Power distribution network dispatching method based on situation awareness

Abstract

The invention discloses a situation awareness-based power distribution network scheduling method, relates to the technical field of power distribution network scheduling, and solves the technical problem that the existing active power distribution network is difficult to quickly respond to the problems of volatility, randomness and the like generated by wind power generation and photovoltaic power generation, so that the scheduling precision of the power distribution network is influenced. The average wind energy power and the average photovoltaic power are respectively calculated based on Weibull distribution and Beta distribution, the new energy generating capacity in the set time period in the target area is predicted through the average wind energy power and the average photovoltaic power, and the new energy generating capacity is compared with the electric energy demand quantity of the target area in the set time period. Setting a dynamic energy storage scheme of the energy storage equipment according to a comparison result; and in the power supply process, the dynamic energy storage scheme is adjusted at any time according to the total error amount, it is ensured that the energy storage equipment does not store too much electric energy, and supplement can be conducted when the new energy generating capacity is insufficient to meet the electric energy demand of the target area.

Description

Technical Field

[0001] This application belongs to the field of distribution network dispatching, and specifically relates to a distribution network dispatching method based on situation awareness. Background Technique

[0002] With the development and utilization of renewable energy, it is difficult to effectively manage renewable energy units connected to the distribution network in a distributed form with the fixed network structure and passive control and protection modes of traditional distribution networks. An active distribution network can use various adjustment technologies to control the power flow of the power grid, solve a series of problems brought by the access of a large number of renewable energy units, and achieve stable and reliable operation of the distribution system.

[0003] Since renewable energy represented by wind and light is affected by weather factors, such as wind power generation is greatly affected by wind speed and photovoltaic power generation is greatly affected by light intensity, problems such as volatility, randomness, and intermittency generated therefrom will bring great trouble to the optimal dispatching of the active distribution network. Although existing solutions have realized the dispatching of the distribution network by establishing a power generation prediction model, the amount of data required for the construction of this power generation prediction model is large and the types are numerous. Once some data appears abnormal, it will seriously affect the comprehensive accuracy of the above power generation prediction model, and the deviation of the comprehensive accuracy cannot be effectively remedied, which will greatly affect the dispatching accuracy of the distribution network.

[0004] This application provides a distribution network dispatching method based on situation awareness to solve the above technical problems. Summary of the Invention

[0005] This application aims to solve at least one of the technical problems existing in the prior art; for this purpose, this application proposes a distribution network dispatching method based on situation awareness, which is used to solve the technical problem that the existing active distribution network is difficult to quickly respond to problems such as volatility and randomness generated by wind power generation and photovoltaic power generation, which affects the dispatching accuracy of the distribution network.

[0006] To achieve the above object, the first aspect of this application provides a distribution network dispatching method based on situation awareness, including:

[0007] Calculating the average wind energy power and the average photovoltaic power based on the Weibull distribution and the Beta distribution respectively; predicting the new energy power generation corresponding to a set time period in the target area based on the average wind energy power and the average photovoltaic power;

[0008] Predicting the electrical energy demand of the target area based on the weather data of the set time period; comparing the new energy power generation and the electrical energy demand, and setting the dynamic energy storage scheme of the energy storage device according to the comparison result;

[0009] Calculate the total error of the new energy power generation during the power supply process; adjust the dynamic energy storage scheme of the energy storage device according to the comparison result between the total error and the set threshold; wherein, the dynamic energy storage scheme is used to guide the electric energy storage or consumption in the energy storage device.

[0010] Preferably, calculating the average wind power based on the Weibull distribution includes:

[0011] Based on the probability density function f(v) of the wind speed distribution described by the Weibull distribution, and where k is the shape parameter and λ is the scale parameter;

[0012] Average wind power where P(v) is the average wind power; ρ is the air density, A is the swept area of the wind turbine rotor, C p (v) is the wind turbine power coefficient, v cut-in is the cut-in wind speed, v cut-out is the cut-out wind speed.

