Multi-constraint consistency control method and system for coordinated operation of source, network, load, storage and charging

Abstract

The embodiment of the application provides a kind of source network load storage and charging coordinated operation multi-constraint consistency control method and system, belong to power system operation control technical field.The method comprises: obtaining the operation data of each energy unit in source network load storage and charging system, and constructing operation state vector based on the operation data;The constraint corresponding to each energy unit is converted into the constraint expression unit of uniform structure, and a multi-constraint set is formed;Based on the operation state vector and the multi-constraint set, the scheduling period is divided into multiple execution fragments, and the transition state constraint corridor covering each execution fragment is constructed;Based on the transition state constraint corridor, generate target scheduling path, and execute corresponding scheduling control in each execution fragment according to the target scheduling path.The scheme of the present application realizes the consistent expression and feasible path screening of source network load storage and charging multi-constraint conditions in the whole scheduling period, avoids the boundary crossing of intermediate state and improves the stability and feasibility of scheduling execution.

Description

Technical Field

[0001] This invention relates to the field of power system operation control technology, and specifically to a multi-constraint consistency control method and system for coordinated operation of power generation, grid, load, storage and charging. Background Technology

[0002] In the operation of existing park-level or regional-level energy systems, fluctuations in source-side output, uncertainties in load-side conditions, and rapid changes in energy storage and charging loads often make the scheduling process a complex steady-state optimization problem. In actual operation, the scheduling system typically calculates a target power allocation result based on the current state and then distributes this result to each energy unit for execution. From the results, the target state often satisfies power balance and various constraints. However, during the transition from the current state to the target state, due to differences in response speed, control granularity, and local control strategies, each energy unit is prone to problems such as short-term limit exceedances, power backlashes, or constraint conflicts. For example, energy storage power ramp-up has rate limitations, air conditioning or industrial load adjustments have execution delays, and charging loads may change rapidly with user behavior. The combination of these factors makes it difficult to effectively constrain the operating state during the intermediate transition phase.

[0003] Most existing methods focus on whether the scheduling result itself meets the constraints, but lack characterization of the continuous evolution during the scheduling process, usually only compensating through simple safety margins or empirical rules. This approach is acceptable in scenarios with small load fluctuations, but in scenarios with multiple coupled factors of source, grid, load, storage and charging, and significant dynamic changes, it often cannot guarantee that various constraints will be continuously met throughout the entire scheduling cycle, leading to the accumulation of operational risks and even triggering protection actions.

[0004] Therefore, how to ensure that the target state not only meets the constraints during the scheduling process, but also to constrain and control the transition process from the current state to the target state so that each execution stage is within the feasible range and thus avoids intermediate state instability has become an urgent problem to be solved in current engineering applications. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-constraint consistency control method and system for coordinated operation of source, grid, load, storage and charging, so as to at least solve the problem that existing scheduling methods only guarantee that the target state meets the constraints but are difficult to constrain the scheduling transition process, resulting in short-term overruns or constraint conflicts during execution.

[0006] To achieve the above objectives, the first aspect of the present invention provides a multi-constraint consistency control method for coordinated operation of energy source, grid, load, storage, and charging systems. The method includes: acquiring operational data of each energy unit in the energy source, grid, load, storage, and charging system, and constructing an operational state vector based on the operational data; converting the constraints corresponding to each energy unit into constraint expression units with a unified structure to form a multi-constraint set; dividing the scheduling cycle into multiple execution segments based on the operational state vector and the multi-constraint set, and constructing a transitional constraint corridor covering each execution segment; generating a target scheduling path based on the transitional constraint corridor, and performing corresponding scheduling control within each execution segment according to the target scheduling path.

[0007] Optionally, the operation data of each energy unit in the source-grid-load-storage-charging system is acquired, and an operation state vector is constructed based on the operation data. This includes: acquiring power data, status data, and task data of each energy unit, and performing time alignment processing on the power data, status data, and task data to form an original operation data sequence; based on the original operation data sequence, feature extraction is performed on the power data, status data, and task data to obtain power parameters, status parameters, and task constraint parameters, respectively; and an operation state vector is constructed based on the power parameters, status parameters, and task constraint parameters.

[0008] Optionally, based on the original operating data sequence, feature extraction is performed on the power data, the status data, and the task data to obtain power parameters, status parameters, and task constraint parameters, respectively. This includes: performing time window statistical processing on the power data to obtain power parameters characterizing the power level and changing trend of each energy unit; performing status identification processing on the status data to obtain status parameters characterizing the operating conditions of each energy unit; and performing constraint parsing processing on the task data to obtain task constraint parameters characterizing the service requirements and time limits of each energy unit.

[0009] Optionally, the constraints corresponding to each energy unit are converted into constraint expression units with a unified structure to form a multi-constraint set, including: obtaining the operational constraints, physical constraints, and task constraints corresponding to each energy unit, and determining the corresponding constraint objects and constraint variables based on each constraint; extracting the upper and lower limit value ranges and constraint effective intervals of each constraint based on the constraint variables, and associating and encapsulating the constraint objects, the constraint variables, the upper and lower limit value ranges, and the constraint effective intervals to construct constraint expression units; and classifying and aggregating each constraint expression unit according to its corresponding constraint object to form a multi-constraint set.

[0010] Optionally, the operation constraints, physical constraints, and task constraints corresponding to each energy unit are obtained, and the corresponding constraint objects and constraint variables are determined based on each constraint. This includes: extracting the power parameters, state parameters, and task constraint parameters of each energy unit from the original operation data sequence, and generating the corresponding operation constraints, physical constraints, and task constraints based on the power parameters, state parameters, and task constraint parameters; for each constraint, determining the corresponding constraint object as the energy unit that generates the corresponding constraint, and determining the control quantity and / or state quantity acting on the constraint as the corresponding constraint variable.

[0011] Optionally, the constraint expression units are categorized and aggregated according to their corresponding constraint objects to form a multi-constraint set, including: merging constraint expression units with the same constraint object to form object constraint groups corresponding to each constraint object; determining the parallel constraint relationship and temporal constraint relationship between constraint expression units within each object constraint group based on the constraint variables, upper and lower limit value ranges, and constraint effective intervals of each constraint expression unit within each object constraint group; and constructing a multi-constraint set based on each object constraint group and its corresponding parallel constraint relationship and temporal constraint relationship to characterize the constraint superposition state and constraint switching state of the same constraint object under different execution segments.

