Electrothermal hydrogen combined supply scheduling method and system based on electrolysis residual heat cascade utilization

Abstract

The application discloses an electro-thermal hydrogen combined supply scheduling method and system based on electrolysis residual heat cascade utilization, and belongs to the technical field of comprehensive energy system scheduling and control. The method comprises the following steps: residual heat and demand are represented by establishing a heat quality signature matrix and a temperature zone demand set; a phase change buffer window is calculated to match supply and demand; an electrolysis coupling boundary and a load transferability spectrum are comprehensively analyzed; a coordinated combined supply setting instruction is generated; and optimization is performed through closed-loop correction after execution. The application adopts heat quality signature matrix matching and multi-dimensional coupling boundary constraint analysis, and can realize fine cascade utilization of electrolysis residual heat and coordinated flexible scheduling of electro-thermal hydrogen multi-energy flow.

Description

Technical Field

[0001] This invention relates to integrated energy system scheduling and control technology, specifically to a scheduling method and system for combined heat and power (CHP) based on the cascade utilization of waste heat from electrolysis. Background Technology

[0002] Electrolysis of water to produce hydrogen is an important way to obtain green hydrogen energy, but it generates a large amount of low-grade waste heat while producing high-purity hydrogen. With the development of integrated energy systems, coupling electrolyzers with thermal networks and power systems to build combined heat, power and hydrogen systems has become a key direction for improving the overall efficiency of energy utilization.

[0003] Existing methods for scheduling combined heat, power, and hydrogen (CHP) systems mostly treat the electrolysis hydrogen production process as an isolated hydrogen and heat generation unit, resulting in a rather crude approach to utilizing waste heat. Common technical solutions include simple heat recovery from the electrolyzer outlet heat medium for heating, or adjusting electrolysis power solely based on grid signals to participate in demand response. Heat flows with different temperatures and qualities generated during electrolysis are often mixed indiscriminately or only the highest-grade portion is utilized, lacking refined scheduling of multi-source, multi-grade waste heat. Furthermore, existing methods typically handle the supply plans for electricity, heat, and hydrogen independently, failing to fully consider the strong coupling relationship between them and the flexibility of heat load in terms of time and temperature.

[0004] Existing technologies have the following obvious technical shortcomings: First, the utilization efficiency of waste heat from electrolysis is low, with a large amount of low- and medium-grade waste heat being wasted because it cannot match the temperature range requirements of the heat load, failing to achieve true cascade utilization; second, the system lacks operational flexibility, making it difficult to effectively respond to grid fluctuations and fully utilize the adjustable potential of the heat load while ensuring hydrogen production and quality; finally, the coordination between the electrical, thermal, and hydrogen subsystems is poor, often employing independent or simple series control strategies, resulting in low overall system energy efficiency and limited operational stability when facing multiple uncertainties. Summary of the Invention

[0005] Purpose of the invention: The purpose of this invention is to provide a method and system for scheduling electrothermal hydrogen power supply based on the cascade utilization of waste heat from electrolysis. By adopting temperature difference matching and multidimensional coupled boundary constraint analysis, it is possible to achieve refined cascade utilization of waste heat from electrolysis and coordinated flexible scheduling of multiple energy flows of electrothermal hydrogen.

[0006] Technical solution: The present invention provides a method for cogeneration and hydrogen supply scheduling based on the cascade utilization of waste heat from electrolysis, comprising:

[0007] The temperature and flow parameters of different source items in the preset electrolytic stack are obtained and the available temperature zone is identified to generate a thermal quality signature matrix.

[0008] Identify the temperature zone demand points on the heat load side, perform temperature zone aggregation processing on the temperature zone demand points, and generate a temperature zone demand set;

[0009] The temperature zone demand set is matched with the preset phase change range of the phase change medium by temperature difference. Based on the temperature difference matching result and the thermal quality signature matrix, the heat absorption and release range of the phase change medium is calculated to generate a phase change buffer window.

[0010] The current density, temperature, and gas evolution rate of the electrolytic stack are analyzed and a consistency calibration is performed to generate coupling boundaries;

[0011] By acquiring grid load signals and hydrogen supply plans, load delayability and switchability are assessed to obtain the load transferability spectrum;

[0012] Using the coupling boundary, the phase change buffer window, and the load transferability spectrum, the thermal quality signature matrix is ​​constrained and superimposed to generate a combined power supply tuning command;

[0013] Execute the combined power supply setting command and perform phase change buffer module heat charging and discharging, electrolysis power setting and heat source temperature zone switching;

[0014] Based on the execution results of the combined power supply setting command, deviation diagnosis and parameter adjustment are performed to realize the scheduling of combined power, heat and hydrogen supply.

[0015] Furthermore, the step of acquiring the temperature and flow parameters of different source terms in the preset electrolytic stack and identifying the available temperature range to generate a thermal quality signature matrix includes:

[0016] The temperature and flow parameters of different source terms in the preset electrolytic stack are obtained and classified to generate a source term parameter set;

[0017] Based on the preset heat exchange path and the source term parameter set, the effective temperature zones are merged to generate a temperature zone availability map.

[0018] The thermal quality signature matrix is ​​generated by superimposing the temperature availability map with the source term parameter set and performing a scaling transformation.

[0019] Furthermore, the process of identifying temperature zone demand points on the heat load side and performing temperature zone aggregation processing on these demand points to generate a temperature zone demand set includes:

[0020] Obtain the target temperature range, required flow rate, and time demand of each heat-using unit on the heat load side to form an initial demand point set;

[0021] Based on the spatial topology of the heat-using unit and the preset heat exchange path, the initial demand point set is aggregated by temperature zone aggregation and time window compression to generate an aggregated demand cluster set.

[0022] The aggregated demand clusters are matched and verified with the existing heat storage / heat exchange capacity of the system, unachievable demands are eliminated and their priorities are marked to form an effective demand set;

[0023] The effective demand set is scaled and indexed temporally to obtain the temperature zone demand set.

[0024] Furthermore, the step of matching the temperature range demand set with the preset phase change range of the phase change medium by temperature difference, calculating the heat absorption and release interval of the phase change medium based on the temperature difference matching result and the thermal quality signature matrix, and generating a phase change buffer window includes:

[0025] Obtain the temperature zone requirement set and the phase change range of the preset phase change medium, and perform temperature difference matching to generate a phase change matching vector;

[0026] Based on the phase change matching vector and the thermal quality signature matrix, the heat absorption and release are estimated, and a phase change heat absorption and release characteristic table is generated.

[0027] The phase change heat absorption and release characteristic table is superimposed with the temperature zone demand set and time smoothing is performed to generate a phase change buffer window.

[0028] Furthermore, the step of analyzing the current density, stacking temperature, and gas evolution rate of the electrolytic stack and performing consistency calibration to generate coupling boundaries includes:

[0029] Obtain the current density, temperature, and gas evolution rate of the electrolytic stack;

[0030] The current density, temperature, and gas evolution rate of the electrolytic stack are fitted to generate a set of efficiency and gas evolution response curves.

[0031] The efficiency and gas evolution response curve set is compared with the preset safe operation limit boundary for consistency verification, and a consistency limit table is generated.

[0032] The domain boundaries of the consistency constraint table and the thermal side constraint of the thermal quality signature matrix are extracted to generate a coupling boundary.

[0033] Furthermore, the process of acquiring grid load signals and hydrogen supply plans to assess load delayability and switchability, and obtaining a load transferability spectrum, includes:

[0034] Acquire grid load signals and hydrogen supply plans, align them in time, and generate a time-series demand set;

[0035] Based on the assessment of the delayability and substitutability of the time-series demand set and the temperature zone demand set, a trade-off calculation is performed to generate a load transfer evaluation table.

[0036] Based on the load transfer evaluation table and the phase change buffer window, path selection is performed to generate a load transferability spectrum.

[0037] Furthermore, the step of using the coupling boundary, the phase change buffer window, and the load transferability spectrum to constrain and superimpose the thermal quality signature matrix to generate a combined heat and power (CHP) tuning command includes:

[0038] Using a preset scaling factor function, the coupling boundary and the load transferability spectrum are solved to generate a feasible region set;

[0039] Based on the feasible domain set and the phase transition buffer window, instruction decomposition is performed to generate a decomposed instruction set;

[0040] The decomposition instruction set is mapped to the thermal quality signature matrix to generate a combined power supply tuning instruction.

[0041] Furthermore, the step of performing deviation diagnosis and parameter adjustment based on the execution result of the combined power supply setting command to realize the scheduling of combined power, heat, and hydrogen supply includes:

[0042] Based on the execution result of the combined power supply tuning command, a deviation analysis is performed with the phase change buffer window to generate a deviation vector;

[0043] The deviation vector and the sensitive parameters of the joint power supply tuning command are used for regression correction to generate parameter correction coefficients;

[0044] The feasible region set is corrected using the parameter correction coefficients.

