Distributed integrated energy variable-resolution planning control method and system

Abstract

The application discloses a distributed comprehensive energy variable resolution planning control method and system, and discloses a planning operation collaborative optimization method of a distributed comprehensive energy system. The method obtains operation data of photovoltaic, wind power, energy storage, charging facilities, generator units, loads and metering terminals, and forms credible state data based on unified time service and data quality marks; a variable time resolution input sequence is constructed according to time sequence fluctuation characteristics, short time dynamic characteristics are retained in high fluctuation intervals, and stationary data is compressed in low fluctuation intervals; a planning design and operation plan unified solving model is constructed based on the input sequence, device capacity configuration and benchmark operation plans are output; further, structured control commands are generated through a semantic attribute-communication interface mapping relationship, and pre-write checking and post-write read confirmation are performed. The method can improve planning accuracy, operation executability and field control reliability.

Description

Technical Field

[0001] This specification relates to the field of power system automation and integrated energy operation control technology. More specifically, this application relates to a distributed integrated energy variable resolution planning and control method and system. Background Technology

[0002] With the continuous integration of distributed photovoltaic, wind power, energy storage, electric vehicle charging facilities, biogas or gas generator sets, and adjustable loads into industrial parks, distribution areas, and industrial user sites, distributed integrated energy systems are characterized by diverse equipment types, rapid fluctuations in source and load, complex operating boundaries, and multiple control objectives. Such systems require not only the completion of equipment capacity configuration and economic evaluation during the planning phase, but also rolling scheduling and control execution based on real-time status and forecast results during the operation phase.

[0003] Existing technologies typically employ an architecture combining planning and design modules, an energy management master station, and field terminal equipment, but significant shortcomings remain. On one hand, planning models struggle to balance high time resolution with solvability. If minute-level, year-round modeling is used, the number of variables and constraints increases dramatically, making the solution difficult. On the other hand, fixed, low-resolution modeling can easily mask key dynamic characteristics such as sudden drops in photovoltaic power, sudden load surges, rapid charging and discharging of energy storage, and unit ramp-up, leading to inaccurate capacity configuration and operational boundary assessments.

[0004] On the other hand, there is a disconnect between the optimization results in the existing system and the execution interface of the field devices. The power setpoints, start / stop statuses, and other results output by the upper-level model usually need to be converted into specific communication protocols, register addresses, function codes, scaling factors, byte order, and readback confirmation logic before they can be executed by the devices. Existing methods mostly rely on manual point table configuration, script conversion, or hard coding, lacking a unified semantic mapping and structured command closed loop, resulting in high device access and maintenance costs and insufficient reliability of control execution.

[0005] In addition, the existing system lacks a unified time synchronization, data quality identification, and execution feedback verification mechanism, which can easily lead to incorrect scheduling in cases of asynchronous data collection, communication timeouts, old value retention, or equipment refusal to execute.

[0006] Therefore, there is an urgent need for a distributed integrated energy system modeling and control scheme that can integrate planning and design, operation optimization, data governance and on-site execution, in order to improve planning accuracy, operational feasibility and reliability of multi-device collaborative control. Summary of the Invention

[0007] The summary section introduces a series of simplified concepts, which will be further explained in detail in the detailed description section. This summary section is not intended to limit the key and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0008] Firstly, this application proposes a distributed integrated energy variable resolution planning and control method, including: Establish standardized equipment instances for field terminal equipment and metering acquisition devices within a regional microgrid, define writable control semantic attributes, and construct a semantic attribute-communication interface mapping table between the aforementioned writable control semantic attributes and communication interfaces. The operation data of the above-mentioned field terminal equipment and metering acquisition device are collected through a unified time synchronization mechanism, and a unified timestamp and data quality mark are added to the above-mentioned operation data to form the original operation dataset under a unified time scale. The original running dataset is segmented, fluctuation is identified, and multi-granularity aggregation is performed to generate a variable time resolution input sequence. Based on the above variable time resolution input sequence, a unified solution model for planning design and operation plan is constructed and solved to obtain the planning design results and the baseline operation plan. Based on the above baseline operation plan, real-time status, equipment availability information and online adjustment results, a structured control command object is generated, and then parsed and converted into a communication message that can be executed by the target device according to the above semantic attribute-communication interface mapping table. Before the above communication message is sent, a boundary validity check is performed; after the message is sent, a readback confirmation is performed, and a command execution result record is generated. Based on feedback data with a unified time scale and data quality indicators meeting preset conditions, the device operation settings in the subsequent scrolling window are further optimized to generate a revised short-term operation plan.

