Distributed intelligent measurement and control scheduling management system based on IPT communication mechanism

Abstract

The invention relates to the technical field of distributed intelligent measurement and control scheduling management, and particularly discloses a distributed intelligent measurement and control scheduling management system based on an IPT communication mechanism, and the system comprises a power station cluster confirmation module, a power station cluster information extraction module, an IPT communication mechanism construction module, a power grid information extraction module, an electric energy scheduling analysis module, and a database. According to the invention, each power station cluster in a region is confirmed from three aspects of a power grid topological structure, power station distance and climate condition similarity, and ordered monitoring of the generating capacity of each power station cluster is realized through an IPT communication mechanism. Scheduling adaptation analysis is carried out from two aspects of whether the generating capacity of the power station cluster is sufficient and energy loss of electric energy transmission between the power station cluster and the power grid, orderliness and intelligence of distributed energy monitoring are improved, reasonability of power station cluster clustering is improved, flexibility limitation of subsequent electric energy scheduling is avoided, and the reliability of distributed energy monitoring is improved. Therefore, the stability of power grid operation is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed intelligent measurement, control and dispatching management, and in particular to a distributed intelligent measurement, control and dispatching management system based on an IPT communication mechanism. Background Art

[0002] With the increasing proportion of renewable energy in electricity supply, such as the emergence of a large number of photovoltaic power station clusters, the demand for refined and intelligent management of power systems has become more urgent. Through distributed photovoltaic clustering to voltage coordinated regulation, cluster autonomy and global power regulation and optimization between clusters are realized, and various flexible resources are reasonably optimized to coordinate power output and achieve complementary advantages. Through the integrated control of main and distribution, the standardized management and unified dispatch of centralized and distributed resources are efficiently realized, which improves the adjustable and controllable capabilities of the power system.

[0003] The prior art still has the following problems:

[0004] 1. For distributed photovoltaic power stations, in the photovoltaic power station monitoring system, the information end of each photovoltaic power station needs to be separately connected to multiple devices or nodes, resulting in complex wiring. At the same time, due to the different installation positions of sensors in the photovoltaic power station, if each intelligent monitoring node is connected to the measurement and control network in the traditional way, it is difficult to ensure the order and regularity of access. There are problems such as low quality of distributed photovoltaic data transmission and poor sharing within the data system, relatively large amount of wiring operations and high costs, and uncontrollable problems.

[0005] 2. With regard to photovoltaic power station clusters, a comprehensive analysis of photovoltaic power station clusters was not conducted from three levels: grid topology, distance between power stations, and similar climate conditions. This reduced the rationality of power station cluster clustering, limited the flexibility of subsequent power dispatch, and increased the operation and maintenance costs of subsequent power dispatch.

[0006] 3. In terms of power generation forecasting of power plant clusters, the impact of climate condition deviations on power generation forecasting was not analyzed, which reduced the accuracy of power generation forecasting of power plant clusters and failed to provide effective data support for subsequent power grid power dispatching.

[0007] 4. In terms of grid electricity consumption scheduling, no scheduling adaptation analysis was conducted from two aspects: whether the power generation of the power station cluster is sufficient and the energy loss of power transmission between the power station cluster and the power grid. The adequacy of the power supply required by the power grid cannot be guaranteed, which reduces the stability of the grid operation and causes a large amount of electricity to be lost in the transmission process, reducing energy utilization efficiency and increasing energy waste. Summary of the invention

[0008] In view of this, in order to solve the problems raised in the above background technology, a distributed intelligent measurement, control and scheduling management system based on the IPT communication mechanism is proposed.

[0009] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides a distributed intelligent measurement, control and dispatching management system based on the IPT communication mechanism, including: a power station cluster confirmation module, which is used to extract the substations connected to each photovoltaic power station in the target area, obtain each preliminary power station cluster in the target area, extract the location and climate condition information of each photovoltaic power station in each preliminary power station cluster, and confirm each power station cluster in the target area.

[0010] The power station cluster information extraction module is used to extract the light intensity and duration of each photovoltaic power station in each power station cluster in the target area on the current monitoring day, and to extract the installation position of each photovoltaic panel in each photovoltaic power station.

[0011] The IPT communication mechanism building module is used to connect the information terminals of each photovoltaic power station in each power station cluster in the target area through a cable to form an intelligent sensing link corresponding to each power station cluster. In the distributed communication network based on the intelligent sensing link corresponding to each power station cluster, each intelligent monitoring node of each photovoltaic power station in each power station cluster is randomly connected to the measurement and control network of the corresponding power station cluster according to the installation position of the sensor, thereby realizing orderly monitoring of the power generation of each power station cluster in the target area during the current monitoring day.

