A dynamic load reduction method for new energy systems based on real-time load analysis

Abstract

The present invention relates to the field of dynamic load reduction of new energy systems, and in particular to a dynamic load reduction method for new energy systems based on real-time load analysis. The method comprises: obtaining real-time load data of the power grid and the output of the new energy system, performing time domain analysis on the real-time load data of the power grid to obtain the load fluctuation rate and the load change acceleration; predicting and normalizing the load fluctuation rate at the future moment to obtain the normalized load characteristic parameters at the future moment; predicting the output of the new energy system at the future moment, calculating the deviation between the output of the new energy system at the future moment and the load power; introducing the normalized load characteristic parameters at the future moment, calculating the initial output constraint factor, performing amplitude limiting processing, and obtaining the output constraint factor; and calculating the adjusted output of the new energy system through a dynamic load reduction adaptive adjustment algorithm. The method solves the technical problem that traditional technologies mostly use low-frequency sampling to obtain load data, which makes it difficult to capture the rapid change characteristics of the load, resulting in distorted analysis results.

Description

Technical Field

[0001] The present invention relates to the field of dynamic load reduction of new energy systems, and in particular to a dynamic load reduction method for new energy systems based on real-time load analysis. Background Art

[0002] With the global energy transition and the rapid growth of renewable energy generation, especially the widespread integration of renewable energy sources such as wind and solar power, traditional power grids are facing increasing challenges in frequency and voltage regulation. The volatility and intermittency of renewable energy sources lead to more frequent frequency fluctuations in power systems, especially in areas with a high proportion of renewable energy generation. Effectively addressing frequency fluctuations has become a critical issue for the safe operation of power grids. In traditional power grids, frequency stability is typically achieved through under-frequency load shedding (UFLS) devices. These devices shed a certain percentage of load in batches when the power system frequency drops, thereby helping to restore the frequency. However, with the increasing proportion of renewable energy generation, traditional UFLS strategies face numerous challenges. In particular, in areas with a high proportion of renewable energy generation, power system inertia decreases and frequency fluctuations intensify, rendering traditional under-frequency load shedding methods inflexible. A dynamic load shedding control method based on real-time load analysis not only addresses the challenges posed by renewable energy volatility but also improves the accuracy and efficiency of power grid dispatch, marking a significant advancement in the intelligent, green, and sustainable development of power grids.

[0003] However, the above-mentioned existing dynamic load reduction methods for new energy systems still have the following technical problems: the intermittent and fluctuating nature of new energy systems (such as wind power and photovoltaics) makes it difficult to match their output with the grid load in real time; there is insufficient coordination between grid load fluctuations and the output of new energy systems; the efficiency and response speed of real-time control are insufficient; and the accuracy and economy of new energy system output reduction are limited. Summary of the Invention

[0004] The present invention provides a dynamic load reduction method for a new energy system based on real-time load analysis to solve the technical problems that traditional technologies mostly use low-frequency sampling (less than 1Hz) to obtain load data, which makes it difficult to capture the rapid change characteristics of the load, resulting in distorted analysis results and the inability to accurately quantify dynamic characteristics such as load fluctuation rate and load change acceleration; existing technologies predict the output of new energy systems based on a single variable (such as wind speed or light), ignoring the combined influence of multiple environmental variables, resulting in large output prediction errors; traditional load reduction strategies are mostly static or linear adjustments, which are difficult to adapt to scenarios with drastic load changes and lack a nonlinear response mechanism to load fluctuations and load change acceleration.

[0005] The present invention provides a method for dynamic load reduction of a new energy system based on real-time load analysis, which specifically includes the following technical solutions:

[0006] A method for dynamic load reduction of a new energy system based on real-time load analysis includes the following steps:

[0007] S1. Obtain real-time grid load data and renewable energy system output, perform time-domain analysis on the data, and determine load fluctuation rate and load change acceleration. Predict future load power, load fluctuation rate, and load change acceleration, and normalize the predicted load fluctuation rate to obtain normalized load characteristic parameters for the future.

[0008] S2. Use a multivariate linear regression model based on environmental variables to predict the future output of the renewable energy system. Subtract the future output of the renewable energy system from the future load power to obtain the deviation between the future output and the load power. Introduce the normalized load characteristic parameters for the future time and calculate the initial output constraint factor. Limit the initial constraint factor to obtain the output constraint factor.

[0009] S3. Based on the future output of the new energy system, normalized load characteristic parameters, load change acceleration, and output constraint factors, the adjusted output of the new energy system is calculated using a dynamic load reduction adaptive adjustment algorithm.