[0009] In one feasible implementation, the standardized equipment example for establishing field terminal equipment and metering acquisition devices within the aforementioned regional microgrid defines writable control semantic attributes and constructs a semantic attribute-communication interface mapping table between the writable control semantic attributes and the communication interface, including: Based on the general information model extension rules, corresponding standardized equipment instances are established for distributed photovoltaic inverters, wind power inverters, energy storage converters, electric vehicle charging facility controllers, biogas or gas generator control units, adjustable load controllers, smart meters, environmental acquisition devices, and grid connection point metering devices. Configure each of the above standardized equipment instances with object identifier, equipment type, rated capacity, readable attributes, writable control semantic attributes, unit, range boundary, control mode, status enumeration, and execution confirmation attributes; Establish a correspondence between the above writable control semantic attributes and communication protocol type, station address or IP address, function code, register address, data type, scaling factor, byte order, pre-write enable condition, effective time window, readback confirmation point, and exception return code interpretation rules to form the above semantic attribute-communication interface mapping table.

[0010] In one feasible implementation, the above-mentioned operational data of the field terminal equipment and metering acquisition device are collected through a unified time synchronization mechanism, and a unified timestamp and data quality flag are added to the above-mentioned operational data to form an original operational dataset under a unified time scale, including: Clock synchronization is performed on the energy management master station, edge computing device, edge semantic gateway and time-synchronizable field terminal equipment based on the Network Time Protocol. The operating data of the above-mentioned field terminal equipment and metering acquisition device are collected according to the preset sampling period or event triggering method. The above-mentioned operating data includes active power, reactive power, voltage, current, energy storage state of charge, switch status, grid connection point switching power, environmental parameters and equipment fault status. The collected operational data is written with a unified timestamp and a quality flag is attached. The data quality flags at least distinguish between valid measured values, communication timeout values, interpolated values, estimated values, manually set values, time synchronization anomalies, and out-of-limit values.

[0011] In one feasible implementation, the above-mentioned segmentation, fluctuation discrimination, and multi-granularity aggregation of the original running dataset to generate a variable time resolution input sequence includes: Extract source-side power, load-side power, grid-connected point switching power, and environmental sequence from the above original operational dataset, and divide the continuous time axis into multiple basic time windows; Fluctuation analysis is performed on the net load changes, distributed power output changes, or energy storage charging and discharging changes within each of the above-mentioned basic time windows. Based on the first and second thresholds, the above-mentioned basic time windows are divided into high volatility range, medium volatility range and low volatility range; The basic time resolution data is retained for the high fluctuation range, the first aggregation granularity processing is performed for the medium fluctuation range, and the second aggregation granularity processing is performed for the low fluctuation range to obtain the variable time resolution input sequence.

[0012] In one feasible implementation, each time interval in the above-mentioned variable time resolution input sequence retains the corresponding time period length identifier, representative value, cumulative energy value, and source data start and end time information, and each time interval in the above-mentioned variable time resolution input sequence corresponds to the corresponding time unit in the unified solution model of planning, design and operation plan.

[0013] In one feasible implementation, the above-mentioned unified solution model for planning, design, and operation is constructed and solved based on the variable time resolution input sequence to obtain the planning and design results and the baseline operation plan, including: By combining equipment investment parameters, operation and maintenance parameters, electricity price parameters, fuel cost parameters, grid connection constraint parameters, and equipment efficiency parameters, a unified solution model for the above planning, design, and operation plan is constructed. The decision-making objects include distributed photovoltaic capacity, wind power capacity, energy storage power capacity, energy storage capacity, biogas or gas generator capacity, grid connection point power exchange boundary, and equipment operation setpoints within each time unit. This ensures that the time units in the unified solution model for the above planning, design, and operation plan correspond one-to-one with the time intervals in the above variable time resolution input sequence, and that the duration of the above time units is consistent with the time period granularity of the corresponding time intervals. Power balance constraints, ramping constraints, charge / discharge mutual exclusion constraints, start / stop constraints, or action count constraints are applied to short-time granular time units, while energy balance constraints, state transition constraints, connection capacity boundary constraints, and periodic boundary constraints are applied to long-time granular time units.