[0012] The power grid information extraction module is used to extract the total load and total output power of each power grid in the target area on the current monitoring day, confirm the power grids that require power in the target area on the current monitoring day, extract the location of the substation of each power grid that requires power, and extract the length of the transmission line between each power grid and each power station cluster.

[0013] The electric energy dispatch analysis module is used to dispatch electric energy to each electric energy dispatching power grid in the target area during the current monitoring day.

[0014] The database is used to store the benchmark light intensity, benchmark light duration and single-day benchmark power generation of each photovoltaic panel at each installation angle in each photovoltaic power station in each power station cluster, and store the power loss corresponding to the unit transmission line length of each power grid in the target area during power transmission.

[0015] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention improves the rationality of power station clustering by confirming the power station clusters in the target area from three levels: grid topology, distance between power stations and similar climate conditions, avoids limited flexibility in subsequent power scheduling, and reduces the operation and maintenance costs of subsequent power scheduling.

[0016] (2) The present invention improves the accuracy of power generation prediction of power station clusters by analyzing the impact of climate condition deviation on power generation prediction, thereby providing effective data support for subsequent power grid power dispatching.

[0017] (3) The present invention conducts scheduling adaptation analysis from two aspects: whether the power generation of the power station cluster is sufficient and the energy loss of power transmission between the power station cluster and the power grid, so as to confirm the adaptive power station cluster corresponding to the required power grid, ensure the adequacy of the power supply required by the power grid, thereby improving the stability of the power grid operation, avoiding the waste of a large amount of power during transmission, improving energy utilization efficiency, and reducing energy waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0019] Figure 1 It is a schematic diagram of the system module structure connection of the present invention.

[0020] Figure 2 This is a flow chart for confirming a cluster of power stations to be adapted in the electric energy dispatching grid of the present invention.

[0021] Figure 3 It is a schematic diagram of a power station cluster in the target area of ​​the present invention.

[0022] Description of the accompanying figures: 1. Target area, 2. Preliminary power station cluster, 3. Power station cluster, 4. Photovoltaic power station. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] See also Figure 1 As shown, the present invention provides a distributed intelligent measurement, control and dispatching management system based on the IPT communication mechanism, including: a power plant cluster confirmation module, a power plant cluster information extraction module, an IPT communication mechanism construction module, a power grid information extraction module, an electric energy dispatching analysis module and a database.

[0025] The power station cluster confirmation module is connected to the power station cluster information extraction module, the power station cluster information extraction module is connected to the IPT communication mechanism construction module, the IPT communication mechanism construction module and the power grid information extraction module are both connected to the electric energy dispatching analysis module, and the IPT communication mechanism construction module and the electric energy dispatching analysis module are both connected to the database.

[0026] See also Figure 3 As shown, the power station cluster confirmation module is used to extract the substations connected to each photovoltaic power station in the target area, obtain each preliminary power station cluster in the target area, extract the location and climate condition information of each photovoltaic power station in each preliminary power station cluster, and confirm each power station cluster in the target area.

[0027] It should be noted that the substations connected to each photovoltaic power station in the target area are extracted from the photovoltaic power station construction manual of the target area, and the locations of each photovoltaic power station in each preliminary power station cluster are extracted from the GPS system.

[0028] In a specific embodiment of the present invention, the method for obtaining the preliminary power station clusters in the target area is: based on the substations to which each photovoltaic power station in the target area is connected, the photovoltaic power stations connected to the same substation are recorded as power stations with the same grid topology structure, and the power stations with the same grid topology structure are clustered into preliminary power station clusters, thereby obtaining the preliminary power station clusters in the target area.

[0029] It should be noted that the grid topology is the most critical factor affecting the access and operation of distributed photovoltaic power stations. From the perspective of safe and stable operation of the grid, clustering according to the grid topology is first considered. This is because the operation mode, power flow distribution, and voltage regulation of the grid are closely related to the physical connection of the grid. For example, the location of photovoltaic power stations connected to the same substation in the grid determines their common influence on grid voltage, frequency and other parameters. If the grid topology is not considered first, clustering power stations in different locations at random may cause chaos in the local power flow distribution of the grid, affecting the safe and stable operation of the grid.

[0030] In a specific embodiment of the present invention, the climate condition information includes the daily average light intensity, the time points of the light intensity peak and light intensity valley of each historical monitoring day, and the daily average temperature, the time points of the highest temperature and the lowest temperature of each historical monitoring day.