[0012] Optionally, based on the running state vector and the multi-constraint set, the scheduling period is divided into multiple execution segments, and a transitional constraint corridor covering each execution segment is constructed, including: discretizing the scheduling period based on a preset time granularity to obtain multiple continuous execution segments; for each execution segment, performing state prediction on the constraint variables corresponding to each constraint object based on the running state vector to obtain the predicted state value under each execution segment; performing constraint verification on the predicted state value under each execution segment based on the multi-constraint set to obtain the constraint satisfaction interval corresponding to each execution segment; and constructing a transitional constraint corridor covering all execution segments based on the constraint satisfaction interval corresponding to each execution segment to characterize the feasible state range of each constraint object within each execution segment.

[0013] Optionally, for each execution segment, state prediction is performed on the constraint variables corresponding to each constraint object based on the running state vector to obtain the predicted state value under each execution segment, including: determining the initial state value of each constraint object under the starting execution segment based on the current value of each constraint variable in the running state vector; for adjacent execution segments, the change of each constraint variable between adjacent execution segments is restricted and calculated based on the power change constraint and state change constraint corresponding to each constraint object to obtain the state change amount corresponding to each execution segment; based on the state change amount corresponding to each execution segment, the initial state value is accumulated segment by segment to obtain the predicted state value sequence corresponding to each execution segment.

[0014] Optionally, based on the multi-constraint set, constraint verification is performed on the predicted state values ​​under each execution segment to obtain the constraint satisfaction interval corresponding to each execution segment. This includes: for each execution segment, extracting the corresponding constraint expression unit from the multi-constraint set, and obtaining the constraint variables, upper and lower limit value ranges, and constraint effective intervals corresponding to each extracted constraint expression unit; filtering constraint expression units that are effective in the current execution segment based on the matching relationship between each execution segment and the constraint effective intervals corresponding to each constraint expression unit; determining the feasible value intervals corresponding to each constraint variable based on the comparison between the predicted state value and the upper and lower limit value ranges corresponding to the filtered constraint expression units; performing interval superposition processing on each feasible value interval for multiple constraint expression units with the same constraint object in the same execution segment to obtain the constraint satisfaction interval of the corresponding constraint object in the execution segment; and aggregating the constraint satisfaction intervals corresponding to each constraint object in each execution segment to form a constraint satisfaction interval set corresponding to each execution segment.

[0015] A second aspect of the present invention provides a multi-constraint consistency control system for coordinated operation of energy sources, grid, load, storage, and charging. The system includes: a data acquisition unit for acquiring operational data of each energy unit in the energy source, grid, load, storage, and charging system, and constructing an operational state vector based on the operational data; a processing unit for converting the constraints corresponding to each energy unit into constraint expression units with a unified structure to form a multi-constraint set; a constraint construction unit for dividing the scheduling cycle into multiple execution segments based on the operational state vector and the multi-constraint set, and constructing a transitional constraint corridor covering each execution segment; and a scheduling unit for generating a target scheduling path based on the transitional constraint corridor, and executing corresponding scheduling control within each execution segment according to the target scheduling path.

[0016] Through the above technical solution, this invention achieves unified representation and collaborative processing of different types of constraints by uniformly modeling the operational data of each energy unit in the source-grid-load-storage-charging system, constructing operational state vectors, and transforming multi-source constraints into constraint expression units with a unified structure. Based on this, the scheduling cycle is discretized into multiple execution segments, and transitional constraint corridors are constructed on each execution segment, extending the scheduling process from traditional target state constraints to continuous constraint control of the entire process. Furthermore, a target scheduling path is generated based on the transitional constraint corridors, and scheduling control is executed segment by segment, ensuring that each execution stage is within the allowable constraint range. This effectively avoids short-term limit violations and constraint conflicts caused by sudden state changes or response differences during scheduling execution, improving the stability and consistency of multi-energy unit collaborative operation.

[0017] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart of the steps of a multi-constraint consistency control method for coordinated operation of source, grid, load, storage, and charging provided by one embodiment of the present invention; Figure 2 This is a flowchart of step S10 of the multi-constraint consistency control method for coordinated operation of source, grid, load, storage and charging provided in one embodiment of the present invention. Figure 3 This is a flowchart of step S30 of the multi-constraint consistency control method for coordinated operation of source, grid, load, storage and charging provided in one embodiment of the present invention. Figure 4 This is a schematic diagram comparing target scheduling paths and risk paths within a transitional constraint corridor, provided by one embodiment of the present invention. Figure 5 This is a system structure diagram of a multi-constraint consistency control system for coordinated operation of source, grid, load, storage, and charging provided by one embodiment of the present invention; Figure 6 This is an internal structural diagram of a computer device provided in one embodiment of the present invention. Detailed Implementation

[0019] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0020] like Figure 1 As shown, embodiments of the present invention provide a multi-constraint consistency control method for coordinated operation of source, grid, load, storage, and charging systems. The method includes: Step S10: Obtain the operating data of each energy unit in the source-grid-load-storage-charging system, and construct an operating state vector based on the operating data.

[0021] Specifically, data is collected from each energy unit in the source-grid-load-storage-charging system. This operational data includes power data reflecting energy exchange relationships, status data characterizing equipment operating conditions, and task data reflecting external demand. Considering the differences in data sources and sampling periods among different energy units, the operational data undergoes unified time-base alignment processing to form a time-consistent original operational data sequence.

[0022] Based on this, various types of data are organized and parameterized. Power data is converted into power parameters characterizing output or energy consumption levels, state data is converted into state parameters reflecting equipment operating status, and task data is converted into task constraint parameters reflecting service demands and time limits. Finally, the power parameters, state parameters, and task constraint parameters are uniformly organized to construct an operating state vector describing the current system operating status, providing the basic input for subsequent multi-constraint modeling and scheduling calculations. Specifically, for example... Figure 2 Step S10 includes the following steps: Step S101: Obtain the power data, status data and task data of each energy unit, and perform time alignment processing on the power data, status data and task data to form the original operation data sequence.

[0023] Specifically, operational data of each energy unit in the power generation, grid, load, energy storage, and charging system is collected. This operational data includes at least power data, status data, and task data. Power data reflects the output or consumption of each energy unit at the current moment, such as the power generation of photovoltaic units, the charging and discharging power of energy storage units, and the power consumption on the load side. Status data characterizes the operating conditions of the equipment, such as the state of charge of energy storage units, the start / stop status of switching equipment, or the connection status of charging piles. Task data reflects external scheduling or service demands, such as the remaining power demand and completion time limit for charging tasks, or the adjustment command range of adjustable loads.

[0024] Since the aforementioned data originates from different acquisition terminals, their sampling periods and timestamps differ, and directly using them for subsequent calculations can easily lead to timing deviations. Therefore, time alignment processing is performed on the power data, status data, and task data. Specifically, all types of data are mapped to the same time base. By interpolating, padding, or truncating asynchronously sampled data, a time-consistent original operational data sequence is formed. This original operational data sequence remains aligned on the time axis, ensuring that data at the same moment corresponds to the same system state, thus providing a consistent data foundation for subsequent operational state vector construction and constraint calculations. It should be noted that the time alignment processing method is not limited to the above form and can be adjusted according to the data sampling characteristics of different systems.

