Power grid energy efficiency optimization management platform and method based on big data

Abstract

The invention relates to industrial data processing, and specifically discloses a power grid energy efficiency optimization management platform and method based on big data. The platform obtains network feature data between the power grid energy efficiency optimization platform and each energy efficiency device, and collects environmental information of the area to which each energy efficiency device belongs, thereby obtaining a network feature index between the power grid energy efficiency optimization platform and each energy efficiency device, and compares it with a network feature threshold, thereby determining whether it is necessary to optimize the features between the power grid energy efficiency optimization platform and each energy efficiency device; the power grid energy efficiency optimization platform obtains energy efficiency parameters of each energy efficiency device, analyzes and obtains an energy efficiency evaluation index of each energy efficiency device, and compares it with an energy efficiency evaluation threshold, thereby determining whether it is necessary to optimize and manage each energy efficiency device in the power grid, thereby ensuring the accuracy and comprehensiveness of the energy efficiency evaluation, and improving the pertinence and effectiveness of the power grid energy efficiency optimization management.

Description

Technical Field

[0001] The present invention relates to industrial data processing, and in particular to a power grid energy efficiency optimization management platform and method based on big data. Background Art

[0002] As an important part of the modern power system, smart grid has promoted the development of grid energy efficiency optimization management, enabling the grid to better achieve safe, reliable and efficient operation; with the continuous development and widespread application of a series of cutting-edge technologies such as the Internet of Things, big data, and cloud computing, grid energy efficiency optimization management has ushered in new development opportunities. These technologies provide the grid with efficient data processing and analysis capabilities, support the intelligent management and optimized operation of the grid, and greatly improve the level of grid energy efficiency optimization management.

[0003] For example, the invention patent with publication number CN111625770A discloses an energy efficiency evaluation method and system for a distribution network containing distributed power sources, including: constructing an energy efficiency evaluation index set for a distribution network containing distributed power sources; combining a quantifiable energy efficiency evaluation index set to construct an energy efficiency function between the operating input and output of the distribution network containing distributed power sources; solving the energy efficiency function based on a super efficiency model with the goal of obtaining the highest energy efficiency value of each distribution network participating in the evaluation; and fully ranking the evaluation objects according to the solved energy efficiency evaluation values ​​to obtain a ranking result of the evaluation objects containing a distributed power distribution network.

[0004] For example, the invention patent with announcement number CN110795681B announces a method for calculating distribution energy efficiency and a low-voltage distribution network energy efficiency monitoring system, in which the method for calculating distribution energy efficiency divides the distribution system into a main line unit, a distributed power supply unit and a load unit through an energy efficiency monitoring device; by dividing the load unit into multiple load sub-units, and dividing the distributed power supply unit into multiple distributed power supply sub-units, device loss calculation and line loss calculation are performed separately, and the same type of load sub-units or distributed power supply sub-units are calculated uniformly.

[0005] Combined with the above technical solutions, it is found that when optimizing the energy efficiency in the power grid, it is usually necessary to directly conduct an overall analysis through the energy efficiency data of the power grid, and it is believed that each energy efficiency device in the power grid is in a normal operating state. This practice will cause errors and loss problems in the data, resulting in inaccurate results of the power distribution energy efficiency of the evaluated power grid, which cannot truly and comprehensively reflect the actual operating conditions of the power grid, and ultimately affect the effect of the optimization management of the power grid energy efficiency. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a power grid energy efficiency optimization management platform and method based on big data, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: The first aspect of the present invention provides a power grid energy efficiency optimization management platform based on big data, including: a power grid network feature determination module, which is used for the power grid energy efficiency optimization platform to obtain network feature data between the power grid energy efficiency optimization platform and each energy efficiency device, and at the same time collect environmental information of the area to which each energy efficiency device belongs, thereby analyzing and obtaining the network feature index between the power grid energy efficiency optimization platform and each energy efficiency device, and comparing it with the network feature threshold preset in the power grid management library, thereby determining whether it is necessary to optimize the features between the power grid energy efficiency optimization platform and each energy efficiency device.

[0008] The power grid energy efficiency optimization management module is used for the power grid energy efficiency optimization platform to obtain the energy efficiency parameters of each energy efficiency device, and combines the network characteristic index between the power grid energy efficiency optimization platform and each energy efficiency device to obtain the energy efficiency evaluation index of each energy efficiency device through comprehensive analysis, and compares it with the energy efficiency evaluation threshold preset in the power grid management library to obtain the comparison results of each energy efficiency evaluation, thereby determining whether it is necessary to optimize the management of each energy efficiency device in the power grid.

[0009] As a further solution, the network characteristic data between the power grid energy efficiency optimization platform and each energy efficiency device specifically includes the maximum network response time, the minimum network response time, the maximum network throughput between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, the network throughput fluctuation rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, the average data transmission rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, and the channel utilization rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period.

[0010] The environmental information of the area to which each energy efficiency device belongs specifically includes the radio interference intensity, electromagnetic radiation intensity, the maximum duration of electromagnetic radiation in the area to which each energy efficiency device belongs in the network characteristic determination period, the maximum environmental temperature in the area to which each energy efficiency device belongs in the network characteristic determination period, and the maximum environmental humidity in the area to which each energy efficiency device belongs in the network characteristic determination period.

[0011] The radio interference intensity and electromagnetic radiation intensity of the area to which each energy efficiency device belongs at each environmental determination time during the network feature determination period are arranged in order from large to small, and the radio interference intensity ranked first is extracted and recorded as the maximum radio interference intensity of the area to which each energy efficiency device belongs during the network feature determination period, and the electromagnetic radiation intensity ranked first is extracted and recorded as the maximum electromagnetic radiation intensity of the area to which each energy efficiency device belongs during the network feature determination period.

[0012] The environmental impact value of each energy efficiency device is obtained through comprehensive analysis of the maximum radio interference intensity of the area to which each energy efficiency device belongs during the network characteristic determination period, the maximum electromagnetic radiation intensity of the area to which each energy efficiency device belongs during the network characteristic determination period, the maximum duration of electromagnetic radiation in the area to which each energy efficiency device belongs during the network characteristic determination period, the maximum ambient temperature of the area to which each energy efficiency device belongs during the network characteristic determination period, and the maximum ambient humidity of the area to which each energy efficiency device belongs during the network characteristic determination period.

[0013] As a further solution, the environmental impact value of each energy efficiency device is obtained in the following specific method:

[0014]

[0015] In the formula, DEPG N Represents the environmental impact value of the Nth energy-efficient equipment, MRI N Indicates the maximum intensity of radio interference in the area where the Nth energy-efficient device belongs during the network feature determination period, MER N Indicates the maximum intensity of electromagnetic radiation in the area where the Nth energy efficiency device belongs during the network feature determination period, MDR N Indicates the maximum duration of electromagnetic radiation in the area where the Nth energy-efficient device belongs during the network feature determination period, MAT N It indicates the maximum ambient temperature of the area where the Nth energy efficiency device belongs during the network feature determination period. ΔMAT indicates the maximum ambient temperature reference value preset by the power grid management library. AAH N represents the maximum environmental humidity of the area to which the Nth energy efficiency device belongs during the network feature determination period, ΔAAH represents the reference value of the maximum environmental humidity preset by the power grid management library, ρ1 represents the power grid environment weight factor of the maximum intensity of radio interference preset by the power grid management library, ρ2 represents the power grid environment weight factor of the maximum intensity of electromagnetic radiation preset by the power grid management library, ρ3 represents the power grid environment weight factor of the maximum duration of electromagnetic radiation preset by the power grid management library, ρ4 represents the power grid environment correction factor of the maximum ambient temperature preset by the power grid management library, ρ5 represents the power grid environment correction factor of the maximum ambient humidity preset by the power grid management library, N is the number of each energy efficiency device, N=1,2,3,...,T, T is the total number of energy efficiency devices.