[0013] Preferably, establishing the average photovoltaic power based on the Beta distribution includes:

[0014] Obtain the probability density function of the Beta distribution as: where α and β are shape parameters, obtained by fitting historical solar irradiance data, and Γ is the Gamma function;

[0015] Average photovoltaic power where η is the conversion efficiency of the photovoltaic panel, I is the actual solar irradiance, I ref is the irradiance at the maximum power point, A is the area of the photovoltaic panel, and s is the normalized solar irradiance.

[0016] Preferably, predicting the new energy power generation corresponding to a set time period in the target area based on the average wind power and the average photovoltaic power includes:

[0017] Obtain the wind turbines and photovoltaic modules associated with the target area, and obtain the weather prediction data corresponding to the wind turbines and photovoltaic modules within the set time period through the meteorological monitoring platform; wherein, the weather prediction data includes wind speed, irradiance, temperature, and humidity;

[0018] Match and calculate the weather prediction data with the average wind power and the average photovoltaic power respectively to obtain the new energy power generation within the set time period.

[0019] Preferably, predicting the electricity demand of the target area based on the weather data of the set time period includes:

[0020] Obtain the business entities and living entities within the target area;

[0021] Predict the influence coefficients of weather forecast data in the target area on business entities and living entities by the triple exponential smoothing method, and mark them as business influence weight and living influence weight respectively;

[0022] Calculate the business electricity demand based on the benchmark electricity consumption of business entities and the business influence weight, and calculate the living electricity demand based on the benchmark electricity consumption of living entities and the living influence weight; superimpose and calculate the business electricity demand and the living electricity demand to obtain the electricity demand in the target area within the set time period.

[0023] Preferably, the predicting the influence coefficients of weather forecast data in the target area on business entities and living entities by the triple exponential smoothing method includes:

[0024] Collect historical weather data in the target area, as well as the corresponding electricity consumption data of business entities and living entities;

[0025] Initialize the level component S0, the trend component t0 and the seasonal component p0, and select three smoothing coefficients δ, θ and μ; among them, δ affects the level component, θ affects the trend component, μ affects the seasonal component, and the value range of the smoothing coefficient is (0, 1);

[0026] Calculate the smoothed value: And optimize the smoothing coefficient by the least squares method; where Y t is the observed value corresponding to time t, and m is the length of the seasonal cycle;

[0027] Calculate the business influence weight based on the observed value of business electricity consumption, and calculate the living influence weight based on the observed value of living electricity consumption.

[0028] Preferably, the calculating the total error of new energy power generation during the power supply process includes:

[0029] Obtain the actual value and the predicted value of new energy power generation, calculate the difference between the predicted value and the actual value and mark it as the power generation error; establish a power generation error curve based on the power generation error;

[0030] Through the formula Calculate the total error WC; where F(t) is the power generation error at time t, T is the total observation time, and w(t) is the weight function.

[0031] Preferably, adjusting the dynamic energy storage scheme of the energy storage device includes:

[0032] When the total error is negative, store the electric energy corresponding to the total error in the energy storage device;

[0033] When the total error is positive and WC ≥ SY, the energy storage supplement CTL of the energy storage device is calculated by the formula CTL = k·(|WC| - SY); where k is a proportionality coefficient greater than 1, determined according to the energy storage cost and energy storage efficiency, and SY is the set threshold, that is, the backup electric energy stored in the energy storage device.

[0034] Compared with the prior art, the beneficial effects of the present application are:

[0035] The present application predicts the new energy power generation in a target area during a set period by calculating the average wind power and average photovoltaic power, compares the new energy power generation with the power demand in the target area during the set period, and sets a dynamic energy storage plan for the energy storage device according to the comparison result; moreover, during the power supply process, the dynamic energy storage plan is adjusted at any time according to the total error, ensuring that neither too much electric energy is stored in the energy storage device, nor can it be supplemented when the new energy power generation is insufficient to meet the power demand in the target area.

[0036] The present application calculates the average wind power and average photovoltaic power based on the Weibull distribution and Beta distribution respectively, and establishes the functional relationship between the average wind power and the wind speed, and the functional relationship between the average photovoltaic power and the solar irradiance; during the process of constructing the functional relationship, the influence of various factors and the degree of influence are fully considered, ensuring the accuracy and reliability of the functional relationship.

