Method and system for evaluating operation flexibility of power system

Abstract

The invention provides a power system operation flexibility evaluation method and system, and the method comprises the following steps: S1, collecting historical data of a power system, analyzing the correlation between a power level and a prediction error, and screening the historical data most similar to a current prediction point according to the correlation; s2, calculating a system flexibility demand according to the net load and the prediction error; s3, calculating a flexibility demand according to the net load of the system and prediction errors of all prediction points in the system; s4, modeling the supply capability of the flexibility resources, and integrating into the overall flexibility supply of the system; s5, analyzing the overall flexibility of the system according to the available power transmission capacity of the key section in combination with the flexible supply and demand conditions of the sending end and the receiving end; and S6, using Monte Carlo simulation to simulate the system flexibility supply and demand condition, and calculating the system flexibility index based on the flexibility supply and demand quantification results in different scenes. According to the invention, more accurate and more comprehensive system operation flexibility evaluation is realized.

Description

Technical Field

[0001] The present invention relates to the field of power system performance evaluation, and in particular to a method and system for evaluating the operational flexibility of a power system. Background Art

[0002] Traditional With the advancement of my country's "dual carbon" goals, the penetration rate of new energy has continued to increase, resulting in a significant increase in the randomness and uncertainty of the power system. The traditional power system relies on controllable resources such as thermal power, and its operation mode is relatively stable. However, the volatility and intermittency of new energy sources such as wind power and photovoltaics pose new challenges to the power system. To ensure the safe and stable operation of the system, it is necessary to know how to evaluate flexibility. Flexibility involves not only the ability of power sources to respond quickly to changes in demand, but also the coordination of energy storage and demand-side management. This flexibility requires the power system to be able to effectively respond to load fluctuations and forecast errors on different time scales. Therefore, quantifying and evaluating the flexibility of the power system is crucial to ensuring its stability.

[0003] At present, the methods for evaluating the flexibility of power systems are divided into two categories: deterministic and uncertain. Deterministic methods rely on fixed indicators and scoring mechanisms, which are difficult to adapt to the complex changes of the real power grid. Although the uncertain methods take into account the random characteristics of the system, they usually ignore the actual operating conditions, such as unit start-up and shutdown and transmission restrictions. Many studies focus on the supply and demand balance of flexibility resources, but ignore the impact of transmission lines and key sections.

[0004] In view of this, the present invention proposes a method for evaluating the operating flexibility of a power system, which combines historical data screening and key section constraints to evaluate flexibility. This method can not only more accurately characterize the flexibility demand, but also consider the impact of spatial attributes on the supply and demand of flexibility, thereby providing a more comprehensive system operating flexibility evaluation. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for evaluating the operational flexibility of an electric power system, so as to achieve a more accurate and comprehensive evaluation of the operational flexibility of the system.

[0006] To achieve the above object, the technical solution of the present invention is as follows: A method for evaluating the operational flexibility of a power system comprises the following steps: S1. Collect historical data of the power system, including power data of wind power, photovoltaic power and load; analyze the correlation between power level and forecast error by calculating Spearman coefficient; and select the historical data most similar to the current forecast point according to the correlation; S2. Calculate the prediction error probability distribution of the current prediction point based on the selected historical data; S3, calculate the net load, and calculate the system flexibility requirement based on the net load and the forecast error; S4. Modeling the supply capacity of flexibility resources and integrating them to obtain the overall flexibility supply of the system; the flexibility resources include thermal power units, energy storage devices and controllable loads; S5. Analyze the overall system flexibility based on the available transmission capacity of key sections and the flexibility supply and demand of the sending and receiving ends; S6. Monte Carlo simulation is used to simulate the supply and demand of system flexibility, and the system flexibility index is calculated based on the quantitative results of flexibility supply and demand under different scenarios.

[0007] Preferably, the S1 specifically comprises the following steps: S11. Establishing a data set of power level, fluctuation degree and prediction error based on historical data, where t The power level at the moment is the power level at the prediction point. t The degree of fluctuation and prediction error at each moment are expressed as follows: in, yes t The degree of fluctuation of wind power / photovoltaic / load at any moment, yes t The forecast error of wind power / photovoltaic / load at each moment, yes t +1 forecast value of wind power / photovoltaic / load at time, yes t The actual value of wind power / photovoltaic / load at the moment; S12, using Spearman coefficient to characterize t The correlation between the time data sets is as follows: in, a is the data volume of the dataset; k Indicates the coefficient type, k =1, The Spearman coefficient representing the power level and prediction error, d Represents data points i The power level of is the order difference with the prediction error; k =2, The Spearman coefficient represents the degree of volatility and prediction error. d Represents data points i The volatility of and the order difference of the forecast error; S13. Based on the calculated Spearman coefficient, select the historical data that is most similar to the power level and fluctuation degree of the current prediction point.

