Intelligent evaluation method and system for distributed photovoltaic resources

Abstract

The invention relates to the technical field of intelligent power grids and renewable energy sources, in particular to an intelligent evaluation method and system for distributed photovoltaic resources. Comprising the following steps: S1, on the basis of acquired static topological data and dynamic operation data of a power distribution network, calculating physical absorption capacity of a grid-connected point by coupling thermal stability constraint and voltage stability constraint; s2, in combination with the physical consumption capacity and expected photovoltaic output predicted based on weather forecast data, calculating to obtain a light abandoning risk index; s3, in combination with the light abandoning risk index, the expected photovoltaic output and the predicted market on-grid electricity price, calculating to obtain a total income after risk adjustment; s4, generating a photovoltaic investment value index through normalization processing based on the total income after risk adjustment and a preset reference total income; and S5, according to the photovoltaic investment value index and a preset value grading threshold value, outputting an investment decision support strategy. The method overcomes the defect that in the prior art, static and fixed limit values are adopted for evaluation, so that the evaluation result is disjointed with the actual operation condition of the power grid.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of smart grid and renewable energy technology, in particular to a smart evaluation method and system for distributed photovoltaic resources. BACKGROUND

[0002] With the rapid development of distributed photovoltaic resources, the scientificity and accuracy of investment decision-making become the key; effective evaluation of the planning and investment of distributed photovoltaic resources is crucial to improving decision-making quality; however, the traditional evaluation method of photovoltaic resource investment faces many challenges in practical application; The existing technology is relatively single and static in evaluation dimension, and fails to fully consider the dynamic changes of the power grid; the traditional evaluation method often uses fixed limits, which cannot reflect the power grid's accommodation capacity in real time, and ignores the influence of power grid dynamic operation data on the evaluation results; in addition, the existing technology generally has the defect of disconnection between physical constraints and economic benefits; the evaluation process fails to effectively combine the physical boundary conditions such as thermal stability and voltage stability of the power grid with economic indicators such as light rejection risk and market electricity price fluctuation, resulting in that the evaluation results cannot accurately reflect the real profitability and potential risks of the project; The current evaluation technology fails to build a logical closed loop from accurate quantification of power grid physical boundaries, to probabilistic evaluation of grid-connected mismatch risk, to risk adjustment of asset economic value, and finally to index output of investment decision-making; in summary, the existing technology lacks a dynamic, forward-looking and highly quantitative decision support tool for distributed photovoltaic resource investment, and it is difficult to converge the calculation results of complex physical risks and economic benefits into a intuitive and standardized top-level evaluation score, thereby significantly affecting the scientificity and accuracy of investment decision-making.

[0003] The above information disclosed in the above background section is only used to strengthen the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0004] To solve the above technical problems, the present application discloses a smart evaluation method and system for distributed photovoltaic resources, specifically, the technical solution of the present application is: The smart evaluation method for distributed photovoltaic resources comprises: S1, based on the collected static topology data and dynamic operation data of the distribution network, the physical accommodation capacity of the grid-connected point is calculated by coupling the thermal stability constraint and the voltage stability constraint; S2, combining the physical accommodation capacity and the expected photovoltaic output predicted based on the meteorological forecast data, the light rejection risk index is calculated; S3, combining the light rejection risk index, the expected photovoltaic output and the predicted market on-grid electricity price, the total income after risk adjustment is calculated; S4, based on the risk-adjusted total return and the preset benchmark total return, a photovoltaic investment value index is generated by normalization processing; S5, according to the photovoltaic investment value index and the preset value classification threshold, an investment decision support strategy is output.

[0005] Preferably, the physical consumption capacity of the grid-connected point is calculated, including: Based on the upper and lower limits of the node voltage of the distribution network and the reference voltage, combined with the predicted node voltage, the voltage health factor is calculated; Based on the transformer rated capacity of the distribution network and the predicted line flow, the residual thermal capacity is determined; The residual thermal capacity is dynamically reduced by the voltage health factor to obtain the physical consumption capacity.

[0006] Preferably, the light rejection risk index is calculated, including: The power amount that the expected photovoltaic output exceeds the physical consumption capacity is defined as the expected light rejection power; The ratio of the expected light rejection power to the expected photovoltaic output is determined as the light rejection risk index.

[0007] Preferably, the risk-adjusted total return is calculated, including: In the preset evaluation period, the expected photovoltaic output at each time is multiplied by the difference between 1 and the light rejection risk index, and then multiplied by the predicted market on-grid price, and then accumulated and summed to obtain the risk-adjusted total return.

[0008] Preferably, the benchmark total return is the ideal total return in the preset evaluation period assuming the light rejection risk index is zero.

