Power flow balancing simulation method and apparatus

Abstract

The application discloses a power balance simulation method and device, relates to the technical field of power system planning and operation simulation, and mainly aims to improve the pertinence of the simulation method through prior discrimination of balance risks. The main technical scheme of the application is as follows: obtaining power load and new energy power generation data of a target power system in an evaluation period; determining power shortage of the target power system according to the power load and the new energy power generation data and system operation characteristic parameters; extracting long-period power balance risk characteristic quantities based on the power load, the new energy power generation data and the power shortage; if the long-period power balance risk characteristic quantities exceed characteristic quantity thresholds, a target simulation kernel will select a 8760-hour time-series production simulation method; if the long-period power balance risk characteristic quantities do not exceed the characteristic quantity thresholds, the target simulation kernel will select a typical day simulation method; and performing the target simulation kernel to obtain power balance analysis results.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system planning and operation simulation, and in particular to a power balance simulation method and device. BACKGROUND

[0002] Power balance simulation is a core link of power system planning and operation analysis, aiming to simulate the production and operation process of the power system in a specific period based on the predicted load demand, power installed capacity and resource constraints, to evaluate whether the system can achieve dynamic balance of power supply and demand, and thus provide decision basis for power source planning and energy storage configuration.

[0003] At present, the power balance simulation method mainly includes typical day simulation and 8760-hour time sequence production simulation. The typical day simulation method selects several representative typical days (such as the maximum load day and the extreme weather day) by experience or clustering, performs time sequence simulation within the day, and combines the simulation results according to the appearance frequency of the typical days to approximately reflect the annual operation, which is simple and has high calculation efficiency. The 8760-hour time sequence production simulation is based on time sequence prediction data to perform continuous simulation every hour throughout the year, which can completely retain the time sequence continuity of load, new energy power generation and energy storage state, and has high calculation accuracy.

[0004] However, the typical day simulation method regards each typical day as independent, and cannot reflect the long-period energy transfer demand. Under high proportion of new energy access, it is easy to underestimate the long-period power supply shortage risk caused by continuous extreme weather. The 8760-hour time sequence production simulation method has high accuracy, but low calculation efficiency, and faces the "dimension disaster" problem in actual planning application. In actual application, the adopted power balance simulation method is usually selected according to the single preference between efficiency and accuracy based on human experience, which may lead to mismatch between the simulation method and the actual demand of the power system: if the typical day simulation is blindly adopted, the power supply gap under extreme conditions may be covered up; if the 8760-hour time sequence production simulation is adopted, it will cause serious waste of calculation resources in weakly coupled and low-risk conventional scenarios. Therefore, how to adaptively match the optimal simulation kernel according to the physical characteristics of the target power system has become a key technical problem to be solved in the field of new-type power system planning. SUMMARY

[0005] In view of the above problems, the present application provides a power balance simulation method and device, the main purpose of which is to realize dynamic allocation of simulation resources through prior discrimination of balance risk of the target power system planning scheme, and significantly improve the pertinence and calculation efficiency of power system balance analysis.

[0006] To solve the above technical problems, the present application proposes the following solutions: In a first aspect, the present application provides a power balance simulation method, comprising: acquiring power load and new energy power generation data of a target power system in an evaluation period; determining power shortage of the target power system in the evaluation period according to the power load and new energy power generation data and system operation characteristic parameters; extracting long-period power balance risk characteristic quantities of the target power system in the evaluation period based on the power load and new energy power generation data and the power shortage; if the long-period power balance risk characteristic quantities exceed corresponding characteristic quantity thresholds, a target simulation kernel selects a 8760-hour time-series production simulation method; if the long-period power balance risk characteristic quantities do not exceed corresponding characteristic quantity thresholds, the target simulation kernel selects a typical day simulation method; based on the target simulation kernel, corresponding power balance simulation algorithms are called to carry out power balance simulation calculation and result analysis of the target power system in the evaluation period.

[0007] In a second aspect, the present application provides a power balance simulation device, comprising: a data access and preprocessing module configured to acquire power load and new energy power generation data of a target power system in an evaluation period; the data access and preprocessing module is further configured to determine power shortage of the target power system in the evaluation period according to the acquired power load and new energy power generation data and system operation characteristic parameters; a multi-dimensional feature extraction module configured to extract long-period power balance risk characteristic quantities of the target power system in the evaluation period based on the power load and new energy power generation data and the determined power shortage acquired by the data access and preprocessing module; a multi-level discrimination selection module configured to, if the long-period power balance risk characteristic quantities determined by the multi-dimensional feature extraction module exceed corresponding characteristic quantity thresholds, a target simulation kernel selects a 8760-hour time-series production simulation method; the multi-level discrimination selection module is further configured to, if the long-period power balance risk characteristic quantities determined by the multi-dimensional feature extraction module do not exceed corresponding characteristic quantity thresholds, the target simulation kernel selects a typical day simulation method; a simulation execution module configured to, based on the target simulation kernel determined and output by the multi-level discrimination selection module, call corresponding power balance simulation algorithms to carry out power balance simulation calculation and result analysis of the target power system in the evaluation period.

[0008] In order to achieve the above object, according to a third aspect of the present application, a storage medium is provided, which comprises a stored program, wherein the storage medium controls a device in which the storage medium is located to execute the power and energy balance simulation method of the first aspect when the program is run.

[0009] In order to achieve the above object, according to a fourth aspect of the present application, a processor is provided, which is used to run a program, wherein the power and energy balance simulation method of the first aspect is executed when the program is run.

[0010] By the above technical solution, the power and energy balance simulation method and device provided by the present application determines the corresponding power shortage by obtaining the power load and new energy power generation data and system operation characteristic parameters of the target power system in the to-be-evaluated period, and extracts long-period power and energy balance risk characteristic quantities based on the power load and new energy power generation data and the power shortage. When the long-period power and energy balance risk characteristic quantities exceed the characteristic quantity threshold, the target simulation kernel will select the annual 8760-hour time-series production simulation method; if the long-period power and energy balance risk characteristic quantities do not exceed the threshold, the target simulation kernel will select the typical day simulation method. On this basis, the determined target simulation kernel is executed, the corresponding power and energy balance simulation algorithm is called, and the power and energy balance simulation calculation and result analysis of the target power system in the to-be-evaluated period are carried out. Compared with the prior art, the present application does not simply select the simulation method according to the calculation efficiency or simulation accuracy, but adaptively selects according to the long-period power and energy balance risk characteristic quantities. When the long-period power and energy balance risk characteristic quantities are greater than the corresponding characteristic quantity threshold, the annual 8760-hour time-series production simulation method is selected to ensure accuracy and avoid underestimating the power supply gap and loss of load risk; when the long-period power and energy balance risk characteristic quantities are lower than the corresponding characteristic quantity threshold, the typical day simulation method is selected to improve efficiency and avoid resource waste. Thus, the adaptive selection of the power and energy balance simulation method from "experience-driven" to "supply and demand risk feature-driven" in the medium and long-term power system planning is realized, and the problem of extreme power supply risk misjudgment or waste of computing resources caused by the mutual restriction of calculation accuracy and efficiency in large-scale power planning tasks is effectively solved.