[0008] Preferably, the specific screening strategy in S13 is as follows: if : First, select the historical data that is most similar to the power level of the current prediction point M The predicted values ​​and their corresponding prediction errors are used as the initial historical data set to ensure that the initial historical data set has power level similarity; then, the initial historical data set is selected N The historical data with the highest volatility similarity; if : First, select the historical data with the most similar fluctuation level to the current forecast point M The predicted values ​​and their corresponding prediction errors are used as the initial historical data set to ensure that the initial historical data set has similar fluctuations; then select N The historical data with the highest power level similarity is selected.

[0009] Preferably, S2 specifically comprises: taking the selected historical data as input, and using a non-parametric kernel density estimation method to calculate the probability distribution of the prediction error, and the kernel density estimation expression is: in, is the probability density function, x is the prediction error of the current prediction point, N is the number of historical data filtered out, It is the first i The prediction error for each data point is h is the bandwidth, K is the kernel function.

[0010] Preferably, the kernel function is a Gaussian kernel function: .

[0011] Preferably, the S3 specifically includes the following steps: S31. Calculate Net Load : in, yes t The load forecast value at that moment, yes t Wind power forecast value at the moment, yes t The photovoltaic forecast value at each moment; S32. Calculate the net load forecast error : in, , and They are the net load prediction errors of load, wind power and photovoltaic respectively, and the net load prediction errors of load, wind power and photovoltaic are obtained by using the acceptance-rejection sampling method after the corresponding prediction error probability distribution is calculated according to the historical data of load, wind power and photovoltaic according to steps S1-S2; S33. Calculate the system flexibility requirements at time t : The flexibility requirements include upward adjustment requirements and downward adjustment requirements: when When t The entire system needs to be more flexible at all times. Indicates an increase in flexibility requirements, when When t The entire system needs to adjust its flexibility at all times. Indicates downward adjustment of flexibility requirements.

[0012] Preferably, the S4 specifically comprises the following steps: S41. Model the supply capacity of flexible resources including thermal power units, energy storage devices and controllable loads: The flexibility supply capacity of thermal power units is limited by the maximum / minimum output and ramp speed of thermal power units; in, and It is a thermal power unit j exist t The ability to adjust the supply of flexibility upward and downward at any time; express t Thermal power unit j contribution; represents a time step; and It is a thermal power unit j Maximum and minimum output; and It is a thermal power unit j Up and down climbing speed; yes t Thermal power unit j The running status, 1 means running, 0 means shutdown; The flexibility of energy storage devices is limited by the maximum charging and discharging power and the current storage capacity: in, and It is an energy storage device n exist t The ability to adjust the supply of flexibility upward and downward at any time; and Indicates the maximum charging and discharging power of energy storage; is the current energy storage capacity, , They are the lower limit of energy storage capacity and the upper limit of energy storage capacity. and is the charge and discharge efficiency; express t Energy storage at all times n contribution; The flexibility of controllable load is provided based on the load characteristic range of the user; in, and It is a controllable load m exist t The ability to adjust the supply of flexibility upward and downward at any time; yes t User load at any moment; and They are the upper and lower bounds of the user load characteristic interval; S42. Integrate the supply capabilities of three types of flexibility resources, namely thermal power units, energy storage devices and controllable loads, to obtain the overall flexibility supply of the system: in, yes t The overall flexibility supply of the time system is increased. yes t The overall flexibility supply of the time system is adjusted downward; , and They respectively represent the number of thermal power units, energy storage devices and controllable loads in the system.