[0009] Preferably, the investment decision support strategy is output, including: In response to the photovoltaic investment value index being greater than or equal to the preset high value threshold, the investment value level is determined as high investment value; In response to the photovoltaic investment value index being less than the high value threshold and greater than or equal to the preset high risk threshold, the investment value level is determined as medium investment value; In response to the photovoltaic investment value index being less than the high risk threshold, the investment value level is determined as high investment risk.

[0010] The intelligent evaluation system of distributed photovoltaic resources includes: The data acquisition module is used to acquire the static topology data and dynamic operation data of the distribution network, and the weather forecast data; The physical capacity calculation module is used to calculate the physical consumption capacity of the grid-connected point based on the static topology data and dynamic operation data collected by the data acquisition module; The risk index calculation module is used to combine the physical absorption capacity calculated by the physical capacity calculation module with the predicted expected photovoltaic output to calculate the curtailment risk index. The revenue assessment module is used to calculate the risk-adjusted total revenue by combining the curtailment risk index calculated by the risk index calculation module, the expected photovoltaic output, and the predicted market feed-in tariff. The value index generation module is used to generate a photovoltaic investment value index based on the risk-adjusted total return calculated by the return assessment module and the preset benchmark total return. The decision support module is used to output investment decision support strategies based on the photovoltaic investment value index generated by the value index generation module and the preset value grading threshold.

[0011] Preferred, including: The health factor calculation unit is used to calculate the voltage health factor based on the upper and lower limits of the node voltage and the reference voltage of the distribution network, combined with the predicted node voltage. The heat capacity calculation unit is used to determine the remaining heat capacity based on the rated capacity of transformers in the distribution network and the predicted line power flow. The absorption capacity correction unit is used to dynamically reduce the remaining heat capacity using the voltage health factor to obtain the physical absorption capacity.

[0012] Preferably, the risk index calculation module is used to define the amount of power that the expected photovoltaic output exceeds the physical absorption capacity as the expected curtailment power, and to determine the ratio of the expected curtailment power to the expected photovoltaic output as the curtailment risk index.

[0013] Preferably, the decision support module is used for: In response to the photovoltaic investment value index being greater than or equal to the preset high value threshold, the investment value level is determined to be high investment value; In response to the photovoltaic investment value index being less than the high value threshold and greater than or equal to the preset high risk threshold, the investment value level is determined to be medium investment value; In response to the photovoltaic investment value index being lower than the high-risk threshold, the investment value level is judged as high investment risk.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention, by coupling the thermal stability and voltage stability constraints of the power grid and combining the static topology data and dynamic operation data of the distribution network, can dynamically calculate the physical absorption capacity of the grid connection point, overcoming the defect of existing technologies that use static and fixed limits for evaluation, resulting in the evaluation results being out of sync with the actual operation of the power grid.

[0015] 2、The application constructs a complete evaluation closed loop from power grid physical constraints, grid-connected risk probability to investment economic value. By converting the physical consumption capacity into the light abandonment risk index, and using it as a dynamic reduction coefficient to modify the ideal power generation income, the physical constraints and economic benefits are closely combined, so that the economic evaluation result can truly reflect the limitation of the power grid receiving capacity.

[0016] 3、The application converges the complex physical risk and economic benefit calculation results into a standardized photovoltaic investment value index through normalization processing. According to the index and the preset threshold, clear investment suggestions such as high investment value, medium investment value or high investment risk level are provided, the transformation from complex technical analysis to intuitive business decision is realized, and the scientificity and practicality of investment decision are improved.

[0017] 4、The application proposes an innovative physical consumption capacity calculation method, which dynamically adjusts the residual heat capacity by introducing a voltage health degree factor, so that the calculated consumption capacity can reflect the voltage health condition of the power grid in real time. The method effectively captures the core constraint relationship and improves the accuracy of the evaluation result, while ensuring high calculation efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0018] The application will be further explained below in combination with the drawings and embodiments: Figure 1 is a flow chart of the method of the application.

[0019] Figure 2 is a flow chart of the system of the application. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical scheme and advantages of the application clearer and more apparent, the application will be further described in detail below in combination with specific embodiments.

