Industrial production method, system and electronic device for green electricity synthesis of ammonia
By constructing a quantitative comparison model and selecting appropriate production scheduling strategies, the problem of coordinating fluctuating green electricity with industrial production tasks was solved, achieving efficient utilization of green electricity resources and flexible adaptability of the production system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- STATE NUCLEAR POWER AUTOMATION SYST ENGCO
- Filing Date
- 2026-02-27
- Publication Date
- 2026-06-19
Smart Images
Figure CN122243026A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the energy and power sector, and in particular to an industrial scheduling method, system, and electronic equipment for green electricity-based ammonia synthesis. Background Technology
[0002] Against the backdrop of a green energy structure transformation, fluctuating green electricity, represented by wind and solar power, is gradually becoming an important power source for energy-intensive industrial production. For example, coupled systems such as green electricity-hydrogen-ammonia, which utilize renewable energy to produce "green hydrogen" and "green ammonia," have become an important path for the decarbonization of process industries. However, the stable operation of such systems faces inherent contradictions: the highly volatile green electricity is difficult to coordinate with chemical equipment that seeks stable operation; at the same time, production needs to meet order demands, while the grid-connected price of green electricity and the market price of chemical products are both dynamically changing, requiring complex cost-effectiveness decisions between absorbing green electricity, selling electricity to the grid, and local production. Existing production scheduling methods mostly rely on fixed plans or experience-based scheduling, making it difficult to effectively adapt and optimize the utilization of fluctuating green electricity under multiple objectives such as ensuring safety, fulfilling orders, and improving efficiency. Summary of the Invention
[0003] The technical problem to be solved by this disclosure is to overcome the shortcomings of the prior art in that it is impossible to dynamically adapt fluctuating green electricity to industrial production tasks in order to generate an optimized production schedule, and to provide an industrial production scheduling method, system and electronic equipment for green electricity ammonia synthesis.
[0004] This disclosure solves the above-mentioned technical problems through the following technical solution:
[0005] This disclosure provides an industrial production scheduling method for green electricity-based ammonia synthesis, the industrial production scheduling method comprising:
[0006] Obtain forecast data on green electricity generation within a preset future time period, as well as task data for production tasks to be completed;
[0007] The predicted data is quantitatively compared with the task data, and the target strategy is determined from the predefined scheduling strategy based on the comparison results.
[0008] The production schedule for the preset future time period is generated based on the target strategy.
[0009] Optionally, the step of quantitatively comparing the predicted data with the task data and determining the target strategy from the predefined scheduling strategy based on the comparison result includes:
[0010] Determine the total power generation provided by green electricity generation as represented by the predicted data, the total electricity consumption required to complete the production task as represented by the task data, and the maximum electricity consumption corresponding to operation at maximum technical capacity within the preset future time period;
[0011] If the total power generation is greater than or equal to the total power consumption, a first strategy is selected; the first strategy is implemented by constructing a first model with the goal of maximizing the total system revenue.
[0012] In response to the total power generation being less than the total power consumption, and the total power consumption being less than or equal to the maximum power consumption, a second strategy is selected; the second strategy is implemented by constructing a second model with production task completion rate as a constraint and minimization of operating cost as the objective function;
[0013] If the total electricity consumption exceeds the maximum electricity consumption, a third strategy is selected; the third strategy is implemented by constructing a third model with the objective function of achieving the maximum technical capacity of the system under safety constraints.
[0014] Optionally, generating the production schedule for the preset future time period based on the target strategy includes:
[0015] In response to the target strategy being the first strategy, the first model is constructed and solved; wherein, the green electricity sold to the grid and the power allocated for local production at each time step within the preset future time period are used as the first decision variables of the first model.
[0016] With the goal of maximizing total revenue, and under the premise of satisfying the constraints of safe equipment operation, the first optimal solution for the first decision variable is obtained based on real-time electricity market price and local production profit data.
[0017] The first optimal solution is used as the production scheduling instruction with time step granularity within the preset future time period.
[0018] Optionally, generating the production schedule for the preset future time period based on the target strategy includes:
[0019] In response to the target strategy being the second strategy, the second model is constructed and solved; wherein, the purchase time and purchase power of external grid electricity at each time step within the preset future time period are used as the second decision variables of the second model;
[0020] With the goal of minimizing operating costs and under the premise of meeting the production task completion rate, the second optimal solution of the second decision variable is obtained based on time-of-use electricity price data;
[0021] The second optimal solution is used as the production scheduling instruction with time step granularity within the preset future time period.
[0022] Optionally, generating the production schedule for the preset future time period based on the target strategy includes:
[0023] In response to the target strategy being the third strategy, the third model is constructed and solved; wherein, the external grid power required to maintain maximum technical capacity is used as the third decision variable of the third model;
[0024] With the goal of achieving the system's maximum technical capacity, and under the premise of satisfying the safety operation constraints of production equipment, the third optimal solution of the third decision variable is obtained based on the difference between the maximum power consumption and the total power generation.
[0025] The third optimal solution is used as the production scheduling instruction with time step granularity within the preset future time period.
[0026] Optionally, the industrial scheduling method further includes:
[0027] The actual operating data during the execution of the production schedule is collected in real time, and the actual operating data is compared with the expected operating data of the production schedule.
