Intra-day flexible adjustment scheduling method for hydrogen production and ammonia synthesis through water electrolysis

Abstract

The invention relates to an electro-hydrogen ammonia coupling system configuration and operation optimization technology, and aims to provide a scheduling method for hydrogen production and ammonia synthesis through water electrolysis with intraday flexible adjustment. Comprising the following steps: slicing annual hourly data monthly and then layering in a rank space, and selecting a representative month to construct a feature matrix; introducing an intraday flexible adjustment mechanism, and describing the synthetic ammonia load as a continuous piecewise linear operation curve; constructing a unit ammonia production power consumption function taking per unit load of synthetic ammonia as an independent variable and a synthetic ammonia section electric power model capable of being linearly solved; and constructing a full-chain mixed integer linear programming model of the system, taking the minimum annual total cost as a target, carrying out annual weighted solution according to the representative monthly weight, and obtaining an annual optimal system configuration and operation scheduling result. According to the method, sparse distribution of the adjusting sections can be achieved, sawtooth fluctuation and invalid micro-amplitude adjustment of a load curve are effectively inhibited, the climbing frequency of the synthetic ammonia load is reduced, and the operation performability and the service life friendliness of equipment are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of configuration and operation optimization of electric hydrogen ammonia coupling system, in particular to a scheduling method of a water electrolysis hydrogen synthesis ammonia system with daily flexible adjustment. BACKGROUND

[0002] With the acceleration of low-carbon transformation of energy structure, renewable energy such as wind power and photovoltaic is connected to the power system in a large scale. However, renewable energy has randomness, intermittency and uncertainty, which puts higher requirements on the safe and stable operation of the power system. Water electrolysis hydrogen synthesis ammonia as an important path to promote renewable energy consumption and promote industrial green transformation, its configuration and operation optimization is directly related to energy utilization efficiency and system economy.

[0003] The output of wind power and photovoltaic has significant seasonality and daily multi-scale fluctuation, and the key state variables such as battery state of charge and hydrogen storage tank inventory have strong interdiurnal correlation, so that the water electrolysis hydrogen synthesis ammonia system has strong time sequence coupling characteristics in configuration and operation level. If the annual data is directly used for modeling, the number of variables and constraints will increase sharply, forming a large-scale mixed integer optimization problem, which has high calculation cost and insufficient convergence stability. If the typical day or week is used for dimension reduction, it is difficult to retain the continuous time sequence structure within the whole month, which reduces the reliability of capacity configuration and daily scheduling conclusion.

[0004] In the model expression and solution level of electric power in the ammonia synthesis section, the existing researches mostly approximate the electric power in the ammonia synthesis section as a linear relationship with the ammonia production flow with a fixed coefficient. Although this treatment is convenient for modeling and solving, it ignores the difference in unit power consumption under different ammonia synthesis load levels, which is easy to cause systematic deviation in energy consumption evaluation and economic optimization. In actual operation process, the electric power in the ammonia synthesis section presents nonlinear characteristics with the change of load level; however, direct use of nonlinear expression will introduce product term and non-convex structure, which will significantly increase the scale and solution difficulty of the optimization problem.

[0005] In the treatment of hydrogen flow direction, the existing researches mostly adopt a single path: the hydrogen produced by the electrolytic cell is directly supplied to the ammonia synthesis device to simplify the material coupling; or the hydrogen production is sent to the hydrogen storage tank, and then the hydrogen storage tank is uniformly supplied to the ammonia synthesis section. The setting of such fixed destination ignores the collaborative adjustment space between direct supply and re-supply through hydrogen storage tank, as well as the comprehensive influence on the device capacity configuration and operation economy, which is difficult to reflect the optimal configuration of hydrogen storage tank.

[0006] At the level of ammonia synthesis load scheduling strategy, the existing methods can be mainly divided into two categories: one is to track renewable energy output continuously per hour, which can increase the local consumption rate of renewable energy in the short term, but it is easy to form a jagged load, induce frequent thermal mechanical cycles of ammonia synthesis device, accelerate equipment aging, and lead to insufficient feasibility and safety margin of the scheme. The second is to run approximately constant load in a longer interval, which is easy to implement, but ignores the time-varying nature and climbing process of the output, and the matching degree of wind and light is low.

[0007] In addition, most of the current researches model the capacity configuration and intraday scheduling of the electric-hydrogen-ammonia coupled system separately, and the researches on integrated joint optimization of system capacity configuration and intraday scheduling under a unified modeling framework are still limited.

[0008] Therefore, it is necessary to propose a new scheme to solve the above problems. SUMMARY

[0009] The technical problem to be solved by the present application is to overcome the deficiencies in the prior art and provide a scheduling method for an electrolytic water hydrogen synthesis ammonia system with intraday flexible adjustment.