[0011] The above description is only a summary of the technical solutions of the present application. In order to enable a clearer understanding of the technical means of the present application, the content of the specification can be implemented, and in order to make the above and other objects, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application are described below. BRIEF DESCRIPTION OF DRAWINGS

[0012] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart of a power balance simulation method provided by an embodiment of the present invention is shown; Figure 2 A flowchart of another power balance simulation method provided by an embodiment of the present invention is shown; Figure 3 This diagram illustrates a block diagram of a power balance simulation device provided in an embodiment of the present invention. Figure 4 A block diagram of another power balance simulation device provided by an embodiment of the present invention is shown. Detailed Implementation

[0013] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0014] As explained above, current power balance simulation methods mainly include typical day simulation and annual 8760-hour time-series production simulation. Among them, the typical day simulation method is highly efficient; however, this method uses independent and decoupled typical days, which cannot reflect long-term power transfers across days or even weeks, thus underestimating the risk of power balance issues. The annual 8760-hour time-series production simulation has high calculation accuracy, but low calculation efficiency, and faces the "curse of dimensionality" problem in engineering applications.

[0015] Therefore, the core concept of this invention is to propose a power balance simulation method. By prior identification of the supply and demand balance risk of the target power system planning scheme, the method avoids underestimating the supply guarantee risk at an acceptable computational cost and adaptively selects either a high-precision annual 8760-hour time-series production simulation or a high-efficiency typical daily simulation.

[0016] In view of this, this invention proposes a power balance simulation method. First, by acquiring power load and renewable energy generation data, as well as system operation characteristic parameters, of the target power system during the evaluation period, the power deficit is quantitatively assessed, thereby generating long-term power balance risk characteristics. The simulation method is decided based on the comparison between the long-term power balance risk characteristics and the corresponding characteristic threshold: when the long-term power balance risk characteristics are greater than the corresponding characteristic threshold, the annual 8760-hour time-series production simulation method is selected to ensure accuracy and avoid underestimating the power supply gap and load shedding risks; when the long-term power balance risk characteristics are lower than the corresponding characteristic threshold, the typical daily simulation method can be selected to improve computational efficiency and avoid resource waste. This achieves an adaptive selection of power balance simulation methods in medium- and long-term power system planning from "experience-driven" to "supply and demand risk characteristic-driven," effectively solving the problem of missed detection of extreme supply guarantee risks or waste of computing resources caused by the mutual constraint between computational accuracy and efficiency in large-scale power planning tasks.

[0017] The power balance simulation method provided by this invention is executed by a power balance simulation device. This device includes a data access and preprocessing module, a multi-dimensional feature extraction module, a multi-level discrimination and selection module, and a simulation execution module.

[0018] The data access and preprocessing module is responsible for accessing the database, reading power load and new energy power generation data, planned power parameters (such as the installed capacity of various power sources, energy storage capacity, etc.) and physical operation constraints, and calculating the power deficit of the target power system during the evaluation period based on these parameters.

[0019] Based on the aforementioned power load and new energy power generation data and power deficit, the multi-dimensional feature extraction module extracts and determines the long-term power balance risk characteristics of the target power system during the evaluation period.

[0020] The multi-level discrimination selection module is used to determine whether the risk characteristics of each long-cycle power balance exceed the corresponding characteristic threshold according to the set discrimination logic, and then select the appropriate target simulation kernel.

[0021] The simulation execution module, based on the selected target simulation kernel, executes the corresponding power balance simulation method to conduct a power balance analysis of the target power system during the evaluation period, obtaining the power balance analysis results. It should be noted that this simulation execution module is internally configured with two parallel sub-engines: a typical daily simulation engine and an annual 8760-hour time-series production simulation engine. The module will activate the corresponding sub-engine to execute the calculation task based on the judgment results of the multi-level discrimination selection module.

[0022] Next, based on Figure 1The present invention provides a method for simulating power balance, the specific execution steps of which are as follows: Figure 1 As shown, steps 101-106 are included: 101. Obtain power load and renewable energy generation data of the target power system during the evaluation period.

[0023] In this invention, the target power system can be the object of medium- and long-term power system planning. The evaluation period is a future planning period set according to planning needs, which can be five years or several decades in the future, without specific limitations.

[0024] In addition, the aforementioned power load and renewable energy power generation data include power load forecast data and renewable energy theoretical power generation data.

[0025] 102. Determine the power deficit of the target power system during the evaluation period based on power load and new energy power generation data and system operation characteristic parameters.

[0026] In this step, in order to quantify the power that the target power system needs to regulate by the energy storage system after considering the dynamic constraints of local conventional generator units and cross-regional power support, the power deficit of the target power system during the evaluation period can be determined based on the power load and new energy power generation data and system operation characteristic parameters.

[0027] System operating characteristic parameters include the planned net incoming power of the tie line, the total maximum technical output of conventional generator sets, and the reduction factor for the gradeability of conventional generator sets.

[0028] The net power received via tie lines refers to the net planned value of power exchange between the target power system and the external power grid via tie lines during the assessment period (i.e., planned power received minus planned power transmitted). This data reflects the target power system's ability to obtain support through inter-regional mutual assistance when its own power supply is insufficient or surplus, and is an external variable for meeting the target power system's power demand.

[0029] Among them, the maximum technical output sum limits the upper limit of the dispatchable power generation resources of conventional generator sets (e.g., coal-fired units and other units with relatively stable output); while the range of the conventional generator set ramp rate reduction coefficient is (0,1], which is used to characterize the degree of reduction of the theoretical maximum output of conventional generator sets due to ramp rate constraints.

[0030] When determining the power deficit, the power load forecast data for each time unit can be subtracted from the theoretical power generation data of new energy sources for the corresponding time unit to obtain the net load demand for each time unit. Then, the net load demand for each time unit is subtracted from the product of the planned net power input of the tie line and the maximum technical output of the conventional generator units and the reduction factor of the ramp rate of the conventional generator units to obtain the power deficit for the corresponding time unit.

[0031] If the calculated power deficit is less than zero, then take zero, indicating that the system has sufficient regulation capability.

[0032] 103. Extract long-term power balance risk characteristics of the target power system during the evaluation period based on power load and new energy power generation data and power deficit.

