Power dispatching big data platform based on informatization management control

Abstract

The invention provides a power dispatching big data platform based on informatization management control. Comprising the following steps: research of a regional reactive voltage autonomous system, a distribution network district level of a first-level target, a grid-connected bus level of a second-level target, a regional control level of a third-level target, reactive voltage margin modeling of a distributed photovoltaic power station, regulation characteristic analysis of regional aggregation resources, and construction of a centralized controller. The invention provides a power dispatching big data platform based on informatization management control. The method comprises the steps of a distribution network area level of a first-level target, a grid-connected bus level of a second-level target, a regional control level of a third-level target, reactive voltage margin modeling of a distributed photovoltaic power station, regional aggregation resource adjustment characteristic analysis and establishment of a central integrated controller. A distributed photovoltaic grid-connected inverter control system can be transformed, and a superior centralized control platform can be built.

Description

Technical Field

[0001] The present invention relates to the field of electric power systems, and in particular to an electric power dispatching big data platform based on information management and control. Background Art

[0002] In today's society, electricity is an important energy source supporting the development of the national economy and people's lives, and the stability, reliability and efficiency of its supply are crucial.

[0003] With the continuous expansion of the scale of power systems and the widespread access to new energy sources, traditional power dispatching methods face many challenges.

[0004] In the past, power dispatching mainly relied on limited monitoring data and empirical judgment, making it difficult to conduct comprehensive, accurate and timely analysis of the complex and changeable power system.

[0005] Due to the large number and wide distribution of equipment in the power system, a huge amount of data is generated, including but not limited to power generation data, transmission data, substation data, distribution data, and user power consumption data.

[0006] The main problems are as follows:

[0007] In the process of promoting the rapid implementation of "dual carbon", in order to cope with the rapid growth of electricity demand, the development of new energy has entered a rapid period. Affected by the randomness and volatility of photovoltaics, areas with a high proportion of photovoltaic access are prone to frequent fluctuations in power generation output, large fluctuations in voltage and reactive power, and serious impacts:

[0008] With the comprehensive promotion of photovoltaic power generation in the whole county, the penetration rate of distributed photovoltaic power generation has increased. Compared with centralized photovoltaic power generation, distributed photovoltaic power generation has management difficulties due to its "many points and wide coverage", and distributed photovoltaic power generation has no SVG, so the current control strategy is difficult to achieve voltage autonomy.

[0009] There is currently no better response strategy for user power factor issues, especially low-voltage substation issues on long lines in suburban areas, during the transition period of switching and modification. Regional voltage problems still rely solely on conventional tap changers or capacitors.

[0010] Therefore, it is necessary to provide a power dispatching big data platform based on information management and control to solve the above technical problems. Summary of the invention

[0011] The present invention provides a power dispatching big data platform based on information management and control, which solves the problem that due to the randomness and volatility of photovoltaics, areas with a high proportion of photovoltaic access are prone to frequent fluctuations in power generation, large fluctuations in voltage and reactive power, and serious impacts. Compared with centralized photovoltaics, distributed photovoltaics have management difficulties and user power factor problems due to their "many points and wide areas".

[0012] In order to solve the above technical problems, the present invention provides a power dispatching big data platform based on information management control, comprising the following steps:

[0013] S1. Research on regional reactive power and voltage autonomous system;

[0014] S11, the distribution network area level of the first-level target;

[0015] S12, grid-connected bus level of secondary target;

[0016] S13, regional control level for level 3 targets;

[0017] S2, modeling of reactive power and voltage margin of distributed photovoltaic power stations;

[0018] S3, analysis of regional aggregation resource regulation characteristics;

[0019] S4. Build a central controller.