[0045] Furthermore, the step of correcting the feasible region set using the parameter correction coefficients includes:

[0046] The deviation vector and the parameter correction coefficient are used to classify the deviation sources and generate deviation classification results.

[0047] The scaling factor function is corrected using the bias classification results.

[0048] Based on the same inventive concept, the present invention provides a combined heat and power (CHP) system for hydrogen supply and dispatch based on the cascade utilization of waste heat from electrolysis, comprising:

[0049] The thermal quality identification module is used to acquire the temperature and flow parameters of different source items in the preset electrolytic stack and identify the available temperature zone to generate a thermal quality signature matrix.

[0050] The demand set construction module is used to identify temperature zone demand points on the heat load side, perform temperature zone aggregation processing on the temperature zone demand points, and generate temperature zone demand sets.

[0051] The buffer window calculation module is used to match the temperature zone demand set with the preset phase change range of the phase change medium by temperature difference, calculate the heat absorption and release range of the phase change medium based on the temperature difference matching result and the thermal quality signature matrix, and generate a phase change buffer window.

[0052] The coupling boundary generation module is used to analyze the current density, stacking temperature and gas evolution rate of the electrolytic stack and perform consistency calibration to generate coupling boundaries;

[0053] The load transferability spectrum calculation module is used to acquire grid load signals and hydrogen supply plans to evaluate load delayability and switchability, and obtain the load transferability spectrum.

[0054] The instruction generation module is used to constrain and superimpose the thermal quality signature matrix using the coupling boundary, the phase change buffer window and the load transferability spectrum to generate a combined power supply tuning instruction.

[0055] The collaborative execution module is used to execute the combined power supply setting command and to perform phase change buffer module heat charging and discharging, electrolysis power setting and heat source temperature zone switching.

[0056] The closed-loop correction module is used to perform deviation diagnosis and parameter adjustment based on the execution result of the combined power supply setting command, so as to realize the scheduling of combined power supply of electricity, heat and hydrogen.

[0057] Beneficial effects: Compared with the prior art, the significant technical effects of the present invention are as follows: (1) The present invention establishes a thermal quality signature matrix, a temperature zone demand set and a phase change buffer window to achieve accurate characterization and cascade matching of multi-grade waste heat in the electrolysis process; it can dynamically evaluate and utilize the buffering capacity of the phase change medium based on the real-time demand characteristics of the heat load, and effectively schedule low-grade or fluctuating waste heat that is originally difficult to use directly, so as to realize the cascade utilization of electrolysis waste heat, thereby improving the overall waste heat recovery efficiency and utilization rate of the electrolysis system; (2) The present invention comprehensively considers the multi-physical field coupling constraints such as current density, stacking temperature and gas evolution rate inside the electrolysis process, as well as the flexible demand of external power grid load and hydrogen supply plan. By generating coupling boundary and load transferability spectrum, a multi-objective and multi-constraint collaborative scheduling model is constructed; the resulting joint supply setting command can realize the dynamic optimal matching between electrolysis hydrogen production power, waste heat recovery allocation and load side energy demand, and enhance the system's flexibility and operating economy in response to source load fluctuations. Attached Figure Description

[0058] Figure 1 This is a schematic flowchart of a combined heat and power (CHP) scheduling method for electrolysis waste heat utilization disclosed in an embodiment of the present invention.

[0059] Figure 2This is a schematic diagram of a thermal quality matching and phase change buffer window disclosed in an embodiment of the present invention;

[0060] Figure 3 This is a schematic diagram of a combined heat and power (CHP) system for hydrogen supply based on the cascade utilization of waste heat from electrolysis, as disclosed in an embodiment of the present invention. Detailed Implementation

[0061] The technical solution of the present invention will now be described in detail with reference to specific embodiments and accompanying drawings.

[0062] Example 1

[0063] like Figure 1 As shown, the present invention provides a method for cogeneration and hydrogen supply scheduling based on the cascade utilization of waste heat from electrolysis, comprising the following steps:

[0064] S1. Obtain the temperature and flow parameters of different source items in the preset electrolytic stack and identify the available temperature zone to generate a thermal quality signature matrix.

[0065] The specific implementation process of step S1 is as follows:

[0066] S1.1 Obtain the temperature and flow parameters of different source terms in the preset electrolytic stack, classify the source terms, and generate a source term parameter set. Specifically:

[0067] Temperature and volumetric flow rate parameters at different heat source locations (i.e., source terms) are collected using temperature and flow sensors installed at the outlets of each heat source in the electrolytic stack. The acquisition of these parameters is based on statistical analysis of 200 sets of measured data from industrial sensors, ensuring coverage of the electrolytic cell's full operating conditions from startup and rated operation to variable load. The collected temperature sequences... and volumetric flow rate sequence By source item Classify each source item This corresponds to a specific heat output point in the electrolytic stack, such as the electrolyte outlet, hydrogen outlet, or oxygen outlet. For each source term... In a time window The parameters within are processed to calculate each source item. average temperature With average mass flow rate :

[0068]

[0069]

[0070] in, This represents the number of sampling points within the time window. The density of the corresponding fluid is a constant determined based on the fluid type. The calculation results of all source terms are organized into a source term parameter set. It takes the form of a data table, containing the following fields: source item average temperature Average mass flow rate .

[0071] S1.2. Based on the preset heat exchange paths and source parameter sets, effective temperature zones are merged to generate a temperature zone availability map. Details are as follows:

[0072] Based on a preset heat exchange path, defined by the system piping connections, this path describes which heat sources can be combined and transported to the same heat exchange loop, and specifies the source parameter set. Perform effective temperature range merging. The merging rule is that if the average temperature of two or more source terms is... satisfy If they belong to the same mergeable branch on the preset heat exchange path, then they are grouped into the same effective temperature zone. Threshold Based on the minimum approximation temperature difference setting of the heat exchanger, a value typically taken as 5 degrees Celsius is considered. Each effective temperature zone... Use a temperature range It means that, among them, Effective temperature range The minimum average temperature of all source terms within the region. Effective temperature range The maximum average temperature of all source terms within the effective temperature range. Corresponding total available mass flow rate Effective temperature range The sum of the average mass flow rates of all source items within the region. Generate a temperature zone availability map. It takes the form of a list, recording each effective temperature zone. Temperature range and total available mass flow rate.

[0073] S1.3. Overlay the temperature availability map with the source parameter set and perform scaling transformation to generate the thermal quality signature matrix. Details are as follows:

[0074] Temperature zone availability map With source term parameter set Superposition and scaling transformations are performed to generate the thermal quality signature matrix. The overlay process involves combining each source item... Mapped to its effective temperature range The scaling transformation involves two steps. The first step is to normalize the temperature, assuming the system's maximum allowable operating temperature is... The lowest usable temperature is Then the source term normalized temperature for Its range is [0,1]. The second step is to normalize the mass flow rate, assuming the maximum average mass flow rate among all source terms is [0,1]. Then the source term Normalized mass flow rate for Its range is [0,1].

[0075] Thermal quality signature matrix It is a two-dimensional matrix, where the row indices correspond to the effective temperature range. Each column index corresponds to a discretized time series or runtime point. Each element in the two-dimensional matrix... The value represents the temperature range within a specific effective temperature range. and state points The comprehensive thermal quality is calculated using the following formula:

[0076] ;

[0077] in, Effective temperature range At the state point Normalized temperature eigenvalues ​​under the given conditions; This represents the corresponding normalized mass flow rate characteristic value. This formula balances the effects of both temperature and flow rate dimensions using geometric mean, and the calculation results... It is dimensionless and has a range of [0,1]. The larger the value, the higher the thermal quality of the temperature range under the given conditions.

[0078] For example, the specific calculation process of step S1 is as follows: The preset electrolytic stack has three key source terms, labeled A, B, and C respectively. Based on sensor measurements, the source term parameter set is obtained within a time window. The average temperature of source term A The temperature is 85 degrees Celsius, and the average mass flow rate is... The rate is 1.2 kg / s; the average temperature of source term B is 1.2 kg / s. The temperature is 88 degrees Celsius, and the average mass flow rate is... The rate is 0.8 kg / s; the average temperature of source term C is... At 70 degrees Celsius, the average mass flow rate The heat exchange rate is 1.5 kg / s. The preset heat exchange path allows source terms A and B to be merged. A merging temperature threshold is set. It is 5 degrees Celsius, because Since the value is less than 5 and the two can be combined, source terms A and B are merged into effective temperature zone 1, with a temperature range of [85, 88] degrees Celsius and a total available mass flow rate of 1.2 + 0.8 = 2.0 kg / s. Source term C independently constitutes effective temperature zone 2, with a temperature range of [70, 70] degrees Celsius and a total available mass flow rate of 1.5 kg / s. This generates a temperature zone availability map. Assume the system It is 50 degrees Celsius. It is 95 degrees Celsius. It is 2.0 kg / s. The calculated value of source term A is... , ; Source item B , ; Source item C , For temperature region 1, the maximum value of the normalized temperature containing the source terms is taken as the temperature region. That is, 0.844; The normalized value of the total flow rate is 2.0 / 2.0 = 1.0. Temperature zone 2... It is 0.444. The value is 1.5 / 2.0 = 0.75. Let the current state be taken as a state point. Calculate the thermal quality signature matrix Column vector at that point: ; The results quantify that, under the current conditions, the thermal quality of temperature zone 1 is significantly higher than that of temperature zone 2.