[0025] Step S102: Based on the original running data sequence, perform feature extraction on the power data, the state data, and the task data to obtain power parameters, state parameters, and task constraint parameters, respectively.

[0026] Specifically, the power data is subjected to time window statistical processing to obtain power parameters that characterize the power level and trend of each energy unit; the state data is subjected to state identification processing to obtain state parameters that characterize the operating conditions of each energy unit; and the task data is subjected to constraint parsing processing to obtain task constraint parameters that characterize the service requirements and time limits of each energy unit.

[0027] In this embodiment of the invention, after the construction of the original running data sequence is completed, the power data, status data and task data contained therein are further processed to transform the original sampled data into parameter form that can be directly used in scheduling calculations.

[0028] For power data, time window statistical processing is performed on the corresponding data, taking into account the scheduling cycle and the time scale of the execution segment. The time window can be set with a fixed length or divided into segments based on the scheduling cycle. Within each time window, the power data undergoes average calculation, extreme value extraction, and change statistics to obtain power parameters that reflect the power level and its trend. For example, the stable output level can be represented by calculating the power average within the window, and the rate of power change can be represented by the difference between adjacent windows, thus providing a basic input for subsequent state prediction. The statistical method is not limited to average or difference forms; weighted statistics or segmented statistical processing are also introduced depending on different application scenarios.

[0029] For state data, state identification processing is performed based on the operating characteristics of each energy unit, mapping continuous or discrete state sample values ​​into state parameters with scheduling significance. Taking energy storage units as an example, the state of charge can be divided into multiple intervals to distinguish between charging, discharging, and restricted areas. For switching equipment, operating identifiers can be extracted based on its opening and closing status and switching frequency. For charging units, operating condition identifiers can be extracted by combining connection status and charging mode. Through the above processing, the raw state data is uniformly transformed into a set of state parameters that can participate in constraint judgment and path generation. The specific identification method is adapted according to the equipment type and is not limited to a fixed classification method.

[0030] For task data, the data is parsed based on scheduling constraints, transforming external task information into task constraint parameters that can participate in constraint calculations. For example, for a charging task, information such as remaining charging capacity and expected completion time can be extracted from the task data, and the corresponding power demand or time constraints can be further calculated. For adjustable loads, their adjustable range and response time can be parsed to form corresponding constraint boundaries. This parsing process essentially converts descriptive task information into quantifiable constraint parameters for unified processing in subsequent multi-constraint modeling.

[0031] After the above processing, the original operational data sequence is transformed into three types of data: power parameters, state parameters, and task constraint parameters, while maintaining a structural correspondence. This parameter set not only preserves the key operational characteristics of each energy unit but also eliminates the differences in time scale and expression of the original data, thus providing a foundation for subsequent construction of operational state vectors and unified expression of multiple constraints.

[0032] Step S103: Construct an operating state vector based on the power parameters, the state parameters, and the task constraint parameters.

[0033] Specifically, after obtaining the power parameters, state parameters, and task constraint parameters, these parameters are uniformly organized and structured to construct an operational state vector characterizing the current operating characteristics of the system. During construction, based on the expression required for scheduling, various parameters are arranged and combined in a preset order, mapping them within the same vector structure. Each component in the vector corresponds to a key operational indicator of different energy units, such as power parameters reflecting the energy exchange level, state parameters characterizing equipment operating conditions, and task constraint parameters describing external demand constraints.

[0034] In practical implementation, different types of parameters can be uniformly scaled according to the input requirements of the scheduling model, ensuring consistency in the numerical range and expression of each component to avoid the impact of dimensional differences in subsequent calculations. Simultaneously, for parameter arrangement in multi-energy unit scenarios, parameters can be grouped according to energy unit category or scheduling priority, giving the operating state vector a clear physical correspondence in structure. For example, relevant parameters from the source side, load side, energy storage side, and charging side can be mapped to different sub-segments, facilitating subsequent invocation for different constraints.

[0035] Through the above processing, the operational state vector can comprehensively reflect the system's output level, operational status, and task requirements within a single data structure, providing a unified input basis for subsequent multi-constraint modeling, state prediction, and scheduling path generation. It should be noted that the specific dimensions and arrangement of the operational state vector can be adjusted according to the actual application scenario and are not limited to a fixed structure.

[0036] Step S20: Convert the constraints corresponding to each energy unit into constraint expression units with a unified structure to form a multi-constraint set.

[0037] Specifically, the operational constraints, physical constraints, and task constraints corresponding to each energy unit are obtained, and the corresponding constraint objects and constraint variables are determined based on each constraint; based on the constraint variables, the upper and lower limit value ranges and constraint effective intervals of each constraint are extracted, and the constraint objects, constraint variables, upper and lower limit value ranges, and constraint effective intervals are associated and encapsulated to construct constraint expression units; each constraint expression unit is classified and aggregated according to its corresponding constraint object to form a multi-constraint set.

[0038] Furthermore, the operation constraints, physical constraints, and task constraints corresponding to each energy unit are obtained, and the corresponding constraint objects and constraint variables are determined based on each constraint. This includes: extracting the power parameters, state parameters, and task constraint parameters of each energy unit from the original operation data sequence, and generating corresponding operation constraints, physical constraints, and task constraints based on the power parameters, state parameters, and task constraint parameters; for each constraint, determining the corresponding constraint object as the energy unit that generates the corresponding constraint, and determining the control quantity and / or state quantity acting on the constraint as the corresponding constraint variable.

[0039] Furthermore, the constraint expression units are categorized and aggregated according to their corresponding constraint objects to form a multi-constraint set. This includes: merging constraint expression units with the same constraint object to form object constraint groups corresponding to each constraint object; determining the parallel constraint relationship and temporal constraint relationship between constraint expression units within each object constraint group based on the constraint variables, upper and lower limit value ranges, and constraint effective intervals of each constraint expression unit within each object constraint group; and constructing a multi-constraint set based on each object constraint group and its corresponding parallel constraint relationship and temporal constraint relationship to characterize the constraint superposition state and constraint switching state of the same constraint object under different execution segments.

[0040] In this embodiment of the invention, the constraints corresponding to different energy units have complex origins, including physical limitations of the device itself, scheduling rules formed during operation, and service constraints introduced by external demands. These constraints often have different expressions in their original form; some are expressed as upper and lower power limits, some as state intervals, and some have explicit time constraints. If these constraints are directly mixed, problems such as inconsistent dimensions or unclear calling relationships can easily occur in subsequent calculations. Therefore, it is necessary to structurally reconstruct various constraints before entering the scheduling calculation, so that they are comparable and callable under the same expression system.

