of the distribution network can be effectively improved. In addition, the maximized score indicates that the generated partitioning scheme performs well in terms of voltage regulation, load balancing, and renewable energy utilization, providing a reliable basis for subsequent dispatch decisions.

[0136] Specifically, the regional modularity index is determined by the following formula:

[0137]

[0138] e ij =1-L ij / max((L)

[0139]

[0140]

[0141]

[0142] Among them, I1 is the regional modularity index; e ij is the electrical distance between node i and node j; m is the half value of the electrical distance between all nodes in the planning area; k i is the sum of the weights of node i; k j is the sum of the weights of node i and node j; δ(i, j) is a 0-1 variable indicating whether nodes i and j are in the same region; L ij is the degree of association between node i and node j; max(L) is the maximum degree of association; d in is the influence degree of node n on node i; d jnis the influence degree of node n on node j; d ij is the influence degree of node j on node i; S VP，jj is the sensitivity in the sensitivity matrix associated with the voltage and active power of node j; S VQ，jj is the sensitivity in the sensitivity matrix associated with the voltage and reactive power of node j; S VP，ij is the sensitivity in the sensitivity matrix associated with the voltage and active power of node i and node j; S VQ，ij is the sensitivity in the sensitivity matrix associated with the voltage and reactive power of node i and node j;

[0143] Exemplary construction of a sensitivity matrix related to voltage and active power involves performing a small perturbation on the active power of each node, such as increasing or decreasing the active power by a certain amount, and then recording the voltage changes at other nodes. This process can reveal the extent to which changes in active power at each node affect changes in the voltages of other nodes, thereby constructing a sensitivity matrix related to voltage and active power.

[0144] For example, the process of constructing a sensitivity matrix related to voltage and reactive power includes: performing a small perturbation on the reactive power of each node, such as increasing or decreasing the reactive power by a certain amount, and then recording the changes in the voltages of other nodes. This process can reflect the degree to which the reactive power changes at each node affect the voltages of other nodes, thereby constructing a sensitivity matrix related to voltage and active power.

[0145] It's understandable that in the structure of a distribution network, the connections between nodes within each region should be relatively close, while the connections between regions should be relatively loose. This facilitates the management and operation of each region. Therefore, the voltage regulation capability indicator is a positive indicator. A larger regional modularity indicator indicates a higher degree of closeness between nodes within the region, a greater degree of looseness between regions, and a better distribution network structure. This structural advantage ensures that the distribution network can quickly and effectively adjust and respond to load changes, faults, or other emergencies.

[0146] Specifically, the load rate construction index is determined by the following formula:

[0147]

[0148]

[0149] Where I2 is the load factor structure index; N is the number of regions; s is the region index; f s is the load rate of each area; f av is the average load factor of each area;

[0150] Exemplarily, the load rate of each area is calculated by the ratio of the actual load in the area to the maximum load in the area; wherein the actual load in the area refers to the total active power of all load nodes in the area, and the maximum load in the area is the highest load value in the area within a specific time period.

[0151] It's understandable that the load factor structural index is a key parameter used to assess the load status of each area in the distribution network. This index is negative, so it's directly set to a negative value in the distribution network zoning model. This means that a larger value means a smaller load factor structural index, a higher load factor in each area, and a more concentrated distribution of these load factors.

[0152] Specifically, the average load difference index is determined by the following formula:

[0153]

[0154] Among them, I3 is the average load difference index; N is the number of regions; s is the region index; P max is the maximum value among the maximum loads in each area; P s max is the maximum load of the sth area;

[0155] It's understandable that the average load difference indicator, as a negative indicator, is primarily used to assess the load distribution between regions within the distribution network. Setting it directly to a negative value in the distribution network zoning model means that larger values, while smaller values ​​for the average load difference indicator itself, indicate closer load levels across regions and a more even load distribution. Furthermore, a smaller average load difference indicator itself indicates more coordinated power supply between regions, which can improve the stability and reliability of the overall power system. By optimizing this indicator, the distribution network can better cope with load fluctuations and meet user power needs, thereby laying the foundation for more efficient energy management and sustainable development.

