A method and system for generating a day-ahead scheduling plan of an integrated energy base

Abstract

This invention relates to the field of dispatch management technology, and in particular to a method and system for generating day-ahead dispatch plans for integrated energy bases. The method includes the following steps: acquiring operating parameters of various energy devices within the integrated energy base through a data acquisition terminal; performing data verification, timing alignment, and anomaly removal by an edge computing unit to form standardized equipment status data; and a centralized processing server, based on an equipment physical constraint model, performing joint calculations on the equipment status data and day-ahead load forecast data to generate day-ahead dispatch instructions that meet output, ramp-up, and energy balance constraints, and issuing these instructions through a dispatch execution interface to complete the day-ahead dispatch plan generation. This invention achieves refined execution of day-ahead dispatch plans for integrated energy bases under multiple physical constraints by introducing a hierarchical processing mechanism for the status of multiple energy devices into the dispatch control system, and combining dynamic ramp-up constraints based on output change-driven types with energy coupling modeling.

Description

Technical Field

[0001] This invention relates to the field of dispatch management technology, and in particular to a method and system for generating day-ahead dispatch plans for integrated energy bases. Background Technology

[0002] With the large-scale application of new energy power generation, energy storage systems, and multi-energy coupling equipment in integrated energy bases, energy systems are gradually exhibiting characteristics such as the coexistence of multiple energy forms, complex equipment types, and mutually coupled operational constraints. To ensure the safe, stable, and economical operation of integrated energy bases, it is usually necessary to generate a day-ahead dispatch plan before the start of the dispatch cycle to coordinate the output arrangements and operating modes of various energy equipment in a unified manner.

[0003] In existing technologies, day-ahead dispatching methods for integrated energy bases are mostly centralized, typically relying on collected equipment operating data and load forecasting results for dispatching calculations. However, these methods are insufficient in handling the temporal consistency of collected data and the removal of outliers, easily introducing noisy data into the dispatching model and affecting the accuracy of the dispatching results. Furthermore, existing dispatching models often employ simplified descriptions of equipment constraints, failing to comprehensively reflect the output limitations, ramp-up rate limitations, and system energy balance constraints faced by power supply equipment, energy storage equipment, and multi-energy coupled equipment during actual operation.

[0004] Furthermore, in multi-energy coupling scenarios, there are energy conversion and mutual influence relationships between different energy forms. Existing scheduling methods do not fully consider the above coupling relationships, making it difficult to reasonably coordinate the output adjustment of each energy device under load change conditions. This can easily lead to a mismatch between scheduling instructions and the actual operating capacity of the equipment, reducing the executability of the scheduling plan. Summary of the Invention

[0005] Therefore, it is necessary to provide a method and system for generating day-ahead scheduling plans for integrated energy bases to solve at least one of the aforementioned technical problems.

[0006] To achieve the above objectives, a method for generating a day-ahead dispatch plan for an integrated energy base is provided. This method employs a dispatch control system for the integrated energy base. The dispatch control system includes at least a data acquisition terminal, an edge computing unit, a centralized processing server, and a dispatch execution interface. The edge computing unit is wirelessly connected to the data acquisition terminal, and the centralized processing server is connected to both the edge computing unit and the dispatch execution interface. The method includes the following steps: Step S1: The operating parameters and status information of the power supply equipment, energy storage equipment and multi-energy coupling equipment are acquired in real time through the data acquisition terminal and transmitted to the edge computing unit through the industrial communication interface; Step S2: The edge computing unit performs local verification, timing alignment, and anomaly removal on the collected operating parameters to form standardized device status data and uploads it to the centralized processing server; Step S3: The centralized processing server, based on its built-in multi-core processor and storage module, calls the equipment physical constraint model to perform joint calculations on the standardized equipment status data and day-ahead load forecast data, and generates a set of day-ahead scheduling instructions that meet the output limits, ramping constraints and energy balance constraints of each energy equipment. Step S4: Send the day-ahead scheduling instruction set to the corresponding power equipment controller, energy storage converter and energy management terminal through the scheduling execution interface to complete the day-ahead scheduling plan generation of the integrated energy base.

[0007] This invention provides a system for generating day-ahead scheduling plans for integrated energy bases, used to execute the method for generating day-ahead scheduling plans for integrated energy bases as described above. The system includes: The data acquisition module is used to acquire the operating parameters and status information of power equipment, energy storage equipment and multi-energy coupling equipment in real time through the data acquisition terminal, and transmit them to the edge computing unit through the industrial communication interface; The edge preprocessing module is used by the edge computing unit to perform local verification, timing alignment and anomaly removal on the collected operating parameters, form standardized device status data and upload it to the centralized processing server; The scheduling calculation module is used by the centralized processing server to call the equipment physical constraint model based on its built-in multi-core processor and storage module, and to perform joint calculations on standardized equipment status data and day-ahead load forecast data to generate a set of day-ahead scheduling instructions that meet the output limits, ramping constraints and energy balance constraints of each energy equipment. The scheduling execution module is used to send the day-ahead scheduling instruction set to the corresponding power equipment controller, energy storage converter and energy management terminal through the scheduling execution interface, so as to complete the day-ahead scheduling plan generation of the integrated energy base.

[0008] The beneficial effects of this invention lie in the fact that by deploying a scheduling and control system within an integrated energy base, consisting of data acquisition terminals, edge computing units, a centralized processing server, and a scheduling execution interface, hierarchical processing and collaborative computation of operating status data from multiple energy devices are achieved. By performing local verification, timing alignment, and anomaly removal on the collected operating parameters at the edge computing unit level, the noise and communication load of the raw data are effectively reduced, improving the consistency and timeliness of data uploaded to the centralized processing server. In the centralized processing server, multi-core processors are used to perform parallel reading and joint computation of standardized equipment status data and day-ahead load forecast data. Combined with a pre-established equipment physical constraint model, the rated output limits, ramp-up constraints, and system energy balance constraints of energy devices can be simultaneously characterized within the same computational framework. This avoids the problems of dispersed constraint modeling and difficulty in unified coordination found in traditional scheduling methods. Furthermore, this invention introduces a time-related feature analysis mechanism based on the output change relationship between adjacent scheduling periods to identify the driving type of output change in energy equipment. Based on this, it dynamically constructs ramp change rate constraint intervals that match different driving characteristics, enabling ramp constraints to adaptively adjust with the evolution trend of output changes. This significantly improves the fit between scheduling results and the actual operating characteristics of the equipment. Simultaneously, by combining the energy conversion capabilities and operating states of multi-energy coupled equipment, it models the equivalent allocation of system load changes across different energy forms. This ensures that scheduling calculations accurately reflect the cross-energy coupling relationships within the integrated energy system, avoiding the problems of distorted or unexecutable scheduling results from a single energy perspective. Attached Figure Description

