Intelligent assessment methods and systems for distributed photovoltaic resources

Abstract

This invention relates to the fields of smart grid and renewable energy technology, specifically to a smart assessment method and system for distributed photovoltaic (PV) resources. It includes: S1, calculating the physical absorption capacity of the grid connection point based on collected static topology data and dynamic operation data of the distribution network, by coupling thermal stability constraints and voltage stability constraints; S2, calculating the curtailment risk index by combining the physical absorption capacity with the expected PV output predicted based on weather forecast data; S3, calculating the risk-adjusted total return by combining the curtailment risk index, expected PV output, and predicted market feed-in tariff; S4, generating a PV investment value index by normalizing the risk-adjusted total return and a preset benchmark total return; and S5, outputting an investment decision support strategy based on the PV investment value index and a preset value grading threshold. This invention overcomes the shortcomings of existing technologies that use static, fixed limits for assessment, leading to a disconnect between the assessment results and the actual operating conditions of the power grid.

Description

Technical Field

[0001] This invention relates to the fields of smart grid and renewable energy technology, specifically to a smart assessment method and system for distributed photovoltaic resources. Background Technology

[0002] With the rapid development of distributed photovoltaic resources, the scientific nature and accuracy of investment decisions have become crucial. Effective evaluation of the planning and investment in distributed photovoltaic resources is essential for improving decision-making quality. However, traditional evaluation methods for photovoltaic resource investment face many challenges in practical applications.

[0003] Existing technologies are relatively singular and static in their assessment dimensions, failing to fully consider the dynamic changes of the power grid. Traditional assessment methods often use fixed limits, which cannot reflect the grid's acceptance capacity in real time and ignore the impact of dynamic grid operation data on the assessment results. In addition, existing technologies generally suffer from the defect of physical constraints being disconnected from economic benefits. The assessment process fails to effectively combine physical boundary conditions such as the thermal stability and voltage stability of the power grid with economic indicators such as curtailment risk and market electricity price fluctuations, resulting in assessment results that cannot accurately reflect the project's true profitability and potential risks.

[0004] Current assessment techniques fail to construct a logical closed loop that extends from precise quantification of the power grid's physical boundaries to probabilistic assessment of grid mismatch risk, then to risk adjustment of asset economic value, ultimately leading to an indexed output for investment decisions. In summary, existing technologies lack a dynamic, forward-looking, and highly quantitative decision support tool for distributed photovoltaic resource investment. They struggle to converge the complex calculations of physical risks and economic returns into an intuitive and standardized top-level assessment score, thus significantly impacting the scientific rigor and accuracy of investment decisions.

[0005] The information disclosed in the background section above is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention discloses an intelligent assessment method and system for distributed photovoltaic resources. Specifically, the technical solution of this invention is as follows:

[0007] Intelligent assessment methods for distributed photovoltaic resources include:

[0008] 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;

[0009] S2, combining the physical absorption capacity and the expected photovoltaic output predicted based on meteorological forecast data, calculates the curtailment risk index;

[0010] S3, combining the curtailment risk index, expected photovoltaic output and predicted market feed-in tariff, calculates the risk-adjusted total return;

[0011] S4 generates a photovoltaic investment value index based on the risk-adjusted total return and the preset benchmark total return through normalization.

[0012] S5 outputs investment decision support strategies based on the photovoltaic investment value index and preset value grading thresholds.

[0013] Preferably, the physical absorption capacity of the grid connection point is calculated, including:

[0014] 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.

[0015] The remaining heat capacity is determined based on the rated capacity of transformers in the distribution network and the predicted line power flow.

[0016] The remaining heat capacity is dynamically reduced using the voltage health factor to obtain the physical absorption capacity.

[0017] Preferably, the calculated curtailment risk index includes:

[0018] The amount of power expected to exceed the physical absorption capacity is defined as the expected curtailment power.

[0019] The ratio of expected curtailment power to expected photovoltaic output is defined as the curtailment risk index.

[0020] Preferably, the risk-adjusted total return is calculated, including:

[0021] 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.

[0022] Preferably, the benchmark total return is the ideal total return within the preset evaluation period, assuming the curtailment risk index is zero.

[0023] Preferred investment decision support strategies include:

[0024] 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;

[0025] 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;

[0026] 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.