[0031] It should be noted that the daily average light intensity, the time points of the light intensity peak and light intensity valley of each historical monitoring day, and the daily average temperature, the time points of the highest temperature and the lowest temperature of each historical monitoring day are all extracted from the meteorological information platform.

[0032] In a specific embodiment of the present invention, the specific method of confirming each power station cluster in the target area is: A1. Randomly select a photovoltaic power station from each preliminary power station cluster as the target power station of each preliminary power station cluster.

[0033] A2. Analyze the comprehensive similarity θ between the target power station of each preliminary power station cluster and the remaining photovoltaic power stations ij , where i represents the number of the preliminary power station cluster, i=1,2,...,n, and j represents the number of the remaining photovoltaic power stations, j=1,2,...,m.

[0034] In a specific embodiment of the present invention, the specific process of analyzing the comprehensive similarity between the target power station of each preliminary power station cluster and the remaining photovoltaic power stations is as follows: B1, based on the location of each photovoltaic power station in each preliminary power station cluster, calculate the distance similarity β between the target power station of each preliminary power station cluster and the remaining photovoltaic power stations ij .

[0035] It should be noted that the specific process of calculating the distance proximity between the target power station of each preliminary power station cluster and the remaining photovoltaic power stations is as follows: obtaining the distance between the target power station of each preliminary power station cluster and the remaining photovoltaic power stations, and recording it as L ij .

[0036] It should be noted that the method for obtaining the distance between the target power station of each preliminary power station cluster and the remaining photovoltaic power stations is: marking the locations of the target power station and the remaining photovoltaic power stations in GIS, and then using the GIS measurement tool to directly calculate the distance between the target power station and the remaining photovoltaic power stations.

[0037] Obtain the distances between adjacent PV power stations in each preliminary power station cluster, calculate the average value, and use the calculated result as the reference value for setting the power station distance of each preliminary power station cluster, and record it as

[0038] It should be noted that the method for obtaining the distance between adjacent photovoltaic power stations in each preliminary power station cluster is: marking the location of each photovoltaic power station in each preliminary power station cluster in GIS, and then using the GIS measurement tool to directly calculate the distance between adjacent photovoltaic power stations in each preliminary power station cluster.

[0039] Calculate the distance proximity β between the target power station of each preliminary power station cluster and the remaining photovoltaic power stations ij ,

[0040] B2. Extract the daily average light intensity, the time points of the peak and valley values ​​of light intensity on each historical monitoring day, and the daily average temperature, the time points of the highest and lowest temperatures on each historical monitoring day from the climate condition information of each photovoltaic power station in each preliminary power station cluster, and calculate the climate similarity χ between the target power station in each preliminary power station cluster and the remaining photovoltaic power stations. ij .

[0041] In a specific embodiment of the present invention, the specific process of calculating the climate similarity between the target power station of each preliminary power station cluster and the remaining photovoltaic power stations is as follows: C1. Calculating the climate similarity between the target power station of each preliminary power station cluster and the remaining photovoltaic power stations at the light intensity level

[0042] It should be noted that the specific process of calculating the climate similarity between the target power station of each preliminary power station cluster and the remaining photovoltaic power stations at the level of light intensity is: subtract the daily average light intensity of the target power station of each preliminary power station cluster from that of the remaining photovoltaic power stations, obtain the daily average light intensity deviation between the target power station of each preliminary power station cluster and the remaining photovoltaic power stations, and record it as τ ij .

[0043] The time points of the peak light intensity of the target power station of each preliminary power station cluster and the other photovoltaic power stations on each historical monitoring day are compared to obtain the deviation duration of the peak light intensity of the target power station and the other photovoltaic power stations on each historical monitoring day, and the maximum value is extracted as the deviation duration of the peak light intensity of the target power station of each preliminary power station cluster and the other photovoltaic power stations, recorded as

[0044] Obtain the deviation duration of the light intensity valley value between the target power station of each preliminary power station cluster and the other photovoltaic power stations

[0045] It should be noted that the method for obtaining the deviation duration of the light intensity valley value between the target power station of each preliminary power station cluster and the remaining photovoltaic power stations is: compare the time points of the occurrence of the light intensity valley value on each historical monitoring day of the target power station of each preliminary power station cluster and the remaining photovoltaic power stations, and obtain the deviation duration of the light intensity valley value on each historical monitoring day between the target power station and the remaining photovoltaic power stations, and extract the maximum value therefrom as the deviation duration of the light intensity valley value on each historical monitoring day of the target power station of each preliminary power station cluster and the remaining photovoltaic power stations.

