Distributed photovoltaic output fitting system and method

Abstract

The invention discloses a distributed photovoltaic output fitting system and method, and aims to solve the problems of insufficient acquisition means and low output measurement and calculation precision of a dispatching automation system after distributed photovoltaic grid connection. The system comprises a capacity statistics module, a template selection module, a data acquisition module, a data transmission module and an output fitting module. The method comprises the following steps: determining a sample substation by counting county-level regional distributed photovoltaic grid-connected capacity, deploying a micro-meteorological acquisition device in the sample substation, transmitting temperature, humidity and irradiance data to a 5000 scheduling system through a 4-20 milliampere interface, and then determining the grid-connected capacity of the distributed photovoltaic grid-connected system on the basis of correlation characteristics of photovoltaic output and meteorological parameters. And fitting and measuring 10-kilovolt and 380-volt distributed photovoltaic output in combination with tracking 110-kilovolt power station data and an algorithm containing multiple correction terms. According to the method, the blank of distributed photovoltaic monitoring is filled, the measurement and calculation precision is improved, the method is compatible with existing power grid equipment, the implementation cost is reduced, support is provided for power grid precise scheduling and new energy efficient consumption, and the method has important engineering application value.

Description

Technical Field

[0001] This invention relates to distributed photovoltaics, specifically to a distributed photovoltaic power output fitting system and method. Background Technology

[0002] As the global energy structure shifts towards cleaner energy, distributed photovoltaic (PV) power, with its advantages of high flexibility and local consumption, has achieved large-scale grid connection and become an important part of new energy development. However, distributed PV is characterized by its wide distribution range, small individual capacity, and diverse access voltage levels (mainly 10 kV and 380 V), which brings many challenges to grid dispatching and management.

[0003] Existing dispatch automation systems primarily rely on centralized large-scale photovoltaic power plants (such as 110 kV and above power plants under unified dispatch) for data acquisition, depending on dedicated acquisition equipment and communication links to achieve direct data collection. However, for distributed photovoltaic systems, due to their large number and dispersed layout, traditional acquisition methods are too costly and difficult to achieve full coverage, resulting in a lack of effective monitoring of their output by the dispatch system and the formation of numerous "data blind spots."

[0004] Meanwhile, distributed photovoltaic (PV) output is significantly affected by meteorological conditions (irradiance, temperature, etc.), exhibiting strong fluctuations. Due to the lack of real-time data support, existing calculation methods mostly rely on empirical estimations, resulting in low accuracy and failing to meet the requirements of precise grid dispatch. This not only increases the pressure on grid frequency regulation and peak shaving but also restricts the efficient absorption of distributed PV power. Therefore, there is an urgent need for a technical solution that can accurately fit the output of distributed PV to fill the monitoring gap, improve calculation accuracy, and ensure the safe and stable operation of the power grid. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to overcome the defects of the above-mentioned technologies and provide a distributed photovoltaic power output fitting system and method.

[0006] To solve the above-mentioned technical problems, the present invention provides a distributed photovoltaic power output fitting system and method: a distributed photovoltaic power output fitting system, comprising:

[0007] The capacity statistics module is used to calculate the total grid-connected capacity of distributed photovoltaic power in each county-level administrative region;

[0008] The template selection module is used to determine at least one representative template substation in each county-level administrative region based on the total grid-connected capacity.

[0009] The data acquisition module includes a micro-meteorological acquisition device deployed in each of the model substations, which is used to collect temperature, humidity and irradiance data at its location.

[0010] The data transmission module connects the micro-meteorological data acquisition device to the common monitoring and control device of the computer monitoring system of the model substation via a 4-20 mA interface. The common monitoring and control device is used to forward the acquired micro-meteorological data to the 5000 dispatch system.

[0011] The output fitting module is used to fit and calculate the actual output of 10 kV and 380 V distributed photovoltaic power in the corresponding county-level administrative region, based on the characteristics that photovoltaic output is directly proportional to irradiance and inversely proportional to temperature, referring to the measured data of the directly sourced 110 kV photovoltaic power station and the preset algorithm, and combining the micro-meteorological data.

[0012] As an improvement, the number of micro-meteorological acquisition devices deployed is one or more per county-level administrative region, with the specific number determined based on the distribution density, geographical range, and grid connection capacity level of the distributed photovoltaic system in that region.

[0013] As an improvement, in the data transmission module, the 4-20 mA interface is a current signal interface, and the public measurement and control device forwards the micro-meteorological data to the 5000 dispatch system through the power dedicated communication network, with a transmission cycle of no more than 1 minute.

[0014] As an improvement, the preset algorithm is an output calculation model constructed based on historical output data and corresponding micro-meteorological data of the 110 kV photovoltaic power station under unified dispatch. This model is trained using a machine learning algorithm and includes a correction term for the temperature-induced degradation coefficient of photovoltaic module efficiency. This correction term satisfies the formula:

[0015] η(T)=η0×(1-α×(T-T0))

[0016] Where η(T) is the photovoltaic module efficiency at temperature T, η0 is the module rated efficiency at a reference temperature T0 (usually taken as 25℃), α is the temperature decay coefficient (unit: 1 / ℃, typical value is 0.004), and T is the measured temperature; this formula is used to quantify the decay effect of temperature rise on module power generation efficiency and improve the model's response accuracy to temperature factors.

[0017] As an improvement, the criteria for determining the model substation by the model selection module include: the substation's power supply range covers more than 60% of the distributed photovoltaic installed capacity within the county-level administrative region, and its geographical location is near the geometric center of the photovoltaic distribution in that region.

