Green electricity hydrogen ammonia flexible scheduling method and system considering wind and light output correction

Abstract

The invention discloses a green electricity hydrogen ammonia flexible scheduling method and system considering wind and light output correction, and relates to the technical field of energy power, and the method comprises the steps: setting the uncertainty of wind and light output prediction, and correcting the wind and light output prediction; determining a wind and light output prediction value participating in a scheduling algorithm; equally dividing a time period in which a scheduling instruction and a predictive variable need to be given into M small sections; determining a decision variable and an optimization interval; performing iterative optimization on the target function; calculating the power of the electrolytic cell at all time points in the corresponding iteration period; for each iteration period, calculating constraint variables of all time points in the scheduling period; judging whether the constraint variable meets a constraint condition or not, and obtaining an alternative set; and taking a decision variable corresponding to the minimum target function from the alternative set as an optimization result. According to the method, segmented optimization is carried out on the time period in which the scheduling instruction needs to be given, the optimization speed of the algorithm is improved, green hydrogen production is safe and stable, and economical and reliable operation is achieved.

Description

Technical Field

[0001] The present invention relates to the field of energy and power technology, and in particular to a green electricity hydrogen and ammonia flexible scheduling method and system taking into account wind and solar power output correction. Background Art

[0002] The green electricity hydrogen and ammonia system uses green renewable energy such as wind power and photovoltaics to produce "green hydrogen" and further produce "green ammonia", reducing greenhouse gas emissions and promoting the low-carbon transformation of the energy and power industry. The green electricity hydrogen and ammonia dispatching system is mainly responsible for coordinating and optimizing various links in the process of hydrogen production, storage, transportation and use. According to the forecast of wind and solar power output, real-time power generation, status of hydrogen production equipment, hydrogen storage capacity, synthetic ammonia load and other needs, considering the uncertainty of wind and solar power output forecast, hydrogen production electrolyzer, hydrogen storage tank and other constraints, it optimizes the strategies of electricity purchase, electricity use, hydrogen production and hydrogen use, makes intelligent decisions and scheduling, and realizes safe, economical and flexible hydrogen production.

[0003] The output of wind and solar power is greatly affected by natural conditions such as wind power and light intensity, so it has a large degree of uncertainty. The dispatching system needs to have a certain degree of flexibility to cope with the real-time changes in wind and solar power output and the high prediction uncertainty, so as to alleviate the subsequent impact on the safe operation of hydrogen production equipment such as electrolyzers and hydrogen storage equipment such as hydrogen storage tanks. In addition, in order to address the problem of long dispatch prediction time, a suitable optimization strategy is needed to speed up the calculation speed of the dispatching algorithm. Summary of the invention

[0004] The present invention provides a green electricity hydrogen-ammonia flexible scheduling method and system taking into account wind and solar power output correction, so as to overcome at least one technical problem existing in the prior art.

[0005] On the one hand, an embodiment of the present invention provides a green electricity hydrogen-ammonia flexible scheduling method considering wind and solar power output correction, including:

[0006] Establish the mathematical model, constraints and objective function of the hydrogen-ammonia system;

[0007] Obtain the wind and solar power output forecast for the future predetermined time period provided by the manufacturer;

[0008] The uncertainty of the wind and solar power output forecast is set according to the time difference between the wind and solar power output forecast time provided by the manufacturer and the scheduling forecast time point;

[0009] According to the wind and solar power output prediction uncertainty, the wind and solar power output prediction provided by the manufacturer is corrected to obtain an uncertainty interval;

[0010] According to the mathematical model, calculating the current utilization rate of the hydrogen storage tank;

[0011] According to the utilization rate, determining a wind and solar power output prediction value participating in the scheduling algorithm within the uncertainty interval;

[0012] The time period for which scheduling instructions and predicted variables need to be given is divided into M small segments, each of which contains L time points;

[0013] Determine the decision variables as the electrolytic cell power, synthetic ammonia load and synthetic ammonia load adjustment time at M+1 time points at the beginning and end of the M small segments;

[0014] Determining the optimization interval of the decision variables according to the current state and constraints of the hydrogen-ammonia system;

[0015] Iteratively optimizing the objective function within the optimization interval to obtain an optimization set; the optimization set includes the electrolytic cell power, synthetic ammonia load and synthetic ammonia load adjustment time at M+1 time points obtained in each iteration;

[0016] According to the electrolytic cell power at M+1 time points obtained in each iteration, the electrolytic cell power at all time points in the corresponding iteration cycle is calculated;

[0017] For each iteration cycle, the constraint variables at all time points in the scheduling cycle are calculated based on the electrolyzer power, wind and solar power output forecast values ​​at all time points and the mathematical model;

[0018] Determine whether the constraint variables at all time points in each iteration cycle meet the constraint conditions. If not, discard the corresponding iteration results; if satisfied, add the corresponding iteration results to the candidate set;

[0019] The decision variable corresponding to the minimum objective function is selected from the candidate set as the optimization result.

[0020] Optionally, the wind and solar power output forecast includes wind power output forecast and photovoltaic power output forecast, wherein:

[0021] The uncertainty of wind power output prediction is expressed as

[0022] Δt wind It indicates the time difference between the wind power output forecast provided by the manufacturer and the dispatch forecast time point. and They represent the upper and lower limits of the time difference between the wind power output forecast provided by the manufacturer and the dispatch forecast time point, and They represent the upper and lower limits of the uncertainty of wind power output prediction respectively;

[0023] The uncertainty interval of wind power output prediction is in, represents the wind power output forecast provided by the manufacturer, It represents the wind power output forecast after correction;

[0024] The uncertainty of the photovoltaic output prediction is expressed as

[0025] The time difference between the photovoltaic output forecast provided by the manufacturer and the scheduling forecast time point, and They represent the upper and lower limits of the time difference between the photovoltaic output forecast time provided by the manufacturer and the scheduling forecast time point, and They represent the upper and lower limits of the uncertainty of photovoltaic output prediction respectively;

[0026] The uncertainty interval of the photovoltaic output prediction is in, It indicates the photovoltaic output forecast provided by the manufacturer. Represents the corrected photovoltaic output forecast.

[0027] Optionally, the hydrogen storage tank usage rate is Among them, P HT Indicates the current pressure of the hydrogen storage tank. Indicates the lower limit of hydrogen storage tank pressure, Indicates the upper pressure limit of the hydrogen storage tank.

[0028] Optionally, according to the utilization rate, a wind / solar power output prediction value participating in the scheduling algorithm is determined in an uncertainty interval, specifically:

[0029] If the current utilization rate of the hydrogen storage tank is less than 50%, the wind power output forecast value participating in the dispatching algorithm is set to Set the photovoltaic output prediction value participating in the scheduling algorithm to If the current utilization rate of the storage tank is greater than or equal to 50%, the wind power output forecast value participating in the dispatching algorithm is set to Set the photovoltaic output prediction value participating in the scheduling algorithm to

[0030] Optionally, the constraints include electrolyzer power constraints, synthetic ammonia load constraints and synthetic ammonia load adjustment time interval constraints, wherein:

[0031] The electrolyzer power constraint is expressed as Indicates the lower limit of electrolytic cell power, represents the upper limit of electrolyzer power; the synthetic ammonia load constraint is expressed as Indicates the lower limit of synthetic ammonia load, represents the upper limit of synthetic ammonia load; the synthetic ammonia load adjustment time interval constraint is represented by t AM,t+1 ≥t AM,t +ΔtAM,Adj , where t AM,t Indicates the last synthetic ammonia load adjustment time, t AM,t+1 Indicates the next synthetic ammonia load adjustment time, Δt AM,Adj Indicates the lower limit of the time interval between two adjustments of synthetic ammonia load.