[0010] To solve the technical problem, the solution of the present application is:

[0011] The present application provides a scheduling method for an electrolytic water hydrogen synthesis ammonia system with intraday flexible adjustment, which is to connect wind power and photovoltaic renewable energy to the power system for electrolytic water hydrogen synthesis ammonia production; which includes:

[0012] (1) Slice the annual hourly wind power and photovoltaic data by month, calculate the monthly wind and light energy, and divide it into four quadrants in the Copula rank space; extract multi-scale fluctuation characteristics and construct inter-month joint distance, select one representative month from each of the four quadrants to construct a feature matrix, and assign weights to the four selected representative months;

[0013] (2) Introduce an intraday flexible adjustment mechanism in the time domain of the representative month, describe the ammonia synthesis load as a continuous piecewise linear operation curve; solve the annual total cost objective function to concentrate the ammonia synthesis load changes in a small number of controllable sections to match the wind and light power fluctuations and reduce the adjustment frequency of the ammonia synthesis equipment;

[0014] (3) Construct a unit ammonia production power consumption function with ammonia synthesis load as the independent variable, and perform equivalent linearization processing; establish a linearly solvable ammonia section electric power model, and superimpose it with the electric power of the air separation device to form a mixed integer optimization framework;

[0015] (4) Construct a mixed integer linear programming model of the whole chain of the system of synthesizing ammonia by electrolyzing water to hydrogen, and optimize on four representative months to minimize the annual total cost of the system, and solve by annual weighting according to the weight of the representative month to obtain the optimal system configuration and operation scheduling result throughout the year.

[0016] Compared with the prior art, the present application has the following beneficial effects:

[0017] 1. The present application is based on rank space stratification and constraint D-optimal combination, and a small number of representative months are selected from four typical scenarios and weighted, so that the frequency of wind and light joint distribution and extreme situation is retained, and the whole month time sequence structure is maintained, and the size and calculation cost of the whole year time modeling are significantly reduced.

[0018] 2. The present application introduces an intraday flexible adjustment mechanism, and the ammonia synthesis load in the non-adjustment section remains constant, and the load in the adjustment section changes at a constant ramping rate. By introducing an ammonia synthesis adjustment cost term into the annual total cost objective function to punish the load change amplitude, change frequency and adjustment section number, the sparse distribution of the adjustment section is realized, the sawtooth fluctuation and invalid slight adjustment of the load curve are effectively inhibited, the ammonia synthesis load ramping frequency is reduced, and the operation executability and equipment life friendliness are improved.

[0019] 3. The present application establishes an ammonia unit load function with ammonia unit load as the independent variable, and performs equivalent linearization, which improves the description accuracy of the ammonia electric power model without significantly increasing the solution difficulty, so that it can be integrated into the mixed integer optimization framework.

[0020] 4. In the present application, the hydrogen produced by electrolyzing water is dynamically allocated between the two paths of directly supplying ammonia synthesis and supplying after storage, which avoids the capacity configuration deviation caused by single path assumption, and takes into account energy utilization, safety and economy.

[0021] 5. The present application optimizes the device size and intraday operation strategy on each representative month to minimize the annual total cost of the system, and aggregates by weight, which avoids the deviation caused by only configuration or only scheduling, and improves the annual decision consistency and reliability of the system. DETAILED DESCRIPTION

[0022] Figure 1 It is the selection result of the four typical months and candidate months of the present application.

[0023] Figure 2 It is the wind and light output and load demand diagram of the four typical months of the present application.

[0024] Figure 3 It is the unit ammonia production electric power and efficiency curve of the present application.

[0025] Figure 4 The unit ammonia production electric power and efficiency curve under the strong wind and strong light scene of the application.

[0026] Figure 5 The flexible time window schematic diagram under the strong wind and strong light scene of the application.

[0027] Figure 6 The ammonia flow and climbing rate graph under the strong wind and strong light scene of the application.

[0028] Figure 7 The hydrogen production shunt graph under the strong wind and strong light scene of the application. DETAILED DESCRIPTION

[0029] The specific implementation process of the application will be described in detail below with reference to the drawings.

[0030] The application proposes a system optimization scheduling method considering capacity configuration and operation optimization based on annual wind power, photovoltaic and hourly load data. First, the wind and light energy of each month is layered in the rank space, four representative months are selected and weighted to maintain the annual statistical representativeness and monthly time sequence continuity. The intraday flexible adjustment mechanism is introduced in the representative month time domain, the ammonia load climbing rate in the non-adjustment section is zero, and the climbing rate in the adjustment section is a constant value, so that a small amount of linearly changed load section replaces the high-frequency fluctuation, and the smooth and controllable adjustment of the ammonia load is realized; the unit power consumption function with ammonia load as the independent variable is established, and the linearization method is used to obtain the linear expression of the ammonia electric power, which is unified into the mixed integer optimization framework with the air separation device electric power; the distribution constraint of electrolytic hydrogen in the direct supply and storage supply paths is established, and the balance relationship of the hydrogen flow in the system is described; the integrated mixed integer programming model of capacity configuration and operation scheduling is constructed, and the minimum annual total cost is taken as the target to solve, and the optimal configuration and scheduling results of the system are output, so that the renewable energy consumption capacity is improved, and the equipment life and economy are considered.

[0031] Specifically, the application comprises the following steps:

[0032] 1. The annual hourly wind power and photovoltaic data are sliced by month, the monthly wind and light energy is calculated, and the four quadrants of strong wind and strong light, strong wind and weak light, weak wind and strong light, and weak wind and weak light are layered in the Copula rank space, the multi-scale fluctuation characteristics are extracted, and the joint distance between months is constructed, four representative months are selected under the constraint of one month in each quadrant, and weights are assigned.