[0020] Preferably, the distribution network area level of the primary target in S11 includes the following steps:

[0021] S111. Based on the principle of node voltage balance of the substation cluster connected to the 10kV distribution line, and taking the 10kV voltage of the root node as the reference voltage, all substation nodes under the 10kV line are sorted from near to far, and the 10kV voltage of each node is traversed to see if it exceeds the limit. If the node voltage exceeds the limit, it is determined according to the safe operating range of the 10kV voltage;

[0022] S112, transform the distributed photovoltaic control system to achieve local control within the unit, and use the distributed photovoltaic inverters within the unit to achieve reactive power autonomy of the distribution network line;

[0023] S113, its node voltage target, and select a controllable reactive unit to perform voltage regulation.

[0024] Preferably, the grid-connected bus level of the secondary target in S12 includes the following steps:

[0025] S121, taking the 10kV bus voltage of the 110kV substation as the control target, manually trigger the command when the distributed power supply support access is required;

[0026] S122, performing reactive coordination allocation for the controllable reactive resources within the virtual machine group of the node, using an equal power factor allocation strategy to allocate, and obtaining a reactive target value for each controllable reactive resource;

[0027] S123, when the reactive power distribution strategy of the source, grid, load and storage resources within the substation cluster adopts the equal power factor distribution strategy, the sum of the reactive power of capacitors, photovoltaics, energy storage and other resources is equal to the target total reactive power, and at the same time, the target power factor of each resource at the common connection point with the low-voltage distribution network is the same. According to the target total reactive power of the substation cluster after the voltage autonomous control optimization allocation, the target power factor of the corresponding adjustable resource is calculated;

[0028] S124, and perform safety verification and correction on the calculated reactive power targets of each controllable resource;

[0029] S125. Safety verification mainly considers factors such as the upper and lower limits of reactive power regulation of controllable resources, equipment output or input voltage constraints, etc. After target correction through safety verification, the safety target is obtained and then sent to each controllable resource to realize regional reactive power voltage autonomous control.

[0030] Preferably, the regional control level of the third-level target in S13 includes the following steps:

[0031] S131, taking the 110kV busbar of the 220kV substation as the control target, manually trigger the command when distributed photovoltaic support access is required;

[0032] S132. Realize the allocation of reactive power control word targets by analyzing the lower-end resources.

[0033] Preferably, the distributed photovoltaic power station reactive voltage margin modeling in S2 comprises the following steps:

[0034] S21. Considering the voltage stability margin and voltage limit of each node in the distributed station, a phased power voltage coordination control strategy for photovoltaic power stations is proposed, and the controllable reactive voltage regulation margin is calculated;

[0035] S22, constructing a steady-state operation model of a photovoltaic power station, wherein the model includes a box transformer, a collector line, a step-up transformer, and a transmission line impedance, so as to calculate the reactive power / voltage target value of the grid connection point and reasonably allocate the reactive power command value of each stage;

[0036] S23. By reasonably arranging the reactive output of reactive power sources at each stage, while ensuring the total reactive demand of the photovoltaic power station and maintaining the voltage at the grid connection point stable, the controllable reactive margin is calculated to participate in the regional reactive voltage regulation.

[0037] Preferably, the regional aggregation resource adjustment characteristic analysis in S3 includes the following steps:

[0038] S31. After collecting data on resources that can participate in reactive power regulation, such as substations, capacitors, distributed photovoltaic inverters, and integrated photovoltaic and storage devices in the region, the various types of source, grid, load and storage resources in the region are aggregated into a substation cluster based on each substation under the 10kV distribution line. Based on the operating status and regulation characteristics of various resources in the substation cluster, the regulation characteristics of the substation cluster are aggregated and analyzed to determine the maximum and minimum values ​​that can participate in reactive power regulation.

[0039] Preferably, the construction of the central controller in S4 includes the following steps:

[0040] S41. Build a first-phase central controller in the smart dispatching platform and integrate the second- and third-level target control strategies into it;

[0041] S42. To ensure the safety of regulation, manual triggering adjustment is adopted on the basis of conventional regulation;

[0042] S43. To ensure the level of network security and adapt to the problem of multiple and wide distributed photovoltaic points;

[0043] S44. Consider using wireless VPN in conjunction with a secure I+ zone to build a control path.