[0032] Optionally, the optimal interval of electrolytic cell power is expressed as P e,i，min ≤P e,i ≤P e,i，max ,in,

[0033] The optimal range of synthetic ammonia load is expressed as P AM,k+1，min ≤P AM,k+1 ≤P AM,k+1，max ,in, P vel,AM represents the upper limit of synthetic ammonia load adjustment rate, Δt represents the time interval between adjacent time points in the scheduling cycle;

[0034] The optimal interval for adjusting the synthetic ammonia load is expressed as t AM,t+1,min ≤t AM,t+1 ≤t AM,t+1,max , where t AM,t+1,min =max(t AM,t +Δt AM,Adj ,t i ), t AM,t+1,max =t1+ΔT,t i It represents the time of the i-th scheduling cycle, t1 represents the time of the first scheduling cycle, and ΔT represents the time length required to give scheduling instructions and predict variables.

[0035] Optionally, the objective function is iteratively optimized within the optimization interval, specifically:

[0036] Setting the initial optimization value of the decision variable;

[0037] Based on the initial optimization value, the objective function is optimized by Bayesian iteration within the optimization interval, and the Bayesian optimization objective function set is obtained as Obj j , j = 1, 2, ..., K, K represents the number of cycles; the electrolytic cell power at M+1 time points obtained by Bayesian iteration is expressed as P eo,i ,i=1,2...M+1; where,

[0038] The electrolytic cell power at the start and end of each segment meets the following requirements:

[0039] P e,i,1 represents the electrolytic cell power at the beginning of the i-th time period, Pe,i,L represents the electrolyzer power at the end of the i-th time period.

[0040] Optionally, the electrolytic cell power at all time points in the corresponding iteration period is calculated according to the electrolytic cell power at M+1 time points obtained in each iteration, specifically:

[0041] Assume that the time interval of the scheduling instruction is Δt h, and the total number of scheduling time points is calculated to be N = ΔT / Δt, then L = N / M+1;

[0042] Assuming that the electrolytic cell power changes linearly at L points in each small segment, the electrolytic cell power at each time point is expressed as P e,i,m =P e,i,1 +(m-1) / (L-1)*(P e,i,L -P e,i,1 ),m=1,2,...L.

[0043] Optionally, the mathematical model at least includes a power balance model, an electrolyzer model and a hydrogen storage tank model; the constraint conditions also include power constraints, electrolyzer power adjustment rate constraints and hydrogen storage tank constraints; wherein,

[0044] The power balance model is expressed as represents the predicted wind power, Represents the predicted photoelectric power, P pur Indicates the purchased power, P gip Indicates the abandoned power, P e Indicates the electrolytic cell power;

[0045] The electrolytic cell model is represented as represents the hydrogen production rate of the electrolyzer, T e represents the electrolyzer temperature, and f() represents the relationship between the electrolyzer hydrogen production rate and the electrolyzer power and temperature;

[0046] The hydrogen storage tank model is expressed as n HT Indicates the molar amount of hydrogen in the hydrogen storage tank, P HT Indicates the hydrogen storage tank pressure, V HT represents the volume of the hydrogen storage tank, R represents the universal gas constant, T HT Indicates the temperature of the hydrogen storage tank;

[0047] The power constraint is expressed as Indicates the upper limit of power purchase;

[0048] The electrolytic cell power adjustment rate constraint is expressed as P e,n -P e,vel Δt≤P e,n+1 ≤P e,n +P e,vel Δt,P e,nrepresents the electrolyzer power at the nth time point in the scheduling period, P e,n+1 represents the electrolyzer power at the n+1th time point in the scheduling period, P e,vel represents the upper limit of the electrolytic cell power adjustment rate, and Δt represents the time interval between adjacent time points;

[0049] The hydrogen tank constraint is expressed as Indicates the lower limit of hydrogen storage tank pressure. Indicates the upper limit of hydrogen storage tank pressure.

[0050] On the other hand, the present invention also provides a green electricity hydrogen-ammonia flexible dispatching system considering wind and solar power output correction, comprising:

[0051] Establishing modules for establishing mathematical models, constraints and objective functions of hydrogen-ammonia systems;

[0052] An acquisition module is used to obtain the wind and solar power output forecast within a future predetermined time period provided by the manufacturer;

[0053] A setting module is used to set the uncertainty of the wind and solar power output forecast according to the time difference between the provision time of the wind and solar power output forecast provided by the manufacturer and the scheduling forecast time point;

[0054] A correction module, used for correcting the wind and solar power output prediction provided by the manufacturer according to the wind and solar power output prediction uncertainty to obtain an uncertainty interval;

[0055] A first calculation module is used to calculate the current utilization rate of the hydrogen storage tank according to the mathematical model;

[0056] A first determination module, configured to determine, according to the utilization rate, a wind / solar power output prediction value participating in the scheduling algorithm in the uncertainty interval;

[0057] A partitioning module is used to divide the time period for which scheduling instructions and predicted variables need to be given into M small segments, each of which contains L time points;

[0058] The second determination module is used to determine the decision variables as the electrolytic cell power, synthetic ammonia load and synthetic ammonia load adjustment time at M+1 time points at the beginning and end of the M small segments;

[0059] A third determination module is used to determine the optimization interval of the decision variable according to the current state and constraints of the hydrogen-ammonia system;

[0060] An optimization module, used for iteratively optimizing the objective function within the optimization interval to obtain an optimization set; the optimization set includes the electrolytic cell power, synthetic ammonia load and synthetic ammonia load adjustment time at M+1 time points obtained in each iteration;

[0061] The second calculation module is used to calculate the electrolytic cell power at all time points in the corresponding iteration period according to the electrolytic cell power at M+1 time points obtained in each iteration;

[0062] The third calculation module is used to calculate the constraint variables at all time points in the scheduling period according to the electrolyzer power, wind and solar power output prediction values ​​and the mathematical model at all time points for each iteration cycle;

[0063] A judgment module is used to judge whether the constraint variables at all time points in each iteration cycle meet the constraint conditions. If not, the corresponding iteration results are discarded; if satisfied, the corresponding iteration results are added to the candidate set;

[0064] The selection module is used to select the decision variable corresponding to the minimum objective function from the candidate set as the optimization result.

[0065] The innovative features of the embodiments of the present invention include:

[0066] 1. In this embodiment, uncertainty correction is performed on the predicted wind and solar power, and uncertainty is used to measure the uncertainty of the predicted wind and solar power, so as to alleviate the impact of wind and solar power output uncertainty on the scheduling system. Then, the wind and solar power output forecast actually used by the scheduling method is determined according to the utilization rate of the hydrogen storage tank, which is one of the innovative points of the embodiment of the present invention.

[0067] 2. In this embodiment, a Bayesian optimization algorithm is used to perform segmented optimization for the time periods where scheduling instructions need to be given, thereby improving the optimization speed of the algorithm, so that "green hydrogen" production can be produced safely, stably, economically and reliably, which is one of the innovations of the embodiment of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0069] Figure 1 A flow chart of a scheduling method provided by an embodiment of the present invention;

[0070] Figure 2 A schematic diagram of the structure of a scheduling system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0071] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0072] It should be noted that the terms "including" and "having" and any variations thereof in the embodiments of the present invention and the accompanying drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products or devices.

[0073] The embodiment of the present invention discloses a green electricity hydrogen-ammonia flexible scheduling method and system considering wind and solar power output correction, which are described in detail below.