[0010] Preferably, the S1 specifically includes:

[0011] The load fluctuation rate is calculated by dividing the difference between the load power at the current moment and the load power at the previous moment by the time interval to obtain the load fluctuation rate; the load change acceleration is calculated based on the difference between the load fluctuation rates of two consecutive time intervals divided by the time interval to obtain the load change acceleration.

[0012] Preferably, the S1 specifically includes:

[0013] The standard deviation of the load fluctuation rate is calculated by using the historical load fluctuation rate as a reference benchmark for standardization. The autoregressive integral moving average model is used to predict the load power, load fluctuation rate and load change acceleration at future moments.

[0014] Preferably, the S2 specifically includes:

[0015] The normalized load characteristic parameters at the future moment are introduced, and the absolute value of the normalized load characteristic parameters at the future moment and the product of the output of the new energy system at the future moment and the load power deviation are calculated. Then, the product is divided by the maximum output of the new energy system and normalized to obtain the initial output constraint factor.

[0016] Preferably, the S2 specifically includes:

[0017] The initial output constraint factor is limited: the maximum value of the initial output constraint factor and 0 is taken to obtain the intermediate value; the minimum value of the intermediate value and 1 is taken to obtain the output constraint factor, and the output amplitude that needs to be adjusted for the new energy system is quantified.

[0018] Preferably, the S3 specifically includes:

[0019] In the implementation process of the dynamic load reduction adaptive adjustment algorithm, nonlinear coupling is performed by combining the absolute value of the normalized load characteristic parameter at the future moment with the square of the standardized load change acceleration at the future moment to amplify the response to the drastic load change.

[0020] Preferably, the S3 specifically includes:

[0021] In the implementation process of the dynamic load reduction adaptive adjustment algorithm, the dynamic factor is processed through the exponential decay function, the value of the exponential decay term is adjusted, and adaptive control is performed; the dynamic factor is combined with the output constraint factor to generate a comprehensive adjustment ratio.

[0022] Preferably, the S3 specifically includes:

[0023] In the implementation process of the dynamic load reduction adaptive adjustment algorithm, the comprehensive adjustment ratio obtained by combining the dynamic factor and the output constraint factor is multiplied by the output of the new energy system at the future moment to obtain the adjusted new energy system output.

[0024] The beneficial effects of the technical solution of the present invention are:

[0025] 1. Through high-frequency data acquisition and time-domain analysis, the dynamic change characteristics of the load are accurately captured, and the load behavior at future moments is predicted, providing an accurate basis for adjusting the output of the new energy system. The normalized load characteristic parameters effectively quantify the intensity of load fluctuations, improving the adaptability and accuracy of the subsequent dynamic load reduction adaptive adjustment algorithm, thereby optimizing the dynamic response capability of the power system and reducing frequency fluctuations and voltage instability caused by sudden load changes.

[0026] 2. By predicting the output of the new energy system at future times using a multivariate linear regression model based on environmental variables, the power generation capacity of the new energy system is accurately estimated. Combined with the deviation between the output of the new energy system at future times and the load power and the load fluctuation characteristics, a reasonable output constraint factor is generated, which effectively balances the mismatch between the output of the new energy system and the load demand of the power grid, quantifies the adjustment range, prevents over-adjustment or under-adjustment, improves the efficiency of new energy consumption, and avoids the impact of excessive output deviation on the stability of the power grid.

[0027] 3. By introducing a dynamic load reduction adaptive adjustment algorithm, it can respond quickly to drastic changes in load, ensuring that the output of the new energy system is quickly adjusted when the load changes suddenly, and reducing unnecessary reductions when the load is stable. This significantly improves the coordination between the new energy system and the power grid, and suppresses the frequency fluctuations and voltage instability of the power system caused by output fluctuations. At the same time, with the rapid application of power electronic equipment, it overcomes the response lag problem caused by time delay in traditional methods and meets the high efficiency requirements of real-time control. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a flow chart of a dynamic load reduction method for a new energy system based on real-time load analysis according to the present invention. DETAILED DESCRIPTION

[0029] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0030] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0031] The following describes in detail a specific solution of a dynamic load reduction method for a new energy system based on real-time load analysis provided by the present invention with reference to the accompanying drawings.