[0033] Among them, the long-term power balance risk characteristics include the maximum deficit and its corresponding duration, as well as the system local balance complexity index. Specifically, the maximum deficit and the duration of the maximum deficit are extracted based on the power deficit generated in step 102, aiming to quantify the maximum deficit and deficit persistence risk faced by the target power system over a long period.

[0034] The system local balance complexity index is calculated based on power load and renewable energy generation data. It is used to characterize the difficulty of power balance of the target power system caused by the anti-peak shaving characteristics of source load and random fluctuations of source load during a specified risk period (which can be the specified risk period with the largest power deficit in the period to be evaluated). Thus, it describes the long-term power balance risk of the target power system from three dimensions: "maximum deficit", "duration of maximum deficit" and "local balance complexity index".

[0035] In this embodiment, to determine the maximum deficit and its duration, the obtained power deficits are iterated through, continuous time periods with a power deficit greater than zero are identified, and each continuous time period is defined as a power deficit event. Of course, if there is an interval between multiple continuous time periods where the power deficit is equal to zero and the duration does not exceed a preset number of hours, it is also considered as the same deficit event. For each power deficit event, the products of each power deficit contained therein and the sampling time interval are accumulated to obtain the cumulative deficit power corresponding to the power deficit event. Finally, the maximum value of the cumulative deficit power corresponding to the power deficit event is determined as the maximum deficit, and the corresponding time span is determined as the maximum deficit duration.

[0036] Of course, in addition to summing the products of each power deficit and the sampling time interval to obtain the cumulative power deficit corresponding to the power deficit event, it is also possible to sum the power deficits in each power deficit event and multiply the sum by the sampling time interval.

[0037] It should be noted that if only one power deficit event is identified, the cumulative deficit power corresponding to that period is directly determined as the maximum deficit, and its duration is determined as the maximum deficit duration.

[0038] To determine the local balance complexity index of the system, a specific risk period with the largest power deficit can be identified. Power load forecast data and theoretical power generation data of new energy sources during the specified risk period are extracted to obtain a risk data set. Within this set, the fluctuation dispersion index (such as standard deviation or variance) of the power load forecast data and the theoretical power generation data of new energy sources, as well as the linear correlation index (such as Pearson correlation coefficient or normalized covariance value) between the two are calculated. The output is then normalized by combining the average load value of the period to be evaluated to quantify the local balance complexity index of the target power system.

[0039] 104. If the long-term power balance risk characteristic exceeds the corresponding characteristic threshold, the target simulation kernel will select the annual 8760-hour time-series production simulation method.

[0040] 105. If the long-term power balance risk characteristic does not exceed the corresponding characteristic threshold, the target simulation kernel will select the typical daily simulation method.

[0041] 106. Based on the target simulation kernel, call the corresponding power balance simulation algorithm to carry out power balance simulation calculation and result analysis of the target power system during the evaluation period.

[0042] In steps 104-105, the three long-cycle power balance risk characteristics—the maximum deficit, the duration of the maximum deficit, and the system local balance complexity index—can be compared with their respective preset thresholds according to the set discrimination logic.

[0043] If any one of the features exceeds the corresponding threshold, the target power system can be considered to have a long-term power balance risk that is difficult to summarize by typical scenarios. The target simulation kernel will then select the 8760-hour time-series production simulation method throughout the year to ensure accurate calculation of the energy deficit. Only when the values ​​of all three features do not exceed the corresponding thresholds will the target simulation kernel use the computationally simple typical daily simulation method.

[0044] In terms of specific execution strategy, a cascading progressive judgment can be adopted: First, it is determined whether the maximum deficit exceeds the corresponding threshold. If it does, the subsequent judgment is terminated directly, and the target simulation kernel calls the annual 8760-hour time-series production simulation method. If it does not exceed the threshold, it is determined whether the duration of the maximum deficit exceeds the corresponding threshold. Once it does, the subsequent judgment is stopped immediately, and the target simulation kernel calls the annual 8760-hour time-series production simulation method. Only when the first two indicators do not exceed the threshold is it further determined whether the system local balance complexity index exceeds the corresponding threshold.

[0045] In addition to the aforementioned cascaded progressive discrimination strategy, a parallel discrimination strategy can also be adopted. This involves simultaneously comparing the maximum deficit with the corresponding threshold, the duration of the maximum deficit with the corresponding threshold, and the system local balance complexity index with the corresponding threshold. If at least one of the above three comparison results exceeds the corresponding threshold, the target power balance simulation kernel will select the annual 8760-hour time-series production simulation method. If none of the above three features exceed the corresponding feature threshold, the target power balance simulation kernel will select the typical day simulation method.

[0046] The advantage of the first strategy is that it can save computing resources in the discrimination process through cascaded criteria; in contrast, the second parallel strategy has simpler logic and enhances the robustness of the decision-making mechanism in complex and repetitive scenarios.

[0047] Therefore, in practical applications, one can flexibly choose between the two strategies according to actual needs.

[0048] Subsequently, in step 106, the target simulation kernel can be executed, and the corresponding power balance simulation method can be called to carry out the power balance analysis of the target power system during the evaluation period. The system reliability indicators (such as the probability of load failure and the expected value of insufficient power), the renewable energy absorption rate, the operating cost and other evaluation results can be output, thereby guiding the power planning and energy storage configuration of the target power system.

[0049] Based on the above Figure 1As can be seen from the implementation method, the power balance simulation method provided by this invention obtains the power load and renewable energy generation data of the target power system during the evaluation period, as well as the system operation characteristic parameters, to determine the power deficit of the target power system during the evaluation period. Based on the power load and renewable energy generation data and the power deficit, long-term power balance risk characteristics are extracted. When the long-term power balance risk characteristics exceed the characteristic threshold, the target simulation kernel will select the 8760-hour time-series production simulation method for the whole year; if the long-term power balance risk characteristics do not exceed the threshold, the typical daily simulation method is selected. Based on this, the adaptively selected target simulation kernel is invoked to conduct power balance analysis of the target power system during the evaluation period, obtaining the power balance analysis results. Compared with the prior art, this invention does not simply select the power balance simulation method based on human experience and preferences for computational efficiency or simulation accuracy, but rather achieves adaptive selection of the simulation method by prioritizing the supply and demand balance risk through long-term power balance risk characteristics. When the long-term power balance risk characteristic is greater than the corresponding characteristic threshold, the annual 8760-hour time-series production simulation method is selected to ensure accuracy and avoid underestimating the risks of energy shortages and load shedding. When the long-term power balance risk characteristic is lower than the corresponding characteristic threshold, the typical daily simulation method can be used to improve computational efficiency and avoid resource waste. This achieves an adaptive selection of power balance simulation methods in medium- and long-term power system planning from "experience-driven" to "supply and demand risk characteristic-driven," effectively solving the problem of missed detection of extreme supply guarantee risks or waste of computing resources caused by the mutual constraints between computational accuracy and efficiency in large-scale power planning tasks.

