Green electricity hydrogen ammonia optimal configuration method and system, computer equipment and storage medium

Abstract

The invention discloses a green electricity hydrogen ammonia optimal configuration method and system, computer equipment and a storage medium. The method comprises the steps that firstly, relevant parameters in green electricity hydrogen ammonia optimal configuration are obtained; setting change ranges of wind power scale, photovoltaic scale, off-grid electric quantity, electric energy storage scale, electrolytic bath scale, hydrogen storage scale, synthetic tower scale and ammonia storage scale; initializing a particle swarm in the change range; designing a fitness function, and calculating a fitness value of each particle in the particle swarm in combination with wind power, photovoltaic, electric energy storage, hydrogen energy storage, hydrogen production electrolytic bath, synthetic tower equipment unit investment construction cost and annual operation and maintenance cost, synthetic ammonia annual output and off-grid electricity price; constraint conditions are set, a simulated annealing particle swarm algorithm is adopted to carry out optimization solution on fitness values of particles in a particle swarm, a global optimal solution is obtained, and wind power, photovoltaic, electric energy storage, hydrogen storage, a hydrogen production electrolytic bath, a synthesis tower and ammonia storage scales are uniformly configured. And under the condition of increasing the optimization variables, the calculation time can be remarkably shortened, and the calculation efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of weakly grid-connected wind-solar-hydrogen-ammonia storage planning, and in particular, relates to a green electricity hydrogen-ammonia optimization configuration method, system, computer equipment and storage medium. Background Art

[0002] The initial investment of weakly grid-connected green power hydrogen-ammonia integrated projects is large. Different power scales and load combinations seriously affect the overall economic efficiency. How to balance reliable energy supply, economic feasibility, and stable operation is a practical problem. The capacity optimization configuration method of traditional wind-solar-hydrogen storage and ammonia synthesis system is mainly based on traversal. After adding unknown variables that need to be optimized, the calculation time increases exponentially, and there are problems such as low calculation efficiency and long iteration time. Summary of the invention

[0003] In view of the above technical problems, the present invention provides a green electricity hydrogen and ammonia optimization configuration method, system, computer equipment and storage medium.

[0004] The technical solution adopted by the present invention to solve the technical problem is: A method for optimizing the configuration of green electricity hydrogen and ammonia, the method comprising the following steps: S100: Obtain the unit investment and construction costs and annual operation and maintenance costs, annual output of synthetic ammonia, and off-grid electricity prices of wind power, photovoltaic power, electric energy storage, hydrogen energy storage, hydrogen production electrolyzer, and synthesis tower equipment in the green electricity hydrogen and ammonia integrated projects that need to be uniformly configured; S200: Set the range of wind power scale, photovoltaic scale, off-grid power, electric energy storage scale, electrolyzer scale, hydrogen storage scale, synthesis tower scale, and ammonia storage scale; initialize the particle swarm, each particle represents a combination of wind power scale, photovoltaic scale, off-grid power, electric energy storage scale, electrolyzer scale, hydrogen storage scale, synthesis tower scale, and ammonia storage scale strategies; S300: Design a fitness function, and calculate the fitness value of each particle in the particle swarm according to the wind power scale, photovoltaic scale, grid-connected power, electric energy storage scale, electrolyzer scale, hydrogen storage scale, synthesis tower scale, and ammonia storage scale of the particles in the particle swarm, combined with the unit investment and construction cost and annual operation and maintenance cost of wind power, photovoltaic, electric energy storage, hydrogen energy storage, hydrogen electrolyzer, and synthesis tower equipment, the annual output of synthetic ammonia, and the grid-connected electricity price, i.e., the unit ammonia production cost; S400: Set constraints and use simulated annealing particle swarm algorithm to optimize the fitness values ​​of particles in the particle swarm to obtain the global optimal solution. According to the global optimal solution, uniformly configure the scale of wind power, photovoltaics, electric energy storage, hydrogen storage, hydrogen production electrolyzer, synthesis tower, and ammonia storage.