[0033] (1) To represent the monthly total, the annual hourly sequence is sliced by natural month.

[0034] The specific calculation formula of the monthly wind energy and the monthly light energy is as follows:

[0035]

[0036]

[0037] wherein, , are the total wind and solar energy of the mth month respectively; , are the wind and solar power of the tth hour respectively; is the hourly set of the mth month.

[0038] The and are subjected to empirical distribution rank transformation to obtain rank coordinates. Specifically,

[0039] The wind and solar energy of each month are mapped to the interval (0, 1) according to rank transformation to form corresponding Copula coordinates, and the 12 months are divided into four quadrants by a threshold pair , ; when the wind energy rank or the solar energy rank is not lower than the threshold, it is respectively judged as strong wind or strong light, otherwise as weak wind or weak light; if any quadrant is empty, the corresponding threshold is contracted to 0.5 until the four quadrants are all non-empty or the threshold returns to 0.5; the weight of each representative month is equal to the proportion of the months in the quadrant; and the ranks are processed by the average method.

[0040] The threshold pair , refers to two threshold parameters for dividing the wind energy rank value and the solar energy rank value, wherein , are the thresholds of the wind and solar energy rank values respectively.

[0041] The Copula coordinates are calculated according to the following formula:

[0042]

[0043]

[0044] wherein, , are the normalized rank positions in the wind and solar dimensions of the mth month respectively; is the ascending rank and the average is taken for ties; , are the total wind and solar energy of the mth month respectively; and M is the total number of months in a year.

[0045] To maintain the wind-solar joint structure, the Copula rank space is stratified according to the four quadrants of strong wind and strong light, strong wind and weak light, weak wind and strong light, and weak wind and weak light, and each quadrant is ensured to be non-empty.

[0046]

[0047]

[0048]

[0049]

[0050] in, , , , These correspond to strong wind and strong light, strong wind and weak light, weak wind and strong light, and weak wind and weak light, respectively. , This is the threshold value for the rank space, initially set to 0.5; if an empty group is found, then... The system automatically adjusts to ensure that each group is not empty.

[0051] (2) Monthly total data alone cannot reflect the multi-scale fluctuation characteristics of wind and solar energy. Therefore, multi-dimensional features are constructed for each month to characterize these multi-scale fluctuations. These multi-dimensional features specifically include the intensity of wind and solar energy fluctuations, intraday amplitude of wind and solar energy, and the intraday fundamental frequency energy percentage.

[0052] The formulas for measuring the intensity of wind and solar radiation are as follows:

[0053]

[0054] in, This represents the total variation in wind or light in the m-th month sequence; a larger index indicates greater fluctuations. This refers to the hourly sequence of wind and solar power output in month m. The solar power output value for hour t.

[0055] The formula for calculating the intraday amplitude of scenery is as follows:

[0056]

[0057] in, An indicator for measuring the daily range of scenic views; for Quantile operator.

[0058] The formula for calculating the proportion of fundamental frequency energy during the day is as follows:

[0059]

[0060] in, The percentage of spectral energy in the fundamental frequency neighborhood of the m-th month sequence over a 24-hour period; is the spectral power of wind power or photovoltaic power after discrete Fourier transform of the de-meaned time series; k is the index of the corresponding frequency component; is the index set of 24h fundamental frequency.

[0061] (3) For simultaneously representing the distribution pattern and time series fluctuation of wind power and photovoltaic power, the following formula is used to calculate the distance between any two months: i j The joint distance is defined as the weighted Euclidean sum of the difference between wind power quantile curve vectors, the difference between photovoltaic quantile curve vectors and the difference between multi-scale feature vectors; the joint distance is calculated by the following formula:

[0062]

[0063] wherein, represents the joint distance between month i and month j , the smaller the value is, the more similar the two months are; taking month i as an example, , are the wind power and photovoltaic equidistant quantile curve vectors respectively, is the multi-scale feature vector; is the Euclidean norm; α , β , gamma are the weights.

[0064] (4) To improve the information coverage and recognition, candidate month sets are formed in the four quadrants respectively, and the representative months are selected by using the constraint D-optimal criterion; the specific steps include the following:

[0065] (4.1) The month set s in the object limit Calculate the medoid score in the group:

[0066]

[0067] wherein, is the medoid score of month m in the quadrant s .

[0068] (4.2) Arrange in ascending order, and take the first K to form the candidate set:

[0069]

[0070] wherein, is the candidate month set of quadrant s ; is the month set of quadrant s ; represents the medoid score of month m in the set .​ ascending rank, and average if tie; K is the upper bound of candidate number.

[0071] (4.3) Take one month from each quadrant to form candidate combination and construct feature matrix X (c) as follows:

[0072]

[0073] where, is the multi-scale feature row vector of month m; is the month index of the selected representative month in quadrant s. s

[0074] (4.4) Take the value of the representative month selection objective function of each candidate combination as the target, and select the combination that makes the objective function reach the maximum value; if there are multiple combinations that reach the same maximum objective function value, preferentially select the smallest combination. is the sum of the medoid scores of each month in the selected month combination.