[0016] As a further solution, the network characteristic index between the power grid energy efficiency optimization platform and each energy efficiency device is specifically analyzed as follows:

[0017] The maximum network response time and the minimum network response time between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period are processed with difference to obtain the network response time difference between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period.

[0018] The maximum network throughput between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period is specifically obtained by obtaining the network throughput curve between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period, and extracting the network throughput corresponding to the highest position point from it, which is recorded as the maximum network throughput between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period.

[0019] The network throughput fluctuation rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period is specifically obtained by extracting the network throughput corresponding to each throughput change position point from the network throughput curve between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period, and performing standard deviation processing to obtain the network throughput fluctuation rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period.

[0020] The network characteristic index between the power grid energy efficiency optimization platform and each energy efficiency device is obtained by comprehensive analysis of the network response time difference between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, the maximum network throughput between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, the network throughput fluctuation rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, the average data transmission rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, the channel utilization rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, and the environmental impact degree value of each energy efficiency device.

[0021] As a further solution, the determination of whether it is necessary to optimize the characteristics between the power grid energy efficiency optimization platform and each energy efficiency device is specifically performed as follows:

[0022] If the network characteristic index between the power grid energy efficiency optimization platform and a certain energy efficiency device is less than the network characteristic threshold, there is no need to optimize the network characteristics between the power grid energy efficiency optimization platform and the energy efficiency device.

[0023] If the network characteristic index between the power grid energy efficiency optimization platform and a certain energy efficiency device is greater than or equal to the network characteristic threshold, it is necessary to optimize the network characteristics between the power grid energy efficiency optimization platform and the energy efficiency device.

[0024] The optimization of the network characteristics between the power grid energy efficiency optimization platform and the energy efficiency device may specifically involve replacing the energy efficiency device with a router with a higher bandwidth, increasing the TCP window size, and installing a TCP accelerator on the energy efficiency device.

[0025] As a further solution, the energy efficiency evaluation index of each energy efficiency device is specifically analyzed as follows:

[0026] The active power of each energy efficiency device at each energy efficiency evaluation time in the energy efficiency evaluation period is averaged to obtain the average active power of each energy efficiency device in the energy efficiency evaluation period.

[0027] The environmental information of the area to which each energy efficiency device belongs during the energy efficiency evaluation period is obtained, thereby obtaining the environmental impact degree value of each energy efficiency device during the energy efficiency evaluation period.

[0028] The energy efficiency evaluation index of each energy efficiency device is obtained through comprehensive analysis of the average power factor of each energy efficiency device during the energy efficiency evaluation period, the average voltage deviation value of each energy efficiency device during the energy efficiency evaluation period, the load rate of each energy efficiency device during the energy efficiency evaluation period, the average active power of each energy efficiency device during the energy efficiency evaluation period, the environmental impact value of each energy efficiency device during the energy efficiency evaluation period, and the network characteristic index between the power grid energy efficiency optimization platform and each energy efficiency device.

[0029] As a further solution, the energy efficiency evaluation comparison results are specifically a first energy efficiency evaluation comparison result and a second energy efficiency evaluation comparison result.

[0030] The first energy efficiency evaluation comparison result is specifically that the energy efficiency evaluation index of a certain energy efficiency equipment is less than the energy efficiency evaluation threshold.

[0031] The second energy efficiency evaluation comparison result is specifically that the energy efficiency evaluation index of a certain energy efficiency equipment is greater than or equal to the energy efficiency evaluation threshold.

[0032] As a further solution, the determination of whether it is necessary to optimize the management of each energy efficiency device in the power grid is specifically carried out as follows:

[0033] If the energy efficiency evaluation comparison result of a certain energy efficiency device is the first energy efficiency evaluation comparison result, there is no need to perform optimization management on the energy efficiency device.

[0034] If the energy efficiency evaluation comparison result of a certain energy efficiency device is the second energy efficiency evaluation comparison result, it is necessary to optimize the management of the energy efficiency device.

[0035] The energy efficiency equipment is optimized and managed, and the specific optimization process is to install a reactive power compensation device on the energy efficiency equipment and a linear programming algorithm built into the power grid energy efficiency optimization platform.

[0036] The second aspect of the present invention provides a power grid energy efficiency optimization management method based on big data, including: S1. The power grid energy efficiency optimization platform obtains the network characteristic data between the power grid energy efficiency optimization platform and each energy efficiency device, and at the same time collects the environmental information of the area to which each energy efficiency device belongs, thereby analyzing and obtaining the network characteristic index between the power grid energy efficiency optimization platform and each energy efficiency device, and comparing it with the network characteristic threshold preset in the power grid management library, thereby determining whether it is necessary to optimize the characteristics between the power grid energy efficiency optimization platform and each energy efficiency device.

[0037] S2. The power grid energy efficiency optimization platform obtains the energy efficiency parameters of each energy efficiency device, and combines the network characteristic index between the power grid energy efficiency optimization platform and each energy efficiency device to obtain the energy efficiency evaluation index of each energy efficiency device through comprehensive analysis, and compares it with the energy efficiency evaluation threshold preset in the power grid management library to obtain the comparison results of each energy efficiency evaluation, thereby determining whether it is necessary to optimize the management of each energy efficiency device in the power grid.

[0038] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0039] (1) The present invention provides a power grid energy efficiency optimization management platform and method based on big data. The power grid energy efficiency optimization platform obtains network feature data between the power grid energy efficiency optimization platform and each energy efficiency device, and collects environmental information of the area to which each energy efficiency device belongs. The network feature index between the power grid energy efficiency optimization platform and each energy efficiency device is obtained, and compared with the network feature threshold. It is determined whether it is necessary to optimize the features between the power grid energy efficiency optimization platform and each energy efficiency device, thereby ensuring the network performance between the power grid energy efficiency optimization platform and each energy efficiency device, and providing a basis for subsequent acquisition of accurate energy efficiency parameters. The power grid energy efficiency optimization platform obtains the energy efficiency parameters of each energy efficiency device, analyzes and obtains the energy efficiency evaluation index of each energy efficiency device, and compares it with the energy efficiency evaluation threshold. It is determined whether it is necessary to optimize the management of each energy efficiency device in the power grid. By optimizing the management of the energy efficiency equipment that needs to be improved, the overall energy efficiency level of the power grid can be significantly improved, and the level and efficiency of power grid energy efficiency optimization management can be effectively improved.

[0040] (2) The present invention obtains network feature data between the power grid energy efficiency optimization platform and each energy efficiency device through the power grid energy efficiency optimization platform, and collects environmental information of the area to which each energy efficiency device belongs, thereby analyzing and obtaining the network feature index between the power grid energy efficiency optimization platform and each energy efficiency device, and comparing it with the network feature threshold preset by the power grid management library, thereby determining whether it is necessary to optimize the features between the power grid energy efficiency optimization platform and each energy efficiency device, thereby ensuring the accuracy of energy efficiency data, being able to more accurately evaluate the operating status and energy efficiency level of energy efficiency equipment, and providing a reliable basis for subsequent energy efficiency optimization management.

[0041] (3) The present invention obtains the energy efficiency parameters of each energy efficiency device through the power grid energy efficiency optimization platform, and combines the network characteristic index between the power grid energy efficiency optimization platform and each energy efficiency device to obtain the energy efficiency evaluation index of each energy efficiency device through comprehensive analysis, thereby realizing accurate evaluation of the energy efficiency level of each energy efficiency device in the power grid, and laying a reliable data foundation for subsequent energy efficiency optimization management.

[0042] (4) The present invention obtains energy efficiency evaluation comparison results by comparing the energy efficiency evaluation index of each energy efficiency device with the energy efficiency evaluation threshold preset in the power grid management library, thereby determining whether it is necessary to optimize the management of each energy efficiency device in the power grid. It can accurately identify and optimize the management of energy efficiency devices that need to be improved, effectively improving the pertinence and effectiveness of the optimization management. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0044] Figure 1 It is a schematic diagram of system module connection of the present invention.

[0045] Figure 2 The figure is a schematic flow chart of the method steps of the present invention.