[0005] Preferably, S300 includes: S310: according to the wind power scale, photovoltaic scale, grid-connected electricity, electric energy storage scale, electrolyzer scale, hydrogen storage scale, synthesis tower scale, and ammonia storage scale of each particle in the particle group, combined with the unit investment and construction costs of wind power, photovoltaic, electric energy storage, hydrogen energy storage, hydrogen electrolyzer, and synthesis tower equipment, the initial investment costs of wind power, photovoltaic, electric energy storage, hydrogen electrolyzer, hydrogen storage, synthesis tower, and ammonia storage are obtained; S320: Calculate the grid-connected power of the green electricity hydrogen-ammonia integration project based on the wind power scale, photovoltaic scale, grid-connected power, electric energy storage scale, electrolyzer scale, hydrogen storage scale, synthesis tower scale and ammonia storage scale of each particle in the particle group; S330: The present value conversion annual value coefficient is calculated based on the social average capital rate of return and the full life cycle operation period of the green power hydrogen and ammonia integrated project; S340: The total cost of purchasing electricity from the grid is calculated based on the grid-connected electricity volume and grid-connected electricity price of the green electricity hydrogen-ammonia integration project; S350: The fitness value, i.e., the unit ammonia production cost, is calculated based on the initial investment cost of wind power, photovoltaic power, electric energy storage, hydrogen production electrolyzer, hydrogen storage, synthesis tower, and ammonia storage, the present value conversion annual value coefficient, the total cost of purchasing electricity from the offline grid, and the annual operation and maintenance costs of wind power, photovoltaic power, electric energy storage, hydrogen production electrolyzer, hydrogen storage, synthesis tower, and ammonia storage, as well as the annual output of synthetic ammonia.

[0006] Preferably, S310 specifically includes: ; in, For wind power scale, is the unit investment and construction cost of wind power, For photovoltaic scale, is the unit investment and construction cost of photovoltaic power, is the scale of electric energy storage, is the investment and construction cost of the electric energy storage unit, For the scale of hydrogen production electrolyzer, is the unit investment and construction cost of the hydrogen production electrolyzer, For hydrogen storage scale, is the unit investment and construction cost of hydrogen storage, For the scale of the synthesis tower, is the unit investment and construction cost of the synthesis tower, For ammonia storage scale, It is the unit investment and construction cost of ammonia storage.

[0007] Preferably, S330 specifically includes: ; in, is the social average capital return rate, The full life cycle operation period of the green power hydrogen and ammonia integrated project; S340 is specifically: ; in, The electricity generated from the green electricity hydrogen and ammonia integrated project is The grid electricity price; S350 is as follows: ; in, The initial investment cost of wind power, photovoltaic, electric energy storage, hydrogen production electrolyzer, hydrogen storage, synthesis tower, and ammonia storage, is the annual value conversion factor of present value, The annual operation and maintenance costs of wind power, photovoltaic power, electric energy storage, hydrogen production electrolyzer, hydrogen storage, synthesis tower, and ammonia storage. The total cost of purchasing electricity online is: is the annual output of synthetic ammonia.

[0008] Preferably, S400 includes: S410: setting constraints and initial parameters of the simulated annealing algorithm, including initial temperature, termination temperature, annealing rate, and the number of iterations at each temperature; S420: simulated annealing process, first set the current temperature as the initial temperature, then enter the annealing cycle according to the annealing rate, use the simulated annealing particle swarm algorithm to optimize the fitness value of each particle in the particle swarm, and obtain the individual fitness value with the same number of particles. After comparing each individual fitness value with the previous value, the smaller fitness value is selected as the current individual optimal solution for update. After comparing all individual optimal solutions, the minimum value is selected as the current global optimal solution for update. On the basis of the particle parameters corresponding to the global optimal solution, it is iterated again for the same number of particles; wherein, the individual fitness value must meet the constraint conditions; S430: When the current temperature drops below the termination temperature, or the fitness value converges in multiple consecutive iterations, the simulated annealing process ends, and the minimum fitness value corresponding to the global optimal solution at this time is used as the final global optimal solution; S440: Uniformly configure the scale of wind power, photovoltaics, electric energy storage, hydrogen storage, hydrogen production electrolyzers, synthesis towers, and ammonia storage based on the final global optimal solution.