[0044] Preferably, the controllable reactive power margin in S23 participates in the regional reactive power voltage regulation, including the following steps:

[0045] S231, determine coefficient parameters;

[0046] S232, calculating reactive power demand;

[0047] S233, evaluating the output of existing reactive power sources;

[0048] S234, calculating the maximum and minimum reactive power output capabilities of the controllable reactive power source;

[0049] S235, calculating controllable reactive power margin;

[0050] S236. Participate in regional reactive power voltage regulation.

[0051] Preferably, the reactive voltage margin modeling of the distributed photovoltaic power station in S2 also includes: system analysis and parameter collection, establishment of photovoltaic power supply model, inverter modeling, grid model construction, reactive compensation device model, definition of reactive voltage margin index, operation scenario simulation, solution and analysis, and model verification and optimization.

[0052] Compared with related technologies, the power dispatching big data platform based on information management and control provided by the present invention has the following beneficial effects:

[0053] The present invention provides a power dispatching big data platform based on information management and control. Through the distribution network area level of the first-level target, the grid-connected bus level of the second-level target, the regional control level of the third-level target, the reactive voltage margin modeling of distributed photovoltaic power stations, the regional aggregation resource regulation characteristics analysis and the construction of a central centralized controller, the distributed photovoltaic grid-connected inverter control system can be transformed and the upper centralized control platform can be built to realize unit control and regional centralized control, and complete the three-level control targets in the region, namely the distribution network area level, the grid-connected bus level, and the regional control level. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A schematic diagram of the structure of a first embodiment of a power dispatching big data platform based on information management and control provided by the present invention;

[0055] Figure 2 It is a flowchart of the local method;

[0056] Figure 3 It is the operation diagram of the whole device;

[0057] Figure 4 A schematic diagram of the structure of a second embodiment of a power dispatching big data platform based on information management and control provided by the present invention;

[0058] Figure 5 for Figure 4 The schematic diagram of the steps shown;

[0059] Figure 6 A schematic structural diagram of a third embodiment of an electric power dispatching big data platform based on information management and control provided by the present invention. DETAILED DESCRIPTION

[0060] The present invention will be further described below in conjunction with the accompanying drawings and implementation modes.

[0061] First embodiment

[0062] Please refer to Figure 1 , Figure 2 and Figure 3 ,in, Figure 1 A schematic diagram of the structure of a first embodiment of a power dispatching big data platform based on information management and control provided by the present invention; Figure 2 It is a flow chart of the local method; Figure 3 The schematic diagram of the operation of the whole device is shown in FIG. A power dispatching big data platform based on information management control includes the following steps:

[0063] S1. Research on regional reactive power and voltage autonomous system;

[0064] S11, the distribution network area level of the first-level target;

[0065] S12, grid-connected bus level of secondary target;

[0066] S13, regional control level for level 3 targets;

[0067] S2, modeling of reactive power and voltage margin of distributed photovoltaic power stations;

[0068] S3, analysis of regional aggregation resource regulation characteristics;

[0069] S4. Build a central controller.

[0070] The distribution network area level of the primary target in S11 includes the following steps:

[0071] S111. Based on the principle of node voltage balance of the substation cluster connected to the 10kV distribution line, and taking the 10kV voltage of the root node as the reference voltage, all substation nodes under the 10kV line are sorted from near to far, and the 10kV voltage of each node is traversed to see if it exceeds the limit. If the node voltage exceeds the limit, it is determined according to the safe operating range of the 10kV voltage;

[0072] S112, transform the distributed photovoltaic control system to achieve local control within the unit, and use the distributed photovoltaic inverters within the unit to achieve reactive power autonomy of the distribution network line;

[0073] S113, its node voltage target, and select a controllable reactive unit to perform voltage regulation.

