Method for scheduling optimization of electric energy supply based on hydrogen-ammonia energy storage

Abstract

The application discloses a hydrogen-ammonia energy storage-based power supply scheduling optimization method and belongs to the technical field of power supply scheduling; a sampling time period is set, actual power demand of a power supply target area is called and a load change rate is quantified, and scene types under different periods are constructed; response time and actual energy supply values of hydrogen energy storage and ammonia energy storage systems under the same scene type are acquired, and a response credibility factor is calculated; scheduling score parameters are calculated in combination with available capacity data and the response credibility factor in the hydrogen energy storage system and the ammonia energy storage system in the power supply target area under the current sampling time period; error calibration is performed on the scheduling score parameters based on the actual energy supply value average and the actual power demand average in the hydrogen energy storage system and the ammonia energy storage system; power supply scheduling optimization is analyzed and performed, the scheduling precision and the operation efficiency of the energy storage system under a variable load situation are effectively improved, and the reliability and the sustainability of the power supply system are enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric energy supply scheduling, in particular to an electric energy supply scheduling optimization method based on hydrogen-ammonia energy storage. BACKGROUND

[0002] In recent years, hydrogen energy and ammonia energy play an increasingly important role in energy systems due to their advantages such as cleanliness, efficiency and storability. Especially in the context of the continuous increase in the proportion of renewable energy grid connection and the challenges faced by the grid regulation capacity, hydrogen energy storage and ammonia energy storage as new long-time energy storage means have gradually attracted attention. Currently, hydrogen energy systems have achieved preliminary application in fuel cells, industrial hydrogen production, etc., and ammonia energy is also widely studied as a green energy carrier due to its characteristics of easy storage and transportation at normal temperature and pressure. At the same time, under the support of big data and intelligent scheduling strategy, the electric energy scheduling system integrating hydrogen-ammonia energy storage units has become a research hotspot. However, in practical application, due to the differences in response time, available capacity and output power between hydrogen energy storage and ammonia energy storage, how to achieve efficient scheduling of the two in the dynamic changing load scenario has become a key technical problem to be solved.

[0003] Although existing related researches have involved the optimization scheduling strategy of energy storage system, most of them focus on a single type of energy storage (such as batteries or pumped storage) or only start from the supply-demand balance angle, ignoring the influence of energy storage response characteristics on scheduling reliability and efficiency. In addition, the current scheduling method is rough in scene division dimension, which is difficult to accurately describe the variable load fluctuation situation, resulting in a lack of robustness and self-adaptive ability of the scheduling strategy. Especially for hydrogen energy storage and ammonia energy storage systems with obvious dynamic differences, there is a lack of response credibility evaluation mechanism based on scene feature matching, which makes the scheduling decision deviate from the optimal path, and even may lead to waste of energy storage resources or aggravation of power supply risk. Therefore, the existing technology has not formed a systematic, parameter-adjustable and feedback correction-capable hydrogen-ammonia collaborative scheduling method, especially there are still obvious shortcomings in multi-period dynamic optimization and error calibration. SUMMARY

[0004] The present application aims to provide an electric energy supply scheduling optimization method based on hydrogen-ammonia energy storage to solve the problems raised in the background art.

[0005] In order to solve the above technical problems, the present application provides the following technical solutions:

[0006] The application discloses a method for optimizing power supply scheduling based on hydrogen-ammonia energy storage, which comprises the following steps: S1, setting a sampling time period, calling actual power demand of a power supply target area, and quantifying a load change rate; constructing a scene type of the power supply target area under different sampling time periods; S2, acquiring response time data and actual energy supply values of hydrogen energy storage systems and ammonia energy storage systems in the power supply target area with the same scene type, and calculating a response credibility factor; S3, acquiring available capacity data of the hydrogen energy storage systems and the ammonia energy storage systems in the power supply target area under the current sampling time period, and combining the response credibility factor to calculate scheduling score parameters of the hydrogen energy storage systems and the ammonia energy storage systems; S4, error correcting the scheduling score parameters based on the actual energy supply value average and the actual power demand average of the hydrogen energy storage systems and the ammonia energy storage systems; and analyzing and optimizing power supply scheduling.

[0007] As a preferred scheme of the method for optimizing power supply scheduling based on hydrogen-ammonia energy storage, the sampling time period is set, historical energy storage unit scheduling control logs of the power supply target area are periodically called from a power supply system background, the historical energy storage unit scheduling control logs comprise actual power demand of the power supply target area; actual power demand collected in an i-th sampling time period is denoted as , and a load change rate between actual power demand collected in an i-1-th sampling time period and actual power demand collected in the i-th sampling time period is denoted as .

[0008] Based on the actual power demand and the load change rate collected in different sampling time periods, a scene type of the power supply target area under different sampling time periods is constructed, and the scene type is constructed in the following manner:

[0009] A feature interval set composed of a plurality of two-dimensional joint feature intervals is constructed , wherein denotes the n-th two-dimensional joint feature interval, N denotes a total number of the two-dimensional joint feature intervals, the two-dimensional joint feature interval comprises a group of preset actual power demand threshold intervals and a group of preset load change rate threshold intervals, and the feature interval set is used for representing different scene types of the power supply target area.

[0010] Actual power demand and the load change rate are matched with actual power demand threshold intervals and load change rate threshold intervals in the two-dimensional joint feature interval ; if the actual power demand belongs to the actual power demand threshold interval in the two-dimensional joint feature interval , and the load change rate The two-dimensional joint feature interval belongs to the load change rate threshold interval in the step of , the scene type of the power supply target area at the i-th sampling time period is recorded as .