[0027] An intelligent assessment system for distributed photovoltaic resources includes:

[0028] 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;

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] Preferred, including:

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] Preferably, the decision support module is used for:

[0040] 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;

[0041] 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;

[0042] 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.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] 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 the existing technology that uses static and fixed limits for evaluation, resulting in the evaluation results being out of sync with the actual operation of the power grid.

[0045] 2. This invention constructs a complete closed-loop assessment system from grid physical constraints and grid connection risk probability to investment economic value. By converting physical absorption capacity into a curtailment risk index and using it as a dynamic reduction factor to correct ideal power generation revenue, a close integration of physical constraints and economic benefits is achieved, enabling the economic assessment results to truly reflect the limitations of grid absorption capacity.

[0046] 3. This invention, through normalization, converges the complex calculation results of physical risks and economic returns into a standardized photovoltaic investment value index. Based on this index and preset thresholds, it provides clear investment recommendations, such as high investment value, medium investment value, or high investment risk level, realizing the transformation from complex technical analysis to intuitive business decision-making, and improving the scientificity and practicality of investment decisions.

[0047] 4. This invention proposes an innovative method for calculating physical absorption capacity. By introducing a voltage health factor to dynamically adjust the remaining heat capacity, the calculated absorption capacity can reflect the voltage health status of the power grid in real time. This method effectively captures core constraints and improves the accuracy of evaluation results while also ensuring high computational efficiency. Attached Figure Description

[0048] The present invention will be further explained below with reference to the accompanying drawings and embodiments:

[0049] Figure 1 This is a flowchart of the method of the present invention.

[0050] Figure 2 This is a flowchart of the system of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0052] Example 1:

[0053] Please see Figure 1 Intelligent assessment methods for distributed photovoltaic resources include:

[0054] 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;

[0055] S2, combining the physical absorption capacity and the expected photovoltaic output predicted based on meteorological forecast data, calculates the curtailment risk index;

[0056] S3, combining the curtailment risk index, expected photovoltaic output and predicted market feed-in tariff, calculates the risk-adjusted total return;

[0057] S4 generates a photovoltaic investment value index based on the risk-adjusted total return and the preset benchmark total return through normalization.

[0058] S5 outputs investment decision support strategies based on the photovoltaic investment value index and the preset value grading threshold.

[0059] This invention provides an intelligent assessment method for distributed photovoltaic (PV) resources. The purpose of this method is to construct a complete assessment loop, from grid physical constraints and grid connection risk probability to investment economic value, providing a quantitative, dynamic, and highly valuable top-level guiding indicator for distributed PV investment decisions. In this embodiment, the method, as a complete and self-consistent technical process, includes the following steps:

[0060] S1. Based on the collected static topology data and dynamic operation data of the distribution network, the physical absorption capacity of the grid connection point is calculated by coupling thermal stability constraints and voltage stability constraints. The physical absorption capacity refers to the maximum photovoltaic power that a specific grid connection point can accept at a specific time under the premise of ensuring the safe and stable operation of the power grid. Its role is to provide an accurate and reliable physical boundary for subsequent risk assessment. It is calculated by the model proposed in this embodiment.

[0061] The implementation of this step involves two aspects. First, by consulting the power grid geographic information system and asset management database, static topology data of the distribution network in the area where the target grid connection point is located is obtained, such as line impedance, transformer rated capacity, protection settings, and node voltage upper and lower limits. Second, dynamic operating data, such as historical load data and real-time electricity prices, is acquired in real time through measurement units or smart meters deployed in the area. Based on the historical load data, time series prediction models, such as ARIMA and LSTM, are used to predict future node loads, and the prediction results will be used as input for power flow calculation. Based on the above data, thermal stability constraints and voltage stability constraints are innovatively coupled to calculate the dynamic physical absorption capacity, rather than using the static and fixed limits in traditional technologies.

[0062] S2, combining the physical absorption capacity and the expected photovoltaic output predicted based on weather forecast data, calculates the curtailment risk index. The curtailment risk index is a quantitative measure of the risk that the expected photovoltaic power generation will be wasted due to insufficient physical absorption capacity of the grid. Its function is to transform abstract systemic risks into specific, time-based probabilistic indicators, which are calculated based on the physical absorption capacity and photovoltaic output prediction in S1. This step uses integrated weather forecast data to probabilistically predict the expected photovoltaic output of the target grid connection point, and compares the predicted output with the physical absorption capacity calculated in the previous step to quantitatively assess the possibility that the power generation exceeds the absorption limit.

