Method for evaluating credible capacity range of new energy unit

Abstract

This application discloses a method for assessing the credible capacity range of new energy generating units, relating to the field of computer technology. The method includes: after initiating a simulation analysis of the power system based on a preset credible capacity range assessment duration, determining a target index value using the power system load, conventional unit output, and a preset reliability index assessment function; determining the upper limit of the credible capacity of the power system after the integration of new energy generating units based on the target index value, a first preset function, the output status of virtual generators, and the output of new energy generating units; comparing the target index value with a preset index threshold to determine whether a credible capacity lower limit analysis has been triggered; if so, determining the lower limit of the credible capacity based on a second preset function and the output status of virtual generators; and determining the credible capacity range assessment result based on the upper and lower limits of the credible capacity. This application improves the comprehensiveness and accuracy of credible capacity assessment and the reliability of decisions based on credible capacity.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method for assessing the reliable capacity range of new energy power units. Background Technology

[0002] When using LOLP (Loss of Load Probability) as a reliability metric, the solution for reliable capacity is sometimes not unique, but rather an arbitrary value within a range, due to the discrete nature of LOLP values.

[0003] Furthermore, existing reliable capacity assessment schemes are implicit iterative schemes. These schemes cannot directly calculate the reliable capacity of the power system under given reliability conditions. Instead, they continuously explore the value of the reliable capacity until, in a certain round, the reliability index values ​​of the power system before and after the integration of new energy sources are consistent, at which point a reliable capacity value is output and the iteration terminates. In other words, this scheme has low efficiency, accuracy, and comprehensiveness. It exits the iteration when it only obtains a feasible solution for one reliable capacity, and cannot determine the specific boundary values ​​of the reliable capacity range. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a method for assessing the reliable capacity range of new energy generating units, which can effectively determine the boundary values ​​of the reliable capacity range, thereby improving the efficiency, comprehensiveness, and accuracy of reliable capacity assessment, and further enhancing the reliability of decisions based on reliable capacity and the effectiveness of data visualization. The specific solution is as follows:

[0005] This application provides a method for assessing the reliable capacity range of new energy generating units, including:

[0006] After initiating simulation analysis of the power system based on a preset reliable capacity range assessment duration, the target index value of the power system is determined using the corresponding system load, conventional unit output, and preset reliability index assessment function; the preset reliability index assessment function is used to assess the load failure probability of the power system.

[0007] Based on the target index value, the preset reliable capacity range evaluation time, the first preset function, the virtual generator output status, and the output of the new energy units, the upper limit of the reliable capacity of the power system after the new energy units are connected is determined; wherein, the first preset function is the function obtained by explicitly optimizing the inverse function of the preset reliability index evaluation function; the virtual generator output status is used to reflect the load loss situation of the power system;

[0008] The target indicator value is compared with the preset indicator threshold, and based on the corresponding indicator comparison result, it is determined whether the trusted capacity lower limit analysis operation is triggered, so as to determine the operation trigger judgment result.

[0009] If the operation trigger judgment result is yes, then based on the second preset function, the output status of the virtual generator and the output of the new energy unit, the reliable capacity lower limit value after the power system is connected to the new energy unit is determined; the second preset function is the function obtained by explicitly optimizing the inverse function of the preset reliability index evaluation function;

[0010] Based on the upper limit and lower limit of the trusted capacity, the trusted capacity range assessment result is determined.

[0011] Optionally, the step of initiating simulation analysis of the power system based on a preset trusted capacity range assessment duration includes:

[0012] Based on the preset trusted capacity range assessment duration, target operation data of the corresponding time length is selected from the system historical operation database corresponding to the power system.

[0013] Based on the target operating data, the power system is simulated to obtain the corresponding system load and conventional unit output.

[0014] Optionally, the target index value for the power system is determined using the system load, conventional unit output, and a preset reliability index evaluation function, including:

[0015] The system load and the output of the conventional generating units corresponding to the power system are input into a preset reliability index evaluation function to determine the target index value corresponding to the power system; wherein, the target index value is the index value of the power system before the connection of new energy generating units.