[0046] Calculate the climate similarity between the target power station of each preliminary power station cluster and the remaining photovoltaic power stations in terms of light intensity Among them, τ′, T 峰 and T 谷They respectively represent the deviation of the set reference daily average light intensity, the deviation duration of the light intensity peak, and the deviation duration of the light intensity valley.

[0047] It should be noted that the deviation of the daily average light intensity, the deviation duration of the light intensity peak and the deviation duration of the light intensity valley for setting the reference are stipulated by the power station cluster industry.

[0048] C2. Calculate the climate similarity between the target power station of each preliminary power station cluster and the other photovoltaic power stations in terms of light intensity. Similarly, calculate the climate similarity between the target power station of each preliminary power station cluster and the other photovoltaic power stations in terms of temperature.

[0049] C3. Calculate the climate similarity χ between the target power station of each preliminary power station cluster and the remaining photovoltaic power stations ij , Among them, b1 and b2 represent the weights of climate similarity assessment corresponding to the climate similarity at the set light intensity level and temperature level, respectively, and b1+b2=1.

[0050] In a specific embodiment of the present invention, the setting value of b1 is 0.6, and the setting value of b2 is 0.4. Light intensity is the core driving factor of photovoltaic power generation. The similarity of light intensity has a key impact on the power generation income and resource utilization efficiency of the power station. Therefore, when calculating the climate similarity between the target power station of each preliminary power station cluster and the remaining photovoltaic power stations, the climate similarity weight at the light intensity level accounts for a larger proportion.

[0051] B3. Calculate the comprehensive similarity θ between the target power station of each preliminary power station cluster and the remaining photovoltaic power stations ij , Among them, a1 and a2 represent the weights of the set distance similarity and climate similarity corresponding to the comprehensive similarity evaluation, a1+a2=1.

[0052] In a specific embodiment of the present invention, the setting value of a1 is 0.5, and the setting value of a2 is 0.5. When calculating the comprehensive similarity between the target power station of each preliminary power station cluster and the remaining photovoltaic power stations, the distance proximity and the climate similarity are equally important. The two complement each other and influence each other, so a comprehensive analysis is needed.

[0053] A3. Compare the comprehensive similarity between the target power station of each preliminary power station cluster and the remaining photovoltaic power stations with the comprehensive similarity of the set reference. If the comprehensive similarity between the target power station and any other photovoltaic power station is greater than or equal to the comprehensive similarity of the set reference, then the photovoltaic power station is used as the clustered power station of the target power station, and so on to obtain the clustered power stations of the target power station, and cluster them to obtain the target power station cluster, thereby obtaining the target power station cluster of each preliminary power station cluster.

[0054] A4. Eliminate each photovoltaic power station in the target power station cluster of each preliminary power station cluster, and then randomly select a photovoltaic power station as the target power station. In accordance with the above analysis method, obtain each power station cluster in each preliminary power station cluster, thereby obtaining each power station cluster in the target area.

[0055] It should be noted that, after each power station cluster in each preliminary power station cluster is obtained, each power station cluster in the target area is obtained by taking the power station cluster as a unit.

[0056] The embodiment of the present invention improves the rationality of power station clustering by confirming the power station clusters in the target area from three levels: power grid topology, distance between power stations and similar climate conditions, avoids limited flexibility in subsequent power scheduling, and reduces the operation and maintenance costs of subsequent power scheduling.

[0057] The power station cluster information extraction module is used to extract the light intensity and light duration corresponding to each photovoltaic power station in each power station cluster in the target area on the current monitoring day, and to extract the installation position of each photovoltaic power generation panel in each photovoltaic power station.

[0058] It should be noted that the light intensity and duration corresponding to each photovoltaic power station in each power station cluster in the target area on the current monitoring day are collected from the meteorological information platform, and the installation position of each photovoltaic panel in each photovoltaic power station is extracted from the photovoltaic management system of the corresponding photovoltaic power station.

[0059] The IPT communication mechanism building module is used to connect the information terminals of each photovoltaic power station in each power station cluster in the target area through a cable to form an intelligent sensing link corresponding to each power station cluster. In the distributed communication network based on the intelligent sensing link corresponding to each power station cluster, each intelligent monitoring node of each photovoltaic power station in each power station cluster is randomly connected to the measurement and control network of the corresponding power station cluster according to the installation position of the sensor, thereby realizing orderly monitoring of the power generation of each power station cluster in the target area during the current monitoring day.