[0009] Preferably, the constraints in S410 include power balance, electricity balance, hydrogen balance and ammonia balance; The power balance is as follows: ; in, is the load of the hydrogen production electrolyzer at the i-th hour on the j-th day, is the energy storage charge and discharge state at the i-th hour on the j-th day, is the energy storage charging and discharging power coefficient at the i-th hour on the j-th day, The real-time amount of power abandonment at the i-th hour on the j-th day; The specific power balance is: ; ; ; in, The annual electricity consumption for the production of synthetic ammonia is The energy consumption for electric energy storage is is the total amount of abandoned electricity, is the wind power output coefficient at the i-th hour on the j-th day, is the photovoltaic output coefficient for the i-th hour on the j-th day, is the annual equivalent full load utilization hours of wind power, The equivalent full load utilization hours of photovoltaic power generation per year; The hydrogen balance is specifically: ; in, is the hydrogen consumption of the synthesis tower at the i-th hour on the j-th day, is the amount of hydrogen produced by the hydrogen production electrolyzer at the i-th hour on the j-th day, The amount of hydrogen stored and released in the hydrogen storage tank at the i-th hour on the j-th day; The ammonia balance is specifically: ; in, The scale of synthetic ammonia demand at the i-th hour on the j-th day is taken as the goal to keep the amount of ammonia that can be sold every day basically the same. is the ammonia production of the synthesis tower at the i-th hour on the j-th day, The amount of ammonia stored and released in the ammonia storage tank at the i-th hour on the j-th day.

[0010] A green electricity hydrogen and ammonia intelligent optimization configuration system, including a parameter acquisition module, a parameter setting module, a fitness function design module and an optimization configuration module. The parameter acquisition module is used to obtain the unit investment and construction costs and annual operation and maintenance costs, annual production of synthetic ammonia, and off-grid electricity prices of wind power, photovoltaic power, electric energy storage, hydrogen energy storage, hydrogen production electrolyzers, and synthesis tower equipment in the green power hydrogen and ammonia integrated projects that need to be uniformly configured; The parameter setting module is used to set the range of wind power scale, photovoltaic scale, grid-connected power, electric energy storage scale, electrolyzer scale, hydrogen storage scale, synthesis tower scale, and ammonia storage scale; initialize the particle swarm, each particle represents a combination of wind power scale, photovoltaic scale, grid-connected power, electric energy storage scale, electrolyzer scale, hydrogen storage scale, synthesis tower scale, and ammonia storage scale strategies; The fitness function design module is used to design the fitness function. According to the wind power scale, photovoltaic scale, grid-connected power, electric energy storage scale, electrolyzer scale, hydrogen storage scale, synthesis tower scale, and ammonia storage scale of the particles in the particle swarm, the fitness value of each particle in the particle swarm, i.e., the unit ammonia production cost, is calculated by combining the unit investment and construction cost and annual operation and maintenance cost of wind power, photovoltaic, electric energy storage, hydrogen energy storage, hydrogen electrolyzer, and synthesis tower equipment, the annual output of synthetic ammonia, and the grid-connected electricity price; The optimization configuration module is used to set constraints and use the simulated annealing particle swarm algorithm to optimize the fitness values ​​of particles in the particle swarm to obtain the global optimal solution. According to the global optimal solution, the scale of wind power, photovoltaics, electric energy storage, hydrogen storage, hydrogen production electrolyzer, synthesis tower, and ammonia storage are uniformly configured.

[0011] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of a green electricity hydrogen and ammonia optimization configuration method when executing the computer program.

[0012] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a green electricity hydrogen-ammonia optimization configuration method.

[0013] The green electricity hydrogen and ammonia optimization configuration method, system, computer equipment and storage medium proposed in the present invention integrate multiple elements such as wind and solar power sources, hydrogen production tower load, electricity, hydrogen and ammonia storage and grid-connected power, and calculate the capacity ratio with the lowest unit ammonia production cost by adopting the simulated annealing particle swarm algorithm, which solves the contradiction between too many unknown elements and too long calculation time in the configuration algorithm of the green electricity hydrogen and ammonia integrated project, and improves the calculation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The present invention is a flowchart of a method for optimizing the configuration of green electricity hydrogen and ammonia in one embodiment of the present invention. DETAILED DESCRIPTION

[0015] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings.