[0018] A method for fitting distributed photovoltaic power output includes the following steps:

[0019] S1. Calculate the total grid-connected capacity of distributed photovoltaic power in each county-level administrative region, and determine at least one model substation in each county-level administrative region based on the total grid-connected capacity;

[0020] S2. Install a micro-meteorological acquisition device in each of the model substations. Collect temperature, humidity and irradiance data at the location through the micro-meteorological acquisition device. Connect the collected data to the common measurement and control device of the computer monitoring system of the model substation through the 4-20 mA interface. The common measurement and control device forwards the data to the 5000 dispatch system.

[0021] S3. Based on the characteristics that photovoltaic output is directly proportional to irradiance and inversely proportional to temperature, referencing the measured data of the directly sourced 110 kV photovoltaic power station and the preset algorithm, and combining the micro-meteorological data collected in step S2, the actual output of 10 kV and 380 V distributed photovoltaic power in the corresponding county-level administrative region is fitted and calculated.

[0022] As an improvement, the process of determining the model substation in step S1 includes:

[0023] S11. Select the top 30% of substations with the highest grid-connected capacity within the county-level administrative region as candidate model substations.

[0024] S12. Select one or more candidate model substations whose power supply range overlaps most with the main distribution areas of distributed photovoltaic power as the final model substations.

[0025] As an improvement, the temperature data collected in step S2 has a measurement range of -40℃ to 85℃ with an accuracy of ±0.5℃; the humidity data has a measurement range of 0-100%RH with an accuracy of ±3%RH; and the irradiance data has a measurement range of 0-2000W / m². 2 Accuracy ±2%.

[0026] As an improvement, the construction process of the preset algorithm in step S3 includes:

[0027] S31. Using the irradiance and temperature data of the 110 kV photovoltaic power station under unified dispatch as input features and the actual output of the power station as output label, construct a training dataset.

[0028] S32. Use random forest or gradient boosting algorithm to train the training dataset to obtain the initial output calculation model;

[0029] S33. Adjust the parameters of the initial model based on the installed capacity type (monocrystalline / polycrystalline modules) of distributed photovoltaics within the county-level administrative region. The adjustment formula is as follows:

[0030] P adj =P init ×k type

[0031] Among them, P adj To adjust and adapt to the output of distributed photovoltaic power, P init k is the initial model output value. typeThe correction factor is 1.02 for monocrystalline modules and 0.98 for polycrystalline modules, calibrated according to the actual module characteristics. This formula is used to adapt to the differences in power generation characteristics of different photovoltaic module types and improve the model's versatility.

[0032] As an improvement, the fitting and measurement process in step S3 includes:

[0033] S301, Calculate the irradiance contribution term: P irr = k1×G, where P irr k1 is the contribution of irradiance to output power, and k1 is the irradiance proportionality coefficient (unit: kW·m). 2 / W, calibrated from data of the centrally dispatched power station), G is the measured irradiance (unit: W / m²). 2 );

[0034] S302, Calculate the temperature effect term: P temp =k2×(TT) ref ), where P temp Here, k2 represents the effect of temperature on output, k2 is the temperature influence coefficient (unit: kW / ℃, k2<0 indicates that output decreases with increasing temperature), and T is the measured temperature (unit: ℃). ref Reference temperature (unit: °C, usually taken as 25 °C);

[0035] S303. Calculate the final fitted output force: P final =(P irr +P temp )×w+C, where P final For the total output of distributed photovoltaic power in the county-level region, w is the weight of the grid-connected capacity in the region (provided by the capacity statistics module), and C is the algorithm correction value of the 110 kV power station under unified dispatch (unit: kW, used to compensate for model system errors);

[0036] The above formula achieves accurate fitting of distributed photovoltaic power output by quantifying the combined influence of meteorological factors and capacity weights.

[0037] The advantages of this invention compared to existing technologies are as follows: It effectively fills the gap in comprehensive data collection for distributed photovoltaic (PV) power generation. By deploying micro-meteorological data acquisition devices in representative model substations at the county level, and combining this with existing power grid monitoring and dispatching systems to achieve stable transmission of meteorological data, it solves the problem of insufficient monitoring of distributed PV output by traditional dispatching systems. By integrating the inherent correlation between PV output and meteorological parameters, and referencing mature data from centrally dispatched substations to construct a multi-dimensional correction model, it significantly improves the accuracy of 10 kV and 380 V distributed PV output measurement, providing reliable data support for power grid dispatching. The solution is fully compatible with existing power grid equipment, requiring no large-scale modifications; functional upgrades can be achieved simply by adding micro-meteorological devices, significantly reducing engineering implementation and maintenance costs, and possessing strong feasibility for widespread application. Accurate output data helps dispatching departments optimize resource allocation, enhance the capacity for renewable energy absorption, and promote energy structure transformation. Simultaneously, it enables refined management at the county level, providing a basis for formulating differentiated dispatching strategies, effectively improving the intelligence level of power grid dispatching, and providing important technical guarantees for the large-scale development of distributed PV and the safe and stable operation of the power grid. Attached Figure Description

[0038] Figure 1 This is a three-dimensional structural diagram of a distributed photovoltaic power output fitting system and method according to the present invention. Figure 1 .