[0037] When predicting the power demand in the target area based on the weather data of the set period, the present application divides the electricity-consuming entities in the target area into business entities and living entities, expresses the influence of weather changes on the electricity consumption of living entities and business entities through a model, and thus calculates the business influence weight and living influence weight; then combines their respective baseline power consumption to calculate the power demand in the target area during the set period; comprehensively considers the different influences of weather changes on living entities and business entities, improving the calculation accuracy of the power demand. Description of the Drawings

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0039] Figure 1 It is a schematic diagram of the method for distribution network dispatching in the first embodiment of the present application;

[0040] Figure 2 It is a schematic diagram of the modeling process of the average wind power in the second embodiment of the present application;

[0041] Figure 3 Schematic diagram of the modeling process of the average photovoltaic power in the second embodiment of this application;

[0042] Figure 4 Schematic diagram of the calculation process of the power demand in the third embodiment of this application. Specific implementation manners

[0043] Next, the technical solutions of this application will be clearly and completely described in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.

[0044] The first aspect embodiment of this application provides a distribution network scheduling method based on situation awareness, aiming to solve the problem that the existing active distribution network cannot accurately optimize the problems of volatility, randomness, intermittency, etc. brought by the access of renewable energy to the distribution network, which will greatly reduce the scheduling accuracy of the distribution network.

[0045] Embodiment 1: Provide a distribution network scheduling solution based on situation awareness to solve the problem that the volatility, randomness, etc. brought by the access of renewable energy to the distribution network affect the scheduling accuracy. Please refer to Figure 1 .

[0046] A distribution network scheduling method based on situation awareness provided in this embodiment includes:

[0047] Calculate the average wind power and the average photovoltaic power respectively based on the Weibull distribution and the Beta distribution; predict the new energy power generation corresponding to the set time period in the target area based on the average wind power and the average photovoltaic power;

[0048] Predict the power demand of the target area based on the weather data of the set time period; compare the new energy power generation and the power demand, and set the dynamic energy storage scheme of the energy storage device according to the comparison result;

[0049] Calculate the total error of the new energy power generation during the power supply process; adjust the dynamic energy storage scheme of the energy storage device according to the comparison result between the total error and the set threshold; wherein, the dynamic energy storage scheme is used to guide the electric energy storage or consumption in the energy storage device.

[0050] In this embodiment, the average wind energy power of the wind turbine is established through the Weibull distribution, and this average wind energy power is mainly affected by the wind speed, air density, and equipment parameters corresponding to the wind turbine. The average wind energy power is a functional relationship established with the wind speed as the independent variable, and the average wind energy power at the corresponding moment can be calculated by substituting the wind speed value. Similarly, the average photovoltaic power of the photovoltaic module is established through the Beta distribution, and this average photovoltaic power is mainly affected by factors such as solar irradiance and photovoltaic panel area. The average photovoltaic power is a functional relationship established with the irradiance as the independent variable, and the average photovoltaic power at the corresponding moment can be obtained by substituting the irradiance.

[0051] The process of calculating the average wind energy power and the average photovoltaic power based on the Weibull distribution and the Beta distribution respectively in this embodiment can refer to the invention patent with the application number 2021100177571, which provides a method for establishing the power generation models of wind turbines and photovoltaic modules. Of course, other calculation methods disclosed in the existing technologies can also be referred to.

[0052] After obtaining the average wind energy power and the average photovoltaic power in this embodiment, the wind turbines and photovoltaic modules associated with the target area are obtained. On the one hand, the weather prediction data of the associated wind turbines in the corresponding area during the set period is obtained through a third-party meteorological monitoring platform, and the weather prediction data is combined with the average wind power to predict the power generation corresponding to each time period; on the other hand, the weather prediction data of the associated photovoltaic modules during the set period also needs to be obtained through a third-party monitoring platform, and this weather prediction data is combined with the average photovoltaic power to predict the power generation corresponding to each time period. The sum of the power generations of the wind turbines and photovoltaic modules in each time period is used as the new energy power generation corresponding to that time period. The new energy power generation here can be understood as the total power generation of the wind turbines and photovoltaic modules at the corresponding moment.