[0013] Preferably, the S5 specifically includes the following steps: S51. Definition t Available transmission capacity at critical sections at the moment for: in, cRepresents the key section, is the maximum flow limit of the section, is the line flow in the section, b is the number of lines, l Represents a line; S52. The two areas divided by the key section are called the sending end and the receiving end. The upward / downward flexibility demand and upward / downward flexibility supply of the sending end and the receiving end are calculated respectively. The overall flexibility of the system is analyzed by combining the flexibility supply and demand of the sending end and the receiving end. Among them, the flexibility margin is increased. , otherwise, the upward flexibility is insufficient; the downward flexibility is sufficient, which means the downward flexibility margin is sufficient. , otherwise it means insufficient flexibility for downward adjustment; the specific situation of overall system flexibility is as follows: a. When the sending end and the receiving end have insufficient flexibility in both upward and downward adjustments, the system will definitely have insufficient flexibility; when the sending end and the receiving end have sufficient flexibility in both upward and downward adjustments, the system will definitely meet the flexibility requirements; upward flexibility margin and reduce flexibility margin The calculation is: b. Sufficient flexibility at the sending end but insufficient flexibility at the receiving end: in, They are the system upward / downward flexibility margin; They are upward / downward flexible supply at the sending end; They are upward / downward flexibility supply at the receiving end; They are upward / downward flexibility requirements at the sending end; They are upward / downward flexibility requirements of the receiving end; c. Insufficient flexibility at the sending end but sufficient flexibility at the receiving end: d. The sending and receiving ends have insufficient flexibility in upward or downward adjustments, but there is a possibility of complementarity: ① The sending end has insufficient flexibility in upward adjustment, and the receiving end has insufficient flexibility in downward adjustment. ② The sending end has insufficient flexibility in downward adjustment, and the receiving end has insufficient flexibility in upward adjustment. .

[0014] Preferably, the calculation of the system flexibility index based on the quantified results of flexibility supply and demand in different scenarios specifically includes: (1) t The probability of insufficient flexibility in the time system adjustment and expectations : in, d is the sample size; To adjust the system's lack of flexibility index, when the sample e Increase flexibility margin When less than 0 Take 1, otherwise take 0; (2) t The probability of insufficient flexibility in the time system adjustment and expectations : in, d is the sample size; To lower the flexibility index of the system, when the sample e Lowering flexibility margin When less than 0 Take 1, otherwise take 0; (3) Probability of insufficient flexibility in system adjustments on operating days and expectations : (4) Probability of insufficient flexibility in system downgrades on operating days and expectations : in, T The time period for the operating day.

[0015] A system for evaluating the operating flexibility of an electric power system comprises a processor, a memory and a computer program stored in the memory. When the processor executes the computer program, it specifically executes the steps in any one of the above-mentioned methods for evaluating the operating flexibility of an electric power system.

[0016] Compared with the prior art, the present invention has the following beneficial effects: By combining historical data screening and non-parametric kernel density estimation, the present invention can effectively characterize the uncertainty of the power system and improve the accuracy of flexibility demand assessment.

[0017] The present invention provides a complete flexibility supply capacity evaluation method by comprehensively analyzing the flexibility characteristics of thermal power units, energy storage devices and load sides, ensuring that the system can better cope with load fluctuations.

[0018] By incorporating the analysis of transmission constraints at key sections, the present invention can more comprehensively evaluate system flexibility and support safe and stable operation in complex power environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A system power prediction error curve diagram in one embodiment of the present invention; Figure 2 A system flexibility supply curve diagram in one embodiment of the present invention; Figure 3 A bar graph showing the probability of insufficient system flexibility in one embodiment of the present invention; Figure 4 It is a flow chart of a method in one embodiment of the present invention. DETAILED DESCRIPTION

[0020] The technical solution of the present invention is described in detail below in conjunction with the accompanying drawings.

[0021] As the proportion of renewable energy in the power system continues to increase, the randomness and uncertainty of the system have increased significantly. This change has put forward higher flexibility requirements for the stable operation of the power system. Traditional power system evaluation methods are difficult to adapt to this complexity because they are usually based on fixed indicators and cannot fully consider the actual operating conditions and uncertainties. Therefore, it is particularly important to develop a new method that can effectively evaluate the flexibility of the power system. The present invention proposes a flexibility evaluation method that combines historical data screening and key section constraints. By analyzing historical data and applying the Spearman coefficient and non-parametric kernel density estimation method, the uncertainty of the system can be accurately characterized. At the same time, the flexibility characteristics of thermal power units, energy storage devices and load-side resources are comprehensively considered to evaluate the flexibility supply capacity of the system. In addition, the influence of the key section flow limit on the system flexibility balance is also analyzed.

[0022] The present invention proposes a method for evaluating the operational flexibility of a power system, which specifically comprises the following steps: Step 1: Filter historical data. First, collect historical operation data of the power system, including power data of wind power, photovoltaic power and load. These data provide the basis for flexibility assessment. For historical data, analyze the correlation between power level and forecast error by calculating the Spearman coefficient. According to the size of the correlation, filter out the historical data that is most similar to the current forecast point. The specific process is as follows: (1) Establish a data set of power level, fluctuation degree and prediction error based on historical data, where t The power level at the moment is the power level at the prediction point. t The degree of fluctuation and prediction error at each moment are expressed as follows: in, yes tThe degree of fluctuation of wind power / photovoltaic / load at any moment, yes t The forecast error of wind power / photovoltaic / load at each moment, yes t +1 forecast value of wind power / photovoltaic / load at time, yes t The actual value of wind power / photovoltaic / load at the moment.