[0039] Figure 2 This is a three-dimensional structural diagram of a distributed photovoltaic power output fitting system and method according to the present invention. Figure 2 . Detailed Implementation

[0042] To facilitate understanding of this application, a more complete description will be provided below with reference to the accompanying drawings, which illustrate embodiments of the present application. However, the present application can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of this application will be thorough and complete.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0044] It is understood that spatial relation terms such as "below," "under," "below," "below," "above," "over," etc., can be used here to describe the relationship between one element or feature shown in the figure and other elements or features. It should be understood that, in addition to the orientation shown in the figure, spatial relation terms also include different orientations of the device in use and operation. For example, if the device in the figure is flipped, the element or feature described as "below" or "under" or "below" of the other element or feature will be oriented "over" the other element or feature. Therefore, the exemplary terms "below" and "under" can include both upper and lower orientations. Furthermore, the device may also include other orientations, such as being rotated 90 degrees or other orientations, and the spatial descriptive terms used herein will be interpreted accordingly.

[0045] It should be noted that when one element is considered to be "connected" to another element, it can be directly connected to the other element or connected to the other element through an intermediary element. In the following embodiments, "connection" should be understood as "electrical connection," "communication connection," etc., if the connected circuits, modules, units, etc., have the transmission of electrical signals or data between them.

[0046] When used herein, the singular forms of “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising,” “including,” or “having,” etc., specify the presence of the stated feature, whole, step, operation, component, part, or combination thereof, but do not preclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts, or combinations thereof.

[0047] Referring to the attached figures, a distributed photovoltaic power output fitting system and method are described. The distributed photovoltaic power output fitting system includes:

[0048] The capacity statistics module is used to calculate the total grid-connected capacity of distributed photovoltaic power in each county-level administrative region;

[0049] The template selection module is used to determine at least one representative template substation in each county-level administrative region based on the total grid-connected capacity.

[0050] The data acquisition module includes a micro-meteorological acquisition device deployed in each of the model substations, which is used to collect temperature, humidity and irradiance data at its location.

[0051] The data transmission module connects the micro-meteorological data acquisition device to the common monitoring and control device of the computer monitoring system of the model substation via a 4-20 mA interface. The common monitoring and control device is used to forward the acquired micro-meteorological data to the 5000 dispatch system.

[0052] The output fitting module is used to fit and calculate the actual output of 10 kV and 380 V distributed photovoltaic power in the corresponding county-level administrative region, based on the characteristics that photovoltaic output is directly proportional to irradiance and inversely proportional to temperature, referring to the measured data of the directly sourced 110 kV photovoltaic power station and the preset algorithm, and combining the micro-meteorological data.

[0053] As an improvement, the number of micro-meteorological acquisition devices deployed is one or more per county-level administrative region, with the specific number determined based on the distribution density, geographical range, and grid connection capacity level of the distributed photovoltaic system in that region.

[0054] As an improvement, in the data transmission module, the 4-20 mA interface is a current signal interface, and the public measurement and control device forwards the micro-meteorological data to the 5000 dispatch system through the power dedicated communication network, with a transmission cycle of no more than 1 minute.

[0055] As an improvement, the preset algorithm is an output calculation model constructed based on historical output data and corresponding micro-meteorological data of the 110 kV photovoltaic power station under unified dispatch. This model is trained using a machine learning algorithm and includes a correction term for the temperature-induced degradation coefficient of photovoltaic module efficiency. This correction term satisfies the formula:

[0056] η(T)=η0×(1-α×(T-T0))

[0057] Where η(T) is the photovoltaic module efficiency at temperature T, η0 is the module rated efficiency at a reference temperature T0 (usually taken as 25℃), α is the temperature decay coefficient (unit: 1 / ℃, typical value is 0.004), and T is the measured temperature; this formula is used to quantify the decay effect of temperature rise on module power generation efficiency and improve the model's response accuracy to temperature factors.

[0058] As an improvement, the criteria for determining the model substation by the model selection module include: the substation's power supply range covers more than 60% of the distributed photovoltaic installed capacity within the county-level administrative region, and its geographical location is near the geometric center of the photovoltaic distribution in that region.

[0059] A method for fitting distributed photovoltaic power output includes the following steps:

[0060] S1. Calculate the total grid-connected capacity of distributed photovoltaic power in each county-level administrative region, and determine at least one model substation in each county-level administrative region based on the total grid-connected capacity;

[0061] S2. Install a micro-meteorological acquisition device in each of the model substations. Collect temperature, humidity and irradiance data at the location through the micro-meteorological acquisition device. Connect the collected data to the common measurement and control device of the computer monitoring system of the model substation through the 4-20 mA interface. The common measurement and control device forwards the data to the 5000 dispatch system.

[0062] S3. Based on the characteristics that photovoltaic output is directly proportional to irradiance and inversely proportional to temperature, referencing the measured data of the directly sourced 110 kV photovoltaic power station and the preset algorithm, and combining the micro-meteorological data collected in step S2, the actual output of 10 kV and 380 V distributed photovoltaic power in the corresponding county-level administrative region is fitted and calculated.

[0063] As an improvement, the process of determining the model substation in step S1 includes:

[0064] S11. Select the top 30% of substations with the highest grid-connected capacity within the county-level administrative region as candidate model substations.

[0065] S12. Select one or more candidate model substations whose power supply range overlaps most with the main distribution areas of distributed photovoltaic power as the final model substations.

[0066] As an improvement, the temperature data collected in step S2 has a measurement range of -40℃ to 85℃ with an accuracy of ±0.5℃; the humidity data has a measurement range of 0-100%RH with an accuracy of ±3%RH; and the irradiance data has a measurement range of 0-2000W / m². 2 Accuracy ±2%.

