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CN-121984053-A - Multi-time-scale rolling optimization scheduling method and device applied to green ammonia system

CN121984053ACN 121984053 ACN121984053 ACN 121984053ACN-121984053-A

Abstract

The application discloses a multi-time scale rolling optimization scheduling method and a device applied to a green ammonia system, which relate to the field of hydrogen production, and are characterized in that a multi-time scale wind-solar power prediction model is used for obtaining a first day-ahead multi-period prediction power array of an hour level, a second day-ahead multi-period preset power array, a sub-hour level third day-ahead multi-period prediction power array and a fourth day-ahead multi-period prediction power array in a layering manner, so that the prediction precision and fine granularity of renewable energy fluctuation are remarkably improved, wind-solar power surplus and gaps in different periods can be prospectively identified, and further key equipment operation parameters such as battery energy storage charging rate, hydrogen production power value of an electrolytic tank cluster, system power discarding, electrolytic tank hydrogen production flow rate, hydrogen storage flow rate of a hydrogen storage tank cluster and the like are adjusted in real time, and dynamic matching of electric-hydrogen-chemical multi-energy flow response characteristics is realized.

Inventors

  • ZHOU JIAHUI
  • WANG ZHENG
  • LIN WEINING
  • CAO YANG
  • ZHANG CHI
  • SHI YAO
  • LIU QIQI
  • SHAN XIAOYONG
  • LI SHIYU

Assignees

  • 华电科工股份有限公司

Dates

Publication Date
20260505
Application Date
20260126

Claims (13)