[0028] If the deviation between the expected operating data and the actual operating data exceeds a deviation threshold, the production schedule is adjusted.
[0029] This disclosure provides an industrial scheduling system for green electricity-based ammonia synthesis, the industrial scheduling system comprising:
[0030] The acquisition module is used to acquire the predicted data of green electricity generation within a preset future time period, as well as the task data of the production tasks to be completed.
[0031] The planning module is used to quantitatively compare the predicted data with the task data, and determine the target strategy from the predefined scheduling strategy based on the comparison results.
[0032] The generation module is used to generate a production schedule for the preset future time period based on the target strategy.
[0033] Optionally, the planning module is specifically used for:
[0034] Determine the total power generation provided by green electricity generation as represented by the predicted data, the total electricity consumption required to complete the production task as represented by the task data, and the maximum electricity consumption corresponding to operation at maximum technical capacity within the preset future time period;
[0035] If the total power generation is greater than or equal to the total power consumption, a first strategy is selected; the first strategy is implemented by constructing a first model with the goal of maximizing the total system revenue.
[0036] In response to the total power generation being less than the total power consumption, and the total power consumption being less than or equal to the maximum power consumption, a second strategy is selected; the second strategy is implemented by constructing a second model with production task completion rate as a constraint and minimization of operating cost as the objective function;
[0037] If the total electricity consumption exceeds the maximum electricity consumption, a third strategy is selected; the third strategy is implemented by constructing a third model with the objective function of achieving the maximum technical capacity of the system under safety constraints.
[0038] Optionally, the generation module is specifically used for:
[0039] In response to the target strategy being the first strategy, the first model is constructed and solved; wherein, the green electricity sold to the grid and the power allocated for local production at each time step within the preset future time period are used as the first decision variables of the first model.
[0040] With the goal of maximizing total revenue, and under the premise of satisfying the constraints of safe equipment operation, the first optimal solution for the first decision variable is obtained based on real-time electricity market price and local production profit data.
[0041] The first optimal solution is used as the production scheduling instruction with time step granularity within the preset future time period.
[0042] Optionally, the generation module is specifically used for:
[0043] In response to the target strategy being the second strategy, the second model is constructed and solved; wherein, the purchase time and purchase power of external grid electricity at each time step within the preset future time period are used as the second decision variables of the second model;
[0044] With the goal of minimizing operating costs and under the premise of meeting the production task completion rate, the second optimal solution of the second decision variable is obtained based on time-of-use electricity price data;
[0045] The second optimal solution is used as the production scheduling instruction with time step granularity within the preset future time period.
[0046] Optionally, the generation module is specifically used for:
[0047] In response to the target strategy being the third strategy, the third model is constructed and solved; wherein, the external grid power required to maintain maximum technical capacity is used as the third decision variable of the third model;
[0048] With the goal of achieving the system's maximum technical capacity, and under the premise of satisfying the safety operation constraints of production equipment, the third optimal solution of the third decision variable is obtained based on the difference between the maximum power consumption and the total power generation.
[0049] The third optimal solution is used as the production scheduling instruction with time step granularity within the preset future time period.
[0050] Optionally, the industrial scheduling system further includes:
[0051] The comparison module is used to collect actual operating data in real time during the execution of the production schedule and compare the actual operating data with the expected operating data of the production schedule.
[0052] The adjustment module is used to trigger an adjustment to the production schedule in response to a deviation between the expected operating data and the actual operating data that exceeds a deviation threshold.
[0053] This disclosure provides an electronic device, including a memory, a processor, and a computer program stored in the memory and used to run on the processor, wherein the processor executes the computer program to implement the industrial production scheduling method for green electricity-to-ammonia synthesis as described above.
[0054] This disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the industrial production scheduling method for green electricity-to-ammonia synthesis as described above.
[0055] This disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the industrial production scheduling method for green electricity-based ammonia synthesis as described above.
[0056] Based on common knowledge in the field, the above-mentioned preferred conditions can be combined arbitrarily to obtain various preferred embodiments of this disclosure.
[0057] The positive and progressive effects of this disclosure are as follows: The industrial scheduling method for green electricity-based ammonia synthesis significantly improves the systematicness and foresight of scheduling decisions by constructing a data-driven systematic decision-making process, shifting planning from reliance on manual experience to proactive prediction based on future forecasts. Through core quantitative comparison steps, it achieves effective matching of green electricity resources and production demand at the source, fundamentally promoting energy supply and demand matching and improving the localization of green electricity consumption. Furthermore, by introducing a predefined strategy library and dynamically selecting strategies based on real-time quantitative results, it greatly enhances the flexibility and adaptability of production management, enabling flexible adjustment and optimization of objectives for different supply and demand situations, thereby outputting more adaptable scheduling schemes and comprehensively improving the production system's ability to cope with the uncertainties of wind and solar power generation. Attached Figure Description
[0058] Figure 1 A flowchart illustrating an industrial production scheduling method for green electricity-based ammonia synthesis, provided as an exemplary embodiment of this disclosure;
[0059] Figure 2 A flowchart of step 102 provided for an exemplary embodiment of this disclosure;
[0060] Figure 3 A flowchart illustrating an industrial production scheduling method for green electricity-based ammonia synthesis, provided as an exemplary embodiment of this disclosure;
[0061] Figure 4 A schematic diagram of a wind and solar power output prediction curve provided for an exemplary embodiment of this disclosure;
[0062] Figure 5 A schematic diagram illustrating an hourly intelligent scheduling result provided as an exemplary embodiment of this disclosure;
[0063] Figure 6 A schematic diagram of a green electricity ammonia synthesis industrial production scheduling system provided as an exemplary embodiment of this disclosure;
[0064] Figure 7 This is a schematic diagram of the structure of an electronic device provided as an exemplary embodiment of the present disclosure. Detailed Implementation
[0065] The present disclosure is further illustrated below by way of embodiments, but the present disclosure is not limited to the scope of the embodiments described herein.