[0023] (2) Then the Spearman coefficient is used to characterize the correlation between the data sets at time t. The specific formula is as follows: in, a is the data volume of the dataset; k Indicates the coefficient type, k =1, The Spearman coefficient representing the power level and prediction error, d Represents data points i The power level of is the order difference with the prediction error; k =2, The Spearman coefficient represents the degree of volatility and prediction error. d Represents data points i The degree of volatility is related to the order difference of the forecast error.

[0024] (3) Finally, based on the Spearman coefficient calculated in (2), the historical data set that is most similar to the power level and fluctuation degree of the current prediction point is selected. The specific selection strategy is as follows: ① If : First, select the historical data that is most similar to the power level at the prediction point M prediction values ​​and their corresponding prediction errors to ensure that the initial historical data sets all meet certain power level similarities, and then select N The historical data with the highest degree of similarity in volatility.

[0025] ② If : First, select the historical data with the most similar volatility to the predicted point M The predicted values ​​and their corresponding prediction errors ensure that the initial historical data sets meet a certain degree of volatility similarity, and then select N The historical data with the highest power level similarity is selected.

[0026] By executing the above steps, the historical data set is processed and the historical data that is most similar to the characteristics of the current prediction point is accurately selected.

[0027] Step 2: Calculate the forecast error probability distribution. Step 1 screened the historical data set and used the screened data set as the input for Step 2. Use the nonparametric kernel density estimation method to calculate the error probability distribution. This method does not need to assume the probability distribution type of the data and can build an accurate error model from the characteristics of historical data.

[0028] The kernel density estimation function formula is: in, is the probability density function, x is the prediction error of the current prediction point, N is the number of historical data filtered out, It is the first i The prediction error for each data point is h is the bandwidth, K is the kernel function (Gaussian kernel function is used here): By inputting the filtered historical data set, the probability distribution of the forecast error can be calculated. Such input can more realistically reflect the uncertainty of the system.

[0029] Step 3: Quantification of flexibility needs The flexibility demand is defined as the system's demand for upward or downward regulation capacity after the net load fluctuation and forecast error are combined.

[0030] First calculate the net load , which is defined as the system load minus the output of wind power and photovoltaic power: in, yes t The load forecast value at that moment, yes t Wind power forecast value at the moment, yes t Photovoltaic forecast value at each moment.

[0031] According to the historical data of load, wind power and photovoltaic, the corresponding prediction error distribution probability density function is calculated; then the acceptance-rejection sampling method is used to obtain the net load prediction error of load, wind power and photovoltaic. , and , the steps are as follows: (1) Select a proposed distribution, which is an easy-to-sample distribution. Here, we choose the normal distribution. (2) Generate candidate samples from the proposed distribution: Generate a sample from the proposed distribution y ; (3) By target distribution and the proposed distribution Calculate an acceptance probability in, is a constant, ensuring For all y All are established; (4) Generate a uniform random number ,if Accept the sample y , otherwise reject the sample and resample.

[0032] Calculate the net load forecast error at time t: Then calculate the flexibility demand, which includes upward and downward demand. when When t The entire system needs to be more flexible at all times. Indicates an increase in flexibility requirements, when When t The entire system needs to adjust its flexibility at all times. Indicates downward adjustment of flexibility requirements; The upward and downward demand are shown in the following formula: Step 4: Quantitative analysis of flexibility supply The supply capacity of three types of flexibility resources, namely thermal power units, energy storage devices and controllable loads, is analyzed and modeled separately to evaluate the upward and downward adjustment capabilities that various flexibility resources in the system can provide under specific operating conditions.

[0033] (1) Thermal power generation units The flexibility of thermal power units is limited by their maximum / minimum output and ramp speed: in, and It is a thermal power unit j exist t The ability to adjust the supply of flexibility upward and downward at any time; express t Thermal power unit j contribution; represents a time step; and It is a thermal power unitj Maximum and minimum output; and It is a thermal power unit j Up and down climbing speed; yes t Thermal power unit j The running status, 1 means running, 0 means stopping.