[0041] To achieve this goal, a unified model is used to model the constraints corresponding to each energy unit. The constraints are derived from the aforementioned original operational data sequence and the extracted power parameters, state parameters, and task constraint parameters. Power parameters reflect the boundary conditions of force or load changes, such as the charging and discharging power limits of energy storage units. State parameters define the operating range of the equipment, such as the upper and lower bounds of the energy storage state of charge or the feasible range of the equipment's operating state. Task constraint parameters reflect external scheduling requirements, such as the completion time limit of charging tasks or the response time window of adjustable loads. Based on this, constraints from different sources are uniformly categorized into three types: operational constraints, physical constraints, and task constraints. Among them, operational constraints primarily reflect the control requirements at the system scheduling level, physical constraints correspond to the equipment's inherent capability boundaries, and task constraints reflect the limitations imposed on operation by external demands.

[0042] After determining the constraint type, the object and corresponding variables of each constraint are further clarified. The constraint object directly corresponds to a specific energy unit, such as a photovoltaic unit, energy storage unit, or charging unit. The constraint variable is the control or state quantity affected by the constraint, such as power, state of charge, or load level. Determining this correspondence allows all subsequent constraints to be mapped to a unified structure of objects and variables, providing a consistent indexing method for subsequent calculations. For example, a power constraint for an energy storage unit has the energy storage unit itself as the constraint object, and the constraint variable is the energy storage output power. A time constraint for a charging task has the charging unit as the constraint object, and the constraint variable is the charging power or accumulated energy.

[0043] Based on this, boundary information for each constraint is extracted. Specifically, this includes the upper and lower limit value ranges of the constraint variables, and the constraint's effective interval in the time dimension. The upper and lower limit value ranges describe the feasible value boundaries of the variables, such as power ranges or state intervals. The constraint's effective interval describes the duration of the constraint's effect within the scheduling cycle, such as whether the load is adjustable within a certain time period or whether a charging task must be met within a certain time period. This information is then associated and encapsulated with the constraint objects and constraint variables to form a constraint expression unit with a unified structure. This constraint expression unit can be abstracted as a data structure with fixed fields, containing at least four types of information: constraint objects, constraint variables, upper and lower limit value ranges, and constraint effective intervals, thus ensuring that constraints from different sources are completely unified in form.

[0044] After constructing the constraint representation units, they are further organized to form a multi-constraint set that can be directly used for scheduling computation. Simple set stacking is insufficient to represent the relationships between constraints; therefore, a classification method based on constraint objects is introduced during the organization process. Constraint representation units with the same constraint object are merged to form corresponding object constraint groups. Each object constraint group contains all constraints of the same energy unit under different sources. This organization method limits the scope of constraints to specific objects, allowing subsequent computation to be carried out along the object dimension.

[0045] Within an object constraint group, it's also necessary to identify the relationships between different constraints. Some constraints take effect simultaneously within the same time period, creating cumulative restrictions on the same variable; this type of relationship can be categorized as parallel constraint relationships. Other constraints take effect sequentially in different time intervals, exhibiting constraint switching; this type of relationship can be categorized as temporal constraint relationships. By jointly analyzing the upper and lower limit value ranges and the constraint effective intervals, the superposition and switching relationships between constraints can be determined. For example, when the effective intervals of two constraints overlap, the corresponding variable needs to satisfy both constraints simultaneously within that time period, and its effective value range is the intersection of the two intervals; when the effective intervals do not overlap, different constraint boundaries are applied in different time periods.

[0046] Based on object constraint groups and their internal parallel and temporal constraint relationships, a multi-constraint set is constructed. This multi-constraint set is a structured constraint system that reflects the constraint superposition state and constraint switching state of the same constraint object under different execution segments within the scheduling cycle. In this way, multi-source constraints are uniformly mapped to the same expression framework, while preserving the dynamic characteristics in the time dimension, providing a foundation for subsequent execution segment partitioning and the construction of transitional constraint corridors.

[0047] In one specific implementation, suppose the system has a total of The constraint expression unit, the first Each constraint expression unit is denoted as: in, Represents the constraint object, Represents constraint variables. This refers to the upper and lower limits of the variable's value range. To constrain the effective range.

[0048] In a given execution segment Corresponding time interval Within this section, you can filter out the set of constraints that are in effect within the specified segment: For sets The constraint expression unit in the variable The valid range of values ​​can be obtained by overlaying (intersection) the intervals: in, , This interval is the execution segment. Internal, constraint objects The constraints on the corresponding variables satisfy the interval.

[0049] Step S30: Based on the running state vector and the multi-constraint set, the scheduling cycle is divided into multiple execution segments, and a transitional constraint corridor covering each execution segment is constructed.

[0050] Specifically, after obtaining the running state vector and the set of multiple constraints, the scheduling process is refined in the time dimension, dividing the originally continuous scheduling cycle into multiple execution segments with fixed time lengths. This allows the scheduling process to be described and controlled at a segmented scale. Each execution segment corresponds to a different stage in the evolution of the system's running state, facilitating segment-by-segment analysis of the state change process.

[0051] Based on this, and combining the effective range of each constraint in the multi-constraint set along the time dimension and their restrictive relationship on variable values, the feasible state range within each execution segment is calculated and expressed. The feasible state ranges of each execution segment are then connected chronologically to form a transitional constraint corridor covering the entire scheduling cycle. This transitional constraint corridor describes the constraint boundaries that must be satisfied at each stage of the system's evolution from the current state to the target state, providing a constraint basis for the generation and execution of subsequent scheduling paths. Specifically, such as... Figure 3 Step S30 includes the following steps: Step S301: The scheduling period is discretized based on a preset time granularity to obtain multiple continuous execution segments.

[0052] Specifically, the original scheduling cycle is usually defined on a timescale of minutes or even longer to express a complete scheduling decision interval. However, in actual execution, the response of each energy unit has obvious dynamic characteristics. For example, there are ramp-up limitations for changes in energy storage power, load regulation has response delays, and charging behavior may fluctuate within a short period of time. If the overall scheduling cycle is still used as the only calculation unit, it is difficult to characterize these dynamic processes. Therefore, the scheduling cycle is further discretized into multiple execution segments with consistent time granularity, making the scheduling process decomposable on the time axis, thus providing a basis for subsequent segment-by-segment calculations.