[0021] Embodiment 1: Please refer to Figure 1 , the intelligent evaluation method of distributed photovoltaic resources, which comprises: S1, based on the collected static topology data and dynamic operation data of the distribution network, the physical consumption capacity of the grid-connected point is calculated by coupling the thermal stability constraint and the voltage stability constraint; S2, the light abandonment risk index is calculated in combination with the physical consumption capacity and the expected photovoltaic output based on the meteorological forecast data; S3, the total income after risk adjustment is calculated in combination with the light abandonment risk index, the expected photovoltaic output and the predicted market on-grid price; S4, based on the total income after risk adjustment and the preset reference total income, the photovoltaic investment value index is generated through normalization processing; S5, outputting an investment decision support strategy according to the photovoltaic investment value index and a preset value classification threshold value; The embodiment of the present application provides a kind of intelligent evaluation method of distributed photovoltaic resource;The purpose of this method is to build a complete evaluation closed loop from power grid physical constraint, grid-connected risk probability to investment economic value, to provide a quantitative, dynamic and high reference value top-level guidance index for the investment decision of distributed photovoltaic;In this embodiment, the method as a complete and self-consistent technical process includes the following steps: S1, based on the collected distribution network static topology data and dynamic operation data, the physical absorption capacity of the grid-connected point is calculated by coupling thermal stability constraint and voltage stability constraint;Physical absorption capacity refers to the maximum photovoltaic power that a specific grid-connected point can accept at a specific time under the premise of ensuring the safe and stable operation of the power grid, which provides an accurate and reliable physical boundary for subsequent risk assessment, calculated by the model proposed in this embodiment; To achieve this step, first, by consulting the power grid geographic information system and asset management database, the static topology data of the target grid-connected point in the regional distribution network is obtained, such as line impedance, transformer rated capacity, protection setting value, node voltage upper and lower limit, etc.;Second, by deploying measurement units or smart meters in the region, real-time dynamic operation data such as regional historical load data and real-time electricity price are obtained;Based on historical load data, time series prediction models such as ARIMA, LSTM, etc. are used to predict future node load, and the prediction results will be used as input for power flow calculation;Based on the above data, thermal stability constraint and voltage stability constraint are innovatively coupled to calculate dynamic physical absorption capacity, rather than using the traditional static and fixed limit value; S2, combine physical absorption capacity with expected photovoltaic output based on weather forecast data to calculate the curtailment risk index;The curtailment risk index refers to the quantitative measure of the risk that the expected photovoltaic power generation cannot be fully connected to the grid due to insufficient physical absorption capacity of the power grid, which converts abstract systemic risk into specific, time-based probabilistic indicators. Based on the physical absorption capacity of S1 and photovoltaic output prediction, this step uses integrated weather forecast data to probabilistically predict the expected photovoltaic output of the target grid-connected point, and compares the predicted output with the physical absorption capacity calculated in the previous step to quantitatively assess the likelihood of exceeding the absorption limit of power generation; S3, combined with the risk of light abandonment index, the expected photovoltaic output and the predicted market on-grid electricity price, the risk-adjusted total return is calculated; the risk-adjusted total return refers to the real expected profitability of the distributed photovoltaic asset in a specific evaluation period after fully considering the risk of grid-connected light abandonment; its role is to provide an economic indicator closer to the actual operating conditions for investment evaluation; it is calculated based on ideal income and reduced by light abandonment risk index; this step takes the light abandonment risk calculated in S2 as a dynamic reduction factor to modify the power generation income in the ideal case, thereby obtaining an asset net income evaluation result that comprehensively considers power generation potential, grid connection constraints and market price fluctuations; S4, based on the risk-adjusted total return and the preset benchmark total return, a photovoltaic investment value index is generated through normalization processing; the photovoltaic investment value index refers to a direct, standardized top-level evaluation score formed by converging complex physical risks and economic income calculation results; its role is to directly reflect the percentage of risk-adjusted income to ideal income; the benchmark total return is defined as the total theoretical income that the photovoltaic project can generate in the same evaluation period under ideal conditions, i.e. assuming that the light abandonment risk is zero; by comparing the calculated risk-adjusted return with this ideal benchmark, this step generates a standardized index ranging from 0 to 100, making the investment value of different projects comparable; S5, according to the photovoltaic investment value index and the preset value classification threshold, output investment decision support strategy; the investment decision support strategy refers to the clear and executable business decision recommendations provided for investors based on the quantification results of the photovoltaic investment value index; its role is to realize the final transition from complex technical analysis to business feasibility evaluation; it is determined according to the classification interval of the value index; this step compares the value index generated in the previous step with the preset threshold, divides the investment value into different levels, and outputs clear investment recommendations accordingly, such as high investment value, medium investment value or high investment risk; The present application constructs a logical closed loop from accurate quantification of power grid physical boundaries, to probabilistic assessment of grid mismatch risk, to risk adjustment of asset economic value, and finally to index output of investment decision, through the above steps; it overcomes the defects of single evaluation dimension, static, and disconnection between physical constraints and economic benefits in the prior art, and can provide a dynamic, forward-looking and highly quantitative decision support tool for the planning and investment of distributed photovoltaic resources, significantly improving the scientificity and accuracy of investment decisions.