[0074] Figure 1 For a flowchart of the scheduling method provided by an embodiment of the present invention, please refer to Figure 1 The embodiment of the present invention provides a green electricity hydrogen-ammonia flexible scheduling method considering wind and solar power output correction, including:

[0075] Step 1: Establish the mathematical model, constraints and objective function of the hydrogen-ammonia system;

[0076] Step 2: Obtain the wind and solar power output forecast for the future predetermined time period provided by the manufacturer;

[0077] Step 3: Set the uncertainty of the wind and solar power output forecast according to the time difference between the wind and solar power output forecast time provided by the manufacturer and the scheduling forecast time point;

[0078] Step 4: According to the uncertainty of wind and solar power output prediction, correct the wind and solar power output prediction provided by the manufacturer to obtain the uncertainty interval;

[0079] Step 5: Calculate the current utilization rate of the hydrogen storage tank based on the mathematical model;

[0080] Step 6: According to the utilization rate, determine the wind and solar power output forecast value participating in the scheduling algorithm within the uncertainty interval;

[0081] Step 7: Divide the time period for which scheduling instructions and predicted variables need to be given into M small segments, each of which contains L time points;

[0082] Step 8: Determine the decision variables as the electrolytic cell power, synthetic ammonia load and synthetic ammonia load adjustment time at M+1 time points at the beginning and end of the M small segments;

[0083] Step 9: Determine the optimization interval of the decision variables according to the current state and constraints of the hydrogen-ammonia system;

[0084] Step 10: Iteratively optimize the objective function within the optimization interval to obtain an optimization set; the optimization set includes the electrolytic cell power, synthetic ammonia load and synthetic ammonia load adjustment time at M+1 time points obtained in each iteration;

[0085] Step 11: Calculate the electrolytic cell power at all time points in the corresponding iteration period according to the electrolytic cell power at M+1 time points obtained in each iteration;

[0086] Step 12: For each iteration cycle, the constraint variables at all time points in the scheduling cycle are calculated based on the electrolyzer power, wind and solar power output forecast values ​​and mathematical models at all time points;

[0087] Step 13: Determine whether the constraint variables at all time points in each iteration cycle meet the constraint conditions. If not, discard the corresponding iteration results; if satisfied, add the corresponding iteration results to the candidate set;

[0088] Step 14: Take the decision variable corresponding to the minimum objective function from the candidate set as the optimization result.

[0089] Specifically, please refer to Figure 1 The green electricity hydrogen-ammonia flexible scheduling method considering wind and solar output correction provided by the embodiment of the present invention first constructs a mathematical model of the hydrogen-ammonia system through step 1. When constructing the mathematical model, it is necessary to comprehensively consider the state parameters of the hydrogen-ammonia system. The main state parameters of the hydrogen-ammonia system include wind power, photovoltaic power, purchased power, and abandoned power related to power generation; electrolyzer cluster power, electrolyzer cluster temperature, and electrolyzer cluster hydrogen production rate related to the electrolyzer cluster; hydrogen storage tank temperature, hydrogen storage tank pressure, hydrogen storage capacity, hydrogen storage tank utilization rate, hydrogen storage tank hydrogen storage rate, and hydrogen supply rate related to the hydrogen storage tank; synthetic ammonia load, synthetic ammonia output, and the previous synthetic ammonia load adjustment time related to synthetic ammonia production.

[0090] Based on this, the mathematical model of the synthetic ammonia system constructed in the present invention includes a power balance model, an electrolyzer model, a hydrogen storage tank model, a hydrogen balance model and a synthetic ammonia model.

[0091] The power balance model is expressed as In the formula, Indicates the predicted wind power, in MW; Represents the predicted photovoltaic power, in MW; P pur Indicates the purchased power, in MW; P gip Indicates the abandoned power, in MW; P e Indicates the electrolyzer power in MW.

[0092] The electrolytic cell model is represented as Indicates the hydrogen production rate of the electrolyzer, in kNm 3 / h; T e represents the temperature of the electrolyzer, in K; f() represents the relationship between the hydrogen production rate of the electrolyzer and the power and temperature of the electrolyzer.

[0093] The hydrogen storage tank model is expressed as n HT Indicates the molar amount of hydrogen in the hydrogen storage tank, in kmol; P HT Indicates the pressure of the hydrogen storage tank, in MPa; V HT Indicates the volume of the hydrogen storage tank in km 3 ; R represents the universal gas constant, the unit is J / (mol K), and in this embodiment, for example, the value can be 8.314 J / (mol K); T HT Indicates the temperature of the hydrogen storage tank, in K.

[0094] The value of the state parameter in the hydrogen storage tank will change over time, that is, the value at the n+1th time point in the scheduling cycle is different from that at the nth time point. In the formula, Δt represents the time interval between adjacent time points, and the unit is h; Indicates the hydrogen storage rate of the hydrogen storage tank at the nth time point in the scheduling cycle, in kNm 3 / h;n HT,n Indicates the hydrogen molar amount in the hydrogen storage tank at the nth time point in the scheduling cycle, in kmol; n HT,n+1 Indicates the hydrogen molar amount in the hydrogen storage tank at the n+1th time point in the scheduling cycle, in kmol; V m Indicates the molar volume of hydrogen, in Nm 3 / mol.

[0095] The hydrogen balance model is expressed as In the formula, Indicates the hydrogen storage rate of the hydrogen storage tank, in kNm 3 / h; Indicates the hydrogen supply rate of the hydrogen storage tank, in kNm 3 / h.

[0096] The synthetic ammonia model is expressed as Where P AM Indicates the synthetic ammonia load, expressed as a percentage; Indicates the hydrogen supply rate at 100% load of synthetic ammonia, in kNm 3 / h.

[0097] In addition to the model expression, the hydrogen-ammonia system also needs to set corresponding constraints and objective functions. The constraints include power constraints, electrolyzer constraints, and hydrogen storage tank constraints. The power constraint is expressed as represents the upper limit of power purchase, in MW. According to this constraint, P pur and P gip At least one is 0, which means that electricity purchase and power abandonment will not occur at the same time.

[0098] The electrolytic cell constraints include the electrolytic cell power constraint and the electrolytic cell power adjustment rate constraint, where the electrolytic cell power constraint is expressed as Indicates the lower limit of electrolyzer power, in MW; Represents the upper limit of the electrolyzer power, in MW. The electrolyzer power adjustment rate constraint is expressed as P e,n -P e,vel Δt≤P e,n+1 ≤P e,n +P e,vel Δt,P e,n represents the electrolyzer power at the nth time point in the scheduling period, in MW; P e,n+1 represents the electrolyzer power at the n+1th time point in the scheduling period, in MW; P e,vel It indicates the upper limit of the electrolytic cell power adjustment rate, in MW / h; Δt indicates the time interval between adjacent time points.

[0099] The hydrogen tank constraint is expressed as Indicates the lower limit of hydrogen storage tank pressure. Indicates the upper pressure limit of the hydrogen storage tank.

[0100] Manufacturers generally provide wind and solar power output forecasts for a period of time in the future. The uncertainty of forecasts in the near future is lower, while the uncertainty of forecasts in the distant future is higher. In order to alleviate the impact of wind and solar power output uncertainty on the dispatching system, the present invention corrects the wind and solar power output forecast power provided by the manufacturer. Therefore, first, through step 2, the wind and solar power output forecast provided by the manufacturer within a predetermined time period in the future is obtained, including wind power output forecast and photovoltaic output forecast.

[0101] Then, in step 3, the uncertainty of wind power output forecast and the uncertainty of photovoltaic output forecast are set according to the time difference between the wind and photovoltaic output forecast provided by the manufacturer and the scheduling forecast time point. And in step 4, according to the wind and photovoltaic output forecast uncertainty, the wind and photovoltaic output forecast provided by the manufacturer is corrected to obtain the uncertainty interval of wind power output forecast and the uncertainty interval of photovoltaic output forecast.