[0074] The grid-connected bus level of the secondary target in S12 comprises the following steps:

[0075] S121, taking the 10kV bus voltage of the 110kV substation as the control target, manually trigger the command when the distributed power supply support access is required;

[0076] S122, performing reactive coordination allocation for the controllable reactive resources within the virtual machine group of the node, using an equal power factor allocation strategy to allocate, and obtaining a reactive target value for each controllable reactive resource;

[0077] S123, when the reactive power distribution strategy of the source, grid, load and storage resources within the substation cluster adopts the equal power factor distribution strategy, the sum of the reactive power of capacitors, photovoltaics, energy storage and other resources is equal to the target total reactive power, and at the same time, the target power factor of each resource at the common connection point with the low-voltage distribution network is the same. According to the target total reactive power of the substation cluster after the voltage autonomous control optimization allocation, the target power factor of the corresponding adjustable resource is calculated;

[0078] S124, and perform safety verification and correction on the calculated reactive power targets of each controllable resource;

[0079] S125. Safety verification mainly considers factors such as the upper and lower limits of reactive power regulation of controllable resources, equipment output or input voltage constraints, etc. After target correction through safety verification, the safety target is obtained and then sent to each controllable resource to realize regional reactive power voltage autonomous control.

[0080] The regional control level of the three-level target in S13 includes the following steps:

[0081] S131, taking the 110kV busbar of the 220kV substation as the control target, manually trigger the command when distributed photovoltaic support access is required;

[0082] S132. Realize the allocation of reactive power control word targets by analyzing the lower-end resources.

[0083] The distributed photovoltaic power station reactive power voltage margin modeling in S2 includes the following steps:

[0084] S21. Considering the voltage stability margin and voltage limit of each node in the distributed station, a phased power voltage coordination control strategy for photovoltaic power stations is proposed, and the controllable reactive voltage regulation margin is calculated;

[0085] S22, constructing a steady-state operation model of a photovoltaic power station, wherein the model includes a box transformer, a collector line, a step-up transformer, and a transmission line impedance, so as to calculate the reactive power / voltage target value of the grid connection point and reasonably allocate the reactive power command value of each stage;

[0086] S23. By reasonably arranging the reactive output of reactive power sources at each stage, while ensuring the total reactive demand of the photovoltaic power station and maintaining the voltage at the grid connection point stable, the controllable reactive margin is calculated to participate in the regional reactive voltage regulation.

[0087] The regional aggregation resource regulation characteristic analysis in S3 includes the following steps:

[0088] S31. After collecting data on resources that can participate in reactive power regulation, such as substations, capacitors, distributed photovoltaic inverters, and integrated photovoltaic and storage devices in the region, the various types of source, grid, load and storage resources in the region are aggregated into a substation cluster based on each substation under the 10kV distribution line. Based on the operating status and regulation characteristics of various resources in the substation cluster, the regulation characteristics of the substation cluster are aggregated and analyzed to determine the maximum and minimum values ​​that can participate in reactive power regulation.

[0089] The construction of the central controller in S4 includes the following steps:

[0090] S41. Build a first-phase central controller in the smart dispatching platform and integrate the second- and third-level target control strategies into it;

[0091] S42. To ensure the safety of regulation, manual triggering adjustment is adopted on the basis of conventional regulation;

[0092] S43. To ensure the level of network security and adapt to the problem of multiple and wide distributed photovoltaic points;

[0093] S44. Consider using wireless VPN in conjunction with a secure I+ zone to build a control path.

[0094] Compared with related technologies, the power dispatching big data platform based on information management and control provided by the present invention has the following beneficial effects:

[0095] The present invention provides a power dispatching big data platform based on information management and control. Through the distribution network area level of the first-level target, the grid-connected bus level of the second-level target, the regional control level of the third-level target, the reactive voltage margin modeling of distributed photovoltaic power stations, the regional aggregation resource regulation characteristics analysis and the construction of a central centralized controller, the distributed photovoltaic grid-connected inverter control system can be transformed and the upper centralized control platform can be built to realize unit control and regional centralized control, and complete the three-level control targets in the region, namely the distribution network area level, the grid-connected bus level, and the regional control level.