  1. 1. The multi-time scale rolling optimization scheduling method applied to the green ammonia system is characterized by comprising the following steps of: Inputting weather forecast data in a first preset time period of the future day ahead into a pre-constructed multi-time scale wind-light power prediction model at intervals of a first preset time period, and obtaining a first day ahead multi-time period forecast power array and a second day ahead multi-time period forecast power array through the multi-time scale wind-light power prediction model, wherein the first day ahead multi-time period forecast power array comprises short-term wind power generation forecast powers respectively corresponding to each hour in the first preset time period of the day ahead; Inputting weather data in a historical time period into the multi-time scale wind-light power prediction model at intervals of a second preset time period, outputting a third-day multi-period prediction power array and a fourth-day multi-period prediction power array through the multi-time scale wind-light power prediction model, wherein the third-day multi-period prediction power array comprises ultra-short-term wind power generation prediction power corresponding to each target time in a future day second preset time period, the fourth-day multi-period prediction power array comprises ultra-short-term photovoltaic power generation prediction power corresponding to each target time in the day second preset time period, the target time is less than 60 minutes, and the day-ahead first preset time period comprises the day second preset time period; Based on the first day-ahead multi-period predicted power array, the second day-ahead multi-period preset power array, the third day-ahead multi-period predicted power array and the fourth day-ahead multi-period predicted power array, the real-time battery energy storage charging rate is adjusted, the hydrogen production power value of the real-time electrolytic cell cluster is adjusted, the real-time system power discarding power is adjusted, the real-time electrolytic cell hydrogen production flow is adjusted, the real-time hydrogen storage tank cluster hydrogen release flow is adjusted, and the real-time hydrogen storage tank cluster hydrogen storage flow is adjusted.
  2. 2. The multi-time scale rolling optimization scheduling method for a green ammonia system according to claim 1, wherein the steps of adjusting a real-time battery energy storage charging rate, adjusting a hydrogen production power value of a real-time electrolytic cell cluster, adjusting a real-time system power rejection, adjusting a real-time electrolytic cell hydrogen production flow, adjusting a real-time hydrogen storage tank cluster hydrogen release flow, and adjusting a real-time hydrogen storage tank cluster hydrogen storage flow based on the first day-ahead multi-period predicted power array, the second day-ahead multi-period preset power array, the third day-ahead multi-period predicted power array, and the fourth day-ahead multi-period predicted power array include: Obtaining power consumption of the synthesis ammonia equipment corresponding to each hour in the first day-ahead preset time period respectively based on the first day-ahead multi-period predicted power array, the second day-ahead multi-period preset power array, a pre-built electrolytic cell hydrogen production equipment model, a pre-built synthesis ammonia equipment model, a pre-built hydrogen storage tank equipment model, a pre-built battery energy storage equipment model, a first objective function, a first power balance constraint and a first hydrogen balance constraint, and obtaining hydrogen consumption of the synthesis ammonia equipment corresponding to each hour in the first day-ahead preset time period respectively; The system comprises an electrolyzer hydrogen production equipment model, a synthesis ammonia equipment model, a hydrogen storage tank equipment model, a battery energy storage equipment model, a first objective function, a second objective function and a first objective function, wherein the electrolyzer hydrogen production equipment model is used for acquiring hydrogen production power of an electrolyzer cluster and hydrogen production flow of the electrolyzer; The first power balance constraint is that , wherein, Refers to the short-term wind power generation predicted power corresponding to the t hour in the first preset time period before the day, Refers to the short-term photovoltaic power generation predicted power corresponding to the t hour in the first preset time period before the day, Refers to the battery energy storage and discharge power of the battery energy storage device corresponding to the t hour in the first preset time period before the day, Refers to battery energy storage charging power of the battery energy storage device corresponding to the t hour in the first preset time period before the day, The hydrogen production power of the electrolytic tank cluster corresponding to the t hour in the first preset time period before the day, Refers to the power consumption of the ammonia synthesizing equipment corresponding to the t hour in the first preset time period before the day, The system power discarding corresponding to the t hour in the first preset time period before the day is referred to; The first hydrogen balance constraint is that , wherein, Refers to the hydrogen production amount of the electrolytic tank cluster corresponding to the t hour in the first preset time period before the day, Refers to the hydrogen release amount of the hydrogen storage tank cluster corresponding to the t hour in the first preset time period before the day, Means the stored hydrogen amount of the hydrogen storage tank cluster corresponding to the t hour in the first preset time period before the day, The hydrogen consumption amount of the ammonia synthesis equipment corresponding to the t hour in the first preset time period before the day is referred to; for each target time, determining the power consumption of the ammonia synthesis equipment corresponding to the hour to which the target time belongs as the power consumption of the ammonia synthesis equipment corresponding to the target time; For each target time, determining