[0053] It should be noted that the wind turbines and photovoltaic modules are only responsible for supplying power to the target area and do not necessarily need to be located within the target area. When obtaining the weather prediction data, it is necessary to determine the actual locations of the wind turbines and photovoltaic modules to ensure the accuracy and reliability of the weather prediction data. The wind turbine is the general term for wind power generation equipment, including components such as the wind wheel, generator, yaw system, and control system; the photovoltaic module includes devices such as photovoltaic panels, photovoltaic cells, busbar boxes, and tracking systems.

[0054] The target area in this embodiment refers to the area scheduled by the distribution network. The purpose of the distribution network scheduling is to ensure that there is enough electrical energy for use within the target area, and it is preferred to ensure that the electrical energy generated by renewable energy is given priority. The set period is for formulating the distribution network scheduling plan. Since the distribution network scheduling plan is severely affected by the weather, the set period should not be too long to avoid large-scale adjustments to the distribution network scheduling plan due to sudden weather changes.

[0055] After determining the electricity demand of the target area, it is compared with the new energy power generation calculated above. If the new energy power generation is greater than the electricity demand, the electricity generated by the wind turbines and photovoltaic modules can meet the electricity demand of the target area within the set time period; if it is less than the electricity demand, it cannot meet the electricity demand of the target area within the set time period. At this time, a certain amount of electricity needs to be pre-stored through energy storage devices to ensure that the electricity demand of the target area within the set time period can be met. It should be noted that the determination of the electricity demand in the target area can refer to relevant solutions in the prior art, such as calculating the electricity demand through data mining by an artificial intelligence model.

[0056] The above solution has already constructed an electricity supply plan for the target area. However, due to problems such as the volatility and randomness of renewable energy power generation, as well as the accuracy of weather prediction data, it is difficult to ensure that the new energy power generation can be consistent with the predicted value. If it is inconsistent, it will either lead to the inability to meet the electricity demand in the target area, or lead to excessive electricity storage in the energy storage device, thereby increasing the energy storage cost.

[0057] To solve this technical problem, this embodiment calculates the total error of the new energy power generation during the power supply process. When the total error is negative, it means that the actual power generation is greater than the predicted power generation. Then, the excess electricity is stored in the energy storage device. If there is too much electricity stored in the energy storage device, it needs to be supplied to other areas for use. When the total error is positive, it means that the actual power generation is less than the predicted power generation. At this time, the power generation of the photovoltaic modules and wind turbines may not reach the expected value due to various factors. It is difficult for various prediction models to comprehensively consider all influencing factors. At this time, it is necessary to consider whether the remaining electricity in the energy storage device can be replenished (supplemented when the backup electricity in the energy storage device cannot make up for the electricity gap). If the electricity demand of the target area still cannot be met after the replenishment of the remaining electricity, it is necessary to adjust the dynamic energy storage plan of the energy storage device in a timely manner.

[0058] Based on the existing active distribution network, this embodiment sets up an energy storage device dedicated to dynamic energy storage adjustment. Through the mutual complementation of this energy storage device with wind turbines and photovoltaic modules, not only can the excess electricity over the predicted value be stored and utilized in a timely manner through the energy storage device, reducing the energy storage cost and improving the energy utilization rate, but also when the actual power generation is less than the predicted power generation, the electricity stored in the energy storage device can be used for timely replenishment, providing a redundancy guarantee for the volatility and randomness of renewable energy and ensuring that the electricity demand of the target area is met.

[0059] It should be noted that the energy storage device in this example can be an energy storage device set separately for the target area, that is, the energy storage device is isolated from other areas and only serves the target area it is associated with. Of course, multiple target areas can also share an energy storage device, which is beneficial to the distribution and utilization of electric energy. However, it is best to partition the energy storage device, and each partition is separately associated with the target area.

[0060] Embodiment 2: Compared with Embodiment 1, a modeling method for average wind power and average photovoltaic power is provided to improve the result accuracy of average wind power and average photovoltaic power, and further improve the prediction accuracy of new energy power generation.