[0011] It should be noted that the step obtains the actual power demand and the load change rate at different time periods by setting the sampling time period and calling the historical scheduling control log, thereby constructing a two-dimensional joint feature interval set, and realizing the typical scene description of the power supply target area at different time evolution.

[0012] The scene recognition method based on joint feature modeling not only improves the recognition ability of the system running state, but also provides clear classification basis for the classification of the response credibility factor and the determination of the scheduling score parameter in the subsequent step. Therefore, the capture ability of the dynamic load fluctuation characteristics is improved, and the scheduling optimization has stronger situational adaptability and prediction guiding ability.

[0013] As a preferred scheme of the power supply scheduling optimization method based on hydrogen ammonia energy storage, the i-th sampling time period is traversed to obtain the response time data and the actual energy supply value of the hydrogen energy storage system and the ammonia energy storage system in the power supply target area with the same scene type, the response time data of the hydrogen energy storage system and the ammonia energy storage system in the i-th sampling time period are recorded as and , the actual energy supply value of the hydrogen energy storage system and the ammonia energy storage system in the i-th sampling time period is recorded as and .

[0014] The response credibility factors of the hydrogen energy storage system and the ammonia energy storage system in the power supply target area under the scene type are calculated respectively, and the calculation formula is as follows:

[0015] ;

[0016] Wherein, and respectively represent the response credibility factors of the hydrogen energy storage system and the ammonia energy storage system in the power supply target area under the scene type , and respectively represent the average response time data of the hydrogen energy storage system and the ammonia energy storage system, and respectively represent the average actual energy supply value of the hydrogen energy storage system and the ammonia energy storage system, , and , respectively represent the preset adjustment coefficients of the response time data and the actual energy supply value.

[0017] It should be noted that this step realizes the quantitative modeling of the response characteristics of different types of energy storage devices under different working conditions by collecting and analyzing the response time data and actual energy supply values of hydrogen energy storage systems and ammonia energy storage systems under the same scene type in multiple sampling periods, and constructing a response credibility factor accordingly.

[0018] The introduction of the response credibility factor effectively supplements the trade-off mechanism of the scheduling strategy for the dual performance dimensions of "response speed + energy supply capability", thereby providing a more robust evaluation benchmark for subsequent scheduling strategies, avoiding scheduling imbalance caused by abnormal response or insufficient energy supply, and enhancing the system's carrying capacity for uncertain factors.

[0019] As a preferred scheme of the hydrogen-ammonia energy storage-based electric energy supply scheduling optimization method, the available capacity data of the hydrogen energy storage system and the ammonia energy storage system in the electric energy supply target area under the I+1 sampling time period are obtained, respectively denoted as and The scene type of the electric energy supply target area under the I+1 sampling time period is obtained, and the corresponding response credibility factor is obtained based on the scene type;

[0020] Based on the response credibility factor and the available capacity data of the hydrogen energy storage system and the ammonia energy storage system, the scheduling score parameters of the hydrogen energy storage system and the ammonia energy storage system in the electric energy supply target area under the I+1 sampling time period are calculated, and the calculation formula is as follows:

[0021] ;

[0022] Among them, and respectively represent the scheduling score parameters of the hydrogen energy storage system and the ammonia energy storage system in the electric energy supply target area under the I+1 sampling time period, , and , respectively represent the mapping weight coefficients of the preset response credibility factor and the available capacity data, represents a preset error term.

[0023] It should be noted that this step, based on the available capacity of the hydrogen / ammonia energy storage system under the current sampling period, combines the corresponding response credibility factor to construct the scheduling score parameter, which realizes the dynamic comprehensive evaluation of the energy supply advantages and disadvantages of each type of energy storage resource under a given scene.

[0024] The scoring system combines the two key dimensions of available resource capacity and system response performance, and has the beneficial effect of ensuring that the scheduling decision not only considers the current capacity redundancy, but also comprehensively considers the reliability of system response, thereby improving the stability of the scheduling strategy and the safety redundancy guarantee capability.

[0025] As a preferred scheme of the hydrogen-ammonia energy storage-based electric energy supply scheduling optimization method, based on the actual energy supply value mean and the actual electric energy demand mean in the hydrogen energy storage system and the ammonia energy storage system, a scheduling error expectation value is calculated, and based on the scheduling error expectation value, an error correction is performed on the scheduling score parameters of the hydrogen energy storage system and the ammonia energy storage system in the electric energy supply target area in the I+1 sampling time period, and the calculation formula is as follows:

[0026] ;

[0027] Wherein, and respectively represent the scheduling score parameters of the hydrogen energy storage system and the ammonia energy storage system in the electric energy supply target area in the I+1 sampling time period after error correction, represents the actual electric energy demand mean corresponding to the scene type of the electric energy supply target area in the I+1 sampling time period, represents a preset error feedback adjustment coefficient;

[0028] If the scheduling score parameter of the hydrogen energy storage system in the electric energy supply target area in the I+1 sampling time period after error correction is greater than the scheduling score parameter of the ammonia energy storage system, the hydrogen energy storage system is preferentially scheduled for electric energy supply, and if the scheduling score parameter of the hydrogen energy storage system in the electric energy supply target area in the I+1 sampling time period after error correction is less than the scheduling score parameter of the ammonia energy storage system, the ammonia energy storage system is preferentially scheduled for electric energy supply.

[0029] It should be noted that this step introduces an error expectation-based correction mechanism, which feeds back the actual deviation between supply and demand to the scheduling score parameters, achieving dynamic error correction of the scoring system. By using the historical supply and demand mean under the scene type matching as a reference, and setting an error feedback adjustment coefficient.