[0046] Figure 3 This is a network throughput curve involved in the present invention.

[0047] Reference numerals: 1, highest position point; 2, throughput change position point. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0049] Reference Figure 1 As shown, the first aspect of the present invention provides a power grid energy efficiency optimization management platform based on big data, including: a power grid network feature determination module, a power grid energy efficiency optimization management module and a power grid management library.

[0050] The power grid network characteristic determination module is connected to the power grid energy efficiency optimization management module, and the power grid network characteristic determination module and the power grid energy efficiency optimization management module are both connected to the power grid management library.

[0051] The power grid network characteristic determination module is used by the power grid energy efficiency optimization platform to obtain the network characteristic data between the power grid energy efficiency optimization platform and each energy efficiency device, and at the same time collect the environmental information of the area to which each energy efficiency device belongs, thereby analyzing and obtaining the network characteristic index between the power grid energy efficiency optimization platform and each energy efficiency device, and comparing it with the network characteristic threshold preset by the power grid management library, thereby determining whether it is necessary to optimize the characteristics between the power grid energy efficiency optimization platform and each energy efficiency device.

[0052] In a specific embodiment, the above-mentioned power grid energy efficiency optimization platform specifically refers to a platform for performing energy efficiency optimization management on various energy efficiency devices in the power grid. This power grid energy efficiency optimization platform can be connected through the Internet of Things, and receive energy efficiency parameters of various energy efficiency devices through the Internet of Things, thereby performing energy efficiency optimization management operations on each energy efficiency device; the power grid energy efficiency optimization platform is internally equipped with multiple data monitoring technologies such as SIEM (security information and event management) system and network performance monitoring module.

[0053] It should be explained that the above-mentioned energy efficiency equipment includes power generation equipment, transmission equipment, distribution equipment, power consumption equipment, monitoring and analysis equipment, energy storage equipment and microgrid equipment; among them, power generation equipment includes generators, transformers and renewable energy power generation equipment, transmission equipment includes high-voltage direct current transmission equipment, smart grid equipment and power lines, distribution equipment includes smart distribution boxes, reactive power compensation devices and energy-saving distribution transformers, power consumption equipment includes motors, lighting equipment, refrigeration / heating equipment, etc., monitoring and analysis equipment such as power quality monitoring equipment, and energy storage equipment such as pumped storage power stations.

[0054] Specifically, the network characteristic data between the power grid energy efficiency optimization platform and each energy efficiency device include the maximum network response time, the minimum network response time, the maximum network throughput between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, the network throughput fluctuation rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, the average data transmission rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, and the channel utilization rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period.

[0055] It should be explained that the above-mentioned network characteristic determination period refers to the time period from the start of network characteristic determination of the network between the power grid energy efficiency optimization platform and each energy efficiency device to the end of the network characteristic determination, which is recorded as the network characteristic determination period. The network characteristic determination period is divided into multiple network characteristic determination sub-periods according to the length of the time period. A network test is performed in each network characteristic determination sub-period. For example, the network characteristic determination period is 200 seconds, and each 5 seconds is divided into a network characteristic determination sub-period, which is divided into 40 network characteristic determination sub-periods in total. The network characteristic determination period can be obtained through the SI EM system built into the power grid energy efficiency optimization platform.

[0056] It should be explained that the maximum network response time, the minimum network response time, the average data transmission rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period, and the channel utilization rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period can all be obtained through the network performance monitoring module provided in the power grid energy efficiency optimization platform; among them, when the network feature determination is performed between the power grid energy efficiency optimization platform and each energy efficiency device, the time from the power grid energy efficiency optimization platform sending a network test request to the energy efficiency device responding and returning the network test data in each network feature determination sub-period is calculated according to the time. They are arranged in order from large to small, and the duration ranked first is extracted and recorded as the maximum network response duration between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period, and the duration ranked last is extracted and recorded as the minimum network response duration; the average data transmission rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period specifically refers to the average speed of data transmission between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period; the channel utilization rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period specifically refers to the ratio of the time when the channel is effectively utilized during the network feature determination period to the total time of the network feature determination period.

[0057] The environmental information of the area to which each energy efficiency device belongs specifically includes the radio interference intensity, electromagnetic radiation intensity, the maximum duration of electromagnetic radiation in the area to which each energy efficiency device belongs in the network characteristic determination period, the maximum environmental temperature in the area to which each energy efficiency device belongs in the network characteristic determination period, and the maximum environmental humidity in the area to which each energy efficiency device belongs in the network characteristic determination period.

[0058] It should be explained that the above-mentioned environmental determination moments refer to the moments when the environmental determination is performed on the area to which each energy efficiency device belongs.

[0059] It should be explained that the radio interference intensity of the area to which each of the above-mentioned energy efficiency devices belongs at each environmental determination time during the network feature determination period specifically refers to the degree of radio interference received by the area to which each of the energy efficiency devices belongs during the network feature determination period, which can be obtained through the radio interference measuring instrument installed in each energy efficiency device; the electromagnetic radiation intensity specifically refers to the radiation power density per unit time passing through a unit area in the area to which each of the energy efficiency devices belongs during the network feature determination period, which can be obtained through a high-sensitivity electromagnetic field intensity meter installed in each of the energy efficiency devices; the maximum duration of electromagnetic radiation in the area to which each of the above-mentioned energy efficiency devices belongs during the network feature determination period specifically refers to the longest time that the area to which each of the energy efficiency devices belongs continues to be subjected to electromagnetic radiation during the network feature determination period, which can be obtained through an electromagnetic radiation spectrum analyzer; The maximum ambient temperature of the area to which each energy efficiency device belongs during the network feature determination period refers to the maximum ambient temperature of the area to which each energy efficiency device belongs during the network feature determination period, which can be obtained through the temperature sensors installed in each energy efficiency device, wherein the temperature sensor can monitor multiple ambient temperature values ​​during the network feature determination period in real time, and screen out the maximum value as the maximum ambient temperature by comparing the values. The maximum ambient humidity of the area to which each energy efficiency device belongs during the network feature determination period refers to the maximum ambient humidity of the area to which each energy efficiency device belongs during the network feature determination period, which can be obtained through the humidity sensors installed in each energy efficiency device, wherein the humidity sensor can monitor multiple ambient humidity values ​​during the network feature determination period in real time, and screen out the maximum value as the maximum ambient humidity by comparing the values.

[0060] The radio interference intensity and electromagnetic radiation intensity of the area to which each energy efficiency device belongs at each environmental determination time during the network feature determination period are arranged in order from large to small, and the radio interference intensity ranked first is extracted and recorded as the maximum radio interference intensity of the area to which each energy efficiency device belongs during the network feature determination period, and the electromagnetic radiation intensity ranked first is extracted and recorded as the maximum electromagnetic radiation intensity of the area to which each energy efficiency device belongs during the network feature determination period.

[0061] The environmental impact value of each energy efficiency device is obtained through comprehensive analysis of the maximum radio interference intensity of the area to which each energy efficiency device belongs during the network characteristic determination period, the maximum electromagnetic radiation intensity of the area to which each energy efficiency device belongs during the network characteristic determination period, the maximum duration of electromagnetic radiation in the area to which each energy efficiency device belongs during the network characteristic determination period, the maximum ambient temperature of the area to which each energy efficiency device belongs during the network characteristic determination period, and the maximum ambient humidity of the area to which each energy efficiency device belongs during the network characteristic determination period.