[0060] In a specific embodiment of the present invention, the specific process of realizing orderly monitoring of the power generation of each power station cluster in the target area on the current monitoring day is as follows: D1, based on the light intensity and light duration corresponding to each photovoltaic power station in each power station cluster in the target area on the current monitoring day and the installation position of each photovoltaic power generation panel in each photovoltaic power station, set the power generation influence factor λ corresponding to the meteorological level of each photovoltaic power generation panel in each photovoltaic power station cluster gpr , where g represents the number of the power station cluster, g = 1, 2, ..., z, p represents the number of the photovoltaic power station, p = 1, 2, ..., q, and r represents the number of the photovoltaic panel, r = 1, 2, ..., x.

[0061] In a specific embodiment of the present invention, the specific process of setting the power generation influencing factor corresponding to the meteorological level of each photovoltaic panel in each photovoltaic power station in each power station cluster is as follows: E1. According to the installation position of each photovoltaic panel in each photovoltaic power station, the reference light intensity and reference light duration of the installation angle of each photovoltaic panel in each photovoltaic power station in each power station cluster are located from the database, and recorded as and

[0062] E2, the light intensity and light duration of each photovoltaic power station in each power station cluster in the target area on the current monitoring day are recorded as ε gp and T gp .

[0063] E3. Set the power generation impact factor λ corresponding to the meteorological level for each photovoltaic panel in each photovoltaic power station in each power station cluster gpr , Here, e represents a natural constant.

[0064] D2. Extract the daily benchmark power generation of each photovoltaic panel in each photovoltaic power station in each power station cluster from the database, and record it as

[0065] D3. Predict the power generation Q of each power station cluster in the target area during the current monitoring day g , Among them, x represents the number of photovoltaic panels, and q represents the number of photovoltaic power stations.

[0066] The embodiment of the present invention improves the accuracy of power generation prediction of power station clusters by analyzing the impact of climate condition deviation on power generation prediction in power station clusters, and provides effective data support for subsequent power grid power dispatching.

[0067] The power grid information extraction module is used to extract the total load and total output power of each power grid in the target area on the current monitoring day, confirm the power grids that require power in the target area on the current monitoring day, extract the location of the substation of each power grid that requires power, and extract the length of the transmission line between each power grid that requires power and each power station cluster.

[0068] It should be noted that the total load corresponding to each power grid in the target area on the current monitoring day is collected by using smart meters installed at each node in each power grid to collect the load of each node in each power grid, and then add them up to obtain the total load corresponding to each power grid on the current monitoring day. The total output power corresponding to each power grid in the target area on the current monitoring day refers to the cumulative output power of each power plant in each power grid.

[0069] It should be noted that the method for confirming the power grids that require electric energy to be dispatched in the target area on the current monitoring day is: compare the total load and total output power of each power grid in the target area on the current monitoring day. If the total load corresponding to a power grid on the current monitoring day is greater than the total output power, then the power grid is recorded as the power grid that requires electric energy to be dispatched, thereby obtaining the power grids that require electric energy to be dispatched in the target area on the current monitoring day.

[0070] It should also be noted that the location of the substation of each power demand dispatching grid is extracted from the GPS system, and the length of the transmission line between each power demand dispatching grid and each power station cluster is extracted from the power transmission management system of the target area.

[0071] The electric energy dispatch analysis module is used to perform electric energy dispatch on each electric energy dispatching power grid in the target area within the current monitoring day.

[0072] In a specific embodiment of the present invention, the specific process of power dispatching for each power grid requiring power dispatching in the target area on the current monitoring day is as follows: F1. Extract the total load and total output power corresponding to each power grid requiring power dispatching on the current monitoring day from the total load and total output power corresponding to each power grid in the target area on the current monitoring day, and subtract them to obtain the required supplementary power corresponding to each power grid requiring power dispatching on the current monitoring day.

[0073] See also Figure 2 As shown, F2, obtain the clusters of power stations to be adapted corresponding to each power grid requiring electric energy dispatch on the current monitoring day.

[0074] It should be noted that the specific method of obtaining the clusters of power stations to be adapted corresponding to each power grid requiring electric energy on the current monitoring day is: compare the required supplementary electricity corresponding to each power grid requiring electric energy on the current monitoring day with the power generation of each power station cluster in the target area on the current monitoring day. If the power generation of a power station cluster on the current monitoring day is greater than the required supplementary electricity corresponding to a power grid requiring electric energy on the current monitoring day, then the power station cluster will be used as the power station cluster to be adapted for the electric energy dispatching power grid; otherwise, the power station cluster will be used as the unsuitable power station cluster for the electric energy dispatching power grid, thereby obtaining the clusters of power stations to be adapted corresponding to each power grid requiring electric energy on the current monitoring day.