[0016] Optionally, determining the upper limit of the reliable capacity of the power system after connecting new energy units, based on the target index value, the preset reliable capacity range evaluation time, the first preset function, the virtual generator output status, and the output of new energy units, includes:

[0017] The target index value will be used as the index value after the power system is connected to the new energy unit;

[0018] Configure the virtual generator output status as an indicator variable for the power system's load shedding; the virtual generator output status includes virtual generator output and virtual generator start / stop status variables.

[0019] Based on the target index value, the output of the virtual generator, the start-stop state variables of the virtual generator, the preset reliable capacity range evaluation time, the preset constant, the output of the new energy unit, and the first preset function, the effective load capacity of the power system after the new energy unit is connected is analyzed to determine the upper limit of the reliable capacity.

[0020] Optionally, configuring the virtual generator output status as an indicator variable for the power system's load shedding includes:

[0021] If the value of the virtual generator start / stop status variable is a first preset value, and the output of the virtual generator at the corresponding time is zero, it indicates that the power system has not experienced a loss of load.

[0022] Optionally, configuring the virtual generator output status as an indicator variable for the power system's load shedding includes:

[0023] If the value of the virtual generator start / stop status variable is a second preset value, and the output of the virtual generator at the corresponding time is greater than zero, it indicates that the power system has experienced a loss of load.

[0024] Optionally, comparing the target indicator value with a preset indicator threshold and determining whether the trusted capacity lower limit analysis operation is triggered based on the corresponding indicator comparison result, in order to determine the operation trigger judgment result, includes:

[0025] Determine whether the target indicator value is greater than a preset indicator threshold to determine the indicator comparison result;

[0026] If the comparison result of the aforementioned indicators is greater than, then it is determined that the current trusted capacity lower limit analysis operation is triggered to obtain the first operation trigger judgment result.

[0027] Optionally, after determining the index comparison results, the method further includes:

[0028] If the comparison result of the indicators is equal, it is determined that the trusted capacity lower limit analysis operation will not be triggered at present, so as to obtain the second operation trigger judgment result.

[0029] Optionally, after obtaining the second operation trigger determination result, the method further includes:

[0030] The upper limit of the trusted capacity is determined as the evaluation result of the trusted capacity range.

[0031] Optionally, determining the reliable capacity lower limit after the power system connects to the new energy unit based on the second preset function, the output state of the virtual generator, and the output of the new energy unit includes:

[0032] Based on the target indicator value and the preset trusted capacity range evaluation time, the updated indicator value is determined;

[0033] Based on the updated index values, the output status of the virtual generator, the output of the new energy unit, and the second preset function, the effective load capacity of the power system after connecting the new energy unit is analyzed to determine the reliable capacity lower limit.

[0034] As can be seen, in this application, after initiating the simulation analysis of the power system based on the preset reliable capacity range assessment time, the target index value of the power system is determined by utilizing the system load, conventional unit output, and preset reliability index assessment function corresponding to the power system; the preset reliability index assessment function is used to assess the load loss probability of the power system; based on the target index value, the preset reliable capacity range assessment time, the first preset function, the virtual generator output status, and the output of new energy units, the upper limit of the reliable capacity of the power system after the connection of new energy units is determined; wherein, the first preset function is a function obtained by explicitly optimizing the inverse function of the preset reliability index assessment function; The virtual generator output status is used to reflect the load loss situation of the power system; the target index value is compared with the preset index threshold, and based on the corresponding index comparison result, it is determined whether the reliable capacity lower limit analysis operation is triggered to determine the operation trigger judgment result; if the operation trigger judgment result is yes, then based on the second preset function, the virtual generator output status and the output of the new energy unit, the reliable capacity lower limit value after the power system is connected to the new energy unit is determined; the second preset function is a function obtained by explicitly optimizing the inverse function of the preset reliability index evaluation function; based on the reliable capacity upper limit value and the reliable capacity lower limit value, the reliable capacity range evaluation result is determined. In other words, this application first initiates a simulation analysis of the power system based on a preset trusted capacity range assessment duration, and determines the target index value using the corresponding system load, conventional unit output, and a preset reliability index assessment function. Then, based on the target index value, a first preset function, virtual generator output status, and new energy unit output, the upper limit of the trusted capacity after the system connects to new energy units is determined. Next, the target index value is compared with the preset index threshold to determine whether a trusted capacity lower limit analysis operation has been triggered. If so, the lower limit of the trusted capacity after the system connects to new energy units is determined based on a second preset function, virtual generator output status, and new energy unit output. Finally, the trusted capacity range assessment result is obtained based on the upper and lower limits of the trusted capacity. In other words, this application can effectively obtain the boundary values ​​of the trusted capacity range, thereby improving the efficiency, comprehensiveness, and accuracy of trusted capacity assessment, and further improving the reliability of decisions based on trusted capacity and the data visualization effect. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0036] Figure 1 A flowchart of a reliable capacity range assessment method for new energy generating units provided in this application;