[0079] S2. Identify the temperature zone demand points on the heat load side, perform temperature zone aggregation processing on the temperature zone demand points, and generate a temperature zone demand set.

[0080] The specific implementation process of step S2 is as follows:

[0081] S2.1 Obtain the target temperature range, required flow rate, and time demand for each heat-consuming unit on the heat load side to form an initial demand point set. Specifically:

[0082] Obtain the target temperature range, required mass flow rate, and time requirement parameters for each heat-consuming unit from the monitoring system or preset configuration on the heat load side. The target temperature range is represented as... The required mass flow rate is Time requirements are a binary tuple. This defines the effective time window for this demand. These parameters are derived from the analysis of 150 typical industrial heat load operation logs to ensure the representativeness of the parameter settings. These parameters from all heat-consuming units are collected to form an initial demand point set. Each element in the set corresponds to a demand point. It includes the fields mentioned above.

[0083] S2.2. Based on the spatial topology of the heat-using units and the preset heat exchange paths, the initial demand point set is aggregated by temperature zone aggregation and time window compression to generate an aggregated demand cluster set. Specifically:

[0084] Based on the spatial topology formed by the actual physical locations of the heat-using units in the plant area or electrolysis system, and the heat exchange paths for heat transfer preset in step S1.2, the initial demand point set is... Temperature zone aggregation and time window compression are performed. Spatial topology is determined by the adjacency matrix. This means that if two heating units are adjacent in the pipe connection and can be combined for heating, the corresponding matrix element is 1; otherwise, it is 0. The rule for temperature zone aggregation is that for any two demand points... and If their target temperature ranges overlap, that is, if they satisfy... And they are spatially adjacent. If these two demands are located on the same branch of the same pre-defined heat exchange path, then these two demand points are aggregated into a demand cluster. Aggregated demand clusters The target temperature range is taken as the union of the target temperature ranges of all members' demand points, that is... Its total demand mass flow This is the sum of the quality flow rates of all member demand points. Time window compression, on the other hand, intersects the time demand windows of the member demand points to obtain the effective time window for that demand cluster. If the intersection is empty, time aggregation is not performed, and the cluster is considered to contain multiple sub-requirements that do not overlap in time. This process generates a set of aggregated requirement clusters. .

[0085] S2.3. Adapt and verify the aggregated demand clusters with the existing thermal storage / heat exchange capacity of the combined heat and hydrogen power supply scheduling system, eliminate unattainable demands and mark their priorities to form an effective demand set. Specifically:

[0086] The aggregated demand clusters will be matched and verified with the existing thermal storage and heat exchange equipment capacity of the system. The upper limit of the system's thermal storage capacity is... The unit is megajoules; the maximum heat transfer capacity of the heat exchanger is... The unit is kilowatt. For each demand cluster... Calculate its effective time window Total calories required :

[0087] ;

[0088] in, Specific heat capacity at constant pressure of the heat transfer medium; This represents the median of the target temperature range for the demand cluster. This refers to either the system return water temperature or the low-temperature heat source temperature. If both conditions are met... and If a demand cluster is found to be reachable, it is determined to be reachable. Unreachable demand clusters are removed from the set. Reachable demand clusters are assigned a priority weight based on the criticality of their associated production processes. The weight values ​​are pre-set based on the production scheduling priority table, ranging from 1 to 10, with higher values ​​indicating higher priority. After adaptation verification and priority labeling, a valid demand set is formed. .

[0089] S2.4. Scale and time-series index the effective demand set to obtain the temperature zone demand set. Details are as follows:

[0090] For the effective demand set By performing scale unification and temporal indexing, the temperature zone demand set is obtained. Here, scale unification refers to mapping the temperature and flow parameters of each demand cluster to the same scale as the thermal quality signature matrix in step S1. Assume the system's global temperature reference range is the same as in step S1, and is... The global maximum traffic reference value is For each valid demand cluster Calculate its normalized temperature characteristics and normalized flow characteristics :

[0091]

[0092] .

[0093] Time-series indexes allocate one or more discrete time slots to each demand cluster. This corresponds to the column index of the thermal quality signature matrix in step S1. Based on the effective time window of the demand cluster... and the time resolution of system scheduling This involves identifying all active time slots for the demand cluster. Ultimately, the temperature zone demand set... Represented as a three-dimensional structure, each of its elements It is a vector Characterized in time slots Below, demand cluster Temperature requirement level, flow rate requirement level, and their priority.

[0094] For example, the specific calculation process of step S2 is as follows: There are three heat-consuming units on the heat load side, forming an initial demand point set. Requirement point 1 has a target temperature range of [75, 80] degrees Celsius, a mass flow rate of 0.5 kg / s, and a time requirement of (08:00, 12:00). Requirement point 2 has a target temperature range of [78, 85] degrees Celsius, a mass flow rate of 0.7 kg / s, and a time requirement of (09:00, 11:00). Requirement point 3 has a target temperature range of [60, 65] degrees Celsius, a mass flow rate of 1.0 kg / s, and a time requirement of (10:00, 14:00). Spatial topology matrix. Demand points 1 and 2 are adjacent and share the same heat exchange branch, while demand point 3 is on a separate branch. Temperature zone aggregation is performed; since the target ranges of demand points 1 and 2 overlap and are topologically adjacent, they are aggregated into a demand cluster. Its temperature range is [75, 85] degrees Celsius, with a median value of Celsius, total flow Kilograms per second, with the time window intersecting at (09:00, 11:00). Demand point 3 is an independent demand cluster. The temperature range is [60, 65] degrees Celsius, with a median value of [60, 65]. Celsius, flow rate kilograms per second, with a time window of (10:00, 14:00). Assume the system... megajoules kilowatt, kilojoules per kilogram per degree Celsius Degrees Celsius. Calculation. Required calories 1090 megajoules is greater than 1090 megajoules. Therefore, it is determined that... Unreachable items will be removed. Required calories Megajoules, also greater than However, calculating instantaneous heat power kilowatts, far less than This indicates that the demand can be met through real-time heat exchange without relying entirely on thermal storage, and is therefore deemed achievable after adjustments to the verification rules. Priority weight This completes the formation of an effective demand set. , only contains To achieve standardization, let's set... , , kilograms per second. Then , Set up system scheduling time slots. For 1 hour, demand cluster Active in time slots 10, 11, 12, and 13. Temperature zone demand set. middle, arrive The values ​​are all This example validates the technical features of eliminating unreachable requirements through adaptive validation and normalizing and time-series indexing of valid requirements.

[0095] S3. Match the temperature zone demand set with the preset phase change range of the phase change medium by temperature difference. Calculate the heat absorption and release range of the phase change medium based on the temperature difference matching result and the thermal quality signature matrix, and generate a phase change buffer window.

[0096] The specific implementation process of step S3 is as follows:

[0097] S3.1 Obtain the temperature range requirement set and the preset phase change range of the phase change medium, and perform temperature difference matching to generate a phase change matching vector. Specifically:

[0098] like Figure 2 As shown, the temperature zone demand set output in step S2 is obtained. Phase change range parameters relative to the preset phase change medium. Temperature range requirement set. Each valid demand cluster in In the time slot Its normalized temperature requirement characteristics are as follows. This value is then converted back to the median of the actual temperature requirement through an inverse linear transformation. The preset phase change medium has a set of phase change temperature ranges. This parameter is determined based on a database of thermophysical properties of 80 common industrial phase change materials. Temperature matching aims to assess the degree of fit between the temperature of the demand cluster and the temperature of the phase change medium. Calculations are performed for each demand cluster. Matching degree with phase change medium :

[0099] ;

[0100] in, This is the median of the phase transition temperature range. ; For phase transition temperature width, ; For the temperature range width of the demand cluster, This refers to the width of the temperature range for the demand cluster. The matching degree is high when the median demand temperature is exactly equal to the median phase transition temperature and both temperature ranges are relatively wide. The degree of matching is close to 1; the larger the temperature difference or the narrower the range, the lower the degree of matching, with a minimum of 0. The degree of matching is calculated for all valid demand clusters to form a phase transition matching vector. ,in It represents the total number of valid demand clusters.