the hydrogen consumption of the ammonia synthesis equipment corresponding to the hour to which the target time belongs as the hydrogen consumption of the ammonia synthesis equipment corresponding to the target time; Obtaining hydrogen production power ranges of the electrolyzer clusters corresponding to each target time in the second preset time period in the day based on the third multi-period predicted power array in the third day, the fourth multi-period predicted power array in the fourth day, the power consumption of the ammonia synthesis equipment corresponding to each target time in the second preset time period in the day, the hydrogen consumption of the ammonia synthesis equipment corresponding to each target time in the second preset time period in the day, the electrolyzer hydrogen production equipment model, the ammonia synthesis equipment model, the hydrogen storage tank equipment model, the battery energy storage equipment model, a second objective function, a second power balance constraint and a second hydrogen balance constraint; the second objective function characterizes the maximum net gain of scheduling hydrogen production in a second preset time period in the day, and the second power balance constraint is that , wherein, Refers to ultra-short-term wind power generation predicted power corresponding to the t-th target time in the second preset time period in the day, Refers to ultra-short-term photovoltaic power generation predicted power corresponding to the t-th target time in the second preset time period in the day, Refers to the battery energy storage and discharge power of the battery energy storage equipment corresponding to the t-th target time in the second preset time period in the day, Refers to battery energy storage charging power of the battery energy storage device corresponding to the t-th target time in the second preset time period in the day, The hydrogen production power of the electrolytic tank cluster corresponding to the t target time in the second preset time period in the day, Refers to the power consumption of the ammonia synthesizing equipment corresponding to the t target time in the second preset time period in the day, The system power discarding corresponding to the t-th target time in the second preset time period in the day is referred to; The second hydrogen balance constraint is that , wherein, Refers to the hydrogen production amount of the electrolytic tank cluster corresponding to the t target time in the second preset time period in the day, Refers to the hydrogen release amount of the hydrogen storage tank cluster corresponding to the t-th target time in the second preset time period in the day, Means the stored hydrogen amount of the hydrogen storage tank cluster corresponding to the t-th target time in the second preset time period in the day, The hydrogen consumption amount of the ammonia synthesizing equipment corresponding to the t target time in the second preset time period in the day is referred to; determining the power consumption of the ammonia synthesis equipment corresponding to the target time to which the current time belongs, wherein the power consumption is the power consumption of the ammonia synthesis equipment corresponding to the current time; determining the hydrogen consumption of the ammonia synthesis equipment corresponding to the target time to which the current time belongs, wherein the hydrogen consumption is the hydrogen consumption of the ammonia synthesis equipment corresponding to the current time; Determining the hydrogen production power range of the electrolytic cell cluster corresponding to the target time to which the current time belongs, wherein the hydrogen production power range is the hydrogen production power range of the electrolytic cell cluster corresponding to the current time; Based on the wind power generation power of the current time, the photovoltaic power generation prediction power of the current time, the hydrogen production power range of the electrolytic tank cluster corresponding to the current time, the power consumption of the synthesis ammonia equipment corresponding to the current time, the hydrogen consumption of the synthesis ammonia equipment corresponding to the current time, the battery storage and release power of the current time, a third objective function, a third power balance constraint and a third hydrogen balance constraint, the real-time battery energy storage charging rate is adjusted, the hydrogen production power value of the real-time electrolytic tank cluster is adjusted, the system power discarding power is adjusted, the hydrogen production flow of the real-time electrolytic tank is adjusted, the hydrogen release flow of the real-time hydrogen storage tank cluster is adjusted, and the hydrogen storage flow of the real-time hydrogen storage tank cluster is adjusted; wherein the third objective function represents that the current time has the minimum electric discarding penalty coefficient, and the third electric power balance constraint is that , wherein, Refers to the wind power generation predicted power corresponding to the current time t, Refers to the photovoltaic power generation predicted power corresponding to the current time t, Refers to the battery energy storage and discharge power of the battery energy storage equipment corresponding to the current time t, Refers to battery energy storage charging power of battery energy storage equipment corresponding to the current time t, Refers to hydrogen production power of an electrolytic tank cluster corresponding to the current time t, Refers to the power consumption of the ammonia synthesizing equipment corresponding to the current time t, The system power discarding corresponding to the current time t is referred; the third hydrogen balance constraint is that , wherein, Refers to the hydrogen production amount of the electrolytic tank cluster corresponding to the current time t, Refers to the hydrogen release amount of the hydrogen storage tank cluster corresponding to the current time t, Refers to the stored hydrogen quantity of the hydrogen storage tank cluster corresponding to the current time t, Refers to the amount of hydrogen consumed by the ammonia plant corresponding to the current time t.