[0066] The prefixes such as "first" and "second" used in this disclosure are merely for distinguishing different descriptive objects and do not limit the position, order, priority, quantity, or content of the described objects. The use of ordinal numbers and other prefixes used to distinguish descriptive objects in this disclosure does not constitute a limitation on the described objects. The description of the described objects is given in the context of the embodiments, and the use of such prefixes should not constitute unnecessary restrictions. Furthermore, in the description of this embodiment, unless otherwise stated, "multiple" means two or more.
[0067] Example 1
[0068] Figure 1 A flowchart of an industrial production scheduling method for green electricity-based ammonia synthesis, provided as an exemplary embodiment of this disclosure, is included in the following industrial production scheduling method:
[0069] Step 101: Obtain the predicted data of green electricity generation within the preset future time period, as well as the task data of the production tasks to be completed.
[0070] The green power generation forecast data in step 101 mainly includes the output curves of wind power and photovoltaic power, forecast timestamps, spatial location information, and forecast error range. For example, wind power forecast data may include power values derived from wind speed, wind direction, and turbulence intensity; photovoltaic forecast data may include DC / AC power obtained from irradiance, cloud cover, and temperature conversion. Production task data covers order quantity (e.g., tons, kilograms), product specifications (e.g., green ammonia purity), process path constraints (e.g., the series relationship between hydrogen production and ammonia synthesis), and energy consumption coefficients (e.g., unit product power consumption). For example, if the production scheduling system sets the next 24 hours as the scheduling cycle with a time step of 1 hour, then it needs to obtain the hourly forecast and task data for that 24-hour period.
[0071] Step 102: Quantitatively compare the predicted data with the task data, and determine the target strategy from the predefined scheduling strategy based on the comparison results.
[0072] In step 102, key indicators in the green electricity generation forecast data and production task data are numerically compared using mathematical operations. Based on a pre-constructed set of optimization schemes for typical operating scenarios, specifically including: Strategy 1 (Economic Priority, also known as "Load Follows Source"), Strategy 2 (Order Guarantee, also known as "Source Follows Load"), and Strategy 3 (Capacity Saturation), each strategy corresponds to a specific objective function and constraints. Finally, the optimal production scheduling strategy is dynamically selected based on the quantitative comparison results.
[0073] Optionally, see Figure 2 It can be seen that step 102 specifically includes:
[0074] Step 1021: Determine the total power generation provided by green electricity generation as represented by the forecast data, the total electricity consumption required to complete the production task as represented by the task data, and the maximum electricity consumption corresponding to operation at maximum technical capacity within the preset future time period.
[0075] Among them, total power generation refers to the total electrical energy that can be provided by green energy sources such as wind and solar power within a preset future time period. This value can be obtained by integrating the power-time series data in the forecast data. Total electricity consumption refers to the total electrical energy required to complete the predetermined production tasks. This value can be obtained by multiplying the product output in the task data by the unit product electricity consumption coefficient. Maximum electricity consumption refers to the upper limit of electrical energy consumed by all equipment in the system when operating continuously at maximum safe technical capacity within a preset future time period. This value is determined by the equipment operating parameters, operating procedures, and system topology, and is a theoretical extreme value.
[0076] Step 1022: In response to the total power generation being greater than or equal to the total power consumption, the first strategy is selected; the first strategy is implemented by constructing a first model with the goal of maximizing the total system revenue.
[0077] In step 1022, the trigger condition is "total power generation ≥ total power consumption," indicating that available green electricity is sufficient to cover production needs and has a surplus. The objective then becomes how to allocate the surplus green electricity to maximize its value. Therefore, the first strategy is chosen, which involves constructing a first model and optimizing it with the goal of maximizing the total system revenue. This model needs to intelligently balance two profit models—"green electricity sold to the grid" and "green electricity used for local ammonia production"—while meeting equipment safety constraints. For example, during peak electricity price periods at midday, there might be a preference for selling surplus green electricity to the grid; while during off-peak periods at night, there might be a preference for using the electricity to produce higher value-added green ammonia.
[0078] Step 1023: In response to the total power generation being less than the total power consumption, and the total power consumption being less than or equal to the maximum power consumption, the second strategy is selected; the second strategy is implemented by constructing a second model with the production task completion rate as a constraint and the minimization of operating costs as the objective function.
[0079] In step 1023, the triggering conditions are "total power generation < total power consumption" and "total power consumption ≤ maximum power consumption," indicating a shortage of green electricity, but the order demand is within the system's physical capacity and can be met by purchasing electricity from outside sources. The objective here is to minimize the cost of purchased electricity while ensuring 100% order delivery. Therefore, the second strategy is chosen, i.e., constructing the second model. This model uses minimizing operating costs as the objective function, while setting the production task completion rate as a rigid constraint. The optimization focus is on intelligently deciding the time and power of purchased electricity based on time-of-use pricing, for example, purchasing more electricity during off-peak hours and minimizing purchases during peak hours.