[0061] 1. Calculate the average wind power based on the Weibull distribution. Please refer to Figure 2 , including the following steps:

[0062] Step 1: Based on the Weibull distribution, describe the probability density function f(v) of the wind speed distribution, and where k is the shape parameter and λ is the scale parameter;

[0063] Step 2: Establish the relationship between the wind speed and the wind power. The formula is as follows: where ρ is the air density (unit: kg / m 3 ), A is the swept area of the wind turbine (unit: m 2 ), C p is the wind turbine power coefficient, also known as the Betz efficiency, which is an index to measure the efficiency of the wind turbine. Its value is usually between 0 and 0.59 (the theoretical maximum value is 0.59, that is, the Betz limit;

[0064] Step 3: Integrate the probability density function of the wind speed to obtain the average wind power where v cut-in is the cut-in wind speed and v cut-out is the cut-out wind speed. Here, the wind turbine power coefficient is written as a function related to the wind speed. Of course, it can also be directly valued as described above, and the value range is (0, 0.59].

[0065] The average wind power established above is a function of wind speed. When the wind speed is less than the cut-in wind speed or greater than the cut-out wind speed, the wind turbine does not operate, so the average wind power is 0. When the wind speed is between the cut-in wind speed and the cut-out wind speed, the corresponding average wind power is calculated according to the above calculation formula. It should be understood that there is a corresponding relationship between the above average wind power and the cube of the wind speed, that is, a slight change in the wind speed will also cause a sudden change in the average wind power. This is also the reason why randomness and volatility cannot be well solved in the existing distribution network dispatching scheme. At the same time, the above model of average wind power is more comprehensive than the existing models and the calculation process is simpler.

[0066] 2. Establish the average photovoltaic power based on the Beta distribution. Please refer to Figure 3 , including:

[0067] Step 1: Normalize the solar irradiance; assume that the irradiance at the maximum power point of the photovoltaic panel is I ref , and the actual solar irradiance I can be expressed as I = I ref ·s; where s is the normalized solar irradiance and s follows the Beta distribution;

[0068] Step 2: Determine the parameters of the Beta distribution; the Beta distribution is controlled by two shape parameters α and β, and these two parameters can be determined by the expectation and variance of the historical observation data of solar irradiance. The probability density function of the Beta distribution is: where α and β are shape parameters and Γ is the Gamma function;

[0069] Step 3: Calculate the photovoltaic power generation; the photovoltaic power generation is calculated by the solar irradiance I and the conversion efficiency η of the photovoltaic panel. Substitute the normalized solar irradiance s into the photovoltaic power generation formula for calculation, and the obtained formula is: P = ηIA = ηI ref As; where A is the area of the photovoltaic panel;

[0070] Step 4: Calculate the average photovoltaic power; integrate the Beta distribution to obtain the expected value [S] of s, that is

[0071] Average photovoltaic power

[0072] The average photovoltaic power established above is a function of the irradiance at the maximum power point. By substituting the irradiance at the maximum power point in the weather prediction data, the average power of the photovoltaic panel can be calculated, and then the power generation of the photovoltaic panel can be calculated. The construction of the average photovoltaic power also fully considers the influencing factors, which can ensure the accuracy of the power generation of the photovoltaic panel.

[0073] After calculating the average wind power and average photovoltaic power, relevant parameters such as wind speed and maximum power point irradiance can be extracted from the weather forecast data within the set time period. Substituting these parameters into the above calculation formula can calculate the corresponding power generation. Multiplying the power generation by the time period can obtain the corresponding power generation amount.

[0074] Since the average wind power and average photovoltaic power need to be multiplied by a time period to obtain the corresponding power generation amount, the time period can be set to one minute or a quarter of an hour. In this way, there will be a corresponding power generation amount for each minute or quarter of an hour, and connecting them in series will form a power generation curve. It should be noted that both the average wind power and average photovoltaic power can be converted into power curves with time as the independent variable. Vertically superimposing the power curves can obtain the total power generation curve. Integrating the total power curve over the set time period can obtain the new energy power generation amount corresponding to the set time period.

[0075] Embodiment 3: Compared with Embodiment 1, a method for obtaining the electrical energy demand of a target area is provided. Please refer to Figure 4 .

[0076] Predicting the electrical energy demand of the target area based on the weather data within the set time period, including;

[0077] Step 1: Obtain the business entities and living entities within the target area.

