New energy unit auxiliary service demand quantification method based on probability distribution characteristics

Abstract

This invention provides a method for quantifying the ancillary service demand of renewable energy units based on probability distribution characteristics, addressing the challenge of quantifying the dynamic adjustment demand of the power grid caused by the volatility of renewable energy. The method includes: first, establishing a non-time-series ancillary service demand calculation model, determining the basic demand capacity through system load and the maximum theoretical output of renewable energy; second, generating a probability distribution of the ancillary service demand capacity based on the convolution operation of the probability distribution of renewable energy output fluctuation characteristics parameters and the probability density function of the system load; further, introducing a dynamic correction mechanism to transform the minute-level fluctuation of renewable energy into instantaneous power imbalance, which is then added to the basic demand capacity to achieve dynamic demand updates; finally, outputting the expected value of the ancillary service demand through probability distribution integration, supporting the linkage of the automatic generation control system.

Description

Technical Field

[0001] This invention relates to the field of new energy power systems, and in particular to a method for quantifying the ancillary service demand of new energy generating units based on probability distribution characteristics. Background Technology

[0002] The large-scale grid connection of new energy sources (such as wind power and photovoltaics) has increased the randomness and volatility of the power grid, leading to a sharp increase in the demand for ancillary services (such as peak shaving and frequency regulation). Traditional methods for quantifying ancillary service demand mainly rely on time-series simulation and Monte Carlo simulation, for example, by repeatedly sampling new energy output and load data to simulate the system's operating state in the time domain to calculate the required reserve capacity. These methods have significant drawbacks:

[0003] Low computational efficiency: Monte Carlo simulation requires a large number of repeated sampling and simulation processes, which is time-consuming and difficult to meet real-time scheduling requirements.

[0004] Accuracy limitations: Time series models are sensitive to data integrity and resolution, and are prone to accumulating errors in scenarios where the output of new energy sources fluctuates frequently.

[0005] Insufficient practicality: Existing studies mostly focus on long-term planning and ignore the quantitative impact of actual operating parameters (such as peak shaving rate and proportion of self-owned power plants) on ancillary services, resulting in weak dispatch guidance.

[0006] Furthermore, traditional ancillary service demand calculation has two limitations:

[0007] Limitations of static models: They cannot capture instantaneous power imbalances caused by minute-level fluctuations in new energy sources (such as sudden changes in wind speed or cloud movement).

[0008] Lack of dynamic response: Existing methods rely on historical averages and ignore the probability distribution characteristics of instantaneous fluctuation amplitudes, resulting in: insufficient frequency regulation capacity configuration (sudden power shortages cause frequency over-limits) and waste of reserve resources (over-compensation of stable fluctuation periods). Summary of the Invention

[0009] To address the aforementioned technical bottlenecks, this invention provides a method for quantifying the auxiliary service requirements of new energy generating units based on probability distribution characteristics.

[0010] A method for quantifying the auxiliary service demand of new energy generating units based on probability distribution characteristics includes the following steps:

[0011] Step 1: Establish a non-time-series calculation model for ancillary service requirements based on the system to be evaluated; obtain the ancillary service requirement capacity through the non-time-series calculation model for ancillary service requirements.

[0012] Step 2: Calculate the probability distribution of ancillary service demand capacity;

[0013] Step 3: Quantify the demand for ancillary services by completing the quantitative assessment through the expected value of the demand for ancillary services;

[0014] Step 4, dynamic adjustment of ancillary service demand caused by the volatility of new energy sources, specifically:

[0015] Based on the probability distribution of the characteristic parameter γ of the new energy power output fluctuation, the instantaneous power imbalance ΔP(t) of the system is calculated and expressed as:

[0016] ;

[0017] Where S is the installed capacity of new energy, η is the fluctuation confidence coefficient, and 0 < η ≤ 1;

[0018] Combine the instantaneous power imbalance of the system with the ancillary service demand capacity from step 1. By superimposing these values, we can obtain the dynamic auxiliary service demand capacity. , represented as:

[0019] ;

[0020] Convolution calculation The probability distribution is used to update the expected value of auxiliary service demand.

[0021] Furthermore, in step 1, the ancillary service demand capacity The calculation formula is:

[0022] ;

[0023] In the formula, This represents the maximum theoretical output of the new energy source at time t. This represents the system load at time t. Here, K represents the power output fluctuation characteristic parameter of the new energy source, and K is the system adjustment coefficient. This is the maximum evaluation function.

[0024] Furthermore, in step 2, the ancillary service demand capacity... probability distribution function Due to the fluctuation characteristics of new energy power output and system load The probability density function is obtained through convolution, and is expressed as:

[0025] ;

[0026] in, Indicating the fluctuation characteristics of new energy power output The probability density function, Indicates system load The probability density function.

[0027] Furthermore, in step 3, the expected value of ancillary service demand. The calculation formula is:

[0028] ;

[0029] pass Determine the required capacity of auxiliary services for the system.

[0030] Furthermore, the formula for calculating the power output fluctuation characteristic parameter γ of new energy sources is as follows:

[0031] ;

[0032] In the formula, and The new energy output of adjacent sampling points, For new energy installed capacity, This is the sampling interval.