[0050] Furthermore, as a response to Figure 1 Further refinement and extension of the illustrated embodiment, this invention also provides another method for simulating power balance, such as... Figure 2 As shown, the specific steps are as follows: 201. Obtain power load and renewable energy generation data of the target power system during the evaluation period.

[0051] The implementation method of step 201 is the same as that of step 101, and can achieve the same technical effect and solve the same technical problem, so it will not be repeated here.

[0052] 202. Determine the power deficit of the target power system during the evaluation period based on power load and new energy power generation data and system operation characteristic parameters.

[0053] In this embodiment, the following can be obtained first: power load forecast data, theoretical power generation data of new energy sources, and net power input plan of tie lines, total maximum technical output of conventional generator units, and ramp rate reduction factor of conventional generator units from the power load and new energy power generation data. Then, a power deficit is generated based on the power load forecast data, theoretical power generation data of new energy sources, net power input plan of tie lines, total maximum technical output data of conventional regulating units, and ramp rate reduction factor of conventional generator units.

[0054] This method, by taking into account the planned net power input of tie lines and the conventional generator ramp rate reduction factor of conventional generator sets in the process of calculating the power deficit, can more reasonably quantify the power regulation demand that the target power system must meet by the energy storage system at each time period.

[0055] When generating power deficit based on power load forecast data, theoretical renewable energy generation data, planned net power input from tie lines, the total maximum technical output of conventional generating units, and the reduction factor for the ramp rate of conventional generating units, the net load demand can be obtained by first calculating the difference between the power load forecast data and the theoretical renewable energy generation data at each time point. Then, the product of the total maximum technical output of conventional generating units and the reduction factor for the ramp rate of conventional generating units is calculated as the effective regulation capacity of conventional generating units. Finally, the difference between the net load demand at each time point, the planned net power input from tie lines, and the effective regulation capacity of conventional generating units is determined as the power deficit at the corresponding time point. This method uses a time-period step-by-time calculation approach, which can reflect the effective regulation capacity of the system at each time unit within the evaluation period.

[0056] The formula used in this method is: (Formula 1) In the formula, This is the sum of the maximum technical output of the conventional generator sets in the system; This is the reduction factor for the gradeability of conventional generator sets, with a value range of [value range missing]. This is used to characterize the degree to which the theoretical maximum output of conventional generator sets is converted and their actual regulation capability due to the constraint of grade gradient. For power load forecasting data, For theoretical power generation data of new energy sources, The net incoming power plan for the tie line, of which .

[0057] Specifically, when the ramp-up rate reduction factor for conventional generator sets approaches 1, it indicates that the total maximum technical output of conventional generator sets can be fully considered as effective regulation capacity. A lower factor typically corresponds to complex scenarios such as high-proportion renewable energy integration and variable weather conditions. In such scenarios, the total regulation demand of the target power system is prone to drastic fluctuations, imposing strict requirements on the ramp-up rate of the generator sets. By setting a lower factor value to conservatively reduce the theoretical maximum output of conventional generator sets, capacity that cannot be actually utilized due to response lag is eliminated, ensuring that the power deficit reflects the true regulation capacity of the system under extreme operating conditions.

[0058] 203. Extract long-term power balance risk characteristics of the target power system during the evaluation period based on power load and new energy power generation data and power deficit.

[0059] In this embodiment, the maximum deficit and its duration are determined by integrating all consecutive time segments where the calculated power deficit is greater than 0. Simultaneously, based on power load forecasting data and theoretical renewable energy generation data, the system local balance complexity index in the long-term power balance risk characteristic is calculated. This index characterizes the difficulty of achieving power balance in the target power system during a specified risk period due to the anti-peak characteristics of source loads and random fluctuations in source loads. In this way, by using multi-dimensional characteristic variables such as the maximum deficit, the duration of the maximum persistent deficit, and the system local balance complexity index, a multi-dimensional prior judgment of the supply and demand balance risk of the target power system during the evaluation period is achieved: the former describes the target power system's demand for the total rated power and total energy capacity of energy storage (including intraday energy storage and long-term energy storage), while the latter considers the power shortage risk caused by source-load counter-peak regulation and fluctuations, overcoming the limitations of relying on human experience and using a single physical criterion which are prone to failure under complex statistical characteristics.

[0060] When calculating the maximum deficit and the corresponding duration of the maximum deficit in the long-term power balance risk characteristics from the power deficit, the following steps can be taken: First, identify consecutive time periods with values ​​greater than zero in the power deficit and define each consecutive time period as a power deficit event.

[0061] Among them, the power deficit represents the instantaneous regulation pressure of the target power system at the corresponding moment under the support of conventional generators and tie lines, that is, the power regulation demand that must be met by the energy storage system. If the energy storage cannot meet the demand, an energy deficit will occur.

[0062] It should be noted that a power deficit event includes multiple consecutive time periods with a power deficit greater than zero, and any two consecutive time periods must have an interval where the power deficit is equal to zero and the duration does not exceed a preset number of hours. In other words, even if multiple power deficits greater than zero occur at a zero value, as long as the interval between zero values ​​does not exceed the preset number of hours, they are still considered as the same power deficit event. This processing mechanism aims to simulate scenarios where renewable energy output is continuously low due to extreme weather such as "continuous rain," avoiding excessive fragmentation of long-cycle risk events caused by short-term weather fluctuations, thereby more realistically reflecting the balance risk of the target power system.

[0063] The preset number of hours can be determined based on the minimum start-up and shutdown time of conventional generator sets in the power system and the typical short-term fluctuation characteristics of new energy output. Its physical meaning can be the duration of the interruption of regulation demand that can be ignored.

[0064] Second, for each power deficit event, the product of each power deficit and the sampling time interval is accumulated to obtain the cumulative deficit power. The power deficit represents the instantaneous adjustment pressure of the target power system at the corresponding moment under the support of conventional generator sets and tie lines. The cumulative deficit power is the energy deficit that cannot be covered by conventional generator sets and tie lines and must be borne by the energy storage system.

[0065] The formula is: (Formula 2) in, Indicates the maximum shortfall, where For the first A continuous period of vacancies This is due to a power deficit.

[0066] Third, the maximum value among the cumulative power deficits corresponding to all power deficit events is taken as the maximum deficit.

[0067] Fourth, the duration of the power deficit event corresponding to the maximum deficit is determined as the duration of the maximum deficit.