[0034] (2) Energy storage flexibility The flexibility of energy storage devices is limited by the maximum charging and discharging power and the current storage capacity: in, and It is an energy storage device n exist t The ability to adjust the supply of flexibility upward and downward at any time; and Indicates the maximum charging and discharging power of energy storage; is the current energy storage capacity, , They are the lower limit of energy storage capacity and the upper limit of energy storage capacity. and is the charge and discharge efficiency; express t Energy storage at all times n of effort.

[0035] (3) Controllable load flexibility The flexibility of controllable load (IDR) is based on the load characteristics of the user: in, and It is a controllable load m exist t The ability to adjust the supply of flexibility upward and downward at any time; yes t User load at any moment; and They are the upper and lower bounds of the user load characteristic interval respectively.

[0036] After analyzing the flexibility of the above parts, we integrate them to obtain the overall flexibility supply of the system: in, yes t The overall flexibility supply of the time system is increased. yes t The overall flexibility supply of the time system is adjusted downward; , and They respectively represent the number of thermal power units, energy storage devices and controllable loads in the system.

[0037] Step 5: Flexibility assessment considering flow constraints at key sections In the power system, the distribution of flexibility resources is usually unbalanced, and the following situations may occur: (1) Flexibility resources are concentrated in certain areas (sending end): For example, thermal power units or energy storage devices may be concentrated in areas with less load; flexibility resources need to be transmitted from the sending end to the load center (receiving end) through transmission lines.

[0038] (2) Flexibility demand is concentrated at the load center (receiving end): For example, a large number of new energy access may result in less demand in the sending area and higher load demand in the receiving area. Critical sections are some important transmission lines or line groups in the transmission network. Their transmission capacity is limited and may become a bottleneck for the transmission of flexibility resources. Even if there are sufficient flexibility resources at the sending end, if the flow at the critical section reaches the upper limit, the flexibility resources cannot be fully transmitted to the receiving end, the system flexibility will be limited, and insufficient flexibility may occur. Therefore, it is necessary to analyze the transmission capacity of the critical section, which is a further constraint on the balance between flexibility supply and demand. Definition t Available transmission capacity at critical sections at the moment for in, c Represents the key section, is the maximum flow limit of the section, is the line flow in the section, b is the number of lines, l Represents a line.

[0039] The two areas divided by the key section are called the sending end and the receiving end. The upward / downward flexibility demand and upward / downward flexibility supply of the sending end and the receiving end are calculated respectively, and the overall flexibility of the system is analyzed by combining the flexibility supply and demand of the sending end and the receiving end.

[0040] Among them, the flexibility margin is increased. , otherwise, the upward flexibility is insufficient; the downward flexibility is sufficient, which means the downward flexibility margin is sufficient. , otherwise it means insufficient flexibility for downward adjustment; the specific situation of overall system flexibility is as follows: a. When the sending end and the receiving end have insufficient flexibility in both upward and downward adjustments, the system will definitely have insufficient flexibility; when the sending end and the receiving end have sufficient flexibility in both upward and downward adjustments, the system will definitely meet the flexibility requirements; upward flexibility margin and reduce flexibility margin The calculation is: b. Sufficient flexibility at the sending end but insufficient flexibility at the receiving end: in, They are the system upward / downward flexibility margin; They are upward / downward flexible supply at the sending end; They are upward / downward flexibility supply at the receiving end; They are upward / downward flexibility requirements at the sending end; They are upward / downward flexibility requirements of the receiving end; c. Insufficient flexibility at the sending end but sufficient flexibility at the receiving end: d. The sending and receiving ends have insufficient flexibility in upward or downward adjustments, but there is a possibility of complementarity: ① The sending end has insufficient flexibility in upward adjustment, and the receiving end has insufficient flexibility in downward adjustment. ② The sending end has insufficient flexibility in downward adjustment, and the receiving end has insufficient flexibility in upward adjustment. .

[0041] Based on the above formulas, we deeply analyze and clarify how the key section limits the transmission of the flexible supply capacity of the sending / receiving end under different circumstances. These formulas reveal how the behavior of the key section directly affects the overall flexibility and transmission capacity of the system under various operating conditions, providing a theoretical basis for further optimizing system design.

[0042] Step 6: Monte Carlo simulation According to steps 1-5, the flexibility supply and demand evaluation framework of the system is basically established. Since the forecast deviation in the system flexibility demand is a random variable, Monte Carlo simulation is used, which can perform sampling simulation according to a given probability distribution function. In addition, the number of samplings has little to do with the scale of the system, which is simple and easy to implement. It can simulate the supply and demand of system flexibility in a more realistic way, and calculate the system flexibility index based on the quantitative results of flexibility supply and demand in different scenarios.