[0075] The representative month selection objective function is:

[0076]

[0077] where, is the representative month selection objective function, used to measure the overall information amount of different candidate month combinations, and the greater the objective function value, the higher the coverage and the stronger the difference in the feature space, which can better represent the annual operation characteristics. ε>0 is a diagonal regularization coefficient, used to enhance numerical stability; is the identity matrix; is the determinant of the matrix; is the Gram matrix of the candidate month feature matrix X , reflecting the correlation structure of the candidate months in the feature space.

[0078] (4.5) Determine the weight of the selected four representative months in the following manner:

[0079]

[0080] where, is the representative month weight of quadrant s; s is the number of months in quadrant s; is the total number of months in a year.

[0081] ​​​2. A flexible adjustment mechanism is introduced in the representative month time domain, and the ammonia load is described as a continuous piecewise linear operation curve, which is divided into a smooth operation section and a preset flexible adjustment section. In order to avoid the frequent fluctuation of ammonia load leading to the drastic change of ammonia equipment pressure and temperature, causing catalyst damage and safety risk, only in the allowed flexible adjustment interval, the ammonia load changes at a constant ramping rate, and the ammonia load remains unchanged in other periods.

[0082] By introducing the ammonia adjustment cost term in the annual total cost objective function, the ammonia load change and the number of flexible adjustment intervals can be punished, so as to realize the sparsification of the adjustment section; The number, start and end time and duration are automatically determined by the optimization solution of the annual total cost objective function, which can concentrate the ammonia load change in a small number of controllable sections to match the wind and light power fluctuation and reduce the adjustment frequency of ammonia equipment.

[0083] The flexible adjustment modeling constraints corresponding to the annual total cost objective function include the following contents:

[0084] (1) The hourly ammonia load change is related to the maximum ammonia yield and the ramping rate, which is shown in the following formula:

[0085]

[0086] Among them, is the ammonia yield at time t; is the maximum ammonia yield; is the ammonia load ramping rate; represents the total number of hours in the representative month.

[0087] (2) The state of the flexible adjustment interval is described by activating the indicator variable. When the indicator variable is 1, the ramping rate is constrained by its upper and lower limits, and when the indicator variable is 0, the ramping rate is 0, thereby limiting the ammonia load to change only in the selected adjustment interval, and remaining stable in the remaining period. The specific constraint form is as follows:

[0088]

[0089] Among them, is the adjustment interval indicator variable, which is a binary variable indicating whether the interval is in the active state; is the ammonia load ramping rate; , is the lower limit and upper limit of the ammonia ramping rate.

[0090] (3) In order to ensure the smooth connection across the day, the ramping rate at each of the 24 points of the day is set to zero, and the representative month head and tail equivalent conditions are imposed to facilitate annual aggregation:

[0091]

[0092]

[0093] where, is the day serial number; is the total monthly hours.

[0094] (4) Further, to ensure the feasibility of flexible adjustment section operation and obtain sparse solution, the logical consistency constraints between adjustment section state indicator variables and section state switching event variables are established to ensure that the synthetic ammonia load ramping rate remains constant within each adjustment section, and a synthetic ammonia load adjustment penalty term is introduced into the annualized total cost objective function. Specifically as follows:

[0095] (4.1) To accurately describe the start and end changes of the adjustment section and maintain the logical consistency between the event variables and the state indicator variables, the adjustment section start event s ( t ) and the adjustment section end event e ( t ) are defined as follows:

[0096]

[0097] (4.2) To ensure that the synthetic ammonia load ramping rate remains constant within the same adjustment section and achieve a smooth transition of synthetic ammonia load within and outside the section, the adjacent time ramping rate consistency constraint needs to be applied:

[0098]

[0099] where, is a sufficiently large constant; when , it gets , ensuring that the ramping rate is constant within each adjustment interval.

[0100] (4.3) To suppress invalid adjustment and constrain the number of adjustment sections, a penalty cost for opening an adjustment section is introduced into the synthetic ammonia adjustment cost, and the formula is as follows:

[0101]

[0102] where, is the penalty cost for opening an adjustment section; is the penalty coefficient.

[0103] (4.4) To represent the adjustment scheme in the optimization result, the number, starting time, and duration of the adjustment section are determined by the optimization solution:

[0104]

[0105]

[0106]

[0107] in, To adjust the number of sections; Represents the total number of hours in a month; The set representing the starting times of the adjustment segment; For the first The duration of each adjustment segment; For the first The start time of each adjustment segment; To adjust the candidate length of the segment.

[0108] 3. Construct a unit ammonia production power consumption function with the per-unit load of ammonia synthesis as the independent variable. Use SOS2 convex combination to perform piecewise linear characterization of the ammonia synthesis energy consumption curve. Use McCormick envelope linearization to process the bilinear terms introduced by the convex combination and establish a linearly solvable ammonia synthesis section power model. After superimposing it with the power of the air separation unit, incorporate it into the mixed integer optimization framework.