[0068] When determining the system local balance complexity index in the long-term power balance risk characteristics based on power load forecast data and new energy theoretical power generation data, the following steps can be followed: First: Determine the specified risk period with the largest power deficit, extract the power load forecast data and the theoretical power generation data of new energy sources during the specified risk period, and obtain the risk data set; Second: Calculate the standard deviation of load forecasts based on the power load forecast data in the risk dataset; Third: Calculate the standard deviation of theoretical power generation of new energy sources based on the theoretical power generation data of new energy sources in the risk dataset; Fourth: Obtain the Pearson correlation coefficient between the power load forecast data and the theoretical power generation data of new energy sources in the risk data set, and calculate the average load for the period to be evaluated based on the power load forecast data; Fifth: Determine the system local balance complexity index based on the standard deviation of load forecast during high-risk periods, the standard deviation of theoretical power generation of new energy sources, the Pearson correlation coefficient, and the average load.

[0069] The above method, by selecting a risk data set, eliminates the masking effect of traditional full-cycle averaging analysis on extreme operating conditions, thus improving the targeting of risk identification. Secondly, this method couples and models load volatility (standard deviation), theoretical power generation uncertainty (standard deviation) of new energy sources, and spatiotemporal matching characteristics of source and load (Pearson correlation coefficient) during high-risk periods. This not only quantifies the fluctuation amplitude of individual variables but also profoundly reveals the nonlinear impact of source-load mismatch on the target power system's balancing capacity.

[0070] The formulas corresponding to the above steps are as follows: (Formula 3) In the formula, For risk data sets, It is the standard deviation of load forecast calculated based on the power load forecast data in the risk dataset, which characterizes the inherent volatility of the load side; Based on risk data sets The standard deviation of theoretical power generation of new energy sources, calculated from the theoretical power generation data of new energy sources, characterizes the random fluctuations on the power supply side. The Pearson correlation coefficient is the relationship between load forecast data and theoretical renewable energy power generation data, with a value range of [-1, 1]. This indicates a good source-load match, when This indicates a source-load mismatch; To calculate the average load for the evaluation period based on power load forecast data, the index is normalized to eliminate the impact of differences in system capacity on the discrimination threshold.

[0071] 204. Determine the dynamic time threshold based on the total energy storage capacity, total rated power, and energy storage charge-discharge cycle safety factor.

[0072] Since the total energy storage capacity, total rated power, and charge-discharge cycle safety factor directly determine the effective cycle maintenance capability of the energy storage system, this step can determine the dynamic time threshold by integrating the total energy storage capacity, total rated power, and charge-discharge cycle safety factor. This abandons the rigid judgment mode of the traditional static fixed threshold and can adaptively reflect the true continuous support capability of the energy storage system.

[0073] One approach is to multiply the total energy storage capacity by the energy storage charge-discharge cycle safety factor to obtain the effective energy storage regulation capacity. Then, the ratio between the effective energy storage regulation capacity and the total rated power is determined as the dynamic time threshold. This helps identify long-cycle energy transfer demand scenarios where the total energy storage capacity is sufficient but the continuous support duration is insufficient, thereby determining whether the target simulation kernel needs to call upon the 8760-hour time-series production simulation throughout the year.

[0074] The formula is: (Formula 4) In the formula, For dynamic time thresholds; This represents the total energy storage capacity. The total power capacity is set for the energy storage capacity; It is defined as the "energy storage charge-discharge cycle safety factor". This energy storage charge-discharge cycle safety factor comprehensively considers the energy storage charge-discharge efficiency. and depth of discharge The constraint correction factor. For example, considering energy storage charging losses and unavailable capacity reserves, the physical duration required to maintain the same effective discharge energy should be greater than the theoretical duration. Usually in nearby.

[0075] The dynamic time threshold is defined because this indicator constructs an adaptive mapping relationship between the target power system's power supply and energy storage configuration scheme and the accuracy requirements of the power balance simulation kernel. Specifically, this threshold depends on the energy storage physical parameters (such as energy and power) and operational constraints (such as charge and discharge efficiency and depth of discharge) under the current evaluation scenario. This means that when the scale of energy storage configuration changes, battery aging leads to efficiency decline, or safety margin is adjusted, the theoretical upper limit of the deficit duration that the target power system can support will also change accordingly. The deeper physical significance of this dynamic adjustment mechanism is that it defines the effectiveness boundary of typical daily simulation in engineering applications. When the deficit duration does not exceed the dynamic time threshold, even if the typical daily simulation suffers from trajectory distortion caused by forced reset of SOC during the day, the calculated reliability index is engineering equivalent to the annual 8760-hour time-series production simulation because the energy storage system has not reached the power depletion boundary; conversely, it is necessary to switch to the annual 8760-hour time-series production simulation to capture the long-term evolution risk of SOC. In this way, the present invention can more accurately match the actual adjustment capabilities of different energy storage systems, avoiding the risk of missed judgment or computing power redundancy that may occur in traditional static threshold scenarios with a high proportion of new energy.

[0076] 205. If the maximum deficit in the long-term power balance risk characteristics exceeds the total energy storage capacity, or the duration of the maximum deficit exceeds the dynamic time threshold, or the system local balance complexity index exceeds the preset volatility tolerance threshold, then the target simulation kernel will select the annual 8760-hour time-series production simulation method.

[0077] 206. If the maximum deficit in the long-term power balance risk characteristics does not exceed the total energy storage capacity, the duration of the maximum deficit does not exceed the dynamic time threshold, and the system local balance complexity index does not exceed the preset volatility tolerance threshold, then the target simulation kernel will select the typical daily simulation method.

[0078] 207. Based on the target simulation kernel, call the corresponding power balance simulation algorithm to carry out power balance simulation calculation and result analysis of the target power system during the evaluation period.

[0079] Among them, the long-term power balance risk characteristics include the maximum deficit, the duration of the maximum deficit, and the system local balance complexity index.

[0080] The threshold value for the feature quantity corresponding to the maximum deficit can be the total energy storage capacity; the threshold value for the duration of the maximum deficit can be the dynamic time threshold; and the threshold value for the feature quantity corresponding to the system local equilibrium complexity index can be the preset volatility tolerance threshold.

[0081] The volatility tolerance threshold is determined according to the following steps: (1) Select 8760 hours of power load and new energy power generation data for no less than 5 historical years of the target power system or similar systems, covering different new energy penetration rates, and calculate the system local balance complexity index year by year according to the aforementioned method. (2) For each historical year, the annual 8760-hour time-series production simulation method and the typical day simulation method are respectively implemented. Based on the expected value of insufficient electricity in the annual 8760-hour time-series production simulation results, the relative deviation of the typical day simulation results relative to the benchmark is calculated. (3) Monotonic fitting is performed with the system local equilibrium complexity index as the abscissa and the relative deviation as the ordinate. The system local equilibrium complexity index corresponding to the relative deviation being equal to the preset engineering allowable value (e.g., 5%) is taken as the volatility tolerance threshold.