[0043] (1) t The probability of insufficient flexibility in the time system adjustment and expectations : in, d is the sample size; To adjust the system's lack of flexibility index, when the sample e Increase flexibility margin When less than 0 Take 1, otherwise take 0; (2) t The probability of insufficient flexibility in the time system adjustment and expectations : in, d is the sample size; To lower the flexibility index of the system, when the sample e Lowering flexibility margin When less than 0 Take 1, otherwise take 0; (3) Probability of insufficient flexibility in system adjustments on operating days and expectations : (4) Probability of insufficient flexibility in system downgrades on operating days and expectations : in, T The time period for the operating day.

[0044] The present invention also proposes a system for evaluating the operating flexibility of an electric power system, comprising a processor, a memory, and a computer program stored in the memory. When the processor executes the computer program, it specifically executes the steps in any one of the above-mentioned methods for evaluating the operating flexibility of an electric power system.

[0045] The following is an example to illustrate the operation process. Figure 4 As shown, the following steps are mainly covered: Step 1: Simulation environment construction The IEEE 30-node system is a standardized power system test case, which is widely used in power system analysis and simulation research. This system is one of a series of standard test systems developed by IEEE, which is mainly used to verify and evaluate the operation and control algorithms of power systems. The system contains 30 nodes (i.e., buses), including power generation nodes and load nodes. There are also 41 transmission line and transformer connection nodes in the system to simulate the transmission characteristics of the power network. The system also includes 6 generators and 4 transformers. This node system is used here for simulation analysis.

[0046] A 400MW photovoltaic power station and a 900MW wind farm are connected to nodes 6 and 27 respectively, and a 200MW / 400MW·h energy storage device is connected to node 3. It is assumed that there are users at nodes 5 and 21 who have the ability to participate in demand response. Lines 4-12, 6-9, 6-10 and 28-27 are selected as key sections (where line 4-12 refers to the transmission line connecting nodes 4 and 12, and the rest are analogous), forming a typical power system structure with uneven source and load distribution. The flexibility resources are almost concentrated in the upper half of the area, while the flexibility resources in the lower half are less and a large number of flexibility demands may be generated due to the presence of wind power stations.

[0047] Step 2: Calculation of system flexibility demand and supply The flexibility demand that the system may face in each scenario is calculated based on the following formula, and the flexibility supply size of the system is calculated based on the operating status of the system flexibility resources such as thermal power units and energy storage. Figure 1 and Figure 2 As shown. Since unit 5 and unit 6 have been in shutdown state, they are not included in the flexibility calculation. The trough of upward flexibility supply is mainly concentrated in 14:00-16:00 and 19:00-23:00. The former is because after the lunch peak, some units were shut down for economic reasons and the energy storage was exhausted during the peak period. Therefore, the system's upward flexibility decreased significantly during the 14:00-16:00 period, and 19:00-23:00 is mostly the evening peak period. At this time, the net load is large, the unit's upward flexibility is limited by the maximum technical output, and the energy storage power is low and cannot provide enough power. Therefore, the overall upward flexibility supply of the system is low; the system's downward flexibility supply trough is mainly concentrated in the 0:00-9:00 period in the early morning at night. At this time, the net load is low, the energy storage power is high, and some units are running at the minimum output state, so the overall downward flexibility supply of the system is low.

[0048] Step 3: Consider critical section constraints and conduct flexibility assessment based on Monte Carlo simulation The power limit of the key section is set to 1000MW, and the flexibility of the system is evaluated based on the Monte Carlo simulation results and the evaluation indicators proposed in this paper. The probability of insufficient flexibility of the system and its expectation are calculated according to the formula. According to the calculation results, the probability of insufficient flexibility on the day of system operation is much higher than the probability of insufficient flexibility on the day of system operation, that is, the probability of load loss in the system is greater than the probability of wind and solar power abandonment.

[0049] In order to further determine the specific lack of flexibility on the operating day, the probability of insufficient flexibility for the system to adjust upwards or downwards at each time is calculated according to the formula. Figure 3As shown in the figure, the probability of insufficient downward flexibility is concentrated in the late night and early morning hours, when wind power output is large, volatility is strong, forecast errors are large, and units are limited by minimum technical output and cannot provide more downward flexibility; insufficient upward flexibility is concentrated in the afternoon and evening peak hours, which is mainly due to the fact that the system flexibility supply is small during these periods and the key section flow is large, making it difficult to bear large power changes.