[0080] Step 1024: In response to the total electricity consumption exceeding the maximum electricity consumption, the third strategy is selected; the third strategy is implemented by constructing a third model with the objective function of achieving the maximum technical capacity of the system under safety constraints.
[0081] In step 1024, the trigger condition is "total electricity consumption > maximum electricity consumption," indicating that the order demand has exceeded the system's physical capacity limit and cannot be fully met. At this point, the objective shifts to maximizing output and minimizing order arrears under the premise of absolute safety. Therefore, the third strategy is chosen: constructing a third model with the objective function of achieving the system's maximum technical capacity under safety constraints. The model's solution will drive the system to operate at or near its maximum safe load and calculate the total amount of externally purchased electricity required.
[0082] Step 103: Generate a production schedule for a preset future time period based on the target strategy.
[0083] Optionally, step 103 specifically includes: responding to the target strategy being the first strategy, constructing and solving a first model; wherein, the green electricity sold to the grid and the power allocated for local production at each time step within a preset future time period are used as the first decision variables of the first model; with the goal of maximizing total revenue, and under the premise of satisfying the equipment safety operation constraints, based on real-time electricity market prices and local production profit data, solving for the first optimal solution of the first decision variables; and using the first optimal solution as the production scheduling instruction at the time step granularity within the preset future time period.
[0084] Specifically, the first model discretizes the pre-defined future time period into several time steps (e.g., 24 hours divided into 24 steps). For each time step t, the decision variables are defined as follows:
[0085] P grid,sell (t): Green electricity power used for grid connection within time step t.
[0086] P local,use (t): Green electricity power used for localized plant production within time step t.
[0087] These two variables must satisfy power balance: P green (t)=P grid,sell (t)+P local,use (t)+P curtail (t), where P green (t) represents the predicted wind and solar power, P curtail (t) represents the redundant power (which should approach zero during optimization).
[0088] The objective function is to maximize the total revenue, and its mathematical expression is:
[0089] ;
[0090] in:
[0091] T represents the total number of time steps within a preset future time period.
[0092] The feed-in tariff (RMB / kWh) is the feed-in tariff for a time step t.
[0093] The selling price of green ammonia (yuan / ton) needs to be converted from power consumption to ammonia output through energy consumption coefficient.
[0094] The overall conversion efficiency from electricity to ammonia (including hydrogen production and ammonia production efficiency).
[0095] The time step (e.g., 1 hour).
[0096] The first model must satisfy the following set of constraints:
[0097] Equipment safety constraints: The operating parameters of all chemical equipment must be within their safe limits.
[0098] Power balance constraints: As mentioned in the power balance formula above, they will not be repeated here.
[0099] Grid access constraints: Power supplied to the grid P grid,sell (t) The transmission capacity limit of the line shall not be exceeded.
[0100] Nonnegativity constraint: All decision variables are greater than or equal to zero.
[0101] Then, mathematical programming (e.g., mixed-integer linear programming) is used to solve the first model to obtain the first optimal solution, which is the P of a series of time series. grid,sell (t) and P local,use (t) Optimal value. This sequence is the production scheduling instruction with time step granularity, which clearly specifies the specific allocation scheme of green electricity for each hour.
[0102] Optionally, step 103 further includes: in response to the target strategy being the second strategy, constructing and solving a second model; wherein the purchase time and power of external grid electricity at each time step within a preset future time period are used as the second decision variables of the second model; with the goal of minimizing operating costs, and under the premise of satisfying the production task completion rate, the second optimal solution of the second decision variables is solved based on time-of-use electricity price data; and the second optimal solution is used as the production scheduling instruction at the time step granularity within the preset future time period.
[0103] Specifically, the second model discretizes the pre-defined future time period into several time steps (e.g., 24 hours divided into 24 steps). For each time step t, the decision variables are defined as follows:
[0104] P grid,buy (t): The electrical power purchased from the external power grid within a time step t. This variable is a continuous non-negative variable, and its value is limited by the grid connection capacity.
[0105] The objective function is to minimize the total operating cost, i.e., the cost of purchased electricity. Its mathematical expression is:
[0106] ;
[0107] in:
[0108] T represents the total number of time steps within a preset future time period.
[0109] The time-of-use electricity price (RMB / kWh) is the time step t.
[0110] P grid,buy (t) represents the power purchased (kW or MW) at time step t.
[0111] The time step (e.g., 1 hour).
[0112] The model must satisfy the following set of constraints:
[0113] Production task completion rate constraint (hard constraint): Ensure that the total power supply meets production demand. Let the total power consumption of the production task be E. demand Then we have:
[0114] ;
[0115] Where P green (t) represents the predicted green electricity power during time period t. This constraint guarantees the complete delivery of the order.
[0116] Equipment safety operation constraints: The operating parameters of the entire production system under the combined supply of green electricity and purchased electricity must meet its safety limits, such as load range and load change rate. These constraints are consistent with those described in the first strategy and will not be repeated here.
[0117] Grid interaction constraints: Purchased power P grid,buy (t) must not exceed the upper limit of line transmission capacity P grid,max That is, 0≤P grid,buy (t)≤P grid,max .