  3. 3. The multi-time scale rolling optimization scheduling method applied to the green ammonia system according to claim 1, wherein the calculation expression of the synthetic ammonia equipment model is: Wherein, the Is the power consumption of the ammonia synthesizing equipment at the time t, The ammonia production amount of the ammonia synthesis equipment at the time t; The hydrogen consumption of the ammonia synthesizing equipment at the moment t; the water consumption of the ammonia synthesizing equipment at the time t; the power consumption coefficient for the unit ammonia production of the ammonia synthesis equipment; Hydrogen consumption coefficient for ammonia production per unit of ammonia synthesis equipment; Water consumption coefficient for ammonia production per unit of ammonia synthesis equipment; the ammonia synthesis equipment is in a steady state operation state in a first target time period and is in a flexible operation state in a second target time period; The operating state of the ammonia synthesis device is as follows: , wherein, The steady-state operation state variable of the ammonia synthesis equipment at the moment t is 1 if the ammonia synthesis equipment is in a steady-state operation state, otherwise, the steady-state operation variable is 0; the flexible operation state variable of the ammonia synthesis equipment at the moment t is 1 if the ammonia synthesis equipment is in the flexible operation state, otherwise, the flexible operation state variable is 0; The ammonia synthesis equipment model in which the ammonia synthesis equipment is in a steady state operation state is as follows: , wherein, Discrete multistable load rates for the ammonia plant; The synthetic ammonia equipment model in which the synthetic ammonia equipment is in a flexible operation state is as follows: , a time constant for the switching of the ammonia plant from the steady state operating condition to the flexible operating condition switching process.
  4. 4. The multi-time scale rolling optimization scheduling method applied to the green ammonia system according to claim 1, wherein the start-stop scheduling characteristic calculation expression of the electrolytic cell hydrogen production equipment model is as follows: , wherein, The number of the online operation electrolytic cells of the electrolytic cell cluster at the moment t is increased by a state variable, if the number of the online operation electrolytic cells is increased, the number is 1, otherwise, the number is 0; the number of the online operation electrolytic cells of the electrolytic cell cluster at the moment t is reduced by a state variable, if the number of the online operation electrolytic cells is increased, the number is 1, otherwise, the number is 0; The number of the online running electrolytic cells of the electrolytic cell cluster at the moment t is not changed, if the number of the online running electrolytic cells is not changed, the number is 1, otherwise, the number is 0; the number of the electrolytic cells is the number of the electrolytic cell clusters running on line at the time t+1; The calculation expression of the electrolytic cell hydrogen production equipment model is as follows: , wherein, For t time electrolytic cell cluster the number of line operated cells; A total number of all cells in the cell cluster; The hydrogen production power of the online running electrolytic cells in the electrolytic cell cluster at the time t; rated operating power of a single electrolytic tank; Producing hydrogen for the electrolytic cell cluster at the moment t; the water consumption of the electrolytic tank cluster at the moment t; the hydrogen production electricity consumption coefficient of the electrolytic cell cluster; hydrogen production water consumption coefficient for the electrolytic cell cluster; the lower limit coefficient of the operation power of a single electrolytic tank; The upper limit coefficient of the operation power of a single electrolytic tank; Exemplary, the calculation expression of the model time delay and the starting power loss of the electrolyzer hydrogen production equipment is as follows: Wherein, the method comprises the steps of, The power consumption is the starting power consumption of the electrolytic tank cluster at the moment t; The power consumption coefficient is used for starting the electrolytic tank cluster; the calculation expression of the total electric power of the electrolytic cell hydrogen production equipment model is as follows: 。
  5. 5. the multi-time scale rolling optimization scheduling method applied to the green ammonia system according to claim 1, wherein the calculation expression of the hydrogen storage tank equipment model in the stable running state is: , wherein, The mass of hydrogen in the hydrogen storage tank cluster at the time t; the hydrogen storage quality in the hydrogen storage tank cluster at the time t-1; storing the hydrogen mass for the hydrogen storage tank cluster at the time t; Releasing hydrogen flow for the hydrogen storage tank cluster at the moment t; scheduling a time interval for the system; The state variable is stored for the hydrogen storage tank cluster at the time t, if the hydrogen storage tank cluster is in a hydrogen storage state, the state variable is 1, otherwise, the state variable is 0; The hydrogen storage tank cluster release state variable is 1 if the hydrogen storage tank cluster is in a hydrogen release state, otherwise, the hydrogen storage tank cluster release state variable is 0; maximum stored hydrogen mass for a single hydrogen storage tank; The pressure of the hydrogen storage tank cluster at the time t; Minimum pressure for a single hydrogen storage tank; maximum pressure for a single hydrogen storage tank; rated volume for a single hydrogen storage tank; Releasing hydrogen flow for the hydrogen storage tank cluster at the moment t; is hydrogen gas constant; the average hydrogen temperature in the hydrogen storage tank cluster; is the total number of hydrogen storage tanks in the hydrogen storage tank cluster.