[0053] The division of execution segments is typically based on a preset time granularity, which can be selected according to system characteristics. For example, in scenarios with rapid energy storage response and frequent load fluctuations, a smaller time granularity can be selected to improve description accuracy; in scenarios with relatively stable operation, the time granularity can be appropriately widened to reduce computational complexity. The setting of the time granularity does not depend on a fixed value; its selection range is adjusted according to the equipment response time, data sampling period, or scheduling control accuracy requirements. In engineering implementation, the time granularity is usually consistent with the data acquisition period or an integer multiple thereof to ensure good alignment between data and execution segments.

[0054] In the specific partitioning process, the entire scheduling cycle is continuously segmented according to the aforementioned time granularity, forming multiple sequentially arranged execution segments. These execution segments are interconnected on the time axis, covering the entire scheduling cycle while maintaining no overlap or interruption between segments. Each execution segment corresponds to a discrete stage in the evolution of the system's operating state. In subsequent processing, various state predictions, constraint filtering, and interval calculations are all performed independently on this discrete segment. This partitioning method transforms the originally continuously changing system state into a series of computable discrete state nodes, enabling a structured expression of the complex scheduling problem in the time dimension.

[0055] In one specific embodiment, let the scheduling period be... The preset time granularity is The number of execution fragments It can be represented as: in, This indicates the floor function.

[0056] No. The time interval corresponding to each execution segment can be represented as: in, , and They represent the first The start and end times of each execution segment.

[0057] Using the above partitioning method, the scheduling period is represented as a set of ordered execution segments: This set of execution segments serves as a unified time frame in subsequent processes. Matching of various constraint effective intervals, recursive state prediction, and calculation of constraint satisfaction intervals are all performed on this set. It should be noted that the above time division method is only one implementation; without changing the discretization principle of the scheduling cycle, the length of the execution segment can be dynamically adjusted according to actual needs.

[0058] Step S302: For each execution segment, based on the running state vector, perform state prediction on the constraint variables corresponding to each constraint object to obtain the predicted state value under each execution segment.

[0059] Specifically, based on the current values ​​of each constraint variable in the running state vector, the initial state value of each constraint object under the starting execution segment is determined; for adjacent execution segments, based on the power change constraints and state change constraints corresponding to each constraint object, the change amount of each constraint variable between adjacent execution segments is restricted and calculated to obtain the state change amount corresponding to each execution segment; based on the state change amount corresponding to each execution segment, the initial state value is accumulated segment by segment to obtain the predicted state value sequence corresponding to each execution segment.

[0060] In this embodiment of the invention, after the execution segment division is completed, the evolution of the system state needs to be described within this discrete-time framework. During the scheduling process, the state of each energy unit changes gradually due to equipment capabilities and operational constraints. Therefore, establishing a traceable state prediction relationship for each execution segment is helpful for subsequent constraint verification and path generation. Here, instead of directly using the static state at a single moment, the constraint variables corresponding to each constraint object are recursively calculated around the execution segment sequence, forming a continuous predicted state sequence on the time axis.

[0061] Specifically, the values ​​of each constraint variable at the current moment are first extracted from the operating state vector and used as the initial state value corresponding to the starting execution segment. This initial state value is consistent with the current actual operating state of the system and can reflect the true operating condition of each energy unit at the scheduling starting point. For example, the initial state value of an energy storage unit can correspond to its current output power or state of charge, the load side can correspond to the current electricity consumption level, and the charging unit can reflect the current charging power, etc. Through this mapping relationship, the information in the operating state vector obtains a specific starting point in the time dimension.

[0062] Building upon this foundation, a change constraint mechanism, jointly determined by physical and operational constraints, is introduced to address state changes between adjacent execution segments. Different constraint objects correspond to different sources of constraint; for example, energy storage units are typically limited by the rate of change of charging and discharging power, load-side constraints are limited by the speed of regulation response, and some task constraints also impose constraints on the direction or magnitude of state changes. During the calculation process, the changes of each constraint variable between adjacent execution segments are constrained to ensure that the change process satisfies the predetermined constraint conditions. This constraint calculation can be understood as constraining the feasible range of state changes, ensuring that the state remains continuous and achievable as time progresses.

[0063] After obtaining the state changes corresponding to each execution segment, these changes are applied to the initial state value in chronological order, forming a segment-by-segment cumulative state recursion process. Through this process, the initial state is expanded into a sequence of predicted state values ​​covering the entire scheduling cycle. Each state value in this sequence corresponds to a specific execution segment, reflecting the evolution trajectory of the system state under the current constraints. Since the state changes are already constrained, the generated sequence of predicted state values ​​is physically realizable, thus providing reliable input for subsequent constraint verification.

[0064] In terms of expression, the above process is described through a recursive relationship. Let the constraint variables corresponding to the constraint objects be... In its first The predicted state value under each execution segment is The initial state value corresponding to the starting execution segment is then... ,in, This indicates the current value of the corresponding constraint variable in the running state vector.

[0065] The state change between adjacent execution segments is denoted as . Its value is subject to both power change constraints and state change constraints, and can be expressed as: in, and These represent the minimum and maximum allowable variations within a single execution segment, respectively, and this range can be determined by device characteristic parameters or operational constraint parameters.

[0066] Based on the above constraints on the amount of change, the predicted state value can be calculated recursively: in, , This represents the total number of execution segments.

[0067] Based on the above recursive relationship, a complete sequence of predicted state values ​​can be constructed. This predicted state value sequence corresponds one-to-one with the execution segments on the time axis, providing a foundation for subsequent constraint effective interval matching and constraint satisfaction interval calculation. It should be noted that the above recursive calculation method is not limited to a single variable form; for multi-dimensional state variables, a similar approach can be used to extend the processing within the vector space. Without changing the core idea of ​​segment-by-segment recursion, the specific implementation can be adjusted according to the characteristics of different systems.

[0068] Step S303: Based on the multi-constraint set, perform constraint verification on the predicted state values ​​under each execution segment to obtain the constraint satisfaction interval corresponding to each execution segment.

[0069] Specifically, for each execution segment, corresponding constraint expression units are extracted from the multi-constraint set, and the constraint variables, upper and lower limit value ranges, and constraint effective intervals corresponding to each extracted constraint expression unit are obtained. Based on the matching relationship between each execution segment and the constraint effective intervals corresponding to each constraint expression unit, constraint expression units that are effective within the current execution segment are selected. Based on the comparison between the predicted state value and the upper and lower limit value ranges corresponding to the selected constraint expression units, feasible value intervals corresponding to each constraint variable are determined. For multiple constraint expression units with the same constraint object within the same execution segment, interval superposition processing is performed on each feasible value interval to obtain the constraint satisfaction interval of the corresponding constraint object within the execution segment. The constraint satisfaction intervals corresponding to each constraint object within each execution segment are aggregated to form a constraint satisfaction interval set corresponding to each execution segment.