[0082] Based on the above description, in steps 205-206, it can be determined whether the maximum deficit is greater than the total energy storage capacity; whether the duration of the maximum deficit is greater than the dynamic time threshold; and whether the system local balance complexity index is greater than the preset volatility tolerance threshold.

[0083] If any one of them is greater than the corresponding characteristic threshold, the target power balance simulation kernel will select the annual 8760-hour time-series production simulation method; otherwise, if none of them are greater than the corresponding characteristic threshold, the target power balance simulation kernel will select the typical day simulation method.

[0084] In terms of specific execution strategy, a cascading progressive judgment is preferred: First, it is determined whether the maximum deficit exceeds the total energy storage capacity. If it does, subsequent judgments are terminated directly, and the target simulation kernel calls the annual 8760-hour time-series production simulation method. If it does not exceed the limit, it is determined whether the duration of the maximum deficit exceeds the dynamic time threshold. Once it does, subsequent judgments are stopped immediately, and the target simulation kernel calls the annual 8760-hour time-series production simulation method. Only when the first two indicators do not exceed the limit is it further determined whether the local equilibrium complexity index of the system exceeds the preset volatility tolerance threshold. This can save computing resources in the judgment process.

[0085] Furthermore, this invention no longer relies on a single indicator, but comprehensively considers three feature quantities: the maximum deficit, the duration of the maximum deficit, and the system local equilibrium complexity index. It constructs an adaptive selection method for the simulation kernel from two dimensions: physical envelope features and statistical analytical features, which significantly reduces the risk of misjudgment caused by a single or one-sided indicator.

[0086] Specifically, the maximum deficit directly reflects the non-negligible long-cycle energy transfer demand of the target power system during the evaluation period, and is the core constraint for determining the energy storage configuration capacity. Once this indicator exceeds the limit, it means that the target power system has an explicit risk of load shedding. The duration of the maximum deficit reflects the energy support capability of the energy storage system under the power limit. The system local balance complexity index focuses on characterizing the analytical features of source-load fluctuations, and is used to avoid evaluation bias in typical daily simulations when dealing with drastic fluctuation scenarios.

[0087] Finally, in step 207, the corresponding power balance simulation method is called according to the adaptive selection result of the target simulation kernel to carry out the power balance analysis of the target power system during the evaluation period. The simulation output results such as the probability of load failure, the expected value of insufficient power, and the operating cost are used to adjust the power planning scheme or optimize the energy storage configuration of the target power system.

[0088] Based on the above content, the process provided by the embodiments of the present invention can be summarized as follows: First, by accessing the database, the system reads the power load and renewable energy generation data of the target power system during the assessment period, along with system operation characteristic parameters (power load and renewable energy generation data include time-series load, theoretical renewable energy generation, and system operation characteristic parameters include planned net power input from tie lines, total maximum technical output of conventional generator units, and ramp rate reduction coefficients for conventional generator units). A time-series step-by-step calculation is then performed to subtract the theoretical renewable energy generation, planned net power input, and effective regulation capacity of conventional generator units to obtain the system's power deficit. Based on this power deficit, the maximum deficit and corresponding duration of the maximum deficit in the target power system during the assessment period are calculated. The designated risk period with the largest power deficit is selected as the high-risk period. Based on the standard deviation of the load forecast data, the standard deviation of the theoretical renewable energy generation, the Pearson correlation coefficient between the power load forecast data and the theoretical renewable energy generation data, and the average load during the assessment period, the system's local balance complexity index is calculated, thereby comprehensively quantifying the long-term power balance risk.

[0089] Subsequently, the three long-term power balance risk characteristics mentioned above are compared with the total energy storage capacity, the dynamic time threshold calculated based on energy storage parameters, and the preset volatility tolerance threshold, respectively. A step-by-step or parallel execution strategy is used for judgment. If any indicator exceeds the threshold, the target simulation kernel selects the 8760-hour time-series production simulation method for the whole year. Only when all indicators do not exceed the threshold is the target simulation kernel selected the typical daily simulation method. Finally, based on the generated target simulation kernel, the corresponding power balance simulation algorithm is invoked to conduct power balance simulation calculations and result analysis, providing decision support for power system power planning and energy storage configuration optimization.

[0090] Furthermore, as a response to the above Figure 1 In addition to the implementation of the method shown, this embodiment of the invention also provides a power balance simulation device for simulating the above-mentioned power balance. Figure 1 The method shown is implemented accordingly. This device embodiment corresponds to the foregoing method embodiment. For ease of reading, this device embodiment will not repeat the details of the foregoing method embodiment, but it should be clear that the device in this embodiment can implement all the contents of the foregoing method embodiment. Figure 3 As shown, the device includes: The data access and preprocessing module 301 is used to acquire the power load and new energy power generation data of the target power system during the evaluation period; The data access and preprocessing module 301 is also used to determine the power deficit of the target power system during the evaluation period based on the acquired power load and new energy power generation data and system operation characteristic parameters. The multi-dimensional feature extraction module 302 is used to extract the long-term power balance risk characteristics of the target power system during the evaluation period based on the power load and new energy power generation data obtained by the data access and preprocessing module 301 and the calculated power deficit. The multi-level discrimination and selection module 303 is used to select the annual 8760-hour time-series production simulation method if the long-cycle power balance risk feature quantity determined by the multi-dimensional feature extraction module 302 exceeds the corresponding feature quantity threshold. The multi-level discrimination and selection module 303 is also used to select the typical daily simulation method if the long-cycle power balance risk feature determined by the multi-dimensional feature extraction module 302 does not exceed the corresponding feature threshold. The simulation execution module 304 is used to call the corresponding power balance simulation algorithm based on the target simulation kernel determined by the multi-level discrimination and selection module 303, and to carry out power balance simulation calculation and result analysis of the target power system during the evaluation period.

[0091] Furthermore, as a response to the above Figure 2 In addition to the method shown, this embodiment of the invention also provides another power balance simulation device for the above-mentioned implementation. Figure 2 The method shown is implemented accordingly. This device embodiment corresponds to the foregoing method embodiment. For ease of reading, this device embodiment will not repeat the details of the foregoing method embodiment, but it should be clear that the device in this embodiment can implement all the contents of the foregoing method embodiment. Figure 4 As shown, the device includes: The data access and preprocessing module 301 is used to acquire the power load and new energy power generation data of the target power system during the evaluation period; The data access and preprocessing module 301 is also used to determine the power deficit of the target power system during the evaluation period based on the acquired power load and new energy power generation data and system operation characteristic parameters. The multi-dimensional feature extraction module 302 is used to extract the long-term power balance risk characteristics of the target power system during the evaluation period based on the power load and new energy power generation data obtained by the data access and preprocessing module 301 and the determined power deficit. The multi-level discrimination and selection module 303 is used to select the annual 8760-hour time-series production simulation method if the long-cycle power balance risk feature quantity determined by the multi-dimensional feature extraction module 302 exceeds the corresponding feature quantity threshold. The multi-level discrimination and selection module 303 is also used to select the typical daily simulation method if the long-cycle power balance risk feature determined by the multi-dimensional feature extraction module 302 does not exceed the corresponding feature threshold. The simulation execution module 304 is used to call the corresponding power balance simulation algorithm based on the target simulation kernel determined by the multi-level discrimination and selection module 303, and to carry out power balance simulation calculation and result analysis of the target power system during the evaluation period.