[0067] As an improvement, the construction process of the preset algorithm in step S3 includes:

[0068] S31. Using the irradiance and temperature data of the 110 kV photovoltaic power station under unified dispatch as input features and the actual output of the power station as output label, construct a training dataset.

[0069] S32. Use random forest or gradient boosting algorithm to train the training dataset to obtain the initial output calculation model;

[0070] S33. Adjust the parameters of the initial model based on the installed capacity type (monocrystalline / polycrystalline modules) of distributed photovoltaics within the county-level administrative region. The adjustment formula is as follows:

[0071] P adj =P init ×k type

[0072] Among them, P adj To adjust and adapt to the output of distributed photovoltaic power, P init k is the initial model output value. typeThe correction factor is 1.02 for monocrystalline modules and 0.98 for polycrystalline modules, calibrated according to the actual module characteristics. This formula is used to adapt to the differences in power generation characteristics of different photovoltaic module types and improve the model's versatility.

[0073] As an improvement, the fitting and measurement process in step S3 includes:

[0074] S301, Calculate the irradiance contribution term: P irr = k1×G, where P irr k1 is the contribution of irradiance to output power, and k1 is the irradiance proportionality coefficient (unit: kW·m). 2 / W, calibrated from data of the centrally dispatched power station), G is the measured irradiance (unit: W / m²). 2 );

[0075] S302, Calculate the temperature effect term: P temp =k2×(TT) ref ), where P temp Here, k2 represents the effect of temperature on output, k2 is the temperature influence coefficient (unit: kW / ℃, k2<0 indicates that output decreases with increasing temperature), and T is the measured temperature (unit: ℃). ref Reference temperature (unit: °C, usually taken as 25 °C);

[0076] S303. Calculate the final fitted output force: P final =(P irr +P temp )×w+C, where P final For the total output of distributed photovoltaic power in the county-level region, w is the weight of the grid-connected capacity in the region (provided by the capacity statistics module), and C is the algorithm correction value of the 110 kV power station under unified dispatch (unit: kW, used to compensate for model system errors);

[0077] The above formula achieves accurate fitting of distributed photovoltaic power output by quantifying the combined influence of meteorological factors and capacity weights.

[0078] The distributed photovoltaic power output fitting system and method described in this invention will be explained in detail below with reference to specific application scenarios, so as to fully demonstrate its practical operability and technical effect.

[0079] I. Implementation example of a distributed photovoltaic power output matching system:

[0080] This embodiment uses 10 county-level administrative regions under the jurisdiction of a prefecture-level city as the application object. The distributed photovoltaic power generation in this region is mainly connected to 10 kV and 380 V power grids, with a total grid-connected capacity of approximately 800 MW. Due to the dispersed distribution and small individual capacity, existing dispatch automation systems cannot directly collect their output data. The system of this invention achieves fitting calculation of the distributed photovoltaic power output of each county-level region. The specific implementation is as follows:

[0081] Operation of the capacity statistics module:

[0082] The capacity statistics module integrates distributed photovoltaic (PV) grid-connected files (including project address, access voltage level, installed capacity, etc.) from the power grid marketing system and historical grid-connected records from the dispatch automation system to calculate the total distributed PV grid-connected capacity for each county-level administrative region. For example, County A has a grid-connected capacity of 120 MW, County B has 85 MW, County C has 60 MW, and the capacity of other counties ranges from 30 to 90 MW. This statistical result provides basic data support for the subsequent selection of pilot substations.

[0083] Running the template selection module:

[0084] The sample selection module determines sample substations based on the total grid-connected capacity of each county-level region, following the principle of "covering the main distribution areas and having statistical representativeness." Specifically, for county-level regions with a grid-connected capacity ≥ 100 MW (e.g., County A), two sample substations are selected; for regions with a capacity of 50 MW ≤ capacity < 100 MW (e.g., County B, County C), one sample substation is selected; and for regions with a capacity < 50 MW, one sample substation is selected.

[0085] The selection criteria strictly adhere to the principle that "the power supply range covers more than 60% of the distributed photovoltaic installed capacity within the county-level administrative region, and the geographical location is near the geometric center of the photovoltaic distribution in that region." Taking County A as an example, its distributed photovoltaic power is mainly concentrated in the eastern agricultural area and the western industrial park. Therefore, the "Chengdong 110 kV Substation" in the east and the "Chengxi 35 kV Substation" in the west were selected as model substations. Together, they cover 82% of the distributed photovoltaic installed capacity in County A and are located in the center of the two major distribution areas, effectively representing the meteorological characteristics of the region.

[0086] Deployment and operation of the data acquisition module:

[0087] Microclimate monitoring devices should be deployed in each model substation. The selected devices must meet the following parameter requirements: temperature measurement range -40℃ to 85℃, accuracy ±0.5℃; humidity measurement range 0-100%RH, accuracy ±3%RH; irradiance measurement range 0~2000W / m². 2 Accuracy ±2%. The device is installed in an open, unobstructed area of ​​the substation (such as a rooftop platform) to ensure that the collected meteorological data is not affected by buildings or equipment.

[0088] Taking the "Chengguan 110 kV Substation" in County B as an example, its micro-meteorological acquisition device collects temperature, humidity and irradiance data every 10 seconds and caches them locally to provide a stable data source for subsequent data transmission.