[0009] Figure 1 A flowchart illustrating the steps involved in generating a day-ahead scheduling plan for an integrated energy base. Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S3. Figure 3 This is a constraint diagram of the method for generating day-ahead scheduling plans for integrated energy bases according to this application; Figure 4 This is an energy ramp line graph for the method of generating a day-ahead dispatch plan for an integrated energy base according to this application; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0010] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0011] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0012] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0013] To achieve the above objectives, please refer to Figures 1 to 4 A method for generating a day-ahead dispatch plan for an integrated energy base is disclosed. This method employs the dispatch control system of the integrated energy base. The dispatch control system includes at least a data acquisition terminal, an edge computing unit, a centralized processing server, and a dispatch execution interface. The edge computing unit is wirelessly connected to the data acquisition terminal, and the centralized processing server is connected to both the edge computing unit and the dispatch execution interface. The method includes the following steps: Step S1: The operating parameters and status information of the power supply equipment, energy storage equipment and multi-energy coupling equipment are acquired in real time through the data acquisition terminal and transmitted to the edge computing unit through the industrial communication interface; In one embodiment, data acquisition terminals are deployed on the local side of power supply equipment, energy storage equipment, and multi-energy coupling equipment within the integrated energy base, respectively, for real-time acquisition of operating parameters and status information of various types of equipment. The power supply equipment includes photovoltaic power generation devices, wind power generation devices, or conventional power generation devices; the energy storage equipment includes electrochemical energy storage systems or other forms of energy storage units; and the multi-energy coupling equipment includes electro-thermal, electro-cooling, or electro-gas coupling devices.

[0014] In practice, the data acquisition terminal connects to the sensor interface or control interface of the device body to acquire operating parameters and status information such as voltage, current, active power, reactive power, energy output value, state of charge, operating mode, start / stop status, and fault status. The acquisition terminal periodically collects the above parameters according to a preset sampling period, and timestamps and performs basic formatting on the collected raw data to form standardized device operating data.

[0015] After data acquisition is completed, the data acquisition terminal transmits the device operation data to the edge computing unit via an industrial communication interface. The industrial communication interface includes wired or wireless industrial communication methods, specifically industrial Ethernet, fieldbus, or other industrial communication protocols. In one specific embodiment, data from different types of devices is encapsulated using a unified communication protocol to ensure consistency and compatibility of data from multiple sources during transmission.

[0016] During data transmission, the data acquisition terminal performs integrity verification and anomaly detection on the data to be transmitted. When data loss or communication anomaly is detected, a retransmission or supplementary acquisition mechanism is automatically executed to ensure that the running data received by the edge computing unit is complete and accurate.

[0017] Step S2: The edge computing unit performs local verification, timing alignment, and anomaly removal on the collected operating parameters to form standardized device status data and uploads it to the centralized processing server; In one embodiment, the edge computing unit is deployed on the field side of the integrated energy base and maintains communication connections with multiple data acquisition terminals to process the received equipment operating parameters locally. After receiving operating parameter data from the data acquisition terminals, the edge computing unit first performs local verification on the data to determine whether there are any missing, formatted, or abnormal data during the acquisition and transmission process.

[0018] In practice, local verification includes checking data integrity, parameter value range, and data structure consistency. When data records with missing fields, abnormal values, or that do not conform to the preset data structure are found, the edge computing unit marks or removes the corresponding data to prevent abnormal data from entering subsequent computing processes.

[0019] After completing local verification, the edge computing unit performs time-series alignment processing on the operating parameters from different devices or different acquisition terminals. Since the sampling period and data reporting time of each device may differ, the edge computing unit performs time window matching on various operating parameters according to the timestamps carried by the data, so that the data from multiple sources within the same scheduling period are consistent in the time dimension, thereby forming a unified time-series set of device operating data.

[0020] Subsequently, the edge computing unit performs anomaly removal processing on the time-aligned data. Anomaly removal includes identifying and processing abrupt changes, data that significantly deviates from normal operating conditions, and duplicate reports. In one specific implementation, the edge computing unit combines the device's historical operating status and current operating characteristics to filter data that does not conform to normal operating characteristics, retaining only data that truly reflects the device's operating status.

[0021] After completing local verification, timing alignment, and anomaly removal, the edge computing unit organizes the processed data into standardized device status data. This standardized device status data is encapsulated according to a unified data structure, including device identification information, timestamps, and corresponding operating status parameters, to facilitate unified parsing and subsequent scheduling calculations by the centralized processing server.

[0022] Step S3: The centralized processing server, based on its built-in multi-core processor and storage module, calls the equipment physical constraint model to perform joint calculations on the standardized equipment status data and day-ahead load forecast data, and generates a set of day-ahead scheduling instructions that meet the output limits, ramping constraints and energy balance constraints of each energy equipment. In one embodiment, reference may be made to Figure 3 The centralized processing server is deployed at the central control layer of the integrated energy base. It integrates a multi-core processor and a large-capacity storage module to perform high-concurrency data processing and scheduling analysis tasks. After receiving standardized equipment status data uploaded by the edge computing unit, the centralized processing server loads this equipment status data along with the day-ahead load forecast data into the storage module as the basis for joint calculations.

[0023] During the scheduling calculation process, the centralized processing server first identifies the current operating status of various energy devices based on standardized equipment status data, including available output range, operating mode, and start / stop status. Simultaneously, it invokes a pre-built and stored physical constraint model of the devices to describe the operational capabilities of each energy device within the scheduling cycle, reflecting the physical limitations faced by different devices in actual operation.

[0024] Based on this, the centralized processing server jointly processes standardized equipment status data with day-ahead load forecast data, and, while meeting the overall supply and demand relationship of the system, through... Figure 3 The built-in constraint parameter mapping module and constraint condition construction layer calculate the output arrangement of each energy device during each scheduling period. In particular, the joint calculation process simultaneously considers the maximum and minimum output limits of energy devices, the ramp-up characteristics during output changes, and the overall energy balance requirements of the multi-energy system, so as to avoid the situation of equipment operating beyond its limits or system supply and demand imbalance.