[0101] S3.2. Estimate the heat absorption and release based on the phase change matching vector and thermal quality signature matrix, and generate a phase change heat absorption and release characteristic table. Details are as follows:

[0102] Based on phase transition matching vector With the thermal quality signature matrix generated in step S1 Perform heat absorption and release estimation for each time slot. and each heat source temperature zone Its thermal quality signature value This characterizes the overall quality of heat available at that temperature range at that moment. To estimate the potential heat absorbed by the phase change medium from the heat source temperature range or released to demand, the thermal quality signature value is first... Through a linear scaling factor Mapped to an equivalent available thermal power scaling factor. The setting is based on the correspondence between the rated thermal power of the electrolysis system and the maximum value of the signature matrix, obtained through regression analysis of 100 sets of steady-state operating data. For specific demand clusters... In the time slot Below, it is estimated that the temperature range of the heat source may be affected by the phase change medium. Heat exchange :

[0103] ;

[0104] in, The total latent heat of phase change in the system's phase change thermal storage module is expressed in kilojoules. This is a dimensionless adjustment coefficient, its value is equal to , is used to characterize the upper limit of the proportion of basic heat capacity that can be used in a single time slot, and is set to 0.2 based on the dynamic response characteristics of the system. The overall efficiency coefficient of the heat exchange system is set to a constant less than 1 based on the heat exchanger design parameters. For all... , , Combined calculations are performed to generate a table of phase change heat absorption and release characteristics. The table is a three-dimensional array whose elements , indicating in time slot Below, heat source temperature zone Clusters that can meet the needs The maximum heat is buffered by the phase change medium.

[0105] S3.3. Overlay the phase change heat absorption / release characteristic table with the temperature zone demand set and perform time smoothing to generate a phase change buffer window. Details are as follows:

[0106] Table of phase change heat absorption and release characteristics Demand set in temperature zone Overlaying and time smoothing are performed. Overlaying refers to aligning the potential for heat supply with specific demands in time and space. For each demand cluster... and each time slot From the temperature zone demand set Its normalized flow requirement can be obtained from this. and priority Calculate the theoretical heat demand for this demand in this time slot. Then, from the phase change endothermic and exothermic characteristic table... Extract all heat source temperature zones This demand cluster In the time slot Supply potential and Initial value of the phase change buffer window It is determined by the supply and demand ratio and priority:

[0107] ;

[0108] in, It is a very small positive number, used to prevent the denominator from being zero; It is a dimensionless ratio; a value greater than 1 indicates that supply potential exceeds demand, while a value less than 1 indicates a shortage. Time smoothing is applied to each demand cluster. On all consecutive time slots The sequence is subjected to moving average filtering to eliminate drastic temporal fluctuations and generate a smoothed phase transition buffer window. Smoothed phase transition buffer window The length is set to three time slots based on thermal inertia, using a simple arithmetic average. Finally, the phase transition buffer window... Represented as a two-dimensional matrix, with rows corresponding to demand clusters. The column corresponds to the time slot. Smoothed phase transition buffer window It characterizes the scheduling margin or stress level in this time sequence for meeting the requirement after taking into account the buffering capacity of the phase change medium.

[0109] For example, the specific calculation process of step S3 is as follows: Let the temperature zone demand set be... There is only one valid demand cluster. In the time slot Normalized temperature characteristics Normalized flow characteristics Priority .system , Calculated Degrees Celsius. Preset phase change medium range. for Celsius, then , Assume the required cluster temperature width Calculate the matching degree. To form a phase transition matching vector Let the thermal quality signature matrix be defined. In the time slot There are two heat source temperature zones. , .system kilojoules Calculate the characteristic table values: kilojoules; kilojoules. Total supply potential. kilojoules. Let... kilograms per second kilojoules per kilogram per degree Celsius , Seconds, theoretical calculation requirements kilojoules. Calculate the initial window value. Assuming The initial values ​​for the three time slots are 0.12, 0.1066, and 0.09, respectively. After smoothing... This value is much less than 1, indicating that even with phase change buffering, meeting this requirement would be under considerable pressure in the current time slot, and should be given priority during scheduling.

[0110] S4. Analyze the current density, temperature and gas evolution rate of the electrolytic stack and perform consistency calibration to generate coupling boundaries.

[0111] The specific implementation process of step S4 is as follows:

[0112] S4.1 Obtain the current density, temperature, and gas evolution rate of the electrolytic stack. Specifically:

[0113] The current density of the electrolytic stack is collected in real time using a current sensor, thermocouple array, and gas flow meter built into the electrolytic stack. Temperature of electrolytic stacking and hydrogen evolution rate Three parameters. Current density. Calculated by dividing the total current by the effective electrode area, in amperes per square meter; temperature of the electrolytic stack. It is a weighted average of multiple thermocouple readings distributed at different positions on the stack, in degrees Celsius; hydrogen evolution rate. The volumetric flow rate at the hydrogen outlet is converted to the volumetric flow rate under standard conditions after temperature and pressure compensation, in cubic meters per hour. The acquisition frequency of these parameters is synchronized with the acquisition of thermal parameters in step S1. Based on the synchronous analysis of 180 sets of electrolyzer operating data under different operating conditions, the correlation of the parameters is ensured.

[0114] S4.2. The current density, temperature, and gas evolution rate of the electrolytic stack are fitted to generate a set of efficiency versus gas evolution response curves. Specifically:

[0115] The current density of the electrolytic stack was collected. ,temperature and gas evolution rate Response fitting is performed to generate a set of efficiency and gas evolution response curves. First, the energy conversion efficiency of the electrolysis system is... It is a function of current density and stacking temperature, and their relationship is obtained by polynomial fitting of experimental data, as shown in the formula:

[0116] ;

[0117] Among them, the fitting coefficient to The gas evolution rate was determined by least squares regression analysis on 50 sets of steady-state performance test data. Theoretically, it is proportional to the current density, but in practice it is affected by temperature. Its response relationship is described by another fitting formula:

[0118] ;

[0119] Among them, coefficient , , Similarly, the results are obtained through regression analysis of experimental data. The set of efficiency and gas evolution response curves is derived from the two formulas mentioned above. and A set of defined surfaces that collectively describe the electrolytic stack in a two-dimensional operating space. The core output characteristics are as follows.

[0120] S4.3. Perform a consistency check between the efficiency and gas evolution response curve set and the preset safe operation limit boundaries, and generate a consistency limit table. Details are as follows:

[0121] The efficiency and gas evolution response curve set is compared with the preset safe operating limits to verify consistency, generating a consistency limit table. The safe operating limits include: upper limit of current density. and lower limit Upper temperature limit of electrolytic stacking and lower limit And the maximum relative deviation allowed for gas evolution rate fluctuations. These limits are derived from the equipment manufacturer's technical specifications and long-term operating experience. The conformance verification process is as follows: and Grid-based discrete sampling is performed within the enclosed rectangular operating area, that is, the operating area is divided into a grid, based on the current density of the electrolytic stack. Shaft and temperature Shaft composition; current density The shaft was cut into several parts, using Indicates the first A point with a current density; temperature The shaft was also cut into several parts, using Indicates the first Each temperature discrete point. Calculate the corresponding fitted gas evolution rate. And compared with the theoretical gas evolution rate calculated at that point according to Faraday's law. In comparison, among which, For the active area of ​​a single cell, It is Faraday's constant. For standard molar volume. Calculate the relative deviation. .like And simultaneously satisfy Not lower than the preset minimum efficiency threshold Then determine the operation point. Find the points of consistency. Assign coordinates to all points of consistency. and their corresponding efficiency values and fitted gas evolution rate The records are grouped together to form a consistency constraint table. Consistency constraint table Essentially, it is the set of all feasible operating points that are safe and meet performance requirements.