[0089] Operation of the data transmission module:

[0090] The micro-meteorological data acquisition device connects to the common measurement and control device of the computer monitoring system of the model substation via a 4-20 mA current signal interface. This interface converts meteorological parameters into standardized current signals (such as irradiance 0.2000 W / m²). 2 (Corresponding to a current of 420 mA) to ensure interference resistance in data transmission. The public monitoring and control device performs analog-to-digital conversion on the received current signal, restoring it to digital data of temperature, humidity, and irradiance. This data is then packaged according to the IEC 61850 standard protocol and forwarded to the municipal 5000 dispatch system via a dedicated power fiber optic communication network. The data transmission cycle is set to 1 minute, meaning that full data acquisition and uploading are completed every minute, ensuring the real-time nature of the meteorological data acquired by the dispatch system.

[0091] Operation of the output fitting module

[0092] The output fitting module is the core of the system. Based on the characteristics that photovoltaic output is directly proportional to irradiance and inversely proportional to temperature, it combines micrometeorological data and preset algorithms to calculate the output.

[0093] The preset algorithm is a machine learning model trained based on historical data from a 110 kV photovoltaic power station (this embodiment uses the random forest algorithm), and includes a correction term for the temperature-induced degradation coefficient of photovoltaic module efficiency to improve measurement accuracy. The calculation formula for this correction term is:

[0094] η(T)=η0×(1-α×(T-T0))

[0095] Wherein, η(T) is the photovoltaic module efficiency at temperature T, η0 is the rated efficiency of the module at the reference temperature T0 (25℃ in this embodiment) (usually 15% to 22%, with an average of 18% based on the mainstream module types in the region), α is the temperature decay coefficient (0.0041 / ℃, meaning that the module efficiency decreases by 0.4% for every 1℃ increase in temperature), and T is the measured temperature collected from the sample substation.

[0096] For example, when the measured temperature in County B is 35℃, substituting it into the formula yields η(35)=0.18×(1-0.004×(35-25))=0.18×0.96=0.1728 (i.e. 17.28%), which quantifies the impact of temperature rise on the degradation of component efficiency and provides accurate efficiency parameters for subsequent output calculation.

[0097] II. Examples of Distributed Photovoltaic Output Fitting Methods:

[0098] Based on the above system, this embodiment describes the specific steps of the distributed photovoltaic power output fitting method:

[0099] Step S1: Calculate grid-connected capacity and determine the model substation:

[0100] First, the installed capacity of all 10 kV and 380 V distributed photovoltaic projects in each county-level region is exported through the power grid marketing system, and then summed to obtain the total grid-connected capacity of each county-level region (such as 120 MW in County A and 85 MW in County B, as mentioned above).

[0101] Subsequently, the model substation was determined by following these sub-steps:

[0102] S11: Screening candidate sample substations - For each county-level region, extract the power supply range data of all substations within its jurisdiction, calculate the proportion of distributed photovoltaic installed capacity covered by each substation to the total capacity of the region, and screen the substations with the top 30% as candidates (e.g., if there are 8 substations in County A, screen the 3 substations with the top 30% coverage capacity as candidates).

[0103] S12: Determine the final model substation – From the candidate substations, select one or more with the highest overlap between their power supply range and the main distribution area of ​​distributed photovoltaic power (the number is determined according to the capacity level). For example, among the candidate substations in County A, the "Eastern 110kV Substation" covers 90% of the distributed photovoltaic power in the eastern agricultural area, and the "Western 35kV Substation" covers 85% of the distributed photovoltaic power in the western industrial park. The overlap between the two is the highest, therefore, it is determined as the final model substation.

[0104] Step S2: Collect and transmit micro-meteorological data:

[0105] Micro-meteorological data acquisition devices meeting parameter requirements were installed in each model substation (temperature -40℃ to 85℃ ± 0.5℃, humidity 0 to 100% RH ± 3% RH, irradiance 0 to 2000 W / m²). 2 ±2%), complete the 4-20 mA interface wiring between the device and the common measurement and control device (such as connecting the temperature signal to the AI ​​channel 1 of the measurement and control device, and the irradiance to the channel 2, etc.), and ensure stable data acquisition through debugging (no packet loss and error within the allowable range).

[0106] The data collected by the device is processed by the public measurement and control device and then forwarded to the 5000 dispatch system every minute through the power dedicated communication network. The data is stored in the system's real-time database for subsequent fitting and calculation.

[0107] Step S3: Fit and calculate distributed photovoltaic power output:

[0108] This step, based on meteorological data, reference data from centrally dispatched power stations, and a pre-set algorithm, calculates the power output. It specifically includes two processes: constructing the pre-set algorithm and performing fitting calculations.

[0109] (1) Construction of the preset algorithm:

[0110] S31: Construct a training dataset - Extract historical data (one minute of data from the past year) of the five existing 110 kV photovoltaic power stations in the city from the 5000 dispatch system, including measured irradiance, temperature and actual output at the corresponding time, to form a training dataset of "input features (irradiance, temperature) - output labels (output)".

[0111] S32: Training the initial model – The training dataset is trained using the random forest algorithm: with irradiance and temperature as inputs and actual power output as output, the initial power output calculation model is obtained through iterative optimization. The calculation error of the model for the power output of the unified dispatch power station is controlled within ±3%.