[0078] Business entities mainly refer to electricity-consuming entities that provide goods or services, and the electricity they use is mainly for business activities; living entities refer to those whose electricity is mainly used for daily life. The electricity used by business entities and living entities is affected by the weather. For example, when the weather is too hot, the electricity-consuming activities of business entities are relatively less, while those of living entities increase relatively. Therefore, this embodiment provides a calculation method for the electrical energy demand of the target area to improve the accuracy of the calculated electrical energy demand. It should be noted that some entities within the target area do not completely belong to living entities or business entities. For these entities, they can be equivalently regarded as living entities or business entities according to the change law affected by the weather.

[0079] Step 2: Predict the influence coefficients of the weather forecast data in the target area on business entities and living entities through the triple exponential smoothing method, and mark them as business influence weight and living influence weight respectively.

[0080] The electricity consumption of the target area is not only related to business entities and living entities themselves, but also related to the season. In this embodiment, the triple exponential smoothing method is used to combine the weather forecast data in the target area to predict the influence of the weather on business entities and living entities, and obtain the business influence weight and living influence weight.

[0081] Step 3: Calculate the operating electricity demand based on the baseline electricity consumption of the business entity and the business impact weight, and calculate the living electricity demand based on the baseline electricity consumption of the living entity and the living impact weight; superimpose the operating electricity demand and the living electricity demand to calculate the electricity demand of the target area within the set period.

[0082] Calculate the operating electricity demand based on the baseline electricity consumption of the business entity and the business impact weight, and calculate the living electricity demand based on the baseline electricity consumption of the living entity and the living impact weight. Superimpose the operating electricity demand and the living electricity demand, and calculate the electricity demand of the target area within the set period:

[0083] Electricity demand = (baseline operating electricity consumption × business impact weight) + (baseline living electricity consumption × living impact weight).

[0084] In this embodiment, the baseline electricity consumption of the living entity and the business entity can be replaced by the average value of the actual electricity consumption in the past period. Of course, other methods can also be used to calculate the baseline electricity consumption. Based on the analysis of the historical electricity consumption data of the living entity and the business entity, the electricity demand calculated by combining the triple exponential smoothing method is more accurate, with higher prediction efficiency and less data processed compared to existing artificial intelligence models.

[0085] Predict the influence coefficients of weather prediction data in the target area on the business entity and the living entity through the triple exponential smoothing method, including:

[0086] 1. Data collection and preprocessing

[0087] Collect the historical weather data (such as temperature, humidity, wind speed, etc.) in the target area and the corresponding electricity consumption data of the business entity and the living entity. Preprocess these data, including missing value processing, outlier removal, and data normalization.

[0088] 2. Model initialization

[0089] Operating electricity: Initialize the level component Sop t 、trend component Top t and seasonal component Pop t , and these components represent the initial level, trend, and seasonal components respectively. Usually, the level component Sop t can take the average value of the first few observations, the trend component Top t can take the difference between the first two observations, and the seasonal component Pop t is initialized to 0 or 1.

[0090] Living electricity: Initialize the level component Sres t 、trend component Tres t and seasonal component Prest , these components respectively represent the initial level, trend, and seasonal components. Usually, the level component Sres t can take the mean of the first few observations, and the trend component Tres t can take the difference between the first two observations, and the seasonal component Pres t is initialized to 0 or 1.

[0091] 3. Calculate the smoothed values

[0092] Calculate the smoothed values using the following formulas:

[0093] Sop t = δ1Y 1t +(1 - δ1)(Sop t-1 + Top t-1 + Pop t-m )

[0094] Top t = θ1(Sop t - Sop t-1 - Pop t-m )+(1 - θ1)Top t-1 ;

[0095] Pop t = μ1(Y 1t - Sop t - Top t )+(1 - μ1)Top t-m

[0096] Sres t = δ2Y 2t +(1 - δ2)(Sres t-1 + Tres t-1 + Pres t-m )

[0097] Tres t = θ2(Sres t - Sres t-1 - Pres t-m )+(1 - θ2)Tres t-1

[0098] Pres t = μ2(Y 2t - Sres t - Tres t )+(1 - μ2)Tres t-m

[0099] where Y 1t is the observed value of operating electricity consumption corresponding to time t, Y2t is the observed value of domestic electricity consumption corresponding to time t, and m is the length of the seasonal cycle.