[0030] This step builds a closed-loop error compensation mechanism, so that the scoring system tends to be accurate in dynamic operation, reduces the long-term scheduling deviation caused by error accumulation, and improves the adaptive ability of scheduling optimization and the long-period operation efficiency of the energy supply system.

[0031] ​​The application discloses an electric energy supply scheduling optimization system based on hydrogen-ammonia energy storage, and belongs to the technical field of electric energy supply scheduling optimization.

[0032] The data acquisition and scene type construction module sets a sampling time period, calls actual electric energy demand of an electric energy supply target region, and quantifies a load change rate; and constructs scene types of the electric energy supply target region under different sampling time periods.

[0033] The response credibility factor calculation module acquires response time data and actual energy supply values of hydrogen energy storage systems and ammonia energy storage systems in electric energy supply target regions with the same scene types, and calculates response credibility factors.

[0034] The scheduling score parameter calculation module acquires available capacity data of hydrogen energy storage systems and ammonia energy storage systems in the electric energy supply target region under the current sampling time period, and calculates scheduling score parameters of the hydrogen energy storage systems and the ammonia energy storage systems in combination with the response credibility factors.

[0035] The error calibration and scheduling optimization module calibrates errors of the scheduling score parameters based on mean values of the actual energy supply values of the hydrogen energy storage systems and the ammonia energy storage systems and mean values of the actual electric energy demand, and analyzes and optimizes electric energy supply scheduling.

[0036] Further, the data acquisition and scene type construction module comprises a data acquisition unit and a scene type construction unit.

[0037] The data acquisition unit sets a sampling time period, periodically calls a historical energy storage unit scheduling control log of an electric energy supply target region from a background of an electric energy supply system, the historical energy storage unit scheduling control log comprising actual electric energy demand of the electric energy supply target region; actual electric energy demand collected in an i-th sampling time period is recorded as , and a load change rate between actual electric energy demand collected in an i-1-th sampling time period and actual electric energy demand collected in the i-th sampling time period is recorded as .

[0038] The scene type construction unit constructs scene types of the electric energy supply target region under different sampling time periods based on actual electric energy demand and load change rates collected in different sampling time periods, and specifically as follows: a feature interval set composed of a plurality of two-dimensional joint feature intervals is constructed , wherein denotes the nth two-dimensional joint feature interval, N denotes the total number of two-dimensional joint feature intervals, the two-dimensional joint feature interval comprises a set of preset actual power demand threshold intervals and a set of preset load change rate threshold intervals, and the feature interval set is used to represent different scene types of the power supply target area; the actual power demand quantity and the load change rate are matched with the actual power demand threshold interval and the load change rate threshold interval in the two-dimensional joint feature interval , if the actual power demand quantity belongs to the actual power demand threshold interval in the two-dimensional joint feature interval , and the load change rate belongs to the load change rate threshold interval in the two-dimensional joint feature interval , the scene type of the power supply target area at the i th sampling time period is recorded as .

[0039] Further, the response credibility factor calculation module comprises a response credibility factor calculation unit.

[0040] The response credibility factor calculation unit: traverses the I sampling time periods, obtains the response time data and the actual energy supply values in the hydrogen energy storage system and the ammonia energy storage system in the power supply target area with the same scene type, records the response time data in the hydrogen energy storage system and the ammonia energy storage system at the i th sampling time period as and , records the actual energy supply values in the hydrogen energy storage system and the ammonia energy storage system at the i th sampling time period as and , respectively, and calculates the response credibility factors of the hydrogen energy storage system and the ammonia energy storage system in the power supply target area under the scene type .

[0041] Further, the scheduling score parameter calculation module comprises a scheduling score parameter calculation unit.

[0042] The scheduling score parameter calculation unit: obtains the available capacity data in the hydrogen energy storage system and the ammonia energy storage system in the power supply target area at the I+1 th sampling time period, and records them as and , respectively, obtains the scene type of the power supply target area at the I+1 th sampling time period, obtains the corresponding response credibility factors based on the scene type, and calculates the scheduling score parameters of the hydrogen energy storage system and the ammonia energy storage system in the power supply target area at the I+1 th sampling time period based on the response credibility factors and the available capacity data of the hydrogen energy storage system and the ammonia energy storage system.

[0043] Furthermore, the error calibration and scheduling optimization module includes an error calibration unit and a scheduling optimization unit;

[0044] The error calibration unit calculates the expected value of the scheduling error based on the average actual energy supply value and the average actual power demand value of the hydrogen energy storage system and the ammonia energy storage system, and performs error calibration on the scheduling score parameters of the hydrogen energy storage system and the ammonia energy storage system in the power supply target area under the (I+1)th sampling time period based on the expected value of the scheduling error.

[0045] The scheduling optimization unit: if the scheduling score parameters of the hydrogen energy storage system in the power supply target area under the (I+1)th sampling time period after error calibration... Dispatch scoring parameters greater than those of ammonia energy storage systems If the hydrogen energy storage system is prioritized for power supply, then the scheduling score parameters of the hydrogen energy storage system in the power supply target area during the (I+1)th sampling time period after error calibration will be considered. Dispatch scoring parameters of ammonia energy storage system In this case, the ammonia energy storage system will be prioritized for power supply.