[0070] In this embodiment of the invention, unlike the traditional approach of constraining only at a single moment, constraint verification is performed for each execution segment separately, ensuring that the constraint judgment aligns with the state change process over time. This approach is based on the fact that different constraints have dynamic activation characteristics over time, and multiple constraints may have overlapping relationships; verifying only the endpoint state is insufficient to reflect the feasibility of intermediate processes. Therefore, using the execution segment as the basic unit, segment-by-segment constraint verification is performed on the predicted state value, identifying potential non-compliance regions before the scheduling path is formed.

[0071] Specifically, for the current execution segment, all constraint expression units are extracted from the multi-constraint set, and the constraint variables, upper and lower limit value ranges, and constraint effective intervals corresponding to each constraint expression unit are read. Since different constraints have different effective times, the time interval corresponding to the execution segment needs to be matched with the constraint effective intervals of each constraint expression unit, retaining only the constraint expression units that are actually effective within that execution segment. Through this filtering process, the global constraint set is compressed into a locally effective constraint set for the current execution segment, thereby preventing invalid constraints from participating in the calculation.

[0072] After obtaining the constraint expression units that are effective within the current execution segment, the predicted state value of the corresponding execution segment is compared with the upper and lower limit value ranges of each constraint expression unit. This comparison process is not limited to simple out-of-bounds judgment, but expresses the feasible range of variables in the form of intervals. For each constraint variable, based on its corresponding upper and lower limit ranges, the initial feasible value interval of the variable within the current execution segment is obtained. This interval reflects the allowed range of the variable under the action of a single constraint.

[0073] Considering that the same constraint object often corresponds to multiple constraint expression units—for example, an energy storage unit is subject to power limitations, state of charge limitations, and may also be affected by task constraints—it is necessary to superimpose the feasible intervals formed by multiple constraints. Specifically, for multiple constraint expression units with the same constraint object within the same execution segment, the intersection operation of their respective feasible value intervals is performed to obtain the final constraint satisfaction interval for that constraint object within that execution segment. This interval reflects the comprehensive constraint result after the superposition of multiple constraints, and its boundary is jointly determined by the most stringent constraint conditions.

[0074] After the above processing, constraint satisfaction intervals for each constraint object are obtained for each execution segment. These intervals are then aggregated to form a set of constraint satisfaction intervals at the execution segment level. This set structurally preserves the correspondence between execution segments and constraint objects, while numerically characterizing the feasible value range of each variable within that segment. By repeating the above process on all execution segments, a set of constraint satisfaction intervals covering the entire scheduling cycle is obtained, providing direct input for the subsequent construction of the transitional constraint corridor.

[0075] In one specific embodiment, let the first... The predicted state value corresponding to each execution segment is The set of constraint expression units is Each constraint expression unit Corresponding variable range and effective period For execution fragments Its effective constraint set can be expressed as: For the same constraint object In the execution fragment The intervals within which constraints are satisfied can be obtained by calculating the intersection of intervals: in, Indicates belonging to the constraint object And in the execution fragment The set of constraint expression units that are effective within the scope.

[0076] Finally, the first The set of constraint-satisfied intervals corresponding to each execution segment can be represented as: in, This represents the set of all constraint objects.

[0077] Through the above process, multiple constraints are uniformly expressed in the time and object dimensions, and the constraint verification results are expanded in the form of intervals within each execution segment, providing basic support for the construction of the subsequent transitional constraint corridor.

[0078] Step S304: Based on the constraint satisfaction intervals corresponding to each execution segment, construct a transitional constraint corridor covering all execution segments to characterize the feasible state range of each constraint object within each execution segment.

[0079] After obtaining the set of constraint satisfaction intervals corresponding to each execution segment, these intervals are further organized and connected along the time dimension to form a transitional constraint corridor covering the entire scheduling cycle. This corridor is a set of continuous feasible intervals unfolding along the sequence of execution segments, used to describe the range into which the system state can always fall during the time progression. Since the feasible value intervals of each constraint object within each execution segment have been defined in interval form in the previous stage, in this step, the constraints are not recalculated. Instead, these intervals are structurally integrated to ensure continuity and traceability along the time axis.

[0080] In the specific processing, the constraint-satisfying intervals of each execution segment are arranged in chronological order, and an interval sequence is constructed using the execution segment as an index. Each execution segment corresponds to a set of intervals, which are organized internally by constraint objects and externally connected sequentially in chronological order. Through this organization, the intervals of the discrete calculations are pieced together into a feasible path band that unfolds along time. This path band is formally represented as a sequence of intervals changing over time, and physically corresponds to the allowable boundary of the system state as it evolves from the current moment to the future.

[0081] Furthermore, during the construction process, the connection relationship between intervals of adjacent execution segments is considered. Since state changes are constrained by the aforementioned change constraints, the feasible intervals of adjacent execution segments typically exhibit a certain degree of continuity. By aligning or constraining the boundaries of adjacent intervals, interval breaks are avoided, ensuring that the formed transitional constraint corridor remains coherent on the time axis. This coherence does not require complete overlap of intervals, but it must guarantee that a feasible path exists during state recursion to transition from the previous execution segment to the next.

[0082] In terms of expression, the first The set of constraint-satisfied intervals corresponding to each execution segment is denoted as . Then, the transition state constraint corridor can be represented as a sequence of intervals arranged in chronological order: in, The total number of execution segments, This represents a transitional constraint corridor.

[0083] For any constraint object The range of feasible states within the entire scheduling cycle can be represented by the corresponding segment interval sequence as follows: This sequence describes how the allowed value range of the constraint object changes under different execution segments.

[0084] Through the above construction method, the transitional constraint corridor can express the feasible state range of each constraint object over time under a unified structure, providing constraint boundary references for the generation of subsequent scheduling paths. Based on this, if any scheduling path falls within the corresponding interval range in each execution segment, it can be considered that the path satisfies multiple constraint conditions throughout the entire scheduling cycle. It should be noted that the organization method and connection rules of the interval sequence can be adjusted according to the actual system characteristics, and different implementation forms are allowed without changing the basic structure of the transitional constraint corridor.

[0085] In one specific embodiment, such as Figure 4 Taking a coordinated scheduling scenario of energy source, grid, load, storage, and charging in a certain industrial park as an example, the scheduling cycle is discretized to obtain execution segments k1 to k12, and a transitional constraint corridor is constructed based on each execution segment. In the figure, the transitional constraint corridor represents the feasible range of comprehensive power values ​​within each execution segment, and the upper and lower boundaries of the corridor are formed by the superposition of multiple constraints. Energy storage units, charging units, and adjustable loads are the main adjustment objects, and their changes are all subject to power change constraints and state constraints.