[0037] Figure 2 A flowchart of a specific method for assessing the credible capacity range of new energy generating units provided in this application;

[0038] Figure 3 A schematic diagram illustrating the reliable capacity range assessment of a simple single-machine time-period system as provided in this application;

[0039] Figure 4 A schematic diagram illustrating the original relationship between the minimum reliable capacity coverage area and the probability of load failure, provided for this application;

[0040] Figure 5 This application provides a schematic diagram illustrating the relationship between the minimum credible capacity coverage interval and the load loss probability when the load loss probability is assumed to be continuous.

[0041] Figure 6(a) is a schematic diagram of the distribution of trusted capacity assessment results based on different algorithms in scenario one, provided by this application;

[0042] Figure 6(b) is a schematic diagram of the distribution of trusted capacity assessment results based on different algorithms in scenario two, as provided in this application.

[0043] Figure 6(c) is a schematic diagram of the distribution of trusted capacity assessment results based on different algorithms in scenario 3 provided by this application;

[0044] Figure 6(d) is a schematic diagram of the distribution of trusted capacity assessment results based on different algorithms in scenario four, as provided in this application.

[0045] Figure 7 This application provides a schematic diagram of the trusted capacity assessment results based on different algorithms in different scenarios. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] When using LOLP as the reliability metric, the discrete nature of LOLP values ​​means that the solution for reliable capacity is sometimes not unique, but rather an arbitrary value within a range. Furthermore, existing reliable capacity assessment schemes are implicit iterative, meaning they cannot directly calculate the reliable capacity of the power system under a given reliability condition. Instead, they continuously explore reliable capacity values ​​until, in a certain round, the reliability metric values ​​of the power system before and after the integration of new energy sources are consistent, at which point they output a reliable capacity value and terminate the iteration. In other words, this scheme has low efficiency, accuracy, and comprehensiveness; it exits the iteration when it only obtains a feasible solution for one reliable capacity value, and cannot determine the specific boundary values ​​of the reliable capacity range.

[0048] To this end, this application provides a reliable capacity range assessment scheme for new energy units, which can effectively obtain the boundary value of the reliable capacity range, thereby improving the efficiency, comprehensiveness and accuracy of reliable capacity assessment, and further improving the reliability of decision-making and data visualization based on reliable capacity.

[0049] See Figure 1 As shown in the figure, an embodiment of the present invention discloses a method for evaluating the reliable capacity range of new energy generating units, including:

[0050] Step S11: After starting the simulation analysis of the power system based on the preset reliable capacity range assessment time, the target index value of the power system is determined by using the system load, conventional unit output and preset reliability index assessment function corresponding to the power system; the preset reliability index assessment function is used to assess the load failure probability of the power system.

[0051] In this embodiment, combined with Figure 2 As shown, the calculation period for this evaluation is first set. (The total number of system operating time periods selected when conducting new energy reliable capacity assessment), i.e., the preset reliable capacity range assessment duration. And according to... Calculate the reliability index of the power system without new energy generating units Specifically: based on the preset reliable capacity range assessment duration, target operating data of a corresponding time length is selected from the historical operating database of the power system; based on the target operating data, the power system is simulated to obtain the system load and conventional unit output of the power system; the system load and conventional unit output of the power system are input into a preset reliability index evaluation function to determine the target index value of the power system; wherein, the target index value is the index value of the power system before the connection of new energy units.