[0156] Specifically, the average peak-to-valley difference rate index is determined by the following formula:

[0157]

[0158] Among them, I4 is the average peak-to-valley difference rate index; N is the number of regions; s is the region index; f sp is the peak-to-valley difference rate of each region;

[0159] Optionally, the peak-to-valley difference rate of each region is calculated by subtracting the difference between the maximum load and the minimum load in the region, and then dividing the difference by the maximum load, where the maximum load represents the peak load value of the region in the period, and the minimum load represents the valley load value in the period.

[0160] Understandably, the average peak-to-valley difference ratio is a negative indicator that measures load fluctuations in the distribution network. Setting it directly to a negative value in the distribution network zoning model means that a larger value, and a smaller average load difference ratio, indicates smaller load fluctuations within the zoned area and a more stable load demand. A reduction in the original value of this indicator means that the impact of peak loads can be better controlled and mitigated within the region, contributing to more stable load management and scheduling. This improves the distribution network's ability to regulate large peak-to-valley load variations, further ensuring the reliability and efficiency of power supply.

[0161] Specifically, the voltage regulation capability index is determined by the following formula:

[0162]

[0163]

[0164] Where I5 ​​is the voltage regulation capability index; N is the number of regions; s is the region index; is the voltage regulation capability of the region; ΔV i is the voltage regulation amount of node i in region s; is the maximum voltage regulation amount of node i in region s;

[0165] Understandably, voltage overshoot is a common and critical issue when a high proportion of distributed PV is connected to the distribution network. The voltage regulation capability indicator is defined as the ability of the adjustable active and reactive power of PV, energy storage, and load resources within a region to regulate the maximum voltage deviation. The voltage regulation capability indicator is a positive indicator; higher values ​​indicate a more effective region in balancing voltage fluctuations, reflecting better voltage control performance and, consequently, a more appropriate zoning scheme.

[0166] Specifically, the power continuous regulation capability index is determined by the following formula:

[0167]

[0168]

[0169]

[0170] Where, I6 is the power continuous regulation capability indicator; N is the number of regions; s is the region index; is the power regulation capability of region s; is the adjustable power of area s; is the net load of area s at dispatch time t; is the adjustable power of distributed photovoltaic in area s at scheduling time t; is the adjustable power of the flexible load in area s at the scheduling time t.

[0171] For example, the adjustable power of the distributed photovoltaic system connected to the distributed power generation node can be calculated by subtracting the lower limit of the distributed photovoltaic power generation from the current distributed photovoltaic power generation. The adjustable power of the flexible load can be calculated by the difference between the maximum adjustable power and the current load power, where the maximum adjustable power refers to the maximum power value that the flexible load can adjust during the scheduling time, and the current load power refers to the power actually consumed at that time.

[0172] Understandably, the randomness and volatility of photovoltaic and load power exacerbate fluctuations in the net load of the distribution network. The power continuous regulation capability indicator is defined as the ability of the adjustable power of multiple regulation resources within a region to cope with net load fluctuations. The power continuous regulation capability indicator is a positive indicator; higher values ​​indicate that the region's regulation resources are more effectively able to adapt to and mitigate load fluctuations, ensuring the operational stability and reliability of the distribution network.

[0173] Step S3: obtaining a first voltage of each node at a first scheduling moment during the operation day of the distribution network;

[0174] It should be noted that the distribution network operation day refers to the specific date of actual operation and dispatch management of the distribution network, which is usually used to analyze and evaluate the operating status and load characteristics of the distribution network.

[0175] Step S4: Determine whether the first voltage of each node exceeds a preset voltage range. If there is a node that exceeds the preset voltage range, adjust the weight of each evaluation indicator in the distribution network partition model to obtain an updated distribution network partition model, input the scheduling plan into the updated distribution network partition model, and generate a second partition scheme and a second partition score at the first scheduling moment when the partition scheme score is the largest; wherein, under the second partition scheme, the first voltage of each node does not exceed the preset voltage range;

[0176] In specific operation, when the first voltage exceeds the limit, the weight of the voltage regulation capability index of the distribution network partition model is increased, and the weights of other indicators are modified to obtain an updated distribution network partition model. The scheduling plan of the first scheduling moment is input into the updated distribution network partition model to generate the second partition scheme and the second partition score at the first scheduling moment when the partition scheme score is the largest; ensure that under the second partition scheme, the first voltage of each node does not exceed the preset voltage range.