[0050] The above are preferred embodiments of the present invention. Any changes made according to the technical solution of the present invention, as long as the resulting functions do not exceed the scope of the technical solution of the present invention, belong to the protection scope of the present invention.

Claims

1. A method for evaluating the operational flexibility of a power system, characterized in that: The specific steps include: S1. Collect historical data of the power system, including power data of wind power, photovoltaic power and load; analyze the correlation between power level and forecast error by calculating Spearman coefficient; and select the historical data most similar to the current forecast point according to the correlation; S2. Calculate the prediction error probability distribution of the current prediction point based on the selected historical data; S3, calculate the net load, and calculate the system flexibility requirement based on the net load and the forecast error; S4. Modeling the supply capacity of flexibility resources and integrating them to obtain the overall flexibility supply of the system; the flexibility resources include thermal power units, energy storage devices and controllable loads; S5. Analyze the overall system flexibility based on the available transmission capacity of key sections and the flexibility supply and demand of the sending and receiving ends; S6. Monte Carlo simulation is used to simulate the supply and demand of system flexibility, and the system flexibility index is calculated based on the quantitative results of flexibility supply and demand under different scenarios.

2. A method for evaluating the operational flexibility of a power system according to claim 1, characterized in that: The S1 specifically includes the following steps: S11. Establishing a data set of power level, fluctuation degree and prediction error based on historical data, where t The power level at the moment is the power level at the prediction point. t The degree of fluctuation and prediction error at each moment are expressed as follows: in, yes t The degree of fluctuation of wind power / photovoltaic / load at any moment, yes t The forecast error of wind power / photovoltaic / load at each moment, yes t +1 forecast value of wind power / photovoltaic / load at time, yes t The actual value of wind power / photovoltaic / load at the moment; S12, using Spearman coefficient to characterize t The correlation between the time data sets is as follows: in, a is the data volume of the dataset; k Indicates the coefficient type, k =1, The Spearman coefficient representing the power level and prediction error, d Represents data points i The power level of is the order difference with the prediction error; k =2, The Spearman coefficient represents the degree of volatility and prediction error. d Represents data points i The volatility of and the order difference of the forecast error; S13. Based on the calculated Spearman coefficient, select the historical data that is most similar to the power level and fluctuation degree of the current prediction point.

3. A method for evaluating the operational flexibility of a power system according to claim 2, characterized in that: The specific screening strategy in S13 is as follows: if : First, select the historical data that is most similar to the power level of the current prediction point M The predicted values ​​and their corresponding prediction errors are used as the initial historical data set to ensure that the initial historical data set has power level similarity; Then select in the initial historical data set N The historical data with the highest volatility similarity; if : First, select the historical data with the most similar fluctuation level to the current forecast point M The predicted values ​​and their corresponding prediction errors are used as the initial historical data set to ensure that the initial historical data set has similar fluctuation levels; Then select in the initial historical data set N The historical data with the highest power level similarity is selected.

4. A method for evaluating the operational flexibility of a power system according to claim 1, characterized in that: The S2 is specifically: taking the selected historical data as input, using the non-parametric kernel density estimation method to calculate the probability distribution of the prediction error, and the kernel density estimation expression is: in, is the probability density function, x is the prediction error of the current prediction point, N is the number of historical data filtered out, It is the first i The prediction error for each data point is h is the bandwidth, K is the kernel function.

5. A method for evaluating the operational flexibility of a power system according to claim 4, characterized in that: The kernel function uses the Gaussian kernel function: 。 6. A method for evaluating the operational flexibility of a power system according to claim 1, characterized in that: The S3 specifically includes the following steps: S31. Calculate Net Load : in, yes t The load forecast value at that moment, yes t Wind power forecast value at the moment, yes t The photovoltaic forecast value at each moment; S32. Calculate the net load forecast error : in, , and They are the net load prediction errors of load, wind power and photovoltaic respectively, and the net load prediction errors of load, wind power and photovoltaic are obtained by using the acceptance-rejection sampling method after the corresponding prediction error probability distribution is calculated according to the historical data of load, wind power and photovoltaic according to steps S1-S2; S33. Calculate the system flexibility requirements at time t : The flexibility requirements include upward adjustment requirements and downward adjustment requirements: when When t The entire system needs to be more flexible at all times. Indicates an increase in flexibility requirements, when When t The entire system needs to adjust its flexibility at all times. Indicates downward adjustment of flexibility requirements.