[0063] S3, combining the curtailment risk index, expected photovoltaic output, and predicted market feed-in tariff, calculates the risk-adjusted total return. Risk-adjusted total return refers to the true expected profitability of distributed photovoltaic assets within a specific assessment period after fully considering grid curtailment risk. Its purpose is to provide an economic indicator that more closely reflects actual operating conditions for investment assessment. It is calculated based on ideal returns and after discounting using the curtailment risk index. This step uses the curtailment risk calculated in S2 as a dynamic discount factor to correct the power generation revenue under ideal conditions, thus obtaining a comprehensive asset net return assessment result that considers power generation potential, grid connection constraints, and market price fluctuations.

[0064] S4, based on the risk-adjusted total return and the preset benchmark total return, generates a photovoltaic investment value index through normalization. The photovoltaic investment value index refers to an intuitive and standardized top-level evaluation score formed by converging the complex physical risks and economic returns. Its function is to directly reflect the percentage of risk-adjusted return to ideal return. It is calculated by normalizing the risk-adjusted total return and the benchmark total return. The benchmark total return is defined as the total theoretical return that a photovoltaic project can generate in the same evaluation period under ideal conditions, that is, assuming zero curtailment risk. 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.

[0065] S5, based on the photovoltaic investment value index and the preset value grading threshold, outputs an investment decision support strategy; the investment decision support strategy refers to providing investors with clear and actionable business decision suggestions based on the quantitative results of the photovoltaic investment value index. Its role is to achieve the final leap from complex technical analysis to commercial feasibility assessment, and it is determined according to the grading range 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 suggestions accordingly, such as high investment value, medium investment value, or high investment risk;

[0066] Through the above steps, this invention constructs a logical closed loop that extends from precise quantification of the physical boundaries of the power grid to probabilistic assessment of grid mismatch risk, then to risk adjustment of asset economic value, and finally to indexed output of investment decisions. It overcomes the shortcomings of existing technologies, such as single and static assessment dimensions and the disconnect between physical constraints and economic benefits. It provides 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.

[0067] Example 2:

[0068] The physical absorption capacity of the grid connection point is calculated, including:

[0069] 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.

[0070] The remaining heat capacity is determined based on the rated capacity of transformers in the distribution network and the predicted line power flow.

[0071] The remaining heat capacity is dynamically reduced using the voltage health factor to obtain the physical absorption capacity;

[0072] Based on Example 1, this embodiment specifies and optimizes the step of calculating the physical absorption capacity of the grid connection point. Its purpose is to use an innovative dynamic reduction mechanism to enable the calculated absorption capacity to reflect the voltage health of the power grid in real time, thereby obtaining more accurate results than traditional methods. Its specific implementation includes the following aspects:

[0073] Based on the upper and lower limits of node voltages and the reference voltage of the distribution network, and combined with the predicted node voltages, a voltage health factor is calculated. To dynamically quantify the grid voltage's ability to accommodate new power, this embodiment introduces a voltage health factor. The calculation method is as follows:

[0074]

[0075] in, : The voltage health factor at node i at time t; is a dimensionless parameter; is calculated by this formula; the function of this factor is to characterize the margin of the current voltage from the safe upper limit. A value close to 1 indicates that the voltage condition is good and the acceptance capability is strong; the smaller the value, the closer the voltage is to the upper limit and the worse the acceptance capability.

[0076] : Represents the upper limit of the allowable voltage amplitude of node i; its dimension is voltage, such as kV; it can be obtained by referring to power grid design specifications and operating guidelines;

[0077] : Represents the predicted voltage at node i at time t; its dimension is voltage, such as kV; it is the real-time solution obtained by standard power flow calculation tools in combination with predicted load data, and is the input variable of this calculation step;

[0078] The reference voltage characterizes the system; its dimension is voltage, such as kV; it can be obtained by consulting power grid design specifications and operating guidelines.

[0079] Based on the rated capacity of transformers in the distribution network and the predicted line power flow, the remaining thermal capacity is determined. Remaining thermal capacity refers to the capacity of critical lines or transformers that, after carrying the predicted background power flow, is available for transmitting additional power; its function is to determine the thermal stability boundary of the power grid. In this embodiment, it is determined by... The calculation yielded, where This assesses the rated transmission power limit of critical lines or transformers on the feeder where grid connection point i is located. The data can be obtained from the power grid asset management database. It is the background power flow through the critical line or transformer at time t, which is predicted by the power flow calculation model and combined with the predicted load data.

