Day-ahead scheduling method and device for wind, light, hydrogen and ammonia source network load storage integrated park

Abstract

The invention relates to a day-ahead scheduling method and device for a wind-light-hydrogen-ammonia source network load storage integrated park, and is applied to the technical field of day-ahead scheduling management. Comprising the following steps: through collection and preprocessing of input data such as electricity market price data, park equipment parameters, time sequence reference data, park equipment initial operation state data and modeling parameters of public and auxiliary equipment, the integrity and compatibility of the input data are ensured; then, a model is built for core units such as hydrogen production, synthesis ammonia and a storage tank, and a complete constraint system is formed; thirdly, by taking the minimum net operation cost as a target, integrating the operation cost, the penalty term and the operation income to construct an optimization function, solving by adopting a mixed integer linear programming algorithm, and generating a day-ahead 15-minute-level scheduling plan; through standardized, refined and collaborative design, feasibility, accuracy and continuity of a scheduling plan are ensured.

Description

Technical Field

[0001] This invention relates to the field of day-ahead scheduling management technology, specifically to a day-ahead scheduling method and device for an integrated wind-solar-hydrogen-ammonia power-load-storage park. Background Technology

[0002] The inherent intermittency and volatility of wind and solar resources lead to significant curtailment of wind and solar power, resulting not only in resource waste but also impacting the safe and stable operation of the power grid. Meanwhile, the demand for low-carbon energy carriers such as green hydrogen and green ammonia continues to grow in sectors like chemical engineering and transportation, making integrated industrial parks combining wind and solar power generation, water electrolysis for hydrogen production, hydrogen-ammonia conversion, and energy storage a key direction for balancing new energy consumption with decarbonization of high-carbon industries.

[0003] Such industrial parks face a dual core challenge: First, there is significant uncertainty on both the supply and demand sides. On the supply side, wind and solar power output is highly random due to weather factors; on the demand side, the loads of cooling, heating, and electricity, as well as the loads of hydrogen, ammonia, and chemical industries, exhibit spatiotemporal coupling characteristics, further exacerbating the difficulty of matching supply and demand. Second, the operation of multiple units within the park is strongly coupled with constraints. Units such as wind and solar power generation, electrolytic hydrogen production, hydrogen and ammonia synthesis, and hydrogen and ammonia storage need to operate in coordination, and multiple objectives such as economic efficiency and reliability need to be balanced. Traditional dispatching models are no longer suitable for the complex characteristics of the system.

[0004] Existing research methods for daytime dispatching of integrated wind-solar-hydrogen-ammonia systems suffer from several drawbacks: simplified core unit modeling, distorted characterization of equipment operation, incomplete constraint coverage, and insufficient feasibility and compliance of dispatching schemes. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a day-ahead scheduling method and device for an integrated wind-solar-hydrogen-ammonia power-load-storage park, which aims to solve the problems of simplified modeling of core units, distorted characterization of equipment operation characteristics, incomplete coverage of constraint systems, and insufficient feasibility and compliance of scheduling schemes in the prior art.

[0006] According to a first aspect of the present invention, a day-ahead scheduling method for an integrated wind-solar-hydrogen-ammonia power-load-storage park is provided, the method comprising: The input data includes: electricity market price data, park equipment parameters, time series reference data, initial operating status data of park equipment, and modeling parameters of auxiliary equipment; The acquired input data is preprocessed to obtain standardized input data; Set a penalty term for the objective function, and construct a first objective function based on the standardized input data with the goal of minimizing net operating cost; The constraints for the first objective function are constructed, including: power balance constraints, hydrogen storage tank constraints, electrolyzer constraints, and ammonia synthesis constraints. The calculation of demand power increment, grid-connected power deviation, abandoned power deviation and ammonia sales deviation is introduced into the first objective function, and the penalty coefficients of grid-connected power deviation, abandoned power deviation and ammonia sales deviation are added to the penalty term of the first objective function to obtain the second objective function; Set constraints for the demand power increment, on-grid power deviation, abandoned power deviation, and ammonia sales deviation; Under the premise of satisfying all constraints, the second objective function is solved using a mixed-integer linear programming algorithm to obtain the decision variables for the next day.

[0007] Preferably, The electricity market price data includes: spot node electricity prices and time-of-use electricity prices for 96 15-minute time periods on the next day; as well as the electricity prices of medium- and long-term contracts signed with market users for each time period on the next day and the corresponding settlement reference point prices; ammonia sales prices, basic electricity unit prices, and demand electricity unit prices; The parameters of the park equipment include: the rated parameters and operating constraints of the hydrogen production unit, ammonia synthesis unit, storage tank unit, wind power unit, and power grid interaction unit in the park; The time-series reference data includes: ultra-short-term wind power forecast data for 96 15-minute periods the next day, meteorological forecast data for 96 15-minute periods the next day; and reference values ​​for on-grid power generation, abandoned power generation, ammonia sales, and demand control targets for each period the next day, output by multi-day dispatch.

[0008] Preferably, The preprocessing of the acquired input data to obtain standardized input data includes: Verify the completeness of critical equipment parameters and trigger an alarm when required parameters are missing; Data exceeding the equipment's rated range is truncated, and missing temperature forecast data in meteorological forecast data is supplemented using the historical average for the same period. All data are formatted uniformly according to time period to ensure that the data in each time period accurately matches the optimization window, resulting in standardized input data.