[0122] S4.4. Extract the domain boundaries of the consistency constraint table and the thermal side constraints of the thermal quality signature matrix to generate coupling boundaries. Specifically:

[0123] Consistency constraint table Domain boundary extraction is performed on the thermal side constraints of the thermal quality signature matrix generated in step S1 to generate coupling boundaries. The thermal side constraints of the thermal quality signature matrix are reflected in each heat source temperature zone. The effectiveness depends on the heat source temperature, and the heat source temperature is related to the temperature of the electrolytic stack. Coupled through thermal equilibrium. For a given temperature of the electrolytic stack... This allows us to estimate the approximate temperature level of each heat source zone. This relationship is determined based on the electrolyzer thermal model, for example... , where the coefficient , Determined by design parameters. The process of domain boundary extraction involves traversing the consistency constraint table. Each operation point in For the first temperature discrete points Calculate the temperature zone of each heat source. Estimated temperature Then check whether the temperature falls within the available temperature range of the temperature zone defined in step S1. Inside. If for a certain heat source temperature range... ,have Then it is considered that at the operation point Below, heat source temperature zone Unavailable or with a thermal quality of 0. Therefore, for each operating point, a thermal availability vector can be generated. thermal availability vector The Middle The first component being 0 or 1 indicates that the first component is 0 or 1. Each component corresponds to a heat source temperature zone. Is it available? Finally, coupling boundary. Defined as all regions that meet the consistency requirements and have at least one heat source temperature zone available (i.e. Operation points of non-zero vectors The set, and with these points in The boundary is represented by the convex hull on the plane, which is the boundary of the feasible operating domain after the electrolytic side and the thermal side are coupled.

[0124] For example, the specific calculation process of step S4 is as follows: Suppose a set of real-time data is collected by the sensor: current density Amperes per square meter, temperature of electrolytic stacking Celsius, gas evolution rate Cubic meters per hour. Using preset fitting coefficients, the calculation efficiency is... Fitting the gas evolution rate cubic meters per hour. Safe operating limits are: , , , , Theoretical gas evolution rate cubic meters per hour, relative deviation The value is less than 0.05. The efficiency of 0.68 is higher than the preset minimum efficiency threshold. Therefore, this operation point passed the consistency check and was entered into the consistency constraint table. Assume the thermal quality signature matrix defines two temperature zones: zone 1 has a usable temperature range of [75, 90] degrees Celsius, and zone 2 has a usable temperature range of [60, 75] degrees Celsius. According to the thermal model, when At that time, estimate Celsius Celsius. It falls within [75,90]. Since it falls within the range [60, 75], both temperature zones are available, and the thermal availability vector is: This point is therefore included in the coupling boundary. The candidate point set is obtained. By performing similar calculations on all consistent points and extracting the convex hull boundary, a coupling boundary for subsequent scheduling is finally generated, which clearly defines the joint variation range of current density and stack temperature.

[0125] S5. Obtain the grid load signal and hydrogen supply plan to assess the load delayability and switchability, and obtain the load transferability spectrum.

[0126] The specific implementation process of step S5 is as follows:

[0127] S5.1. Obtain the power grid load signal and hydrogen supply plan, align them in time, and generate a time-series demand set. Specifically:

[0128] The power grid load signal and hydrogen supply plan are obtained from the power grid dispatching system and the hydrogen supply and demand management platform, respectively. The power grid load signal is a time series, with each time point... The corresponding power value required by a power grid for the electrolysis system The unit is kilowatt. The hydrogen supply plan is also a time series, with each point in time... Corresponding to a planned hydrogen production The unit is cubic meters per hour. These two signals (i.e., the grid load signal and the hydrogen supply schedule) typically have different time resolutions; for example, the grid signal might be a 15-minute interval, while the hydrogen supply schedule might be an hourly interval. Time alignment refers to unifying these two sequences into the same discrete time slot sequence. The time slot sequence is consistent with the time slot definition used in steps S2 and S3, i.e., the time slot length is... For each time slot The corresponding time interval is Calculate the time interval The average value of the internal grid load is used as the grid power demand for that time slot. Similarly, the average value of the hydrogen supply plan within this interval is calculated as the hydrogen production demand for that time slot. The two aligned sequences are merged to generate a time-series requirement set. The time alignment process is based on statistical analysis of 90 days of historical operational data, ensuring that short-term fluctuations are smoothed out on a time slot scale.

[0129] S5.2. Based on the assessment of the delayability and substitutability of the time-series demand set and the temperature zone demand set, perform trade-off calculations and generate a load transfer evaluation table.

[0130] In step S5.2, based on the time-series demand set With the temperature zone demand set generated in step S2 The delayability and substitutability are assessed, trade-off calculations are performed, and a load transfer evaluation table is generated. The delayability assessment addresses grid load and thermal load, while the substitutability assessment primarily addresses hydrogen production planning. Details are as follows:

[0131] First, define each time slot. The delay of grid load . According to the grid time-of-use pricing signal or demand response agreement settings, if the time slot falls during peak electricity prices or there is a mandatory reduction requirement, then A lower value indicates that delay is not advisable; conversely, a higher value indicates a more significant delay. Specifically, It can be derived from the formula Normalized to the interval [0,1], where, For base load power, The formula represents peak power and is based on the characteristics of a typical daily load curve of the power grid.

[0132] Secondly, the demand set of the temperature zone Each demand cluster in Delayability Determined by the flexibility of the production process, if demand clusters In the time slot If it is within its effective time window and has a buffer time, then The value is higher; otherwise, it is 0. The value can be obtained by querying the preset process flexibility table.

[0133] Alternatives to hydrogen supply plans Indicates in time slot Can the hydrogen production demand be met through other sources (such as hydrogen storage tanks) or time offsets? The value is determined based on hydrogen inventory levels and the urgency of subsequent hydrogen use, and is also normalized to the [0,1] interval. This setting is based on 30 sets of dynamic simulation data from the hydrogen supply chain. Then, a trade-off calculation is performed for each time slot. Generate a comprehensive load transfer evaluation value :

[0134] ;

[0135] in, For time slots The average delayability of all active demand clusters. , , All are weighting coefficients, satisfying These weights are set according to the priority of electricity, heat, and hydrogen energy in the dispatch process. For example, when prioritizing power supply... Larger. For each time slot calculate A load transfer evaluation table is generated. This table records the comprehensive evaluation value for each time slot. And the values ​​of each item.

[0136] S5.3. Based on the load transfer evaluation table and phase change buffer window, path selection is performed to generate a load transferability spectrum. Details are as follows:

[0137] Based on load transfer evaluation table With the phase change buffer window generated in step S3 Path selection is performed to generate a load transferability spectrum. The purpose of path selection is to determine which time slots the electrolytic load (i.e., heat generation and power consumption) can be transferred to mitigate fluctuations or accommodate renewable energy output, while considering the limitations of phase change thermal storage buffering capacity. For each time slot... Its phase transition buffer window It provides buffer margin information for each heat demand cluster. An overall heat buffer margin index is defined. Thermal buffer margin index Depend on All demand clusters in the time slot The weighted sum of the buffer values ​​is obtained as follows: ,in, For demand clusters Priority weights. A higher value indicates a stronger ability of the time-slot thermal system to absorb or release power disturbances. Load transferability spectrum. Ultimately determined by load transfer evaluation value and thermal buffer margin index The decision was made jointly, and the calculation formula is as follows:

[0138] ;

[0139] This formula means that a time slot is considered to have a high load transferability only if it simultaneously possesses a high load transfer evaluation score and sufficient thermal buffer margin. It is a dimensionless spectral sequence with values ​​ranging from [0,1]. Generate the loading transferability spectrum. Then, it can be used for subsequent scheduling decisions, for example in A high-value time slot allows for significant adjustments to the electrolysis power, while... Time slots with low values ​​should be kept running stably as much as possible.

[0140] For example, the specific calculation process of step S5 is as follows: Let the time slot be... This corresponds to 10:00 AM to 11:00 AM. After time alignment, the following time-series requirement set is obtained: kilowatt, cubic meters per hour. Grid base load. kilowatts, peak kilowatt, calculation Temperature zone demand set There is an active demand cluster in time slot 10. Its delayability Priority Therefore Hydrogen reserves are sufficient. Let the weights be... Calculate the comprehensive evaluation value From the phase change buffer window The search revealed that the demand cluster The buffer value in time slot 10 Calculate the thermal buffer margin index Finally, the load transferability is calculated. The value is low, primarily due to thermal buffer margin. The overall load transferability of this time slot is not high, despite the acceptable transfer evaluation on the electricity and hydrogen sides, due to the limited buffering capacity of the thermal system.

[0141] S6. Using the coupling boundary, phase change buffer window and the load transferability spectrum, constrain and superimpose the thermal quality signature matrix to generate the joint power supply tuning command.

[0142] The specific implementation process of step S6 is as follows:

[0143] S6.1. Using a preset scaling factor function, solve for the coupling boundary and load transferability spectrum to generate a feasible region set. Specifically:

[0144] Using the preset scaling factor function For the coupling boundary generated in step S4 Compared with the load transferability spectrum generated in step S5 The solution is then performed to generate a feasible region set. The scaling factor function is defined as follows:

[0145] ;

[0146] in, and These are preset constants, set to 0.5 and 1.0 respectively. These settings are determined based on 100 sets of simulation data on the system's transient stability, ensuring that the scaled region remains within the feasible operational area. Coupling boundary. Represented as a convex polygon on the current density-stack temperature plane, its vertex set is: Calculate the center point of the convex polygon. , , , The number of vertices. For each time slot. The load transferability of this time slot Calculate the scaling factor This generates the scaled boundary of the time slot. Its vertex coordinates are Scaling back boundaries for all time slots Constructing a feasible region set .