[0080] The remaining heat capacity is dynamically reduced using a voltage health factor to obtain the physical absorption capacity; this is a core innovation of this technical solution. Its underlying logic lies in introducing a dynamic correction factor related to the voltage state, transforming the voltage stability constraint into a dynamic adjustment of the thermal stability boundary, thereby achieving an effective combination of the two constraints. This simplifies the complex nonlinear power flow problem into linear algebraic operations, representing an approximate model. The specific calculation method is as follows:

[0081]

[0082] in, : Characterizes the physical absorption capacity of node i at time t; its dimension is power, such as MW; it is calculated by this formula;

[0083] By reflecting voltage health The physical absorption capacity in this embodiment is calculated by multiplying the residual heat capacity by a linear dynamic reduction factor in the previous step. It is no longer a fixed heat capacity limit, but a dynamic boundary that can intelligently shrink according to the real-time voltage health status. This linearization process achieves an effective balance between computational efficiency and physical fidelity. It transforms the complex nonlinear power flow problem into a simple algebraic operation, which greatly improves the computational efficiency for rapid evaluation of massive time series data. At the same time, through the voltage health factor, it effectively captures the decisive constraint relationship between voltage level and absorption capacity on a macroscopic level.

[0084] This calculation method will predict the background current. and node voltage As an independent input, the decoupled calculation of the two constraints is achieved through dynamic reduction coefficients, which greatly improves the calculation efficiency. As an engineering approximation, this approximation model can effectively capture the macroscopic impact of voltage health status on absorption capacity while ensuring calculation speed.

[0085] It should be noted that this approximate model does not take into account the impact of the newly injected photovoltaic power on the background power flow. While the impact of this model may introduce some errors in scenarios with extremely high distributed power penetration, it can provide reliable evaluation results with extremely high computational efficiency in most engineering scenarios. This approximate model is particularly suitable for rapid screening and evaluation in the distribution network planning stage. Its core assumption is that the new photovoltaic capacity is relatively small relative to the total system load and the total line capacity, so its marginal impact on the background power flow and voltage distribution of the power grid can be linearized.

[0086] Example 3:

[0087] The calculated curtailment risk index includes:

[0088] The amount of power expected to exceed the physical absorption capacity is defined as the expected curtailment power.

[0089] The ratio of expected curtailed solar power to expected photovoltaic output is defined as the curtailment risk index.

[0090] Based on Example 1, this embodiment specifies the step of calculating the curtailment risk index. Its purpose is to establish a risk measurement model directly driven by physical constraints to quantify the degree of mismatch between the expected photovoltaic output and the actual grid acceptance capacity. The implementation method is described below:

[0091] To achieve this step, the amount of power that the expected photovoltaic output exceeds the physical absorption capacity is defined as the expected curtailment power. Expected curtailment power refers to the portion of photovoltaic power generated that exceeds the physical absorption capacity of the power grid at a given moment; its function is to quantify the absolute value of wasted power. In this embodiment, it is achieved through... Perform calculations; among which, It is a prediction model based on integrated meteorological forecast data and photovoltaic power plant parameters, obtained through a probabilistic prediction model of the expected photovoltaic output at time t. The physical absorption capacity at time t of node i is calculated from the steps of Example 2;

[0092] Desired curtailment power The ratio of the solar power output to the expected photovoltaic output is determined as the curtailment risk index. To standardize and make the risk assessment results comparable and to ensure the robustness of the model, this embodiment uses the following piecewise function formula to calculate the curtailment risk index. :

[0093]

[0094] This piecewise function ensures that the curtailment risk index is defined as zero at times when the expected photovoltaic output is zero, such as at night, thus avoiding division by zero errors in the calculation.

[0095] in, : Characterizes the curtailment risk index of node i at time t; is a dimensionless proportion; is calculated by this formula; it directly reflects the proportion of expected power generation that is expected to be curtailed due to insufficient absorption capacity;

[0096] : Represents the expected photovoltaic output at time t; its dimension is power, such as MW; the output of a probabilistic photovoltaic output prediction model;

[0097] : Characterizes the physical absorption capacity at time t; its dimension is power, such as MW; it is the output of step 2 in Example 2;

[0098] The numerator of the formula is the expected curtailment power, and the denominator is the expected total output power. The dimensionless ratio obtained by dividing the two is driven entirely by the two core physical quantities: the expected output power of photovoltaic power and the physical absorption capacity of the grid. There are no external adjustable weights, which ensures the objectivity of the assessment.