[0009] Preferably, The net operating cost is calculated using operating costs, penalty items, and operating revenue. The operating costs include electricity purchase costs and demand costs. The electricity purchase costs are calculated using the basic electricity price per unit and the power output to the grid. The demand costs are calculated using the demand electricity price per unit and the demand capacity. The penalties include: penalties for exceeding the limits for electricity output to the grid, penalties for exceeding the limits for electricity output to the grid, penalties for exceeding the limits for electricity abandonment, penalties for the number of start-ups and shutdowns of four-to-one generating units, penalties for the number of start-ups and shutdowns of electrolytic cells, penalties for the operating time of four-to-one generating units, and penalties for the operating time of electrolytic cells. The penalty for exceeding the limit of offline power consumption is calculated by using a preset penalty coefficient for exceeding the limit of offline power consumption and the amount of exceeding the limit of offline power consumption. The penalty for exceeding the internet power limit is calculated based on a preset penalty coefficient for exceeding the internet power limit and the amount of internet power exceeding the limit. The penalty for exceeding the limit on abandoned electricity is calculated using a pre-set penalty coefficient for exceeding the limit on abandoned electricity and the amount of abandoned electricity exceeding the limit. The penalty for the number of start-ups and shutdowns of the four-to-one unit is calculated by using a pre-set penalty coefficient for the uniform distribution of the number of start-ups and shutdowns of the four-to-one unit and the number of start-ups and shutdowns of the four-to-one unit. The penalty for the number of start-ups and shutdowns of the electrolytic cell is calculated by using a pre-set uniform penalty coefficient for the number of start-ups and shutdowns of the electrolytic cell and the number of start-ups and shutdowns of the electrolytic cell. The runtime penalty for the four-to-one unit is calculated by using a pre-set uniform runtime distribution penalty coefficient for the four-to-one unit and the runtime of the four-to-one unit. The running time penalty of the electrolytic cell is calculated by using a preset uniform penalty coefficient for the running time distribution of the electrolytic cell and the running time of the electrolytic cell. The operating revenue includes revenue from ammonia sales and revenue from electricity sales; The revenue from ammonia sales is calculated using the ammonia sales price and the ammonia sales rate. The revenue from electricity sales includes short-term revenue from electricity sales and medium- and long-term revenue from electricity sales. The short-term electricity sales revenue is calculated based on the electricity sales price and the grid-connected power. The revenue from medium- and long-term electricity sales is calculated by the difference between the medium- and long-term contract power with market users, the medium- and long-term price with market users, and the settlement reference point price in the region where the medium- and long-term contract market users are located.

[0010] Preferably, The power balance constraint is the sum of wind power generation and grid-connected power, which is equal to the sum of hydrogen production power, ammonia synthesis-related auxiliary power, other auxiliary power, grid-sold power, and abandoned power.

[0011] Preferably, The constraints on the hydrogen storage tank include state-space equation constraints, upper and lower limits of storage capacity constraints, and constraints linking outlet flow rate and pressure. The state-space equation constraint is the hydrogen storage tank's final storage volume at the end of the time period, which is equal to the initial storage volume of the hydrogen storage tank at the beginning of the time period plus the difference between the inlet flow rate and the outlet flow rate of the hydrogen storage tank during the time period. The upper and lower limits of the storage capacity are that the storage capacity of the hydrogen storage tank shall not exceed the rated minimum and maximum storage capacity range of the equipment. The outlet flow rate and pressure linkage constraint is as follows: when the hydrogen storage tank pressure is higher than the preset critical value, the hydrogen compressor is turned off and the outlet flow rate is unlimited; when the hydrogen storage tank pressure is lower than the preset critical value, the compressor is turned on and the outlet flow rate must not exceed the maximum allowable flow rate.

[0012] Preferably, The constraints of the electrolyzer include: hydrogen production conversion constraints, upper and lower load limits constraints, four-to-one system optimization constraints, and electrolyzer state and power optimization constraints. The hydrogen production conversion constraint is a linear fitting constraint on the hydrogen production cluster power-hydrogen production curve. The upper and lower limits of the load constraint mean that the hydrogen production power must not exceed the upper and lower limits of the hydrogen production load. The four-to-one system optimization constraints include: four-to-one state switching constraints, upper limit constraints on the number of purification start-stop times, and upper and lower limit constraints on the four-to-one unit load. The four-to-one state switching constraint includes: using a set of 0-1 variables to represent the state of the four-to-one unit at any time, the state of the four-to-one unit includes shutdown maintenance state, maintenance restart state, normal operation state, hot standby state, and hot standby restart state; using 0-1 variables to represent the initial state, intermediate and final state of the four-to-one unit, and maintenance / fault state respectively. The upper limit constraint on the number of purification start-stops is that, within the optimization window, the number of purification start-stops for each group of four pairs of one unit shall not exceed the maximum number of purification start-stops within the optimization window. The upper and lower limits of the load of the four-to-one unit are as follows: when the four-to-one unit is in a shutdown or hot standby state, the hydrogen production power is 0; when the four-to-one unit is in operation or start-up state, the hydrogen production power is within the preset upper and lower limits. The electrolytic cell state and power optimization constraints include electrolytic cell state switching constraints, upper limit constraints on the number of switching current cycles, and upper and lower limit constraints on the electrolytic cell load. The electrolytic cell state switching constraints include: using a set of 0-1 variables to represent the state of the electrolytic cell at any time, the state of the electrolytic cell includes shutdown and maintenance state, start-up state after maintenance, normal operation state and hot standby state; using 0-1 variables to represent the initial state, intermediate and final state and maintenance / fault state of the electrolytic cell respectively. The upper limit constraint on the number of switching currents is that, within the optimization window, the number of switching currents for each electrolytic cell shall not exceed the maximum number of switching currents within the optimization window. The upper and lower limits of the electrolyzer load are constrained such that when the electrolyzer is in a shutdown or hot standby state, the hydrogen production power is 0; when the electrolyzer is in a running or start-up state, the hydrogen production power is within the preset upper and lower limits.

[0013] Preferably, The ammonia synthesis constraints include: ammonia synthesis load constraints and upper and lower limits constraints for ammonia synthesis load; The ammonia synthesis load constraint is: the product of the ammonia production rate and the slope of the ammonia synthesis-related auxiliary power consumption-ammonia production rate fitting curve, plus the intercept of the ammonia synthesis-related auxiliary power consumption-ammonia production rate fitting curve, equals the power of the ammonia synthesis-related auxiliary. The upper and lower limits of the ammonia load are as follows: when the ammonia load is in hot standby mode, the outlet hydrogen flow rate of the hydrogen storage tank is 0; when the ammonia load is in normal operation mode, the outlet hydrogen flow rate of the hydrogen storage tank shall not exceed the hydrogen flow rate range corresponding to the upper and lower limits of the ammonia load.