[0062] Furthermore, the environmental impact value of each energy efficiency device is obtained in the following specific method:

[0063]

[0064] In the formula, MRI N Indicates the maximum intensity of radio interference in the area where the Nth energy-efficient device belongs during the network feature determination period, MER N Indicates the maximum intensity of electromagnetic radiation in the area where the Nth energy efficiency device belongs during the network feature determination period, MDR N Indicates the maximum duration of electromagnetic radiation in the area where the Nth energy-efficient device belongs during the network feature determination period, MAT N It indicates the maximum ambient temperature of the area where the Nth energy efficiency device belongs during the network feature determination period. ΔMAT indicates the maximum ambient temperature reference value preset by the power grid management library. AAH N represents the maximum ambient humidity of the area to which the Nth energy efficiency device belongs during the network feature determination period, ΔAAH represents the maximum ambient humidity reference value preset by the power grid management library, DEPG N It indicates the environmental impact value of the Nth energy efficiency device. A high temperature environment will cause the performance of the electronic components inside the energy efficiency device to decline. The high temperature will also be accompanied by a high ambient humidity. High humidity may cause the electronic components inside the device to get damp, thereby increasing the intensity of electromagnetic radiation. When the electromagnetic radiation intensity in the area where the energy efficiency device is located is high, it will also generate strong radio interference, because the spectrum of electromagnetic radiation may include the frequency band of radio communication. At the same time, long-term electromagnetic radiation will also accumulate to generate stronger radio interference, resulting in an increase in the environmental impact value of the energy efficiency device. In a low temperature environment, the ambient humidity is often low, and the moisture content in the air is reduced, which makes it easy for static electricity to be generated on the surface of the device. Static electricity can damage electronic components, thereby increasing the intensity of electromagnetic radiation and the intensity of radio interference, which increases the environmental impact value of the energy efficiency device.

[0065] The maximum ambient temperature reference value specifically refers to the optimal ambient temperature value of the area to which the energy-efficiency device belongs, as preset by the power grid management library. At this ambient temperature, the energy efficiency of the energy-efficiency device is the best; the maximum ambient humidity reference value specifically refers to the optimal adaptation value of the ambient humidity of the area to which the energy-efficiency device belongs, as preset by the power grid management library, so that the energy-efficiency device has the highest operating efficiency.

[0066] ρ1 represents the power grid environment weight factor of the maximum intensity of radio interference preset by the power grid management library, ρ2 represents the power grid environment weight factor of the maximum intensity of electromagnetic radiation preset by the power grid management library, ρ3 represents the power grid environment weight factor of the maximum duration of electromagnetic radiation preset by the power grid management library, ρ4 represents the power grid environment correction factor of the maximum ambient temperature preset by the power grid management library, and ρ5 represents the power grid environment correction factor of the maximum ambient humidity preset by the power grid management library.

[0067] It needs to be explained that the power grid environment weight factors corresponding to the maximum intensity of radio interference, the maximum intensity of electromagnetic radiation, the maximum duration of electromagnetic radiation, the maximum ambient temperature and the maximum ambient humidity are respectively used to adjust the importance of the maximum intensity of radio interference, the maximum intensity of electromagnetic radiation, the maximum duration of electromagnetic radiation, the maximum ambient temperature and the maximum ambient humidity in the process of analyzing and obtaining the environmental impact degree value. For example, there is a pre-set mapping relationship between the real-time environmental information and the corresponding power grid environment weight factors in the power grid management library. The power grid environment weight factors corresponding to the real-time environmental information can be matched through the pre-set mapping relationship. The maximum intensity of radio interference, the maximum intensity of electromagnetic radiation, the maximum duration of electromagnetic radiation, the maximum ambient temperature and the maximum ambient humidity are respectively matched with the pre-set mapping relationship to obtain the power grid environment weight factor corresponding to the maximum intensity of radio interference, the power grid environment weight factor corresponding to the maximum intensity of electromagnetic radiation, the power grid environment weight factor corresponding to the maximum duration of electromagnetic radiation, the power grid environment correction factor corresponding to the maximum ambient temperature and the power grid environment correction factor corresponding to the maximum ambient humidity.

[0068] N is the serial number of each energy efficiency device, N=1, 2, 3, ..., T, and T is the total number of energy efficiency devices.

[0069] Specifically, the network characteristic index between the power grid energy efficiency optimization platform and each energy efficiency device is analyzed in the following process:

[0070] The maximum network response time and the minimum network response time between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period are processed with difference to obtain the network response time difference between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period.

[0071] The maximum network throughput between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period is specifically obtained by obtaining the network throughput curve between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period, and extracting the network throughput corresponding to the highest position point from it, which is recorded as the maximum network throughput between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period.

[0072] It should be explained that the network throughput curve between the above-mentioned power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period can obtain the network throughput continuous change data through the network performance monitoring module set up in the power grid energy efficiency optimization platform, and then draw a network throughput curve.

[0073] according to Figure 3As shown in FIG. 1 , it is a network throughput curve between the power grid energy efficiency optimization platform and a certain energy efficiency device during the network feature determination period. It can be seen from the figure that the horizontal axis is the network feature determination sub-period, the unit is seconds, and the vertical axis is the network throughput, the unit is bits / second; the network throughput corresponding to the highest position point 1 of the network throughput curve in the figure is recorded as the maximum network throughput between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period.

[0074] The network throughput fluctuation rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period is specifically obtained by extracting the network throughput corresponding to each throughput change position point from the network throughput curve between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period, and performing standard deviation processing to obtain the network throughput fluctuation rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period.

[0075] according to Figure 3 As shown, each throughput change position point is a number of throughput change position points 2 in the network throughput curve, and the network throughput corresponding to each throughput change position point is obtained from the network throughput curve, and the network throughput fluctuation rate between the power grid energy efficiency optimization platform and each energy efficiency device in the network feature determination period is obtained by standard deviation processing; wherein, this embodiment is not limited to Figure 3 The limited number of throughput change location points 2 clearly marked in the figure are only exemplary representatives. In fact, a large number of points are selected for calculation and analysis. When selecting research points, location points with more significant or representative throughput changes are selected for research. These points can better reflect the performance of the network under different conditions.

[0076] The network characteristic index between the power grid energy efficiency optimization platform and each energy efficiency device is obtained by comprehensive analysis of the network response time difference between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, the maximum network throughput between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, the network throughput fluctuation rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, the average data transmission rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, the channel utilization rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, and the environmental impact degree value of each energy efficiency device.

[0077] The environmental impact degree value of each of the above-mentioned energy efficiency devices refers to the value obtained by comprehensively evaluating the environment of the area to which each energy efficiency device belongs during the network characteristic determination period through the maximum intensity of radio interference in the area to which each energy efficiency device belongs during the network characteristic determination period, the maximum intensity of electromagnetic radiation in the area to which each energy efficiency device belongs during the network characteristic determination period, the maximum duration of electromagnetic radiation in the area to which each energy efficiency device belongs during the network characteristic determination period, the maximum ambient temperature in the area to which each energy efficiency device belongs during the network characteristic determination period, and the maximum ambient humidity in the area to which each energy efficiency device belongs during the network characteristic determination period.

[0078] The network characteristic index between the power grid energy efficiency optimization platform and each energy efficiency device is specifically obtained as follows:

[0079]

[0080] Where, NRL N MNT represents the network response time difference between the power grid energy efficiency optimization platform and the Nth energy efficiency device during the network feature determination period. N represents the maximum network throughput of the network between the power grid energy efficiency optimization platform and the Nth energy efficiency device during the network feature determination period, ΔMNT represents the reference value of the maximum network throughput preset by the power grid management library, and NTF N ADR represents the network throughput fluctuation rate between the power grid energy efficiency optimization platform and the Nth energy efficiency device during the network feature determination period. N represents the average data transmission rate between the power grid energy efficiency optimization platform and the Nth energy efficiency device during the network feature determination period, CU N represents the channel utilization rate of the network between the power grid energy efficiency optimization platform and the Nth energy efficiency device during the network feature determination period, ΔCU represents the channel reference utilization rate preset by the power grid management library, and DEPG N It represents the environmental impact value of the Nth energy-efficient equipment, e is a natural constant, GNCI N It represents the network characteristic index between the power grid energy efficiency optimization platform and the Nth energy efficiency device. When the channel utilization rate and the maximum network throughput are too high, network congestion will occur, thereby reducing the average data transmission rate and increasing the network response time difference. Therefore, network congestion will make the network performance unstable, resulting in large fluctuations in throughput, increasing the network throughput volatility, and increasing the network characteristic index between the power grid energy efficiency optimization platform and the energy efficiency device. When the environmental impact value of the energy efficiency device is too high, the radio interference intensity is too large, which will reduce the network performance, reduce the channel utilization rate and the maximum network throughput, and reduce the average data transmission rate, thereby increasing the network characteristic index between the power grid energy efficiency optimization platform and the energy efficiency device.