[0099] Example 4:

[0100] The risk-adjusted total return is calculated, including:

[0101] Within the preset assessment period, the expected photovoltaic output at each moment is multiplied by (1 - curtailment risk index), then multiplied by the predicted market feed-in tariff, and then summed to obtain the risk-adjusted total revenue.

[0102] Building upon Example 3, this paper specifies the step of calculating the risk-adjusted total return. The aim is to establish a quantitative calculation model that accurately transmits the physical risk of curtailment to the economic return level. The risk-adjusted total return... The specific calculation formula is as follows:

[0103]

[0104] in, To assess the risk-adjusted total return over period T; The expected photovoltaic output at time t; Let be the light-wasting risk index at time t; The market feed-in tariff is the predicted price of electricity at time t. The time step is used for calculation; this formula introduces a reduction term. The potential revenue loss caused by the risk of curtailment is deducted at each time segment, so that the final revenue assessment result can truly reflect the economic impact of insufficient grid capacity.

[0105] Example 5:

[0106] The benchmark total return is the ideal total return within the preset evaluation period, assuming the curtailment risk index is zero.

[0107] Based on Example 1, this example provides a clear definition of the benchmark total return; its purpose is to provide a standard and ideal reference benchmark for the subsequent normalization calculation of the investment value index.

[0108] In this embodiment, the benchmark total revenue is defined as the ideal total revenue assuming the curtailment risk index is zero within a preset evaluation period; this means that when calculating the benchmark total revenue... At that time, we assume that the grid's absorption capacity is infinite, and all expected photovoltaic output can be fully absorbed and fed into the grid. That is, for all times t, the curtailment risk index is... The calculation formula is as follows:

[0109]

[0110] in, : Represents the benchmark total return within the evaluation period T; its dimension is monetary; it is calculated by this formula;

[0111] : Represents the total duration of the evaluation cycle; its dimension is time; it is pre-defined according to the evaluation requirements;

[0112] : Represents the expected photovoltaic output at time t; its dimension is power, such as MW; the output of a probabilistic photovoltaic output prediction model;

[0113] : Represents the predicted 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 by using a time series prediction model based on historical electricity prices, such as the ARIMA model;

[0114] : Represents the time step of the calculation; its dimension is time, such as hours; it is preset according to the evaluation requirements.

[0115] Example 6:

[0116] Output investment decision support strategies, including:

[0117] 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;

[0118] 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;

[0119] 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;

[0120] Based on Example 1, this example specifies the step of outputting investment decision support strategies. Its purpose is to transform the calculated quantitative investment value index into investment grades and recommendations that are user-friendly, intuitive and actionable for decision-makers without a technical background.

[0121] In this embodiment, based on the calculated photovoltaic investment value index A three-tiered evaluation system was established. The thresholds in this system, such as the high-value threshold and the high-risk threshold, are set based on the following technical logic: statistical calibration is performed using a calibration dataset containing a large amount of historical project data. Each data point in this calibration dataset contains two independent variables: one is the historical investment value index calculated according to the method of this invention, and the other is the corresponding real financial performance indicator for the project, such as the internal rate of return. ; Statistical methods such as regression analysis are used to determine index thresholds that can effectively distinguish different investment return ranges; for example, a high-risk threshold can be set at a level that historically led to a project's... The value index percentile was significantly lower than expected; the specific decision support strategy output logic is as follows:

[0122] In response to the photovoltaic investment value index being greater than or equal to a preset high-value threshold, the investment value level is determined to be high investment value; in this embodiment, the high-value threshold is set to 95; when At that time, the system outputs a conclusion of high investment value; this indicates that the grid connection point has sufficient absorption capacity, extremely low risk of curtailment, and strong commercial feasibility of the project, and recommends prioritizing investment.