[0102] Among them, the uncertainty of wind power output prediction is expressed as

[0103] Δtwind It indicates the time difference between the wind power output forecast provided by the manufacturer and the dispatch forecast time point. and They represent the upper and lower limits of the time difference between the wind power output forecast provided by the manufacturer and the dispatch forecast time point, and They represent the upper and lower limits of the wind power output prediction uncertainty respectively. In this embodiment, the wind power output prediction uncertainty is set to like Can be set to 0.8, Can be set to 0.4.

[0104] According to the uncertainty of wind power output prediction, the wind power output prediction is corrected, and the uncertainty interval of wind power output prediction is obtained as follows: in, represents the wind power output forecast provided by the manufacturer, Represents the corrected wind power output forecast.

[0105] The uncertainty of photovoltaic output prediction is expressed as Δt sol It indicates the time difference between the photovoltaic output forecast provided by the manufacturer and the scheduling forecast time point. and They represent the upper and lower limits of the time difference between the photovoltaic output forecast time provided by the manufacturer and the scheduling forecast time point, and They represent the upper and lower limits of the uncertainty of the photovoltaic output prediction. In this embodiment, the uncertainty of the photovoltaic output prediction is like Can be set to 0.4, Can be set to 0.2.

[0106] According to the uncertainty of photovoltaic output prediction, the photovoltaic output prediction is corrected, and the uncertainty interval of photovoltaic output prediction is obtained as follows: in, It indicates the photovoltaic output forecast provided by the manufacturer. Represents the corrected photovoltaic output forecast.

[0107] Since the wind and solar power output prediction value participating in the scheduling algorithm is related to the current utilization rate of the hydrogen storage tank, the present invention calculates the current utilization rate of the hydrogen storage tank in step 5. The calculation formula of the hydrogen storage tank utilization rate is: Where P HT Indicates the current pressure of the hydrogen storage tank. Indicates the lower limit of hydrogen storage tank pressure, It indicates the upper limit of the hydrogen storage tank pressure. It can be seen that the utilization rate of the hydrogen storage tank is related to the pressure of the hydrogen storage tank. Therefore, when calculating the utilization rate of the hydrogen storage tank, it is also necessary to calculate the pressure of the hydrogen storage tank in the current hydrogen-ammonia system through the hydrogen storage tank model.

[0108] After calculating the utilization rate, in step 6, according to the current utilization rate of the hydrogen storage tank, the upper or lower limit of the wind and solar power output is selected in the uncertainty interval as the predicted value of the wind and solar power output participating in the scheduling algorithm.

[0109] For example, if the current utilization rate of the hydrogen storage tank is less than 50%, it means that the hydrogen storage capacity of the hydrogen storage tank is low. and Take the lower limits of the uncertainty intervals of wind power output prediction and photovoltaic output prediction respectively, that is, set the wind power output prediction value participating in the dispatching algorithm to Set the photovoltaic output prediction value participating in the scheduling algorithm to

[0110] If the current tank utilization rate is greater than or equal to 50%, it indicates that the hydrogen storage capacity of the hydrogen storage tank is too high. and Take the upper limits of the uncertainty intervals of wind power output prediction and photovoltaic output prediction respectively, that is, set the wind power output prediction value participating in the dispatching algorithm to Set the photovoltaic output prediction value participating in the scheduling algorithm to

[0111] When optimizing the objective function, in order to speed up the optimization speed, the present invention optimizes the scheduling time period in segments. Therefore, in step 7, the time period for which the scheduling instructions and the predicted variables need to be given is divided into M small segments, and each small segment contains a total of L time points at the beginning and the end. For example, assuming that the time length for which the scheduling instructions and the predicted variables need to be given is ΔT h, and the time interval of the scheduling instructions is Δt h, then the total number of scheduling time points can be calculated to be N = ΔT / Δt, and each small segment contains L = N / M+1 time points.

[0112] After the segmentation is completed, in step 8, the electrolytic cell power, synthetic ammonia load and synthetic ammonia load adjustment time at the M+1 time points at the beginning and end of the M small segments are used as decision variables, that is, there are a total of M+3 decision variables.

[0113] After setting the decision variables, in step 9, the optimization interval of the decision variables is determined according to the current state of the hydrogen-ammonia system and the constraints. Since the decision variables include the electrolyzer power, the synthetic ammonia load and the synthetic ammonia load adjustment time, in addition to the aforementioned constraints, it is also necessary to set constraints for the synthetic ammonia load and the synthetic ammonia load adjustment time, that is, the constraints also include the synthetic ammonia load constraint and the synthetic ammonia load adjustment time interval constraint.

[0114] The synthetic ammonia load constraint is expressed as Indicates the lower limit of synthetic ammonia load, Indicates the upper limit of synthetic ammonia load.

[0115] The synthetic ammonia load adjustment time interval constraint is expressed as t AM,t+1 ≥t AM,t +Δt AM,Adj , t AM,t Indicates the last synthetic ammonia load adjustment time, t AM,t+1 Indicates the next synthetic ammonia load adjustment time, Δt AM,Adj Indicates the lower limit of the time interval between two adjustments of synthetic ammonia load.

[0116] According to the electrolytic cell power constraint, the optimal interval of electrolytic cell power is set to P e,i，min ≤P e,i ≤P e,i，max ,in,

[0117] According to the synthetic ammonia load constraint, the optimization interval of synthetic ammonia load is set to P AM,k+1，min ≤P AM,k+1 ≤P AM,k+1，max ,in, P vel,AM It represents the upper limit of synthetic ammonia load adjustment rate, and Δt represents the time interval between adjacent time points in the scheduling cycle.

[0118] According to the synthetic ammonia load adjustment time interval constraint, the optimal interval for synthetic ammonia load adjustment time is set to t AM,t+1,min ≤t AM,t+1 ≤t AM,t+1,max , where t AM,t+1,min =max(t AM,t +Δt AM,Adj ,t i ), t AM,t+1,max =t1+ΔT,t i It represents the time of the i-th scheduling cycle, and t1 represents the time of the first scheduling cycle.

[0119] After obtaining the optimization interval, step 10 is performed to iteratively optimize the objective function in the optimization interval to obtain an optimization set. In this embodiment, the Bayesian iterative optimization method is adopted to iteratively optimize the objective function. When performing Bayesian iterative optimization, it is first necessary to set the initial optimization value for each decision variable, and then based on the given initial optimization value, perform Bayesian iterative optimization on the objective function in the optimization interval. The specific algorithm of Bayes can refer to the prior art and will not be described here.

[0120] Assuming the number of loops is K, the Bayesian optimization objective function set obtained at the end of the loop is Objj , where j = 1, 2, ..., K. Each iteration cycle can obtain a set of decision variable values, including the electrolytic cell power, synthetic ammonia load and synthetic ammonia load adjustment time at M+1 time points. The Bayesian optimization objective function set Obj obtained by K cycles is j contains K sets of values ​​of decision variables.

[0121] Assume that the electrolytic cell power at M+1 time points obtained in each Bayesian iteration is expressed as P eo,i , i=1,2...M+1, then the electrolytic cell power at the start and end of each small segment satisfies:

[0122] Among them, P e,i,1 represents the electrolytic cell power at the beginning of the i-th time period, P e,i,L represents the electrolyzer power at the end of the i-th time period.

[0123] In step 11, assuming that the electrolytic cell power changes linearly at L points in each small segment, then according to the electrolytic cell power at M+1 time points obtained in each iteration, the electrolytic cell power at each time point in the corresponding iteration cycle can be calculated as P e,i,m =P e,i,1 +(m-1) / (L-1)*(P e,i,L -P e,i,1 ),m=1,2,...L.

[0124] After obtaining the electrolyzer power at all time points in each iteration cycle, in step 12, for each iteration cycle, the electrolyzer power at all time points, the synthetic ammonia load size and synthetic ammonia load adjustment time given by Bayesian optimization, and the wind and solar power output forecast value are substituted into the mathematical model to calculate the constraint variables at all time points in the scheduling cycle. Then, step 13 is used to determine whether the constraint variables at all time points in each iteration cycle meet the constraint conditions.