[0016] In one embodiment, Figure 1 As shown, a method for optimizing the configuration of green electricity hydrogen and ammonia comprises the following steps: S100: Obtain the unit investment and construction costs and annual operation and maintenance costs, annual output of synthetic ammonia, and off-grid electricity prices of wind power, photovoltaic power, electric energy storage, hydrogen energy storage, hydrogen production electrolyzer, and synthesis tower equipment in the green electricity hydrogen and ammonia integrated projects that need to be uniformly configured; S200: Set the range of wind power scale, photovoltaic scale, off-grid power, electric energy storage scale, electrolyzer scale, hydrogen storage scale, synthesis tower scale, and ammonia storage scale; initialize the particle swarm, each particle represents a combination of wind power scale, photovoltaic scale, off-grid power, electric energy storage scale, electrolyzer scale, hydrogen storage scale, synthesis tower scale, and ammonia storage scale strategies; S300: Design a fitness function, and calculate the fitness value of each particle in the particle swarm according to the wind power scale, photovoltaic scale, grid-connected power, electric energy storage scale, electrolyzer scale, hydrogen storage scale, synthesis tower scale, and ammonia storage scale of the particles in the particle swarm, combined with the unit investment and construction cost and annual operation and maintenance cost of wind power, photovoltaic, electric energy storage, hydrogen energy storage, hydrogen electrolyzer, and synthesis tower equipment, the annual output of synthetic ammonia, and the grid-connected electricity price, i.e., the unit ammonia production cost; S400: Set constraints and use simulated annealing particle swarm algorithm to optimize the fitness values ​​of particles in the particle swarm to obtain the global optimal solution. According to the global optimal solution, uniformly configure the scale of wind power, photovoltaics, electric energy storage, hydrogen storage, hydrogen production electrolyzer, synthesis tower, and ammonia storage.

[0017] In one embodiment, S300 includes: S310: according to the wind power scale, photovoltaic scale, grid-connected electricity, electric energy storage scale, electrolyzer scale, hydrogen storage scale, synthesis tower scale, and ammonia storage scale of each particle in the particle group, combined with the unit investment and construction costs of wind power, photovoltaic, electric energy storage, hydrogen energy storage, hydrogen electrolyzer, and synthesis tower equipment, the initial investment costs of wind power, photovoltaic, electric energy storage, hydrogen electrolyzer, hydrogen storage, synthesis tower, and ammonia storage are obtained; S320: Calculate the grid-connected power of the green electricity hydrogen-ammonia integration project based on the wind power scale, photovoltaic scale, grid-connected power, electric energy storage scale, electrolyzer scale, hydrogen storage scale, synthesis tower scale and ammonia storage scale of each particle in the particle group; S330: The present value conversion annual value coefficient is calculated based on the social average capital rate of return and the full life cycle operation period of the green power hydrogen and ammonia integrated project; S340: The total cost of purchasing electricity from the grid is calculated based on the grid-connected electricity volume and grid-connected electricity price of the green electricity hydrogen-ammonia integration project; S350: The fitness value, i.e., the unit ammonia production cost, is calculated based on the initial investment cost of wind power, photovoltaic power, electric energy storage, hydrogen production electrolyzer, hydrogen storage, synthesis tower, and ammonia storage, the present value conversion annual value coefficient, the total cost of purchasing electricity from the offline grid, and the annual operation and maintenance costs of wind power, photovoltaic power, electric energy storage, hydrogen production electrolyzer, hydrogen storage, synthesis tower, and ammonia storage, as well as the annual output of synthetic ammonia.

[0018] Specifically, the average social capital return rate and the full life cycle operating period of the green electricity hydrogen-ammonia integrated project are generally 6% and 25 years.

[0019] In one embodiment, S310 specifically includes: ; in, For wind power scale, is the unit investment and construction cost of wind power, For photovoltaic scale, is the unit investment and construction cost of photovoltaic power, is the scale of electric energy storage, is the investment and construction cost of the electric energy storage unit, For the scale of hydrogen production electrolyzer, is the unit investment and construction cost of the hydrogen production electrolyzer, For hydrogen storage scale, is the unit investment and construction cost of hydrogen storage, For the scale of the synthesis tower, is the unit investment and construction cost of the synthesis tower, For ammonia storage scale, It is the unit investment and construction cost of ammonia storage.

[0020] In one embodiment, S330 specifically includes: ; in, is the social average capital return rate, The full life cycle operation period of the green power hydrogen and ammonia integrated project; S340 is specifically: ; in, The electricity generated from the green electricity hydrogen and ammonia integrated project is The grid electricity price; S350 is as follows: ; in, The initial investment cost of wind power, photovoltaic, electric energy storage, hydrogen production electrolyzer, hydrogen storage, synthesis tower, and ammonia storage, is the annual value conversion factor of present value, The annual operation and maintenance costs of wind power, photovoltaic power, electric energy storage, hydrogen production electrolyzer, hydrogen storage, synthesis tower, and ammonia storage. The total cost of purchasing electricity online is: is the annual output of synthetic ammonia.