[0177] Step S5: Calculate the difference between the first partition score and the second partition score at the first scheduling moment. If the difference exceeds the set threshold, use the corresponding second partition scheme as the actual partition scheme for the first scheduling moment; if the difference does not exceed the set threshold, use the corresponding first partition scheme as the actual partition scheme for the first scheduling moment.

[0178] It can be understood that by calculating the difference between the first partition score and the second partition score at the first scheduling moment and comparing it with the set threshold, the pros and cons of the partitioning scheme can be effectively evaluated. This process helps to ensure that a more reasonable and efficient partitioning scheme is selected in the actual operation of the distribution network, thereby improving the operating efficiency and stability of the power grid. If the difference does not exceed the preset threshold, the first partitioning scheme is still selected instead of the second partitioning scheme after weight adjustment, indicating that the necessity of partition adjustment is not strong. Even if there is a voltage limit violation, the original scheme has little impact on the overall stability and scheduling effect of the system. This selection is based on the setting of the difference threshold, which ensures that the system will not frequently adjust the partition due to minor optimization effects, thereby reducing unnecessary partition switching.

[0179] Step S6: If no node exceeds the preset voltage range, the corresponding first partitioning scheme is used as the actual partitioning scheme at the first scheduling moment;

[0180] It can be understood that when no node exceeds the preset voltage range, selecting the first partitioning scheme as the actual partitioning scheme at the first scheduling moment helps maintain the operational stability and safety of the distribution network, reduces frequent adjustments to the partitioning scheme, and thus reduces the complexity of scheduling decisions.

[0181] Step S7: For other scheduling moments except the first scheduling moment, calculate the difference between the first partition score and the actual partition score of the previous scheduling moment. If the difference exceeds the set threshold, the first partition scheme at the current moment is used as the actual partition scheme; if the difference does not exceed the set threshold, the actual partition scheme at the previous scheduling moment is used as the actual partition scheme of the distribution network at the current moment.

[0182] It can be understood that by calculating the difference between the current first partition score and the actual partition score at the previous scheduling moment, the stability and continuity of the partitioning scheme can be effectively monitored. If the difference exceeds a preset threshold, the current first partitioning scheme is selected as the actual partitioning scheme. If the difference does not exceed the threshold, the actual partitioning scheme at the previous moment is retained. This avoids unnecessary frequent adjustments, helps optimize the operation and management of the distribution network, and improves overall efficiency and reliability.

[0183] Based on the above method embodiments, the present invention provides corresponding device embodiments.

[0184] like Figure 2 As shown, an embodiment of the present invention provides a dynamic partitioning device for a distribution network, including: a scheduling plan acquisition module 101, a first partitioning scheme generation module 102, a second partitioning scheme generation module 103 and an actual partitioning scheme confirmation module 104.

[0185] The dispatch plan acquisition module 101 is used to obtain a dispatch plan for a typical day of the distribution network.

[0186] The first partitioning scheme generating module 102 is configured to input the scheduling plan into the distribution network partitioning model, and generate a first partitioning scheme and a first partitioning score at each scheduling moment when the partitioning scheme score is maximized;

[0187] The second partitioning scheme generating module 103 is configured to determine whether the first voltage of each node exceeds a preset voltage range. If there is a node that exceeds the preset voltage range, an updated distribution network partitioning model is obtained by adjusting the weights of the evaluation indicators in the distribution network partitioning model, and the scheduling plan is input into the updated distribution network partitioning model to generate a second partitioning scheme and a second partitioning score at the first scheduling moment when the partitioning scheme score is the maximum. Under the second partitioning scheme, the first voltage of each node does not exceed the preset voltage range.