[0022] Example 2: The physical absorption capacity of the grid connection point is calculated, including: a voltage health factor is calculated based on the upper and lower limits of the node voltage of the power distribution network and the reference voltage, combined with the predicted node voltage; a residual thermal capacity is determined based on the rated capacity of the transformer of the power distribution network and the predicted line power flow; a physical accommodation capacity is obtained by dynamically reducing the residual thermal capacity using the voltage health factor; In this embodiment, the step of calculating the physical accommodation capacity of the grid-connected point is specified and optimized based on Embodiment 1. The purpose is to make the calculated accommodation capacity reflect the voltage health status of the power grid in real time through an innovative dynamic reduction mechanism, so as to obtain more accurate results than traditional methods. The specific implementation includes the following aspects: a voltage health factor is calculated based on the upper and lower limits of the node voltage of the power distribution network and the reference voltage, combined with the predicted node voltage; to dynamically quantify the accommodation capacity of the power grid voltage to the newly added power, this embodiment introduces a voltage health factor , which is calculated as follows:

[0023] wherein, : represents the voltage health factor of the i node at time t; is a dimensionless parameter; is calculated by the formula; the role of this factor is to represent the margin of the current voltage from the safe upper limit, and the value close to 1 indicates that the voltage state is good and the accommodation capacity is strong; the smaller the value, the closer the voltage is to the upper limit, and the worse the accommodation capacity; : represents the upper limit of the voltage amplitude allowed by the i node; its dimension is voltage, such as kV; it is obtained by consulting the power grid design specification and operation criteria; : represents the predicted voltage of the i node at time t; its dimension is voltage, such as kV; the real-time calculation result obtained by combining the predicted load data through the standard power flow calculation tool is the input variable of this calculation step; : represents the reference voltage of the system; its dimension is voltage, such as kV; it is obtained by consulting the power grid design specification and operation criteria; a residual thermal capacity is determined based on the rated capacity of the transformer of the power distribution network and the predicted line power flow; the residual thermal capacity refers to the capacity of the key line or transformer that can be used to transmit the newly added power after bearing the predicted background power flow, and its role is to determine the thermal stability boundary of the power grid; in this embodiment, it is calculated by , wherein is the rated transmission power limit of the key line or transformer on the feeder where the grid-connected point i is located, and the power grid asset management database can obtain it from the power grid asset management database, The background power flow flowing through the key line or transformer at the time t is calculated by a power flow calculation model and combined with the predicted load data; The physical accommodation capacity is obtained by dynamically reducing the residual thermal capacity by using the voltage health factor; this is a core innovation point of the technical solution, and the internal logic is that by introducing a dynamic correction factor related to the voltage state, the voltage stability constraint is converted into dynamic adjustment of the thermal stability boundary, thereby realizing effective combination of the two constraints, simplifying the complex nonlinear power flow problem into linear algebraic operation, which is an approximate model, and the specific calculation method is as follows:

[0024] wherein, : represents the physical accommodation capacity of the i node at the time t; the dimension is power, such as MW; obtained by calculation from the formula; By multiplying the voltage health reflecting as a linear dynamic reduction coefficient on the residual thermal capacity calculated in the previous step, the physical accommodation capacity of the embodiment is no longer a fixed thermal capacity limit, but a dynamic boundary that can intelligently contract according to the real-time voltage health status; this linearization processing is an effective balance between calculation efficiency and physical fidelity; it converts the complex nonlinear power flow problem into a simple algebraic operation, greatly improving the calculation efficiency of rapid evaluation of massive time series data, and through the voltage health factor, the decisive constraint relationship between the voltage level and the accommodation capacity is effectively captured in a macroscopic way; This calculation method takes the predicted background power flow and the node voltage as independent inputs, realizes decoupling calculation of the two constraints through the dynamic reduction coefficient, and greatly improves the calculation efficiency; as an engineering approximation, the approximate model can effectively capture the macroscopic influence of the voltage health status on the accommodation capacity while ensuring the calculation speed; It should be pointed out that the approximate model does not consider the influence of the newly added photovoltaic injection power on the background power flow , which may introduce certain errors in the scenario with high penetration rate of distributed power sources, but in most engineering scenarios, it can provide reliable evaluation results with very high calculation efficiency; the approximate model is especially suitable for rapid screening and evaluation in the planning stage of distribution networks, and its core assumption is that the newly added photovoltaic capacity is small compared to the total load and the total capacity of the line, so the marginal influence of the background power flow and the voltage distribution can be linearized.