[0100] 5. Optimize the smoothing coefficient

[0101] Optimize δ, θ, and μ by minimizing the prediction error (such as the mean square error). Specifically, the least squares method can be used to optimize the three smoothing parameters.

[0102] 6. Calculate the influence weights

[0103] Based on the difference between the smoothed value and the actual observed value, the operation influence weight and the domestic influence weight can be calculated. Specifically:

[0104]

[0105]

[0106] Example 4: Compared with Example 1, a dynamic energy storage scheme for an energy storage device is provided to ensure that the energy storage device can reserve sufficient electric energy as a supplement to the new energy power generation and can also timely distribute the excess electric energy to other areas for utilization to reduce the energy storage cost.

[0107] Monitor the measured value of the new energy power generation at any time, and at the same time calculate the predicted value of the new energy power generation at the corresponding moment according to the total power curve obtained above. Take the difference between the predicted value and the measured value at the corresponding moment as the power generation error, and establish a power generation error curve F(t) with the power generation error as the dependent variable and time as the independent variable.

[0108] Through the formula Calculate the total error WC; where F(t) is the power generation error at time t, T is the total observation time, w(t) is the weight function, and this weight function can be dynamically adjusted according to factors such as weather conditions, equipment performance, and data quality. Of course, in some other preferred examples, the influence of weather conditions, equipment performance, and data quality on the total error can be not considered, and the gap between the predicted value and the measured value at the corresponding moment can be directly calculated.

[0109] When the total error is positive, it means that the predicted value minus the measured value is positive during the total error calculation period, indicating that the actual power generation has not reached the predicted amount. Then, calculate the energy storage supplement amount CTL of the energy storage device through the formula CTL = k·(|WC| - SY), that is, other methods are needed to replenish the energy storage device to make up for the power generation that the previous photovoltaic module or wind turbine did not complete. k is a proportionality coefficient greater than 1, and SY is the standby electric energy stored in the energy storage device.

[0110] When the total error is negative, it indicates that the predicted value is less than the actual value, that is, the actual power generation of the photovoltaic module and the wind turbine is greater than the previous predicted value. At this time, the excess electric energy is stored in the energy storage device.

[0111] It should be noted that a reasonable amount of electric energy should be stored in the energy storage device. Storing too much electric energy without using it will be a waste and increase the energy storage cost; storing too little electric energy will not be enough to supplement the electric energy demand of the target area in time. Therefore, the backup electric energy of the energy storage device, that is, SY, can be set according to the average value of the difference between the predicted value and the measured value when the predicted value is greater than the measured value. Of course, the difference between the electric energy demand and the predicted new energy power generation can also be used as the backup electric energy and stored in the energy storage device.

[0112] This backup electric energy can also be used as the set threshold when analyzing the total error subsequently. That is, when the total error is greater than the backup electric energy, the energy storage device needs to be supplemented with electric energy, and when it is less than the backup electric energy, the energy storage device does not need to be supplemented with electric energy.

[0113] Some of the data in the above formula are taken as numerical values after removing the dimension. The formula is obtained by software simulation of a large amount of collected data to get a formula closest to the real situation; the preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0114] The above embodiments are only used to illustrate the technical method of the present application and not to limit it. Although the present application has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present application.

Claims

1. A distribution network scheduling method based on situation awareness, characterized in that, including: calculating the average wind power and the average photovoltaic power based on the Weibull distribution and the Beta distribution respectively; predicting the new energy power generation corresponding to a set time period in the target area based on the average wind power and the average photovoltaic power; predicting the electricity demand of the target area based on the weather data of the set time period; comparing the new energy power generation and the electricity demand, and setting a dynamic energy storage plan for the energy storage device according to the comparison result; calculating the total error of the new energy power generation during the power supply process; adjusting the dynamic energy storage plan of the energy storage device according to the comparison result between the total error and the set threshold; wherein, the dynamic energy storage plan is used to guide the electricity storage or consumption in the energy storage device.