[0188] The actual partitioning scheme confirmation module 104 is used to calculate the difference between the first partitioning score and the second partitioning score at the first scheduling moment. If the difference exceeds the set threshold, the corresponding second partitioning scheme is used as the actual partitioning scheme at the first scheduling moment; if the difference does not exceed the set threshold, the corresponding first partitioning scheme is used as the actual partitioning scheme at the first scheduling moment; if there is no node exceeding the preset voltage range, the corresponding first partitioning scheme is used as the actual partitioning scheme at the first scheduling moment; for other scheduling moments except the first scheduling moment, the difference between the first partitioning score and the actual partitioning score at the previous scheduling moment is calculated. If the difference exceeds the set threshold, the first partitioning scheme at the current moment is used as the actual partitioning scheme; if the difference does not exceed the set threshold, the actual partitioning scheme at the previous scheduling moment is used as the actual partitioning scheme of the distribution network at the current moment.

[0189] It should be noted that the embodiments of the device described above correspond to the above-mentioned embodiments of the present invention, and can implement any of the methods described above in the present invention. In addition, the embodiments of the above-mentioned device are merely schematic, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the embodiment of the device provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0190] Based on the above method embodiment of the present invention, a corresponding electronic device embodiment is provided.

[0191] An embodiment of the present invention provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the dynamic partitioning method of the distribution network described in any one of the present invention is implemented, or when the processor executes the computer program, the functions of the modules in the above-mentioned device embodiments are implemented.

[0192] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0193] The terminal device may be a computing device such as a desktop computer, a notebook computer, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0194] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, connecting various parts of the entire terminal device using various interfaces and lines.

[0195] The memory can be used to store the computer programs and / or modules, and the processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created based on the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0196] Based on the above method embodiment, the present invention provides a corresponding storage medium embodiment;

[0197] Another embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is executed, the device where the storage medium is located is controlled to execute any one of the above-mentioned methods for dynamic partitioning of a distribution network of the present invention.

[0198] The above-mentioned storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0199] In the description of this specification, the reference terms "one embodiment," "some embodiments," "example," "specific example," or "some examples" mean that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials, or characteristics described may be combined in any appropriate manner in any one or more embodiments or examples. In addition, those skilled in the art may combine and integrate different embodiments or examples described in this specification, as well as features of different embodiments or examples, unless they are mutually inconsistent.

[0200] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A dynamic partitioning method for a distribution network, characterized in that: include: Obtain the dispatch plan for a typical day of the distribution network; Inputting the dispatch plan into a distribution network partition model to generate a first partition scheme and a first partition score at each dispatching moment when the partition scheme score is the largest; Obtain the first voltage of each node at the first scheduling moment during the operation day of the distribution network; Determine whether the first voltage of each node exceeds a preset voltage range; if there is a node that exceeds the preset voltage range, adjust the weight of each evaluation indicator in the distribution network partition model to obtain an updated distribution network partition model, input the scheduling plan into the updated distribution network partition model, and generate a second partition scheme and a second partition score at the first scheduling time when the partition scheme score is the maximum; wherein, under the second partition scheme, the first voltage of each node does not exceed the preset voltage range; Calculate the difference between the first partition score and the second partition score at the first scheduling time. If the difference exceeds the set threshold, the corresponding second partition scheme is used as the actual partition scheme for the first scheduling time. If the difference does not exceed the set threshold, the corresponding first partition scheme is used as the actual partition scheme for the first scheduling time. If there is no node exceeding the preset voltage range, the corresponding first partitioning scheme is used as the actual partitioning scheme at the first scheduling moment; For other scheduling moments except the first scheduling moment, the difference between the first partition score and the actual partition score of the previous scheduling moment is calculated. If the difference exceeds the set threshold, the first partition scheme at the current moment is used as the actual partition scheme; if the difference does not exceed the set threshold, the actual partition scheme at the previous scheduling moment is used as the actual partition scheme of the distribution network at the current moment.