[0123] In response to the photovoltaic investment value index being less than the high value threshold but greater than or equal to the preset high risk threshold, the investment value level is determined to be medium investment value; in this embodiment, the high risk threshold is set to 85; when At that time, the system outputs a conclusion of moderate investment value; this indicates that there is a certain risk of curtailment of solar power at the grid connection point, and there may be a bottleneck in the absorption of solar power during specific periods such as peak solar irradiance. It is recommended that investors make a careful assessment and consider risk mitigation measures such as configuring energy storage systems to enhance the value of the project.

[0124] In response to the photovoltaic investment value index being lower than the high-risk threshold, the investment value level is classified as high investment risk; when When the system outputs a conclusion of high investment risk, it indicates that the grid connection point has serious grid absorption problems, high risk of curtailment, and significant expected revenue loss. It is recommended to avoid the investment or re-evaluate the project plan.

[0125] Example 7:

[0126] Please see Figure 2 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] This invention provides an intelligent evaluation system for distributed photovoltaic resources. This system is designed to execute the intelligent evaluation method described in any of the foregoing embodiments. The purpose of this system is to provide an integrated, automated hardware or software platform to achieve a comprehensive evaluation of the investment value of distributed photovoltaic resources. In this embodiment, the system includes:

[0133] The data acquisition module aims to provide all the necessary raw data inputs for the entire evaluation system. In this embodiment, it is configured to collect static topology data and dynamic operation data of the distribution network, as well as weather forecast data. Static data is obtained through an interface connected to the power grid geographic information system (GIS) and asset management database. Dynamic operation data is acquired in real time by communicating with smart meters or phasor measurement units (PMUs) deployed in the area. Weather forecast data is obtained by calling a third-party weather service API.

[0134] The physical capacity calculation module is designed to perform S1 in the aforementioned method. In this embodiment, it is configured 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 module internally contains a calculation model that couples thermal stability and voltage stability constraints.

[0135] The risk index calculation module is designed to perform S2 in the aforementioned method. In this embodiment, it is configured 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 module receives the output of the physical capacity calculation module and combines it with the internal photovoltaic output prediction model to perform the risk index calculation.

[0136] The purpose of the revenue assessment module is to perform S3 in the aforementioned method; in this embodiment, it is configured to combine the curtailment risk index calculated by the risk index calculation module, the expected photovoltaic output, and the predicted market feed-in tariff to calculate the risk-adjusted total revenue; this module integrates the calculation logic of the risk-adjusted total revenue.

[0137] The value index generation module is designed to execute S4 in the aforementioned method. In this embodiment, it is configured to generate a photovoltaic investment value index based on the risk-adjusted total return calculated by the return assessment module and a preset benchmark total return. This module performs normalization processing and outputs the final value index.

[0138] The decision support module is designed to execute S5 in the aforementioned method. In this embodiment, it is configured to output an investment decision support strategy based on the photovoltaic investment value index generated by the value index generation module and a preset value grading threshold. The module stores the value grading threshold internally and makes a judgment based on the input index, ultimately displaying clear investment advice on the user interface.

[0139] This invention, through modular system design, solidifies complex evaluation methods into a set of automated functional units. Each module has a clear responsibility and works collaboratively, achieving full-process automation from raw data collection to final decision support. This not only greatly improves evaluation efficiency and reduces errors that may be caused by human intervention, but also enables this advanced evaluation method to be provided in the form of a product or service, making it highly practical and deployable.

[0140] Example 8:

[0141] The physical capacity calculation module includes:

[0142] 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.

[0143] 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.

[0144] 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;

[0145] Based on Example 7, this embodiment further specifies and optimizes the internal structure and function of the physical capacity calculation module. Its purpose is to clearly reveal the calculation logic and implementation path of the physical absorption capacity through more refined unit division. In this embodiment, the physical capacity calculation module specifically includes:

[0146] The voltage health factor calculation unit is used to calculate the voltage health factor in Example 2. This unit is configured 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. It receives voltage-related parameters from the data acquisition module and executes... The calculation is used as the basis for adjusting the absorption capacity;

[0147] The heat capacity calculation unit is designed to determine the remaining heat capacity of the power grid. This unit is configured to determine the remaining heat capacity based on the rated capacity of transformers in the distribution network and predicted line power flow. It receives transformer rated capacity and predicted line power flow data and calculates... The value;

[0148] The absorption capacity correction unit is responsible for calculating the final physical absorption capacity. This unit is configured to dynamically reduce the remaining heat capacity using a voltage health factor to obtain the physical absorption capacity. It receives data from the health factor calculation unit. and from the heat capacity calculation unit Multiply the two together to obtain the final physical absorption capacity. It is then output to the risk index calculation module.