[0025] Embodiment 3: The light rejection risk index is calculated, including: The power amount that exceeds the physical accommodation capacity is defined as the expected curtailment power; The ratio of the expected curtailment power to the expected photovoltaic output is determined as the curtailment risk index; The embodiment is based on embodiment 1, and the step of calculating the curtailment risk index is specified; the purpose is to establish a risk measurement model directly driven by physical constraints to quantify the mismatch between the expected photovoltaic output and the actual accommodation capacity of the power grid; the implementation is described as follows: To achieve this step, the power amount that exceeds the physical accommodation capacity is defined as the expected curtailment power ; the expected curtailment power refers to the part of the predicted photovoltaic power that exceeds the upper limit of the physical accommodation capacity of the power grid at a certain moment, and its role is to quantify the absolute power value of waste; in this embodiment, it is calculated by ; wherein, is the expected photovoltaic output at time t obtained by a probabilistic prediction model, and the prediction model is established based on integrated meteorological forecast data and photovoltaic power station parameters; is the physical accommodation capacity of node i at time t calculated from step of embodiment 2; The ratio of the expected curtailment power to the expected photovoltaic output is determined as the curtailment risk index; in order to standardize and make the risk assessment results comparable and ensure the robustness of the model, the curtailment risk index is calculated by the following piecewise function formula in this embodiment :

[0026] This piecewise function ensures that the curtailment risk index is defined as zero at the moment when the expected photovoltaic output is zero, such as at night, thereby avoiding division by zero error in calculation; wherein, : represents the curtailment risk index of node i at time t; it is a dimensionless ratio; it is calculated by this formula; it directly reflects the proportion of the expected power that is expected to be curtailed due to insufficient accommodation capacity; : represents the expected photovoltaic output at time t; its dimension is power, such as MW; it is the output of the probabilistic photovoltaic power prediction model; : represents the physical accommodation capacity at time t; its dimension is power, such as MW; it is the output of the step of embodiment 2; The numerator of this formula is the expected curtailment power, and the denominator is the expected total output; the dimensionless ratio obtained by dividing the two is completely driven by the two core physical quantities of the expected output of the photovoltaic and the physical accommodation capacity of the power grid, without any external adjustable weight, which ensures the objectivity of the evaluation.

[0027] Embodiment 4 The total risk-adjusted revenue is calculated, including: In the preset evaluation period, the expected photovoltaic output at each time is multiplied by (1-abandoned light risk index), and then multiplied by the predicted market on-grid electricity price, and then accumulated and summed to obtain the risk-adjusted total revenue; On the basis of embodiment 3, the step of calculating the risk-adjusted total revenue is specified; the purpose is to establish a quantitative calculation model that can accurately conduct the physical layer abandoned light risk to the economic benefit layer; the risk-adjusted total revenue The specific calculation formula is as follows:

[0028] Among them, is the risk-adjusted total revenue in the evaluation period T; is the expected photovoltaic output at time t; is the abandoned light risk index at time t; is the predicted market on-grid electricity price at time t; is the time step for calculation; the formula introduces a discount item , which deducts the potential revenue loss caused by abandoned light risk at each time section, so that the final revenue evaluation result can truly reflect the economic impact of insufficient grid accommodation capacity.

[0029] Embodiment 5 The benchmark total revenue is the ideal total revenue in the preset evaluation period, assuming that the abandoned light risk index is zero; This embodiment is based on embodiment 1, and the benchmark total revenue is defined in this embodiment; the purpose is to provide a standard, ideal reference for subsequent investment value index normalization calculation; In this embodiment, the benchmark total revenue is defined as the ideal total revenue in the preset evaluation period, assuming that the abandoned light risk index is zero; this means that when calculating the benchmark total revenue , we assume that the accommodation capacity of the power grid is infinite, and all expected photovoltaic output can be fully accommodated and on-grid, that is, for all time t, the abandoned light risk index ; the calculation formula is as follows:

[0030] Among them, : represents the benchmark total revenue in the evaluation period T; the dimension is currency; calculated by the formula; : represents the total duration of the evaluation period; the dimension is time; pre-set according to the evaluation requirements; : represents the expected photovoltaic output at time t; its dimension is power, such as MW; the output of the probabilistic photovoltaic output prediction model; : represents the predicted market on-grid electricity price at time t; its dimension is currency / power-time, such as yuan / MWh; it can be predicted by calling the day-ahead electricity price published by the electricity market or using a time series prediction model, such as an ARIMA model, based on historical electricity prices; : represents the calculated time step; its dimension is time, such as hours; it is pre-set according to the evaluation requirements.