[0075] F3. Calculate the dispatch adaptability between each power demand dispatching grid and its corresponding power station clusters to be adapted based on the location of the substation of each power demand dispatching grid and the length of the transmission line between each power demand dispatching grid and each power station cluster. Wherein, y represents the number of the power grid to be dispatched, y=1, 2, ..., s, and u represents the number of the power station cluster to be adapted, u=1, 2, ..., l.

[0076] In a specific embodiment of the present invention, the specific process of calculating the dispatching adaptability between each power grid requiring power dispatching and its corresponding power station clusters to be adapted is as follows: G1, obtaining the distance between the substation of each power grid requiring power dispatching and each photovoltaic power station in its corresponding power station clusters to be adapted, and performing mean calculation to obtain the distance between the substation of each power grid requiring power dispatching and its corresponding power station clusters to be adapted, recorded as L y ' u .

[0077] It should be noted that the method for obtaining the distance between the substation of each power grid requiring electric energy dispatching and each photovoltaic power station in the corresponding power station cluster to be adapted is: marking the location of the substation of each power grid requiring electric energy dispatching in GIS, and then using the GIS measurement tool, the distance between the substation of each power grid requiring electric energy dispatching and each photovoltaic power station in the corresponding power station cluster to be adapted can be directly calculated.

[0078] G2, extract the length of the transmission line between each power demand dispatching grid and its corresponding power station clusters to be adapted from the length of the transmission line between each power demand dispatching grid and each power station cluster, and record it as

[0079] G3. Extract the power loss corresponding to the unit transmission line length of each power grid in the target area from the database, and then match the power loss corresponding to the unit transmission line length of each power grid in the power transmission, and record it as

[0080] G4. Calculate the dispatch adaptability between each power grid that needs to be dispatched and its corresponding power station clusters to be adapted Among them, L′ and Q 损 They respectively represent the distance and power loss between the reference grid and the power station cluster.

[0081] F4. Extract the power station cluster to be adapted corresponding to the maximum dispatching adaptability from the dispatching adaptability between each power grid requiring electric energy and its corresponding power station cluster to be adapted, and use it as the adapted power station cluster of each power grid requiring electric energy. Thus, the adapted power station cluster of each power grid requiring electric energy supplements electric energy to each power grid requiring electric energy.

[0082] The embodiment of the present invention performs scheduling adaptation analysis from two aspects: whether the power generation of the power station cluster is sufficient and the energy loss of power transmission between the power station cluster and the power grid, so as to confirm the adaptation power station cluster corresponding to the required power grid, ensure the adequacy of the power grid's required power replenishment, thereby improving the stability of the grid operation, avoiding a large amount of power loss in the transmission process, improving energy utilization efficiency, and reducing energy waste.

[0083] The database is used to store the reference light intensity, reference light duration and single-day reference power generation of each photovoltaic panel in each photovoltaic power station in each power station cluster, and store the power loss corresponding to the unit transmission line length of each power grid in the target area during power transmission. The data sources in the database of this embodiment are shown in Table 1.

[0084] Table 1 Data sources in the database

[0085]

[0086]

[0087] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A distributed intelligent measurement, control and dispatching management system based on IPT communication mechanism, characterized in that: include: The power station cluster confirmation module is used to extract the substations connected to each photovoltaic power station in the target area, obtain each preliminary power station cluster in the target area, extract the location and climate condition information of each photovoltaic power station in each preliminary power station cluster, and confirm each power station cluster in the target area; The power station cluster information extraction module is used to extract the light intensity and duration of each photovoltaic power station in each power station cluster in the target area on the current monitoring day, and to extract the installation position of each photovoltaic panel in each photovoltaic power station; The IPT communication mechanism building module is used to connect the information terminals of each photovoltaic power station in each power station cluster in the target area through a cable to form an intelligent sensing link corresponding to each power station cluster. In the distributed communication network based on the intelligent sensing link corresponding to each power station cluster, each intelligent monitoring node of each photovoltaic power station in each power station cluster is connected to the measurement and control network of the corresponding power station cluster in a disorderly manner according to the installation position of the sensor, thereby realizing orderly monitoring of the power generation of each power station cluster in the target area within the current monitoring day; The power grid information extraction module is used to extract the total load and total output power of each power grid in the target area on the current monitoring day, confirm the power grids that require power in the target area on the current monitoring day, extract the location of the substation of each power grid that requires power, and extract the length of the transmission line between each power grid and each power station cluster; The power dispatch analysis module is used to dispatch power to each power grid that needs power dispatch in the target area during the current monitoring day; The database is used to store the benchmark light intensity, benchmark light duration and single-day benchmark power generation of each photovoltaic panel at each installation angle in each photovoltaic power station in each power station cluster, and store the power loss corresponding to the unit transmission line length of each power grid in the target area during power transmission.