[0033] Furthermore, when the peak shaving rate β of a conventional unit is reached, the calculation formula for the system regulation coefficient K is as follows:

[0034] ;

[0035] Where β is the peak shaving rate of conventional units and α is the system reserve rate.

[0036] Furthermore, considering the proportion ω of self-owned power plants, the calculation formula for the system regulation coefficient K is updated as follows:

[0037] ;

[0038] Where β is the peak shaving rate of conventional units and ω is the proportion of self-owned power plants.

[0039] Furthermore, consider DC power transmission. At that time, the system load The calculation formula has been updated to:

[0040] ;

[0041] in, For external power transmission.

[0042] Furthermore, it also includes step 5:

[0043] Step 5: Optimize the system peak shaving rate or new energy installation plan by quantifying the evaluation results and target curtailment rate parameters.

[0044] The positive and progressive effects of this invention are as follows:

[0045] This invention calculates the basic ancillary service capacity using a non-time-series model, introduces new energy fluctuation characteristic parameters to generate instantaneous power imbalance in real time and dynamically corrects demand, utilizes probabilistic convolution to fuse system load and fluctuation distribution to achieve minute-level fluctuation response and risk adaptive configuration, and finally outputs the expected value of ancillary service demand that can be linked to the automatic generation control system, thereby improving the transient stability of the power grid and resource economy. Attached Figure Description

[0046] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0047] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention.

[0048] Reference Figure 1 A method for quantifying the auxiliary service demand of new energy generating units based on probability distribution characteristics includes the following steps:

[0049] Step 1: Establish a non-time-series calculation model for ancillary service requirements based on the system to be evaluated; obtain the ancillary service requirement capacity through the non-time-series calculation model for ancillary service requirements.

[0050] Step 2: Calculate the probability distribution of ancillary service demand capacity;

[0051] Step 3: Quantify the demand for ancillary services by completing the quantitative assessment through the expected value of the demand for ancillary services;

[0052] Step 4, dynamic adjustment of ancillary service demand caused by the volatility of new energy sources, specifically:

[0053] Based on the probability distribution of the characteristic parameter γ of the new energy power output fluctuation, the instantaneous power imbalance ΔP(t) of the system is calculated and expressed as:

[0054] ;

[0055] Where S is the installed capacity of new energy, η is the fluctuation confidence coefficient, and 0 < η ≤ 1;

[0056] Combine the instantaneous power imbalance of the system with the ancillary service demand capacity from step 1. By superimposing these values, we can obtain the dynamic auxiliary service demand capacity. , represented as:

[0057] ;

[0058] Convolution calculation The probability distribution is used to update the expected value of auxiliary service demand.

[0059] Furthermore, in step 1, the ancillary service demand capacity The calculation formula is:

[0060] ;

[0061] In the formula, This represents the maximum theoretical output of the new energy source at time t. This represents the system load at time t. Here, K represents the power output fluctuation characteristic parameter of the new energy source, and K is the system adjustment coefficient. This is the maximum evaluation function.

[0062] Furthermore, in step 2, the ancillary service demand capacity... probability distribution function Due to the fluctuation characteristics of new energy power output and system load The probability density function is obtained through convolution, and is expressed as:

[0063] ;

[0064] in, Indicating the fluctuation characteristics of new energy power output The probability density function, Indicates system load The probability density function.

[0065] Furthermore, in step 3, the expected value of ancillary service demand. The calculation formula is:

[0066] ;

[0067] pass Determine the required capacity of auxiliary services for the system.

[0068] Furthermore, the formula for calculating the power output fluctuation characteristic parameter γ of new energy sources is as follows:

[0069] ;

[0070] In the formula, and The new energy output of adjacent sampling points, For new energy installed capacity, This is the sampling interval.

[0071] Furthermore, when the peak shaving rate β of a conventional unit is reached, the calculation formula for the system regulation coefficient K is as follows:

[0072] ;

[0073] Where β is the peak shaving rate of conventional units and α is the system reserve rate.

[0074] Furthermore, considering the proportion ω of self-owned power plants, the calculation formula for the system regulation coefficient K is updated as follows:

[0075] ;

[0076] Where β is the peak shaving rate of conventional units and ω is the proportion of self-owned power plants.

[0077] Furthermore, consider DC power transmission. At that time, the system load The calculation formula has been updated to:

[0078] ;

[0079] in, For external power transmission.

[0080] Furthermore, it also includes step 5:

[0081] Step 5: Optimize the system peak shaving rate or new energy installation plan by quantifying the evaluation results and target curtailment rate parameters.

[0082] When the curtailment rate exceeds the standard, priority should be given to adjusting adjustable parameters (such as increasing the peak shaving rate β of conventional units), followed by adjusting the installed capacity plan (such as limiting the grid connection capacity of new energy sources) to avoid a decrease in power supply capacity due to installed capacity adjustments. The optimization process must simultaneously meet grid security constraints (such as frequency deviation ≤ ±0.2Hz), environmental indicators (such as carbon emission limits), and economic indicators (such as cost per kilowatt-hour ≤ 0.5 yuan). Particle swarm optimization (PSO) algorithm can be used to solve the multi-objective function.