[0149] Example 9:

[0150] 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.

[0151] Based on Example 7, this embodiment further clarifies the specific functions of the risk index calculation module; its purpose is to reveal the operational logic within this module used to implement the method of Example 3.

[0152] In this embodiment, the risk index calculation module is specifically configured to: define the amount of power exceeding the physical absorption 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, the module receives data from the physical capacity calculation module. And by calling the internal probabilistic photovoltaic output prediction model, it obtains... Based on the above input, the processor inside the module executes... The desired power of light discard is calculated, and then divided by... The calculation ultimately yields the light-wasting risk index. Then output it to the revenue evaluation module.

[0153] Example 10:

[0154] Decision support module, used for:

[0155] 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;

[0156] 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;

[0157] 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;

[0158] Based on Example 7, this embodiment elaborates on the specific functions and internal logic of the decision support module; its purpose is to reveal how the module transforms a quantitative index into hierarchical and actionable decision recommendations, thereby realizing the strategy output of Example 6.

[0159] In this embodiment, the decision support module is internally configured with a corresponding comparator and a logic judgment unit, and its specific working logic is as follows:

[0160] This module is configured to: in response to a photovoltaic investment value index being greater than or equal to a preset high-value threshold, determine the investment value level as high investment value; specifically, the module internally stores a high-value threshold, and when the value index is received from the value index generation module... satisfy When the condition is met, the logic unit triggers and outputs a judgment result indicating high investment value;

[0161] The module is further configured to: in response to a photovoltaic investment value index being less than a high-value threshold but greater than or equal to a preset high-risk threshold, determine the investment value level as medium investment value; the module also stores a high-risk threshold internally, and when a high-risk threshold is received... satisfy When the condition is met, the logic unit triggers the output of the judgment result of medium investment value;

[0162] This module was ultimately configured to: In response to a photovoltaic investment value index falling below a high-risk threshold, classify the investment value level as high-risk; when receiving... satisfy When the condition is met, the logic unit triggers and outputs a judgment result indicating high investment risk.

[0163] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention; any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0164] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

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 the preset value grading threshold. 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 curtailment risk index and the predicted market feed-in tariff. The results are then summed to obtain the risk-adjusted total return. The curtailment risk index is calculated using the following piecewise function formula. : in, Characterization node The risk index of wasted light at any given moment; Characterization node The expectation of photovoltaic power output at all times; Characterization node Physical absorption capacity at any given moment; Risk-adjusted total return The specific calculation formula is as follows: in, To assess the risk-adjusted total return over period T; Characterization node The expectation of photovoltaic power output at all times; Characterization node The risk index of wasted light at any given moment; The market feed-in tariff is the predicted price of electricity at time t. The time step is used for calculation; this formula introduces a reduction term. The potential revenue loss caused by the risk of curtailment is deducted at each time segment, so that the final revenue assessment result can truly reflect the economic impact of insufficient grid acceptance capacity.

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; Voltage health factor The calculation method is as follows: in, : The voltage health factor at node i at time t; : Represents the upper limit of the allowed voltage amplitude at node i; its dimension is voltage; : Represents the predicted voltage at node i at time t; its dimension is voltage; : The reference voltage that characterizes the system; its dimension is voltage.

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 curtailed solar power to expected photovoltaic output is defined as the curtailment risk index. The specific calculation method for the physical absorption capacity of node i at time t is as follows: in, : Characterizes the physical absorption capacity of node i at time t; Characterizes the rated transmission power limit of the i-node; Characterizes the background power flow at time t of node i; : Characterizes the voltage health factor at node i at time t.

4. 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.

5. 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.

6. An intelligent assessment system for distributed photovoltaic resources, based on the intelligent assessment method for distributed photovoltaic resources according to any one of claims 1-5, 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.

7. The intelligent assessment system for distributed photovoltaic resources according to claim 6, 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.

8. The intelligent assessment system for distributed photovoltaic resources according to claim 6, characterized in that, The risk index calculation module defines the amount of power that the expected photovoltaic output exceeds the physical absorption capacity as the expected curtailment power, and determines the ratio of the expected curtailment power to the expected photovoltaic output as the curtailment risk index.

9. The intelligent assessment system for distributed photovoltaic resources according to claim 6, 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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