[0052] Furthermore, it is necessary to understand that, in combination Figure 3 As shown, taking a single-unit time-period system as an example, this paper briefly analyzes why the solution for the reliable capacity is an interval. The maximum output of a conventional unit in a power system is... For conventional generating units, constraints other than the unit's maximum output are not considered. Figure 3 Before the inclusion of new energy sources in China, the power system only had When load loss occurs, LOLP = 1 / T, which corresponds to the left side of equation (1). Based on this, the output of new energy units is added. If the available load of the power system can be increased from the original... Increase to Then the net load of the power system can be expressed as Assume that the system's maximum net load occurs at this time. At that time, the maximum net load value was The second largest net load of the system occurred At that time, the second largest net load was recorded as It is easy to see that in order to ensure that the LOLP of the power system remains unchanged before and after the addition of new energy units, that is, to ensure that the right side of equation (1) holds, it is necessary to satisfy the following: Ultimately obtained That is, for this simple system when When the reliability capacity of new energy sources is within a range, the solution is a range. Regarding equation (1), since the output of new energy units is intermittent, the load-carrying capacity of new energy units with the same capacity is not the same as that of conventional units. Therefore, new energy units cannot be treated the same as conventional units in adequacy analysis. For this reason, researchers have proposed the concept of reliability capacity. Reliability capacity has multiple definitions, with equivalent load-carrying capacity being a commonly used one. It refers to the difference in load that the power system can supply before and after the integration of new energy sources, under the premise of equal reliability, as shown below:

[0053] (1).

[0054] In the formula, Indicates the load of the power system; This indicates the output of a conventional generating unit; Indicates the output of the new energy unit; This indicates the reliable capacity of the equivalent supply load of new energy units, i.e., ELCC (Effective Load Carrying Capability). This represents a preset reliability index evaluation function, often using the failure probability as the reliability index. The result of the function calculation representing the reliability index.

[0055] Step S12: Based on the target index value, the preset reliable capacity range evaluation time, the first preset function, the virtual generator output status, and the output of the new energy unit, determine the upper limit of the reliable capacity of the power system after the new energy unit is connected; wherein, the first preset function is the function obtained by explicitly optimizing the inverse function of the preset reliability index evaluation function; the virtual generator output status is used to reflect the load loss situation of the power system.

[0056] In this embodiment, after determining the target index value, the trusted capacity calculation definition requires that the new energy source be connected after... Same as before access, so in Figure 2 In the third step of the process shown, the parameters after new energy access are set according to the target indicator values, either manually or through other means. The target index value is used as the index value after the power system is connected to the new energy generating units; the virtual generator output status is configured as the load shedding indicator variable of the power system; the virtual generator output status includes virtual generator output and virtual generator start-stop status variables; based on the target index value, the virtual generator output, the virtual generator start-stop status variables, the preset reliable capacity range evaluation time, the preset constant, the output of the new energy generating units, and the first preset function, the effective load capacity of the power system after the connection of the new energy generating units is analyzed to determine the reliable capacity upper limit value.

[0057] It's important to understand that the virtual generator, or ideal virtual generator, is not a real power generation device, but rather a mathematical auxiliary variable. Introducing the ideal virtual generator as an indicator variable, its start-stop state and output variable reflect whether the system has experienced a load shedding, thus transforming the reliability assessment problem into a solvable optimization problem. Compared to a real generator, it has no output or ramp-up limitations. The indication of this variable includes: if the value of the virtual generator start-stop state variable is a first preset value, and the virtual generator output at the corresponding time is zero, it indicates that the power system has not experienced a load shedding; if the value of the virtual generator start-stop state variable is a second preset value, and the virtual generator output at the corresponding time is greater than zero, it indicates that the power system has experienced a load shedding.

[0058] It is further necessary to understand that the actual process of determining the first preset function can be described as follows:

[0059] 1) Constructing an explicit optimization problem based on the direct representation of the inverse function of reliability calculation.

[0060] Equation (1) can be rewritten in the following form:

[0061] (2).

[0062] In the formula, It is a preset reliability index evaluation function The inverse function of the equation can be used to determine the load level that the power system can supply under given generation parameters and reliability values.