[0025] In one specific implementation, a centralized processing server uses multi-core processors to execute scheduling computation tasks from different devices or at different times in parallel. This breaks down the complex joint computation process into multiple subtasks that can be processed in parallel, thereby improving scheduling computation efficiency and shortening the time for generating scheduling results. Intermediate results and constraint verification information generated during the computation process can be temporarily stored in a storage module for backtracking analysis or anomaly handling.

[0026] After completing the joint calculations, the centralized processing server generates a set of day-ahead scheduling instructions that satisfy the operational constraints of each energy device. This day-ahead scheduling instruction set includes at least the target output instructions for each energy device within each scheduling period and the corresponding execution sequence information, used to guide the subsequent scheduling execution interface in uniformly controlling each energy device.

[0027] Step S4: Send the day-ahead scheduling instruction set to the corresponding power equipment controller, energy storage converter and energy management terminal through the scheduling execution interface to complete the day-ahead scheduling plan generation of the integrated energy base.

[0028] In one embodiment, the scheduling execution interface is deployed in the control execution layer of the integrated energy base to realize instruction interaction between the centralized processing server and various energy devices. After the centralized processing server generates a day-ahead scheduling instruction set, the day-ahead scheduling instruction set is uniformly encapsulated through the scheduling execution interface, and the instructions are classified and processed according to device type and control level.

[0029] Based on the instruction classification results, the scheduling execution interface issues scheduling instructions for power supply equipment to the corresponding power supply equipment controller, and scheduling instructions for energy storage equipment to the energy storage converter. Simultaneously, scheduling instructions involving multi-energy coordinated control or operational status management are sent to the energy management terminal. All types of scheduling instructions can be transmitted via industrial communication networks during the issuance process to ensure the real-time performance and reliability of instruction transmission.

[0030] In one specific implementation, when issuing a pre-schedule dispatch instruction, the dispatch execution interface performs integrity verification and format matching on the instruction content to ensure that the dispatch instruction can be correctly parsed and executed by the corresponding power equipment controller, energy storage converter, and energy management terminal. After receiving the dispatch instruction, each execution terminal performs local verification of the instruction parameters and sends back the instruction reception status or execution confirmation information to the dispatch execution interface.

[0031] After receiving confirmation messages from each execution terminal, the scheduling execution interface summarizes the command issuance results and feeds the confirmation results back to the centralized processing server, thus forming a closed-loop management process for scheduling command issuance. The set of scheduling commands becomes effective only when all target devices have completed command reception and are in an executable state.

[0032] As an example of the present invention, reference is made to Figure 2 As shown, step S3 in this example includes: Step S31: The centralized processing server calls its built-in multi-core processor to perform parallel reading and cache scheduling of standardized equipment status data and day-ahead load forecast data in order to build the input dataset required for joint computing; Step S32: Based on the input dataset, call the device physical constraint model pre-stored in the storage module to map constraint parameters for the rated output range, ramp change rate and available energy boundary of each energy device; Step S33: Based on the constraint parameters, construct a set of joint constraint conditions that includes energy equipment output limit constraints, ramping constraints, and system energy balance constraints; Step S34: Under the premise of satisfying the set of joint constraints, perform joint calculations on the standardized equipment status data and the day-ahead load forecast data to obtain the target output results of each energy equipment in each day-ahead period; Step S35: Based on the target output results, generate a set of day-ahead dispatch instructions that correspond one-to-one with each energy device.

[0033] In one embodiment, after receiving standardized equipment status data and day-ahead load forecast data uploaded by the edge computing unit, the centralized processing server utilizes its built-in multi-core processor resources to perform parallel data reading and task allocation. By implementing thread-based loading and cache management for data corresponding to different energy devices, the standardized equipment status data and day-ahead load forecast data are uniformly organized into the input dataset required for joint computation, thereby improving data reading efficiency and reducing computation waiting time.

[0034] After constructing the input dataset, the centralized processing server retrieves pre-configured physical constraint models of the devices from its storage module. These physical constraint models correspond to power supply devices, energy storage devices, and multi-energy coupled devices, describing the capability boundaries and variation characteristics of each type of device during actual operation. Based on the input dataset, the centralized processing server maps the operating state of each energy device to the corresponding physical constraint model, obtaining constraint parameters such as output range limits, rate of change limits, and available energy boundaries that match each energy device.

[0035] In one specific implementation, the centralized processing server, based on the obtained constraint parameters, uniformly organizes the operating conditions of each energy device within the day-ahead scheduling cycle, constructing a set of joint constraints that includes energy device output limits, output variation constraints, and overall system energy balance requirements. This set of joint constraints is used to limit the feasible operating range of each energy device during subsequent calculations, thereby avoiding the generation of scheduling results that do not conform to the actual capabilities of the devices or the system's operational safety requirements.

[0036] Under the constraints of a joint set of constraints, the centralized processing server performs joint calculations on standardized equipment status data and day-ahead load forecast data. By comprehensively analyzing the relationship between system load demand and energy equipment availability during each scheduling period, the target output of each energy device is calculated for each day-ahead period, ensuring that the target output meets both load demand and all constraints.

[0037] After obtaining the target output results of each energy device, the centralized processing server organizes the target output results according to the device type and device identifier, and generates a set of day-ahead scheduling instructions that correspond one-to-one with each energy device.

[0038] Preferably, step S33 includes: Based on the rated output range in the constraint parameters, equipment-level output constraints are constructed for each energy device, where the equipment-level output constraints are used to limit the maximum available output range of each energy device in each time period; Based on the output change relationship of the ramp change rate between adjacent scheduling periods, ramp constraints are constructed for each energy device to limit the output change rate of each energy device within adjacent scheduling periods. Based on the available energy boundary analysis, the system load demand and the energy supply capacity of each energy device are analyzed, and system-level energy balance constraints are constructed to limit the balance relationship between energy supply and load demand in each time period. The equipment-level output constraints, ramping constraints, and system-level energy balance constraints are combined to form a set of joint constraints for joint calculations.

[0039] In one embodiment, after obtaining the constraint parameters corresponding to each energy device, the centralized processing server first constructs device-level output constraints for each energy device based on the rated output range in the constraint parameters. Specifically, the centralized processing server reads the upper and lower limits of the rated output for each energy device and, in conjunction with the time period division within the scheduling cycle, maps the rated output range to each scheduling time period, forming device-level output constraints to limit the maximum available output range of the energy device within each time period. For example, for a power generation device with a rated output range of a preset range, its target output within each scheduling time period is limited to within that preset range.