[0092] In one optional implementation, the power load and renewable energy generation data include power load forecast data and renewable energy theoretical generation data. System operating characteristic parameters include the planned net power input from tie lines, the total maximum technical output of conventional generator units, and the ramp-up rate reduction factor for conventional generator units. When determining the power deficit of the target power system during the evaluation period based on the power load and renewable energy generation data, the data access and preprocessing module 301 includes: The net load demand is obtained by calculating the difference between the predicted power load data and the theoretical power generation data of new energy sources at each time point. The product of the maximum technical output of the conventional generator sets and the grade reduction factor of the conventional generator sets is calculated as the effective regulation capacity of the conventional generator sets. The difference between the net load demand at each time point and the net power input plan of the tie line and the effective regulation capacity of the conventional generator set is determined as the power deficit at the corresponding time point.

[0093] In one optional implementation, the multidimensional feature extraction module 302 includes: The physical envelope feature extraction submodule 3021 is used to determine the maximum deficit and the maximum deficit duration corresponding to the maximum deficit in the long-cycle power balance risk feature quantity based on the power deficit. The statistical analysis feature extraction submodule 3022 is used to determine the system local balance complexity index in the long-cycle power balance risk feature quantity based on the power load forecast data and the new energy theoretical power generation data. The system local balance complexity index is used to characterize the difficulty of power balance of the target power system caused by the source-load anti-peak characteristics and random fluctuations of source-load during a specified risk period.

[0094] In one optional implementation, the physical envelope feature extraction submodule 3022 is specifically used for: The power deficit event is determined based on the power deficit. The power deficit event includes multiple consecutive time periods with a power deficit greater than zero, and there is an interval between any two consecutive time periods with a power deficit equal to zero and a duration not exceeding a preset number of hours. For each power deficit event, the product of each power deficit and the sampling time interval is accumulated to obtain the cumulative deficit power. The power deficit represents the instantaneous adjustment pressure of the target power system at the corresponding moment under the support of conventional generator sets and tie lines. The cumulative deficit power is the energy deficit borne by the energy storage system. The maximum value among the cumulative power deficits corresponding to all power deficit events is taken as the maximum deficit. The duration of the power deficit event corresponding to the maximum deficit is determined as the maximum deficit duration.

[0095] In one optional implementation, the statistical parsing feature extraction submodule 3021 is specifically used for: Identify the specified risk period with the largest power deficit, extract the power load forecast data and the theoretical power generation data of new energy sources during the specified risk period, and obtain the risk data set; Calculate the standard deviation of load forecasts based on the power load forecast data in the aforementioned risk dataset; Calculate the standard deviation of theoretical power generation of new energy sources based on the theoretical power generation data of new energy sources in the aforementioned risk dataset; Obtain the Pearson correlation coefficient between the load forecast data and the theoretical power generation data of new energy sources in the risk data set, and calculate the average load for the period to be evaluated based on the power load forecast data; The system local balance complexity index is determined based on the load forecast standard deviation, the theoretical power generation standard deviation of the new energy source, the Pearson correlation coefficient, and the average load.

[0096] In an optional implementation, before the multi-level discrimination and selection module 303 selects the annual 8760-hour time-series production simulation method, the device further includes a threshold calculation module 305, the threshold calculation module 305 comprising: The coefficient acquisition submodule 3051 is used to acquire the total energy storage capacity, total rated power, and energy storage charge-discharge cycle safety factor. The threshold calculation submodule 3052 is used to determine the dynamic time threshold based on the total energy storage capacity, the total rated power and the energy storage charge-discharge cycle safety factor obtained by the acquisition submodule 3051 according to the coefficient.

[0097] In one optional implementation, the threshold calculation submodule 3052 is specifically used for: Multiplying the total energy storage capacity by the energy storage charge-discharge cycle safety factor yields the effective energy storage regulation capacity; The ratio between the effective energy storage regulation capacity and the total rated power is determined as the dynamic time threshold.

[0098] In one optional implementation, the multi-level discrimination selection module 303 includes: The feature acquisition submodule 3031 is used to acquire the maximum deficit, the duration of the maximum deficit, and the system local balance complexity index in the long-cycle power balance risk feature quantities. The first discrimination submodule 3032 is used to determine whether the duration of the maximum deficit extracted by the feature acquisition submodule 3031 is greater than the dynamic time threshold if the maximum deficit extracted by the feature acquisition submodule 3031 is not greater than the total energy storage capacity. The second discrimination submodule 3033 is used to determine whether the system local balance complexity index extracted by the feature acquisition submodule 3031 is greater than the preset volatility tolerance threshold if the first discrimination submodule 3032 determines that the duration of the maximum deficit is not greater than the dynamic time threshold. The third discrimination submodule 3034 is used to select the typical daily simulation method if the second discrimination submodule 3033 determines that the local equilibrium complexity index of the system is not greater than the preset volatility tolerance threshold.

[0099] Furthermore, embodiments of the present invention also provide a storage medium for storing a computer program, wherein the computer program, when running, controls the device where the storage medium is located to execute the above-described... Figures 1-2 The power balance simulation method described in [the document].

[0100] Furthermore, embodiments of the present invention also provide a processor for running a program, wherein the program executes the above-described... Figures 1-2 The power balance simulation method described in [the document].

[0101] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0102] It is understood that the relevant features in the above methods and apparatus can be referenced interchangeably. Furthermore, the terms "first," "second," etc., in the above embodiments are used to distinguish between embodiments and do not represent the superiority or inferiority of any particular embodiment.

[0103] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0104] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0105] In addition, the memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0106] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0107] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0108] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0109] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0110] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0111] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0112] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0113] It should also be noted that 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 process, method, article, or apparatus. Unless otherwise specified, 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 that element.