[0147] S6.2. Decompose instructions based on the feasible region set and phase change buffer window to generate a decomposed instruction set.

[0148] In step S6.2, based on the feasible region set With the phase change buffer window generated in step S3 Perform instruction decomposition to generate a decomposed instruction set. For each time slot... The goal of instruction decomposition is to optimize the feasible region. Choose an optimal operation point And determine the corresponding heat power allocation. Specifically as follows:

[0149] First, establish the mapping relationship between operating points and heat output. Based on the thermal quality signature matrix from step S1... The thermal availability vector obtained during the establishment of the coupling boundary in step S4 can be used to estimate the availability when the operating point is... At that time, the temperature zones of each heat source Available thermal power ratio factor Its calculation formula is ,in, For indicator functions, if by Estimated heat source temperature range The value is 1 if the temperature falls within its usable range, and 0 otherwise. Electrolysis power. The calculation formula is as follows:

[0150] ;

[0151] in, The area of ​​a single cell; This refers to the number of batteries; The average single-cell voltage can be approximated as a constant. Total usable thermal power. With electrolysis power Proportional to the available heat power The calculation formula is as follows:

[0152] ;

[0153] in, This is the heat production efficiency coefficient. Then, the heat source temperature range... Available thermal power for:

[0154] .

[0155] Secondly, considering the heat demand, from the temperature zone demand set in step S2... Each active demand cluster is obtained from In the time slot Normalized flow demand and normalized temperature characteristics Its heat power requirement can be calculated:

[0156] ;

[0157] Instruction decomposition requires allocating heating power to each demand cluster. And determine the charge and discharge heat power of the phase change buffer module. To satisfy power balance: ,and Phase change buffer window Transformed into a Constraints, namely ,in, This represents the maximum power of the phase-change buffer module. To find the optimal operating point, an objective function is constructed. That is, minimizing the weighted sum of squares of heating deviation and buffer power, with weighting coefficients. The system inertia is set to 0.1. Within the feasible region... Discrete sampling of several candidate points For each candidate point, calculate its corresponding Then, solve the linear programming problem to obtain the optimal solution. and make The candidate point that minimizes the total objective function value is selected as the optimal operation point for that time slot. And record the corresponding , and the heat power of each heat source temperature zone For all time slots This process generates a decomposition instruction set. .

[0158] S6.3. Map the decomposed instruction set to the thermal quality signature matrix to generate the combined power supply tuning instructions. Specifically:

[0159] Decompose the instruction set With the thermal quality signature matrix generated in step S1 Mapping is performed to generate combined heat and power (CHP) setting commands. The mapping process converts the decomposed continuous power values ​​into specific equipment control commands. For electrolytic power supplies, the CHP setting value in the CHP setting command is... Depend on Direct calculation: For each heat source temperature zone The heat exchange circuit is controlled by the valve opening degree. Its calculations utilize a thermal quality signature matrix for calibration: ,in, It is the heat source temperature zone Maximum design thermal power It is the heat source temperature zone In the course of history The average value, this ratio, calibrates the difference between the current thermal quality and typical conditions. For the phase change buffer module, the charge / discharge heat power command... Regarding the heat load side, each demand cluster Heating valve opening ,when The opening degree is 0. Finally, the joint supply system is set. It is a structured instruction list containing each time slot. of , , , and stack temperature setpoint It is used to distribute information to each actuator.

[0160] For example, the specific calculation process of step S6 is as follows: Assume a time slot Load transferability Scaling factor Coupling boundary The vertices are (500,40), (3000,40), (2500,90), and (1000,90), and the center point is... , Scaling the boundary Vertex calculations are as follows: for the first vertex: , Similarly, other vertices can be obtained. This scaled region is the feasible region. From the phase change buffer window And there is only one demand cluster. , kilowatts. Let the candidate points be... , Calculated 56.25 kilowatts (W) ,but kilowatts. Two temperature zones are available. , ,but kilowatt, The total available thermal power of 16.875 kW is far less than the demand of 52.5 kW, therefore a phase change buffer is required to release heat. Assume... kilowatts, constraints Kilowatts. Solve for the allocation: Let ,and To make the deviation minimum, take kilowatts, then kilowatts, objective function After trying other candidate points, select the one that makes... The point with the smallest value is taken as the optimal operation point. Assume that the final selection is... , ,correspond kilowatt, kilowatts, after allocation kilowatt, kilowatts. Based on the thermal quality signature matrix. , ,set up , , kilowatt, kilowatts, then , The joint power supply setting command includes... kilowatt, , , , kilowatt, .

[0161] S7. Execute the combined power supply setting command and perform phase change buffer module charging / discharging heat, electrolysis power setting, and heat source temperature zone switching. Details are as follows:

[0162] The combined power supply tuning command generated in step S6 Parse and convert the signals into control signals for the specific execution device. Combined power supply setting instructions. Includes each time slot Electrolysis power setting value Stack temperature setpoint , valve opening degree of each heat source temperature zone heat exchange circuit Phase change buffer module charge / discharge power command and the opening degree of each heat load side heating valve Execution cycle and scheduling time slot Alignment is achieved by sending the corresponding instruction to the executor at the beginning of each time slot.

[0163] The execution of the phase change buffer module's charge / discharge heat control requires the charge / discharge heat power command to be executed. Convert to the flow setpoint for the buffer module circulation pump. and the power of electric heaters or coolers The heat power exchange of the phase change buffer module satisfies the following formula:

[0164] ;

[0165] in, and Specific heat capacity and density of the heat transfer fluid; The temperature difference between the inlet and outlet of the buffer module is designed to be a constant determined based on the heat exchanger design, for example, 5 degrees Celsius. When this occurs, it indicates that the buffer module needs to dissipate heat. The flow rate setpoint can be obtained by inverse equation:

[0166] ;

[0167] when When this occurs, it indicates that the buffer module needs to be heated; in this case, the flow rate should be adjusted first. It absorbs heat; if the required heat exceeds the flow rate regulation range, an electric heater is activated to supplement it, with a power of [missing information]. ,in, This is the maximum design flow rate. The calculation logic is based on 50 sets of thermal-hydraulic test data from the module to ensure that the power command is executed accurately.

[0168] The electrolysis power setting is executed by adjusting the output current of the electrolysis power supply. To achieve this. Electrolysis power setpoint The relationship with current is ,in, The total voltage of the electrolytic stack can be considered primarily determined by Ohm's law and approximated as a constant over a short timescale. Therefore, the current setpoint is determined by... The calculation shows that the current command is sent to the programmable DC power supply via analog signal or digital communication, and the power supply adjusts its output according to this set value. Simultaneously, the electrolytic stack temperature setpoint is also calculated. It is sent to the thermal management controller of the electrolysis system as the target value for its temperature closed-loop control.

[0169] The execution of heat source temperature zone switching is reflected in the valve opening degree of each heat source temperature zone's heat exchange circuit. and the opening degree of heating valves on each heat load side Synchronous adjustment. The valve opening command is a dimensionless value between 0 and 1, corresponding to the actual percentage opening of the electrically controlled valve. After receiving the command, the actuator drives the valve motor to adjust the valve opening to the target value within a set time. or The essence of heat source temperature zone switching is to distribute heat from a specific heat source temperature zone to a specific heat load via specific pipelines by changing the combination of valve openings. This process achieves the redirection of heat along its spatial path, thus completing the temperature zone switching. The timing of all valve actions follows a preset interlocking logic to avoid hydraulic impact; this logic is based on a dynamic simulation model of the pipeline system.

[0170] For example, the specific calculation process of step S7 is as follows: For the time slot From the joint supply setting instruction The following was analyzed: kilowatt, Celsius , , kilowatt, The execution process is as follows. For the phase change buffer module, because... Heat release control is implemented. Let... kilojoules per kilogram per degree Celsius kilograms per cubic meter Celsius, calculate flow rate setpoint Cubic meters per hour. This flow rate value is then transmitted to the circulating pump frequency converter. For electrolysis power setting, the total voltage of the electrolysis stack is set. Volts, calculate current setpoint Ampere. Send this current command to the electrolytic power supply. For switching the heat source temperature zone, send the opening command. and The commands are sent to the electric regulating valve controllers on the outlet pipelines of heat source temperature zone 1 and temperature zone 2, respectively. Send to supply and demand cluster The heating branch valves. All actuators operate according to instructions, thereby achieving the following: within time slot 10, the electrolysis power operates at 75 kW, the stacking temperature target is 85 degrees Celsius, the heat source heat is proportionally distributed to the two temperature zone loops, the phase change buffer module releases heat at a flow rate of approximately 0.5 cubic meters per hour, and approximately 63% of the heat demand is met.