[0081] The reference value of the maximum network throughput specifically refers to the optimal adaptation value of the maximum network throughput preset by the power grid management library, so that the network between the power grid energy efficiency optimization platform and each energy efficiency device successfully transmits the maximum amount of data per unit time and no network congestion occurs; the reference channel utilization specifically refers to the optimal adaptation value of the channel utilization preset by the power grid management library, so that the network performance between the power grid energy efficiency optimization platform and each energy efficiency device is the best.

[0082] δ1 represents the network characteristic impact weight of the network response time difference preset by the power grid management library, δ2 represents the network characteristic correction weight of the network maximum throughput preset by the power grid management library, δ3 represents the network characteristic impact weight of the network throughput fluctuation rate preset by the power grid management library, δ4 represents the network characteristic impact weight of the average data transmission rate preset by the power grid management library, δ5 represents the network characteristic correction weight of the channel utilization rate preset by the power grid management library, and δ6 represents the network characteristic impact weight of the environmental impact degree value preset by the power grid management library.

[0083] It should be explained that the network characteristic impact weights corresponding to the network response time difference, the network maximum throughput, the network throughput fluctuation rate, the average data transmission rate, the channel utilization rate and the environmental impact degree values ​​are respectively used to adjust the importance of the network response time difference, the network maximum throughput, the network throughput fluctuation rate, the average data transmission rate, the channel utilization rate and the environmental impact degree values ​​in the process of analyzing and obtaining the network characteristic index. For example, there is a pre-set mapping relationship between the real-time network characteristic data and the corresponding network characteristic impact weights in the power grid management library. The network characteristic impact weights corresponding to the real-time network characteristic data can be matched through the pre-set mapping relationship. The network response time difference, the network maximum throughput, the network throughput fluctuation rate, the average data transmission rate, the channel utilization rate and the environmental impact degree values ​​are respectively matched with the pre-set mapping relationship to obtain the network characteristic impact weight corresponding to the network response time difference, the network characteristic correction weight corresponding to the network maximum throughput, the energy efficiency assessment correction coefficient of the network throughput fluctuation rate, the network characteristic impact weight corresponding to the average data transmission rate, the network characteristic correction weight corresponding to the channel utilization rate and the network characteristic impact weight corresponding to the environmental impact degree value.

[0084] N is the serial number of each energy efficiency device, N=1, 2, 3, ..., T, and T is the total number of energy efficiency devices.

[0085] Furthermore, the determination of whether it is necessary to optimize the characteristics between the power grid energy efficiency optimization platform and each energy efficiency device is specifically performed as follows:

[0086] If the network characteristic index between the power grid energy efficiency optimization platform and a certain energy efficiency device is less than the network characteristic threshold, there is no need to optimize the network characteristics between the power grid energy efficiency optimization platform and the energy efficiency device; the network characteristic threshold represents the critical value used to determine whether the network characteristic index between the power grid energy efficiency optimization platform and a certain energy efficiency device reaches the network performance standard when performing network characteristic determination, and is used to quantitatively evaluate the characteristic state of the network and help the power grid energy efficiency optimization platform determine whether it is necessary to optimize the network characteristics between the power grid energy efficiency optimization platform and the energy efficiency device; the network characteristic index between the above-mentioned power grid energy efficiency optimization platform and a certain energy efficiency device is less than the network characteristic threshold, indicating that the network is good and there is no need to optimize the network characteristics between the power grid energy efficiency optimization platform and the energy efficiency device.

[0087] If the network characteristic index between the power grid energy efficiency optimization platform and a certain energy efficiency device is greater than or equal to the network characteristic threshold, it means that the network is not good and the network characteristics between the power grid energy efficiency optimization platform and the energy efficiency device need to be optimized.

[0088] The optimization of the network characteristics between the power grid energy efficiency optimization platform and the energy efficiency device may specifically involve replacing the energy efficiency device with a router with a higher bandwidth, increasing the TCP window size, and installing a TCP accelerator on the energy efficiency device.

[0089] The energy efficiency device is replaced with a router with higher bandwidth. Specifically, the amount of data that the network can process is directly increased by increasing the bandwidth, thereby improving the maximum network throughput, while reducing network latency, and reducing the network response time difference; a router with higher bandwidth can support a higher data transmission rate, thereby increasing the average data transmission rate, reducing the network characteristic index between the power grid energy efficiency optimization platform and the energy efficiency device, and optimizing the network characteristics.

[0090] The increase in the TCP window size specifically allows more data to be sent in a single TCP connection through the increase in the TCP window size, shortening the response time and thereby reducing the network response time difference; by allowing more data to be transmitted in a single connection, the increase in the TCP window size improves the maximum throughput of the network; at the same time, it can reduce network congestion and uncertainty in data transmission, thereby reducing the volatility of network throughput; it can also reduce unnecessary waiting and retransmissions, thereby increasing the average rate of data transmission; and it can more effectively utilize channel resources, thereby improving channel utilization, reducing the network characteristic index between the power grid energy efficiency optimization platform and the energy efficiency device, and optimizing the network characteristics.

[0091] The installation of a TCP accelerator on the energy efficiency device specifically reduces the delay and jitter in data transmission through the TCP accelerator, thereby further shortening the network response time difference, and also helping to reduce throughput fluctuations and improve the average rate of data transmission; by reducing the overhead and delay in data transmission, the network can transmit more data in the same time, thereby improving the maximum network throughput; the TCP accelerator optimizes the performance of the TCP protocol so that the channel can transmit data more efficiently, further improving the channel utilization rate, reducing the network characteristic index between the power grid energy efficiency optimization platform and the energy efficiency device, and optimizing the network characteristics.

[0092] The power grid energy efficiency optimization management module is used for the power grid energy efficiency optimization platform to obtain the energy efficiency parameters of each energy efficiency device, and combines the network characteristic index between the power grid energy efficiency optimization platform and each energy efficiency device to obtain the energy efficiency evaluation index of each energy efficiency device through comprehensive analysis, and compares it with the energy efficiency evaluation threshold preset in the power grid management library to obtain the comparison results of each energy efficiency evaluation, thereby determining whether it is necessary to optimize the management of each energy efficiency device in the power grid.

[0093] Specifically, the energy efficiency parameters of each energy efficiency device include the power factor of each energy efficiency device at each energy efficiency evaluation moment in the energy efficiency evaluation period, the average voltage of each energy efficiency device in the energy efficiency evaluation period, the load rate of each energy efficiency device in the energy efficiency evaluation period, and the active power of each energy efficiency device at each energy efficiency evaluation moment in the energy efficiency evaluation period.

[0094] It should be explained that the above-mentioned energy efficiency evaluation period specifically refers to the time period from the start of the energy efficiency evaluation of each energy efficiency device to the end of the energy efficiency evaluation, which is recorded as the energy efficiency evaluation period and can be obtained through the SI EM system built into the power grid energy efficiency optimization platform; the above-mentioned energy efficiency evaluation moments refer to the moments when the energy efficiency evaluation of each energy efficiency device is carried out, which can also be obtained through the SI EM system built into the power grid energy efficiency optimization platform.