[0014] Preferably, The second objective function includes: In the first objective function, the demand charge is calculated by introducing the demand power increment; Add follow-up penalties for grid-connected electricity, abandoned electricity, and ammonia sales to the penalty term of the first objective function; The penalty for internet power consumption is calculated using a pre-set penalty coefficient for internet power consumption deviation and the internet power consumption deviation itself. The penalty for abandoned power is calculated using a pre-set penalty coefficient for abandoned power deviation and abandoned power deviation. The penalty for the ammonia sales volume is calculated using a pre-set penalty coefficient for the ammonia sales volume deviation and the ammonia sales volume deviation. The deviation of the on-grid power is the difference between the on-grid power calculated through monthly scheduling optimization and the on-grid power output for each time period of the next day as output by the multi-day scheduling; the deviation of the abandoned power is the difference between the abandoned power calculated through monthly scheduling optimization and the abandoned power output for each time period of the next day as output by the multi-day scheduling; the deviation of the ammonia sales is the difference between the ammonia sales calculated through monthly scheduling optimization and the ammonia sales output for each time period of the next day as output by the multi-day scheduling. The demand power increment is the difference between the demand control target and the actual power output.

[0015] According to a second aspect of the present invention, a day-ahead dispatching device for an integrated wind-solar-hydrogen-ammonia power-load-storage park is provided, the device comprising: Data acquisition module: used to acquire input data, including: electricity market price data, park equipment parameters, time series reference data, initial operating status data of park equipment, and modeling parameters of auxiliary equipment; Data preprocessing module: used to preprocess the acquired input data to obtain standardized input data; First objective function construction module: used to set the penalty term of the objective function, and construct the first objective function with the goal of minimizing the net operating cost based on the standardized input data; First constraint construction module: used to construct the constraint conditions of the first objective function, the constraint conditions include: power balance constraint, hydrogen storage tank constraint, electrolyzer constraint and ammonia synthesis constraint; The second objective function construction module is used to introduce the calculation of demand power increment, grid-connected power deviation, abandoned power deviation and ammonia sales deviation into the first objective function, and to add penalty coefficients for grid-connected power deviation, abandoned power deviation and ammonia sales deviation to the penalty term of the first objective function to obtain the second objective function. The second constraint construction module is used to set the constraint conditions for the demand power increment, the on-grid power deviation, the abandoned power deviation, and the ammonia sales deviation. The solution module is used to solve the second objective function using a mixed-integer linear programming algorithm, under the premise of satisfying all constraints, to obtain the decision variables for the next day.

[0016] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This application ensures the integrity and compatibility of input data by collecting and preprocessing input data such as electricity market price data, park equipment parameters, time-series reference data, initial operating status data of park equipment, and modeling parameters of auxiliary equipment. Then, models are built for core units such as hydrogen production, ammonia synthesis, and storage tanks to form a complete constraint system. Next, with the goal of minimizing net operating cost, an optimization function is constructed by integrating operating cost, penalty term, and operating revenue, and solved using a mixed-integer linear programming algorithm to generate a 15-minute-ahead scheduling plan. Through standardized, refined, and collaborative design, the feasibility, accuracy, and continuity of the scheduling plan are ensured.

[0017] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0019] Figure 1 This is a flowchart illustrating a day-ahead scheduling method for an integrated wind-solar-hydrogen-ammonia power grid-load-storage park according to an exemplary embodiment; Figure 2 This is a schematic diagram of a day-ahead dispatching device for an integrated wind-solar-hydrogen-ammonia power grid-load-storage park, according to another exemplary embodiment. In the attached diagram: 1-Data acquisition module, 2-Data preprocessing module, 3-First objective function construction module, 4-First constraint construction module, 5-Second objective function construction module, 6-Second constraint construction module, 7-Solving module. Detailed Implementation

[0020] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0021] Example 1 Figure 1 This is a flowchart illustrating a day-ahead scheduling method for an integrated wind-solar-hydrogen-ammonia power grid-load-storage park according to an exemplary embodiment, as shown below. Figure 1 As shown, the method includes: S1, Obtain input data, which includes: electricity market price data, park equipment parameters, time series reference data, initial operating status data of park equipment, and modeling parameters of auxiliary equipment; S2, preprocess the acquired input data to obtain standardized input data; S3, Set the penalty term for the objective function, and construct a first objective function based on the standardized input data with the goal of minimizing net operating cost; S4, construct the constraints of the first objective function, the constraints include: power balance constraints, hydrogen storage tank constraints, electrolyzer constraints and ammonia synthesis constraints; S5, the calculation of demand power increment, grid-connected power deviation, abandoned power deviation and ammonia sales deviation is introduced into the first objective function, and the penalty coefficients of grid-connected power deviation, abandoned power deviation and ammonia sales deviation are added to the penalty term of the first objective function to obtain the second objective function; S6, set the constraints for the demand power increment, the on-grid power deviation, the abandoned power deviation, and the ammonia sales deviation; S7. Under the premise of satisfying all constraints, the second objective function is solved by a mixed integer linear programming algorithm to obtain the decision variables for the next day. It is understood that the overall concept of this invention revolves around the entire process of day-ahead scheduling in an integrated wind-solar-hydrogen-ammonia power-load-storage park: "standardized data input - full-unit modeling - multi-objective optimization solution - standardized result output." With "minimizing net operating cost" as the core objective, it considers equipment safety and the coordination between upper and lower level scheduling to construct a closed-loop scheduling management scheme. This includes: First, collecting and preprocessing basic parameters, time-series predictions, and upper-level reference values ​​based on standardized data formats to ensure the integrity and compatibility of the input data; then, transforming the reference values ​​of grid-connected electricity, abandoned electricity, and ammonia sales output from multiple days of scheduling into day-ahead scheduling constraints through a multi-time-series connection mechanism, while simultaneously constructing models for core units such as hydrogen production, ammonia synthesis, and storage tanks to form a complete constraint system; next, with the goal of minimizing net operating cost, integrating operating costs, penalty terms, and operating benefits to construct an optimization function, and solving it using a mixed-integer linear programming algorithm to generate a day-ahead 15-minute level scheduling plan; finally, outputting the scheduling results in a standardized format, ensuring the feasibility, accuracy, and continuity of the scheduling plan through standardized, refined, and collaborative design. Specifically, it includes the following: Data collection: The optimization mode was determined to be day-ahead scheduling, and the optimization period was determined to be 96 15-minute time slots on the next day. Optimization configuration parameters such as calculation accuracy, time limit, and penalty coefficient were obtained. Electricity market price data: Collects spot node electricity prices and time-of-use electricity prices for 96 15-minute time periods the following day, electricity prices for medium- and long-term contracts signed with market users for each time period the following day and the corresponding settlement reference point prices, ammonia sales prices, basic electricity price per unit, and demand electricity price per unit; Park equipment parameters: Obtain equipment data such as rated parameters and operating constraints of hydrogen production unit, ammonia synthesis unit, storage tank unit, wind power unit, and power grid interaction unit; Time-series reference data: ultra-short-term wind power forecast data for 96 15-minute periods the next day; ultra-short-term meteorological forecast data for 96 15-minute periods the next day; reference values ​​for grid-connected power, abandoned power, ammonia sales, and demand control targets output by multi-day dispatch. Initial operating status data of park equipment: Collect initial operating status data of equipment such as initial storage capacity of hydrogen storage tank, initial storage capacity of ammonia storage tank, and initial value of synthetic ammonia load to ensure that the initial scheduling status is consistent with the actual operation; Modeling parameters for auxiliary equipment: Obtain modeling parameters for other auxiliary equipment such as circulating water and heating boilers to support power balance calculations for the entire park; Data preprocessing: Validity verification: Verify the completeness of key equipment parameters, trigger an alarm when required parameters are missing, and ensure that the data meets the modeling requirements; Outlier handling: Data exceeding the equipment's rated range is truncated, and missing temperature prediction data is supplemented with the historical average value to ensure that the data conforms to the equipment's operating logic; Standardized formatting: All data is formatted uniformly according to time period to ensure that data for each time period accurately matches the optimization window; cross-time period data is split and mapped according to time period to meet the requirements of calculation granularity.