[0112] S33: Parameter Adjustment for Distributed Photovoltaics – Due to potential differences in module types between distributed photovoltaic systems and centrally regulated 110kV power stations (e.g., distributed systems often use monocrystalline or polycrystalline modules, while centrally regulated power stations may use high-efficiency modules), the initial model needs to be adjusted. The adjustment formula is as follows:

[0113] P adj =P init ×k type

[0114] Among them, P adj To adjust and adapt to the output of distributed photovoltaic power, P init k is the output value of the initial model. type The component type correction factor is calculated as follows: (Statistics show that monocrystalline modules account for 60% and polycrystalline modules account for 40% of distributed photovoltaic systems in this region. Therefore, a coefficient of 1.02 for monocrystalline and 0.98 for polycrystalline are used, and the weighted average is k). type =1.02×0.6+0.98×0.4=1.004). This adjustment ensures that the model can adapt to the characteristics of distributed photovoltaic modules and reduce errors caused by type differences.

[0115] (2) Fitting and calculation process:

[0116] Based on the adjusted preset algorithm and combined with the micro-meteorological data collected in step S2, the total output of distributed photovoltaic power in the county-level area is calculated according to the following sub-steps:

[0117] S301: Calculation of Irradiance Contribution Term—Irradiance is the core driving factor of photovoltaic power output, and its contribution term is calculated using the following formula:

[0118] P irr =k1×G

[0119] Among them, P irr k is the contribution of irradiance to output power (unit: kW), and k1 is the irradiance proportionality coefficient (unit: kW·m). 2 / W, calibrated from data of centrally dispatched power plants: The irradiance increases by 100W / m² by fitting historical data from centrally dispatched power plants. 2 The output increases by approximately 15% of its rated capacity. Therefore, for a centrally dispatched power station with a rated capacity of 100 MW, 22k1 = 15000kW / 100W / m 2 =150kW·m 2 / W), G is the measured irradiance collected by the sample substation (unit: W / m²). 2 For example, the measured irradiance in County B. 2 G = 800 W / m 2 Then P irr =150×800=120000kW.

[0120] S302: Calculation of the impact of temperature – Increased temperature leads to a decrease in photovoltaic module efficiency. The formula for calculating this impact is as follows:

[0121] P temp =k2×(TT) ref )

[0122] Among them, P temp Let be the effect of temperature on power output (unit: kW), k2 be the temperature influence coefficient (unit: kW / ℃, obtained from the data of the centrally dispatched power station, ℃k2=-2000kW / ℃, the negative sign indicates that the power output decreases as the temperature increases), and T be the measured temperature (unit: ℃). ref For reference temperature (taken as 25℃). For example, if the measured temperature in County B is T = 30℃, then P temp =-2000×(30-25)=-10000kW, that is, a 5℃ increase in temperature leads to a 10000kW decrease in output.

[0123] S303: Calculate the final fitted output power – taking into account the contribution of irradiance, temperature effect, capacity weight, and systematic error correction. The formula is:

[0124] P final =(P irr +P temp )×w+C

[0125] Among them, P final Let P be the total distributed photovoltaic output of the county-level region (unit: kW), w be the weight of the region's grid-connected capacity (County B's total capacity is 85 MW, accounting for 10.625% of the city's 800 MW, so w = 0.10625), and C be the algorithm correction value for the centrally dispatched power station (taken as 500 kW after long-term verification, used to compensate for the model's system error). Substituting the data from County B, we get: P final= (120000-10000)×0.10625+500=110000×0.10625+500=11687.5+500=12187.5kW (i.e. 12.19 MW), which is the fitted output of distributed photovoltaic power in County B.

[0126] Through the implementation of the above system and methods, the prefecture-level city has achieved real-time measurement of the output of 10 kV and 380 V distributed photovoltaic power in all county-level areas. After comparison with the measured data of some areas, the measurement error is controlled within ±5%, which significantly improves the measurability and scheduling accuracy of distributed photovoltaic power and provides strong support for the safe and stable operation of the power grid.

[0127] Beneficial effects:

[0128] The distributed photovoltaic (PV) output fitting system and method proposed in this invention address the industry pain points of insufficient data acquisition capabilities and low output measurement accuracy in current automated dispatch systems after large-scale grid connection of distributed PV systems. Through scientific system architecture design and refined methodology, it achieves comprehensive optimization of distributed PV output monitoring and measurement. Its beneficial effects are reflected in multiple dimensions such as technological innovation, engineering application, grid dispatch, economic and environmental benefits, as detailed below:

[0129] Filling the gap in distributed photovoltaic (PV) data acquisition and constructing a comprehensive monitoring system: Existing technologies for distributed PV systems, due to their wide distribution range, diverse access voltage levels (mainly 10 kV and 380 V), and small individual unit capacity, make it difficult for dispatch automation systems to directly collect data across the entire region, resulting in numerous "monitoring blind spots." This invention deploys micro-meteorological data acquisition devices in representative model substations within county-level administrative regions to accurately capture core meteorological parameters (temperature, humidity, and irradiance) affecting PV output. It seamlessly integrates with existing substation computer monitoring systems and the 5000 dispatch system using a 4-20 mA current signal interface, constructing a distributed data acquisition network of "model acquisition - full coverage." This acquisition mode not only breaks through the geographical limitations of traditional acquisition methods but also ensures the stability and real-time performance of data transmission (transmission cycle no more than 1 minute) through standardized interfaces. It completely fills the gap in the acquisition of meteorological and output data for distributed PV systems, providing solid data source support for subsequent accurate matching and achieving a leap from "locally measurable" to "fully controllable."