[0118] Power balance constraint: The total power supplied by the system (green electricity + purchased electricity) equals the total power consumed (production load + plant auxiliary load).
[0119] Then, mathematical programming is used to solve the second model to obtain the second optimal solution, which is a series of time series P. grid,buy (t) Optimal value. This sequence specifies the power that needs to be purchased from the grid at each time step. Combined with the predicted power of green electricity, a complete production scheduling instruction with time step granularity can be obtained.
[0120] Optionally, step 103 further includes: in response to the target strategy being the third strategy, constructing and solving a third model; wherein the external power grid electricity required to maintain maximum technical capacity is used as the third decision variable of the third model; with the goal of achieving the maximum technical capacity of the system, and under the premise of satisfying the safety operation constraints of production equipment, the third optimal solution of the third decision variable is solved based on the difference between the maximum power consumption and the total power generation; and the third optimal solution is used as a production scheduling instruction with time step granularity within a preset future time period.
[0121] Specifically, the third model discretizes the pre-defined future time period into several time steps (e.g., 24 hours divided into 24 steps). For each time step t, the decision variables are defined as follows:
[0122] P grid,supplement (t): The electrical power required from the external power grid to maintain the system's maximum technical capacity within a time step t.
[0123] The objective function is to maximize system output, i.e., to drive the system to operate at its maximum technical capacity. Since the maximum technical capacity is a fixed value, this objective is equivalent to ensuring that the total energy consumption of the system reaches the maximum power consumption E. max Its mathematical expression is:
[0124] ;
[0125] And limited by , where P local,use (t) represents the power used for local production during time period t, which is the green electricity power P. green (t) and supplementary grid power P grid,supplement The objective can be expressed as finding a set of P(t) functions. grid,supplement (t) makes the total energy consumption of the system... Infinitely close to (less than or equal to) E max At the same time, it satisfies all safety constraints.
[0126] The third model must satisfy the following set of constraints:
[0127] Maximum capacity energy consumption constraint: The total energy consumption of the system should reach the maximum power consumption E. max :
[0128] ;
[0129] Equipment safety operation constraints: When the system is running at maximum load, the operating parameters of all equipment must strictly meet their safety limits. For example, the ammonia synthesis load NH3 load (t) must satisfy NH3 lmin ≤NH3 load (t)≤NH3 lmax The variable load rate must meet the rate requirement. nh3min ≤ NH3 load (t) / t≤rate nh3max These constraints ensure the safety of the optimization results during the production process.
[0130] Power balance constraint: At each time step, we have:
[0131] P green (t)+Pgrid,supplement (t)=P local,use (t)+P loss (t);
[0132] Where P local,use (t) is the local production load, P loss (t) represents the system loss.
[0133] Grid interaction constraints: Supplementary power purchase P grid,supplement (t) must not exceed the upper limit of line transmission capacity P grid,max That is, 0≤P grid,supplement (t)≤P grid,max .
[0134] Based on the maximum power consumption E max With total power generation The difference The key to solving the model is how to reduce the total difference while satisfying all safety constraints. Appropriately allocate time steps, that is, determine P. grid,supplement The time series of (t) is then analyzed. Mathematical programming is then used to solve for the third optimal solution. This solution defines the required grid power replenishment at each time step, thus forming a production scheduling instruction.
[0135] Optionally, see Figure 3 It can be seen that industrial production scheduling methods also include:
[0136] Step 104: Collect actual operating data in real time during the production scheduling process and compare the actual operating data with the expected operating data of the production scheduling plan;
[0137] Step 105: If the deviation between the expected operating data and the actual operating data is greater than the deviation threshold, the production schedule will be adjusted.
[0138] Based on steps 101 to 105 above, a green electricity-hydrogen-ammonia coupling system will be used as an example for explanation. Assume a green electricity-hydrogen-ammonia chemical plant needs to plan its production schedule for the next 24 hours. The plant's production system includes wind and solar power generation facilities, a water electrolysis hydrogen production unit, an air separation nitrogen production unit, an ammonia synthesis unit, and a hydrogen storage device. The output of wind and solar power generation fluctuates due to natural conditions; its typical prediction curve is shown below. Figure 4 As shown, assume the factory needs to fulfill its daily orders (e.g., producing 1000 tons of green ammonia) while considering market electricity price fluctuations and equipment safety constraints. The production scheduling objective is to maximize the green electricity consumption ratio and economic benefits while meeting order fulfillment requirements. This can be achieved through the following steps:
[0139] Step 1: Acquisition of multi-source input data
[0140] The production system collects multi-source data in real time from external data sources for a preset future time period (24 hours in this example):
[0141] Wind and solar forecast data: including short-term forecasts (wind and solar power output in the next 10 days) and ultra-short-term forecasts (wind and solar power output in the next 4 hours), used to assess the availability of green electricity.
[0142] Market data: Time-of-use electricity price and capacity fee, spot transaction on-grid electricity price (based on real-time contracts), and order ammonia price (based on sales agreements).
[0143] Production task data: The order demand is 1,000 tons of green ammonia, which needs to be converted into equivalent electricity demand (based on the unit product electricity consumption coefficient).