2. According to claim 1, a distributed intelligent measurement, control and dispatching management system based on the IPT communication mechanism is characterized in that: The method for obtaining the preliminary power station clusters in the target area is: based on the substations to which the photovoltaic power stations in the target area are connected, the photovoltaic power stations connected to the same substation are recorded as power stations with the same grid topology structure, and the power stations with the same grid topology structure are clustered into preliminary power station clusters, thereby obtaining the preliminary power station clusters in the target area.

3. According to claim 1, a distributed intelligent measurement, control, dispatching and management system based on IPT communication mechanism is characterized in that: The climate condition information includes the daily average light intensity, the time points at which the light intensity peak and light intensity valley values ​​of each historical monitoring day appear, and the daily average temperature, the time points at which the highest temperature and the lowest temperature of each historical monitoring day appear.

4. A distributed intelligent measurement, control and dispatching management system based on IPT communication mechanism according to claim 3, characterized in that: The specific method of confirming each power station cluster in the target area is: A1. Randomly select a photovoltaic power station from each preliminary power station cluster as the target power station of each preliminary power station cluster; A2. Analyze the comprehensive similarity θ between the target power station of each preliminary power station cluster and the remaining photovoltaic power stations ij , where i represents the number of the preliminary power station cluster, i=1,2,...,n, and j represents the number of the remaining photovoltaic power stations, j=1,2,...,m; A3. Compare the comprehensive similarity between the target power station of each preliminary power station cluster and the remaining photovoltaic power stations with the comprehensive similarity of the set reference. If the comprehensive similarity between the target power station and any other photovoltaic power station is greater than or equal to the comprehensive similarity of the set reference, then use the photovoltaic power station as the clustered power station of the target power station, and so on to obtain the clustered power stations of the target power station, and cluster them to obtain the target power station cluster, thereby obtaining the target power station cluster of each preliminary power station cluster; A4. Eliminate each photovoltaic power station in the target power station cluster of each preliminary power station cluster, and then randomly select a photovoltaic power station as the target power station. In accordance with the above analysis method, obtain each power station cluster in each preliminary power station cluster, thereby obtaining each power station cluster in the target area.

5. A distributed intelligent measurement, control and dispatching management system based on IPT communication mechanism according to claim 4, characterized in that: The specific process of analyzing the comprehensive similarity between the target power station of each preliminary power station cluster and the remaining photovoltaic power stations is as follows: B1. Based on the location of each photovoltaic power station in each preliminary power station cluster, calculate the distance proximity β between the target power station of each preliminary power station cluster and the remaining photovoltaic power stations ij ; B2. Extract the daily average light intensity, the time points of the peak and valley values ​​of light intensity on each historical monitoring day, and the daily average temperature, the time points of the highest and lowest temperatures on each historical monitoring day from the climate condition information of each photovoltaic power station in each preliminary power station cluster, and calculate the climate similarity χ between the target power station in each preliminary power station cluster and the remaining photovoltaic power stations. ij ; B3. Calculate the comprehensive similarity θ between the target power station of each preliminary power station cluster and the remaining photovoltaic power stations ij , Among them, a1 and a2 represent the weights of the set distance similarity and climate similarity corresponding to the comprehensive similarity evaluation, a1+a2=1.

6. A distributed intelligent measurement, control and dispatching management system based on IPT communication mechanism according to claim 5, characterized in that: The specific process of calculating the climate similarity between the target power station of each preliminary power station cluster and the remaining photovoltaic power stations is as follows: C1. Calculate the climate similarity between the target power station of each preliminary power station cluster and the remaining photovoltaic power stations in terms of light intensity C2. Calculate the climate similarity between the target power station of each preliminary power station cluster and the other photovoltaic power stations in terms of light intensity. Similarly, calculate the climate similarity between the target power station of each preliminary power station cluster and the other photovoltaic power stations in terms of temperature. C3. Calculate the climate similarity χ between the target power station of each preliminary power station cluster and the remaining photovoltaic power stations ij , Among them, b1 and b2 represent the weights of climate similarity assessment corresponding to the climate similarity at the set light intensity level and temperature level, respectively, and b1+b2=1.