[0096] Second embodiment

[0097] Please refer to Figure 4 and Figure 5 Based on the first embodiment of the present application, which provides a power dispatching big data platform based on information management and control, the second embodiment of the present application proposes another power dispatching big data platform based on information management and control. The second embodiment is only a preferred method of the first embodiment, and the implementation of the second embodiment will not affect the independent implementation of the first embodiment.

[0098] Specifically, the difference of the power dispatching big data platform based on information management control provided by the second embodiment of the present application is that, in a power dispatching big data platform based on information management control, the controllable reactive margin in S23 participates in the regional reactive voltage regulation, including the following steps:

[0099] S231, determine coefficient parameters;

[0100] S232, calculating reactive power demand;

[0101] S233, evaluating the output of existing reactive power sources;

[0102] S234, calculating the maximum and minimum reactive power output capabilities of the controllable reactive power source;

[0103] S235, calculating controllable reactive power margin;

[0104] S236. Participate in regional reactive power voltage regulation.

[0105] Determine system parameters

[0106] Collect parameters of power equipment in the area, such as the rated capacity and current operating status of generators, transformers, reactive compensation devices (such as capacitors and reactors), etc.

[0107] Calculate reactive power demand

[0108] According to the load conditions and grid structure in the area, the reactive power required by the system under the current operating conditions is determined through power flow calculation or other power system analysis methods.

[0109] Evaluate the output of existing reactive sources

[0110] Measure or calculate the reactive power actually provided by existing reactive sources (such as the reactive output of generators and the reactive output of reactive compensation devices that have been put into use).

[0111] Calculate the maximum and minimum reactive power output capabilities of controllable reactive power sources

[0112] For adjustable reactive power sources, such as switchable capacitor banks and reactor banks, determine their maximum and minimum reactive power output limits.

[0113] Calculation of controllable reactive power margin

[0114] Controllable reactive power margin = maximum reactive power output capacity of controllable reactive power source - current reactive power output of controllable reactive power source + minimum reactive power output absolute value of controllable reactive power source

[0115] For example, if the maximum reactive power output of an adjustable capacitor bank is 100 kvar, the current output is 50 kvar, and the minimum reactive power output is 0 kvar, then the controllable reactive power margin is 100-50+0=50 kvar.

[0116] Participate in regional reactive power and voltage regulation

[0117] According to the calculated controllable reactive power margin and the reactive power voltage demand of the system, a reactive power regulation strategy is formulated. For example, if the system voltage is low and the controllable reactive power margin is sufficient, reactive power compensation can be increased to increase the voltage; conversely, if the voltage is high, reactive power compensation can be reduced.

[0118] Compared with related technologies, the power dispatching big data platform based on information management and control provided by the present invention has the following beneficial effects:

[0119] The present invention provides a power dispatching big data platform based on information management and control. In the process of controllable reactive power margin participating in regional reactive voltage regulation, by respectively determining coefficient parameters, calculating reactive power demand, evaluating the output of existing reactive sources, calculating the maximum and minimum reactive output capacity of controllable reactive sources, calculating controllable reactive power margin and participating in regional reactive voltage regulation, the voltage quality can be improved, the system operation efficiency can be optimized, the system stability can be enhanced, the equipment life can be extended, the adaptability of the power grid can be improved, the economic operation can be achieved and the access of renewable energy can be promoted.

[0120] Third embodiment

[0121] Please refer to Figure 6 Based on the first embodiment of the present application, which provides a power dispatching big data platform based on information management control, the third embodiment of the present application proposes another power dispatching big data platform based on information management control. The third embodiment is only a preferred method of the first embodiment, and the implementation of the third embodiment will not affect the independent implementation of the first embodiment.

[0122] Specifically, the difference of the power dispatching big data platform based on information management and control provided by the third embodiment of the present application is that, in a power dispatching big data platform based on information management and control, the reactive voltage margin modeling of distributed photovoltaic power stations in S2 also includes: system analysis and parameter collection, establishment of photovoltaic power supply model, inverter modeling, power grid model construction, reactive compensation device model, definition of reactive voltage margin index, operation scenario simulation, solution and analysis, and model verification and optimization.