[0040] Based on the established device-level output constraints, the centralized processing server further constructs ramp-up constraints for each energy device based on the output change relationship between adjacent scheduling periods. In specific implementation, the centralized processing server obtains the maximum allowable output change rate for each energy device and limits the output change amount between two adjacent scheduling periods according to the scheduling period sequence. This ensures that the output adjustment range of each energy device within adjacent scheduling periods does not exceed the corresponding change rate limit, thereby avoiding equipment malfunctions or system fluctuations caused by excessively rapid output changes.

[0041] Subsequently, the centralized processing server analyzes the system load demand and the energy supply capacity of each energy device based on the available energy boundary in the constraint parameters, and constructs system-level energy balance constraints. In one specific implementation, within each scheduling period, the centralized processing server uses the load demand represented by the day-ahead load forecast data as the energy demand-side input, and the energy that each energy device can provide under the current constraints as the supply-side input. By restricting the matching relationship between the demand side and the supply side, it ensures that the overall energy supply capacity of the system can cover the corresponding load demand within each scheduling period.

[0042] After constructing the aforementioned constraints, the centralized processing server organizes and combines the device-level output constraints, ramp-up constraints, and system-level energy balance constraints. By associating various constraints according to energy device identifiers and scheduling periods, a set of joint constraints is formed for subsequent joint calculations. This set of joint constraints allows for simultaneous constraints on both individual device operating characteristics and overall system operating requirements during scheduling calculations, thus providing a complete constraint foundation for solving the day-ahead scheduling target output.

[0043] Preferably, based on the output change relationship of the ramp change rate between adjacent scheduling periods, the ramp constraints for each energy device are constructed as follows: Based on the output change sequence of each energy device in adjacent scheduling periods, the output change amount and direction between adjacent scheduling periods are extracted to form a time-related feature that reflects the evolution trend of power change. Based on the time period correlation characteristics, the output change driving type of each energy device in adjacent scheduling time periods is identified, where the driving type is used to characterize whether the output change is dominated by load change, constraint release, or state switching. Based on the type of power output change drive, a corresponding climbing rate limit interval is constructed to ensure that the climbing rate of change and the power output change drive characteristics maintain consistency constraints. The climbing rate limit interval is mapped to the interval between adjacent scheduling periods, forming a climbing constraint condition that is dynamically adjusted according to the evolution trend of output change.

[0044] In one embodiment, before generating the day-ahead scheduling instruction, the centralized processing server first analyzes the output changes of each energy device within adjacent scheduling periods. Specifically, the centralized processing server reads the target output value of the same energy device in two consecutive scheduling periods in the order of scheduling periods, compares the output magnitude of the two periods, and extracts the output change amount and direction between adjacent scheduling periods. For example, it determines whether the output is increasing, decreasing, or remaining unchanged, thereby forming a time-related feature reflecting the evolution trend of the energy device's output change.

[0045] After acquiring the time-period correlation characteristics, the centralized processing server further combines the day-ahead load forecast data and equipment operating status to identify the driving source of output changes. In one specific implementation, if the direction of output change within adjacent scheduling periods is consistent with the direction of system load change, and the change amplitude is mainly used to match load fluctuations, then the output change is identified as a driving type dominated by load change; if the output change occurs after the original output constraints of the equipment are lifted, such as when the equipment's available capacity is restored or the constraints are relaxed, then the output change is identified as a driving type dominated by constraint release; if the output change is accompanied by a switch in the equipment's operating state, such as from standby to running or from running to load reduction, then the output change is identified as a driving type dominated by state switching.

[0046] After identifying the type of output change driver, the centralized processing server sets corresponding ramp-up rate limit ranges for different driver types. For example, for output adjustments driven by load changes, a relatively gentle rate limit is used to ensure stable system operation; for output adjustments driven by constraint release, the rate limit is allowed to be appropriately relaxed within a safe range to improve scheduling flexibility; and for output adjustments driven by state switching, a stricter rate limit is set to avoid operational risks caused by rapid start-up or large-scale adjustments.

[0047] Subsequently, the centralized processing server maps the slope change rate limit interval to specific adjacent scheduling time periods. During the scheduling calculation process, the output change of each energy device in adjacent scheduling time periods is constrained by the change rate limit under its corresponding drive type.

[0048] In another embodiment, a reference may be used. Figure 4 The power output curves and corresponding ramp rate distribution characteristics of each energy device during the scheduling cycle are analyzed. The scheduling control system uses the ramp rate of change between adjacent scheduling periods as the core constraint variable to construct the power output change relationship of each energy device. Specifically, for the different power output fluctuation patterns exhibited by photovoltaic power plants, thermal power units, wind power units, and energy storage devices in each scheduling period, the power output change in adjacent scheduling periods is calculated. Combined with the positive and negative directions of the power output change, a time-related characteristic reflecting the power output increase, decrease, and smooth transition is formed. Among them, photovoltaic power output is constrained by its upper and lower limit range, showing a characteristic of concentrated increase during the day and rapid decrease in the evening, while the power output of thermal power, wind power, and energy storage shows a relatively continuous or controllable adjustment curve.

[0049] Furthermore, based on the aforementioned time-period correlation characteristics, the driving types of output changes for each energy device within adjacent scheduling periods are identified. These driving types include output adjustments dominated by system load changes, output adjustments dominated by the release of ramp-up constraints or capacity constraints, and output adjustments dominated by equipment operating state switching or charging / discharging state transitions. For example, when photovoltaic output approaches its upper or lower limit range, its output changes are mainly driven by natural resource conditions and upper / lower limit constraints; when thermal power and energy storage equipment undertake system balance regulation, their output changes are mainly dominated by load changes.

[0050] Based on this, according to different output change driving types, ramp change rate limit intervals matching the change characteristics of each energy device are constructed. This ensures that the ramp change rate maintains consistency with the output change driving characteristics of the corresponding device in both numerical range and direction, thereby avoiding drastic output fluctuations exceeding the actual adjustment capacity of the devices during adjacent scheduling periods. Finally, the ramp change rate limit intervals are mapped to adjacent scheduling periods, forming a ramp constraint condition that dynamically adjusts with the evolution trend of output changes. This allows each energy device to achieve coordinated and stable output adjustment across multiple energy sources while meeting the upper and lower limits of photovoltaic constraints, system load balancing requirements, and scheduling smoothness requirements.