7. The distributed intelligent measurement, control and dispatching management system based on the IPT communication mechanism according to claim 1 is characterized by: The specific process of orderly monitoring the power generation of each power station cluster in the target area during the current monitoring day is as follows: D1. Based on the light intensity and duration of each photovoltaic power station in each power station cluster in the target area on the current monitoring day and the installation position of each photovoltaic panel in each photovoltaic power station, set the power generation impact factor λ corresponding to the meteorological level of each photovoltaic panel in each photovoltaic power station cluster gpr , where g represents the number of the power station cluster, g = 1, 2, ..., z, p represents the number of the photovoltaic power station, p = 1, 2, ..., q, and r represents the number of the photovoltaic panel, r = 1, 2, ..., x; D2. Extract the daily benchmark power generation of each photovoltaic panel in each photovoltaic power station in each power station cluster from the database, and record it as D3. Predict the power generation Q of each power station cluster in the target area during the current monitoring day g , Among them, x represents the number of photovoltaic panels, and q represents the number of photovoltaic power stations.

8. A distributed intelligent measurement, control and dispatching management system based on IPT communication mechanism according to claim 7, characterized in that: The specific process of setting the power generation influencing factor corresponding to the meteorological level for each photovoltaic panel in each photovoltaic power station in each power station cluster is as follows: E1. According to the installation position of each photovoltaic panel in each photovoltaic power station, locate the reference light intensity and reference light duration of the installation angle of each photovoltaic panel in each photovoltaic power station in each power station cluster from the database, and record them as and E2, the light intensity and light duration of each photovoltaic power station in each power station cluster in the target area on the current monitoring day are recorded as ε gp and T gp ; E3. Set the power generation impact factor λ corresponding to the meteorological level for each photovoltaic panel in each photovoltaic power station in each power station cluster gpr , Here, e represents a natural constant.

9. A distributed intelligent measurement, control and dispatching management system based on IPT communication mechanism according to claim 8, characterized in that: The specific process of dispatching electric energy to each electric energy dispatching grid in the target area within the current monitoring day is as follows: F1. Extract the total load and total output power of each power grid in the target area on the current monitoring day, and then make a difference to obtain the required supplementary power of each power grid on the current monitoring day. F2. Obtain the clusters of power stations to be adapted corresponding to each power grid requiring electric energy dispatching on the current monitoring day; F3. Calculate the dispatch adaptability between each power demand dispatching grid and its corresponding power station clusters to be adapted based on the location of the substation of each power demand dispatching grid and the length of the transmission line between each power demand dispatching grid and each power station cluster. Wherein, y represents the number of the power grid to be dispatched, y=1, 2, ..., s, and u represents the number of the power station cluster to be adapted, u=1, 2, ..., l; F4. Extract the power station cluster to be adapted corresponding to the maximum dispatching adaptability from the dispatching adaptability between each power grid requiring electric energy and its corresponding power station cluster to be adapted, and use it as the adapted power station cluster of each power grid requiring electric energy. Thus, the adapted power station cluster of each power grid requiring electric energy supplements electric energy to each power grid requiring electric energy.

10. A distributed intelligent measurement, control and dispatching management system based on IPT communication mechanism according to claim 9, characterized in that: The specific process of calculating the dispatching adaptability between each power grid requiring electric energy dispatching and its corresponding power station clusters to be adapted is as follows: G1. Obtain the distance between each substation of each power grid that needs to dispatch electricity and each photovoltaic power station in its corresponding power station cluster to be adapted, and calculate the average value to obtain the distance between each substation of each power grid that needs to dispatch electricity and each photovoltaic power station cluster to be adapted, recorded as L y ' u ; G2, extract the length of the transmission line between each power demand dispatching grid and its corresponding power station clusters to be adapted from the length of the transmission line between each power demand dispatching grid and each power station cluster, and record it as G3. Extract the power loss corresponding to the unit transmission line length of each power grid in the target area from the database, and then match the power loss corresponding to the unit transmission line length of each power grid in the power transmission, and record it as G4. Calculate the dispatch adaptability between each power grid that needs to be dispatched and its corresponding power station clusters to be adapted Among them, L′ and Q 损 They respectively represent the distance and power loss between the reference grid and the power station cluster.

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