[0014] In one feasible implementation, the generation of structured control command objects based on the aforementioned baseline operation plan, real-time status, equipment availability information, and online adjustment results includes: The structured control command object described above is encapsulated using a self-describing message format; Write the device object identifier, writable control semantic attribute label, target set value, unit, command generation time, planned execution time, effective time window, priority, source plan identifier, idempotency check field and expected readback attribute identifier into the above structured control command object; The structured control command object is sent to the edge semantic gateway so that the edge semantic gateway can query the semantic attribute-communication interface mapping table based on the device object identifier and the writable control semantic attribute tag.

[0015] In one feasible implementation, the above-mentioned parsing and conversion of the semantic attribute-communication interface mapping table into a communication message executable by the target device includes: Based on the device object identifier and the writable control semantic attribute tag, obtain the communication protocol, communication address, function code, register address, data type, scaling factor, byte order and writing method corresponding to the target device; The target setting value is formatted according to the scaling factor and data type mentioned above. When the target device is a Modbus type device, select either single register write message or multi register write message according to the register word length and write method corresponding to the target parameters. When the target device has remote-to-local switching requirements or write-before-enable requirements, control enable sub-commands and set value write sub-commands are generated in a preset order to form the underlying control message queue corresponding to the above communication message.

[0016] In one feasible implementation, the boundary validity check is performed before the communication message is sent, and a readback confirmation is performed after the message is sent, and a command execution result record is generated, including: Before the above communication messages are sent, the rated power boundary, energy storage state of charge boundary, current operating mode of the equipment, interlocking conditions, grid connection point switching power boundary and command priority conflict relationship are verified. After the verification is successful, the above communication message is sent to the target device, and the readback confirmation point or status point is read within the preset valid time window; Compare the consistency between the equipment's returned results, feedback settings, actual execution values, and target settings; Based on the consistency comparison results, a command execution result record associated with the above structured control command object is generated. The command execution result record includes at least the following: execution success, device rejection, communication timeout, readback missing, and execution deviation exceeding the limit. Feedback data with a unified time scale and whose data quality indicators meet preset conditions will be used to update short-term forecast deviations, equipment availability status, energy storage adjustability margin, and grid connection point power exchange status, so as to further optimize the equipment operation settings in subsequent rolling windows.

[0017] Secondly, this application proposes a distributed integrated energy variable resolution planning and control system, comprising: The construction unit is used to establish standardized equipment instances of field terminal equipment and metering acquisition devices in the regional microgrid, define writable control semantic attributes, and construct a semantic attribute-communication interface mapping table between the above writable control semantic attributes and the communication interface. The data acquisition unit is used to collect the operating data of the above-mentioned field terminal equipment and metering acquisition device through a unified time synchronization mechanism, and to add a unified timestamp and data quality mark to the above-mentioned operating data to form the original operating dataset under a unified time scale. The generation unit is used to perform segmentation, fluctuation discrimination, and multi-granularity aggregation on the original running dataset to generate a variable time resolution input sequence. The solution unit is used to construct and solve a unified solution model for planning, design and operation plans based on the above variable time resolution input sequence, and obtain the planning and design results and the baseline operation plan. The conversion unit is used to generate structured control command objects based on the above-mentioned baseline operation plan, real-time status, equipment availability information and online adjustment results, and to parse and convert them into communication messages that can be executed by the target device according to the above-mentioned semantic attribute-communication interface mapping table. The verification unit is used to perform boundary legality verification before the above communication message is sent, perform readback confirmation after the message is sent, and generate a record of command execution results. The optimization unit is used to further optimize the equipment operation settings in the subsequent scrolling window based on feedback data under a unified time scale and whose data quality indicators meet preset conditions, and to generate a revised short-term operation plan.