[0109] (1) Construct an electrical power model for the ammonia synthesis section (Haber-Bosch section). This model uses a unit power consumption function with the ammonia synthesis per-unit load level as the independent variable. Specifically, it is expressed as the formula for ammonia synthesis power minus ammonia production flow rate based on the ammonia synthesis per-unit load:

[0110]

[0111]

[0112]

[0113] in, Per unit load for ammonia synthesis; Electricity consumption per unit of ammonia production; This represents the electrical power of the non-linearized ammonia synthesis section. For the first Hourly ammonia production flow rate; This refers to the rated maximum ammonia production flow rate of the ammonia synthesis unit. This represents the minimum per-unit load level for ammonia synthesis that is permissible for the ammonia synthesis unit. This is a function of electricity consumption per unit of ammonia production; , , , These are the polynomial coefficients of the unit ammonia production power consumption function.

[0114] (2) The above formula is linearized by equivalence, SOS2 convex combination is used on the breakpoint set, and McCormick envelope is used to process the product term, to obtain the linear expression of the synthetic ammonia electric power;

[0115] First, a set of monotonic breakpoints is constructed in the unit load value interval, and the unit load is segmented and convexly combined by interpolation with SOS2 weight, as follows:

[0116]

[0117]

[0118] wherein, SOS2 weight; synthetic ammonia unit load; synthetic ammonia unit load breakpoint.

[0119] Then, in order to convert the product of weight and synthetic ammonia yield into linear form, the variable is introduced to represent the product of weight and synthetic ammonia yield, and McCormick envelope is applied under the conditions that the upper and lower bounds of capacity and are known, as follows:

[0120]

[0121] wherein, lower bound of synthetic ammonia ammonia flow rate; upper bound of synthetic ammonia ammonia flow rate; McCormick auxiliary variable, representing the product of weight and synthetic ammonia yield.

[0122] According to the above formula, the ammonia flow rate and the synthetic ammonia section electric power are uniformly expressed as a linear weighted sum, as follows:

[0123]

[0124] (computing function of the synthetic ammonia section electric power model)

[0125]

[0126] wherein, synthetic ammonia section (Haber-Bosch section) electric power; pre-computed constant at the breakpoint, used to write the electric power as a linear combination.

[0127] The electric power model of the ammonia synthesis section is used to describe the energy consumption variation law of the ammonia synthesis device under different ammonia synthesis loads, and is linearized through the SOS2 and McCormick methods to form an ammonia synthesis section electric power expression that can be directly called in mixed integer optimization, as a basic input for system optimization.

[0128] (3) According to the linear relationship between the air demand of the ammonia synthesis process and the ammonia production, the electric power of the air separation device can be expressed as:

[0129]

[0130] wherein, is the electric power of the air separation device; is the electric power coefficient of the air separation device.

[0131] The electric power of the air separation device and the electric power of the ammonia synthesis section will be included in the mixed integer optimization framework for constructing a mixed integer programming model.

[0132] (4) The electric power of the ammonia synthesis section and the air separation device is superimposed to obtain the total electric power of the ammonia synthesis section, and the calculation formula is as follows:

[0133]

[0134] wherein, is the total electric power of the ammonia synthesis section.

[0135] (5) According to the stoichiometric relationship between hydrogen and ammonia, the hydrogen used in the ammonia synthesis section is related to the ammonia production, and the calculation formula is as follows:

[0136]

[0137] wherein, is the flow rate of the electrolytic hydrogen directly supplied to the synthesis section; is the hydrogen flow rate from the hydrogen storage tank; is the stoichiometric coefficient of hydrogen to ammonia.

[0138] 4. A full-chain mixed integer programming model of the electrolytic water hydrogen synthesis ammonia system is constructed, with the minimum annual total cost of the system as the objective, based on four representative months for joint optimization; the annual weighted solution is obtained according to the weight of the representative month, and the optimal system configuration and operation scheduling result of the whole year is obtained.

[0139] (1) According to the influencing factors in the system operation process, the constraint conditions corresponding to the full-chain mixed integer programming model are determined; specifically as follows:

[0140] The actual output of wind power cannot exceed the available power, and the wind power output constraint is as follows:

[0141]

[0142] wherein, is the wind power actual output; is the wind power available.

[0143] The photovoltaic actual output cannot exceed the available photovoltaic power at the time, and the photovoltaic output constraint is as follows:

[0144]

[0145] wherein, is the photovoltaic actual output; is the photovoltaic available power.

[0146] The electrolyzer start-stop constraint is as follows:

[0147]

[0148]

[0149] wherein, represents the electrolyzer start state; represents the electrolyzer start state at time t, and respectively represent the electrolyzer start state at time t, the electrolyzer start state at time t.

[0150] The electrolyzer electric power upper and lower limit constraint is as follows:

[0151]

[0152]

[0153] wherein, is the electrolysis power minimum value; is the electrolyzer power; is the electrolyzer rated power.

[0154] The electrolyzer electric power calculation formula is as follows:

[0155]

[0156] wherein, is the electrolyzer electric power; is the hydrogen production flow rate; is the hydrogen production electricity consumption.

[0157] The compressor electric power calculation formula is as follows:

[0158]

[0159] wherein, For the compressor electric power; For the compressor power coefficient.

[0160] Other auxiliary equipment electric power calculation formula is:

[0161]

[0162] Wherein, For other auxiliary equipment electric power (such as cooling water pump, circulating fan or control system and other equipment); For the fixed electric power after starting; For the auxiliary power coefficient.