[0022] Definition of decision variables: Decision variables are key parameters that need to be determined through optimization in day-ahead scheduling. They are divided into continuous variables and discrete variables, and comprehensively cover the entire process of energy production, conversion, storage and interaction.

[0023] Continuous variables: hydrogen production power, describing the operating load of the hydrogen production unit; ammonia production rate, characterizing the production intensity of the ammonia synthesis unit; hydrogen storage tank / ammonia storage tank charging and discharging flow rate, representing the charging and discharging energy status of the storage tank; grid-connected power and grid-disconnected power, describing the scale of energy interaction between the park and the power grid; abandoned power, reflecting the scale of unutilized wind power.

[0024] Discrete variables: four-to-one unit status, describing the unit's operating mode; electrolytic cell switch status, indicating the start-up and shutdown status of the electrolytic cell; grid switching status, clarifying the interaction direction between the park and the power grid.

[0025] Construction of the first objective function: The first objective function is an optimization-oriented approach for day-ahead scheduling. In this embodiment, the core objective is to minimize net operating costs. A mathematical model is constructed by integrating cost, benefit, and penalty terms. The first objective function is shown below:

[0026] In the formula, C Indicates operating costs, P Indicates a penalty item. R Indicates operating revenue;

[0027] The formula includes the cost of purchased electricity and the cost of electricity demand. The time scale is 1 hour or 15 minutes. , These are the start and end times of the optimization window, respectively. Electricity cost (RMB / kWh) The power output (kW) is the power supplied to the grid. , These represent the start and end months of the optimization window. The electricity cost is based on demand (RMB / kW). This refers to the demand capacity (i.e., the maximum monthly power output to the grid, in kW).

[0028]

[0029] The penalties include penalties for exceeding the limits for electricity discharged from the grid, exceeding the limits for electricity fed into the grid, exceeding the limits for electricity abandoned, penalties for the number of start-ups and shutdowns of four-to-one generating units, penalties for the number of start-ups and shutdowns of electrolytic cells, penalties for the operating time of four-to-one generating units, and penalties for the operating time of electrolytic cells. In the formula, This refers to the penalty coefficient for exceeding the offline power consumption limit. To exceed the power limit for offline electricity, The penalty coefficient for exceeding the power consumption limit when using the internet. To avoid exceeding the battery limit for internet access, The penalty coefficient for exceeding the limit on abandoned electricity. To prevent the amount of abandoned electricity from exceeding the limit, , The uniform penalty coefficients are allocated to the number of start-ups and shutdowns of the four-to-one unit and the electrolytic cell, respectively. , These are the start-up and shutdown times for the four-to-one generator unit and the electrolytic cell, respectively. , The uniform penalty coefficients are allocated to the operating time of the four-to-one unit and the electrolytic cell, respectively. , The operating time of the four-to-one unit and the electrolytic cell, respectively;

[0030] Operating revenue includes revenue from ammonia sales and revenue from electricity sales; revenue from electricity sales includes both short-term and long-term revenue. Price of ammonia (RMB / t) The ammonia sales rate (t / h) The electricity price is RMB / kWh. Power supplied to the grid (kW). for t Time and Market Users j Power (kW) of the medium- and long-term contracts signed. for t Time and Market Users j The agreed medium- to long-term price (RMB / kWh). for t Long-term contracted market users j The settlement reference point price for the region; First constraint construction: Power balance constraints: Construct power balance constraints to ensure tThe energy conservation principle of the power system during a given period means that the sum of wind and solar power output and electricity purchased from the grid equals the sum of power generated from hydrogen production, ammonia synthesis auxiliaries, other auxiliaries, electricity sold from the grid, and abandoned electricity. The formula is as follows:

[0031] In the formula, Wind power generation capacity (kW). The power output (kW) is the power supplied to the grid. Hydrogen production capacity (kW). The power output (kW) related to ammonia synthesis. The power (kW) of the remaining auxiliary facilities. Power supplied to the grid (kW). Forgone power (kW); Hydrogen storage tank constraints: Construct constraints for the hydrogen storage tank, including state-space equation constraints, upper and lower limits of storage capacity constraints, and constraints linking outlet flow rate and pressure. State-space equation constraint description t The dynamic change in hydrogen storage tank capacity over a given period, i.e., the storage capacity at the end of the period equals the initial storage capacity plus the difference between the inlet and outlet flow rates during the period, is calculated using the following formula:

[0032] In the formula, for t Hydrogen storage tank capacity at any time (Nm3), The hydrogen flow rate at the inlet of the hydrogen storage tank is (Nm3 / h). The hydrogen flow rate at the outlet of the hydrogen storage tank (Nm3 / h); Upper and lower limits of reserves t The storage capacity of the hydrogen storage tank during a given time period must not exceed the equipment's rated minimum and maximum storage capacity range, as shown in the following formula:

[0033] In the formula, , These are the upper and lower limits (Nm3) of the hydrogen storage tank capacity. The outlet flow rate of the hydrogen storage tank is linked to the internal pressure; when the pressure exceeds a critical value... At 14 bar, the hydrogen compressor shuts off, and the outlet flow rate is unlimited; when the pressure is below the critical value... When the compressor is turned on (at 14 bar), the outlet flow rate must not exceed the maximum allowable flow rate, as shown in the formula:

[0034]

[0035]

[0036] In the formula, This is the maximum hydrogen flow rate (Nm3 / h) that the compressor can handle. It is a large constant. For 0-1 variables, =1 indicates that the hydrogen compressor is off. =0 indicates that the hydrogen compressor is on. This refers to the pressure (bar) of the hydrogen storage tank. This refers to the upper limit of the hydrogen storage tank pressure (bar). Electrolytic cell constraints: Electrolyzer constraints include hydrogen production conversion constraints, upper and lower load limits, four-to-one system optimization constraints, and electrolyzer state and power optimization constraints. (1) The hydrogen production rate is converted by applying a linear fit constraint to the hydrogen production cluster power-hydrogen production rate curve, and the formula is:

[0037] In the formula, The slope of the hydrogen production-power fitting curve (kWh / Nm3) is the value collected. The intercept (kW); (2) The formulas for the upper and lower limits of hydrogen production load are as follows:

[0038] In the formula, , These are the power (kW) corresponding to the upper and lower limits of hydrogen production load, respectively. (3) Four-to-one state optimization constraints: Use a set of 0-1 variables , , Indicates a four-to-one generator set n At any moment t The state. When , , At that time, the four-to-one generating units were in a state of shutdown and maintenance; when , , At that time, the four-to-one generating unit was in the state of starting up after maintenance; when , , At that time, the four-to-one generating unit was in normal operating condition; when , , At that time, the four-to-one generating units were in hot standby mode; when , , At that time, the four-to-one generating units are in a hot standby startup state, then:

[0039]

[0040]

[0041] The state transition constraint formula between adjacent time steps is as follows: Initial moment:

[0042]

[0043]

[0044]

[0045] Mid-term and end-term:

[0046]

[0047]

[0048]

[0049] Maintenance / Fault Constraints:

[0050]

[0051]

[0052]

[0053] In the formula, The variable is 0-1, which is input by the user to represent the maintenance or fault status. 0 means that the unit is in maintenance or fault status and the unit is forced to shut down. 1 means that the unit is in normal status and can make its own decision. When the unit is in normal status, it cannot switch from other statuses to shutdown status.

[0054] Maximum number of purification start / stop cycles: Within the optimization window, there is a limit to the number of purification start-stop cycles for each group of four to one. The transition from shutdown to running state or from hot standby state to non-hot standby state is counted as one start-stop cycle.

[0055]

[0056]

[0057] In the formula, 0-1 variables are introduced. This indicates the switch between startup and running states. When switching from startup to running state, =0, all other cases are equal to 0. =1; To optimize the maximum number of purification start-stop cycles within the window.

[0058] Load upper and lower limits constraints for four-to-one generator units:

[0059]

[0060] In the formula, 0-1 variables are introduced. When the value is 0, the unit is in shutdown or hot standby mode, and the hydrogen production capacity is 0. When =1, the unit is in operation or startup state, and the hydrogen production capacity is within the upper and lower limits. Within the range; (4) Electrolyzer state and power optimization constraints: Use a set of 0-1 variables , , Indicates electrolytic cell m At any moment t The state. When =0, =0, At that time, the electrolytic cell was in a state of shutdown and maintenance; when =0, =1, At that time, the electrolytic cell was in the state of starting up after maintenance; when =1, =0, At that time, the electrolytic cell is in normal operating condition; when =1, =0, When the electrolytic cell is in hot standby mode, then:

[0061]

[0062] Constraints for state transitions between adjacent time points: Initial moment:

[0063]

[0064]

[0065] Mid-term and end-term:

[0066]

[0067]

[0068] Maintenance / Fault Constraints:

[0069]

[0070]

[0071] In the formula, The variable is 0-1, which is input by the user to represent the maintenance or fault status. 0 means that the unit is in maintenance or fault status and the unit is forced to shut down. 1 means that the unit is in normal status and can make its own decision. When the unit is in normal status, it cannot switch from other statuses to shutdown status.

[0072] Upper limit constraint on the number of switching current cycles: Within the optimization window, each electrolytic cell has a switching current limit, where the switching current is counted once for each state transitioning from shutdown to operation or from hot standby to non-hot standby.

[0073]

[0074]

[0075] In the formula, 0-1 variables are introduced. This indicates the switch between startup and running states. When switching from startup to running state, =0, all other cases are equal to 0. =1. To optimize the maximum number of switching current cycles within the window.

[0076] Electrolytic cell load upper and lower limit constraints:

[0077]

[0078] In the formula, 0-1 variables are introduced. At this time, the unit is in shutdown or hot standby mode, and the hydrogen production capacity is 0. At this time, the unit is in operation or startup state, and the upper and lower limits of hydrogen production capacity are within the range of... Inside.

[0079] Ammonia synthesis constraints: Ammonia synthesis loading constraints: The power consumption of auxiliary equipment related to ammonia synthesis includes hydrogen compressors, nitrogen compressors, fresh gas compressors, recirculating gas compressors, and ammonia refrigeration machines. The lumped model is as follows:

[0080] In the formula, The ammonia production rate is expressed in t / h. The slope (kWh / t) of the fitted curve of ammonia synthesis-related auxiliary power consumption and ammonia production rate. The intercept (kW) of the fitted curve of ammonia synthesis-related auxiliary power consumption and ammonia production rate.

[0081] Ammonia synthesis loading limits: In addition to the maximum flow rate constraint that the compressor can handle, there are also constraints on the upper and lower limits of the ammonia synthesis load for the hydrogen flow rate at the outlet of the hydrogen storage tank.

[0082] in , The values ​​represent the hydrogen flow rates (Nm3 / h) corresponding to the upper and lower limits of the ammonia synthesis load. The variable is 0-1, representing the hot standby status of ammonia synthesis. 0 indicates that ammonia synthesis is in a hot standby state with 0 hydrogen consumption, and 1 indicates that ammonia synthesis is in normal operation.