[0018] In summary, compared to existing technologies, this invention can connect the planning, design, operation optimization, data governance, and on-site execution processes of distributed integrated energy systems. By establishing writable attribute modeling and semantic attribute-communication interface mapping relationships, this invention corresponds the capacity variables and operating setpoints in the upper-level optimization model with the communication protocols, register addresses, function codes, scaling factors, and readback points of field equipment. This allows planning results and online optimization results to be automatically converted into on-site executable control commands, reducing manual point table configuration and hard-coding adaptation work, and improving the efficiency of multi-type equipment access and maintenance. Based on fluctuation characteristics, this invention constructs a variable time resolution input sequence, retaining short-term spikes, ramp-up, and net load mutation characteristics during high-fluctuation periods, and compressing stable data segments during low-fluctuation periods. This reduces the variable and constraint scale of the annual optimization model while ensuring that key dynamic features are not lost, balancing planning accuracy and model solvability. Furthermore, this invention incorporates equipment capacity configuration, grid connection boundaries, and baseline operation plans into a unified solution model, ensuring that the planning and design results are consistent with the actual operating boundaries, reducing the evaluation deviation of energy storage capacity, unit ramp-up capability, and grid connection switching capability. This invention, through unified time synchronization, data quality flags, structured control commands, pre-write security checks, and post-write readback confirmation mechanisms, can identify situations such as data anomalies, communication timeouts, device rejections, missing readbacks, and execution deviations, avoiding the substitution of successful communication transmission for successful device execution. Furthermore, this invention performs online rolling corrections based on effective feedback data under a unified time scale, enabling the operating plan to continuously adjust according to changes in load, output deviations, and equipment availability, thereby improving the system's reliability, security, and adaptability.

[0019] The distributed integrated energy variable resolution planning and control method proposed in this application, along with other advantages, objectives, and features of this application, will be partly apparent from the following description and partly understood by those skilled in the art through research and practice of this application. Attached Figure Description

[0020] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit this specification. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating a distributed integrated energy variable resolution planning and control method provided in this application embodiment; Figure 2 A schematic diagram illustrating the working principle of a distributed integrated energy variable resolution planning and control system provided in this application embodiment; Figure 3 A schematic diagram illustrating the variable time resolution data aggregation, fluctuation discrimination, and granular reconstruction process provided in an embodiment of this application; Figure 4 A schematic diagram of the unified solution model architecture for planning, design, and operation plans provided in this application embodiment; Figure 5 A schematic diagram illustrating the structured control command object, semantic attribute-communication interface mapping, and communication message conversion provided in the embodiments of this application; Figure 6 A schematic diagram illustrating the closed-loop process of pre-write boundary validity verification, post-write readback confirmation, and command execution result provided in this application embodiment; Figure 7 A schematic diagram of the unified time synchronization, data quality labeling, signal interaction and online rolling correction closed loop provided for the embodiments of this application; Figure 8 This is a structural schematic diagram of a distributed integrated energy variable resolution planning and control system provided in an embodiment of this application. Detailed Implementation

[0021] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0022] Please see Figure 1 and Figure 2 , Figure 1 This is a flowchart illustrating a distributed integrated energy variable resolution planning and control method provided in an embodiment of this application. Figure 2 This application provides a schematic diagram illustrating the working principle of a distributed integrated energy variable resolution planning and control system. Specifically, the method may include: S110. Establish standardized equipment instances for field terminal equipment and metering acquisition devices within the regional microgrid, define writable control semantic attributes, and construct a semantic attribute-communication interface mapping table between the above writable control semantic attributes and the communication interface. S120. Collect the operating data of the above-mentioned field terminal equipment and metering acquisition device through a unified time synchronization mechanism, and add a unified timestamp and data quality mark to the above-mentioned operating data to form the original operating dataset under a unified time scale. S130. Perform segmentation, fluctuation discrimination, and multi-granularity aggregation on the above original running dataset to generate a variable time resolution input sequence. S140. Based on the above variable time resolution input sequence, construct and solve a unified solution model for planning design and operation plan to obtain planning design results and baseline operation plan; S150. Generate a structured control command object based on the above-mentioned baseline operation plan, real-time status, equipment availability information and online adjustment results, and parse and convert it into a communication message that can be executed by the target device according to the above-mentioned semantic attribute-communication interface mapping table. S160. Before the above communication message is sent, perform boundary legality verification; after the message is sent, perform readback confirmation and generate a command execution result record. S170. Based on feedback data under a unified time scale and where the data quality flags meet preset conditions, further optimize the equipment operation settings in the subsequent scrolling window to generate a revised short-term operation plan.