[0144] Step 2: Setting Equipment Safety Operation Constraints
[0145] To ensure equipment safety, the following hard constraints are set:
[0146] 1. Electrolyzer confinement (hydrogen production unit):
[0147] Start-stop time limit: H2 start ≥stime set (The startup time must be greater than the minimum safe interval).
[0148] Load range limit: H2 lmin ≤H2 load ≤H2 lmax (The operating load is between the minimum and maximum values that are technically permissible).
[0149] Variable load rate limit: rate h2min ≤rate h2 ≤rate h2max (The rate of load change per unit time must be within the equipment's tolerance range).
[0150] 2. Constraints of the air separation unit (nitrogen production unit):
[0151] Load range limit: N2 lmin ≤N2 load ≤N2 lmax .
[0152] Variable load rate limit: rate n2min ≤rate n2 ≤rate n2max .
[0153] 3. Synthesis tower constraint (ammonia production unit):
[0154] Load range limitation: NH3 lmin ≤NH3 load ≤NH3lmax .
[0155] Variable load rate limit: rate nh3min ≤rate nh3 ≤rate nh3max .
[0156] 4. Constraints of hydrogen storage tanks:
[0157] Capacity Limit: SOH min ≤SOH≤SOH max (The hydrogen storage capacity is between the upper and lower limits of safety).
[0158] Hydrogen charge / discharge rate limit: rate sohmin ≤rate soh ≤rate sohmax .
[0159] Step 3: Setting Electricity Usage Principles
[0160] Define clear energy dispatch rules as boundary conditions for model optimization:
[0161] Priority rule: Local wind and solar power generation will be fully utilized first, with surplus electricity sold to the grid, and any shortfall supplemented by the municipal power grid.
[0162] Physical equilibrium constraints:
[0163] ;
[0164] Where, p solor p wind For wind and solar power generation, p nh3 p h2 p n2 For ammonia production, hydrogen production, and air separation power consumption, p aux p is an auxiliary load within the plant. ele For the power sold to the grid, p1 supply p2 supply Inject power into the power supply circuit, p1 consume p2 consume To extract power for the power circuit.
[0165] Step 4: Building an intelligent scheduling model
[0166] Multi-objective optimization models can be constructed based on mixed-integer linear programming, with the core elements including:
[0167] Decision variables: Define the hourly power allocation sequence within 24 hours (e.g., wind and solar grid-connected power, hydrogen production load, and purchased electricity power).
[0168] Objective function: Combining economic efficiency and green objectives, the expression is:
[0169] obj=a1(rev ele +rev nh3 )-a2·p grid ;
[0170] Where a1 and a2 are weighting coefficients, rev ele For electricity sales revenue, rev nh3 For the revenue from ammonia sales, p grid This represents the amount of mains electricity used (the negative sign indicates minimization).
[0171] Constraint integration: The device constraints of step 2 and the power principles of step 3 are embedded into the model as hard constraints.
[0172] Step 5: Pre-calculation of production targets
[0173] To avoid setting targets that deviate from actual production capacity, key production capacity indicators should be calculated in advance:
[0174] Optimal daily capacity: Based on wind and solar forecast data, simulate the maximum economic output under the "load follows source" mode (such as the saturated output when only green electricity is used).
[0175] Maximum daily capacity: Calculate the theoretical maximum output considering the upper limit of equipment capacity and capacity fee constraints.
[0176] Order demand comparison: In this example, the order is 1000 tons. If the optimal capacity is 1200 tons and the maximum capacity is 1500 tons, then the demand is between the two.
[0177] Step 6: Production Mode and Target Setting
[0178] Automatic strategy selection through quantitative comparison:
[0179] Condition judgment: The order demand (1000 tons) is less than the optimal capacity (1200 tons). Therefore, the production system selects the "load follows source" mode to maximize the consumption of green electricity and economic benefits.
[0180] Target setting: The production target is set at 1200 tons (making full use of surplus green electricity).
[0181] Step 7: Generation and Output of Production Scheduling Results
[0182] After solving the model, an hourly production schedule is generated, and its visualization results are as follows: Figure 5 The diagram shows the timing arrangement of load and power distribution for each device:
[0183] Production scheduling plans are output at two levels of granularity:
[0184] Current plan: hourly load commands (e.g., the hydrogen production unit is set to 80% load from 08:00 to 12:00), with fixed output times daily.
[0185] Rolling planning: The instructions for the next 4 hours are updated every 15 minutes based on ultra-short-term forecasts to correct deviations caused by wind and light fluctuations.
[0186] Steps 8-9: Dynamic Monitoring and Plan Updates
[0187] The production system collects actual operating data (such as wind and solar power output, equipment load) in real time and compares it with expected values:
[0188] Deviation detection: If the actual wind and solar power is lower than the predicted value by more than a threshold (e.g., 10%), re-optimization is triggered.
[0189] Plan Update: Re-execute steps 5-7 to generate adjusted production scheduling instructions and ensure the adaptability of the production system.
[0190] This embodiment achieves the following through the steps described above: Equipment constraints serve as hard boundaries, eliminating the risk of overload operation. Under the "load follows source" mode, surplus green electricity is prioritized for grid connection and sale (such as during midday peak electricity price periods), increasing revenue. Local consumption of wind and solar power is maximized to reduce dependence on grid power. A rolling update mechanism effectively addresses fluctuations in wind and solar power. An hourly intelligent scheduling solution is provided for the green electricity-hydrogen-ammonia production system, significantly improving the precision of production management.