[0130] Improve the accuracy of output measurement and provide a scientific basis for dispatching decisions: Photovoltaic output is significantly affected by meteorological conditions, and there are differences in output characteristics among photovoltaic systems in different regions and with different component types. Traditional measurement methods often have large errors due to ignoring these details. This invention makes full use of the physical characteristics that photovoltaic output is proportional to irradiance and inversely proportional to temperature, combines the mature data and algorithms of the unified-regulated 110-kV photovoltaic power station, and constructs a fitting model with multiple correction mechanisms. On the one hand, by introducing the temperature attenuation coefficient correction formula η(T) = η0×(1 - α×(T - T0)), the attenuation effect of temperature increase on the efficiency of photovoltaic components is accurately quantified, avoiding measurement deviations caused by temperature factors; on the other hand, considering the diverse types of distributed photovoltaic components, a component type correction formula P adj = P init ×k type is designed to achieve the adaptation of the model to different component types such as monocrystalline and polycrystalline. At the same time, in the final output measurement, the accuracy of the measurement results is further improved through the comprehensive calculation of the irradiance contribution term, temperature influence term and capacity weight. Verified by practical applications, the output measurement error of this invention can be controlled within ±5%, far better than traditional methods, providing accurate and reliable output data for the power grid dispatching department, effectively reducing the pressure of power grid frequency modulation and peak regulation caused by inaccurate output prediction, and ensuring the safe and stable operation of the power grid.

[0131] Be compatible with existing power grid equipment and reduce the engineering implementation cost: This invention fully considers the compatibility with the existing power grid system during the design process and does not require large-scale renovation of existing dispatching automation equipment. In the data transmission link, the existing computer monitoring system common measuring and controlling device and the power dedicated communication network of the template substation are directly used, and data access and forwarding are realized through the mature 4-20 mA current signal interface, avoiding the high investment in new dedicated communication lines or equipment; the output fitting module runs based on the hardware resources of the existing 5000 dispatching system without additional configuration of dedicated servers or computing devices. This design concept of "leveraging existing resources" not only simplifies the engineering implementation process, shortens the project implementation cycle, but also greatly reduces the system construction and operation and maintenance costs, and has strong engineering operability and promotion value. For the existing power grid system, the function can be quickly upgraded by installing a micro meteorological acquisition device without overthrowing the existing architecture, providing an economically feasible solution for the large-scale deployment of the distributed photovoltaic monitoring system.

[0132] Facilitating New Energy Consumption and Driving Energy Structure Transformation: Distributed photovoltaic (PV) power, as an important component of clean energy, plays a significant role in optimizing the energy structure and reducing carbon emissions through its large-scale development. However, due to the volatility and uncertainty of output, the high proportion of distributed PV connected to the grid also poses challenges to grid absorption. This invention enables dispatching departments to clearly understand the actual situation of PV power generation in various regions by accurately calculating the real-time output of distributed PV, thereby formulating more scientific and reasonable dispatching plans, optimizing grid resource allocation, and improving the absorption capacity of distributed PV. Simultaneously, accurate output data provides important reference for grid planning, helping to rationally deploy energy storage devices and optimize the grid structure, laying the foundation for the further large-scale development of distributed PV. Furthermore, the application of this invention can promote the efficient utilization of clean energy, reduce the consumption of fossil fuels, lower the carbon emission intensity of grid operation, and provide technical support for achieving coordinated development of energy transformation and environmental protection.

[0133] Achieving refined management and enhancing the intelligence level of power grid dispatch: This invention uses county-level administrative regions as the basic calculation unit and achieves accurate fitting of distributed photovoltaic (PV) output within a region through representative data from model substations. This breaks away from the traditional "extensive" regional output estimation model and realizes refined management of distributed PV output. Dispatchers can obtain real-time PV output data for each county-level region through the 5000 dispatch system, clearly understanding the power generation status of different regions and providing a basis for formulating differentiated dispatch strategies. For example, for regions with high PV output, load transfer or energy storage device charging can be arranged in advance; for regions with large output fluctuations, power grid operation monitoring can be strengthened, and dispatch plans can be adjusted in a timely manner. This refined management model not only improves the intelligence level of power grid dispatch but also provides data support for distributed PV to participate in electricity market transactions and ancillary services, promoting the transformation of the power system from traditional dispatch to intelligent and market-oriented dispatch.

[0134] In summary, this invention, through its innovative system architecture and scientific fitting method, solves the problem of distributed photovoltaic power output monitoring while taking into account economy, compatibility, and practicality. It provides important technical support for the safe and stable operation of the power grid, the efficient consumption of new energy sources, and the transformation and upgrading of the energy structure, and has significant technological innovation value and broad engineering application prospects.

Claims

1. A distributed photovoltaic power output fitting system, characterized in that: include: The capacity statistics module is used to calculate the total grid-connected capacity of distributed photovoltaic power in each county-level administrative region; The template selection module is used to determine at least one representative template substation in each county-level administrative region based on the total grid-connected capacity. The data acquisition module includes a micro-meteorological acquisition device deployed in each of the model substations, which is used to collect temperature, humidity and irradiance data at its location. The data transmission module connects the micro-meteorological data acquisition device to the common monitoring and control device of the computer monitoring system of the model substation via a 4-20 mA interface. The common monitoring and control device is used to forward the acquired micro-meteorological data to the 5000 dispatch system. The output fitting module is used to fit and calculate the actual output of 10 kV and 380 V distributed photovoltaic power in the corresponding county-level administrative region, based on the characteristics that photovoltaic output is directly proportional to irradiance and inversely proportional to temperature, referring to the measured data of the directly sourced 110 kV photovoltaic power station and the preset algorithm, and combining the micro-meteorological data.