[0023] For example, the distributed integrated energy variable-resolution planning and control method can be collaboratively executed by an energy management master station 101, an edge computing device 102, an edge semantic gateway 103, a network time synchronization unit 104, and field terminal equipment and metering acquisition devices 105. The energy management master station 101 can be used to configure system operation strategies, issue planning tasks, maintain equipment models, and view command execution results. The edge computing device 102 can be used to complete operational data processing, variable time resolution aggregation, planning design and online rolling optimization, and control command generation. The edge semantic gateway 103 can be used to establish standardized equipment instances, maintain semantic attribute-communication interface mapping relationships, and parse upper-layer structured control commands into lower-level communication messages executable by field equipment. The network time synchronization unit 104 can provide a unified time reference for data acquisition, modeling, command issuance, and execution confirmation. The field terminal equipment and metering acquisition devices 105 may include distributed photovoltaic inverters, wind power inverters, energy storage converters, electric vehicle charging facility controllers, biogas or gas generator control units, adjustable load controllers, smart meters, environmental acquisition devices, and grid-connected point metering devices, etc.

[0024] In step S110, the edge semantic gateway 103 can establish standardized device instances for field terminal devices and metering acquisition devices 105 within the regional microgrid through its internal standardized device instance library and mapping table storage area 203a. Each standardized device instance can include information such as object identifier, device type, rated capacity, readable attributes, writable control semantic attributes, unit, range boundary, control mode, status enumeration, and execution confirmation attributes. For attributes that require execution control, such as the setpoint for energy storage active power, the setpoint for photovoltaic power limiting, the upper limit of charging pile power, or the target output value of generator set, the system defines them as writable control semantic attributes and establishes a semantic attribute-communication interface mapping table between the writable control semantic attribute and the communication interface in the standardized device instance library and mapping table storage area 203a. This mapping table can record communication protocol type, station address or IP address, function code, register address, data type, scaling factor, byte order, pre-write enable condition, effective time window, readback confirmation point, and exception return code interpretation rules, so that the upper-layer semantic control object can accurately correspond to the communication interface of the lower-layer field device.

[0025] In step S120, the network time synchronization unit 104 can provide unified time synchronization for the energy management master station 101, edge computing device 102, edge semantic gateway 103, and time-synchronized field terminal devices, ensuring that the data collected by different devices are under a unified time reference. The data acquisition and quality management unit 202a in the edge computing device 102 can collect operating data from the field terminal devices and metering acquisition device 105 through the edge semantic gateway 103. This operating data may include active power, reactive power, voltage, current, energy storage state of charge, switch status, grid connection point switching power, environmental parameters, and equipment fault status. After receiving the operating data, the data acquisition and quality management unit 202a adds a unified timestamp and data quality flag to each piece of operating data. The data quality flag can distinguish between valid measured values, communication timeout values, interpolated values, estimated values, manually set values, time synchronization anomalies, and out-of-limit values, thereby forming a raw operating dataset under a unified time scale, preventing low-quality data from directly entering the subsequent optimization process.

[0026] In step S130, the variable time resolution data aggregation unit 202b in the edge computing device 102 can perform segmentation processing, fluctuation discrimination, and multi-granularity aggregation on the original operating dataset. Specifically, the variable time resolution data aggregation unit 202b can extract source-side power, load-side power, grid-connected point switching power, and environmental sequence, and divide the continuous time axis into multiple basic time windows. Subsequently, based on the severity of net load changes, distributed power output changes, or energy storage charging and discharging changes within each basic time window, it is determined whether the basic time window belongs to a high-fluctuation range, a medium-fluctuation range, or a low-fluctuation range. For high-fluctuation ranges, the system retains the basic time resolution data to preserve short-term dynamic characteristics such as photovoltaic power drop, load surge, and rapid energy storage charging and discharging. For medium-fluctuation and low-fluctuation ranges, the system uses different granularities for aggregation processing to reduce the number of time units in subsequent models. Each aggregation result can retain the time period length identifier, representative value, cumulative energy value, and source data start and end time information, thereby generating a variable time resolution input sequence.