[0031] Embodiment 6: The output investment decision support strategy includes: In response to the photovoltaic investment value index being greater than or equal to a pre-set high value threshold, the investment value level is determined as high investment value; In response to the photovoltaic investment value index being less than the high value threshold and greater than or equal to a pre-set high risk threshold, the investment value level is determined as medium investment value; In response to the photovoltaic investment value index being less than the high risk threshold, the investment value level is determined as high investment risk; This embodiment is based on Embodiment 1, and this embodiment specifically implements the step of outputting the investment decision support strategy; the purpose is to convert the calculated quantitative investment value index into an investment level and suggestion that is friendly, intuitive and executable for decision makers without technical background; In this embodiment, based on the calculated photovoltaic investment value index , a three-level evaluation system is established; the threshold values of the system, such as the high value threshold and the high risk threshold, are set based on the technical logic that a calibration data set containing a large amount of historical project data is statistically calibrated; each data point in the calibration data set contains two independent dimensional variables: one is the historical investment value index calculated according to the method of the present application, and the other is the real financial performance indicator of the project, such as internal rate of return ; by regression analysis and other statistical methods, the index threshold value that can effectively distinguish different investment return intervals is determined; for example, the high risk threshold can be set as the value index quantile that has caused the project to be significantly lower than expected in history; the specific decision support strategy output logic is as follows: In response to the photovoltaic investment value index being greater than or equal to a pre-set high value threshold, the investment value level is determined as high investment value; in this embodiment, the high value threshold is set to 95; when , the system outputs the conclusion of high investment value; this indicates that the consumption capacity of the grid-connected point is sufficient, the light rejection risk is extremely low, the commercial feasibility of the project is strong, and investment is recommended to be prioritized; In response to the photovoltaic investment value index being less than the high value threshold and greater than or equal to a preset high risk threshold, the investment value level is determined as medium investment value; in the embodiment, the high risk threshold is set to 85; when , the system outputs a conclusion of medium investment value; this indicates that there is a certain light abandonment risk at the grid-connected point, and there may be a consumption bottleneck at a certain period such as a light peak, and it is recommended that the investor evaluate carefully, and a risk mitigation measure such as a storage system can be considered to improve the project value; In response to the photovoltaic investment value index being less than the high risk threshold, the investment value level is determined as high investment risk; when , the system outputs a conclusion of high investment risk; this indicates that there is a serious consumption problem at the grid-connected point, the light abandonment risk is high, the expected income loss is large, and it is recommended to avoid investment or re-evaluate the project scheme.

[0032] Embodiment 7: Referring to Figure 2 , a data acquisition module is configured to acquire static topology data and dynamic operation data of the power distribution network, and meteorological forecast data; a physical capacity calculation module is configured to calculate the physical consumption capacity of the grid-connected point based on the static topology data and the dynamic operation data acquired by the data acquisition module; a risk index calculation module is configured to calculate the light abandonment risk index in combination with the physical consumption capacity calculated by the physical capacity calculation module and the predicted expected photovoltaic output; a benefit evaluation module is configured to calculate the risk-adjusted total benefit in combination with the light abandonment risk index calculated by the risk index calculation module, the expected photovoltaic output, and the predicted market on-grid price; a value index generation module is configured to generate the photovoltaic investment value index based on the risk-adjusted total benefit calculated by the benefit evaluation module and a preset benchmark total benefit; a decision support module is configured to output an investment decision support strategy according to the photovoltaic investment value index generated by the value index generation module and a preset value classification threshold; The embodiment of the present application provides an intelligent evaluation system for distributed photovoltaic resources, which is designed to execute the intelligent evaluation method in any of the preceding embodiments; the purpose of the system is to provide an integrated, automated hardware or software platform to realize comprehensive evaluation of the investment value of distributed photovoltaic resources; in the embodiment, the system includes: a data collection module, which is configured to collect static topology data and dynamic operation data of the power distribution network, and weather forecast data, and provide all necessary raw data inputs for the entire evaluation system; the static data is obtained by connecting with a power grid geographic information system (GIS) and an asset management database through an interface, the dynamic operation data is obtained by communicating with smart meters or phasor measurement units (PMUs) deployed in the region in real time, and the weather forecast data is obtained by calling a third-party weather service API; a physical capacity calculation module, which is configured to calculate the physical accommodation capacity of the grid-connected point based on the static topology data and the dynamic operation data collected by the data collection module, and execute S1 in the foregoing method; the calculation model coupled with thermal stability and voltage stability constraints is fixed in the module; a risk index calculation module, which is configured to calculate the light rejection risk index in combination with the physical accommodation capacity calculated by the physical capacity calculation module and the predicted expected photovoltaic output, and execute S2 in the foregoing method; the module receives the output of the physical capacity calculation module, and executes the calculation of the risk index in combination with the internal photovoltaic output prediction model; a benefit evaluation module, which is configured to calculate the risk-adjusted total benefit in combination with the light rejection risk index calculated by the risk index calculation module, the expected photovoltaic output and the predicted market on-grid price, and execute S3 in the foregoing method; the module integrates the calculation logic of the risk-adjusted total benefit; a value index generation module, which is configured to generate the photovoltaic investment value index based on the risk-adjusted total benefit calculated by the benefit evaluation module and the preset benchmark total benefit, and execute S4 in the foregoing method; the module performs normalization processing and outputs the final value index; a decision support module, which is configured to output the investment decision support strategy according to the photovoltaic investment value index generated by the value index generation module and the preset value classification threshold, and execute S5 in the foregoing method; the module stores the value classification threshold in the internal storage, judges according to the input index, and finally displays the explicit investment suggestion on the user interface; The present application solidifies the complex evaluation method into a set of automatic functional units through modular system design; each module has clear responsibilities and works cooperatively, and realizes the full-process automation from raw data collection to final decision support; this not only greatly improves the evaluation efficiency and reduces the errors caused by manual intervention, but also enables the advanced evaluation method to be provided in the form of productization or service, and has strong practicability and deployability.