2. A method for dynamic partitioning of a distribution network according to claim 1, characterized in that: The obtaining of a typical day's dispatch plan for the distribution network includes: Obtain load data, distributed photovoltaic data, and distribution network operation data for a typical day of the distribution network; The load data includes: the active power of the node load; the distributed photovoltaic data includes: the predicted power of the distributed photovoltaic at the distributed power node and the capacity of the distributed photovoltaic at the distributed power node; the distribution network operation data includes: the topology information of the distribution network, the impedance of the branch, the current upper limit of the branch, the voltage upper limit of the node, the voltage lower limit of the node, the active power upper limit of the node, and the active power lower limit of the node; constructing an optimal power flow model for the distribution network based on the distribution network operation data of the typical day; Based on the load data of the typical day and the distributed photovoltaic forecast data, the optimal power flow model of the distribution network is solved to generate a scheduling plan for the typical day; wherein the scheduling plan includes: node voltage, node active power, branch current, branch active power, distributed photovoltaic active power and distributed photovoltaic reactive power.

3. A method for dynamic partitioning of a distribution network according to claim 2, characterized in that: The distribution network optimal power flow model includes an objective function and constraints; The objective function includes: in, The network loss power of the distribution network; For branch At the scheduling time Current; For branch resistance; Gather for scheduling time; For branch collection; The constraints include: node power balance constraint, Ohm's law constraint, branch head end power constraint, node voltage amplitude constraint, node injection power constraint, branch current amplitude constraint and distributed photovoltaic constraint.

4. A method for dynamic partitioning of a distribution network according to claim 3, characterized in that: The node power balance constraint is: in, For branch At the scheduling time Active power; For branch At the scheduling time Active power; For nodes At the scheduling time Active power; For branch Impedance; For branch At the scheduling time Current; For nodes The distributed photovoltaic connected to the Active power; For nodes At the scheduling time Active power of the load; The Ohm's law constraints are: in, For nodes At the scheduling time voltage; For nodes At the scheduling time voltage; For branch Impedance; For branch At the scheduling time Current; The power constraint at the head end of the branch is: in, For branch At the scheduling time Active power; For nodes At the scheduling time voltage; For branch At the scheduling time The conjugate of the current; The node voltage amplitude constraint is: in, For nodes The lower voltage limit; For nodes The upper voltage limit; For nodes At the scheduling time voltage; The node injection power constraint is: in, For nodes Active power upper limit; node The lower limit of active power; For nodes At the scheduling time Active power; The branch current amplitude constraint is: in, For branch At the scheduling time Current; For branch The upper limit of current; The distributed photovoltaic constraints are: in, For nodes The distributed photovoltaic connected to the Active power; For nodes The distributed photovoltaic connected to the The predicted power.

5. A method for dynamic partitioning of a distribution network according to claim 1, characterized in that: The distribution network partition model includes an objective function; in, scoring zoning proposals; For scheduling time Partition structure; is the regional modularity index; is the weight of the regional modularity index; Construct an indicator for the load factor; constructing the weight of the indicator for the load factor; is the average load difference indicator; is the weight of the average load difference indicator; is the average peak-to-valley difference rate indicator; is the weight of the average peak-to-valley difference rate indicator; It is an indicator of voltage regulation capability; is the weight of the voltage regulation capability indicator; It is the power regulation capability indicator; is the weight of the power regulation capability indicator.

6. A method for dynamic partitioning of a distribution network according to claim 5, characterized in that: The regional modularity index is determined by the following formula: in, is the regional modularity index; For nodes and nodes The electrical distance between is the half value of the electrical distance between all nodes in the planning area; For nodes The sum of the weights of For nodes The sum of the weights of To represent a node and nodes A 0-1 variable indicating whether they are in the same region; For nodes and nodes degree of association; is the maximum degree of association; For nodes For Node the extent of the impact; For nodes For Node the extent of the impact; For nodes For Node the extent of the impact; For nodes The sensitivity of the voltage active power associated with the sensitivity matrix; For nodes The sensitivity of the voltage-reactive power associated with the sensitivity matrix; For nodes and nodes The sensitivity of the voltage active power associated with the sensitivity matrix; For nodes and nodes The sensitivity of the voltage-reactive power associated with the sensitivity matrix; The load factor construction index is determined by the following formula: in, Construct an indicator for the load factor; is the number of regions; is the regional index; is the load rate of each area; is the average load factor of each area; The average load difference index is determined by the following formula: in, is the average load difference indicator; is the number of regions; is the regional index; is the maximum value among the maximum loads in each area; For the Maximum load of each area; The average peak-to-valley rate indicator is determined by the following formula: in, is the average peak-to-valley difference rate indicator; is the number of regions; is the regional index; is the peak-to-valley difference rate of each region; The voltage regulation capability index is determined by the following formula: in, It is an indicator of voltage regulation capability; is the number of regions; is the regional index; For the region Voltage regulation capability; For the region Internal Node The voltage regulation amount; For the region Internal Node Maximum voltage regulation; The power continuous regulation capability index is determined by the following formula: in, It is an indicator of the power continuous regulation capability; is the number of regions; is the regional index; For the region Power regulation capability; For the region Adjustable power; For scheduling time area Net load; For scheduling time area The adjustable power of distributed photovoltaics; For scheduling time area Adjustable power for flexible loads; The scheduling time collection.