[0027] In step S140, the planning and design and online rolling optimization unit 202c in the edge computing device 102 can construct a unified solution model for planning, design, and operation based on a variable time resolution input sequence. This model can combine equipment investment parameters, operation and maintenance parameters, electricity price parameters, fuel cost parameters, grid connection constraint parameters, and equipment efficiency parameters, and take distributed photovoltaic capacity, wind power capacity, energy storage power capacity, energy storage energy capacity, biogas or gas generator capacity, grid connection point power exchange boundary, and equipment operation setpoints within each time unit as decision objects. Since each time interval in the variable time resolution input sequence carries time period length information, the time units in the unified solution model can correspond one-to-one with each time interval, enabling power balance constraints, ramp-up constraints, charge / discharge mutual exclusion constraints, start / stop constraints, or action count constraints to be applied to short-term high-fluctuation intervals, and energy balance constraints, state transition constraints, interconnection capacity boundary constraints, and periodic boundary constraints to be applied to long-term low-fluctuation intervals. By solving this model, the planning and design and online rolling optimization unit 202c can obtain the planning and design results and the baseline operation plan.

[0028] In step S150, the control instruction set generation module 202d in the edge computing device 102 can generate a structured control command object based on the baseline operation plan, real-time status, device availability information, and online adjustment results. This structured control command object may include a device object identifier, writable control semantic attribute tags, target setpoints, units, command generation time, planned execution time, effective time window, priority, source plan identifier, idempotency check field, and expected readback attribute identifier. After receiving the structured control command object, the edge semantic gateway 103 can query the semantic attribute-communication interface mapping table in the standardized device instance library and mapping table storage area 203a through the underlying control message parsing module 203b. Based on the device object identifier and writable control semantic attribute tags, it determines the communication protocol, communication address, function code, register address, data type, scaling factor, byte order, and writing method corresponding to the target device, and converts the target setpoints into a data format conforming to the target device's communication protocol, thereby generating a communication message executable by the target device.

[0029] In step S160, the security verification and execution confirmation unit 203c in the edge semantic gateway 103 can perform boundary legality verification before the communication message is sent. Verification content may include rated power boundaries, energy storage charge state boundaries, current equipment operating mode, interlocking conditions, grid connection point switching power boundaries, and command priority conflict relationships. When the verification passes, the underlying control message parsing module 203b sends the communication message to the target field terminal device; when the verification fails, the security verification and execution confirmation unit 203c can prevent the message from being sent and generate a device rejection record. After the communication message is sent, the security verification and execution confirmation unit 203c can read the readback confirmation point or status point within a preset valid time window, and compare the consistency of the device return result, feedback set value, actual execution value, and target set value to generate a command execution result record. This command execution result record may include states such as successful execution, device rejection, communication timeout, readback missing, and execution deviation exceeding limits, enabling the system to distinguish between successful communication transmission and successful actual device execution.

[0030] In step S170, the planning and design and online rolling optimization unit 202c can further optimize the equipment operation setpoints within the subsequent rolling window based on feedback data under a unified time scale and whose data quality indicators meet preset conditions. Specifically, the system can use the effective feedback data obtained by the data acquisition and quality management unit 202a to update the short-term prediction deviation, equipment availability status, energy storage adjustability margin, and grid connection point power exchange status, and recalculate the equipment operation setpoints within the subsequent rolling window to generate a revised short-term operation plan. This revised short-term operation plan can be encapsulated again into a structured control command object by the control command set generation module 202d, and then the communication message conversion, boundary legality verification, and readback confirmation are completed by the underlying control message parsing module 203b and the security verification and execution confirmation unit 203c in the edge semantic gateway 103, thereby forming a closed-loop control process from unified time synchronization acquisition, variable time resolution modeling, unified solution, structured command issuance to feedback correction.