[0125] It should be noted that since the electrolytic cell power, synthetic ammonia load, synthetic ammonia load adjustment range and adjustment time have been taken into consideration when setting the Bayesian optimization interval, it is only necessary to check whether the purchased electricity power, electrolytic cell power adjustment range and hydrogen storage tank pressure meet the constraints.

[0126] During the judgment process, if there is a constraint variable that does not meet the constraint conditions at any of the N time points, it means that the result of this iteration does not meet the constraint, and the corresponding iteration result is discarded and the next judgment is continued. On the contrary, if all constraint variables at all time points meet the constraint conditions, it means that the result of this iteration meets the constraint, and the corresponding iteration result is added to the candidate set.

[0127] After the candidate set is obtained, step 14 is performed to select the value that minimizes the objective function from the candidate set, and the decision variable corresponding to the minimum objective function is used as the optimization result.

[0128] The green electricity hydrogen-ammonia flexible dispatching method considering wind and solar output correction provided by the present invention performs uncertainty correction processing on the wind and solar predicted power, and uses uncertainty to measure the uncertainty of the wind and solar predicted power, so as to alleviate the impact of wind and solar output uncertainty on the dispatching system, and then determines the wind and solar output forecast actually used by the dispatching method according to the utilization rate of the hydrogen storage tank. In addition, the Bayesian optimization algorithm is used to segmentally optimize the time period where the dispatching instruction needs to be given, so as to improve the optimization speed of the algorithm, so that the "green hydrogen" production can be safely and stably produced and economically and reliably operated.

[0129] A green electricity hydrogen and ammonia flexible dispatching system considering wind and solar output correction, Figure 2 For a schematic diagram of a scheduling system provided by an embodiment of the present invention, please refer to Figure 2 The green electricity hydrogen-ammonia flexible dispatching system 100 considering wind and solar power output correction provided by the embodiment of the present invention includes:

[0130] Establishing modules for establishing mathematical models, constraints and objective functions of hydrogen-ammonia systems;

[0131] An acquisition module is used to obtain the wind and solar power output forecast within a future predetermined time period provided by the manufacturer;

[0132] A setting module is used to set the uncertainty of the wind and solar power output forecast according to the time difference between the provision time of the wind and solar power output forecast provided by the manufacturer and the scheduling forecast time point;

[0133] A correction module is used to correct the wind and solar power output prediction provided by the manufacturer according to the wind and solar power output prediction uncertainty to obtain an uncertainty interval;

[0134] The first calculation module is used to calculate the current utilization rate of the hydrogen storage tank according to the mathematical model;

[0135] A first determination module is used to determine the wind and solar power output prediction value participating in the scheduling algorithm in an uncertainty interval according to the utilization rate;

[0136] A partitioning module is used to divide the time period for which scheduling instructions and predicted variables need to be given into M small segments, each of which contains L time points;

[0137] The second determination module is used to determine the decision variables as the electrolytic cell power, synthetic ammonia load and synthetic ammonia load adjustment time at M+1 time points at the beginning and end of the M small segments;

[0138] The third determination module is used to determine the optimization interval of the decision variable according to the current state and constraints of the hydrogen-ammonia system;

[0139] The optimization module is used to iteratively optimize the objective function within the optimization interval to obtain an optimization set; the optimization set includes the electrolytic cell power, synthetic ammonia load and synthetic ammonia load adjustment time at M+1 time points obtained in each iteration;

[0140] The second calculation module is used to calculate the electrolytic cell power at all time points in the corresponding iteration period according to the electrolytic cell power at M+1 time points obtained in each iteration;

[0141] The third calculation module is used to calculate the constraint variables at all time points in the scheduling cycle for each iteration cycle according to the electrolyzer power, wind and solar power output prediction values ​​and mathematical models at all time points;

[0142] The judgment module is used to judge whether the constraint variables at all time points in each iteration cycle meet the constraint conditions. If not, the corresponding iteration results are discarded; if satisfied, the corresponding iteration results are added to the candidate set;

[0143] The selection module is used to select the decision variables corresponding to the minimum objective function from the candidate set as the optimization result.

[0144] Specifically, please refer to Figure 2 The green electricity hydrogen-ammonia flexible dispatching system 100 considering wind and solar output correction provided by the embodiment of the present invention first constructs a mathematical model of the hydrogen-ammonia system by establishing modules. When constructing the mathematical model, it is necessary to comprehensively consider the state parameters of the hydrogen-ammonia system. The main state parameters of the hydrogen-ammonia system include wind power, photovoltaic power, purchased power, and abandoned power related to power generation; electrolyzer cluster power, electrolyzer cluster temperature, and electrolyzer cluster hydrogen production rate related to the electrolyzer cluster; hydrogen storage tank temperature, hydrogen storage tank pressure, hydrogen storage capacity, hydrogen storage tank utilization rate, hydrogen storage tank hydrogen storage rate, and hydrogen supply rate related to the hydrogen storage tank; synthetic ammonia load, synthetic ammonia output, and the previous synthetic ammonia load adjustment time related to synthetic ammonia production, etc.

[0145] Based on this, the mathematical model of the synthetic ammonia system constructed in the present invention includes a power balance model, an electrolyzer model, a hydrogen storage tank model, a hydrogen balance model and a synthetic ammonia model.

[0146] The power balance model is expressed as In the formula, Indicates the predicted wind power, in MW; Represents the predicted photovoltaic power, in MW; P pur Indicates the purchased power, in MW; P gip Indicates the abandoned power, in MW; P e Indicates the electrolyzer power in MW.

[0147] The electrolytic cell model is represented as Indicates the hydrogen production rate of the electrolyzer, in kNm 3 / h; T e represents the temperature of the electrolyzer, in K; f() represents the relationship between the hydrogen production rate of the electrolyzer and the power and temperature of the electrolyzer.

[0148] The hydrogen storage tank model is expressed as n HT Indicates the molar amount of hydrogen in the hydrogen storage tank, in kmol; P HT Indicates the pressure of the hydrogen storage tank, in MPa; V HT Indicates the volume of the hydrogen storage tank in km 3 ; R represents the universal gas constant, the unit is J / (mol K), and in this embodiment, for example, the value can be 8.314 J / (mol K); T HT Indicates the temperature of the hydrogen storage tank, in K.

[0149] The value of the state parameter in the hydrogen storage tank will change over time, that is, the value at the n+1th time point in the scheduling cycle is different from that at the nth time point. In the formula, Δt represents the time interval between adjacent time points, and the unit is h; Indicates the hydrogen storage rate of the hydrogen storage tank at the nth time point in the scheduling cycle, in kNm 3 / h;n HT,n Indicates the hydrogen molar amount in the hydrogen storage tank at the nth time point in the scheduling cycle, in kmol; n HT,n+1 Indicates the hydrogen molar amount in the hydrogen storage tank at the n+1th time point in the scheduling cycle, in kmol; V m Indicates the molar volume of hydrogen, in Nm 3 / mol.

[0150] The hydrogen balance model is expressed as In the formula, Indicates the hydrogen storage rate of the hydrogen storage tank, in kNm 3 / h; Indicates the hydrogen supply rate of the hydrogen storage tank, in kNm 3 / h.

[0151] The synthetic ammonia model is expressed as Where P AM Indicates the synthetic ammonia load, expressed as a percentage; Indicates the hydrogen supply rate at 100% load of synthetic ammonia, in kNm 3 / h.

[0152] In addition to the model expression, the hydrogen-ammonia system also needs to set corresponding constraints and objective functions. The constraints include power constraints, electrolyzer constraints, and hydrogen storage tank constraints. The power constraint is expressed as represents the upper limit of power purchase, in MW. According to this constraint, P pur and P gip At least one is 0, which means that electricity purchase and power abandonment will not occur at the same time.