[0191] Example 2
[0192] Corresponding to the aforementioned embodiments of the industrial production scheduling method for green electricity-based ammonia synthesis, this disclosure also provides embodiments of the industrial production scheduling system for green electricity-based ammonia synthesis.
[0193] Figure 6 A schematic diagram of an industrial production scheduling system for green electricity-based ammonia synthesis, provided as an exemplary embodiment of this disclosure, is shown. The system includes:
[0194] This disclosure provides an industrial scheduling system for green electricity-based ammonia synthesis, the industrial scheduling system comprising:
[0195] The acquisition module 21 is used to acquire the predicted data of green electricity generation within a preset future time period, as well as the task data of the production tasks to be completed.
[0196] The planning module 22 is used to quantitatively compare the forecast data with the task data and determine the target strategy from the predefined scheduling strategy based on the comparison results.
[0197] The generation module 23 is used to generate a production schedule for a preset future time period based on the target strategy.
[0198] Optionally, the planning module 22 is specifically used for:
[0199] Determine the total power generation provided by green electricity generation as represented by the forecast data, the total electricity consumption required to complete the production tasks as represented by the task data, and the maximum electricity consumption corresponding to operation at maximum technical capacity within a preset future time period.
[0200] If the total power generation is greater than or equal to the total power consumption, the first strategy is selected; the first strategy is implemented by constructing a first model with the objective function of maximizing the total system revenue.
[0201] If the total power generation is less than the total power consumption, and the total power consumption is less than or equal to the maximum power consumption, then the second strategy is selected. The second strategy is implemented by constructing a second model with the production task completion rate as a constraint and the minimization of operating costs as the objective function.
[0202] If the total electricity consumption exceeds the maximum electricity consumption, the third strategy is selected; the third strategy is implemented by constructing a third model with the objective function of achieving the maximum technical capacity of the system under safety constraints.
[0203] Optionally, module 23 is generated, specifically for:
[0204] In response to the target strategy being the first strategy, a first model is constructed and solved; wherein, the green electricity sold to the grid and the power allocated for local production at each time step within a preset future time period are used as the first decision variables of the first model;
[0205] With the goal of maximizing total revenue, and under the premise of satisfying the constraints of safe equipment operation, the first optimal solution for the first decision variable is obtained based on real-time electricity market price and local production profit data.
[0206] The first optimal solution is used as the production scheduling instruction with time step granularity within a preset future time period.
[0207] Optionally, module 23 is generated, specifically for:
[0208] In response to the objective strategy being the second strategy, a second model is constructed and solved; wherein, the purchase time and power of external grid electricity at each time step within a preset future time period are used as the second decision variables of the second model;
[0209] With the goal of minimizing operating costs and under the premise of meeting the production task completion rate, the second optimal solution for the second decision variable is obtained based on time-of-use electricity price data.
[0210] The second optimal solution is used as the production scheduling instruction with time step granularity within a preset future time period.
[0211] Optionally, module 23 is generated, specifically for:
[0212] In response to the objective strategy being the third strategy, a third model is constructed and solved; wherein, the external grid power required to maintain maximum technical capacity is used as the third decision variable of the third model;
[0213] With the goal of achieving the system's maximum technical capacity, and under the premise of satisfying the safety operation constraints of production equipment, the third optimal solution of the third decision variable is obtained based on the difference between the maximum power consumption and the total power generation.
[0214] The third optimal solution is used as the production scheduling instruction with time step granularity within a preset future time period.
[0215] Optionally, the industrial scheduling system may also include:
[0216] The comparison module is used to collect actual operating data in real time during the production scheduling process and compare the actual operating data with the expected operating data of the production scheduling plan.
[0217] The adjustment module is used to trigger adjustments to the production schedule when the deviation between the expected operating data and the actual operating data exceeds a deviation threshold.
[0218] For the system embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs.
[0219] Example 3
[0220] Figure 7 This is a schematic diagram of the structure of an electronic device according to an example embodiment of the present disclosure. The electronic device includes a memory, a processor, and a computer program stored in the memory and used to run on the processor. When the processor executes the computer program, it implements the industrial production scheduling method for green electricity-to-ammonia synthesis as described in any of the above embodiments. Figure 7 The electronic device 90 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.
[0221] like Figure 7 As shown, the electronic device 90 can be manifested as a general-purpose computing device, such as a server device. The components of the electronic device 90 may include, but are not limited to: at least one processor 91, at least one memory 92, and a bus 93 connecting different system components (including memory 92 and processor 91).
[0222] Bus 93 includes a data bus, an address bus, and a control bus.
[0223] The memory 92 may include volatile memory, such as random access memory (RAM) 921 and / or cache memory 922, and may further include read-only memory (ROM) 923.
[0224] The memory 92 may also include a program tool 925 (or utility) having a set (at least one) program module 924, such program module 924 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.
[0225] The processor 91 executes various functional applications and data processing by running computer programs stored in the memory 92, such as the industrial production scheduling method for green electricity ammonia synthesis provided in any of the above embodiments.
[0226] Electronic device 90 can also communicate with one or more external devices 94 (e.g., keyboard, pointing device, etc.). This communication can be performed through input / output (I / O) interface 95. Furthermore, electronic device 90 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public network, such as the Internet) via network adapter 96. As shown, network adapter 96 communicates with other modules of electronic device 90 via bus 93. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with electronic device 90, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems.