[0083] In summary, this invention utilizes probabilistic convolution to fuse the fluctuation characteristics of new energy sources with the probability distribution of system load, replacing traditional static mean calculations. This achieves a probabilistic description of demand capacity, enabling the quantification results to reflect the probability of occurrence under different fluctuation scenarios and avoiding "over-allocation" or "under-allocation" of reserve resources. Traditional methods often use historical averages or fixed safety factors (such as allocating 10% of new energy installed capacity as reserves), failing to consider the probabilistic distribution characteristics of fluctuation amplitudes. This results in insufficient frequency regulation capacity during periods of strong fluctuations and redundancy during stable periods, leading to low resource utilization.

[0084] It is worth noting that the power output fluctuation characteristics γ of new energy sources typically follow a normal distribution (such as the randomness of wind speed and solar intensity), while system loads often conform to a Weibull distribution or a log-normal distribution. The distribution fit needs to be verified using the Kolmogorov-Smirnov test based on historical data to avoid probability calculation errors due to incorrect distribution assumptions. When using Fast Fourier Transform (FFT) for convolution calculations, attention must be paid to matching the sampling interval with the frequency resolution (e.g., when the sampling interval Δt = 10 min, the frequency resolution must be ≤ 1 / (24 × 60 min) = 0.0007 Hz) to prevent spectral aliasing.

[0085] The present invention has been described in detail above with reference to the accompanying drawings and embodiments. Those skilled in the art can make various modifications to the present invention based on the above description. Therefore, certain details in the embodiments should not be construed as limiting the present invention, and the scope of protection of the present invention shall be defined by the appended claims.

Claims

1. A method for quantifying the auxiliary service demand of new energy generating units based on probability distribution characteristics, characterized in that, Includes the following steps: Step 1: Establish a non-time-series calculation model for ancillary service requirements based on the system to be evaluated; obtain the ancillary service requirement capacity through the non-time-series calculation model for ancillary service requirements. Step 2: Calculate the probability distribution of ancillary service demand capacity; Step 3: Quantify the demand for ancillary services by completing the quantitative assessment through the expected value of the demand for ancillary services; Step 4, dynamic adjustment of ancillary service demand caused by the volatility of new energy sources, specifically: Based on the probability distribution of the characteristic parameter γ of new energy power output fluctuation, the instantaneous power imbalance of the system is calculated. , is represented as: ； Where S is the installed capacity of new energy, η is the fluctuation confidence coefficient, and 0 < η ≤ 1; Combine the instantaneous power imbalance of the system with the ancillary service demand capacity from step 1. By superimposing these values, we can obtain the dynamic auxiliary service demand capacity. , is represented as: ； Convolution calculation The probability distribution is used to update the expected value of auxiliary service demand.

2. The method for quantifying the auxiliary service demand of new energy generating units based on probability distribution characteristics according to claim 1, characterized in that, In step 1, the capacity demand for ancillary services The calculation formula is: ； In the formula, This represents the maximum theoretical output of the new energy source at time t. This represents the system load at time t. Here, K represents the power output fluctuation characteristic parameter of the new energy source, and K is the system adjustment coefficient. This is the maximum evaluation function.

3. The method for quantifying the auxiliary service demand of new energy generating units based on probability distribution characteristics according to claim 1, characterized in that, In step 3, the expected value of ancillary service demand. The calculation formula is: ； pass Determine the required auxiliary service capacity of the system. Capacity for ancillary services The probability distribution function.

4. The method for quantifying the auxiliary service demand of new energy generating units based on probability distribution characteristics according to claim 2, characterized in that, The formula for calculating the power output fluctuation characteristic parameter γ of new energy sources is: ； In the formula, and The new energy output of adjacent sampling points, For new energy installed capacity, This represents the sampling interval.

5. The method for quantifying the auxiliary service demand of new energy generating units based on probability distribution characteristics according to claim 2, characterized in that, When the peak shaving rate β of a conventional unit is reached, the formula for calculating the system regulation coefficient K is as follows: ； Where β is the peak shaving rate of conventional units and α is the system reserve rate.

6. The method for quantifying the auxiliary service demand of new energy generating units based on probability distribution characteristics according to claim 2, characterized in that, When considering the proportion of self-owned power plants ω, the formula for calculating the system regulation coefficient K is updated as follows: ； Where β is the peak shaving rate of conventional units and ω is the proportion of self-owned power plants.

7. The method for quantifying the auxiliary service demand of new energy generating units based on probability distribution characteristics according to claim 2, characterized in that, Consider DC power transmission At that time, the system load The calculation formula has been updated to: ； in, For external power transmission.

8. The method for quantifying the auxiliary service demand of new energy generating units based on probability distribution characteristics according to claim 1, characterized in that, It also includes step 5: Step 5: Optimize the system peak shaving rate or new energy installation plan by quantifying the evaluation results and target curtailment rate parameters.

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