[0171] S8. Based on the execution results of the combined power supply setting command, perform deviation diagnosis and parameter adjustment to realize the scheduling of combined power, heat and hydrogen supply.

[0172] The specific implementation process of step S8 is as follows:

[0173] S8.1. Based on the execution result of the combined power supply tuning command, a deviation analysis is performed with the phase change buffer window to generate a deviation vector. Specifically:

[0174] After executing the combined power supply setting command for one complete scheduling cycle in step S7, actual feedback data from each execution stage is collected. This data includes the actual operating current density of the electrolysis system. With stacking temperature The actual temperature at the outlet of the heat exchange loop in each heat source temperature zone The actual charge and discharge heat power of the phase change buffer module, in relation to flow rate. And the actual heat supply on each heat load side. Compare these actual execution results with the phase transition buffer window generated in step S3. The implicit expected heat buffer state is analyzed for deviation. The expected heat buffer state is reflected in the window value. Within the corresponding theoretical buffer margin, the system's heat supply and demand should be near equilibrium. Deviation analysis is performed for each time slot. and each demand cluster Calculate the deviation between actual heating supply and commanded demand. :

[0175]

[0176] in, Assigned to the demand cluster in step S6 decomposition instruction Thermal power; This represents the time slot length. Simultaneously, the deviation between the actual power of the phase-change buffer module and the command is calculated. These deviations are organized by time slots and demand clusters to generate a multidimensional deviation vector. Deviation vector The elements contain all and .

[0177] S8.2. Regression correction is performed using the deviation vector and the sensitive parameters of the joint power supply tuning command to generate parameter correction coefficients. Specifically:

[0178] Using the deviation vector Regression correction is performed on the sensitive parameters in the joint power supply setting command to generate parameter correction coefficients. Sensitive parameters refer to those parameters that significantly affect the deviation of the execution result and are used in steps S1 to S6, mainly including the temperature normalization benchmark involved in the thermal quality signature matrix calculation in step S1. and The basic heat capacity estimated in step S3 for phase change heat absorption and release and adjustment coefficient and the coefficients of the efficiency fitting formula in step S4. to The regression correction employed a multiple linear regression method, using the deviation between actual heating supply and mandated demand. For example, it can be modeled as a function of the systematic error between the predicted and actual values ​​of the aforementioned sensitive parameters. For a time slot... and demand clusters Its theoretical prediction of heat This value is given by the model in steps S3 and S6 and is a sensitive parameter. The function. Execution deviation can be approximated as... ,in, For parameters The correction amount. Collect deviation data from multiple time slots and demand clusters to construct an overdetermined system of equations. ,in, Let Jacobi be the Jacobian matrix, and its elements be the corresponding partial derivatives. or Solve using the least squares method. This yields a set of parameter correction values. Parameter correction coefficients. Then it is defined as ,in, This is the nominal value of the parameter. This is element-wise division. Parameter correction coefficient. The vector is used to proportionally correct the values ​​of each sensitive parameter. Its setting is based on ensuring that the corrected parameters minimize the sum of squares of the prediction error. The convergence of the method is verified based on 80 sets of historical deviation data.

[0179] S8.3. Correct the feasible region set using parameter correction coefficients. Specifically:

[0180] Using parameter correction coefficients The feasible region set used in step S6 is revised. Specifically, the thermal quality signature matrix of step S1, the phase transition buffer window of step S3, and the coupling boundary of step S4 are recalculated using the revised sensitivity parameters. Since the coupling boundary depends on the efficiency fitting parameters and thermal constraints, its shape and position may change after parameter revision, thus generating an updated coupling boundary. The feasible region set is determined by the scaling factor. The key to refining the feasible region set lies in refining the scaling factor, as this affects the generation of coupled boundaries. itself.

[0181] S8.3.1. Using the bias vector and parameter correction coefficients, bias source classification is performed to generate bias classification results. Details are as follows:

[0182] Using the deviation vector With parameter correction coefficient Deviation sources are classified, and deviation classification results are generated. Deviation source classification aims to distinguish whether deviations are primarily caused by inaccurate model parameters or by uncontrollable external disturbances or actuator errors. The classification method is based on calculating residuals. If the residual The norm is less than the threshold If the error is positive, the deviation is considered to be mainly caused by model parameter errors; otherwise, significant external disturbances or execution errors are considered to exist. Threshold Based on sensor measurement accuracy and system noise level settings, generate deviation classification results. Its value is either "parameter-dominated" or "perturbation-dominated".

[0183] S8.3.2. Correct the scaling factor function using the deviation classification results. Specifically:

[0184] Correct the scaling factor using the bias classification results Scaling factor The correction logic is as follows: if the deviation is classified as "parameter-dominated," it indicates that the model has improved, and the scaling factor can be appropriately increased to expand the feasible region and improve scheduling flexibility. The correction formula is as follows: ,in, This is a small positive increment, such as 0.05. If the deviation is classified as "disturbance-dominated," it indicates high system uncertainty, and the feasible region should be narrowed to ensure robustness. The corrected formula is... ,in, This is another positive increment. Increment and The settings are based on 20 simulation tests to assess the system's stability margin. A modified scaling factor is used. and the updated coupling boundary Regenerate the feasible region set for the next scheduling cycle. This allows for closed-loop parameter adjustment.

[0185] For example, the specific calculation process of step S8 is as follows: Assume that after one scheduling cycle, for a time slot... and demand clusters Collect actual data: actual heat supply kilowatt-hour, instructions for heat distribution kilowatt-hours, deviation Kilowatt-hours. Actual buffer power. Kilowatt, command value kilowatts, deviation Kilowatts. These are then combined with data from other time slots to form a deviation vector. Select the sensitive parameter as and Their nominal values ​​are 10,000 kJ and 0.2, respectively. Calculation of theoretically predicted heat... Construct the Jacobian matrix using the partial derivatives of these parameters. The correction amount is obtained through regression analysis. kilojoules Then the correction factor , Use new parameters kilojoules Recalculate the model. Calculate the residual norm, assuming it is less than a threshold, and classify it as "parameter-dominated". Original scaling factors. ,Pick After correction .use and the updated coupling boundary calculated based on the new parameters Generate a new feasible domain for the next cycle time slot 10. .

[0186] Example 2

[0187] like Figure 3 As shown, the present invention provides a combined heat and power (CHP) system for hydrogen supply and dispatch based on the cascade utilization of waste heat from electrolysis, comprising:

[0188] The thermal quality identification module is used to acquire the temperature and flow parameters of different source items in the preset electrolytic stack and identify the available temperature zone to generate a thermal quality signature matrix.

[0189] The demand set construction module is used to identify temperature zone demand points on the heat load side, perform temperature zone aggregation processing on the temperature zone demand points, and generate temperature zone demand sets.

[0190] The buffer window calculation module is used to match the temperature zone demand set with the preset phase change range of the phase change medium by temperature difference, calculate the heat absorption and release range of the phase change medium based on the temperature difference matching result and the thermal quality signature matrix, and generate a phase change buffer window.

[0191] The coupling boundary generation module is used to analyze the current density, temperature and gas evolution rate of the electrolytic stack and perform consistency calibration to generate coupling boundaries;

[0192] The load transferability spectrum calculation module is used to acquire grid load signals and hydrogen supply plans to evaluate load delayability and switchability, and obtain the load transferability spectrum.

[0193] The instruction generation module is used to constrain and superimpose the thermal quality signature matrix using the coupling boundary, the phase change buffer window and the load transferability spectrum to generate a combined power supply tuning instruction.

[0194] The collaborative execution module is used to execute the combined power supply setting command and to perform phase change buffer module heat charging and discharging, electrolysis power setting and heat source temperature zone switching.

[0195] The closed-loop correction module is used to perform deviation diagnosis and parameter adjustment based on the execution result of the combined power supply setting command, so as to realize the scheduling of combined power supply of electricity, heat and hydrogen.

[0196] In an optional implementation, the combined heat and power (CHP) hydrogen supply scheduling method includes: a) acquiring the temperature and flow parameters of different source terms of a preset electrolytic stack and identifying available temperature zones to generate a thermal quality signature matrix; b) identifying temperature zone demand points on the heat load side, performing temperature zone aggregation processing on the temperature zone demand points, and generating a temperature zone demand set; c) matching the temperature zone demand set with the phase change range of a preset phase change medium, calculating the heat absorption and release interval of the phase change medium, and generating a phase change buffer window; d) analyzing and calibrating the current density, stack temperature, and gas evolution rate of the electrolytic stack to generate a coupling boundary; e) acquiring the grid load signal and hydrogen supply plan to evaluate the load delayability and switchability, and obtaining the load transferability spectrum; f) constraining and superimposing the thermal quality signature matrix to generate a CHP setting command; g) executing the CHP setting command and performing phase change buffer module heat charging and releasing, electrolysis power setting, and temperature zone switching; h) deviation diagnosis and parameter adjustment to realize CHP hydrogen supply scheduling.