[0063] However, it is still impossible to directly solve using equation (2). This is because calculating the reliability index under a given credible capacity is relatively easy, while the inverse process is relatively difficult, requiring a complex reliability calculation function. It is difficult to represent directly. Therefore, an ideal virtual generator is introduced. As an indicator, the output state of an ideal virtual generator at various times reflects the power system's load loss situation, thereby indirectly representing the inverse function of reliability calculation, as shown below:

[0064] (3).

[0065] (4).

[0066] In the formula, Indicates the preset trusted capacity range assessment duration; Represents the set of real numbers; It is a binary variable, representing The start-stop status of the ideal virtual generator at all times; This indicates that the ideal virtual generator is in a shut-down state. At this time, the ideal virtual generator does not generate power, meaning that the power system does not experience load loss. This indicates that the ideal virtual generator is in operation, and at this time the ideal virtual generator is outputting power, meaning that the power system is experiencing a loss of load. Indicating an ideal virtual generator in Constant effort and Let represent a maximum and a minimum constant, respectively. When the ideal virtual generator shuts down... Then, by the constraints get =0; When the ideal virtual generator is running By constraints get >0. express Regular unit output at all times express The output of new energy units at all times; express The system load value of the power system at any given time. Indicates a given reliability index The feasible region of the power system operation optimization formula; It is the set of all variables, including the confidence capacity. Ideal virtual generator output variables Ideal virtual generator start-stop state variables Other variables Output variables of conventional units and the output variables of new energy units Also included middle; express The number of 01 variables in the middle. express The number of continuous variables; and It is a general representation of the start-up, shutdown, and operation constraints of other units in an equivalent system containing new energy sources, excluding system power balance constraints and constraints related to ideal virtual generators. Indicates ELCC trusted capacity The feasible solution set; In addition to Other than variables The set of other variables in the set.

[0067] (2) Solving for the reliable capacity range.

[0068] According to the principles of reliability analysis, LOLP increases superlinearly with the increase of power system load, that is... It is a superlinear increasing function of the load, therefore we can obtain Similarly, the reliability index is an increasing function, meaning the available load of the power system increases with the increase of the LOLP (Likely Limiting Capacity). Therefore, the relationship between the LOLP and the reliability index can be expressed as follows: Figure 4 As shown. Figure 4 middle and These represent the reliability indexes of LOLP as follows: When, in equation (4) The maximum and minimum values ​​of the feasible solution set, i.e. The range is . This indicates that LOLP is The reliable capacity range at that time.

[0069] In order to obtain Based on equation (4), the objective function is set to maximize the trust capacity, and the explicit optimization formula for the maximum ELCC trust capacity based on the LOLP reliability index is as follows:

[0070] (5).

[0071] when When >0, in order to obtain Naturally, this embodiment attempts to solve the objective function of minimizing the credible capacity based on equation (4), but finds that the problem is unbounded and cannot be solved directly. Therefore, the following equation is calculated to obtain To approximate :

[0072] (6).

[0073] Theorem 1: When When >0, = .

[0074] Proof 1: As Figure 5 As shown, if we assume the LOLP reliability index is within the range It is continuous above, and it is easy to prove that when the reliability index is... When the internal changes, the variables in equation (3) The feasible solution remains unchanged, therefore the feasible region is... If it remains unchanged, then in equation (4) feasible solution set Similarly, its range is always [missing information]. Therefore, we have:

[0075] (7).

[0076] Furthermore, equation (6) can be expressed as:

[0077] (8).

[0078] Combining equations (7) and (8), we have:

[0079] (9).

[0080] In the formula, Representation function exist and The derivative between.

[0081] Depend on The continuity condition of the domain is easily obtained. The range of values ​​is continuous, and It is a monotonic function, therefore It is derivable almost everywhere. If it is bounded, then it has:

[0082] (10).

[0083] Theorem 1 is now proven. Furthermore, based on the above analysis, the reliable capacity range that can be obtained using the explicit optimization algorithm described above in this embodiment is: .

[0084] Step S13: Compare the target indicator value with the preset indicator threshold, and based on the corresponding indicator comparison result, determine whether the trusted capacity lower limit analysis operation is triggered, so as to determine the operation trigger judgment result.