7. A dynamic partitioning device for a distribution network, characterized in that: include: Scheduling plan acquisition module, first partitioning scheme generation module, second partitioning scheme generation module and actual partitioning scheme confirmation module; The dispatch plan acquisition module is used to obtain the dispatch plan of the distribution network on a typical day; The first partitioning scheme generating module is configured to input the scheduling plan into the distribution network partitioning model, and generate a first partitioning scheme and a first partitioning score at each scheduling moment when the partitioning scheme score is maximized; The second partitioning scheme generating module is configured to determine whether the first voltage of each node exceeds a preset voltage range. If there is a node that exceeds the preset voltage range, an updated distribution network partitioning model is obtained by adjusting the weights of the evaluation indicators in the distribution network partitioning model, and the scheduling plan is input into the updated distribution network partitioning model to generate a second partitioning scheme and a second partitioning score at the first scheduling moment when the partitioning scheme score is the largest. Under the second partitioning scheme, the first voltage of each node does not exceed the preset voltage range. The actual partitioning scheme confirmation module is used to calculate the difference between the first partitioning score and the second partitioning score at the first scheduling moment. If the difference exceeds the set threshold, the corresponding second partitioning scheme is used as the actual partitioning scheme at the first scheduling moment; if the difference does not exceed the set threshold, the corresponding first partitioning scheme is used as the actual partitioning scheme at the first scheduling moment; if there is no node exceeding the preset voltage range, the corresponding first partitioning scheme is used as the actual partitioning scheme at the first scheduling moment; for other scheduling moments except the first scheduling moment, the difference between the first partitioning score and the actual partitioning score at the previous scheduling moment is calculated. If the difference exceeds the set threshold, the first partitioning scheme at the current moment is used as the actual partitioning scheme; if the difference does not exceed the set threshold, the actual partitioning scheme at the previous scheduling moment is used as the actual partitioning scheme of the distribution network at the current moment.

8. A dynamic partitioning device for a power distribution network according to claim 7, characterized in that: The obtaining of a typical day's dispatch plan for the distribution network includes: Obtain load data, distributed photovoltaic data, and distribution network operation data for a typical day of the distribution network; The load data includes: the active power of the node load; the distributed photovoltaic data includes: the predicted power of the distributed photovoltaic at the distributed power node and the capacity of the distributed photovoltaic at the distributed power node; the distribution network operation data includes: the topology information of the distribution network, the impedance of the branch, the current upper limit of the branch, the voltage upper limit of the node, the voltage lower limit of the node, the active power upper limit of the node, and the active power lower limit of the node; constructing an optimal power flow model for the distribution network based on the distribution network operation data of the typical day; Based on the load data of the typical day and the distributed photovoltaic forecast data, the optimal power flow model of the distribution network is solved to generate a scheduling plan for the typical day; wherein the scheduling plan includes: node voltage, node active power, branch current, branch active power, distributed photovoltaic active power and distributed photovoltaic reactive power.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method for dynamic partitioning of a distribution network as claimed in any one of claims 1 to 6 can be implemented.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it can implement the dynamic partitioning method of the distribution network as described in any one of claims 1 to 6.

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