[0163] Energy storage charging and discharging power is subject to power upper limit, not at the same time, its constraints are as follows:

[0164]

[0165]

[0166]

[0167] Wherein, , Charging and discharging power, respectively; Charging and discharging state, respectively; For the rated power of energy storage.

[0168] Energy storage SOC constraints are as follows:

[0169]

[0170]

[0171]

[0172]

[0173] Wherein, For the actual capacity of energy storage; For the minimum value of energy storage capacity; For the rated capacity of energy storage; For the efficiency.

[0174] Hydrogen storage tank constraints are as follows:

[0175]

[0176]

[0177]

[0178]

[0179] wherein, is the lower limit of the hydrogen storage energy; is the hydrogen storage tank energy; is the rated capacity of the hydrogen storage tank to be stored; is the hydrogen heat value; is the hydrogen flow into the hydrogen storage tank; is the hydrogen flow out of the hydrogen storage tank.

[0180] The upper and lower limits of the synthetic ammonia flow are:

[0181]

[0182] wherein, is the rated flow of synthetic ammonia; is the synthetic ammonia flow.

[0183] The electric power balance constraint is:

[0184]

[0185] wherein, is the actual output of wind power; is the actual output of photovoltaic power; is the energy storage discharge power; is the electrolyzer power; is the energy storage charging power; is the actual satisfaction of the electrical load; is the auxiliary equipment electric power; is the compressor electric power; is the total electric power of the synthetic ammonia section.

[0186] (2) An integrated mixed integer linear programming model for system configuration and dispatch is established with the goal of minimizing the annual total cost.

[0187] The annual total cost objective function is constructed with the goal of minimizing the annual total cost, which is used as the objective function of the mixed integer linear programming model for comprehensive optimization of resource allocation and operation coordination, and the annual economic optimization of the system is achieved.

[0188] The objective function is specifically shown in the following formula:

[0189]

[0190] wherein, is the annual total cost target value; is the penalty cost of abandoned wind; is the penalty cost of abandoned light; is the penalty cost of abandoned electrical load; is the annual investment cost; The cost of equipment operation and maintenance; The replacement cost of the equipment during its lifetime; The revenue from the sale of ammonia; The cost of adjusting the synthetic ammonia;

[0191] The formula for calculating the penalty cost of abandoned wind is as follows:

[0192]

[0193] Wherein, The penalty coefficient of abandoned wind.

[0194] The formula for calculating the penalty cost of abandoned light is as follows:

[0195]

[0196] Wherein, The penalty coefficient of abandoned light.

[0197] The formula for calculating the penalty cost of abandoned electric load is as follows:

[0198]

[0199] Wherein, The penalty coefficient of abandoned electric load; The demand for electric load; The actual satisfaction of electric load.

[0200] The formula for calculating the annual investment cost is as follows:

[0201]

[0202] Wherein, The annualization coefficient; , , , , , , The unit cost of electrolytic cell, energy storage, hydrogen storage tank, compressor, auxiliary equipment, air separation device, and synthetic ammonia equipment, respectively; The rated installed capacity of the compressor; The rated installed capacity of the auxiliary equipment.

[0203] The formula for calculating the annual equipment operation and maintenance cost is as follows:

[0204] Wherein, , , , , , , The operation and maintenance cost coefficients of electrolytic tank, energy storage, hydrogen storage tank, compressor, auxiliary machine, air separation, and synthetic ammonia equipment, respectively.

[0205] The equipment (battery) replacement cost calculation formula is as follows:

[0206]

[0207] Among them, The battery replacement cost coefficient.

[0208] The ammonia sales revenue calculation formula is as follows:

[0209]

[0210] Among them, The ammonia sales unit price.

[0211] The synthetic ammonia adjustment cost is composed of synthetic ammonia load amplitude variation, synthetic ammonia adjustment frequency, and opening adjustment interval penalty, and its expression is:

[0212]

[0213] Among them, The synthetic ammonia adjustment cost; , The synthetic ammonia load amplitude variation penalty coefficient and the adjustment frequency penalty coefficient, respectively; Indicates whether the synthetic ammonia load changes; The penalty cost of opening the adjustment section.

[0214] The mixed integer linear programming model is the overall model for realizing the overall optimization of the system, the electrolytic water hydrogen production, energy storage, hydrogen storage, air separation, and synthetic ammonia links are uniformly modeled, and the minimum annual total cost of the system is taken as the target; the four representative months are jointly optimized and the annual weighted solution is carried out according to the weight of the representative month, so that the optimal system configuration and operation scheduling result of the whole year can be obtained.

[0215] The annual total cost objective function introduces a synthetic ammonia regulation cost term, which can automatically suppress frequent load fluctuations and reduce the number of regulation segments by penalizing the load change amplitude and the number of regulation segments, and concentrate the necessary load changes into a limited number of regulation segments, so as to obtain a stable, sparse and flexible regulation operation curve matched with wind and light fluctuations. The optimization is automatically selected by the synthetic ammonia regulation cost term whether to start the regulation segment, and the boundary of the regulation segment is determined by the related constraint, so that the constant ramp rate is maintained in each regulation segment, and the load of the non-regulation segment is kept stable, so as to form an executable segmented linear flexible regulation curve. The target function introduces a synthetic ammonia regulation cost term, which tends to generate fewer regulation segments by penalizing the regulation segment start event, so as to obtain a sparse, stable and engineering implementable flexible regulation scheme; and the number, start and end time and duration of the regulation segment are determined together with the segment switching event variable and consistency constraint.