[0046] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: The power supply scheduling optimization method based on hydrogen-ammonia energy storage provided by this invention, by setting a sampling time period, retrieving historical scheduling logs, quantifying power demand and load change rates, and constructing scenario types composed of joint feature intervals, achieves accurate identification of the operating status of the power supply target area, laying a classification foundation for subsequent scheduling. Furthermore, by acquiring the response time and power supply data of hydrogen and ammonia energy storage systems under the same scenario type, a response reliability factor is constructed, realizing a quantitative evaluation of the response capability and power supply reliability of different energy storage systems, enhancing the robustness of system scheduling. Based on this, by combining real-time available capacity data and the response reliability factor, scheduling scoring parameters are calculated, achieving a dynamic comprehensive evaluation of the advantages and disadvantages of energy storage system scheduling, effectively improving the adaptability and accuracy of scheduling strategies. Finally, an error expectation calibration mechanism based on historical averages is introduced to provide feedback correction to the scheduling scoring parameters, constructing a closed-loop optimization system for the scoring results, realizing dynamic adaptation and precise matching in the scheduling process. Overall, this method systematically optimizes the energy supply scheduling strategy of hydrogen-ammonia energy storage system by constructing a four-step mechanism of scene recognition, response evaluation, capacity fusion and error calibration, which significantly improves the intelligence level, reliability and operating efficiency of the power supply system. Attached Figure Description

[0047] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0048] Figure 1 is a schematic diagram of the steps of the method for scheduling optimization of electricity supply based on hydrogen-ammonia energy storage according to the present application;

[0049] Figure 2 is a schematic diagram of the structure of the system for scheduling optimization of electricity supply based on hydrogen-ammonia energy storage according to the present application. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0051] Please refer to Figure 1 In the first embodiment, the method for scheduling optimization of electricity supply based on hydrogen-ammonia energy storage comprises the following steps:

[0052] Step S1: Set a sampling time period, retrieve the actual electricity demand of the target area of electricity supply, and quantify the load change rate; build the scene types of the target area of electricity supply under different sampling time periods.

[0053] Specifically, set a sampling time period, periodically retrieve the historical energy storage unit scheduling control log of the target area of electricity supply from the background of the electricity supply system, wherein the historical energy storage unit scheduling control log comprises the actual electricity demand of the target area of electricity supply; the actual electricity demand collected in the i-th sampling time period is denoted as The load change rate between the actual electricity demand collected in the i-1-th sampling time period and the actual electricity demand collected in the i-th sampling time period is denoted as

[0054] Based on the actual electricity demand and the load change rate collected in different sampling time periods, the scene types of the target area of electricity supply under different sampling time periods are built, specifically as follows:

[0055] A feature interval set composed of a plurality of two-dimensional joint feature intervals is built , wherein the n-th two-dimensional joint feature interval is denoted as N, which represents the total number of two-dimensional joint feature intervals, the two-dimensional joint feature interval comprises a group of preset actual electricity demand threshold intervals and a group of preset load change rate threshold intervals, and the feature interval set is used to represent different scene types of the target area of electricity supply;

[0056] The actual electricity demand and the load change rate ​Joint feature intervals in two dimensions Match the actual electricity demand threshold range and the load change rate threshold range in the data. If the actual electricity demand... Belongs to the two-dimensional joint feature interval The actual electricity demand threshold range in the data, and the load change rate. Belongs to the two-dimensional joint feature interval If the load change rate threshold range is defined, then the scenario type of the power supply target area in the i-th sampling time period is denoted as . .

[0057] Step S2: Obtain response time data and actual energy supply values ​​from hydrogen energy storage systems and ammonia energy storage systems in the target area of ​​the same scenario type, and calculate the response reliability factor.

[0058] Specifically, the process iterates through I sampling time periods to obtain the response time data and actual energy supply value of hydrogen and ammonia energy storage systems in the target area of ​​the same scenario type. The response time data of the hydrogen and ammonia energy storage systems in the i-th sampling time period are denoted as follows: and The actual energy supply values ​​of the hydrogen energy storage system and the ammonia energy storage system during the i-th sampling time period are denoted as follows: and ;

[0059] Calculate scene type separately The response reliability factors of hydrogen energy storage systems and ammonia energy storage systems in the target area for electricity supply are calculated using the following formula:

[0060] ;

[0061] in, and Representing scene type The response reliability factors of hydrogen energy storage systems and ammonia energy storage systems in the target area for electricity supply. and These represent the average response time data for the hydrogen energy storage system and the ammonia energy storage system, respectively. and These represent the average actual energy supply values ​​of the hydrogen energy storage system and the ammonia energy storage system, respectively. , and , These represent the preset response time data and the adjustment coefficients for the actual energy supply value, respectively.

[0062] In this invention, and The average response time of the hydrogen energy storage / ammonia energy storage system under the same scenario type (the shorter the response time, the smaller the average, and the greater the positive contribution to the reliability factor), and The average actual energy supply value of the hydrogen energy storage / ammonia energy storage system under the same scenario type (the closer the energy supply value to the demand, the more reasonable the average, and the greater the positive contribution).

[0063] The traditional scheduling only uses a single indicator of response time or energy supply value to judge the system performance, which is easy to lead to one-sided decision-making (such as a system with fast response but insufficient energy supply being misjudged as reliable). The formula comprehensively reflects the comprehensive performance of the energy storage system in fast response and accurate energy supply through the weighted fusion of and (the average response time), and (the average actual energy supply value), which is more in line with the actual power supply demand (in actual scenarios, both fast startup and energy supply value matching load demand are required).

[0064] The formula is strictly based on historical data calculation of the same scenario type (that is, scenarios with the same load size and change rate), ensuring that the evaluation results are highly adapted to the current load characteristics. For example, the reliability evaluation data of high load + fast growth scenarios and low load + slow fluctuation scenarios are completely separated, avoiding the misjudgment of reliable systems caused by mixing different scenario data (such as a system performing well in a low load scenario but responding poorly in a high load scenario, which would mask the problem if mixed calculation is used).

[0065] Step S3: Obtain the available capacity data of the hydrogen energy storage system and the ammonia energy storage system in the target area of the power supply at the current sampling time period, and calculate the scheduling score parameters of the hydrogen energy storage system and the ammonia energy storage system in combination with the response reliability factor.