[0033] Example 8: a physical capacity calculation module comprising: a health factor calculation unit configured to calculate a voltage health factor based on the upper and lower limits of the node voltage of the power distribution network and the reference voltage, in combination with the predicted node voltage; a thermal capacity calculation unit configured to determine the residual thermal capacity based on the transformer rated capacity of the power distribution network and the predicted line flow; a curtailment capacity correction unit configured to dynamically reduce the residual thermal capacity using the voltage health factor to obtain the physical curtailment capacity; In this embodiment, the physical capacity calculation module specifically comprises: a health factor calculation unit configured to calculate the voltage health factor based on the upper and lower limits of the node voltage of the power distribution network and the reference voltage, in combination with the predicted node voltage; it receives voltage-related parameters from the data acquisition module and performs the calculation of the voltage health factor, the output of which serves as the basis for curtailment capacity correction; a thermal capacity calculation unit configured to determine the residual thermal capacity based on the transformer rated capacity of the power distribution network and the predicted line flow; it receives the transformer rated capacity and the predicted line flow data and calculates the value of the residual thermal capacity; a curtailment capacity correction unit configured to dynamically reduce the residual thermal capacity using the voltage health factor to obtain the physical curtailment capacity; it receives the voltage health factor from the health factor calculation unit and the residual thermal capacity from the thermal capacity calculation unit, multiplies the two to obtain the final physical curtailment capacity, and outputs it to the risk index calculation module.

[0034] Embodiment 9: a risk index calculation module configured to define the amount of power that exceeds the physical curtailment capacity as the expected curtailment power, and determine the ratio of the expected curtailment power to the expected photovoltaic output as the curtailment risk index; In this embodiment, the specific function of the risk index calculation module is further clarified based on embodiment 7; the purpose is to reveal the operation logic inside the module for implementing the method of embodiment 3; ​​​​​In this embodiment, the risk index calculation module is specifically configured to define the amount of power by which the expected photovoltaic output exceeds the physical accommodation capacity as the expected curtailment power, and determine the ratio of the expected curtailment power to the expected photovoltaic output as the curtailment risk index; to achieve this purpose, the module receives from the physical capacity calculation module and calls an internal probabilistic photovoltaic output prediction model to obtain ; based on the above inputs, the processor inside the module performs operation to obtain the expected curtailment power, and then performs division operation, and finally obtains the curtailment risk index and outputs it to the benefit evaluation module.

[0035] Embodiment 10: The decision support module is configured to: determine the investment value level as high investment value in response to the photovoltaic investment value index being greater than or equal to a preset high value threshold; determine the investment value level as medium investment value in response to the photovoltaic investment value index being less than the high value threshold and greater than or equal to a preset high risk threshold; determine the investment value level as high investment risk in response to the photovoltaic investment value index being less than the high risk threshold. This embodiment is based on embodiment 7, and the specific functions and internal logic of the decision support module are described in detail; the purpose is to reveal how the module converts a quantitative index into a graded and operable decision suggestion, so as to realize the strategy output of embodiment 6; In this embodiment, the decision support module is internally configured with a corresponding comparator and logic judgment unit, and the specific working logic is as follows: The module is configured to determine the investment value level as high investment value in response to the photovoltaic investment value index being greater than or equal to a preset high value threshold; specifically, the module internally stores a high value threshold, and when the received meets the condition of , the logic unit triggers the output of the determination result of high investment value; The module is further configured to determine the investment value level as medium investment value in response to the photovoltaic investment value index being less than the high value threshold and greater than or equal to a preset high risk threshold; the module internally stores a high risk threshold at the same time, and when the received meets the condition of , the logic unit triggers the output of the determination result of medium investment value; The module is finally configured to determine the investment value level as high investment risk in response to the photovoltaic investment value index being less than the high risk threshold; when the received meets the condition of When the conditions of the investment risk are high, the logic unit triggers output of a determination result of high investment risk.