[0085] Combination Figure 2 As shown in this embodiment, the upper limit of the trusted capacity range is determined. Next, determine the indicator value. Whether it is zero is used to determine whether a lower limit value needs to be calculated. This is because the definition of trusted capacity calculation requires that the new energy source be connected after... Same as before access, therefore here This refers to the indicator values ​​before and after new energy access, i.e., the target indicator values ​​obtained in step S11. Specifically: it is determined whether the target indicator value is greater than a preset indicator threshold to determine the indicator comparison result; if the indicator comparison result shows that it is greater than, it is determined that the current trusted capacity lower limit analysis operation is triggered to obtain the first operation trigger judgment result; if the indicator comparison result shows that it is equal to, it is determined that the current trusted capacity lower limit analysis operation is not triggered to obtain the second operation trigger judgment result.

[0086] Furthermore, if the judgment is... =0, then output a single trust capacity value. In other words, after obtaining the result of the second operation trigger judgment, the upper limit of the trusted capacity is determined as the evaluation result of the trusted capacity range.

[0087] Step S14: If the operation trigger judgment result is yes, then based on the second preset function, the output status of the virtual generator and the output of the new energy unit, determine the reliable capacity lower limit value after the power system is connected to the new energy unit; the second preset function is the function obtained by explicitly optimizing the inverse function of the preset reliability index evaluation function.

[0088] In this embodiment, combined with Figure 2 As shown, if the determination If the value is greater than 0, then the lower limit of the credible capacity is calculated based on equation (6), that is: based on the target index value and the preset credible capacity range evaluation time, the updated index value is determined; based on the updated index value, the output status of the virtual generator, the output of the new energy unit and the second preset function, the effective load capacity of the power system after connecting to the new energy unit is analyzed to determine the lower limit of the credible capacity.

[0089] Step S15: Determine the trusted capacity range assessment result based on the trusted capacity upper limit and the trusted capacity lower limit.

[0090] In this embodiment, the lower limit of the trusted capacity is determined. Then, it can be combined Output reliable capacity range .

[0091] In summary, this embodiment proposes an explicit optimization method for assessing the reliable capacity of new energy sources. By constructing an explicit optimization formula for assessing the reliable capacity of new energy sources with LOLP as the reliability index, the inverse function of reliability calculation is directly represented, thereby enabling the determination of the boundary values ​​of the reliable capacity interval solution of new energy sources with LOLP as the reliability index.

[0092] Therefore, the process involves several steps. First, based on a preset trusted capacity range assessment duration, a simulation analysis of the power system is initiated. The target index value is determined using the corresponding system load, conventional unit output, and a preset reliability index assessment function. Then, based on the target index value, a first preset function, virtual generator output status, and new energy unit output, the upper limit of the trusted capacity after the integration of new energy units is determined. Next, the target index value is compared with the preset index threshold to determine if a trusted capacity lower limit analysis operation has been triggered. If so, the lower limit of the trusted capacity after the integration of new energy units is determined based on a second preset function, virtual generator output status, and new energy unit output. Finally, the trusted capacity range assessment result is obtained based on the upper and lower limits of the trusted capacity. In other words, this application can effectively determine the boundary values ​​of the trusted capacity range, thereby improving the efficiency, comprehensiveness, and accuracy of trusted capacity assessment, and consequently enhancing the reliability of trusted capacity-based decision-making and data visualization.

[0093] The following refers to Figure 6(a) to... Figure 7 The schematic diagram disclosed herein provides a detailed description of the technical solutions of the embodiments of this application.

[0094] In one specific implementation, a time-series simulation example analysis of a power system is conducted based on a modified IEEE-CASE24. The calculation cycle for this example is... The calculation period is 168 hours, indicating that the calculated weekly renewable energy capacity is reliable. The power system baseline capacity is 100 MVA; it includes 33 thermal power units with a total capacity of 3405 MW; 3 cascade hydropower units with a total capacity of 500 MW; wind power installed capacity of 350 MW; photovoltaic installed capacity of 300 MW; and a renewable energy penetration rate of 14.27%. All parameters are taken as per-unit values. Wind and solar power output data are from a certain region from September 1st to September 7th, 2020. IEEE-CASE24 refers to the 2024 IEEE International Conference on Automation Science and Engineering; IEEE stands for Institute of Electrical and Electronics Engineers.