[0021] In one embodiment, S400 includes: S410: setting constraints and initial parameters of the simulated annealing algorithm, including initial temperature, termination temperature, annealing rate, and the number of iterations at each temperature; S420: simulated annealing process, first set the current temperature as the initial temperature, then enter the annealing cycle according to the annealing rate, use the simulated annealing particle swarm algorithm to optimize the fitness value of each particle in the particle swarm, and obtain the individual fitness value with the same number of particles. After comparing each individual fitness value with the previous value, the smaller fitness value is selected as the current individual optimal solution for update. After comparing all individual optimal solutions, the minimum value is selected as the current global optimal solution for update. On the basis of the particle parameters corresponding to the global optimal solution, it is iterated again for the same number of particles; wherein, the individual fitness value must meet the constraint conditions; S430: When the current temperature drops below the termination temperature, or the fitness value converges in multiple consecutive iterations, the simulated annealing process ends, and the minimum fitness value corresponding to the global optimal solution at this time is used as the final global optimal solution; S440: Uniformly configure the scale of wind power, photovoltaics, electric energy storage, hydrogen storage, hydrogen production electrolyzers, synthesis towers, and ammonia storage based on the final global optimal solution.

[0022] In one embodiment, the constraints in S410 include power balance, electricity balance, hydrogen balance, and ammonia balance; The power balance is as follows: ; in, is the load of the hydrogen production electrolyzer at the i-th hour on the j-th day, is the energy storage charge and discharge state at the i-th hour on the j-th day, is the energy storage charging and discharging power coefficient at the i-th hour on the j-th day, The real-time amount of power abandonment at the i-th hour on the j-th day; The specific power balance is: ; ; ; in, The annual electricity consumption for the production of synthetic ammonia is The energy consumption for electric energy storage is is the total amount of abandoned electricity, is the wind power output coefficient at the i-th hour on the j-th day, is the photovoltaic output coefficient for the i-th hour on the j-th day, is the annual equivalent full load utilization hours of wind power, The equivalent full load utilization hours of photovoltaic power generation per year; The hydrogen balance is specifically: ; in, is the hydrogen consumption of the synthesis tower at the i-th hour on the j-th day, is the amount of hydrogen produced by the hydrogen production electrolyzer at the i-th hour on the j-th day, The amount of hydrogen stored and released in the hydrogen storage tank at the i-th hour on the j-th day; The ammonia balance is specifically: ; in, The scale of synthetic ammonia demand at the i-th hour on the j-th day is taken as the goal to keep the amount of ammonia that can be sold every day basically the same. is the ammonia production of the synthesis tower at the i-th hour on the j-th day, The amount of ammonia stored and released in the ammonia storage tank at the i-th hour on the j-th day.

[0023] Specifically, the charging and discharging state of the electric energy storage, the charging and discharging power coefficient of the electric energy storage, the real-time amount of abandoned electricity, the load size of the hydrogen production electrolyzer, the annual electricity consumption for producing synthetic ammonia, the loss of electric energy storage, the total amount of abandoned electricity, the hydrogen consumption of the synthesis tower, the hydrogen production of the hydrogen production electrolyzer, the amount of hydrogen stored and released in the hydrogen storage tank, the required scale of synthetic ammonia, the ammonia production of the synthesis tower, the amount of ammonia stored and released in the ammonia storage tank are calculated based on the strategy combination of each particle and satisfying the set constraints.

[0024] Take a synthetic ammonia project with an annual output of 150,000 tons in a province in the northwest as an example.

[0025] According to the wind power and photovoltaic output coefficients provided by the region, the annual equivalent full-load utilization hours of wind power and photovoltaic are calculated based on the output coefficient curve to be 3384.1h and 2059.0h respectively.

[0026] According to the actual situation at that time, the unit investment and construction costs of wind power, photovoltaic power, electric energy storage, hydrogen energy storage, hydrogen electrolyzer, and synthesis tower equipment in the green power hydrogen and ammonia integration project, as well as the annual operation and maintenance costs, annual synthetic ammonia production, and off-grid electricity prices are shown in Table 1 below: Table 1 ;

[0027] This green electricity hydrogen and ammonia intelligent optimization configuration method is adopted, and the unit ammonia production cost is used as the fitness function. After reaching the maximum number of iterations, the final wind power scale, photovoltaic scale, grid-connected power, electric energy storage scale, electrolyzer scale, hydrogen storage scale, synthesis tower scale, and ammonia storage scale are output as the optimal solution obtained in the simulated annealing intelligent algorithm process, as shown in Table 2. In this example, there are 8 unknown elements to be optimized, which saves more than 60% of the calculation time compared with the traditional traversal algorithm and improves the calculation accuracy by 3.1%.