[0086] During path generation, a target scheduling path satisfying continuity constraints is selected from the transitional constraint corridor. This path is represented by a line connecting hollow circles and always remains within the corridor boundary, maintaining a feasible state within each execution segment. Meanwhile, a risky path is compared, which exhibits boundary overflow between execution segments k6 and k8, with its corresponding state value falling below the lower boundary of the corridor, indicating that multiple constraint conditions cannot be satisfied within this interval.

[0087] Step S40: Generate a target scheduling path based on the transitional constraint corridor, and perform corresponding scheduling control in each execution segment according to the target scheduling path.

[0088] Specifically, the transitional constraint corridor has already given the feasible state range of each constraint object within each execution segment. The process of generating the target scheduling path is essentially to select a continuous, executable state trajectory that meets the system's operational requirements within this feasible range. Once this trajectory is determined, it can be converted into specific scheduling control instructions segment by segment according to the execution segment.

[0089] During path generation, for each execution segment, the system state can be determined jointly by the constraint variables corresponding to each constraint object. Therefore, the first... The system state of each execution segment is represented by a state vector. Each component in this state vector corresponds to a control variable or state variable for a different constraint, such as energy storage power, load level, or charging power. The target scheduling path is represented as a sequence of state vectors arranged in chronological order. in, The number of execution segments.

[0090] Two basic conditions must be met when generating a path. Firstly, for any execution segment... State vector It must fall within the set of constraint-satisfied intervals corresponding to that segment. Within the constraint corridor, the boundary constraints must be met. On the other hand, state changes between adjacent execution segments must comply with the aforementioned change constraints, such as power change rate limits or state change amplitude limits, to ensure the path is physically feasible. By considering both conditions simultaneously, a set of candidate paths is selected within the constraint corridor.

[0091] To determine the target scheduling path from multiple candidate paths, an evaluation metric is further introduced to compare the paths. This metric typically reflects the scheduling objective, such as power balance deviation, regulation cost, or state fluctuation. In one embodiment, a path cost function is defined. This is used to measure the quality of a path, and its form is: in, Indicates the first The reference state vector of each execution segment, and These are weighting coefficients, used to adjust the degree of attention paid to target deviation and state smoothness, respectively. By evaluating the candidate path set, the path that minimizes the cost function is selected as the target scheduling path.

[0092] After determining the target scheduling path, the state sequence within the path needs to be transformed into specific scheduling control actions. Since adjacent state vectors within the path already satisfy change constraints, the state difference between adjacent execution segments is directly used as the basis for adjusting the control variables. For example, for a constrained object... Corresponding variables In the execution fragment arrive The control adjustment amount between them can be expressed as: This adjustment amount is the control change that needs to be applied within the corresponding execution segment.

[0093] During execution, the entire scheduling cycle is divided into multiple execution segments, and control commands are issued segment by segment in chronological order. At the beginning of each execution segment, a control command is sent to the corresponding energy unit, adjusting its state from the current value to the target state value. Since change constraints have already been considered in the path generation stage, no additional constraint judgments are needed during execution, thus simplifying the control logic. Furthermore, because the control target of each execution segment originates from the same path, consistency across the entire scheduling process in the time dimension is guaranteed.

[0094] Furthermore, in the specific implementation, a centralized or distributed execution method is selected based on the system characteristics. In the centralized method, a unified scheduling unit generates control commands for each energy unit according to the target scheduling path and distributes them uniformly. In the distributed method, the target status information in the path is sent to each energy unit, and each unit adjusts its status according to its own control strategy. Neither of the above execution methods changes the basic idea of ​​path-driven scheduling.

[0095] Through the above process, a complete mapping from constraint corridors to scheduling execution is achieved, ensuring that the scheduling path always remains within the feasible range, thereby avoiding situations where intermediate states exceed limits during execution. It should be noted that the path generation and execution methods can be adjusted according to specific application requirements. For example, different forms of evaluation functions or control strategies can be introduced. All of these, without altering the core mechanism of selecting paths based on constraint corridors, fall within the protection scope of this solution.

[0096] In another possible implementation, considering the uncertainties in source-side output prediction, load changes, and charging behavior during actual operation, when constructing the constraint satisfaction interval for each execution segment, the interval boundary is adaptively shrunk based on the fluctuation amplitude of the corresponding constraint variables.

[0097] Specifically, for each constrained object, within its constraint satisfaction interval Based on this, an uncertainty margin parameter is introduced. Construct the shrunken effective interval The margin parameter can be determined by historical fluctuation statistics or real-time prediction errors, thereby prioritizing the selection of state trajectories located within the interval during the path generation process and reducing the risk of operational deviation.

[0098] The transitional constraint corridor constructed based on this contraction interval reserves a safety margin for the scheduling path while ensuring that the original constraints are not broken, so that the system can remain within the feasible range when facing short-term disturbances.

[0099] like Figure 5 As shown, this invention provides a multi-constraint consistency control system for coordinated operation of energy sources, grid, load, storage, and charging. The system includes: a data acquisition unit for acquiring operational data of each energy unit in the energy source, grid, load, storage, and charging system, and constructing an operational state vector based on the operational data; a processing unit for converting the constraints corresponding to each energy unit into constraint expression units with a unified structure to form a multi-constraint set; a constraint construction unit for dividing the scheduling cycle into multiple execution segments based on the operational state vector and the multi-constraint set, and constructing a transitional constraint corridor covering each execution segment; and a scheduling unit for generating a target scheduling path based on the transitional constraint corridor, and executing corresponding scheduling control within each execution segment according to the target scheduling path.

[0100] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor A01, a network interface A02, memory (not shown), and a database (not shown) connected via a system bus. The processor A01 provides computing and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02, and a database (not shown). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A04. The network interface A02 is used for communication with external terminals via a network connection. When the computer program B02 is executed by the processor A01, it implements a multi-constraint consistency control method for coordinated operation of source, network, load, storage, and charging.

[0101] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a microcontroller, chip, or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0102] The optional embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details described above. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention. It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not further describe the various possible combinations.

[0103] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the embodiments of the present invention, they should also be regarded as the content disclosed by the embodiments of the present invention.

Claims

1. A multi-constraint consistency control method for coordinated operation of source-grid-load-storage-charging systems, characterized in that, The method includes: Obtain the operating data of each energy unit in the source-grid-load-storage-charging system, and construct an operating state vector based on the operating data; The constraints corresponding to each energy unit are converted into constraint expression units with a unified structure, forming a multi-constraint set; Based on the running state vector and the set of multiple constraints, the scheduling cycle is divided into multiple execution segments, and a transitional constraint corridor covering each execution segment is constructed. Based on the transitional constraint corridor, a target scheduling path is generated, and corresponding scheduling control is performed in each execution segment according to the target scheduling path.