[0032] Refer to the attached Figure 1 , which shows a flow chart of a method for dynamic load reduction of a new energy system based on real-time load analysis provided by an embodiment of the present invention, the method comprising the following steps:

[0033] S1. Obtain real-time grid load data and new energy system output, perform time-domain analysis on the real-time grid load data, and obtain load fluctuation rate and load change acceleration; predict the load power, load fluctuation rate, and load change acceleration at future times, and normalize the predicted load fluctuation rate to obtain normalized load characteristic parameters at future times;

[0034] Use high-frequency acquisition equipment (sampling frequency ≥ 1Hz) to obtain real-time load data of the power grid, including load power , and simultaneously collect the output of new energy systems ; Perform time domain analysis on real-time load data of power grid and calculate load fluctuation rate and load change acceleration The load fluctuation rate is calculated by dividing the difference between the load power at the current moment and the load power at the previous moment by the time interval to obtain the load fluctuation rate; the load change acceleration is calculated based on the difference between the load fluctuation rates of two consecutive time intervals, divided by the time interval to obtain the load change acceleration; the standard deviation of the load fluctuation rate is calculated using the historical load fluctuation rate obtained from the database as a reference benchmark for standardization; the autoregressive integral moving average model is used to predict the future moment Load power , load fluctuation rate and load change acceleration , the load fluctuation rate at the predicted future moment is normalized to obtain Normalized load characteristic parameters at time ;

[0035] S2. Predict the output of the new energy system at a future time using a multivariate linear regression model based on environmental variables. Subtract the output of the new energy system at a future time from the load power at a future time to obtain the deviation between the output of the new energy system at a future time and the load power. Introduce the normalized load characteristic parameters at a future time to calculate an initial output constraint factor. Limit the initial constraint factor to obtain the output constraint factor.

[0036] Prediction using a multiple linear regression model based on environmental variables New energy system output at all times The input of the multivariate linear regression model based on environmental variables includes environmental variables obtained through meteorological forecasts, including wind speed, wind direction, light intensity, and temperature;

[0037] Subtract the predicted future energy system output from the future load power to obtain The deviation between the output of the new energy system and the load power at the moment ;

[0038] The normalized load characteristic parameter at the future moment is further introduced. The absolute value of the normalized load characteristic parameter at the future moment and the product of the output of the new energy system and the load power deviation at the future moment are calculated. The product is then divided by the maximum output of the new energy system to obtain the initial output constraint factor. The initial output constraint factor is then clipped: the maximum value of the initial output constraint factor and 0 is taken to obtain the intermediate value, ensuring that it is non-negative. The minimum value of the intermediate value and 1 is further taken to ensure that it does not exceed 1. The output constraint factor is used to quantify the output amplitude that needs to be adjusted for the new energy system.

[0039] The calculation formula of the output constraint factor is:

[0040] ,

[0041] in, express Output constraint factor at the moment; Indicates taking the minimum value and limiting the output constraint factor to range, preventing unreasonable load reduction caused by excessive output constraint factors and ensuring calculation stability; It means taking the maximum value to ensure that the initial output constraint factor is non-negative, avoiding the interference of negative values ​​on subsequent calculations, and conforming to the physical meaning that the load reduction amplitude should not be negative; The initial constraint factor is calculated by combining the deviation between the output of the new energy system and the load power at the future moment and the absolute value of the normalized load characteristic parameter at the future moment, divided by the maximum output of the new energy system. The deviation between the output of the new energy system and the load power at the future moment and the intensity of the load fluctuation are combined to reflect the relative magnitude of the adjustment required for the new energy system. express The deviation between the output of the renewable energy system and the load power at the moment reflects the degree of mismatch between the output of the renewable energy system and the load demand of the power grid; express The absolute value of the normalized load characteristic parameter at the time is used. The load fluctuation intensity is introduced as a weight to amplify or reduce the impact of the deviation between the output of the new energy system and the load power at the future time. This ensures the sensitivity of the initial output constraint factor to load fluctuations. The absolute value is taken to avoid negative values ​​affecting the calculation. Represents the maximum output of the new energy system. As the normalized denominator, it quantifies the deviation between the output of the new energy system and the load power at the future moment into a relative value, ensuring that the value of the initial output constraint factor is within a reasonable range to facilitate limiting processing.

[0042] The output constraint factor comprehensively reflects the load fluctuation intensity and the interaction between the output of the new energy system and the load power deviation at the future moment, providing a quantitative basis for dynamic load reduction.

[0043] S3. Based on the future output of the new energy system, normalized load characteristic parameters, load change acceleration, and output constraint factors, the adjusted output of the new energy system is calculated using a dynamic load reduction adaptive adjustment algorithm;

[0044] The dynamic load shedding adaptive adjustment algorithm combines the absolute value of the normalized load characteristic parameter at a future moment with the square of the normalized load change acceleration at a future moment to perform nonlinear coupling, thereby amplifying the response to drastic load changes and ensuring that the adjustment amplitude is rapidly increased when the load suddenly changes. The dynamic factor is further processed through an exponential decay function to ensure that the exponential decay term is close to 0 when the load fluctuates greatly, thereby achieving a larger adjustment amplitude, and the exponential decay term is close to 1 when the load is stable, so that the adjustment amplitude tends to 0, thereby achieving adaptive control.