[0095] It should be explained that the power factor of each energy efficiency device at each energy efficiency evaluation moment in the energy efficiency evaluation period, the average voltage of each energy efficiency device in the energy efficiency evaluation period, and the active power of each energy efficiency device at each energy efficiency evaluation moment in the energy efficiency evaluation period can all be obtained through the multi-function electric energy meter built into the energy efficiency device; among them, the power factor of each energy efficiency device at each energy efficiency evaluation moment in the energy efficiency evaluation period specifically refers to the ratio of the active power to the apparent power of each energy efficiency device at each energy efficiency evaluation moment; the average voltage of each energy efficiency device in the energy efficiency evaluation period specifically refers to the ratio of the active power to the apparent power of each energy efficiency device at each energy efficiency evaluation moment. The average voltage value at each energy efficiency evaluation moment; the active power of each energy efficiency device at each energy efficiency evaluation moment in the energy efficiency evaluation period specifically refers to the electric energy power actually converted and consumed by the energy efficiency device at each energy efficiency evaluation moment; the load rate of each of the above energy efficiency devices in the energy efficiency evaluation period specifically refers to the ratio between the actual load of each energy efficiency device in the energy efficiency evaluation period and its rated power, which can be calculated from the power consumption data of the energy efficiency device (current, voltage, active power, reactive power, and equipment operating status and working time, etc.) collected by the multi-function electric energy meter built into the energy efficiency device.

[0096] The power factors of each energy efficiency device at each energy efficiency evaluation time in the energy efficiency evaluation period are averaged to obtain the average power factor of each energy efficiency device in the energy efficiency evaluation period.

[0097] The average voltage of each energy efficiency device during the energy efficiency evaluation period is processed with the average voltage reference value preset by the power grid management library one by one to obtain the average voltage deviation value of each energy efficiency device during the energy efficiency evaluation period.

[0098] Furthermore, the energy efficiency evaluation index of each energy efficiency device is specifically analyzed as follows:

[0099] The active power of each energy efficiency device at each energy efficiency evaluation time in the energy efficiency evaluation period is averaged to obtain the average active power of each energy efficiency device in the energy efficiency evaluation period.

[0100] The environmental information of the area to which each energy efficiency device belongs during the energy efficiency evaluation period is obtained, thereby obtaining the environmental impact degree value of each energy efficiency device during the energy efficiency evaluation period.

[0101] It should be explained that the environmental impact degree values ​​of the above-mentioned energy efficiency devices in the energy efficiency assessment period are specifically obtained by comprehensive analysis of the maximum intensity of radio interference in the area to which each energy efficiency device belongs in the energy efficiency assessment period, the maximum intensity of electromagnetic radiation in the area to which each energy efficiency device belongs in the energy efficiency assessment period, the maximum duration of electromagnetic radiation in the area to which each energy efficiency device belongs in the energy efficiency assessment period, the maximum ambient temperature in the area to which each energy efficiency device belongs in the energy efficiency assessment period, and the maximum ambient humidity in the area to which each energy efficiency device belongs in the energy efficiency assessment period when the environmental assessment is performed on each energy efficiency device in the energy efficiency assessment period; the environmental information of the area to which each energy efficiency device belongs in the energy efficiency assessment period is the same as the parameters in the environmental information of the area to which each energy efficiency device belongs, except that the assessment period is the energy efficiency assessment period. Therefore, through the analysis method of the environmental impact degree values ​​of the above-mentioned energy efficiency devices, the environmental impact degree values ​​of each energy efficiency device in the energy efficiency assessment period corresponding to the environmental information of the area to which each energy efficiency device belongs in the energy efficiency assessment period can be obtained.

[0102] The energy efficiency evaluation index of each energy efficiency device is obtained through comprehensive analysis of the average power factor of each energy efficiency device during the energy efficiency evaluation period, the average voltage deviation value of each energy efficiency device during the energy efficiency evaluation period, the load rate of each energy efficiency device during the energy efficiency evaluation period, the average active power of each energy efficiency device during the energy efficiency evaluation period, the environmental impact value of each energy efficiency device during the energy efficiency evaluation period, and the network characteristic index between the power grid energy efficiency optimization platform and each energy efficiency device.

[0103] The network characteristic index between the above-mentioned power grid energy efficiency optimization platform and each energy efficiency device refers to the index obtained by comprehensively analyzing the network response time difference between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, the maximum network throughput between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, the network throughput fluctuation rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, the average data transmission rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, the channel utilization rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, and the environmental impact degree value of each energy efficiency device when judging the network between the power grid energy efficiency optimization platform and each energy efficiency device.

[0104] The specific method for obtaining the energy efficiency evaluation index of each energy efficiency device is as follows:

[0105]

[0106] In the formula, PF N Represents the average power factor of the Nth energy-efficient device during the energy efficiency evaluation period, AVD N Indicates the average voltage deviation value of the Nth energy efficiency device during the energy efficiency evaluation period, ELF Nrepresents the load rate of the Nth energy efficiency device during the energy efficiency evaluation period, ΔELF represents the load rate reference value preset by the power grid management library, and MAP N Indicates the average active power of the Nth energy efficiency device during the energy efficiency evaluation period, GNCI N Denotes the network characteristic index between the power grid energy efficiency optimization platform and the Nth energy efficiency device, DEEG N Represents the environmental impact value of the Nth energy efficiency equipment during the energy efficiency assessment period, EOPG N It represents the energy efficiency evaluation index of the Nth energy efficiency device. When the load rate is high, the voltage of the power grid will drop, resulting in an increase in voltage deviation. When the voltage deviation is too large, the reactive power of the energy efficiency device may increase, resulting in a decrease in power factor and a decrease in average active power. When the load rate is high, congestion and delays will occur in the power grid, resulting in an increase in the network characteristic index, thereby increasing the energy efficiency evaluation index of the energy efficiency device. When the load rate is too low, the inductive load will be in a long-term light load or no-load operation state, resulting in an increase in reactive power component, thereby reducing the power factor and the average active power, ultimately increasing the energy efficiency evaluation index of the energy efficiency device. When the environmental impact degree value is large, the maximum intensity of radio interference and the maximum intensity of electromagnetic radiation are also large, which will lead to network instability and an increase in the network characteristic index, which will increase the energy efficiency evaluation index of the energy efficiency device.

[0107] The load rate reference value specifically refers to the optimal load rate adaptation value preset by the power grid management library to ensure the optimal energy efficiency of energy-efficient equipment.

[0108] ω1 represents the energy efficiency assessment influence coefficient of the average power factor preset by the power grid management library, ω2 represents the energy efficiency assessment influence coefficient of the average voltage deviation value preset by the power grid management library, ω3 represents the energy efficiency assessment correction coefficient of the load rate preset by the power grid management library, ω4 represents the energy efficiency assessment influence coefficient of the average active power preset by the power grid management library, ω5 represents the energy efficiency assessment influence coefficient of the network characteristic index preset by the power grid management library, and ω6 represents the energy efficiency assessment influence coefficient of the environmental impact degree value preset by the power grid management library.

[0109] It needs to be explained that the energy efficiency assessment influence coefficients corresponding to the average power factor, the average voltage deviation value, the load rate, the average active power, the network characteristic index and the environmental impact degree value are respectively used to adjust the importance of the average power factor, the average voltage deviation value, the load rate, the average active power, the network characteristic index and the environmental impact degree value in the process of analyzing and obtaining the energy efficiency assessment index. For example, there is a pre-set mapping relationship between the real-time energy efficiency parameters and the corresponding energy efficiency assessment influence coefficients in the power grid management library. The energy efficiency assessment influence coefficients corresponding to the real-time energy efficiency parameters can be matched through the pre-set mapping relationship. The average power factor, the average voltage deviation value, the load rate, the average active power, the network characteristic index and the environmental impact degree value are respectively matched with the pre-set mapping relationship to obtain the energy efficiency assessment influence coefficient corresponding to the average power factor, the energy efficiency assessment influence coefficient corresponding to the average voltage deviation value, the energy efficiency assessment correction coefficient of the load rate, the energy efficiency assessment influence coefficient corresponding to the average active power, the energy efficiency assessment influence coefficient corresponding to the network characteristic index and the energy efficiency assessment influence coefficient corresponding to the environmental impact degree value.

[0110] N is the serial number of each energy efficiency device, N=1, 2, 3, ..., T, and T is the total number of energy efficiency devices.