  6. 6. The multi-time scale rolling optimization scheduling method applied to the green ammonia system according to claim 1, wherein the calculation expression of the battery energy storage equipment model is: Wherein, the The energy storage electric quantity of the battery energy storage device at the moment t; the energy storage electric quantity of the battery energy storage device at the time t-1; the battery energy storage and charging power of the battery energy storage equipment at the moment t; The battery energy storage and discharge power of the battery energy storage equipment at the moment t; The battery energy storage and charging efficiency of the battery energy storage device; The battery energy storage and discharge efficiency of the battery energy storage equipment; The battery energy storage self-loss rate of the battery energy storage equipment; the method comprises the steps that a battery energy storage state variable of the battery energy storage device at t moment is 1 if the battery energy storage device is in a state of charge, and is 0 if the battery energy storage device is in the state of charge; the method comprises the steps that the energy storage and discharge state variable of the battery energy storage device at the moment t is 1 if the battery energy storage device is in a discharge state, and is 0 if the battery energy storage device is in the discharge state; the maximum energy storage capacity is stored for the battery; a lower limit coefficient of the energy storage capacity of the battery; and the energy storage capacity upper limit coefficient of the battery.
  7. 7. The multi-time scale rolling optimized scheduling method applied to a green ammonia system according to claim 1, wherein the first objective function is: Wherein, the Scheduling a net gain of synthetic ammonia for the system day before; Scheduling product revenue for the system day before; Scheduling raw material costs for the system day before; and scheduling the running cost of the equipment for the system day before.
  8. 8. The multi-time scale rolling optimized scheduling method applied to a green ammonia system according to claim 1, wherein the second objective function is: , wherein, Scheduling a net hydrogen production benefit (meta) for the system within a day; Scheduling product revenue for the system within a day; scheduling raw material costs for a system day; and scheduling the running cost of the equipment for the system in a day.
  9. 9. The multi-time scale rolling optimized scheduling method for use in a green ammonia system according to claim 1, wherein the third objective function is , wherein, Penalty coefficients for discarding electricity; the electric quantity is discarded at the moment of real time T, and T is the preset accumulated time length.
  10. 10. A multi-time scale rolling optimized scheduling device applied to a green ammonia system, comprising: The system comprises a first acquisition module, a second acquisition module and a first prediction module, wherein the first acquisition module is used for inputting weather forecast data in a first preset time period in the future into a pre-built multi-time scale wind-light power prediction model at intervals of a first preset time period, acquiring a first day-ahead multi-time-period prediction power array and a second day-ahead multi-time-period prediction power array through the multi-time scale wind-light power prediction model, wherein the first day-ahead multi-time-period prediction power array comprises short-term wind power generation prediction powers respectively corresponding to each hour in the first preset time period in the day-ahead; The system comprises a first acquisition module, a second acquisition module, a third day multi-period prediction power array, a fourth day multi-period prediction power array, a third day multi-period prediction power array, a fourth day multi-period prediction power array, a first day pre-period prediction power set and a second day pre-period prediction power set, wherein weather data in a historical time period are input into the multi-time scale wind-light power prediction model at intervals of a first preset time period, the weather data in the historical time period are input into the multi-time scale wind-light power prediction model, the target time is smaller than 60 minutes, the third day multi-period prediction power array comprises ultra-short-period wind power generation prediction power respectively corresponding to each target time in a future day second preset time period, the fourth day multi-period prediction power array comprises ultra-short-period photovoltaic power respectively corresponding to each target time in the day second preset time period, and the first day pre-period comprises the day second preset time period; The adjusting module is used for adjusting the real-time battery energy storage charging rate, adjusting the real-time hydrogen production power value of the electrolytic tank cluster, adjusting the real-time system power discarding, adjusting the real-time electrolytic tank hydrogen production flow, adjusting the real-time hydrogen storage tank cluster release hydrogen flow and adjusting the real-time hydrogen storage tank cluster store hydrogen flow based on the first day-ahead multi-period predicted power array, the second day-ahead multi-period preset power array, the third day-ahead multi-period predicted power array and the fourth day-ahead multi-period predicted power array.
  11. 11. A computer program product comprising computer readable instructions which, when run on an electronic device, cause the electronic device to implement the multi-time scale rolling optimized scheduling method of any one of claims 1 to 9 applied to a green ammonia system.
  12. 12. An electronic device comprising at least one processor and a memory coupled to the processor, wherein: The memory is used for storing a computer program; the processor is configured to execute the computer program to enable the electronic device to implement the multi-time scale rolling optimized scheduling method for application to a green ammonia system according to any one of claims 1 to 9.
  13. 13. A computer storage medium carrying one or more computer programs which, when executed by an electronic device, enable the electronic device to implement the multi-time scale rolling optimized scheduling method for use in a green ammonia system according to any one of claims 1 to 9.