7. A method for evaluating the operational flexibility of a power system according to claim 1, characterized in that: The S4 specifically comprises the following steps: S41. Model the supply capacity of flexible resources including thermal power units, energy storage devices and controllable loads: The flexibility supply capacity of thermal power units is limited by the maximum / minimum output and ramp speed of thermal power units; in, and It is a thermal power unit j exist t The ability to adjust the supply of flexibility upward and downward at any time; express t Thermal power unit j contribution; represents a time step; and It is a thermal power unit j Maximum and minimum output; and It is a thermal power unit j Up and down climbing speed; yes t Thermal power unit j The running status, 1 means running, 0 means shutdown; The flexibility of energy storage devices is limited by the maximum charging and discharging power and the current storage capacity: in, and It is an energy storage device n exist t The ability to adjust the supply of flexibility upward and downward at any time; and Indicates the maximum charging and discharging power of energy storage; is the current energy storage capacity, , They are the lower limit of energy storage capacity and the upper limit of energy storage capacity. and is the charge and discharge efficiency; express t Energy storage at all times n contribution; The flexibility of controllable load is provided based on the load characteristic range of the user; in, and It is a controllable load m exist t The ability to adjust the supply of flexibility upward and downward at any time; yes t User load at any moment; and They are the upper and lower bounds of the user load characteristic interval; S42. Integrate the supply capabilities of three types of flexibility resources, namely thermal power units, energy storage devices and controllable loads, to obtain the overall flexibility supply of the system: in, yes t The overall flexibility supply of the time system is increased. yes t The overall flexibility supply of the time system is adjusted downward; , and They respectively represent the number of thermal power units, energy storage devices and controllable loads in the system.

8. A method for evaluating the operational flexibility of a power system according to claim 1, characterized in that: The S5 specifically includes the following steps: S51. Definition t Available transmission capacity at critical sections at the moment for: in, c Represents the key section, is the maximum flow limit of the section, is the line flow in the section, b is the number of lines, l Represents a line; S52. The two areas divided by the key section are called the sending end and the receiving end. The upward / downward flexibility demand and upward / downward flexibility supply of the sending end and the receiving end are calculated respectively. The overall flexibility of the system is analyzed by combining the flexibility supply and demand of the sending end and the receiving end. Among them, the flexibility margin is increased. , otherwise, the upward flexibility is insufficient; the downward flexibility is sufficient, which means the downward flexibility margin is sufficient. , otherwise it means insufficient flexibility for downward adjustment; the specific situation of overall system flexibility is as follows: a. When the sending end and the receiving end have insufficient flexibility in both upward and downward adjustments, the system will definitely have insufficient flexibility; when the sending end and the receiving end have sufficient flexibility in both upward and downward adjustments, the system will definitely meet the flexibility requirements; upward flexibility margin and reduce flexibility margin The calculation is: b. Sufficient flexibility at the sending end but insufficient flexibility at the receiving end: in, They are the system upward / downward flexibility margin; They are upward / downward flexible supply at the sending end; They are upward / downward flexibility supply at the receiving end; They are upward / downward flexibility requirements at the sending end; They are upward / downward flexibility requirements of the receiving end; c. Insufficient flexibility at the sending end but sufficient flexibility at the receiving end: d. The sending and receiving ends have insufficient flexibility in upward or downward adjustments, but there is a possibility of complementarity: The sending end has insufficient flexibility for upward adjustment, and the receiving end has insufficient flexibility for downward adjustment: The sending end has insufficient flexibility in downward adjustment, and the receiving end has insufficient flexibility in upward adjustment: 。 9. A method for evaluating the operational flexibility of a power system according to claim 1, characterized in that: The calculation of system flexibility index based on the quantitative results of flexibility supply and demand in different scenarios specifically includes: (1) t The probability of insufficient flexibility in the time system adjustment and expectations : in, d is the sample size; To adjust the system's lack of flexibility index, when the sample e Increase flexibility margin When less than 0 Take 1, otherwise take 0; (2) t The probability of insufficient flexibility in the time system adjustment and expectations : in, d is the sample size; To lower the flexibility index of the system, when the sample e Lowering flexibility margin When less than 0 Take 1, otherwise take 0; (3) Probability of insufficient flexibility in system adjustments on operating days and expectations : (4) Probability of insufficient flexibility in system downgrades on operating days and expectations : in, T The time period for the operating day.

10. A system for evaluating the operational flexibility of a power system, characterized in that: It includes a processor, a memory and a computer program stored in the memory. When the processor executes the computer program, it specifically executes the steps in the method for evaluating the operating flexibility of an electric power system as described in any one of claims 1 to 9.

Images