[0111] Specifically, the energy efficiency evaluation comparison results are specifically a first energy efficiency evaluation comparison result and a second energy efficiency evaluation comparison result.

[0112] The first energy efficiency evaluation comparison result is specifically that the energy efficiency evaluation index of a certain energy efficiency device is less than the energy efficiency evaluation threshold; the energy efficiency evaluation threshold represents the critical value used to determine whether the energy efficiency evaluation index of a certain energy efficiency device reaches the optimization management standard when performing energy efficiency evaluation, and is used to quantitatively evaluate the operating energy efficiency status of the energy efficiency device and help the power grid energy efficiency optimization platform determine whether it is necessary to optimize the management of the energy efficiency device.

[0113] The second energy efficiency evaluation comparison result is specifically that the energy efficiency evaluation index of a certain energy efficiency equipment is greater than or equal to the energy efficiency evaluation threshold.

[0114] Furthermore, the specific process of determining whether it is necessary to optimize the management of each energy efficiency device in the power grid is as follows:

[0115] If the energy efficiency evaluation comparison result of a certain energy efficiency device is the first energy efficiency evaluation comparison result, it means that the energy efficiency of the energy efficiency device is good, and there is no need to perform optimization management on the energy efficiency device.

[0116] If the energy efficiency evaluation comparison result of a certain energy efficiency device is the second energy efficiency evaluation comparison result, it means that the energy efficiency of the energy efficiency device is not good, and the energy efficiency device needs to be optimized and managed.

[0117] The energy efficiency equipment is optimized and managed, and the specific optimization process is to install a reactive power compensation device on the energy efficiency equipment and to embed a linear programming algorithm in the power grid energy efficiency optimization platform.

[0118] It should be explained that the above-mentioned installation of a reactive power compensation device in the energy efficiency device specifically reduces the reactive power flow in the power grid through the reactive power compensation device, which can make the power factor of the energy efficiency device close to 1, thereby reducing reactive power loss, improving the transmission efficiency of the power grid, and further reducing the energy efficiency evaluation index of each energy efficiency device.

[0119] It should be explained that the above-mentioned built-in linear programming algorithm in the power grid energy efficiency optimization platform specifically uses a linear programming algorithm to find the optimal solution according to the actual operation conditions of each energy efficiency device in the power grid. By adjusting the transformer tap, reactive power compensation and other means, the voltage fluctuation can be reduced and the average voltage deviation value can be reduced; by optimizing the load distribution, the load of each device can be balanced to avoid the situation where some devices are overloaded while other devices are lightly loaded, thereby improving the energy efficiency of the overall equipment; by adjusting the output power of the generator and the power demand of the load, the distribution of active power can be optimized, the average active power of each energy efficiency device can be increased, and finally the energy efficiency evaluation index of each energy efficiency device can be reduced.

[0120] The power grid management library is used to store network characteristic thresholds, energy efficiency evaluation thresholds, reference values ​​for maximum ambient temperature, reference values ​​for maximum ambient humidity, power grid environment weight factors for maximum intensity of radio interference, power grid environment weight factors for maximum intensity of electromagnetic radiation, power grid environment weight factors for maximum duration of electromagnetic radiation, power grid environment correction factors for maximum ambient temperature, power grid environment correction factors for maximum ambient humidity, reference values ​​for maximum network throughput, reference channel utilization, network characteristic impact weights for network response time difference, network characteristic correction weights for maximum network throughput, network characteristic impact weights for network throughput fluctuation rate, network characteristic impact weights for average data transmission rate, network characteristic correction weights for channel utilization rate, network characteristic impact weights for environmental impact degree values ​​of various energy efficiency devices, reference values ​​for load rates, energy efficiency evaluation impact coefficients for average power factor, energy efficiency evaluation impact coefficients for average voltage deviation values, energy efficiency evaluation correction coefficients for load rates, energy efficiency evaluation impact coefficients for average active power, and energy efficiency evaluation impact coefficients for network characteristic indexes.

[0121] Reference Figure 2As shown, the second aspect of the present invention provides a power grid energy efficiency optimization management method based on big data, including: S1. The power grid energy efficiency optimization platform obtains network feature data between the power grid energy efficiency optimization platform and each energy efficiency device, and at the same time collects environmental information of the area to which each energy efficiency device belongs, thereby analyzing and obtaining the network feature index between the power grid energy efficiency optimization platform and each energy efficiency device, and comparing it with the network feature threshold preset in the power grid management library, thereby determining whether it is necessary to optimize the features between the power grid energy efficiency optimization platform and each energy efficiency device.

[0122] S2. The power grid energy efficiency optimization platform obtains the energy efficiency parameters of each energy efficiency device, and combines the network characteristic index between the power grid energy efficiency optimization platform and each energy efficiency device to obtain the energy efficiency evaluation index of each energy efficiency device through comprehensive analysis, and compares it with the energy efficiency evaluation threshold preset in the power grid management library to obtain the comparison results of each energy efficiency evaluation, thereby determining whether it is necessary to optimize the management of each energy efficiency device in the power grid.

[0123] The above contents are merely examples and explanations of the structure 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 structure 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 power grid energy efficiency optimization management platform based on big data, characterized by: include: The power grid network feature determination module is used for the power grid energy efficiency optimization platform to obtain the network feature data between the power grid energy efficiency optimization platform and each energy efficiency device, and at the same time collect the environmental information of the area to which each energy efficiency device belongs, thereby analyzing and obtaining the network feature index between the power grid energy efficiency optimization platform and each energy efficiency device, and comparing it with the network feature threshold preset by the power grid management library, thereby determining whether it is necessary to optimize the features between the power grid energy efficiency optimization platform and each energy efficiency device; The power grid energy efficiency optimization management module is used for the power grid energy efficiency optimization platform to obtain the energy efficiency parameters of each energy efficiency device, and combines the network characteristic index between the power grid energy efficiency optimization platform and each energy efficiency device to obtain the energy efficiency evaluation index of each energy efficiency device through comprehensive analysis, and compares it with the energy efficiency evaluation threshold preset in the power grid management library to obtain the comparison results of each energy efficiency evaluation, thereby determining whether it is necessary to optimize the management of each energy efficiency device in the power grid.

2. The power grid energy efficiency optimization management platform based on big data according to claim 1 is characterized by: The network characteristic data between the power grid energy efficiency optimization platform and each energy efficiency device specifically includes the maximum network response time, the minimum network response time, the maximum network throughput, the fluctuation rate of the network throughput between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, the average data transmission rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, and the channel utilization rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period; The environmental information of the area to which each energy efficiency device belongs specifically includes the radio interference intensity, electromagnetic radiation intensity, the maximum duration of electromagnetic radiation in the area to which each energy efficiency device belongs in the network feature determination period, the maximum environmental temperature in the area to which each energy efficiency device belongs in the network feature determination period, and the maximum environmental humidity in the area to which each energy efficiency device belongs in the network feature determination period at each environmental determination time; The radio interference intensity and electromagnetic radiation intensity of the area to which each energy efficiency device belongs at each environmental determination time in the network feature determination period are arranged in order from large to small, thereby extracting the radio interference intensity ranked first as the maximum radio interference intensity of the area to which each energy efficiency device belongs in the network feature determination period, and extracting the electromagnetic radiation intensity ranked first as the maximum electromagnetic radiation intensity of the area to which each energy efficiency device belongs in the network feature determination period; The environmental impact value of each energy efficiency device is obtained through comprehensive analysis of the maximum radio interference intensity of the area to which each energy efficiency device belongs during the network characteristic determination period, the maximum electromagnetic radiation intensity of the area to which each energy efficiency device belongs during the network characteristic determination period, the maximum duration of electromagnetic radiation in the area to which each energy efficiency device belongs during the network characteristic determination period, the maximum ambient temperature of the area to which each energy efficiency device belongs during the network characteristic determination period, and the maximum ambient humidity of the area to which each energy efficiency device belongs during the network characteristic determination period.