[0083] In summary, the input data includes 15-minute-ahead wind power forecast data, 15-minute-ahead electricity price forecast data, ammonia price forecast data, initial values ​​of hydrogen storage tank capacity, initial values ​​of ammonia storage tank capacity, initial values ​​of synthetic ammonia load, multi-day optimized calculation results of grid-connected electricity volume, multi-day optimized calculation results of abandoned electricity volume, and multi-day optimized calculation results of ammonia sales volume. High-resolution models are used for hydrogen production, synthetic ammonia, and auxiliary equipment to accurately depict the characteristics of equipment multi-state switching and segmented operation. Constraints on the grid-connected electricity ratio, offline electricity ratio, and abandoned electricity ratio are relaxed; only the grid-connected electricity volume and abandoned electricity volume for the corresponding time period transmitted by multi-day optimization are followed. Since the current-day optimization time window is less than one month, the demand charge is calculated in the first objective function by introducing demand power increments. At the same time, penalties for grid-connected electricity, abandoned electricity, and ammonia sales are added to the penalty term of the current-day scheduling optimization, resulting in the second objective function, which is expressed as follows:

[0084] In the formula, This represents the increase in demand power, which is the difference between the actual power supplied to the grid and the demand control target.

[0085] Operating revenue R Consistent with the first objective function; In the above formula, This is a penalty coefficient for deviations in internet usage. This is the deviation in power consumption during internet access. This is the penalty coefficient for the deviation in the amount of abandoned electricity. This refers to the deviation in the amount of abandoned electricity. This is the penalty coefficient for deviations in ammonia sales volume. This refers to the deviation in ammonia sales volume. Based on the optimized model, the calculation of demand power increment is added, along with calculations of grid-connected power deviation, abandoned power deviation, and ammonia sales volume deviation. This is the input value, representing the maximum offline power from the beginning of the month to the current time. If the current optimization starts at 0:00 on the first day of the month, then... ,otherwise Record and output the results of the previous optimization. The on-grid power generation results are obtained from monthly scheduling optimization calculations. The result of the abandoned power volume obtained from the monthly scheduling optimization calculation. The monthly scheduling optimization calculation yields the ammonia sales volume result. By comparing the monthly scheduling optimization result with the reference values ​​of the next day's grid-connected electricity, abandoned electricity, and ammonia sales volume obtained from the multi-day scheduling output, the deviation of grid-connected electricity, abandoned electricity, and ammonia sales volume can be obtained.

[0086] Simultaneously, additional constraints are added regarding the increase in demand power, deviation of grid-connected power, deviation of abandoned power, and deviation of ammonia sales:

[0087]

[0088]

[0089]

[0090] The second objective function is solved using a mixed-integer linear programming algorithm, satisfying all constraints, and the decision variables for the next day are output. The scheduling results for the next day include reference values ​​for grid-connected electricity, abandoned electricity, and ammonia sales. The reference values ​​for grid-connected electricity and abandoned electricity are calculated based on the corresponding power reference values ​​passed from multi-day scheduling optimization, providing a clear target-following basis for intraday scheduling optimization. The reference value for ammonia sales is determined by combining ammonia price forecasts, ammonia storage tank constraints, and multi-day ammonia sales targets. Intraday scheduling must completely follow this result without re-determining the ammonia sales volume. At the same time, an electricity price curve is output, which clearly shows the tradable grid-connected power and corresponding price for each time period of the next day, and is directly used for electricity market trading quotations between the park and the power grid.

[0091] This embodiment takes "minimizing net operating costs" as its core guiding principle, incorporating electricity market compliance cost accounting and multi-dimensional operational revenue into a unified objective function. This achieves a precise synergistic balance between costs and revenues, resolving the imbalance of comprehensive benefits caused by traditional single-objective optimization. On the cost side, it strictly adheres to electricity market settlement rules, fully covering core day-ahead dispatch costs. The cost of purchasing electricity from the grid is precisely calculated based on 15-minute time intervals, including both the basic electricity fee of "time-of-use purchase and sale price × grid power × 0.25 hours" and the demand fee calculated by introducing "demand power increment" combined with the basic electricity unit price. Furthermore, the cost calculation dimension perfectly matches the granularity of the 15-minute day-ahead dispatch time interval, ensuring that cost data is synchronized with the time scale of dispatch decisions. On the revenue side, it comprehensively integrates core revenue items from the park's commercial operation to maximize operational revenue. Ammonia sales revenue is calculated based on 15... The calculation of ammonia sales rate and preset ammonia sales price for each minute period ensures that the accuracy of revenue calculation is consistent with the granularity of dispatching periods. Electricity sales revenue covers transaction revenue from multiple scenarios in the power market, and the calculation of revenue items is strictly linked to price parameters such as spot node electricity price, medium- and long-term contract electricity price, and ammonia sales price. Ultimately, through the quantitative coupling of cost and revenue, day-ahead dispatch optimization can maximize core revenues such as electricity and ammonia sales while accurately controlling compliance costs in the power market. This meets the comprehensive benefit requirements of commercial operation in the park and avoids the problem of insufficient practicality of dispatching schemes caused by incomplete cost accounting or single revenue dimension in traditional optimization.

[0092] Example 2 Figure 2 This is a schematic diagram of a day-ahead dispatching device for an integrated wind-solar-hydrogen-ammonia power grid-load-storage park, according to another exemplary embodiment. The device includes: Data acquisition module 1: used to acquire input data, including: electricity market price data, park equipment parameters, time series reference data, initial operating status data of park equipment, and modeling parameters of auxiliary equipment; Data preprocessing module 2: Used to preprocess the acquired input data to obtain standardized input data; First objective function construction module 3: used to set the penalty term of the objective function, and construct the first objective function with the goal of minimizing the net operating cost based on the standardized input data; First constraint construction module 4: used to construct the constraint conditions of the first objective function, the constraint conditions include: power balance constraint, hydrogen storage tank constraint, electrolyzer constraint and ammonia synthesis constraint; Second objective function construction module 5: used to introduce the calculation of demand power increment, grid-connected power deviation, abandoned power deviation and ammonia sales deviation into the first objective function, and to add penalty coefficients for grid-connected power deviation, abandoned power deviation and ammonia sales deviation to the penalty term of the first objective function to obtain the second objective function; Second constraint construction module 6: used to set the constraint conditions for the demand power increment, the on-grid power deviation, the abandoned power deviation, and the ammonia sales deviation; Solution module 7: Used to solve the second objective function using a mixed-integer linear programming algorithm, under the premise of satisfying all constraints, to obtain the decision variables for the next day.