2. The multi-constraint consistency control method for coordinated operation of source-grid-load-storage-charging as described in claim 1, characterized in that, Obtain the operating data of each energy unit in the source-grid-load-storage-charging system, and construct an operating state vector based on the operating data, including: The power data, status data, and task data of each energy unit are acquired, and time alignment processing is performed on the power data, status data, and task data to form the original operation data sequence. Based on the original operating data sequence, feature extraction is performed on the power data, the state data, and the task data to obtain power parameters, state parameters, and task constraint parameters, respectively. An operating state vector is constructed based on the power parameters, the state parameters, and the task constraint parameters.

3. The multi-constraint consistency control method for coordinated operation of source-grid-load-storage-charging as described in claim 2, characterized in that, Based on the original operational data sequence, feature extraction is performed on the power data, the state data, and the task data to obtain power parameters, state parameters, and task constraint parameters, respectively, including: The power data is subjected to time window statistical processing to obtain power parameters that characterize the power level and trend of each energy unit. The state data is processed for state identification to obtain state parameters that characterize the operating conditions of each energy unit. The task data is subjected to constraint parsing to obtain task constraint parameters that characterize the service requirements and time limits of each energy unit.

4. The multi-constraint consistency control method for coordinated operation of source-grid-load-storage-charging as described in claim 1, characterized in that, The constraints corresponding to each energy unit are converted into constraint expression units with a unified structure, forming a multi-constraint set, including: Obtain the operational constraints, physical constraints, and task constraints corresponding to each energy unit, and determine the corresponding constraint objects and constraint variables based on each constraint; Based on the constraint variables, the upper and lower limit value ranges and constraint effective intervals of each constraint are extracted, and the constraint object, the constraint variables, the upper and lower limit value ranges and the constraint effective intervals are associated and encapsulated to construct a constraint expression unit; Each constraint expression unit is categorized and aggregated according to its corresponding constraint object to form a multi-constraint set.

5. The multi-constraint consistency control method for coordinated operation of source-grid-load-storage-charging as described in claim 4, characterized in that, Obtain the operational constraints, physical constraints, and task constraints corresponding to each energy unit, and determine the corresponding constraint objects and constraint variables based on each constraint, including: The power parameters, state parameters, and task constraint parameters of each energy unit are extracted from the original operation data sequence, and corresponding operation constraints, physical constraints, and task constraints are generated based on the power parameters, state parameters, and task constraint parameters. For each constraint, the corresponding constraint object is determined as the energy unit that generates the corresponding constraint, and the control quantity and / or state quantity of the constraint action are determined as the corresponding constraint variable.

6. The multi-constraint consistency control method for coordinated operation of source-grid-load-storage-charging as described in claim 4, characterized in that, Each constraint expression unit is categorized and aggregated according to its corresponding constraint object to form a multi-constraint set, including: Constraint expression units with the same constraint object are merged to form object constraint groups corresponding to each constraint object; Based on the constraint variables, upper and lower limit value ranges, and constraint effective intervals of each constraint expression unit within each object constraint group, the parallel constraint relationships and temporal constraint relationships between each constraint expression unit within each object constraint group are determined. Based on the constraint groups of each object and the corresponding parallel constraint relationships and temporal constraint relationships, a multi-constraint set is constructed to characterize the constraint superposition state and constraint switching state of the same constraint object under different execution segments.

7. The multi-constraint consistency control method for coordinated operation of source-grid-load-storage-charging as described in claim 1, characterized in that, Based on the running state vector and the multi-constraint set, the scheduling cycle is divided into multiple execution segments, and a transitional constraint corridor covering each execution segment is constructed, including: The scheduling period is discretized based on a preset time granularity to obtain multiple continuous execution segments; For each execution segment, the state prediction of the constraint variables corresponding to each constraint object is performed based on the running state vector to obtain the predicted state value under each execution segment; Based on the aforementioned multi-constraint set, constraint verification is performed on the predicted state values ​​under each execution segment to obtain the constraint satisfaction interval corresponding to each execution segment. Based on the constraint satisfaction intervals corresponding to each execution segment, a transitional constraint corridor covering all execution segments is constructed to characterize the feasible state range of each constraint object within each execution segment.

8. The multi-constraint consistency control method for coordinated operation of source-grid-load-storage-charging as described in claim 7, characterized in that, For each execution segment, based on the running state vector, the state prediction of the constraint variables corresponding to each constraint object is performed to obtain the predicted state value under each execution segment, including: Based on the current values ​​of each constraint variable in the running state vector, the initial state value of each constraint object under the starting execution segment is determined; For adjacent execution segments, based on the power change constraints and state change constraints corresponding to each constraint object, the change of each constraint variable between adjacent execution segments is restricted and calculated to obtain the state change amount corresponding to each execution segment. Based on the state change amount corresponding to each execution segment, the initial state value is accumulated segment by segment to obtain the predicted state value sequence corresponding to each execution segment.

9. The multi-constraint consistency control method for coordinated operation of source-grid-load-storage-charging as described in claim 7, characterized in that, Based on the aforementioned multi-constraint set, constraint verification is performed on the predicted state values ​​under each execution segment to obtain the constraint satisfaction interval corresponding to each execution segment, including: For each execution segment, the corresponding constraint expression unit is extracted from the multi-constraint set, and the constraint variable, upper and lower limit value range and constraint effective interval corresponding to each extracted constraint expression unit are obtained; Based on the matching relationship between each execution segment and the constraint effective interval corresponding to each constraint expression unit, the constraint expression units that are effective within the current execution segment are selected; Based on the comparison between the predicted state value and the upper and lower limit value ranges corresponding to the selected constraint expression units, the feasible value ranges corresponding to each constraint variable are determined. For multiple constraint expression units with the same constraint object within the same execution segment, interval superposition processing is performed on each feasible value interval to obtain the constraint satisfaction interval of the corresponding constraint object within the execution segment. The constraint satisfaction intervals corresponding to each constraint object within each execution segment are aggregated to form a set of constraint satisfaction intervals for each execution segment.

10. A multi-constraint consistency control system for coordinated operation of source, grid, load, storage, and charging, characterized in that, The system includes: The data acquisition unit is used to acquire the operating data of each energy unit in the source-grid-load-storage-charging system, and to construct an operating status vector based on the operating data; The processing unit is used to convert the constraints corresponding to each energy unit into constraint expression units with a unified structure, forming a multi-constraint set. The constraint construction unit is used to divide the scheduling cycle into multiple execution segments based on the running state vector and the multi-constraint set, and to construct a transitional constraint corridor covering each execution segment. The scheduling unit is used to generate a target scheduling path based on the transitional constraint corridor, and to perform corresponding scheduling control in each execution segment according to the target scheduling path.

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