[0031] In one feasible implementation, step S110 above establishes standardized equipment instances of field terminal equipment and metering acquisition devices within the regional microgrid, defines writable control semantic attributes, and constructs a semantic attribute-communication interface mapping table between the writable control semantic attributes and the communication interface, including: S1101. In accordance with the general information model extension rules, establish corresponding standardized equipment instances for distributed photovoltaic inverters, wind power inverters, energy storage converters, electric vehicle charging facility controllers, biogas or gas generator control units, adjustable load controllers, smart meters, environmental acquisition devices, and grid connection point metering devices. S1102. Configure object identifier, equipment type, rated capacity, readable attributes, writable control semantic attributes, unit, range boundary, control mode, status enumeration and execution confirmation attributes for each of the above standardized equipment instances. S1103. Establish a correspondence between the above-mentioned writable control semantic attributes and the communication protocol type, station address or IP address, function code, register address, data type, scaling factor, byte order, pre-write enable condition, effective time window, readback confirmation point, and exception return code interpretation rules to form the above-mentioned semantic attribute-communication interface mapping table.

[0032] For example, in this embodiment, step S110 can be executed by the edge semantic gateway 103 and its internal standardized device instance library and mapping table storage area 203a. Regional microgrids typically connect to multiple types of field terminal devices and metering acquisition devices 105, including distributed photovoltaic inverters, wind power inverters, energy storage converters, electric vehicle charging facility controllers, biogas or gas generator control units, adjustable load controllers, smart meters, environmental acquisition devices, and grid connection point metering devices. Since these devices come from different manufacturers and use different communication protocols and register addresses, directly outputting control quantities from the planning model or scheduling model makes it difficult to guarantee that the control quantities can be accurately identified and executed by specific devices. Therefore, the system first abstracts each type of field device into a standardized device instance with a unified format according to the general information model extension rules, so that different devices can be described in the upper-level system through a unified object identifier and attribute structure.

[0033] In S1101, the edge semantic gateway 103 can establish a standardized device instance for each field device based on its device type, access location, communication address, and functional purpose. For example, it can establish an energy storage converter device instance for a specific energy storage converter, a photovoltaic inverter device instance for a specific distributed photovoltaic inverter, and a grid connection point metering device instance for a grid connection point meter. Through this instantiation process, the field devices are no longer merely represented as scattered register points or communication messages, but are organized into device objects with a unified device type, a unified object identifier, and a unified set of attributes. This facilitates consistent data interaction and control coordination between the energy management master station 101, the edge computing device 102, and the edge semantic gateway 103.

[0034] In S1102, the system further configures each standardized device instance with an object identifier (UUID), device type, rated capacity, readable attributes (such as current active power), writable control semantic attributes (such as current active power), unit (kW, kV, etc.), range boundaries (such as maximum charge / discharge power limit), control mode (PQ mode, V / f mode), state enumeration (running, standby, fault), and execution confirmation attributes. The object identifier is used to uniquely identify the target device. The device type is used to distinguish between photovoltaic, energy storage, charging piles, generator sets, adjustable loads, or metering devices. Rated capacity and range boundaries are used to limit the acceptable operating range of the device. Readable attributes may include current active power, reactive power, voltage, current, energy storage state of charge, switching status, fault status, etc. Writable control semantic attributes may include target active power setpoint, reactive power setpoint, charge / discharge command, start / stop control command, power upper limit setpoint, operating mode switching command, etc. Control mode and state enumeration are used to describe the device's state, such as remote control, local control, running, standby, fault, or lockout. The execution confirmation attribute is used to indicate the feedback points or status points that need to be read after subsequent commands are issued. In this way, the upper-level system can clearly define what a certain device "can read", "can control", and "how to confirm after control".