[0028] Table 2 ; The above-mentioned green electricity hydrogen and ammonia optimization configuration method incorporates multiple elements such as wind and solar power sources, hydrogen production tower load, electricity, hydrogen and ammonia storage, and grid-connected power. It calculates the capacity ratio with the lowest unit ammonia production cost by adopting the simulated annealing particle swarm algorithm. It can significantly reduce the calculation time by increasing the optimization variables, and solves the problems of low efficiency in traditional traversal algorithms, too many unknown elements in the green electricity hydrogen and ammonia integrated project configuration algorithm, and the contradiction between long calculation time, thereby improving the calculation efficiency.

[0029] The green electricity hydrogen and ammonia optimization configuration system includes a parameter acquisition module, a parameter setting module, a fitness function design module and an optimization configuration module. The parameter acquisition module is used to obtain the unit investment and construction costs and annual operation and maintenance costs, annual production of synthetic ammonia, and off-grid electricity prices of wind power, photovoltaic power, electric energy storage, hydrogen energy storage, hydrogen production electrolyzers, and synthesis tower equipment in the green power hydrogen and ammonia integrated projects that need to be uniformly configured; The parameter setting module is used to set the range of wind power scale, photovoltaic scale, grid-connected power, electric energy storage scale, electrolyzer scale, hydrogen storage scale, synthesis tower scale, and ammonia storage scale; initialize the particle swarm, each particle represents a combination of wind power scale, photovoltaic scale, grid-connected power, electric energy storage scale, electrolyzer scale, hydrogen storage scale, synthesis tower scale, and ammonia storage scale strategies; The fitness function design module is used to design the fitness function. According to the wind power scale, photovoltaic scale, grid-connected power, electric energy storage scale, electrolyzer scale, hydrogen storage scale, synthesis tower scale, and ammonia storage scale of the particles in the particle swarm, the fitness value of each particle in the particle swarm, i.e., the unit ammonia production cost, is calculated by combining the unit investment and construction cost and annual operation and maintenance cost of wind power, photovoltaic, electric energy storage, hydrogen energy storage, hydrogen electrolyzer, and synthesis tower equipment, the annual output of synthetic ammonia, and the grid-connected electricity price; The optimization configuration module is used to set constraints and use the simulated annealing particle swarm algorithm to optimize the fitness values ​​of particles in the particle swarm to obtain the global optimal solution. According to the global optimal solution, the scale of wind power, photovoltaics, electric energy storage, hydrogen storage, hydrogen production electrolyzer, synthesis tower, and ammonia storage are uniformly configured.

[0030] For the specific definition of the green electricity hydrogen-ammonia optimization configuration system, please refer to the definition of the green electricity hydrogen-ammonia optimization configuration method above, which will not be repeated here. Each module in the above green electricity hydrogen-ammonia optimization configuration system can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0031] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of a green electricity hydrogen and ammonia optimization configuration method when executing the computer program.

[0032] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a green electricity hydrogen-ammonia optimization configuration method.

[0033] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0034] The above is a detailed introduction to the green electricity hydrogen and ammonia optimization configuration method, system, computer equipment and storage medium provided by the present invention. This article uses specific examples to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the core idea of ​​the present invention. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. A method for intelligent optimization configuration of green electricity hydrogen and ammonia, characterized in that: The method comprises the following steps: S100: Obtain the unit investment and construction costs and annual operation and maintenance costs, annual output of synthetic ammonia, and off-grid electricity prices of wind power, photovoltaic power, electric energy storage, hydrogen energy storage, hydrogen production electrolyzer, and synthesis tower equipment in the green electricity hydrogen and ammonia integrated projects that need to be uniformly configured; S200: Set the range of wind power scale, photovoltaic scale, off-grid power, electric energy storage scale, electrolyzer scale, hydrogen storage scale, synthesis tower scale, and ammonia storage scale; initialize the particle swarm, each particle represents a combination of wind power scale, photovoltaic scale, off-grid power, electric energy storage scale, electrolyzer scale, hydrogen storage scale, synthesis tower scale, and ammonia storage scale strategies; S300: Design a fitness function, and calculate the fitness value of each particle in the particle swarm according to the wind power scale, photovoltaic scale, grid-connected power, electric energy storage scale, electrolyzer scale, hydrogen storage scale, synthesis tower scale, and ammonia storage scale of the particles in the particle swarm, combined with the unit investment and construction cost and annual operation and maintenance cost of wind power, photovoltaic, electric energy storage, hydrogen energy storage, hydrogen electrolyzer, and synthesis tower equipment, the annual output of synthetic ammonia, and the grid-connected electricity price, i.e., the unit ammonia production cost; S400: Set constraints and use simulated annealing particle swarm algorithm to optimize the fitness values ​​of particles in the particle swarm to obtain the global optimal solution. According to the global optimal solution, uniformly configure the scale of wind power, photovoltaics, electric energy storage, hydrogen storage, hydrogen production electrolyzer, synthesis tower, and ammonia storage.