[0066] Specifically, the available capacity data of the hydrogen energy storage system and the ammonia energy storage system in the target area of the power supply at the I+1th sampling time period are obtained, denoted as and respectively; the scenario type of the target area of the power supply at the I+1th sampling time period is obtained, and the corresponding response reliability factor is obtained based on the scenario type;

[0067] Based on the response reliability factor and the available capacity data of the hydrogen energy storage system and the ammonia energy storage system, the scheduling score parameters of the hydrogen energy storage system and the ammonia energy storage system in the target area of the power supply at the I+1th sampling time period are calculated, and the calculation formula is as follows:

[0068] ;

[0069] wherein, and These represent the scheduling score parameters of the hydrogen energy storage system and the ammonia energy storage system in the power supply target area during the (I+1)th sampling time period. , and , These represent the preset response confidence factor and the mapping weight coefficients of available capacity data, respectively. This indicates the preset error term.

[0070] In this invention, traditional scheduling methods may only focus on historical performance (e.g., systems with high reliability but insufficient current capacity are prioritized for scheduling, resulting in power supply gaps) or only focus on current capacity (e.g., systems with sufficient capacity but extremely poor response are over-reliant, resulting in power supply lag when load fluctuates).

[0071] This formula uses a weighted sum of two terms, taking into account historical reliability (through...). (Response credibility factor) reflects that the higher the reliability, the better. The smaller (combined) After weighting, the greater the actual positive contribution to the score) and the currently available capabilities (through and This is reflected in the fact that the more sufficient the capacity, the larger this value becomes; the combination of the two ensures that scheduling decisions are both reliable and feasible.

[0072] , and , It can flexibly adapt to different scheduling priorities. For example, when the target area faces power shortages and priority is given to ensuring supply continuity, it can improve efficiency. , Weighting prioritizes scheduling systems with sufficient capacity; when the target area has sufficient power supply, prioritizing power quality can improve [the system's performance]. , Weighting prioritizes scheduling systems with high reliability, solving the problem of "difficulty in balancing capacity and reliability" in traditional "one-size-fits-all" scheduling.

[0073] Step S4: Based on the average actual energy supply and average actual electricity demand of the hydrogen energy storage system and the ammonia energy storage system, perform error calibration on the scheduling scoring parameters; analyze and optimize the power supply scheduling.

[0074] Specifically, based on the average actual energy supply and average actual electricity demand of the hydrogen and ammonia energy storage systems, the expected value of the scheduling error is calculated. Then, based on this expected value, the scheduling score parameters of the hydrogen and ammonia energy storage systems in the target area for electricity supply are calibrated for error during the (I+1)th sampling time period. The calculation formula is as follows:

[0075] ;

[0076] wherein, and respectively represent the scheduling score parameters of the hydrogen energy storage system and the ammonia energy storage system in the power supply target area at the I+1th sampling time period after error calibration, represents the actual power demand average corresponding to the scene type of the power supply target area at the I+1th sampling time period, represents a preset error feedback adjustment coefficient;

[0077] In the present application, the scoring system of the conventional scheduling method is usually open-loop calculation, that is, the score calculated based on historical data is directly used for scheduling, but if there is a long-term deviation of the system that the theoretical score is high but the actual energy supply is insufficient / overabundant, it will lead to the failure of continuous optimization of the scheduling strategy.

[0078] The formula forms a closed-loop optimization by introducing a historical supply-demand deviation correction term: if a certain system is long-term energy insufficient under the same scene, the correction term is positive, and the scheduling score parameter after calibration is reduced, reducing the dependence on the system; if the energy supply is excessive , the correction term is negative, and the scheduling score parameter is appropriately improved, but the control range is controlled by the error feedback adjustment coefficient to avoid waste.

[0079] The calculation of the correction term is strictly based on the historical supply-demand average corresponding to the current scene type , ensuring that the correction direction is consistent with the current load characteristics. For example, the energy supply deviation of the high-load scene will not affect the score calibration of the low-load scene, avoiding the problem of correct systems being miscorrected caused by cross-scene correction.

[0080] If the scheduling score parameter of the hydrogen energy storage system in the power supply target area at the I+1th sampling time period after error calibration is greater than the scheduling score parameter of the ammonia energy storage system , the hydrogen energy storage system is preferentially scheduled for power supply, and if the scheduling score parameter of the hydrogen energy storage system in the power supply target area at the I+1th sampling time period after error calibration is less than the scheduling score parameter of the ammonia energy storage system , the ammonia energy storage system is preferentially scheduled for power supply.

[0081] Please refer to Figure 2In the second embodiment, an electricity supply scheduling optimization system based on hydrogen-ammonia energy storage is provided, which comprises a data acquisition and scenario type construction module, a response credibility factor calculation module, a scheduling score parameter calculation module, and an error calibration and scheduling optimization module.

[0082] The data acquisition and scenario type construction module sets a sampling time period, calls actual electricity demand of the electricity supply target area, and quantifies the load change rate; and constructs the scenario types of the electricity supply target area under different sampling time periods.

[0083] The response credibility factor calculation module acquires the response time data and actual energy supply values of the hydrogen energy storage system and the ammonia energy storage system in the electricity supply target area with the same scenario type, and calculates the response credibility factor.

[0084] The scheduling score parameter calculation module acquires the available capacity data of the hydrogen energy storage system and the ammonia energy storage system in the electricity supply target area under the current sampling time period, and calculates the scheduling score parameters of the hydrogen energy storage system and the ammonia energy storage system in combination with the response credibility factor.