3. The power grid energy efficiency optimization management platform based on big data according to claim 2 is characterized by: The environmental impact value of each energy efficiency device is obtained in the following specific method: In the formula, DEPG N Represents the environmental impact value of the Nth energy-efficient equipment, MRI N Indicates the maximum intensity of radio interference in the area where the Nth energy-efficient device belongs during the network feature determination period, MER N Indicates the maximum intensity of electromagnetic radiation in the area where the Nth energy efficiency device belongs during the network feature determination period, MDR N Indicates the maximum duration of electromagnetic radiation in the area where the Nth energy efficiency device belongs during the network feature determination period, MAT N It indicates the maximum ambient temperature of the area where the Nth energy efficiency device belongs during the network feature determination period. ΔMAT indicates the maximum ambient temperature reference value preset by the power grid management library. AAH N represents the maximum environmental humidity of the area to which the Nth energy efficiency device belongs during the network feature determination period, ΔAAH represents the reference value of the maximum environmental humidity preset by the power grid management library, ρ1 represents the power grid environment weight factor of the maximum intensity of radio interference preset by the power grid management library, ρ2 represents the power grid environment weight factor of the maximum intensity of electromagnetic radiation preset by the power grid management library, ρ3 represents the power grid environment weight factor of the maximum duration of electromagnetic radiation preset by the power grid management library, ρ4 represents the power grid environment correction factor of the maximum ambient temperature preset by the power grid management library, ρ5 represents the power grid environment correction factor of the maximum ambient humidity preset by the power grid management library, N is the number of each energy efficiency device, N=1,2,3,...,T, T is the total number of energy efficiency devices.

4. The power grid energy efficiency optimization management platform based on big data according to claim 2 is characterized by: The network characteristic index between the power grid energy efficiency optimization platform and each energy efficiency device, the specific analysis process is as follows: The maximum network response time and the minimum network response time between the power grid energy efficiency optimization platform and each energy efficiency device in the network feature determination period are processed by difference, and the network response time difference between the power grid energy efficiency optimization platform and each energy efficiency device in the network feature determination period is obtained; The maximum network throughput between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period is specifically to obtain the network throughput curve between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period, and extract the network throughput corresponding to the highest position point from it, which is recorded as the maximum network throughput between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period; The network throughput fluctuation rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period is specifically obtained by extracting the network throughput corresponding to each throughput change position point from the network throughput curve between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period, and performing standard deviation processing to obtain the network throughput fluctuation rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network feature determination period; The network characteristic index between the power grid energy efficiency optimization platform and each energy efficiency device is obtained by comprehensive analysis of the network response time difference between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, the maximum network throughput between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, the network throughput fluctuation rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, the average data transmission rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, the channel utilization rate between the power grid energy efficiency optimization platform and each energy efficiency device during the network characteristic determination period, and the environmental impact degree value of each energy efficiency device.

5. The power grid energy efficiency optimization management platform based on big data according to claim 1 is characterized by: The specific process of determining whether it is necessary to optimize the characteristics between the power grid energy efficiency optimization platform and each energy efficiency device is as follows: If the network characteristic index between the power grid energy efficiency optimization platform and a certain energy efficiency device is less than the network characteristic threshold, there is no need to optimize the network characteristics between the power grid energy efficiency optimization platform and the energy efficiency device; If the network characteristic index between the power grid energy efficiency optimization platform and a certain energy efficiency device is greater than or equal to the network characteristic threshold, it is necessary to optimize the network characteristics between the power grid energy efficiency optimization platform and the energy efficiency device; The optimization of the network characteristics between the power grid energy efficiency optimization platform and the energy efficiency device may specifically involve replacing the energy efficiency device with a router with a higher bandwidth, increasing the TCP window size, and installing a TCP accelerator on the energy efficiency device.

6. The power grid energy efficiency optimization management platform based on big data according to claim 1 is characterized by: The energy efficiency parameters of each energy efficiency device specifically include the power factor of each energy efficiency device at each energy efficiency evaluation time in the energy efficiency evaluation period, the average voltage of each energy efficiency device in the energy efficiency evaluation period, the load rate of each energy efficiency device in the energy efficiency evaluation period, and the active power of each energy efficiency device at each energy efficiency evaluation time in the energy efficiency evaluation period; The power factors of each energy efficiency device at each energy efficiency evaluation time in the energy efficiency evaluation period are averaged to obtain the average power factor of each energy efficiency device in the energy efficiency evaluation period; The average voltage of each energy efficiency device during the energy efficiency evaluation period is processed with the average voltage reference value preset by the power grid management library one by one to obtain the average voltage deviation value of each energy efficiency device during the energy efficiency evaluation period.

7. The power grid energy efficiency optimization management platform based on big data according to claim 6 is characterized by: The specific analysis process of the energy efficiency evaluation index of each energy efficiency device is as follows: The active power of each energy efficiency device at each energy efficiency evaluation time in the energy efficiency evaluation period is averaged to obtain the average active power of each energy efficiency device in the energy efficiency evaluation period; Obtaining environmental information of the area to which each energy efficiency device belongs during the energy efficiency assessment period, thereby obtaining the environmental impact value of each energy efficiency device during the energy efficiency assessment period; The energy efficiency evaluation index of each energy efficiency device is obtained through comprehensive analysis of the average power factor of each energy efficiency device during the energy efficiency evaluation period, the average voltage deviation value of each energy efficiency device during the energy efficiency evaluation period, the load rate of each energy efficiency device during the energy efficiency evaluation period, the average active power of each energy efficiency device during the energy efficiency evaluation period, the environmental impact value of each energy efficiency device during the energy efficiency evaluation period, and the network characteristic index between the power grid energy efficiency optimization platform and each energy efficiency device.

8. The power grid energy efficiency optimization management platform based on big data according to claim 1 is characterized by: The energy efficiency evaluation comparison results are specifically a first energy efficiency evaluation comparison result and a second energy efficiency evaluation comparison result; The first energy efficiency evaluation comparison result is specifically that the energy efficiency evaluation index of a certain energy efficiency equipment is less than the energy efficiency evaluation threshold; The second energy efficiency evaluation comparison result is specifically that the energy efficiency evaluation index of a certain energy efficiency equipment is greater than or equal to the energy efficiency evaluation threshold.

9. The power grid energy efficiency optimization management platform based on big data according to claim 1 is characterized by: The specific process of determining whether it is necessary to optimize the management of each energy efficiency device in the power grid is as follows: If the energy efficiency evaluation comparison result of a certain energy efficiency equipment is the first energy efficiency evaluation comparison result, it is not necessary to optimize the management of the energy efficiency equipment; If the energy efficiency evaluation comparison result of a certain energy efficiency device is the second energy efficiency evaluation comparison result, it is necessary to optimize the management of the energy efficiency device; The energy efficiency equipment is optimized and managed, and the specific optimization process is to install a reactive power compensation device on the energy efficiency equipment and a linear programming algorithm built into the power grid energy efficiency optimization platform.

10. A method for the power grid energy efficiency optimization management platform based on big data applied to any one of claims 1-9, characterized in that: include: S1. The power grid energy efficiency optimization platform obtains network feature data between the power grid energy efficiency optimization platform and each energy efficiency device, and collects environmental information of the area to which each energy efficiency device belongs, thereby analyzing and obtaining the network feature index between the power grid energy efficiency optimization platform and each energy efficiency device, and comparing it with the network feature threshold preset by the power grid management library, thereby determining whether it is necessary to optimize the features between the power grid energy efficiency optimization platform and each energy efficiency device; S2. The power grid energy efficiency optimization platform obtains the energy efficiency parameters of each energy efficiency device, and combines the network characteristic index between the power grid energy efficiency optimization platform and each energy efficiency device to obtain the energy efficiency evaluation index of each energy efficiency device through comprehensive analysis, and compares it with the energy efficiency evaluation threshold preset in the power grid management library to obtain the comparison results of each energy efficiency evaluation, thereby determining whether it is necessary to optimize the management of each energy efficiency device in the power grid.

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