[0045] The dynamic load shedding adaptive adjustment algorithm combines the dynamic factor with the output constraint factor to generate a comprehensive adjustment ratio that reflects the output ratio that the renewable energy system needs to reduce in the future. It comprehensively considers the interaction between load fluctuation characteristics and output constraint factors, and directly reduces the output of the renewable energy system in the predicted future time proportionally, ensuring that the adjusted renewable energy system output can both match the grid load demand and avoid excessive output reduction.

[0046] The calculation formula for the adjusted new energy system output is:

[0047] ,

[0048] in, express The output of the new energy system after adjustment at all times; express The output of new energy systems at all times; represents the exponential decay term, which converts the nonlinear effects of load fluctuation and load change acceleration at future moments into dynamic factors of load reduction amplitude through the exponential decay function; It represents the adjustment coefficient, which is used to control the decay speed of the exponential decay term and adjust the sensitivity of the load reduction amplitude. The value range is ; Represents the maximum value of the load change acceleration, which is used as the normalized denominator to normalize the load change acceleration at future moments into a dimensionless value to ensure the rationality of the exponential decay term; express The square of the load change acceleration is always standardized. Through the square operation, the sensitivity to the sudden change of load is enhanced, and the load reduction amplitude is significantly increased when the load change acceleration is large.

[0049] Furthermore, the adjusted output of the new energy system is directly output, and the power electronic equipment is quickly applied to meet the high efficiency requirements of real-time control, solving the problem of response lag caused by time delay in traditional methods. Through precise load reduction control, the frequency fluctuation and voltage instability of the power system are effectively suppressed, and the coordination between the new energy system and the power grid is improved.

[0050] In summary, a dynamic load reduction method for new energy systems based on real-time load analysis has been completed.

[0051] The order in which the embodiments of the invention are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0052] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0053] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A dynamic load reduction method for a new energy system based on real-time load analysis, characterized in that: The following steps are involved: S1. Obtain real-time grid load data and renewable energy system output, perform time-domain analysis on the data, and determine load fluctuation rate and load change acceleration. Predict future load power, load fluctuation rate, and load change acceleration, and normalize the predicted load fluctuation rate to obtain normalized load characteristic parameters for the future. S2. Use a multivariate linear regression model based on environmental variables to predict the output of the new energy system at future times. Subtract the output of the new energy system at future times from the load power at future times to obtain the deviation between the output of the new energy system and the load power at future times. Introduce the normalized load characteristic parameter at future times, calculate the product of the absolute value of the normalized load characteristic parameter at future times and the deviation between the output of the new energy system and the load power at future times, and then divide the product by the maximum output of the new energy system to obtain the initial output constraint factor. Limit the initial constraint factor to obtain the output constraint factor. S3. Based on the future output of the new energy system, normalized load characteristic parameters, load change acceleration, and output constraint factors, the adjusted output of the new energy system is calculated using a dynamic load reduction adaptive adjustment algorithm. In the implementation process of the dynamic load reduction adaptive adjustment algorithm, nonlinear coupling is performed by combining the absolute value of the normalized load characteristic parameter at the future moment with the square of the standardized load change acceleration at the future moment; the dynamic factor is processed by an exponential decay function composed of the normalized load characteristic parameter and the load change acceleration, and the value of the exponential decay term is adjusted to perform adaptive control; the dynamic factor is combined with the output constraint factor to generate a comprehensive adjustment ratio; the comprehensive adjustment ratio is multiplied by the new energy system output at the future moment to obtain the adjusted new energy system output.

2. A method for dynamic load reduction of a new energy system based on real-time load analysis according to claim 1, characterized in that: Said S1 specifically includes: The load fluctuation rate is calculated by dividing the difference between the load power at the current moment and the load power at the previous moment by the time interval to obtain the load fluctuation rate; the load change acceleration is calculated based on the difference between the load fluctuation rates of two consecutive time intervals divided by the time interval to obtain the load change acceleration.

3. A method for dynamic load reduction of a new energy system based on real-time load analysis according to claim 2, characterized in that: Said S1 specifically includes: The standard deviation of the load fluctuation rate is calculated by using the historical load fluctuation rate as a reference benchmark for standardization. The autoregressive integral moving average model is used to predict the load power, load fluctuation rate and load change acceleration at future moments.

4. The method for dynamic load reduction of a new energy system based on real-time load analysis according to claim 1, characterized in that: Said S2 specifically includes: The initial output constraint factor is limited: the maximum value of the initial output constraint factor and 0 is taken to obtain the intermediate value; the minimum value of the intermediate value and 1 is taken to obtain the output constraint factor, and the output amplitude that needs to be adjusted for the new energy system is quantified.

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