[0085] The error calibration and scheduling optimization module performs error calibration on the scheduling score parameters based on the mean values of the actual energy supply values of the hydrogen energy storage system and the ammonia energy storage system and the mean value of the actual electricity demand, and analyzes and optimizes the electricity supply scheduling.

[0086] Further, the data acquisition and scenario type construction module comprises a data acquisition unit and a scenario type construction unit.

[0087] The data acquisition unit sets a sampling time period, periodically calls the historical energy storage unit scheduling control log of the electricity supply target area from the electricity supply system background, wherein the historical energy storage unit scheduling control log comprises the actual electricity demand of the electricity supply target area; and records the actual electricity demand collected in the ith sampling time period as , and records the load change rate between the actual electricity demand collected in the i-1th sampling time period and the actual electricity demand collected in the ith sampling time period as .

[0088] The scenario type construction unit constructs the scenario types of the electricity supply target area under different sampling time periods based on the actual electricity demand and the load change rate collected in different sampling time periods, specifically as follows: constructs a feature interval set composed of a plurality of two-dimensional joint feature intervals , wherein represents the nth two-dimensional joint feature interval, N represents the total number of two-dimensional joint feature intervals, the two-dimensional joint feature interval includes a set of preset actual power demand threshold intervals and a set of preset load change rate threshold intervals, and the feature interval set is used to represent different scene types of the power supply target area; the actual power demand quantity and the load change rate are matched with the actual power demand threshold interval and the load change rate threshold interval in the two-dimensional joint feature interval , if the actual power demand quantity belongs to the actual power demand threshold interval in the two-dimensional joint feature interval , and the load change rate belongs to the load change rate threshold interval in the two-dimensional joint feature interval , the scene type of the power supply target area at the i th sampling time period is recorded as .

[0089] Further, the response credibility factor calculation module includes a response credibility factor calculation unit.

[0090] The response credibility factor calculation unit: traverses the I th sampling time period, obtains the response time data and the actual energy supply value of the hydrogen energy storage system and the ammonia energy storage system in the power supply target area with the same scene type, records the response time data of the hydrogen energy storage system and the ammonia energy storage system in the i th sampling time period as and , respectively, records the actual energy supply value of the hydrogen energy storage system and the ammonia energy storage system in the i th sampling time period as and , respectively, and calculates the response credibility factor of the hydrogen energy storage system and the ammonia energy storage system in the power supply target area under the scene type .

[0091] Further, the scheduling score parameter calculation module includes a scheduling score parameter calculation unit.

[0092] The scheduling score parameter calculation unit: obtains the available capacity data of the hydrogen energy storage system and the ammonia energy storage system in the power supply target area at the I+1 th sampling time period, and records them as and , respectively; obtains the scene type of the power supply target area at the I+1 th sampling time period, obtains the corresponding response credibility factor based on the scene type; and calculates the scheduling score parameter of the hydrogen energy storage system and the ammonia energy storage system in the power supply target area at the I+1 th sampling time period based on the response credibility factor and the available capacity data of the hydrogen energy storage system and the ammonia energy storage system.

[0093] Further, the error calibration and scheduling optimization module comprises an error calibration unit and a scheduling optimization unit.

[0094] The error calibration unit calculates a scheduling error expectation value based on the actual energy supply value mean and the actual electricity demand mean in the hydrogen energy storage system and the ammonia energy storage system, and performs error calibration on the scheduling score parameters of the hydrogen energy storage system and the ammonia energy storage system in the electricity supply target area at the I+1 sampling time period based on the scheduling error expectation value.

[0095] The scheduling optimization unit prioritizes the hydrogen energy storage system for electricity supply if the scheduling score parameter of the hydrogen energy storage system in the electricity supply target area at the I+1 sampling time period after error calibration is greater than the scheduling score parameter of the ammonia energy storage system. The scheduling optimization unit prioritizes the ammonia energy storage system for electricity supply if the scheduling score parameter of the hydrogen energy storage system in the electricity supply target area at the I+1 sampling time period after error calibration is less than the scheduling score parameter of the ammonia energy storage system.

[0096] Please refer to Table 1. In this embodiment three, a hydrogen-ammonia energy-based electricity supply scheduling optimization method is provided. In order to verify the beneficial effects of the present application, scientific demonstration is carried out through simulation experiments.

[0097] It is assumed that the sampling time period is set to 1 hour, and the electricity consumption data of a certain industrial park in 5 historical periods (i.e. I is 5) is selected to construct a two-dimensional joint feature interval set, wherein the typical scenario Definition : actual electricity demand threshold interval [150kWh, 300kWh], load change rate threshold interval [20kWh / h, 60kWh / h].

[0098] It is assumed that after matching, the 5 historical periods all belong to the scenario , the current optimization period is the 6th period (I+1), and the scenario type is still .

[0099] Table 1 Parameter Data Table

[0100] ;

[0101] It can be known by substituting into the formula that the scenario type The response credibility factor of the hydrogen energy storage system and the ammonia energy storage system in the electricity supply target area is:

[0102] ;

[0103] ​​​The scheduling score parameter of the hydrogen energy storage system and the ammonia energy storage system in the power supply target area under the 6th sampling time period is:

[0104] ;

[0105] The scheduling score parameter of the hydrogen energy storage system and the ammonia energy storage system in the power supply target area under the 6th sampling time period is error calibrated, and the formula calculation result is:

[0106] ;

[0107] From the above calculation, the error calibrated ammonia energy storage score (282.3) is still higher than the hydrogen energy storage (148.6), so the 6th sampling time period preferentially schedules the ammonia energy storage system for power supply.