[0036] The above merely describes the preferred embodiments of the present application, but not for limiting the protection scope of the present application; any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0037] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, but not for limiting, although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. An intelligent assessment method for distributed photovoltaic resources, characterized in that, include: S1, based on the collected static topology data and dynamic operation data of the distribution network, calculates the physical absorption capacity of the grid connection point by coupling thermal stability constraints and voltage stability constraints; S2, combining the physical absorption capacity and the expected photovoltaic output predicted based on meteorological forecast data, calculates the curtailment risk index; S3, combining the curtailment risk index, expected photovoltaic output and predicted market feed-in tariff, calculates the risk-adjusted total return; S4 generates a photovoltaic investment value index based on the risk-adjusted total return and the preset benchmark total return through normalization. S5 outputs investment decision support strategies based on the photovoltaic investment value index and preset value grading thresholds.

2. The intelligent assessment method for distributed photovoltaic resources according to claim 1, characterized in that, The physical absorption capacity of the grid connection point is calculated, including: Based on the upper and lower limits of node voltage and the reference voltage of the distribution network, and combined with the predicted node voltage, the voltage health factor is calculated. The remaining heat capacity is determined based on the rated capacity of transformers in the distribution network and the predicted line power flow. The remaining heat capacity is dynamically reduced using the voltage health factor to obtain the physical absorption capacity.

3. The intelligent assessment method for distributed photovoltaic resources according to claim 1, characterized in that, The calculated curtailment risk index includes: The amount of power expected to exceed the physical absorption capacity is defined as the expected curtailment power. The ratio of expected curtailment power to expected photovoltaic output is defined as the curtailment risk index.

4. The intelligent assessment method for distributed photovoltaic resources according to claim 1, characterized in that, The risk-adjusted total return is calculated, including: Within the preset assessment period, the expected photovoltaic output at each moment is multiplied by 1 by the difference between the expected output and the curtailment risk index, then multiplied by the predicted market feed-in tariff, and finally summed to obtain the risk-adjusted total return.

5. The intelligent assessment method for distributed photovoltaic resources according to claim 1, characterized in that, The benchmark total return is the ideal total return assuming the curtailment risk index is zero within the preset evaluation period.

6. The intelligent assessment method for distributed photovoltaic resources according to claim 1, characterized in that, Output investment decision support strategies, including: In response to the photovoltaic investment value index being greater than or equal to the preset high value threshold, the investment value level is determined to be high investment value; In response to the photovoltaic investment value index being less than the high value threshold and greater than or equal to the preset high risk threshold, the investment value level is determined to be medium investment value; In response to the photovoltaic investment value index being lower than the high-risk threshold, the investment value level is judged as high investment risk.

7. An intelligent assessment system for distributed photovoltaic resources, based on the intelligent assessment method for distributed photovoltaic resources according to any one of claims 1-6, characterized in that, include: The data acquisition module is used to collect static topology data and dynamic operation data of the power distribution network, as well as weather forecast data; The physical capacity calculation module is used to calculate the physical absorption capacity of the grid connection point based on the static topology data and dynamic operation data collected by the data acquisition module. The risk index calculation module is used to combine the physical absorption capacity calculated by the physical capacity calculation module with the predicted expected photovoltaic output to calculate the curtailment risk index. The revenue assessment module is used to calculate the risk-adjusted total revenue by combining the curtailment risk index calculated by the risk index calculation module, the expected photovoltaic output, and the predicted market feed-in tariff. The value index generation module is used to generate a photovoltaic investment value index based on the risk-adjusted total return calculated by the return assessment module and the preset benchmark total return. The decision support module is used to output investment decision support strategies based on the photovoltaic investment value index generated by the value index generation module and the preset value grading threshold.

8. The intelligent assessment system for distributed photovoltaic resources according to claim 7, characterized in that, The physical capacity calculation module includes: The health factor calculation unit is used to calculate the voltage health factor based on the upper and lower limits of the node voltage and the reference voltage of the distribution network, combined with the predicted node voltage. The heat capacity calculation unit is used to determine the remaining heat capacity based on the rated capacity of transformers in the distribution network and the predicted line power flow. The absorption capacity correction unit is used to dynamically reduce the remaining heat capacity using the voltage health factor to obtain the physical absorption capacity.

9. The intelligent assessment system for distributed photovoltaic resources according to claim 7, characterized in that, The risk index calculation module is used to define the amount of power that the expected photovoltaic output exceeds the physical absorption capacity as the expected curtailment power, and to determine the ratio of the expected curtailment power to the expected photovoltaic output as the curtailment risk index.

10. The intelligent assessment system for distributed photovoltaic resources according to claim 7, characterized in that, Decision support module, used for: In response to the photovoltaic investment value index being greater than or equal to the preset high value threshold, the investment value level is determined to be high investment value; In response to the photovoltaic investment value index being less than the high value threshold and greater than or equal to the preset high risk threshold, the investment value level is determined to be medium investment value; In response to the photovoltaic investment value index being lower than the high-risk threshold, the investment value level is judged as high investment risk.

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