[0114] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0115] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for simulating power balance, characterized in that, The method includes: Obtain data on the power load and renewable energy generation of the target power system during the evaluation period; The power deficit of the target power system during the evaluation period is determined based on the power load and new energy power generation data and system operation characteristic parameters. Based on the power load and new energy power generation data and the power deficit, the long-term power balance risk characteristics of the target power system during the evaluation period are extracted. If the long-cycle power balance risk characteristic exceeds the corresponding characteristic threshold, the target simulation kernel will select the annual 8760-hour time-series production simulation method. If the long-cycle power balance risk characteristic does not exceed the corresponding characteristic threshold, the target simulation kernel will select the typical daily simulation method. Based on the target simulation kernel, the corresponding power balance simulation algorithm is invoked to carry out power balance simulation calculation and result analysis of the target power system during the evaluation period.

2. The method according to claim 1, characterized in that, The power load and renewable energy generation data include power load forecast data and renewable energy theoretical generation data. System operating characteristic parameters include the planned net power input from tie lines, the total maximum technical output of conventional generating units, and the ramp-up rate reduction factor for conventional generating units. Based on the power load and renewable energy generation data and system operating characteristic parameters, the power deficit of the target power system during the evaluation period is determined, including: The net load demand is obtained by calculating the difference between the predicted power load data and the theoretical power generation data of new energy sources at each time point. The product of the maximum technical output of the conventional generator sets and the grade reduction factor of the conventional generator sets is calculated as the effective regulation capacity of the conventional generator sets. The difference between the net load demand at each time point and the net power input plan of the tie line and the effective regulation capacity of the conventional generator set is determined as the power deficit at the corresponding time point.

3. The method according to claim 1, characterized in that, Based on the aforementioned power load and renewable energy generation data, and the aforementioned power deficit, the long-term power balance risk characteristics of the target power system during the assessment period are extracted, including: Based on the power deficit, determine the maximum deficit in the long-cycle power balance risk characteristic quantity and the maximum deficit duration corresponding to the maximum deficit; Based on power load forecast data and new energy theoretical power generation data, the system local balance complexity index in the long-cycle power balance risk characteristic quantity is determined. The system local balance complexity index is used to characterize the difficulty of power balance of the target power system caused by the source-load anti-peak characteristics and random fluctuations of source-load during a specified risk period.

4. The method according to claim 3, characterized in that, Based on the power deficit, determine the maximum deficit in the long-term power balance risk characteristics and the maximum deficit duration corresponding to the maximum deficit, including: The power deficit event is determined based on the power deficit. The power deficit event includes multiple consecutive time periods with a power deficit greater than zero, and there is an interval between any two consecutive time periods with a power deficit equal to zero and a duration not exceeding a preset number of hours. For each power deficit event, the product of each power deficit and the sampling time interval is accumulated to obtain the cumulative deficit power. The power deficit represents the instantaneous adjustment pressure of the target power system at the corresponding moment under the support of conventional generator sets and tie lines. The cumulative deficit power is the energy deficit borne by the energy storage system. The maximum value among the cumulative power deficits corresponding to all power deficit events is taken as the maximum deficit. The duration of the power deficit event corresponding to the maximum deficit is determined as the maximum deficit duration.

5. The method according to claim 3, characterized in that, Based on power load forecast data and theoretical power generation data from new energy sources, the system local balance complexity index in the long-term power balance risk characteristic quantity is determined, including: Identify the specified risk period with the largest power deficit, extract the power load forecast data and the theoretical power generation data of new energy sources during the specified risk period, and obtain the risk data set; Calculate the standard deviation of load forecasts based on the power load forecast data in the aforementioned risk dataset; Calculate the standard deviation of theoretical power generation of new energy sources based on the theoretical power generation data of new energy sources in the aforementioned risk dataset; Obtain the Pearson correlation coefficient between the power load forecast data and the theoretical power generation data of new energy sources in the risk data set, and calculate the average load for the period to be evaluated based on the power load forecast data; The system local balance complexity index is determined based on the load forecast standard deviation, the theoretical power generation standard deviation of the new energy source, the Pearson correlation coefficient, and the average load.

6. The method according to claim 1, characterized in that, Before determining whether the long-cycle power balance risk characteristic exceeds the corresponding characteristic threshold and thus selecting the annual 8760-hour time-series production simulation method, the method further includes: Obtain the total energy storage capacity, total rated power, and energy storage charge / discharge cycle safety factor; The dynamic time threshold is determined based on the total energy storage capacity, the total rated power, and the energy storage charge-discharge cycle safety factor.

7. The method according to claim 6, characterized in that, Based on the total energy storage capacity, the total rated power, and the energy storage charge-discharge cycle safety factor, a dynamic time threshold is determined, including: Multiplying the total energy storage capacity by the energy storage charge-discharge cycle safety factor yields the effective energy storage regulation capacity; The ratio between the effective energy storage regulation capacity and the total rated power is determined as the dynamic time threshold.

8. The method according to any one of claims 1-7, characterized in that, If the long-cycle power balance risk characteristic does not exceed the corresponding characteristic threshold, the target simulation kernel will select a typical daily simulation method, including: Obtain the maximum deficit, the duration of the maximum deficit, and the system local balance complexity index from the long-term power balance risk characteristics; If the maximum deficit is not greater than the total energy storage capacity, then determine whether the duration of the maximum deficit is greater than the dynamic time threshold. If the duration of the maximum deficit is not greater than the dynamic time threshold, then determine whether the system local equilibrium complexity index is greater than the preset volatility tolerance threshold. If the local equilibrium complexity index of the system is not greater than the preset volatility tolerance threshold, the target simulation kernel will select the typical daily simulation method.

9. A power balance simulation device, characterized in that, The device includes: The data access and preprocessing module is used to acquire power load and new energy power generation data of the target power system during the evaluation period; The data access and preprocessing module is also used to determine the power deficit of the target power system during the evaluation period based on the acquired power load and new energy power generation data and system operation characteristic parameters; The multi-dimensional feature extraction module is used to extract the long-term power balance risk characteristics of the target power system during the evaluation period based on the power load and new energy power generation data obtained by the data access and preprocessing module and the determined power deficit. A multi-level discrimination and selection module is used to select the annual 8760-hour time-series production simulation method if the long-cycle power balance risk feature quantity determined by the multi-dimensional feature extraction module exceeds the corresponding feature quantity threshold. The multi-level discrimination and selection module is also used to select the typical daily simulation method if the long-cycle power balance risk feature determined by the multi-dimensional feature extraction module does not exceed the corresponding feature threshold. The simulation execution module is used to call the corresponding power balance simulation algorithm based on the target simulation kernel determined by the multi-level discrimination and selection module, and to carry out power balance simulation calculation and result analysis of the target power system during the evaluation period.

10. A storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the storage medium to perform the power balance simulation method as described in any one of claims 1 to 8.

11. A processor, characterized in that, The processor is used to run a program, wherein the program executes the power balance simulation method as described in any one of claims 1 to 8.

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