[0108] The decision meets the actual demand:

[0109] The current capacity of the ammonia energy storage is sufficient , which can guarantee the supply;

[0110] Although the hydrogen energy storage responds faster, the historical energy supply is slightly insufficient, the score decreases after calibration, and over-reliance is avoided to cause a power supply gap.

[0111] It should be noted that in this article, relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the term "includes", "contains" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device.

[0112] Finally, it should be noted that: the above only describes the preferred embodiments of the present application, and is not used to limit the present application, although the present application has been described in detail with reference to the foregoing embodiments, and those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for scheduling optimization of electricity supply based on hydrogen-ammonia energy storage, characterized in that, The method includes the following steps: Step S1: Set the sampling time period, retrieve the actual power demand of the power supply target area, and quantify the load change rate; construct the scenario types of the power supply target area under different sampling time periods; Step S2: Obtain response time data and actual energy supply value from hydrogen energy storage systems and ammonia energy storage systems in the target area of ​​the same scenario type, and calculate the response reliability factor; Step S3: Obtain the available capacity data of the hydrogen energy storage system and the ammonia energy storage system in the target area of ​​power supply under the current sampling time period, and calculate the scheduling score parameters of the hydrogen energy storage system and the ammonia energy storage system in combination with the response confidence factor; Step S4: Based on the average actual energy supply and average actual electricity demand of the hydrogen energy storage system and the ammonia energy storage system, perform error calibration on the scheduling scoring parameters; analyze and optimize the power supply scheduling. The specific implementation process of step S1 includes: Setting a sampling time period, periodically calling a historical energy storage unit dispatching control log of an energy supply target area from an energy supply system background, the historical energy storage unit dispatching control log including an actual energy demand of the energy supply target area; recording the actual energy demand collected in the i th sampling time period as Recording a load change rate between the actual energy demand collected in the i th sampling time period and the actual energy demand collected in the i-1 th sampling time period as ; Based on the actual power demand and load change rate collected at different sampling time periods, scenario types for the power supply target area under different sampling time periods are constructed as follows: Constructing a feature interval set composed of several two-dimensional joint feature intervals wherein, represents the nth two-dimensional joint feature interval, N represents the total number of two-dimensional joint feature intervals, the two-dimensional joint feature interval comprises a group of preset actual power demand threshold intervals and a group of preset load change rate threshold intervals, and the feature interval set is used to represent different scene types of the power supply target region. actual power demand quantity and load change rate actual power demand threshold interval and load change rate threshold interval in the two-dimensional joint feature interval , if the actual power demand quantity belongs to the actual power demand threshold interval in the two-dimensional joint feature interval , and the load change rate belongs to the load change rate threshold interval in the two-dimensional joint feature interval , the scene type of the power supply target region in the i th sampling time period is recorded as ; The specific implementation process of step S2 includes: The response time data and the actual energy supply values in the hydrogen energy storage system and the ammonia energy storage system in the same scene type target area of the power supply are obtained by traversing I sampling time periods, and the response time data in the hydrogen energy storage system and the ammonia energy storage system in the i-th sampling time period are respectively denoted as and The actual energy supply values in the hydrogen energy storage system and the ammonia energy storage system in the i-th sampling time period are denoted as and ; Respectively calculate scene type The response credibility factor of the hydrogen energy storage system and the ammonia energy storage system in the power supply target area is calculated, and the calculation formula is as follows: ； in, and Representing scene type The response reliability factors of hydrogen energy storage systems and ammonia energy storage systems in the target area for electricity supply. and These represent the average response time data for the hydrogen energy storage system and the ammonia energy storage system, respectively. and These represent the average actual energy supply values ​​of the hydrogen energy storage system and the ammonia energy storage system, respectively. , and , These represent the adjustment coefficients for the preset response time data and the actual energy supply value, respectively. The specific implementation process of step S3 includes: Obtain the available capacity data of the hydrogen energy storage system and the ammonia energy storage system in the target area of ​​power supply during the (I+1)th sampling time period, and denote them as follows: and ; Obtain the scenario type of the power supply target area under the (I+1)th sampling time period, and obtain the corresponding response confidence factor based on the scenario type; Based on the response reliability factors and available capacity data of hydrogen and ammonia energy storage systems, the scheduling score parameters of the hydrogen and ammonia energy storage systems in the power supply target area are calculated in the (I+1)th sampling time period. The calculation formula is as follows: ； in, and These represent the scheduling score parameters of the hydrogen energy storage system and the ammonia energy storage system in the power supply target area during the (I+1)th sampling time period. , and , These represent the preset response confidence factor and the mapping weight coefficients of available capacity data, respectively. This indicates the preset error term.

2. The power supply scheduling optimization method based on hydrogen ammonia energy storage according to claim 1, characterized in that, The specific implementation process of step S4 includes: Based on the average actual energy supply and average actual electricity demand of the hydrogen and ammonia energy storage systems, the expected value of the scheduling error is calculated. Then, based on this expected value, the scheduling score parameters of the hydrogen and ammonia energy storage systems in the target area for electricity supply are calibrated for error during the (I+1)th sampling time period. The calculation formula is as follows: ； in, and These represent the scheduling score parameters of the hydrogen energy storage system and the ammonia energy storage system in the power supply target area during the (I+1)th sampling time period after error calibration. This represents the average actual power demand corresponding to the scenario type of the power supply target area during the (I+1)th sampling time period. This represents the preset error feedback adjustment coefficient; If the scheduling score parameters of the hydrogen energy storage system in the power supply target area during the (I+1)th sampling time period after error calibration are... Dispatch scoring parameters greater than those of ammonia energy storage systems If the hydrogen energy storage system is prioritized for power supply, then the scheduling score parameters of the hydrogen energy storage system in the power supply target area during the (I+1)th sampling time period after error calibration will be considered. Dispatch scoring parameters of ammonia energy storage system In this case, the ammonia energy storage system will be prioritized for power supply.