[0093] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0094] It should be noted that in the description of this invention, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this invention, unless otherwise stated, "a plurality of" means at least two.

[0095] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0096] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0097] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0098] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0099] The storage media mentioned above can be read-only memory, disk, or optical disk, etc.

[0100] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0101] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A day-ahead dispatching method for an integrated wind-solar-hydrogen-ammonia power-load-storage park, characterized in that, The method includes: The input data includes: electricity market price data, park equipment parameters, time series reference data, initial operating status data of park equipment, and modeling parameters of auxiliary equipment; The acquired input data is preprocessed to obtain standardized input data; Set a penalty term for the objective function, and construct a first objective function based on the standardized input data with the goal of minimizing net operating cost; The constraints for the first objective function are constructed, including: power balance constraints, hydrogen storage tank constraints, electrolyzer constraints, and ammonia synthesis constraints. The calculation of demand power increment, grid-connected power deviation, abandoned power deviation and ammonia sales deviation is introduced into the first objective function, and the penalty coefficients of grid-connected power deviation, abandoned power deviation and ammonia sales deviation are added to the penalty term of the first objective function to obtain the second objective function; Set constraints for the demand power increment, on-grid power deviation, abandoned power deviation, and ammonia sales deviation; Under the premise of satisfying all constraints, the second objective function is solved using a mixed-integer linear programming algorithm to obtain the decision variables for the next day.

2. The method according to claim 1, characterized in that, The electricity market price data includes: spot node electricity prices and time-of-use electricity prices for 96 15-minute time periods on the next day; as well as the electricity prices of medium- and long-term contracts signed with market users for each time period on the next day and the corresponding settlement reference point prices; ammonia sales prices, basic electricity unit prices, and demand electricity unit prices; The parameters of the park equipment include: the rated parameters and operating constraints of the hydrogen production unit, ammonia synthesis unit, storage tank unit, wind power unit, and power grid interaction unit in the park; The time-series reference data includes: ultra-short-term wind power forecast data for 96 15-minute periods the next day, meteorological forecast data for 96 15-minute periods the next day; and reference values ​​for on-grid power generation, abandoned power generation, ammonia sales, and demand control targets for each period the next day, output by multi-day dispatch.

3. The method according to claim 2, characterized in that, The preprocessing of the acquired input data to obtain standardized input data includes: Verify the completeness of critical equipment parameters and trigger an alarm when required parameters are missing; Data exceeding the equipment's rated range is truncated, and missing temperature forecast data in meteorological forecast data is supplemented using the historical average for the same period. All data are formatted uniformly according to time period to ensure that the data in each time period accurately matches the optimization window, resulting in standardized input data.

4. The method according to claim 3, characterized in that, The net operating cost is calculated using operating costs, penalty items, and operating revenue. The operating cost includes the cost of electricity purchased and the cost of electricity demanded. The cost of electricity purchased is calculated based on the basic electricity price and the power output to the grid. The demand charge is calculated using the demand charge per unit price and the demand capacity. The penalties include: penalties for exceeding the limits for electricity output to the grid, penalties for exceeding the limits for electricity output to the grid, penalties for exceeding the limits for electricity abandonment, penalties for the number of start-ups and shutdowns of four-to-one generating units, penalties for the number of start-ups and shutdowns of electrolytic cells, penalties for the operating time of four-to-one generating units, and penalties for the operating time of electrolytic cells. The penalty for exceeding the limit of offline power consumption is calculated by using a preset penalty coefficient for exceeding the limit of offline power consumption and the amount of exceeding the limit of offline power consumption. The penalty for exceeding the internet power limit is calculated based on a preset penalty coefficient for exceeding the internet power limit and the amount of internet power exceeding the limit. The penalty for exceeding the limit on abandoned electricity is calculated using a pre-set penalty coefficient for exceeding the limit on abandoned electricity and the amount of abandoned electricity exceeding the limit. The penalty for the number of start-ups and shutdowns of the four-to-one unit is calculated by using a pre-set penalty coefficient for the uniform distribution of the number of start-ups and shutdowns of the four-to-one unit and the number of start-ups and shutdowns of the four-to-one unit. The penalty for the number of start-ups and shutdowns of the electrolytic cell is calculated by using a pre-set uniform penalty coefficient for the number of start-ups and shutdowns of the electrolytic cell and the number of start-ups and shutdowns of the electrolytic cell. The runtime penalty for the four-to-one unit is calculated by using a pre-set uniform runtime distribution penalty coefficient for the four-to-one unit and the runtime of the four-to-one unit. The running time penalty of the electrolytic cell is calculated by using a preset uniform penalty coefficient for the running time distribution of the electrolytic cell and the running time of the electrolytic cell. The operating revenue includes revenue from ammonia sales and revenue from electricity sales; The revenue from ammonia sales is calculated using the ammonia sales price and the ammonia sales rate. The revenue from electricity sales includes short-term revenue from electricity sales and medium- and long-term revenue from electricity sales. The short-term electricity sales revenue is calculated based on the electricity sales price and the grid-connected power. The revenue from medium- and long-term electricity sales is calculated by the difference between the medium- and long-term contract power with market users, the medium- and long-term price with market users, and the settlement reference point price in the region where the medium- and long-term contract market users are located.

5. The method according to claim 4, characterized in that, The power balance constraint is the sum of wind power generation and grid-connected power, which is equal to the sum of hydrogen production power, ammonia synthesis-related auxiliary power, other auxiliary power, grid-sold power, and abandoned power.