2. The distributed photovoltaic power output fitting system according to claim 1, characterized in that: The number of micro-meteorological data acquisition devices deployed is one or more per county-level administrative region, with the specific number determined based on the distribution density, geographical range, and grid-connected capacity level of the distributed photovoltaic system in that region.

3. The distributed photovoltaic power output fitting system according to claim 1, characterized in that: In the data transmission module, the 4-20 mA interface is a current signal interface. The public measurement and control device forwards the micro-meteorological data to the 5000 dispatch system through the power dedicated communication network, with a transmission cycle of no more than 1 minute.

4. The distributed photovoltaic power output fitting system according to claim 1, characterized in that: The preset algorithm is an output calculation model built based on historical output data and corresponding micro-meteorological data of the 110 kV photovoltaic power station under unified dispatch. The model is trained by a machine learning algorithm and includes a correction term for the attenuation coefficient of photovoltaic module efficiency due to temperature. The correction term satisfies the formula: η(T)=η0×(1-α×(T-T0)) Where η(T) is the photovoltaic module efficiency at temperature T, η0 is the module rated efficiency at a reference temperature T0 (usually taken as 25℃), α is the temperature decay coefficient (unit: 1 / ℃, typical value is 0.004), and T is the measured temperature; this formula is used to quantify the decay effect of temperature rise on module power generation efficiency and improve the model's response accuracy to temperature factors.

5. The distributed photovoltaic power output fitting system according to claim 1, characterized in that: The criteria for determining the model substation by the model selection module include: the substation's power supply range covers more than 60% of the distributed photovoltaic installed capacity within the county-level administrative region, and its geographical location is near the geometric center of the photovoltaic distribution in that region.

6. A method for fitting distributed photovoltaic power output, characterized in that: Includes the following steps: S1. Calculate the total grid-connected capacity of distributed photovoltaic power in each county-level administrative region, and determine at least one model substation in each county-level administrative region based on the total grid-connected capacity; S2. Install a micro-meteorological acquisition device in each of the model substations. Collect temperature, humidity and irradiance data at the location through the micro-meteorological acquisition device. Connect the collected data to the common measurement and control device of the computer monitoring system of the model substation through the 4-20 mA interface. The common measurement and control device forwards the data to the 5000 dispatch system. S3. Based on the characteristics that photovoltaic output is directly proportional to irradiance and inversely proportional to temperature, referencing the measured data of the directly sourced 110 kV photovoltaic power station and the preset algorithm, and combining the micro-meteorological data collected in step S2, the actual output of 10 kV and 380 V distributed photovoltaic power in the corresponding county-level administrative region is fitted and calculated.

7. The distributed photovoltaic power output fitting method according to claim 6, characterized in that: The process of determining the model substation in step S1 includes: S11. Select the top 30% of substations with the highest grid-connected capacity within the county-level administrative region as candidate model substations. S12. Select one or more candidate model substations whose power supply range overlaps most with the main distribution areas of distributed photovoltaic power as the final model substations.

8. The distributed photovoltaic power output fitting method according to claim 6, characterized in that: In step S2, the temperature data measurement range is -40℃ to 85℃ with an accuracy of ±0.5℃; the humidity data measurement range is 0-100%RH with an accuracy of ±3%RH; and the irradiance data measurement range is 0-2000W / m². 2 Accuracy ±2%.

9. The distributed photovoltaic power output fitting method according to claim 6, characterized in that: The construction process of the preset algorithm in step S3 includes: S31. Using the irradiance and temperature data of the 110 kV photovoltaic power station under unified dispatch as input features and the actual output of the power station as output label, construct a training dataset. S32. Use random forest or gradient boosting algorithm to train the training dataset to obtain the initial output calculation model; S33. Adjust the parameters of the initial model based on the installed capacity type (monocrystalline / polycrystalline modules) of distributed photovoltaics within the county-level administrative region. The adjustment formula is as follows: P adj =P init ×k type Among them, P adj To adjust and adapt to the output of distributed photovoltaic power, P init k is the initial model output value. type The correction factor is 1.02 for monocrystalline modules and 0.98 for polycrystalline modules, calibrated according to the actual module characteristics. This formula is used to adapt to the differences in power generation characteristics of different photovoltaic module types and improve the model's versatility.

10. The distributed photovoltaic power output fitting method according to claim 6, characterized in that: The fitting and calculation process in step S3 includes: S301, Calculate the irradiance contribution term: P irr = k1×G, where P irr k1 is the contribution of irradiance to output power, and k1 is the irradiance proportionality coefficient (unit: kW·m). 2 / W, calibrated from data of the centrally dispatched power station), G is the measured irradiance (unit: W / m²). 2 ); S302, Calculate the temperature effect term: P temp =k2×(TT) ref ), where P temp Here, k2 represents the effect of temperature on output, k2 is the temperature influence coefficient (unit: kW / ℃, k2<0 indicates that output decreases with increasing temperature), and T is the measured temperature (unit: ℃). ref Reference temperature (unit: °C, usually taken as 25 °C); S303. Calculate the final fitted output force: P final =(P irr +P temp )×w+C, where P final For the total output of distributed photovoltaic power in the county-level area, w is the weight of the grid-connected capacity of the area (provided by the capacity statistics module), and C is the algorithm correction value of the 110 kV power station under unified dispatch (unit: kW, used to compensate for model system errors); The above formula achieves accurate fitting of distributed photovoltaic power output by quantifying the combined influence of meteorological factors and capacity weights.

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