[0051] Preferably, based on the type of power output change drive, the corresponding climbing rate limit ranges are constructed as follows: Based on the type of power output change drive, determine the power output change formation path corresponding to that type of drive. Based on the path of output change, calculate the distribution of output change with respect to time step within adjacent scheduling periods, and extract the extreme value envelope of the output change rate in the distribution. The extreme value envelope is used as the rate change constraint boundary under this type of output change drive, so as to limit the output change rate from exceeding the extreme value envelope range during the continuous scheduling period. Based on the extreme value envelope constraint boundary, the corresponding slope change rate limit interval is generated.

[0052] In one embodiment, the centralized processing server determines the output change formation path corresponding to the identified output change drive type. For example, when the drive type is "load change dominant", the output change formation path is determined to be a change path that smoothly tracks the system load prediction curve; when the drive type is "constraint release dominant", the output change formation path is determined to be a release path that gradually increases after the upper limit of available output of the equipment is restored; when the drive type is "state switching dominant", the output change formation path is determined to be a switching path that transitions from zero or low output state to stable operating output state.

[0053] After determining the path of power output change, the centralized processing server uses a preset scheduling time step as a benchmark to discretize the power output change within adjacent scheduling periods. Specifically, by dividing the target power output difference between adjacent scheduling periods by the corresponding time step, the rate of power output change at each time step is calculated, forming a distribution reflecting the evolution of power output change over time.

[0054] Subsequently, the centralized processing server performs statistical analysis on the change distribution, extracting the maximum and minimum rate of change of output change rate within the distribution, and combining these maximum and minimum values ​​to form the extreme value envelope of the output change rate. This extreme value envelope describes the upper and lower limits of the possible output change rate under the current output change drive type.

[0055] Next, the centralized processing server uses the extreme value envelope as the rate change constraint boundary corresponding to the output change drive type, and constrains and controls the output change rate within consecutive scheduling periods during the scheduling calculation process, so that the output change rate within any adjacent scheduling period shall not exceed the range limited by the extreme value envelope.

[0056] Finally, the centralized processing server generates a ramp change rate limit interval corresponding to the output change drive type based on the extreme value envelope constraint boundary, and uses the ramp change rate limit interval as part of the ramp constraint condition in the subsequent joint constraint calculation, so that the ramp change rate and the corresponding output change drive feature maintain consistency constraint.

[0057] Preferably, based on the type of power output change drive, determining the power output change formation path corresponding to that drive type includes: Based on the type of output change drive, the initial output state and target output state of the energy equipment in adjacent scheduling periods are determined, where the initial output state and target output state are used to define the starting boundary and ending boundary of the output change. Based on the driving characteristics corresponding to the type of power output change, the generation sequence and state switching conditions of each intermediate transition state during the power output change process are determined to form a state evolution sequence of power output change. Based on the state evolution sequence, the trajectory of output change as the scheduling period progresses is constructed; The path validity of the change trajectory is verified to obtain the output change formation path corresponding to the drive type.

[0058] In one embodiment, the centralized processing server determines the initial and target output states of the energy equipment within adjacent scheduling periods based on the output change drive type. The initial output state is the actual operating output state of the energy equipment at the end of the current scheduling period, and the target output state is the output state that the energy equipment needs to achieve in the next scheduling period to meet load demand or system scheduling objectives. By determining the initial and target output states, the starting and ending boundaries of the output change are defined.

[0059] Subsequently, the centralized processing server determines the generation sequence and state switching conditions of each intermediate transition state during the output change process based on the driving characteristics corresponding to the output change driving type. Specifically, when the driving type is dominated by load change, the intermediate transition states are generated sequentially according to the load change trend, so that the output change presents a continuous and smooth increasing or decreasing process; when the driving type is dominated by constraint release, the intermediate transition states are generated in the order of gradual release of equipment constraints, so that the output change presents a phased increase evolution characteristic; when the driving type is dominated by state switching, the intermediate transition states are generated in the logical order of equipment start-up or shutdown or operation mode switching, so that the output change presents an evolution process of transitioning from a low output state to a stable output state.

[0060] After determining the generation order and state switching conditions of each intermediate transition state, the centralized processing server constructs a trajectory of output change that gradually advances with the scheduling period based on the intermediate transition states. This trajectory describes the entire process of energy equipment evolving from its initial output state to its target output state during a continuous scheduling period.

[0061] Finally, the centralized processing server verifies the path validity of the constructed change trajectory. This path validity verification includes determining whether the change trajectory meets the operational safety requirements of the energy equipment, whether it complies with the continuity requirements of the scheduling period, and whether it violates established equipment output limits and ramp constraints. When a change trajectory passes the path validity verification, it is determined as the output change formation path corresponding to the current output change drive type, and is used for subsequent ramp constraint construction and joint scheduling calculations.

[0062] Preferably, based on time-period correlation characteristics, the types of output changes driven by each energy device within adjacent scheduling periods include: The actual output changes of each energy device are obtained based on the time-related characteristics. Based on the energy coupling relationship of each energy device, the equivalent distribution relationship of the load change of the dispatch control system in adjacent dispatch periods among different energy forms is determined. By using the equivalent allocation relationship, the equivalent output change of each energy device corresponding to the system load change in adjacent scheduling periods is calculated. The actual output change of each energy device is aligned and compared with the corresponding equivalent output change to generate an output coupling feature that characterizes the degree of deviation between the two. Based on the output coupling characteristics, the driving type of output change of each energy device in adjacent scheduling periods is determined.

[0063] In one embodiment, the centralized processing server extracts the actual output changes of each energy device within adjacent scheduling periods based on time-period correlation characteristics. These actual output changes reflect the actual output adjustments made by the energy devices within adjacent scheduling periods, including increases, decreases, or stable changes.

[0064] Subsequently, the centralized processing server analyzes the system load changes of the dispatch control system within adjacent dispatch periods based on the energy coupling relationships between various energy devices, and determines the equivalent distribution relationship of this load change among different energy forms. This equivalent distribution relationship characterizes the equivalent output demand of various energy devices caused by system load changes through energy conversion, energy coupling, or complementary regulation under multi-energy collaborative operation conditions.