[0095] This example sets up the following four scenarios by changing the load and wind and solar power output.

[0096] 1) Scenario 1: Basic scenario, using raw data, the correlation between the total wind and solar power output curve and the system load curve of the power system is 0.4896;

[0097] 2) Scenario 2: The output of wind and solar power changes while other parameters remain unchanged, so that the correlation between the total output curve of wind and solar power and the system load curve of the power system is 0. At this time, wind and solar power are smoothly complementary.

[0098] 3) Scenario 3: Change the wind and solar power output while keeping other parameters unchanged, so that the correlation between the total wind and solar power output curve and the system load curve of the power system is 1;

[0099] 4) Scenario 4: Change the wind and solar power output while keeping other parameters unchanged, so that the correlation between the total wind and solar power output curve and the system load curve of the power system is -1.

[0100] The results of calculating the reliable capacity of new energy sources are compared between the implicit iterative method and the explicit optimization method proposed in this embodiment in four different scenarios. The implicit iterative method uses a bisection method for iterative calculation, with a maximum iteration limit of 20, a minimum iteration limit of 1, and initial iteration values ​​ranging from 1 to 20.

[0101] Subsequently, Figures 6(a), 6(b), 6(c), and 6(d) sequentially illustrate the ELCC reliability capacity results calculated using the implicit iterative method in existing related schemes and the explicit optimization method in this embodiment under these four scenarios. In Figures 6(a), 6(b), 6(c), and 6(d), the hollow squares represent the reliability capacity evaluation results based on the implicit iterative method under different initial iteration values ​​in different scenarios. The solid line represents the maximum reliability capacity calculated using the explicit optimization method, and the dashed line represents the minimum reliability capacity calculated using the explicit optimization method. In Figures 6(a), 6(b), 6(c), and 6(d), as the initial iteration value changes, the reliability capacity calculation results based on the implicit iterative method are not fixed but scattered within a certain range, indicating that the implicit iterative method has an initial value dependency. Simultaneously, regardless of how the initial iteration value changes, the reliability capacity calculation results of the implicit iterative method always fall within the range calculated by the explicit optimization method, proving that the explicit optimization method effectively achieves accurate evaluation of the reliability capacity range.

[0102] At the same time, such as Figure 7 As shown in the figure, this diagram illustrates the numerical results of reliable capacity assessment using implicit iteration and explicit optimization methods, and provides the average number of iterations. The comparison reveals that the implicit iteration algorithm requires an average of more than 5 iterations to obtain a feasible solution for reliable capacity. Even after 20 iterations with different initial values, the implicit iteration method still fails to accurately determine the reliable capacity boundary values, and the obtained minimum and maximum reliable capacities still differ somewhat from the actual boundary values. In contrast, the explicit optimization method can obtain the global minimum or maximum reliable capacity in a single direct calculation. Its solution process requires no iteration and is unaffected by additional parameters, resulting in a fixed and reliable solution.

[0103] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0104] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for evaluating a credible capacity range of a new energy unit, characterized in that, include: After initiating simulation analysis of the power system based on the preset reliable capacity range assessment time, the target index value of the power system is determined by using the system load, conventional unit output and preset reliability index assessment function corresponding to the power system. The preset reliability index evaluation function is used to evaluate the probability of load failure of the power system. Based on the target index value, the preset reliable capacity range evaluation time, the first preset function, the virtual generator output status, and the output of the new energy units, the upper limit of the reliable capacity of the power system after the new energy units are connected is determined; wherein, the first preset function is the function obtained by explicitly optimizing the inverse function of the preset reliability index evaluation function; the virtual generator output status is used to reflect the load loss situation of the power system; The target indicator value is compared with the preset indicator threshold, and based on the corresponding indicator comparison result, it is determined whether the trusted capacity lower limit analysis operation is triggered, so as to determine the operation trigger judgment result. If the operation trigger judgment result is yes, then based on the second preset function, the output status of the virtual generator and the output of the new energy unit, the reliable capacity lower limit value after the power system is connected to the new energy unit is determined; the second preset function is the function obtained by explicitly optimizing the inverse function of the preset reliability index evaluation function; Based on the upper limit and lower limit of the trusted capacity, the trusted capacity range assessment result is determined.