[0123] System analysis and parameter collection

[0124] Conduct a detailed analysis of the electrical structure of distributed photovoltaic power stations, including the parameters of photovoltaic arrays, inverters, transformers, lines and other equipment.

[0125] Collect relevant operating data, such as photovoltaic output power and grid connection point voltage under different light intensities and temperatures.

[0126] Building a photovoltaic power source model

[0127] Based on the physical properties and mathematical equations of photovoltaic cells, an accurate photovoltaic power output model is established, taking into account the effects of light and temperature on its output characteristics.

[0128] Inverter Modeling

[0129] The inverter is the key device for controlling reactive power output. Its control model is established, including active and reactive power decoupling control strategy.

[0130] Power grid model construction

[0131] Considering the distribution network structure to which distributed photovoltaic power stations are connected, mathematical models of components such as lines and transformers are established.

[0132] Modeling of reactive power compensation devices

[0133] If the power station is equipped with reactive power compensation devices (such as capacitor banks, reactors, etc.), establish the corresponding model.

[0134] Defining reactive voltage margin indicators

[0135] Determine specific indicators for measuring reactive voltage margin, such as voltage stability margin, reactive reserve capacity, etc.

[0136] Run scenario simulation

[0137] Set different operating scenarios, such as different load levels, changes in photovoltaic output, etc.

[0138] Solution and analysis

[0139] Use power system analysis software or algorithms to solve the system status in various scenarios and analyze the distribution of reactive voltage.

[0140] Model validation and optimization

[0141] Compare the model calculation results with the actual measurement data to verify the accuracy of the model and optimize and improve the model.

[0142] Compared with related technologies, the power dispatching big data platform based on information management and control provided by the present invention has the following beneficial effects:

[0143] The present invention provides a power dispatching big data platform based on information management and control. In the reactive voltage margin modeling of distributed photovoltaic power stations, through system analysis and parameter collection, establishment of photovoltaic power supply model, inverter modeling, power grid model construction, reactive compensation device model, definition of reactive voltage margin index, operation scenario simulation, solution and analysis, and model verification and optimization, the system stability prediction capability can be improved, power station configuration and operation can be optimized, power grid adaptability can be enhanced, reactive compensation can be effectively planned, costs and risks can be reduced, and new energy consumption can be promoted.

[0144] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A power dispatching big data platform based on information management and control, characterized in that: The following steps are involved: S1. Research on regional reactive power and voltage autonomous system; S11, the distribution network area level of the first-level target; S12, grid-connected bus level of secondary target; S13, regional control level for level 3 targets; S2, modeling of reactive power and voltage margin of distributed photovoltaic power stations; S3, analysis of regional aggregation resource regulation characteristics; S4. Build a central controller.

2. The power dispatching big data platform based on information management and control according to claim 1 is characterized in that: The distribution network area level of the primary target in S11 includes the following steps: S111. Based on the principle of node voltage balance of the substation cluster connected to the 10kV distribution line, and taking the 10kV voltage of the root node as the reference voltage, all substation nodes under the 10kV line are sorted from near to far, and the 10kV voltage of each node is traversed to see if it exceeds the limit. If the node voltage exceeds the limit, it is determined according to the safe operating range of the 10kV voltage; S112, transform the distributed photovoltaic control system to achieve local control within the unit, and use the distributed photovoltaic inverters within the unit to achieve reactive power autonomy of the distribution network line; S113, its node voltage target, and select a controllable reactive unit to perform voltage regulation.