3. The power supply dispatch optimization method based on hydrogen ammonia energy storage according to any one of claims 1-2, characterized in that, The power supply scheduling optimization method is executed through a power supply scheduling optimization system based on hydrogen ammonia energy storage. The power supply scheduling optimization system includes: a data acquisition and scenario type construction module, a response credibility factor calculation module, a scheduling score parameter calculation module, and an error calibration and scheduling optimization module. The data acquisition and scenario type construction module: sets the sampling time period, retrieves the actual power demand of the power supply target area, and quantifies the load change rate; constructs scenario types for the power supply target area under different sampling time periods; The response reliability factor calculation module: acquires response time data and actual energy supply value of hydrogen energy storage system and ammonia energy storage system in the target area of ​​power supply with the same scenario type, and calculates the response reliability factor; The scheduling score parameter calculation module: obtains the available capacity data of the hydrogen energy storage system and the ammonia energy storage system in the power supply target area under the current sampling time period, and calculates the scheduling score parameters of the hydrogen energy storage system and the ammonia energy storage system in combination with the response reliability factor; The error calibration and scheduling optimization module: based on the average actual energy supply and average actual electricity demand of the hydrogen energy storage system and the ammonia energy storage system, performs error calibration on the scheduling scoring parameters; analyzes and optimizes the power supply scheduling.

4. The power supply scheduling optimization method based on hydrogen ammonia energy storage according to claim 3, characterized in that: The data acquisition and scene type construction module includes a data acquisition unit and a scene type construction unit; The data acquisition unit: sets a sampling time period and periodically retrieves historical energy storage unit scheduling and control logs for the target area of ​​the power supply system from the background of the power supply system. The historical energy storage unit scheduling and control logs include the actual power demand of the target area. The actual power demand collected in the i-th sampling time period is recorded as... The rate of change of load between the actual power demand collected in the (i-1)th sampling period and the actual power demand collected in the ith sampling period is denoted as . ; The scenario type construction unit: Based on the actual power demand and load change rate collected at different sampling time periods, it constructs scenario types for the power supply target area under different sampling time periods, specifically as follows: It constructs a feature interval set composed of several two-dimensional joint feature intervals. ,in, Let N represent the nth two-dimensional joint feature interval, and N represent the total number of two-dimensional joint feature intervals. The two-dimensional joint feature intervals include a set of preset actual electricity demand threshold intervals and a set of preset load change rate threshold intervals. This set of feature intervals is used to characterize different scenario types within the target area for electricity supply. The actual electricity demand... and load change rate Joint feature intervals in two dimensions Match the actual electricity demand threshold range and the load change rate threshold range in the data. If the actual electricity demand... Belongs to the two-dimensional joint feature interval The actual electricity demand threshold range in the data, and the load change rate. Belongs to the two-dimensional joint feature interval If the load change rate threshold range is defined, then the scenario type of the power supply target area in the i-th sampling time period is denoted as . .

5. The power supply scheduling optimization method based on hydrogen ammonia energy storage according to claim 4, characterized in that: The response confidence factor calculation module includes a response confidence factor calculation unit; The response reliability factor calculation unit: traverses a total of I sampling time periods, acquiring the response time data and actual energy supply value of the hydrogen energy storage system and the ammonia energy storage system in the power supply target area with the same scenario type. The response time data of the hydrogen energy storage system and the ammonia energy storage system in the i-th sampling time period are respectively denoted as... and The actual energy supply values ​​of the hydrogen energy storage system and the ammonia energy storage system during the i-th sampling time period are denoted as follows: and ; Calculate the scene type separately The response reliability factor of hydrogen energy storage system and ammonia energy storage system in the target area of ​​power supply.

6. The power supply scheduling optimization method based on hydrogen ammonia energy storage according to claim 5, characterized in that: The scheduling score parameter calculation module includes a scheduling score parameter calculation unit; The scheduling scoring parameter calculation unit: acquires the available capacity data of the hydrogen energy storage system and the ammonia energy storage system in the power supply target area during the (I+1)th sampling time period, and records them as follows: and ; Obtain the scenario type of the power supply target area under the (I+1)th sampling time period, and obtain the corresponding response reliability factor based on the scenario type; based on the response reliability factor and available capacity data of the hydrogen energy storage system and the ammonia energy storage system, calculate the scheduling score parameters of the hydrogen energy storage system and the ammonia energy storage system in the power supply target area under the (I+1)th sampling time period.

7. The power supply scheduling optimization method based on hydrogen ammonia energy storage according to claim 6, characterized in that: The error calibration and scheduling optimization module includes an error calibration unit and a scheduling optimization unit; The error calibration unit calculates the expected value of the scheduling error based on the average actual energy supply value and the average actual power demand value of the hydrogen energy storage system and the ammonia energy storage system, and performs error calibration on the scheduling score parameters of the hydrogen energy storage system and the ammonia energy storage system in the power supply target area under the (I+1)th sampling time period based on the expected value of the scheduling error. The scheduling optimization unit: if the scheduling score parameters of the hydrogen energy storage system in the power supply target area under the (I+1)th sampling time period after error calibration... Dispatch scoring parameters greater than those of ammonia energy storage systems If the hydrogen energy storage system is prioritized for power supply, then the scheduling score parameters of the hydrogen energy storage system in the power supply target area during the (I+1)th sampling time period after error calibration will be considered. Dispatch scoring parameters of ammonia energy storage system In this case, the ammonia energy storage system will be prioritized for power supply.

Images