[0216] A specific practical case:

[0217] In this embodiment, the wind power, photovoltaic and load data of a certain place in China are taken as an example for simulation, a mixed integer optimization model of capacity configuration and operation scheduling of the electric hydrogen ammonia system based on intraday flexible regulation is constructed on the MATLAB simulation platform, and the optimization calculation of the wind-solar-hydrogen-ammonia island power system is carried out.

[0218] The input conditions are specifically set: according to the selection results of the typical months and the candidate months in the four quadrants obtained from the annual data of the place, as shown in Figure 1 , the wind and light output and load diagrams of the four typical months are as shown in Figure 2 , the electric power model diagram of synthetic ammonia segment is as shown in Figure 3 . The electrolytic cell regulation range is 25%~100%; the upper and lower limits of the energy storage SOC are 10%~90%; the energy storage charge and discharge efficiency is 0.95; the upper and lower limits of the hydrogen storage tank are 10%~100%; the synthetic ammonia ramp rate range is-20%~20%; the upper and lower limits of the synthetic ammonia load are 20%~110%; the initial wind power installation is 7MW; the initial photovoltaic installation is 12MW; the system operation cycle is 20 years.

[0219] The above input parameters are input into the mixed integer optimization model of capacity configuration and operation scheduling of the electric hydrogen ammonia system based on intraday flexible regulation, and are solved simultaneously in the four representative months and are aggregated according to the weight. According to the minimum annual total cost as the target, iterative optimization calculation is carried out, and the optimal results of system configuration are output, as shown in Table 1. The output operation scheduling results are as shown in Figures 4-7 .

[0220] Table 1 Optimal results of system configuration

[0221]

[0222] As can be seen from Table 1 and Figures 4-7 Compared with the traditional method, the configuration and operation optimization method of the electric hydrogen ammonia system based on the intra-day flexible adjustment can control the abandoned wind and light rate within 3% while ensuring that the optimization problem size is solvable, effectively improves the matching degree of wind and light output, reduces the abandoned energy, the annual window number is 513, and significantly reduces the fatigue loss caused by the frequent change of ammonia load. It provides technical support and optimization path for the reasonable configuration and safe operation of the wind and light hydrogen synthesis ammonia device in the island power system.

[0223] The specific embodiments of the application are described above. It should be understood that the application is not limited to the specific implementation described above, and various modifications or changes can be made by those skilled in the art within the scope of the claims, which does not affect the essential content of the application.

Claims

1. A scheduling method for a water electrolysis hydrogen production and ammonia synthesis system with intraday flexible regulation, characterized in that, This involves integrating wind and solar renewable energy sources into the power system for hydrogen production through water electrolysis and ammonia synthesis; this includes: (1) The hourly wind power and photovoltaic data of the whole year are sliced ​​by month. After calculating the monthly wind and solar energy, the data is divided into four quadrants in the Copula rank space. Multi-scale fluctuation features are extracted and the joint distance between months is constructed. A representative month is selected from each of the four quadrants to construct a feature matrix. Weights are assigned to the four selected representative months. (2) Introduce a flexible adjustment mechanism within the time domain of the representative month, and describe the synthetic ammonia load as a continuous piecewise linear operating curve; by solving the annualized total cost objective function, concentrate the synthetic ammonia load change in a few preset controllable sections to match the fluctuation of wind and solar power and reduce the adjustment frequency of synthetic ammonia equipment. (3) Construct a unit ammonia production power consumption function with the per-unit load of ammonia synthesis as the independent variable, and perform equivalent linearization; establish a linearly solvable ammonia synthesis section power model, and incorporate it into the mixed integer optimization framework after superimposing it with the power of the air separation unit. (4) Construct a full-chain mixed integer linear programming model for the electrolysis of water to produce hydrogen and synthesize ammonia. With the goal of minimizing the annual total cost of the system, perform joint optimization on four representative months and solve the annual weighted solution according to the weight of the representative month to obtain the optimal system configuration and operation scheduling results for the whole year.

2. The method according to claim 1, characterized in that, In step (1), the wind and solar energy of each month are mapped to the (0,1) interval by rank transformation to form the corresponding Copula coordinates, and a threshold is used to set the (0,1) interval to the (0,1) interval. , The 12 months are divided into four quadrants: strong wind and strong light, strong wind and weak light, weak wind and strong light, and weak wind and weak light; when the wind energy rank or light energy rank If the value is not lower than the threshold, it is judged as strong wind or strong light respectively; otherwise, it is weak wind or weak light. If any quadrant is empty, the corresponding threshold is shrunk to 0.5 until all four quadrants are not empty or the threshold returns to 0.

5. The weight of each representative month is equal to the proportion of the month in that quadrant. Parallel ranks are processed by the average method.

3. The method according to claim 1, characterized in that, In step (1), the multi-scale fluctuation characteristics refer to the multi-dimensional characteristics constructed for each month's wind power and photovoltaic data, specifically including the intensity of wind and solar fluctuations, the intraday amplitude of wind and solar power, and the intraday fundamental frequency energy ratio.