[0065] After determining the equivalent allocation relationship, the centralized processing server calculates the equivalent output change of each energy device corresponding to the system load change in adjacent scheduling periods, based on the equivalent allocation relationship. The equivalent output change is used to describe the magnitude and direction of the output change that each energy device should undertake in response to the system load change under ideal load sharing conditions.

[0066] Furthermore, the centralized processing server performs time alignment and amplitude comparison between the actual output changes of each energy device and the corresponding equivalent output changes, generating an output coupling feature that characterizes the degree of deviation between the two. This output coupling feature reflects the degree of matching between the actual operating behavior of the energy device and the system load change demand, as well as whether the output change of the energy device is primarily driven by system load changes.

[0067] Finally, based on the aforementioned output coupling characteristics, the centralized processing server comprehensively determines the driving type of output changes for each energy device within adjacent scheduling periods. When the actual output change is highly consistent with the equivalent output change, it is determined that the output change of that energy device is dominated by system load changes; when the actual output change significantly lags behind or leads the equivalent output change, it is determined that the output change of that energy device is dominated by constraint release or operational restrictions; when the actual output change exhibits abrupt changes or segmented characteristics that do not directly correspond to load changes, it is determined that the output change of that energy device is dominated by device state switching.

[0068] Preferably, based on the energy coupling relationship of each energy device, determining the equivalent distribution relationship of the load change of the dispatch control system in adjacent dispatch periods among different energy forms includes: Based on the energy coupling relationship of each energy device, determine the actual available conversion channels between different energy forms; Based on the available conversion channels and the changes in the load of the dispatch control system during adjacent dispatch periods, determine the allocated load changes corresponding to each energy form. By summarizing the changes in the allocated load, the equivalent distribution relationship of the load changes in the dispatch control system among different energy forms can be obtained.

[0069] In one embodiment, a centralized processing server obtains equipment type information, rated conversion capacity information, and current operating status information of coupling devices with cross-energy conversion functions in the integrated energy base from the equipment configuration database and real-time operation monitoring data. These coupling devices include, but are not limited to, electro-thermal coupling devices, electro-gas coupling devices, or bidirectional energy storage-electricity conversion devices. By analyzing the above information, the server determines the actually available energy conversion channels between different energy forms during the current scheduling period and eliminates unusable conversion channels that are under maintenance, faulty, or restricted.

[0070] Subsequently, the centralized processing server, based on the available conversion channels, obtains the system load change of the scheduling control system within adjacent scheduling periods. Combining this with the energy conversion direction and rated conversion capacity of each coupled device, the server distributes the load change. Specifically, the system load change is categorized according to the conversion direction of each available conversion channel. Within the same conversion direction, the load change is proportionally allocated based on the conversion capacity of the corresponding coupled device, thereby obtaining the distributed load change corresponding to each energy form.

[0071] Furthermore, the centralized processing server aggregates and processes the load changes of each energy form obtained from the allocation, forming an equivalent allocation result that characterizes the distribution of load changes in the dispatch control system among different energy forms. This equivalent allocation result describes the share of load change that each energy form should bear in adjacent dispatch periods under the current energy coupling conditions.

[0072] Preferably, the determination of the allocated load change corresponding to each energy form based on the available conversion channels and the change in the load of the dispatch control system during adjacent dispatch periods includes: The change in load of the dispatch control system within adjacent dispatch periods is decomposed according to the direction and capacity of available switching channels, and the maximum distributable energy that each channel can carry within that period is calculated. The relationship between the energy capacity of each channel and the total load change is determined based on the maximum divisible energy, and the actual divisible load change of each energy form during the current scheduling period is calculated. Extract the energy transmission sequence and multi-channel interaction effects of available conversion channels, and adjust the load changes of each coupling device.

[0073] In one embodiment, a centralized processing server acquires the overall load change of the scheduling control system within adjacent scheduling periods, and decomposes the load change directionally according to the energy transmission direction and corresponding conversion capacity of each available conversion channel. For each available conversion channel, based on its current operating status and upper limit of conversion capacity, the maximum distributable energy range that the conversion channel can carry within the current scheduling period is determined, thereby forming the carrying capacity information corresponding to each conversion channel.

[0074] Subsequently, the centralized processing server determines the allocation of load changes based on the correspondence between the maximum distributable energy of each conversion channel and the total system load change. If the total load change is less than or equal to the channel's carrying capacity, the load change is allocated to the corresponding energy type according to the direction of the conversion channel. If the total load change exceeds the carrying capacity of a portion of the channels, it is preferentially allocated to other available conversion channels with remaining carrying capacity, thereby calculating the actual distributable load change for each energy type during the current scheduling period.

[0075] Furthermore, after the initial allocation is completed, the centralized processing server extracts the energy transmission sequence information of each available conversion channel within the current scheduling period and analyzes the potential interactive effects when multiple conversion channels participate in energy transmission simultaneously. Based on the energy transmission sequence and multi-channel interactive effects, the load changes corresponding to each coupled device are coordinated and adjusted to avoid allocation deviations caused by channel competition or superposition of energy transmission.

[0076] Of particular importance are the methods for obtaining day-ahead load forecast data, including: The load data of multi-energy coupling equipment by energy type within the historical scheduling cycle is obtained, and the load data by energy type is aggregated according to the scheduling period of the historical scheduling cycle to obtain the aggregated load data by energy type. Based on the historical operation records of multi-energy coupling equipment, the load data of different energy forms are correlated and organized to identify the load linkage relationship between different energy forms. Based on the load linkage relationship, the load change trend of standardized equipment status data in adjacent scheduling periods is derived to generate the day-ahead load forecast data for the corresponding scheduling cycle.

[0077] In one embodiment, a centralized processing server retrieves load data by energy type from the scheduling and control system for multi-energy coupled devices across multiple historical scheduling cycles, including load records corresponding to electrical energy, thermal energy, and other coupled energy types. The historical load data is divided and aggregated according to the scheduling periods within each historical scheduling cycle, so that the load data by energy type within the same scheduling period form a corresponding relationship in the time dimension, thereby obtaining the aggregated load dataset by energy type.

[0078] Subsequently, based on the historical operation records of multi-energy coupling equipment, the collected load data by energy type were correlated and organized. By analyzing the load changes of different energy types in the same or adjacent scheduling periods, the load linkage relationship between each energy type was identified, including load synchronous change relationship, substitution change relationship, or delayed response relationship, thereby forming a load linkage characteristic that reflects the multi-energy coupling characteristics.