6. The method according to claim 5, characterized in that, The constraints on the hydrogen storage tank include state-space equation constraints, upper and lower limits of storage capacity constraints, and constraints linking outlet flow rate and pressure. The state-space equation constraint is the hydrogen storage tank's final storage volume at the end of the time period, which is equal to the initial storage volume of the hydrogen storage tank at the beginning of the time period plus the difference between the inlet flow rate and the outlet flow rate of the hydrogen storage tank during the time period. The upper and lower limits of the storage capacity are that the storage capacity of the hydrogen storage tank shall not exceed the rated minimum and maximum storage capacity range of the equipment. The outlet flow rate and pressure linkage constraint is as follows: when the hydrogen storage tank pressure is higher than the preset critical value, the hydrogen compressor is turned off and the outlet flow rate is unlimited; when the hydrogen storage tank pressure is lower than the preset critical value, the compressor is turned on and the outlet flow rate must not exceed the maximum allowable flow rate.

7. The method according to claim 6, characterized in that, The constraints of the electrolyzer include: hydrogen production conversion constraints, upper and lower load limits constraints, four-to-one system optimization constraints, and electrolyzer state and power optimization constraints. The hydrogen production conversion constraint is a linear fitting constraint on the hydrogen production cluster power-hydrogen production curve. The upper and lower limits of the load constraint mean that the hydrogen production power must not exceed the upper and lower limits of the hydrogen production load. The four-to-one system optimization constraints include: four-to-one state switching constraints, upper limit constraints on the number of purification start-stop times, and upper and lower limit constraints on the four-to-one unit load. The four-to-one state switching constraint includes: using a set of 0-1 variables to represent the state of the four-to-one unit at any time, the state of the four-to-one unit includes shutdown maintenance state, maintenance restart state, normal operation state, hot standby state, and hot standby restart state; using 0-1 variables to represent the initial state, intermediate and final state of the four-to-one unit, and maintenance / fault state respectively. The upper limit constraint on the number of purification start-stops is that, within the optimization window, the number of purification start-stops for each group of four pairs of one unit shall not exceed the maximum number of purification start-stops within the optimization window. The upper and lower limits of the load of the four-to-one unit are as follows: when the four-to-one unit is in a shutdown or hot standby state, the hydrogen production power is 0; when the four-to-one unit is in operation or start-up state, the hydrogen production power is within the preset upper and lower limits. The electrolytic cell state and power optimization constraints include electrolytic cell state switching constraints, upper limit constraints on the number of switching current cycles, and upper and lower limit constraints on the electrolytic cell load. The electrolytic cell state switching constraints include: using a set of 0-1 variables to represent the state of the electrolytic cell at any time, the state of the electrolytic cell includes shutdown and maintenance state, start-up state after maintenance, normal operation state and hot standby state; using 0-1 variables to represent the initial state, intermediate and final state and maintenance / fault state of the electrolytic cell respectively. The upper limit constraint on the number of switching currents is that, within the optimization window, the number of switching currents for each electrolytic cell shall not exceed the maximum number of switching currents within the optimization window. The upper and lower limits of the electrolyzer load are constrained such that when the electrolyzer is in a shutdown or hot standby state, the hydrogen production power is 0; when the electrolyzer is in a running or start-up state, the hydrogen production power is within the preset upper and lower limits.

8. The method according to claim 7, characterized in that, The ammonia synthesis constraints include: ammonia synthesis load constraints and upper and lower limits constraints for ammonia synthesis load; The ammonia synthesis load constraint is: the product of the ammonia production rate and the slope of the ammonia synthesis-related auxiliary power consumption-ammonia production rate fitting curve, plus the intercept of the ammonia synthesis-related auxiliary power consumption-ammonia production rate fitting curve, equals the power of the ammonia synthesis-related auxiliary. The upper and lower limits of the ammonia load are as follows: when the ammonia load is in hot standby mode, the outlet hydrogen flow rate of the hydrogen storage tank is 0; when the ammonia load is in normal operation mode, the outlet hydrogen flow rate of the hydrogen storage tank shall not exceed the hydrogen flow rate range corresponding to the upper and lower limits of the ammonia load.

9. The method according to claim 8, characterized in that, The second objective function includes: In the first objective function, the demand charge is calculated by introducing the demand power increment; Add follow-up penalties for grid-connected electricity, abandoned electricity, and ammonia sales to the penalty term of the first objective function; The penalty for internet power consumption is calculated using a pre-set penalty coefficient for internet power consumption deviation and the internet power consumption deviation itself. The penalty for abandoned power is calculated using a pre-set penalty coefficient for abandoned power deviation and abandoned power deviation. The penalty for the ammonia sales volume is calculated using a pre-set penalty coefficient for the ammonia sales volume deviation and the ammonia sales volume deviation. The deviation of the on-grid power is the difference between the on-grid power calculated through monthly scheduling optimization and the on-grid power output for each time period of the next day as output by the multi-day scheduling; the deviation of the abandoned power is the difference between the abandoned power calculated through monthly scheduling optimization and the abandoned power output for each time period of the next day as output by the multi-day scheduling; the deviation of the ammonia sales is the difference between the ammonia sales calculated through monthly scheduling optimization and the ammonia sales output for each time period of the next day as output by the multi-day scheduling. The demand power increment is the difference between the demand control target and the actual power output.

10. A day-ahead dispatching device for an integrated wind-solar-hydrogen-ammonia power-load-storage industrial park, characterized in that, The device includes: Data acquisition module: used to acquire input data, including: electricity market price data, park equipment parameters, time series reference data, initial operating status data of park equipment, and modeling parameters of auxiliary equipment; Data preprocessing module: used to preprocess the acquired input data to obtain standardized input data; First objective function construction module: used to set the penalty term of the objective function, and construct the first objective function with the goal of minimizing the net operating cost based on the standardized input data; First constraint construction module: used to construct the constraint conditions of the first objective function, the constraint conditions include: power balance constraint, hydrogen storage tank constraint, electrolyzer constraint and ammonia synthesis constraint; The second objective function construction module is used to introduce the calculation of demand power increment, grid-connected power deviation, abandoned power deviation and ammonia sales deviation into the first objective function, and to add penalty coefficients for grid-connected power deviation, abandoned power deviation and ammonia sales deviation to the penalty term of the first objective function to obtain the second objective function. The second constraint construction module is used to set the constraint conditions for the demand power increment, the on-grid power deviation, the abandoned power deviation, and the ammonia sales deviation. The solution module is used to solve the second objective function using a mixed-integer linear programming algorithm, under the premise of satisfying all constraints, to obtain the decision variables for the next day.

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