[0227] It should be noted that although several units / modules or sub-units / modules of the electronic device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.
[0228] Example 4
[0229] This disclosure also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the industrial production scheduling method for green electricity-based ammonia synthesis provided in any of the above embodiments.
[0230] The readable storage medium may be more specifically adopted, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.
[0231] Example 5
[0232] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the industrial production scheduling method for green electricity-based ammonia synthesis as described above.
[0233] The program code for executing the computer program product of this disclosure can be written in any combination of one or more programming languages, and the program code can be executed entirely on a user device, partially on a user device, as a stand-alone software package, partially on a user device and partially on a remote device, or entirely on a remote device.
[0234] While specific embodiments of this disclosure have been described above, those skilled in the art should understand that these are merely illustrative examples, and the scope of protection of this disclosure is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principles and essence of this disclosure, but all such changes and modifications fall within the scope of protection of this disclosure.
Claims
1. A method for industrial production of ammonia by green electricity, characterized in that, The industrial production scheduling method includes: Obtain forecast data on green electricity generation within a preset future time period, as well as task data for production tasks to be completed; The predicted data is quantitatively compared with the task data, and the target strategy is determined from the predefined scheduling strategy based on the comparison results. The production schedule for the preset future time period is generated based on the target strategy.
2. The industrial scheduling method according to claim 1, characterized in that, The step of quantitatively comparing the predicted data with the task data and determining the target strategy from the predefined scheduling strategy based on the comparison results includes: Determine the total power generation provided by green electricity generation as represented by the predicted data, the total electricity consumption required to complete the production task as represented by the task data, and the maximum electricity consumption corresponding to operation at maximum technical capacity within the preset future time period; If the total power generation is greater than or equal to the total power consumption, a first strategy is selected; the first strategy is implemented by constructing a first model with the goal of maximizing the total system revenue. In response to the total power generation being less than the total power consumption, and the total power consumption being less than or equal to the maximum power consumption, a second strategy is selected; the second strategy is implemented by constructing a second model with production task completion rate as a constraint and minimization of operating cost as the objective function; If the total electricity consumption exceeds the maximum electricity consumption, a third strategy is selected; the third strategy is implemented by constructing a third model with the objective function of achieving the maximum technical capacity of the system under safety constraints.
3. The industrial scheduling method according to claim 2, characterized in that, The step of generating the production schedule for the preset future time period based on the target strategy includes: In response to the target strategy being the first strategy, the first model is constructed and solved; wherein, the green electricity sold to the grid and the power allocated for local production at each time step within the preset future time period are used as the first decision variables of the first model. With the goal of maximizing total revenue, and under the premise of satisfying the constraints of safe equipment operation, the first optimal solution for the first decision variable is obtained based on real-time electricity market price and local production profit data. The first optimal solution is used as the production scheduling instruction with time step granularity within the preset future time period.
4. The industrial scheduling method according to claim 2, characterized in that, The step of generating the production schedule for the preset future time period based on the target strategy includes: In response to the target strategy being the second strategy, the second model is constructed and solved; wherein, the purchase time and purchase power of external grid electricity at each time step within the preset future time period are used as the second decision variables of the second model; With the goal of minimizing operating costs and under the premise of meeting the production task completion rate, the second optimal solution of the second decision variable is obtained based on time-of-use electricity price data; The second optimal solution is used as the production scheduling instruction with time step granularity within the preset future time period.
5. The industrial scheduling method according to claim 2, wherein, The step of generating the production schedule for the preset future time period based on the target strategy includes: In response to the target strategy being the third strategy, the third model is constructed and solved; wherein, the external grid power required to maintain maximum technical capacity is used as the third decision variable of the third model; With the goal of achieving the system's maximum technical capacity, and under the premise of satisfying the safety operation constraints of production equipment, the third optimal solution of the third decision variable is obtained based on the difference between the maximum power consumption and the total power generation. The third optimal solution is used as the production scheduling instruction with time step granularity within the preset future time period.
6. The industrial scheduling method according to claim 1, characterized in that, The industrial production scheduling method also includes: The actual operating data during the execution of the production schedule is collected in real time, and the actual operating data is compared with the expected operating data of the production schedule. If the deviation between the expected operating data and the actual operating data exceeds a deviation threshold, the production schedule is adjusted.
7. A green electricity-based ammonia synthesis industrial production system, characterized in that, The industrial scheduling system includes: The acquisition module is used to acquire the predicted data of green electricity generation within a preset future time period, as well as the task data of the production tasks to be completed. The planning module is used to quantitatively compare the predicted data with the task data, and determine the target strategy from the predefined scheduling strategy based on the comparison results. The generation module is used to generate a production schedule for the preset future time period based on the target strategy.
8. An electronic device comprising a memory, a processor, and a computer program stored on the memory for running on the processor, characterized in that, When the processor executes the computer program, it implements the industrial production scheduling method for green electrolytic ammonia synthesis as described in any one of claims 1 to 6.
9. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by the processor, it implements the industrial production scheduling method for green electricity-synthesized ammonia as described in any one of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the industrial production scheduling method for green electricity-synthesized ammonia as described in any one of claims 1 to 6.