3. The electric power dispatching big data platform based on information management and control according to claim 1 is characterized in that: The grid-connected bus level of the secondary target in S12 comprises the following steps: S121, taking the 10kV bus voltage of the 110kV substation as the control target, manually trigger the command when the distributed power supply support access is required; S122, performing reactive coordination allocation for the controllable reactive resources within the virtual machine group of the node, using an equal power factor allocation strategy to allocate, and obtaining a reactive target value for each controllable reactive resource; S123, when the reactive power distribution strategy of the source, grid, load and storage resources within the substation cluster adopts the equal power factor distribution strategy, the sum of the reactive power of capacitors, photovoltaics, energy storage and other resources is equal to the target total reactive power, and at the same time, the target power factor of each resource at the common connection point with the low-voltage distribution network is the same. According to the target total reactive power of the substation cluster after the voltage autonomous control optimization allocation, the target power factor of the corresponding adjustable resource is calculated; S124, and perform safety verification and correction on the calculated reactive power targets of each controllable resource; S125. Safety verification mainly considers factors such as the upper and lower limits of reactive power regulation of controllable resources, equipment output or input voltage constraints, etc. After target correction through safety verification, the safety target is obtained and then sent to each controllable resource to realize regional reactive power voltage autonomous control.

4. The electric power dispatching big data platform based on information management and control according to claim 1 is characterized in that: The regional control level of the three-level target in S13 includes the following steps: S131, taking the 110kV busbar of the 220kV substation as the control target, manually trigger the command when distributed photovoltaic support access is required; S132. Realize the allocation of reactive power control word targets by analyzing the lower-end resources.

5. The electric power dispatching big data platform based on information management and control according to claim 1 is characterized in that: The distributed photovoltaic power station reactive power voltage margin modeling in S2 includes the following steps: S21. Considering the voltage stability margin and voltage limit of each node in the distributed station, a phased power voltage coordination control strategy for photovoltaic power stations is proposed, and the controllable reactive voltage regulation margin is calculated; S22, constructing a steady-state operation model of a photovoltaic power station, wherein the model includes a box transformer, a collector line, a step-up transformer, and a transmission line impedance, so as to calculate the reactive power / voltage target value of the grid connection point and reasonably allocate the reactive power command value of each stage; S23. By reasonably arranging the reactive output of reactive power sources at each stage, while ensuring the total reactive demand of the photovoltaic power station and maintaining the voltage at the grid connection point stable, the controllable reactive margin is calculated to participate in the regional reactive voltage regulation.

6. The electric power dispatching big data platform based on information management and control according to claim 1 is characterized in that: The regional aggregation resource regulation characteristic analysis in S3 includes the following steps: S31. After collecting data on resources that can participate in reactive power regulation, such as substations, capacitors, distributed photovoltaic inverters, and integrated photovoltaic and storage devices in the region, the various types of source, grid, load and storage resources in the region are aggregated into a substation cluster based on each substation under the 10kV distribution line. Based on the operating status and regulation characteristics of various resources in the substation cluster, the regulation characteristics of the substation cluster are aggregated and analyzed to determine the maximum and minimum values ​​that can participate in reactive power regulation.

7. The electric power dispatching big data platform based on information management and control according to claim 1 is characterized in that: The construction of the central controller in S4 includes the following steps: S41. Build a first-phase central controller in the smart dispatching platform and integrate the second- and third-level target control strategies into it; S42. To ensure the safety of regulation, manual triggering adjustment is adopted on the basis of conventional regulation; S43. To ensure the level of network security and adapt to the problem of multiple and wide distributed photovoltaic points; S44. Consider using wireless VPN in conjunction with a secure I+ zone to build a control path.

8. The electric power dispatching big data platform based on information management and control according to claim 5 is characterized in that: The controllable reactive power margin in S23 participates in the regional reactive power voltage regulation, including the following steps: S231, determine coefficient parameters; S232, calculating reactive power demand; S233, evaluating the output of existing reactive power sources; S234, calculating the maximum and minimum reactive power output capabilities of the controllable reactive power source; S235, calculating controllable reactive power margin; S236. Participate in regional reactive power voltage regulation.

9. The electric power dispatching big data platform based on information management and control according to claim 1 is characterized in that: The reactive voltage margin modeling of the distributed photovoltaic power station in S2 also includes: system analysis and parameter collection, establishment of photovoltaic power model, inverter modeling, grid model construction, reactive compensation device model, definition of reactive voltage margin index, operation scenario simulation, solution and analysis, and model verification and optimization.

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