2. The method according to claim 1, characterized in that S300 includes: S310: according to the wind power scale, photovoltaic scale, grid-connected electricity, electric energy storage scale, electrolyzer scale, hydrogen storage scale, synthesis tower scale, and ammonia storage scale of each particle in the particle group, combined with the unit investment and construction costs of wind power, photovoltaic, electric energy storage, hydrogen energy storage, hydrogen electrolyzer, and synthesis tower equipment, the initial investment costs of wind power, photovoltaic, electric energy storage, hydrogen electrolyzer, hydrogen storage, synthesis tower, and ammonia storage are obtained; S320: Calculate the grid-connected power of the green electricity hydrogen-ammonia integration project based on the wind power scale, photovoltaic scale, grid-connected power, electric energy storage scale, electrolyzer scale, hydrogen storage scale, synthesis tower scale and ammonia storage scale of each particle in the particle group; S330: The present value conversion annual value coefficient is calculated based on the social average capital rate of return and the full life cycle operation period of the green power hydrogen and ammonia integrated project; S340: The total cost of purchasing electricity from the grid is calculated based on the grid-connected electricity volume and grid-connected electricity price of the green electricity hydrogen-ammonia integration project; S350: The fitness value, i.e., the unit ammonia production cost, is calculated based on the initial investment cost of wind power, photovoltaic power, electric energy storage, hydrogen production electrolyzer, hydrogen storage, synthesis tower, and ammonia storage, the present value conversion annual value coefficient, the total cost of purchasing electricity from the offline grid, and the annual operation and maintenance costs of wind power, photovoltaic power, electric energy storage, hydrogen production electrolyzer, hydrogen storage, synthesis tower, and ammonia storage, as well as the annual output of synthetic ammonia.

3. The method according to claim 2, characterized in that S310 is specifically: ； in, For wind power scale, is the unit investment and construction cost of wind power, For photovoltaic scale, is the unit investment and construction cost of photovoltaic power, For the scale of electric energy storage, is the investment and construction cost of the electric energy storage unit, For the scale of hydrogen production electrolyzer, is the unit investment and construction cost of the hydrogen production electrolyzer, For hydrogen storage scale, is the unit investment and construction cost of hydrogen storage, For the scale of the synthesis tower, is the unit investment and construction cost of the synthesis tower, For ammonia storage scale, It is the unit investment and construction cost of ammonia storage.

4. The method according to claim 3, characterized in that S330 is specifically: ； in, is the social average capital return rate, The full life cycle operation period of the green power hydrogen and ammonia integrated project; S340 is specifically: ； in, The electricity generated from the green electricity hydrogen and ammonia integrated project is The grid electricity price; S350 is as follows: ； in, The initial investment cost of wind power, photovoltaic, electric energy storage, hydrogen production electrolyzer, hydrogen storage, synthesis tower, and ammonia storage, is the annual value conversion factor of present value, The annual operation and maintenance costs of wind power, photovoltaic power, electric energy storage, hydrogen production electrolyzer, hydrogen storage, synthesis tower, and ammonia storage. The total cost of purchasing electricity online is: is the annual output of synthetic ammonia.