[0079] Furthermore, after completing the collection of load data by energy type and the identification of load linkage relationships, the centralized processing server first uses historical scheduling cycles as samples to statistically analyze the direction and magnitude of load changes by energy type within each scheduling period, obtaining typical load change characteristics for each energy type under the same scheduling period. These typical load change characteristics are used to characterize the normal change trend of loads of different energy types as the scheduling period progresses under the same operating scenario.

[0080] Subsequently, the centralized processing server determines the operational status of each energy device in adjacent scheduling periods based on standardized equipment status data within the current scheduling cycle, including whether the device is in start-up or shutdown state, available output range, and energy reserve level. By matching the current equipment operating status with the load change characteristics of the corresponding status in historical scheduling cycles, historical load evolution samples similar to the current operating status are selected.

[0081] Based on this, the centralized processing server deduces the load changes in adjacent scheduling periods segment by segment according to the scheduling period sequence: based on the load of the previous scheduling period, it introduces the load change direction and change magnitude from the matched historical samples, and combines the load linkage relationship to adjust the load changes between different energy forms in a coordinated manner, so that the load changes of each energy form maintain a consistent evolutionary logic.

[0082] Furthermore, during the derivation process, the rationality of the load change results of each energy form is verified. When the derivation load change of a certain energy form exceeds the carrying capacity of the corresponding equipment in the current state, the load change is corrected, and the load change results of other energy forms that are linked to it are adjusted simultaneously.

[0083] Of particular importance is the calculation of the distribution of output change over time step within adjacent scheduling periods, based on the path of output change formation, including: Based on the path of output change, the starting state and target state of output change of each energy device in adjacent scheduling periods are identified, and the effective time step range in the corresponding scheduling period is determined. Within the effective time step, the output change within adjacent scheduling periods is broken down according to the time progression order to obtain the stage output change corresponding to each time step; The output change at each time step is continuously verified to ensure that the output change between adjacent time steps meets the requirements for continuous operation of the equipment. Based on the continuity verification results, the output variation distribution is obtained.

[0084] In one embodiment, the centralized processing server first identifies the actual output status of each energy device at the end of the previous scheduling period and the target output status to be achieved in the next scheduling period based on the output change formation path, and divides the effective time step range for output change derivation using the duration of the adjacent scheduling period as the time range.

[0085] After determining the effective time step range, the centralized processing server gradually breaks down the total output change in adjacent scheduling periods according to the time progression sequence, dividing the overall output change process into multiple continuous stage output change processes, and assigning a corresponding stage output change amount to each time step, so that the output gradually transitions from the initial state to the target state.

[0086] Subsequently, the continuity of the phased output changes corresponding to each time step is verified. This includes checking whether the direction of output change is consistent between adjacent time steps, whether there are abrupt changes in output change, and whether the output change matches the adjustment characteristics of the equipment in continuous operation. When a discontinuity is detected in the phased output change of a certain time step compared to the preceding and following time steps, the output change of that time step is adjusted to ensure a smooth transition between output changes of adjacent time steps.

[0087] After completing the continuity check, the verified phased output changes within each time step are summarized in chronological order to form an output change distribution that reflects the evolution of output changes over time within adjacent scheduling periods.

[0088] This invention provides a system for generating day-ahead scheduling plans for integrated energy bases, used to execute the method for generating day-ahead scheduling plans for integrated energy bases as described above. The system includes: The data acquisition module is used to acquire the operating parameters and status information of power equipment, energy storage equipment and multi-energy coupling equipment in real time through the data acquisition terminal, and transmit them to the edge computing unit through the industrial communication interface; The edge preprocessing module is used by the edge computing unit to perform local verification, timing alignment and anomaly removal on the collected operating parameters, form standardized device status data and upload it to the centralized processing server; The scheduling calculation module is used by the centralized processing server to call the equipment physical constraint model based on its built-in multi-core processor and storage module, and to perform joint calculations on standardized equipment status data and day-ahead load forecast data to generate a set of day-ahead scheduling instructions that meet the output limits, ramping constraints and energy balance constraints of each energy equipment. The scheduling execution module is used to distribute day-ahead scheduling instructions to the corresponding power equipment controllers, energy storage converters, and energy management terminals through the scheduling execution interface, in order to generate the day-ahead scheduling plan for the integrated energy base. For example, the centralized processing server, edge computing unit, and scheduling execution interface can be wirelessly connected.

[0089] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0090] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for generating a day-ahead scheduling plan for an integrated energy base, characterized in that, The method employs a comprehensive energy base dispatch and control system, which includes at least a data acquisition terminal, an edge computing unit, a centralized processing server, and a dispatch execution interface. The edge computing unit is wirelessly connected to the data acquisition terminal, and the centralized processing server is connected to the edge computing unit and the dispatch execution interface. The method includes the following steps: Step S1: The operating parameters and status information of the power supply equipment, energy storage equipment and multi-energy coupling equipment are acquired in real time through the data acquisition terminal and transmitted to the edge computing unit through the industrial communication interface; Step S2: The edge computing unit performs local verification, timing alignment, and anomaly removal on the collected operating parameters to form standardized device status data and uploads it to the centralized processing server; Step S3: The centralized processing server, based on its built-in multi-core processor and storage module, calls the equipment physical constraint model to perform joint calculations on the standardized equipment status data and day-ahead load forecast data, and generates a set of day-ahead scheduling instructions that meet the output limits, ramping constraints and energy balance constraints of each energy equipment. Step S4: Send the day-ahead scheduling instruction set to the corresponding power equipment controller, energy storage converter and energy management terminal through the scheduling execution interface to complete the day-ahead scheduling plan generation of the integrated energy base.

2. The method for generating a day-ahead dispatch plan for an integrated energy base according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: The centralized processing server calls its built-in multi-core processor to perform parallel reading and cache scheduling of standardized equipment status data and day-ahead load forecast data in order to build the input dataset required for joint computing; Step S32: Based on the input dataset, call the device physical constraint model pre-stored in the storage module to map constraint parameters for the rated output range, ramp change rate and available energy boundary of each energy device; Step S33: Based on the constraint parameters, construct a set of joint constraint conditions that includes energy equipment output limit constraints, ramping constraints, and system energy balance constraints; Step S34: Under the premise of satisfying the set of joint constraints, perform joint calculations on the standardized equipment status data and the day-ahead load forecast data to obtain the target output results of each energy equipment in each day-ahead period; Step S35: Based on the target output results, generate a set of day-ahead dispatch instructions that correspond one-to-one with each energy device.