Description

Multi-time-scale rolling optimization scheduling method and device applied to green ammonia system Technical Field The application relates to the technical field of hydrogen production, in particular to a multi-time scale rolling optimization scheduling method and device applied to a green ammonia system. Background With the deep advancement of low carbonization transformation of global energy structures, hydrogen energy is considered as one of the most potential clean energy sources due to its efficient energy carrier properties and important industrial raw material values. The hydrogen can realize energy storage and allocation across time, region and field, and has wide downstream application fields in the fields of electric power, traffic, industry and the like. However, due to the inherent physicochemical properties of hydrogen, such as extremely low volumetric energy density, poor material compatibility, high energy consumption for liquefaction, and the like, the large-scale storage and economic transportation of hydrogen face significant technical bottlenecks and high cost constraints, and the large-scale development of the hydrogen energy industry is severely restricted. In this context, a novel electrochemical technology path for preparing green hydrogen by electrolyzing water by using renewable energy sources such as wind energy, solar energy and the like and further synthesizing green ammonia (green ammonia) provides a systematic solution for realizing the above challenges. The path takes hydrogen as an energy conversion medium, and the fluctuating electric energy is stored in green chemical products with stable chemical properties and mature storage and transportation infrastructure, so that the method not only can realize large-scale absorption and efficient utilization of renewable energy sources and effectively solve the problem of hydrogen storage and transportation, but also can assist the industrial field to replace the traditional fossil energy raw materials and remarkably reduce carbon emission. However, the green ammonia system still faces the intrinsic technical contradiction in engineering operation practice that the strong randomness and intermittence of wind-light power generation have fundamental conflict with the rigid operation requirement of the large chemical ammonia synthesis process. Particularly, the significant response dynamics difference exists among the electric energy flow, the hydrogen flow and the chemical material flow in the green ammonia system, namely the electric energy response speed can reach millisecond to second, the hydrogen energy is regulated and controlled to be adapted to minute, and the chemical ammonia synthesis process is slow in reaction and requires long-time stability of load working conditions due to the fact that complex chemical reaction dynamics and strict process safety boundaries are involved. Based on this, how to coordinate such trans-order multi-energy flow coupling characteristics is a technical problem that those skilled in the art are urgently required to solve. Disclosure of Invention In view of the above problems, the application provides a multi-time scale rolling optimization scheduling method and device applied to a green ammonia system, so as to achieve the purpose of coordinating cross-order multi-energy flow coupling characteristics. The specific scheme is as follows: the first aspect of the application provides a multi-time scale rolling optimization scheduling method applied to a green ammonia system, which comprises the following steps: Inputting weather forecast data in a first preset time period of the future day ahead into a pre-constructed multi-time scale wind-light power prediction model at intervals of a first preset time period, and obtaining a first day ahead multi-time period forecast power array and a second day ahead multi-time period forecast power array through the multi-time scale wind-light power prediction model, wherein the first day ahead multi-time period forecast power array comprises short-term wind power generation forecast powers respectively corresponding to each hour in the first preset time period of the day ahead; Inputting weather data in a historical time period into the multi-time scale wind-light power prediction model at intervals of a second preset time period, outputting a third-day multi-period prediction power array and a fourth-day multi-period prediction power array through the multi-time scale wind-light power prediction model, wherein the third-day multi-period prediction power array comprises ultra-short-term wind power generation prediction power corresponding to each target time in a future day second preset time period, the fourth-day multi-period prediction power array comprises ultra-short-term photovoltaic power generation prediction power corresponding to each target time in the day second preset time period, the target time is less than 60 minutes, and the day-ahead first prese