4. The method according to claim 1, characterized in that, In step (1), for any month i , j The joint distance is defined as the weighted Euclidean sum of the differences in wind power quantile curve vectors, photovoltaic quantile curve vectors, and multi-scale eigenvectors, and is used to simultaneously characterize the distribution pattern and temporal fluctuations of wind and solar power output. The joint distance is calculated using the following formula: ; in, Indicates month i With Month j The joint distance, the smaller the value, the more similar the two months are; by month i For example, , These are the equidistant quantile curve vectors for wind power and photovoltaic power, respectively. These are multi-scale feature row vectors; It is the Euclidean norm; α , β , γ As weight.

5. The method according to claim 1, characterized in that, In step (1), the representative month for constructing the feature matrix is ​​selected using the constrained D-optimal criterion, specifically including: a. Calculate the medoid score within each group for the monthly set in each quadrant, then sort them in ascending order and take the top K to form a candidate set; b. Select one month from the candidate sets of each of the four quadrants to form candidate combinations, and construct the feature matrix. X (c); c. Using the constrained D-optimal criterion as the objective, calculate the value of the objective function for each candidate combination, and select the combination that maximizes the objective function. If multiple combinations achieve the same maximum objective function value, prioritize the combination with the smallest sum of medoid scores for each month among the selected month combinations. The objective function for selecting the representative month is: ; in, Choose an objective function to represent the month; It refers to the determinant of a matrix; Refers to the candidate month feature matrix X The Gram matrix; ε > 0 are the diagonal regularization coefficients; It is an identity matrix.

6. The method according to claim 1, characterized in that, In step (2), the linear operating curve is divided into a stable operating segment and a preset flexible adjustment segment; within the flexible adjustment range, the ammonia synthesis load changes at a constant ramp rate, while the others are stable operating segments where the load remains unchanged; the number of flexible adjustment segments, the start and end times, and the duration are automatically determined by the optimization solution process of the annualized total cost objective function. The flexible adjustment modeling constraints corresponding to the annualized total cost objective function include: a. The hourly variation of ammonia synthesis load is related to the maximum ammonia synthesis yield and the ramp-up rate; b. Flexible adjustment of interval states can be described by activating indicator variables; c. Set the daily 24-hour ramp rate to zero, and apply a condition that represents the first and last months of the month being equal in value.

7. The method according to claim 1, characterized in that, In step (2), to ensure the executability of the flexible adjustment section operation and obtain a sparse solution, a logical consistency constraint is established between the adjustment section state indicator variable and the section state switching event variable to ensure that the synthetic ammonia load ramp rate in each adjustment section remains constant, and a penalty term for load changes and the number of flexible adjustment sections is introduced into the annualized total cost objective function; specifically, the following content is included: a. Accurately describe the start and end changes of the adjustment range, and keep the event variables and status indicators logically consistent; b. Apply a consistent ramp rate constraint between adjacent time points to ensure that the ramp rate of ammonia synthesis load is constant within the same regulation section and to achieve a continuous and smooth transition of ammonia synthesis load inside and outside the section; c. Introduce a penalty cost for enabling the adjustment segment into the objective function to suppress ineffective adjustment and constrain the number of adjustment segments; d. The number of adjustment segments, their start time, and duration are all determined by the optimization solution, representing the adjustment scheme in the optimization results.

8. The method according to claim 1, characterized in that, Step (3) specifically includes: a. Construct an electrical power model for the ammonia synthesis section, in which a unit power consumption function with the per-unit load level of ammonia synthesis as the independent variable is used; b. The unit power consumption function is equivalently linearized by using SOS2 convex combination on the set of breakpoints and processing the product terms with McCormick envelope to obtain the linearized expression of the power of ammonia synthesis. c. Based on the linear relationship between air demand and ammonia production in the ammonia synthesis process, construct the power function of the air separation unit; d. The total electrical power of the ammonia synthesis section is obtained by superimposing the electrical power of the ammonia synthesis section and the air separation unit; e. Based on the stoichiometric relationship between hydrogen and ammonia, establish the connection between the amount of hydrogen used in the ammonia synthesis stage and the amount of ammonia produced.

9. The method according to claim 1, characterized in that, In step (4), a full-chain hybrid integer programming model is used to achieve integrated optimization of system configuration and scheduling; wherein, a. Based on the influencing factors during system operation, determine the constraints corresponding to the full-chain mixed integer programming model; the constraints include: wind power output constraints, photovoltaic power output constraints, electrolyzer start-up and shutdown constraints, electrolyzer power upper and lower limit constraints, energy storage charging and discharging power constraints, energy storage SOC constraints, hydrogen storage tank constraints, synthetic ammonia flow upper and lower limit constraints, and power balance constraints. b. Construct an annualized total cost objective function with the goal of minimizing the annualized total cost, and use it as the objective function of the mixed-integer linear programming model, as shown in the following formula: ; in, This represents the annualized total cost target value. The cost of curtailing wind power; The cost of penalties for abandoning light; Penalty costs for abandoned power loads; Annual investment cost; For equipment operation and maintenance costs; The replacement cost of the equipment during its lifespan; For revenue from ammonia sales; To adjust the cost of ammonia synthesis.

Images