[0153] The electrolytic cell constraints include the electrolytic cell power constraint and the electrolytic cell power adjustment rate constraint, where the electrolytic cell power constraint is expressed as Indicates the lower limit of electrolyzer power, in MW; The power limit of the electrolyzer is expressed in MW. The power adjustment rate constraint of the electrolyzer is expressed as P e,n -P e,vel Δt≤P e,n+1 ≤P e,n +P e,vel Δt,P e,n represents the electrolyzer power at the nth time point in the scheduling period, in MW; P e,n+1 represents the electrolyzer power at the n+1th time point in the scheduling period, in MW; P e,vel It indicates the upper limit of the electrolytic cell power adjustment rate, in MW / h; Δt indicates the time interval between adjacent time points.

[0154] The hydrogen tank constraint is expressed as Indicates the lower limit of hydrogen storage tank pressure. Indicates the upper limit of hydrogen storage tank pressure.

[0155] Manufacturers generally provide wind and solar power output forecasts for a period of time in the future. The uncertainty of forecasts in the near future is lower, while the uncertainty of forecasts in the distant future is higher. In order to alleviate the impact of wind and solar power output uncertainty on the dispatching system, the present invention corrects the wind and solar power output forecast power provided by the manufacturer. Therefore, firstly, the wind and solar power output forecast provided by the manufacturer within a predetermined time period in the future is obtained through the acquisition module, including wind power output forecast and photovoltaic output forecast.

[0156] Then, the setting module sets the uncertainty of wind power output prediction and the uncertainty of photovoltaic output prediction respectively according to the time difference between the wind and solar power output prediction provided by the manufacturer and the scheduling prediction time point. The correction module corrects the wind and solar power output prediction provided by the manufacturer according to the wind and solar power output prediction uncertainty, and obtains the uncertainty interval of wind power output prediction and the uncertainty interval of photovoltaic output prediction respectively.

[0157] Among them, the uncertainty of wind power output prediction is expressed as

[0158] Δtwind It indicates the time difference between the wind power output forecast provided by the manufacturer and the dispatch forecast time point. and They represent the upper and lower limits of the time difference between the wind power output forecast provided by the manufacturer and the dispatch forecast time point, and They represent the upper and lower limits of the wind power output prediction uncertainty respectively. In this embodiment, the wind power output prediction uncertainty is set to like Can be set to 0.8, Can be set to 0.4.

[0159] According to the uncertainty of wind power output prediction, the wind power output prediction is corrected, and the uncertainty interval of wind power output prediction is obtained as follows: in, represents the wind power output forecast provided by the manufacturer, Represents the corrected wind power output forecast.

[0160] The uncertainty of photovoltaic output prediction is expressed as Δt sol It indicates the time difference between the photovoltaic output forecast provided by the manufacturer and the scheduling forecast time point. and They represent the upper and lower limits of the time difference between the photovoltaic output forecast time provided by the manufacturer and the scheduling forecast time point, and They represent the upper and lower limits of the uncertainty of the photovoltaic output prediction. In this embodiment, the uncertainty of the photovoltaic output prediction is like Can be set to 0.4, Can be set to 0.2.

[0161] According to the uncertainty of photovoltaic output prediction, the photovoltaic output prediction is corrected, and the uncertainty interval of photovoltaic output prediction is obtained as follows: in, It indicates the photovoltaic output forecast provided by the manufacturer. Represents the corrected photovoltaic output forecast.

[0162] Since the wind and solar power output prediction value participating in the scheduling algorithm is related to the current utilization rate of the hydrogen storage tank, the first calculation module calculates the current utilization rate of the hydrogen storage tank. The calculation formula for the utilization rate of the hydrogen storage tank is: Where P HT Indicates the current pressure of the hydrogen storage tank. Indicates the lower limit of hydrogen storage tank pressure, It indicates the upper limit of the hydrogen storage tank pressure. It can be seen that the utilization rate of the hydrogen storage tank is related to the pressure of the hydrogen storage tank. Therefore, when calculating the utilization rate of the hydrogen storage tank, it is also necessary to calculate the pressure of the hydrogen storage tank in the current hydrogen-ammonia system through the hydrogen storage tank model.

[0163] After calculating the utilization rate, the first determination module selects the upper or lower limit of the wind and solar power output in the uncertainty interval according to the current utilization rate of the hydrogen storage tank as the predicted value of the wind and solar power output participating in the scheduling algorithm.

[0164] For example, if the current utilization rate of the hydrogen storage tank is less than 50%, it means that the hydrogen storage capacity of the hydrogen storage tank is low. and Take the lower limits of the uncertainty intervals of wind power output prediction and photovoltaic output prediction respectively, that is, set the wind power output prediction value participating in the dispatching algorithm to Set the photovoltaic output prediction value participating in the scheduling algorithm to

[0165] If the current tank utilization rate is greater than or equal to 50%, it indicates that the hydrogen storage capacity of the hydrogen storage tank is too high. and Take the upper limits of the uncertainty intervals of wind power output prediction and photovoltaic output prediction respectively, that is, set the wind power output prediction value participating in the dispatching algorithm to Set the photovoltaic output prediction value participating in the scheduling algorithm to

[0166] When optimizing the objective function, in order to speed up the optimization speed, the present invention optimizes the scheduling time period in segments. Therefore, the division module divides the time period for which the scheduling instructions and the predicted variables need to be given into M small segments, and each small segment contains a total of L time points from the beginning to the end. For example, assuming that the time length for which the scheduling instructions and the predicted variables need to be given is ΔT h, and the time interval of the scheduling instructions is Δt h, then the total number of scheduling time points can be calculated to be N = ΔT / Δt, and each small segment contains L = N / M+1 time points.

[0167] After the segmentation is completed, the second determination module uses the electrolytic cell power, synthetic ammonia load and synthetic ammonia load adjustment time at M+1 time points at the beginning and end of the M small segments as decision variables, that is, there are a total of M+3 decision variables.

[0168] After setting the decision variables, the third determination module determines the optimization interval of the decision variables according to the current state of the hydrogen-ammonia system and the constraints. Since the decision variables include the electrolyzer power, the synthetic ammonia load and the synthetic ammonia load adjustment time, in addition to the aforementioned constraints, it is also necessary to set constraints for the synthetic ammonia load and the synthetic ammonia load adjustment time, that is, the constraints also include the synthetic ammonia load constraint and the synthetic ammonia load adjustment time interval constraint.

[0169] The synthetic ammonia load constraint is expressed as Indicates the lower limit of synthetic ammonia load, Indicates the upper limit of synthetic ammonia load.

[0170] The synthetic ammonia load adjustment time interval constraint is expressed as t AM,t+1 ≥t AM,t +Δt AM,Adj , t AM,t Indicates the last synthetic ammonia load adjustment time, t AM,t+1 Indicates the next synthetic ammonia load adjustment time, Δt AM,Adj Indicates the lower limit of the time interval between two adjustments of synthetic ammonia load.

[0171] According to the electrolytic cell power constraint, the optimal interval of electrolytic cell power is set to P e,i，min ≤P e,i ≤P e,i，max ,in,

[0172] According to the synthetic ammonia load constraint, the optimization interval of synthetic ammonia load is set to P AM,k+1，min ≤P AM,k+1 ≤P AM,k+1，max ,in, P vel,AM It represents the upper limit of synthetic ammonia load adjustment rate, and Δt represents the time interval between adjacent time points in the scheduling cycle.