2. The method of claim 1, wherein, The step of initiating simulation analysis of the power system based on a preset trusted capacity range assessment duration includes: Based on the preset trusted capacity range assessment duration, target operation data of the corresponding time length is selected from the system historical operation database corresponding to the power system. Based on the target operating data, the power system is simulated to obtain the corresponding system load and conventional unit output.

3. The method of claim 1, wherein the method further comprises: The process of determining the target index value for the power system using the system load, conventional unit output, and a preset reliability index evaluation function includes: Based on the preset trusted capacity range assessment duration, target operation data of the corresponding time length is selected from the system historical operation database corresponding to the power system. Based on the target operating data, the power system is simulated to obtain the corresponding system load and conventional unit output. The system load and the output of the conventional generating units corresponding to the power system are input into a preset reliability index evaluation function to determine the target index value corresponding to the power system; wherein, the target index value is the index value of the power system before the connection of new energy generating units.

4. The method of claim 1, wherein, The determination of the upper limit of the reliable capacity of the power system after the connection of new energy units, based on the target index value, the preset reliable capacity range evaluation time, the first preset function, the virtual generator output status, and the output of new energy units, includes: The target index value will be used as the index value after the power system is connected to the new energy unit; Configure the virtual generator output status as an indicator variable for the power system's load shedding; the virtual generator output status includes virtual generator output and virtual generator start / stop status variables. Based on the target index value, the output of the virtual generator, the start-stop state variables of the virtual generator, the preset reliable capacity range evaluation time, the preset constant, the output of the new energy unit, and the first preset function, the effective load capacity of the power system after the new energy unit is connected is analyzed to determine the upper limit of the reliable capacity.

5. The method of claim 4, wherein the method further comprises: The configuration of the virtual generator output status as an indicator variable for the power system's load shedding includes: If the value of the virtual generator start / stop status variable is a first preset value, and the output of the virtual generator at the corresponding time is zero, it indicates that the power system has not experienced a loss of load.

6. The reliable capacity range assessment method for new energy generating units according to claim 4, characterized in that, The configuration of the virtual generator output status as an indicator variable for the power system's load shedding includes: If the value of the virtual generator start / stop status variable is a second preset value, and the output of the virtual generator at the corresponding time is greater than zero, it indicates that the power system has experienced a loss of load.

7. The method for assessing the reliable capacity range of new energy generating units according to any one of claims 1 to 6, characterized in that, The step of comparing the target indicator value with a preset indicator threshold and determining whether a trusted capacity lower limit analysis operation is triggered based on the corresponding indicator comparison result, to determine the operation trigger judgment result, includes: Determine whether the target indicator value is greater than a preset indicator threshold to determine the indicator comparison result; If the comparison result of the aforementioned indicators is greater than, then it is determined that the current trusted capacity lower limit analysis operation is triggered to obtain the first operation trigger judgment result.

8. The reliable capacity range assessment method for new energy generating units according to claim 7, characterized in that, After determining the comparison results of the indicators, the following is also included: If the comparison result of the indicators is equal, it is determined that the trusted capacity lower limit analysis operation will not be triggered at present, so as to obtain the second operation trigger judgment result.

9. The reliable capacity range assessment method for new energy generating units according to claim 8, characterized in that, After obtaining the second operation trigger judgment result, the process also includes: The upper limit of the trusted capacity is determined as the evaluation result of the trusted capacity range.

10. The reliable capacity range assessment method for new energy generating units according to claim 1, characterized in that, The determination of the reliable capacity lower limit after the power system connects to the new energy unit, based on the second preset function, the output status of the virtual generator, and the output of the new energy unit, includes: Based on the target indicator value and the preset trusted capacity range evaluation time, the updated indicator value is determined; Based on the updated index values, the output status of the virtual generator, the output of the new energy unit, and the second preset function, the effective load capacity of the power system after connecting the new energy unit is analyzed to determine the reliable capacity lower limit.

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