2. The method for dispatching a distribution network based on situation awareness according to claim 1, characterized in that calculating the average wind power based on the Weibull distribution, including: Based on the probability density function f(v) that describes the wind speed distribution using the Weibull distribution, and where k is the shape parameter and λ is the scale parameter; Average wind power where P(v) is the average wind power; ρ is the air density, A is the swept area of the wind turbine rotor, C p (v) is the power coefficient of the wind turbine rotor, v cut-in is the cut-in wind speed, v cut-out is the cut-out wind speed.

3. A distribution network scheduling method based on situation awareness according to claim 1, characterized in that, establishing the average photovoltaic power based on the Beta distribution, including: The probability density function of the Beta distribution is obtained as follows: where α and β are shape parameters obtained by fitting historical solar irradiance data, and Γ is the Gamma function; Average photovoltaic power Among them, η is the conversion efficiency of the photovoltaic panel, I is the actual solar irradiance, and I ref is the irradiance at the maximum power point, A is the area of the photovoltaic panel, and s is the normalized solar irradiance.

4. A distribution network scheduling method based on situation awareness according to claim 2 or 3, characterized in that predicting the new energy power generation corresponding to a set time period in the target area based on the average wind power and the average photovoltaic power, including: obtaining the wind turbines and photovoltaic modules associated with the target area, and obtaining the corresponding weather prediction data of the wind turbines and photovoltaic modules during the set time period through a meteorological monitoring platform; wherein, the weather prediction data includes wind speed, irradiance, temperature and humidity; matching and calculating the weather prediction data with the average wind power and the average photovoltaic power respectively to obtain the new energy power generation during the set time period.

5. A distribution network scheduling method based on situation awareness according to claim 1, characterized in that, predicting the electricity demand of the target area based on the weather data of the set time period, including: obtaining the business entities and living entities in the target area; predicting the influence coefficients of the weather prediction data on the business entities and living entities in the target area by the triple exponential smoothing method, and respectively marking them as the business influence weight and the living influence weight; calculating the business electricity demand according to the benchmark electricity consumption of the business entities and the business influence weight, and calculating the living electricity demand according to the benchmark electricity consumption of the living entities and the living influence weight; adding the business electricity demand and the living electricity demand to calculate the electricity demand of the target area during the set time period.

6. The method for dispatching a distribution network based on situation awareness according to claim 5, characterized in that, predicting the influence coefficients of the weather prediction data on the business entities and living entities in the target area by the triple exponential smoothing method, including: collecting the historical weather data in the target area and the corresponding electricity consumption data of the business entities and the living entities; initializing the level component S0, the trend component t0 and the seasonal component p0, and selecting three smoothing coefficients δ, θ and μ; wherein, δ affects the level component, θ affects the trend component, μ affects the seasonal component, and the value range of the smoothing coefficient is (0, 1); calculating the smoothed value: and optimize the smoothing coefficient by the least squares method; where Y t is the observed value corresponding to time t, and m is the length of the seasonal period; calculating the business influence weight according to the observed value of the business electricity consumption, and calculating the living influence weight according to the observed value of the living electricity consumption.

7. A distribution network scheduling method based on situation awareness according to claim 1, characterized in that calculating the total error of the new energy power generation during the power supply process, including: obtaining the actual value and the predicted value of the new energy power generation, calculating the difference between the predicted value and the actual value and marking it as the power generation error; establishing a power generation error curve according to the power generation error; The total error WC is calculated through the formula where F(t) is the power generation error at time point t, T is the total observation time, and w(t) is the weight function.

8. A distribution network scheduling method based on situation awareness according to claim 1, characterized in that adjusting the dynamic energy storage plan of the energy storage device, including: when the total error is negative, storing the electric energy corresponding to the total error in the energy storage device; When the total error is positive and WC ≥ SY, the energy storage replenishment amount CTL of the energy storage device is calculated by the formula CTL = k·(|WC| - SY); where k is a proportionality coefficient greater than 1, determined according to the energy storage cost and energy storage efficiency, SY is the set threshold, that is, the standby electric energy stored in the energy storage device.

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