[0173] According to the synthetic ammonia load adjustment time interval constraint, the optimal interval for synthetic ammonia load adjustment time is set to t AM,t+1,min ≤t AM,t+1 ≤t AM,t+1,max , where t AM,t+1,min =max(t AM,t +Δt AM,Adj ,t i ), t AM,t+1,max =t1+ΔT,t i It represents the time of the i-th scheduling cycle, and t1 represents the time of the first scheduling cycle.

[0174] After obtaining the optimization interval, the optimization module iteratively optimizes the objective function within the optimization interval to obtain an optimization set. In this embodiment, the Bayesian iterative optimization method is adopted to iteratively optimize the objective function. When performing Bayesian iterative optimization, it is first necessary to set the initial optimization value for each decision variable, and then based on the given initial optimization value, perform Bayesian iterative optimization on the objective function within the optimization interval. The specific algorithm of Bayes can refer to the prior art and will not be repeated here.

[0175] Assuming the number of loops is K, the Bayesian optimization objective function set obtained at the end of the loop is Objj , where j = 1, 2, ..., K. Each iteration cycle can obtain a set of decision variable values, including the electrolytic cell power, synthetic ammonia load and synthetic ammonia load adjustment time at M+1 time points. The Bayesian optimization objective function set Obj obtained by K cycles is j contains K sets of values ​​of decision variables.

[0176] Assume that the electrolytic cell power at M+1 time points obtained in each Bayesian iteration is expressed as P eo,i , i=1,2...M+1, then the electrolytic cell power at the start and end of each small segment satisfies:

[0177] Among them, P e,i,1 represents the electrolytic cell power at the beginning of the i-th time period, P e,i,L represents the electrolyzer power at the end of the i-th time period.

[0178] Assuming that the electrolytic cell power changes linearly at L points in each small segment, the second calculation module can calculate the electrolytic cell power at each time point in the corresponding iteration cycle as P according to the electrolytic cell power at M+1 time points obtained in each iteration. e,i,m =P e,i,1 +(m-1) / (L-1)*(P e,i,L -P e,i,1 ),m=1,2,...L.

[0179] After obtaining the electrolyzer power at all time points in each iteration cycle, the third calculation module substitutes the electrolyzer power at all time points, the synthetic ammonia load size and synthetic ammonia load adjustment time given by Bayesian optimization, and the wind and solar power output forecast value into the mathematical model for each iteration cycle, and calculates the constraint variables at all time points in the scheduling cycle. Then, the judgment module is used to determine whether the constraint variables at all time points in each iteration cycle meet the constraint conditions.

[0180] It should be noted that since the electrolytic cell power, synthetic ammonia load, synthetic ammonia load adjustment range and adjustment time have been taken into consideration when setting the Bayesian optimization interval, it is only necessary to check whether the purchased electricity power, electrolytic cell power adjustment range and hydrogen storage tank pressure meet the constraints.

[0181] During the judgment process, if there is a constraint variable that does not meet the constraint conditions at any of the N time points, it means that the result of this iteration does not meet the constraint, and the corresponding iteration result is discarded and the next judgment is continued. On the contrary, if all constraint variables at all time points meet the constraint conditions, it means that the result of this iteration meets the constraint, and the corresponding iteration result is added to the candidate set.

[0182] After obtaining the candidate set, the selection module selects the value that minimizes the objective function from the candidate set, and takes the decision variable corresponding to the minimum objective function as the optimization result.

[0183] The green electricity hydrogen-ammonia flexible dispatching system considering wind and solar output correction provided by the present invention performs uncertainty correction processing on wind and solar predicted power, and uses uncertainty to measure the uncertainty of wind and solar predicted power, so as to alleviate the impact of wind and solar output uncertainty on the dispatching system, and then determines the wind and solar output forecast actually used by the dispatching method according to the utilization rate of the hydrogen storage tank. In addition, the Bayesian optimization algorithm is used to segmentally optimize the time period where the dispatching instruction needs to be given, so as to improve the optimization speed of the algorithm, so that the "green hydrogen" production can be safely and stably produced and economically and reliably operated.

[0184] Those skilled in the art can understand that the accompanying drawings are only schematic diagrams of an embodiment, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0185] Those skilled in the art can understand that the modules in the device in the embodiment can be distributed in the device in the embodiment according to the description of the embodiment, or can be changed accordingly and located in one or more devices different from the embodiment. The modules in the above embodiment can be combined into one module, or can be further divided into multiple sub-modules.

[0186] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A green electricity hydrogen and ammonia flexible scheduling method considering wind and solar output correction, characterized in that: include: Establish the mathematical model, constraints and objective function of the hydrogen-ammonia system; Obtain the wind and solar power output forecast for the future predetermined time period provided by the manufacturer; The uncertainty of the wind and solar power output forecast is set according to the time difference between the wind and solar power output forecast time provided by the manufacturer and the scheduling forecast time point; According to the wind and solar power output prediction uncertainty, the wind and solar power output prediction provided by the manufacturer is corrected to obtain an uncertainty interval; According to the mathematical model, calculating the current utilization rate of the hydrogen storage tank; According to the utilization rate, determining a wind and solar power output prediction value participating in the scheduling algorithm within the uncertainty interval; The time period for which scheduling instructions and predicted variables need to be given is divided into M small segments, each of which contains L time points; Determine the decision variables as the electrolytic cell power, synthetic ammonia load and synthetic ammonia load adjustment time at M+1 time points at the beginning and end of the M small segments; Determining the optimization interval of the decision variables according to the current state and constraints of the hydrogen-ammonia system; Iteratively optimizing the objective function within the optimization interval to obtain an optimization set; the optimization set includes the electrolytic cell power, synthetic ammonia load and synthetic ammonia load adjustment time at M+1 time points obtained in each iteration; According to the electrolytic cell power at M+1 time points obtained in each iteration, the electrolytic cell power at all time points in the corresponding iteration cycle is calculated; For each iteration cycle, the constraint variables at all time points in the scheduling cycle are calculated based on the electrolyzer power, wind and solar power output forecast values ​​at all time points and the mathematical model; Determine whether the constraint variables at all time points in each iteration cycle meet the constraint conditions. If not, discard the corresponding iteration results; if satisfied, add the corresponding iteration results to the candidate set; The decision variable corresponding to the minimum objective function is selected from the candidate set as the optimization result.

2. The green electricity hydrogen and ammonia flexible scheduling method considering wind and solar output correction according to claim 1 is characterized in that: Wind and solar output forecast includes wind power output forecast and photovoltaic output forecast, among which: The uncertainty of wind power output prediction is expressed as Δt wind It indicates the time difference between the wind power output forecast provided by the manufacturer and the dispatch forecast time point. and They represent the upper and lower limits of the time difference between the wind power output forecast provided by the manufacturer and the dispatch forecast time point, and They represent the upper and lower limits of the uncertainty of wind power output prediction respectively; The uncertainty interval of wind power output prediction is in, represents the wind power output forecast provided by the manufacturer, represents the wind power output forecast after correction; The uncertainty of the photovoltaic output prediction is expressed as Δt sol It indicates the time difference between the photovoltaic output forecast provided by the manufacturer and the scheduling forecast time point. and They represent the upper and lower limits of the time difference between the photovoltaic output forecast time provided by the manufacturer and the scheduling forecast time point, and They represent the upper and lower limits of the uncertainty of photovoltaic output prediction respectively; The uncertainty interval of the photovoltaic output prediction is in, It indicates the photovoltaic output forecast provided by the manufacturer. Represents the corrected photovoltaic output forecast.

3. The green electricity hydrogen and ammonia flexible scheduling method considering wind and solar output correction according to claim 2 is characterized in that: The utilization rate of hydrogen storage tank is Among them, P HT Indicates the current pressure of the hydrogen storage tank. Indicates the lower limit of hydrogen storage tank pressure, Indicates the upper limit of hydrogen storage tank pressure.