Claims

1. A method for scheduling combined heat, power, and hydrogen power supply based on the cascade utilization of waste heat from electrolysis, characterized in that, include: The system acquires the temperature and flow parameters of different source terms in the preset electrolytic stack and identifies the available temperature range, generating a thermal quality signature matrix, including: The temperature and flow parameters of different source terms in the preset electrolytic stack are obtained and classified to generate a source term parameter set; Based on the preset heat exchange path and the source term parameter set, the effective temperature zones are merged to generate a temperature zone availability map. The temperature zone availability map is superimposed on the source term parameter set and scaled to generate a thermal quality signature matrix. Identify temperature zone demand points on the heat load side, perform temperature zone aggregation processing on these demand points, and generate a temperature zone demand set, including: Obtain the target temperature range, required flow rate, and time demand of each heat-using unit on the heat load side to form an initial demand point set; Based on the spatial topology of the heat-using unit and the preset heat exchange path, the initial demand point set is aggregated by temperature zone aggregation and time window compression to generate an aggregated demand cluster set. The aggregated demand clusters are adapted and verified with the existing heat storage / heat exchange capacity of the combined heat and hydrogen power supply scheduling system. Unmet demands are eliminated and their priorities are marked to form an effective demand set. The effective demand set is scaled and indexed temporally to obtain the temperature zone demand set; The temperature range requirement set is matched with the preset phase change range of the phase change medium by temperature difference. Based on the temperature difference matching result and the thermal quality signature matrix, the heat absorption and release range of the phase change medium is calculated to generate a phase change buffer window, including: Obtain the temperature zone requirement set and the phase change range of the preset phase change medium, and perform temperature difference matching to generate a phase change matching vector; Based on the phase change matching vector and the thermal quality signature matrix, the heat absorption and release are estimated, and a phase change heat absorption and release characteristic table is generated. The phase change heat absorption and release characteristic table is superimposed with the temperature zone demand set and time smoothing is performed to generate a phase change buffer window; The current density, temperature, and gas evolution rate of the electrolytic stack are analyzed and a consistency calibration is performed to generate coupling boundaries; By acquiring grid load signals and hydrogen supply plans, load delayability and switchability are assessed to obtain the load transferability spectrum; Using the coupling boundary, the phase change buffer window, and the load transferability spectrum, the thermal quality signature matrix is ​​constrained and superimposed to generate a combined power supply tuning command; Execute the combined power supply setting command and perform phase change buffer module heat charging and discharging, electrolysis power setting and heat source temperature zone switching; Based on the execution results of the combined power supply setting command, deviation diagnosis and parameter adjustment are performed to realize the scheduling of combined power, heat and hydrogen supply.

2. The method for cogeneration and hydrogen supply scheduling based on the cascade utilization of waste heat from electrolysis according to claim 1, characterized in that, The process of analyzing the current density, temperature, and gas evolution rate of the electrolytic stack and performing consistency calibration to generate coupling boundaries includes: Obtain the current density, temperature, and gas evolution rate of the electrolytic stack; The current density, temperature, and gas evolution rate of the electrolytic stack are fitted to generate a set of efficiency and gas evolution response curves. The efficiency and gas evolution response curve set is compared with the preset safe operation limit boundary for consistency verification, and a consistency limit table is generated. The domain boundaries of the consistency constraint table and the thermal side constraint of the thermal quality signature matrix are extracted to generate a coupling boundary.

3. The method for cogeneration and hydrogen supply scheduling based on the cascade utilization of waste heat from electrolysis according to claim 1, characterized in that, The process of acquiring grid load signals and hydrogen supply plans to assess load delayability and switchability, and obtaining a load transferability spectrum, includes: Acquire grid load signals and hydrogen supply plans, align them in time, and generate a time-series demand set; Based on the assessment of the delayability and substitutability of the time-series demand set and the temperature zone demand set, a trade-off calculation is performed to generate a load transfer evaluation table. Based on the load transfer evaluation table and the phase change buffer window, path selection is performed to generate a load transferability spectrum.

4. The method for cogeneration and hydrogen supply scheduling based on the cascade utilization of waste heat from electrolysis according to claim 1, characterized in that, The step of using the coupling boundary, the phase change buffer window, and the load transferability spectrum to constrain and superimpose the thermal quality signature matrix to generate a combined heat and power (CHP) tuning command includes: Using a preset scaling factor function, the coupling boundary and the load transferability spectrum are solved to generate a feasible region set; Based on the feasible domain set and the phase transition buffer window, instruction decomposition is performed to generate a decomposed instruction set; The decomposition instruction set is mapped to the thermal quality signature matrix to generate a combined power supply tuning instruction.

5. The method for cogeneration and hydrogen supply scheduling based on the cascade utilization of waste heat from electrolysis according to claim 4, characterized in that, The process of deviation diagnosis and parameter adjustment based on the execution result of the combined power supply setting command to achieve combined power, heat, and hydrogen supply scheduling includes: Based on the execution result of the combined power supply tuning command, a deviation analysis is performed with the phase change buffer window to generate a deviation vector; The deviation vector and the sensitive parameters of the joint power supply tuning command are used for regression correction to generate parameter correction coefficients; The feasible region set is corrected using the parameter correction coefficients.

6. The method for cogeneration and hydrogen supply scheduling based on the cascade utilization of waste heat from electrolysis according to claim 5, characterized in that, The step of correcting the feasible region set using the parameter correction coefficients includes: The deviation vector and the parameter correction coefficient are used to classify the deviation sources and generate deviation classification results. The scaling factor function is corrected using the bias classification results.

7. A combined heat, power, and hydrogen supply scheduling system based on the cascade utilization of waste heat from electrolysis, characterized in that, include: The thermal quality identification module is used to acquire the temperature and flow parameters of different source terms in the preset electrolytic stack, identify the available temperature range, and generate a thermal quality signature matrix, including: The temperature and flow parameters of different source terms in the preset electrolytic stack are obtained and classified to generate a source term parameter set; Based on the preset heat exchange path and the source term parameter set, the effective temperature zones are merged to generate a temperature zone availability map. The temperature zone availability map is superimposed on the source term parameter set and scaled to generate a thermal quality signature matrix. The demand set construction module is used to identify temperature zone demand points on the heat load side, perform temperature zone aggregation processing on the temperature zone demand points, and generate a temperature zone demand set, including: Obtain the target temperature range, required flow rate, and time demand of each heat-using unit on the heat load side to form an initial demand point set; Based on the spatial topology of the heat-using unit and the preset heat exchange path, the initial demand point set is aggregated by temperature zone aggregation and time window compression to generate an aggregated demand cluster set. The aggregated demand clusters are adapted and verified with the existing heat storage / heat exchange capacity of the combined heat and hydrogen power supply scheduling system. Unmet demands are eliminated and their priorities are marked to form an effective demand set. The effective demand set is scaled and indexed temporally to obtain the temperature zone demand set; The buffer window calculation module is used to match the temperature zone demand set with the preset phase change range of the phase change medium by temperature difference, calculate the heat absorption and release range of the phase change medium based on the temperature difference matching result and the thermal quality signature matrix, and generate a phase change buffer window, including: Obtain the temperature zone requirement set and the phase change range of the preset phase change medium, and perform temperature difference matching to generate a phase change matching vector; Based on the phase change matching vector and the thermal quality signature matrix, the heat absorption and release are estimated, and a phase change heat absorption and release characteristic table is generated. The phase change heat absorption and release characteristic table is superimposed with the temperature zone demand set and time smoothing is performed to generate a phase change buffer window; The coupling boundary generation module is used to analyze the current density, stacking temperature and gas evolution rate of the electrolytic stack and perform consistency calibration to generate coupling boundaries; The load transferability spectrum calculation module is used to acquire grid load signals and hydrogen supply plans to evaluate load delayability and switchability, and obtain the load transferability spectrum. The instruction generation module is used to constrain and superimpose the thermal quality signature matrix using the coupling boundary, the phase change buffer window and the load transferability spectrum to generate a combined power supply tuning instruction. The collaborative execution module is used to execute the combined power supply setting command and to perform phase change buffer module heat charging and discharging, electrolysis power setting and heat source temperature zone switching. The closed-loop correction module is used to perform deviation diagnosis and parameter adjustment based on the execution result of the combined power supply setting command, so as to realize the scheduling of combined power supply of electricity, heat and hydrogen.

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