5. The method according to claim 4, characterized in that S400 includes: S410: setting constraints and initial parameters of the simulated annealing algorithm, including initial temperature, termination temperature, annealing rate, and the number of iterations at each temperature; S420: simulated annealing process, first set the current temperature as the initial temperature, then enter the annealing cycle according to the annealing rate, use the simulated annealing particle swarm algorithm to optimize the fitness value of each particle in the particle swarm, and obtain the individual fitness value with the same number of particles. After comparing each individual fitness value with the previous value, the smaller fitness value is selected as the current individual optimal solution for update. After comparing all individual optimal solutions, the minimum value is selected as the current global optimal solution for update. On the basis of the particle parameters corresponding to the global optimal solution, it is iterated again for the same number of particles; wherein, the individual fitness value must meet the constraint conditions; S430: When the current temperature drops below the termination temperature, or the fitness value converges in multiple consecutive iterations, the simulated annealing process ends, and the minimum fitness value corresponding to the global optimal solution at this time is used as the final global optimal solution; S440: Uniformly configure the scale of wind power, photovoltaics, electric energy storage, hydrogen storage, hydrogen production electrolyzers, synthesis towers, and ammonia storage based on the final global optimal solution.

6. The method according to claim 5, characterized in that The constraints in S410 include power balance, electricity balance, hydrogen balance, and ammonia balance; The power balance is as follows: ； in, is the load of the hydrogen production electrolyzer at the i-th hour on the j-th day, is the energy storage charge and discharge state at the i-th hour on the j-th day, is the energy storage charging and discharging power coefficient at the i-th hour on the j-th day, The real-time amount of power abandonment at the i-th hour on the j-th day; The specific power balance is: ； ； ； in, The annual electricity consumption for the production of synthetic ammonia is The energy consumption for electric energy storage is is the total amount of abandoned electricity, is the wind power output coefficient at the i-th hour on the j-th day, is the photovoltaic output coefficient for the i-th hour on the j-th day, is the annual equivalent full load utilization hours of wind power, The equivalent full load utilization hours of photovoltaic power generation per year; The hydrogen balance is specifically: ； in, is the hydrogen consumption of the synthesis tower at the i-th hour on the j-th day, is the amount of hydrogen produced by the hydrogen production electrolyzer at the i-th hour on the j-th day, The amount of hydrogen stored and released in the hydrogen storage tank at the i-th hour on the j-th day; The ammonia balance is specifically: ； in, The scale of synthetic ammonia demand at the i-th hour on the j-th day is taken as the goal to keep the amount of ammonia that can be sold every day basically the same. is the ammonia production of the synthesis tower at the i-th hour on the j-th day, The amount of ammonia stored and released in the ammonia storage tank at the i-th hour on the j-th day.

7. A green electricity hydrogen and ammonia intelligent optimization configuration system, characterized in that: It includes parameter acquisition module, parameter setting module, fitness function design module and optimization configuration module. The parameter acquisition module is used to obtain the unit investment and construction costs and annual operation and maintenance costs, annual production of synthetic ammonia, and off-grid electricity prices of wind power, photovoltaic power, electric energy storage, hydrogen energy storage, hydrogen production electrolyzers, and synthesis tower equipment in the green power hydrogen and ammonia integrated projects that need to be uniformly configured; The parameter setting module is used to set the range of wind power scale, photovoltaic scale, grid-connected power, electric energy storage scale, electrolyzer scale, hydrogen storage scale, synthesis tower scale, and ammonia storage scale; initialize the particle swarm, each particle represents a combination of wind power scale, photovoltaic scale, grid-connected power, electric energy storage scale, electrolyzer scale, hydrogen storage scale, synthesis tower scale, and ammonia storage scale strategies; The fitness function design module is used to design the fitness function. According to the wind power scale, photovoltaic scale, grid-connected power, electric energy storage scale, electrolyzer scale, hydrogen storage scale, synthesis tower scale, and ammonia storage scale of the particles in the particle swarm, the fitness value of each particle in the particle swarm, i.e., the unit ammonia production cost, is calculated by combining the unit investment and construction cost and annual operation and maintenance cost of wind power, photovoltaic, electric energy storage, hydrogen energy storage, hydrogen electrolyzer, and synthesis tower equipment, the annual output of synthetic ammonia, and the grid-connected electricity price; The optimization configuration module is used to set constraints and use the simulated annealing particle swarm algorithm to optimize the fitness values ​​of particles in the particle swarm to obtain the global optimal solution. According to the global optimal solution, the scale of wind power, photovoltaics, electric energy storage, hydrogen storage, hydrogen production electrolyzer, synthesis tower, and ammonia storage are uniformly configured.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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