4. The green electricity hydrogen and ammonia flexible scheduling method considering wind and solar output correction according to claim 3 is characterized in that: According to the utilization rate, the wind and solar power output prediction value participating in the scheduling algorithm is determined in the uncertainty interval, specifically: If the current utilization rate of the hydrogen storage tank is less than 50%, the wind power output forecast value participating in the dispatching algorithm is set to Set the photovoltaic output prediction value participating in the scheduling algorithm to If the current utilization rate of the storage tank is greater than or equal to 50%, the wind power output forecast value participating in the dispatching algorithm is set to Set the photovoltaic output prediction value participating in the scheduling algorithm to 5. The green electricity hydrogen and ammonia flexible scheduling method considering wind and solar output correction according to claim 4 is characterized in that: The constraints include electrolyzer power constraints, synthetic ammonia load constraints and synthetic ammonia load adjustment time interval constraints, wherein: The electrolyzer power constraint is expressed as Indicates the lower limit of electrolytic cell power, represents the upper limit of electrolyzer power; the synthetic ammonia load constraint is expressed as Indicates the lower limit of synthetic ammonia load, represents the upper limit of synthetic ammonia load; the synthetic ammonia load adjustment time interval constraint is represented by t AM,t+1 ≥t AM,t +Δt AM,Adj , where t AM,t Indicates the last synthetic ammonia load adjustment time, t AM,t+1 Indicates the next synthetic ammonia load adjustment time, Δt AM,Adj Indicates the lower limit of the time interval between two adjustments of synthetic ammonia load.

6. The green electricity hydrogen and ammonia flexible scheduling method considering wind and solar output correction according to claim 5 is characterized in that: The optimal interval of electrolytic cell power is expressed as P e,i , min ≤P e,i ≤P e,i , max ,in, The optimal range of synthetic ammonia load is expressed as P AM,k+1 , min ≤P AM,k+1 ≤P AM,k+1 , max ,in, P vel,AM represents the upper limit of synthetic ammonia load adjustment rate, Δt represents the time interval between adjacent time points in the scheduling cycle; The optimal interval for adjusting the synthetic ammonia load is expressed as t AM,t+1,min ≤t AM,t+1 ≤t AM,t+1,max , where t AM,t+1,min =max(t AM,t +Δt AM,Adj ,t i ), t AM,t+1,max =t1+ΔT,t i It represents the time of the i-th scheduling cycle, t1 represents the time of the first scheduling cycle, and ΔT represents the time length required to give scheduling instructions and predict variables.

7. The green electricity hydrogen and ammonia flexible scheduling method considering wind and solar output correction according to claim 6 is characterized in that: The objective function is iteratively optimized within the optimization interval, specifically: Setting the initial optimization value of the decision variable; Based on the initial optimization value, the objective function is optimized by Bayesian iteration within the optimization interval, and the Bayesian optimization objective function set is obtained as Obj j , j = 1, 2, ..., K, K represents the number of cycles; the electrolytic cell power at M+1 time points obtained by Bayesian iteration is expressed as P eo,i ,i=1,2...M+1; where, The electrolytic cell power at the start and end of each segment meets the following requirements: P e,i,1 represents the electrolytic cell power at the beginning of the i-th time period, P e,i,L represents the electrolyzer power at the end of the i-th time period.

8. The green electricity hydrogen and ammonia flexible scheduling method considering wind and solar output correction according to claim 7 is characterized in that: According to the electrolytic cell power at M+1 time points obtained in each iteration, the electrolytic cell power at all time points in the corresponding iteration cycle is calculated, specifically: Assume that the time interval of the scheduling instruction is Δt h, and the total number of scheduling time points is calculated to be N = ΔT / Δt, then L = N / M+1; Assuming that the electrolytic cell power changes linearly at L points in each small segment, the electrolytic cell power at each time point is expressed as P e,i,m =P e,i,1 +(m-1) / (L-1)*(P e,i,L -P e,i,1 ),m=1,2,...L.

9. The green electricity hydrogen and ammonia flexible scheduling method considering wind and solar output correction according to claim 8 is characterized in that: The mathematical model at least includes a power balance model, an electrolyzer model and a hydrogen storage tank model; the constraint conditions also include power constraints, electrolyzer power adjustment rate constraints and hydrogen storage tank constraints; wherein, The power balance model is expressed as represents the predicted wind power, Represents the predicted photoelectric power, P pur Indicates the purchased power, P gip Indicates the abandoned power, P e Indicates the electrolytic cell power; The electrolytic cell model is represented as represents the hydrogen production rate of the electrolyzer, T e represents the electrolyzer temperature, and f() represents the relationship between the electrolyzer hydrogen production rate and the electrolyzer power and temperature; The hydrogen storage tank model is expressed as n HT Indicates the molar amount of hydrogen in the hydrogen storage tank, P HT Indicates the hydrogen storage tank pressure, V HT represents the volume of the hydrogen storage tank, R represents the universal gas constant, T HT Indicates the temperature of the hydrogen storage tank; The power constraint is denoted as P pur *P gip =0, Indicates the upper limit of power purchase; The electrolytic cell power adjustment rate constraint is expressed as P e,n -P e,vel Δt≤P e,n+1 ≤P e,n +P e,vel Δt,P e,n represents the electrolyzer power at the nth time point in the scheduling period, P e,n+1 represents the electrolyzer power at the n+1th time point in the scheduling period, P e,vel represents the upper limit of the electrolytic cell power adjustment rate, and Δt represents the time interval between adjacent time points; The hydrogen tank constraint is expressed as Indicates the lower limit of hydrogen storage tank pressure. Indicates the upper limit of hydrogen storage tank pressure.

10. A green electricity hydrogen and ammonia flexible dispatching system considering wind and solar output correction, characterized in that: include: Establishing modules for establishing mathematical models, constraints and objective functions of hydrogen-ammonia systems; An acquisition module is used to obtain the wind and solar power output forecast within a future predetermined time period provided by the manufacturer; A setting module is used to set the uncertainty of the wind and solar power output forecast according to the time difference between the provision time of the wind and solar power output forecast provided by the manufacturer and the scheduling forecast time point; A correction module, used for correcting the wind and solar power output prediction provided by the manufacturer according to the wind and solar power output prediction uncertainty to obtain an uncertainty interval; A first calculation module is used to calculate the current utilization rate of the hydrogen storage tank according to the mathematical model; A first determination module, configured to determine, according to the utilization rate, a wind / solar power output prediction value participating in the scheduling algorithm in the uncertainty interval; A partitioning module is used to divide the time period for which scheduling instructions and predicted variables need to be given into M small segments, each of which contains L time points; The second determination module is used to determine the decision variables as the electrolytic cell power, synthetic ammonia load and synthetic ammonia load adjustment time at M+1 time points at the beginning and end of the M small segments; A third determination module is used to determine the optimization interval of the decision variable according to the current state and constraints of the hydrogen-ammonia system; An optimization module, used for iteratively optimizing the objective function within the optimization interval to obtain an optimization set; the optimization set includes the electrolytic cell power, synthetic ammonia load and synthetic ammonia load adjustment time at M+1 time points obtained in each iteration; The second calculation module is used to calculate the electrolytic cell power at all time points in the corresponding iteration period according to the electrolytic cell power at M+1 time points obtained in each iteration; The third calculation module is used to calculate the constraint variables at all time points in the scheduling period according to the electrolyzer power, wind and solar power output prediction values ​​and the mathematical model at all time points for each iteration cycle; A judgment module is used to judge whether the constraint variables at all time points in each iteration cycle meet the constraint conditions. If not, the corresponding iteration results are discarded; if satisfied, the corresponding iteration results are added to the candidate set; The selection module is used to select the decision variable corresponding to the minimum objective function from the candidate set as the optimization result.

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