3. The method for generating a day-ahead dispatch plan for an integrated energy base according to claim 2, characterized in that, Step S33 includes: Based on the rated output range in the constraint parameters, equipment-level output constraints are constructed for each energy device, where the equipment-level output constraints are used to limit the maximum available output range of each energy device in each time period; Based on the output change relationship of the ramp change rate between adjacent scheduling periods, ramp constraints are constructed for each energy device to limit the output change rate of each energy device within adjacent scheduling periods. Based on the available energy boundary analysis, the system load demand and the energy supply capacity of each energy device are analyzed, and system-level energy balance constraints are constructed to limit the balance relationship between energy supply and load demand in each time period. The equipment-level output constraints, ramping constraints, and system-level energy balance constraints are combined to form a set of joint constraints for joint calculations.

4. The method for generating a day-ahead dispatch plan for an integrated energy base according to claim 3, characterized in that, Based on the output change relationship of the ramp rate between adjacent scheduling periods, the ramp constraints for each energy device are constructed as follows: Based on the output change sequence of each energy device in adjacent scheduling periods, the output change amount and direction between adjacent scheduling periods are extracted to form a time-related feature that reflects the evolution trend of power change. Based on the time period correlation characteristics, the output change driving type of each energy device in adjacent scheduling time periods is identified, where the driving type is used to characterize whether the output change is dominated by load change, constraint release, or state switching. Based on the type of power output change drive, a corresponding climbing rate limit interval is constructed to ensure that the climbing rate of change and the power output change drive characteristics maintain consistency constraints. The climbing rate limit interval is mapped to the interval between adjacent scheduling periods, forming a climbing constraint condition that is dynamically adjusted according to the evolution trend of output change.

5. The method for generating a day-ahead dispatch plan for an integrated energy base according to claim 4, characterized in that, Based on the type of drive force change, corresponding climbing rate limit intervals are constructed, including: Based on the type of power output change drive, determine the power output change formation path corresponding to that type of drive. Based on the path of output change, calculate the distribution of output change with respect to time step within adjacent scheduling periods, and extract the extreme value envelope of the output change rate in the distribution. The extreme value envelope is used as the rate change constraint boundary under this type of output change drive, so as to limit the output change rate from exceeding the extreme value envelope range during the continuous scheduling period. Based on the extreme value envelope constraint boundary, the corresponding slope change rate limit interval is generated.

6. The method for generating a day-ahead dispatch plan for an integrated energy base according to claim 5, characterized in that, Based on the type of power output change drive, the path for the power output change formation corresponding to that drive type is determined as follows: Based on the type of output change drive, the initial output state and target output state of the energy equipment in adjacent scheduling periods are determined, where the initial output state and target output state are used to define the starting boundary and ending boundary of the output change. Based on the driving characteristics corresponding to the type of power output change, the generation sequence and state switching conditions of each intermediate transition state during the power output change process are determined to form a state evolution sequence of power output change. Based on the state evolution sequence, the trajectory of output change as the scheduling period progresses is constructed; The path validity of the change trajectory is verified to obtain the output change formation path corresponding to the drive type.

7. The method for generating a day-ahead dispatch plan for an integrated energy base according to claim 4, characterized in that, Based on time-period correlation characteristics, the types of power output changes driven by each energy device within adjacent scheduling periods can be identified as follows: The actual output changes of each energy device are obtained based on the time-related characteristics. Based on the energy coupling relationship of each energy device, the equivalent distribution relationship of the load change of the dispatch control system in adjacent dispatch periods among different energy forms is determined. By using the equivalent allocation relationship, the equivalent output change of each energy device corresponding to the system load change in adjacent scheduling periods is calculated. The actual output change of each energy device is aligned and compared with the corresponding equivalent output change to generate an output coupling feature that characterizes the degree of deviation between the two. Based on the output coupling characteristics, the driving type of output change of each energy device in adjacent scheduling periods is determined.

8. The method for generating a day-ahead dispatch plan for an integrated energy base according to claim 7, characterized in that, Based on the energy coupling relationship of each energy device, the equivalent distribution relationship of the load change of the dispatch control system in adjacent dispatch periods among different energy forms is determined, including: Based on the energy coupling relationship of each energy device, determine the actual available conversion channels between different energy forms; Based on the available conversion channels and the changes in the load of the dispatch control system during adjacent dispatch periods, determine the allocated load changes corresponding to each energy form. By summarizing the changes in the allocated load, the equivalent distribution relationship of the load changes in the dispatch control system among different energy forms can be obtained.

9. The method for generating a day-ahead dispatch plan for an integrated energy base according to claim 8, characterized in that, Based on available conversion channels and the changes in load of the dispatch control system during adjacent dispatch periods, the allocated load changes for each energy form are determined as follows: The change in load of the dispatch control system within adjacent dispatch periods is decomposed according to the direction and capacity of available switching channels, and the maximum distributable energy that each channel can carry within that period is calculated. The relationship between the energy capacity of each channel and the total load change is determined based on the maximum divisible energy, and the actual load change that can be divisible for each energy form during the current scheduling period is calculated. Extract the energy transmission sequence and multi-channel interaction effects of available conversion channels, and adjust the load changes of each coupling device.

10. A comprehensive energy base day-ahead dispatch plan generation system, characterized in that, For executing the integrated energy base day-ahead scheduling plan generation method as described in claim 1, the integrated energy base day-ahead scheduling plan generation system includes: The data acquisition module is used to acquire the operating parameters and status information of power equipment, energy storage equipment and multi-energy coupling equipment in real time through the data acquisition terminal, and transmit them to the edge computing unit through the industrial communication interface; The edge preprocessing module is used by the edge computing unit to perform local verification, timing alignment and anomaly removal on the collected operating parameters, form standardized device status data and upload it to the centralized processing server; The scheduling calculation module is used by the centralized processing server to call the equipment physical constraint model based on its built-in multi-core processor and storage module, and to perform joint calculations on standardized equipment status data and day-ahead load forecast data to generate a set of day-ahead scheduling instructions that meet the output limits, ramping constraints and energy balance constraints of each energy equipment. The scheduling execution module is used to send the day-ahead scheduling instruction set to the corresponding power equipment controller, energy storage converter and energy management terminal through the scheduling execution interface, so as to complete the day-ahead scheduling plan generation of the integrated energy base.

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