[0035] In S1103, the system establishes a correspondence between the aforementioned writable control semantic attributes and specific communication interfaces, forming a semantic attribute-communication interface mapping table. This mapping table can be stored in the standardized device instance library and the mapping table storage area 203a. Its contents may include communication protocol type (such as Modbus RTU / TCP, IEC 104), station address or IP address, function code (such as 03 read, 06 single write, 16 multiple write), register address (such as 0x010A), data type (such as Float32, Int16), scaling factor (such as 0.1, used to resolve the integer 1000 to 100.0), byte order (such as big-endian mode AB CD or little-endian mode CD AB), pre-write enable condition (such as needing to write the unlock password first), valid time window, readback confirmation point, and exception return code interpretation rules, etc. For example, for the writable control semantic attribute "energy storage active power setpoint", the mapping table can record the corresponding energy storage converter communication address, write function code, target power register address, numerical scaling ratio, data byte order, and feedback register that needs to be read back after writing; for devices that need to switch remote control mode before writing the setpoint, the mapping table can also record the pre-write enable conditions and the corresponding control enable points.

[0036] Through the above processing, when the edge computing device 102 generates a structured control command object based on the baseline operation plan or online adjustment results in subsequent steps, this command object only needs to carry semantic information such as device object identifier, writable control semantic attribute tag, and target setpoint. The edge semantic gateway 103 can then convert it into a specific communication message that the target device can execute through the semantic attribute-communication interface mapping table. This avoids the disconnect between the planning and optimization results and the underlying device interface, and also reduces the need to modify the upper-layer control logic when connecting devices from different manufacturers. It allows device expansion, replacement, or protocol difference adaptation to be mainly completed at the mapping table maintenance level, thereby improving the scalability, maintainability, and control execution reliability of the distributed integrated energy system.

[0037] The method proposed in this embodiment solves the problem that offline planning results and online optimization results remain in the space of mathematical variables and lack a unified semantic mapping and register-level execution link for heterogeneous field devices. It achieves the beneficial effect of establishing a regular correspondence between model variables, device objects, writable attributes and communication messages, enabling control setpoints to be automatically converted into executable commands for field devices, and focusing the work of device expansion, replacement or protocol difference adaptation on the maintenance of mapping items.

[0038] In one feasible implementation, the above-mentioned operational data of the field terminal equipment and metering acquisition device are collected through a unified time synchronization mechanism, and a unified timestamp and data quality flag are added to the above-mentioned operational data to form an original operational dataset under a unified time scale, including: Clock synchronization is performed on the energy management master station, edge computing device, edge semantic gateway and time-synchronizable field terminal equipment based on the Network Time Protocol. The operating data of the above-mentioned field terminal equipment and metering acquisition device are collected according to the preset sampling period or event triggering method. The above-mentioned operating data includes active power, reactive power, voltage, current, energy storage state of charge, switch status, grid connection point switching power, environmental parameters and equipment fault status. The collected operational data is written with a unified timestamp and a quality flag is attached. The data quality flags at least distinguish between valid measured values, communication timeout values, interpolated values, estimated values, manually set values, time synchronization anomalies, and out-of-limit values.

[0039] For example, such as Figure 7 As shown, the above-mentioned process of unified time synchronization, data acquisition, quality labeling, and the formation of the original operational dataset can be followed... Figure 7The diagram illustrates the logical execution of the sensing layer's asynchronous signal acquisition and NTP global high-precision time synchronization, data cleaning and Quality Flag seven-class labeling, and the model layer's online rolling correction and advance scheduling closed loop. This process primarily addresses the issues of asynchronous sampling by multi-source devices, inconsistent timestamps, difficulty in identifying data quality, and low-quality samples contaminating subsequent model optimization in regional microgrids.

[0040] Specifically, at the perception layer, field terminal devices and metering acquisition devices in the regional microgrid, such as photovoltaic inverters, smart meters, and energy storage BMS, typically upload asynchronous sampled data according to their respective sampling mechanisms. Since the local clocks, communication links, and upload cycles of different devices are not entirely consistent, directly incorporating this data into the edge-side computation process could easily lead to the system state at the same moment being pieced together from data from different physical times. Therefore, the system first sends absolute time pulses to devices such as photovoltaic inverters, smart meters, and energy storage BMS via the Network Time Protocol (NTP) time reference, enabling each device to carry its corresponding local time stamp when collecting data. Subsequently, the edge-side unified time rearrangement alignment engine receives the sampled local time stamp data uploaded by various devices and eliminates time deviations caused by network transmission jitter and clock drift, thereby outputting a cross-sectional data stream with absolute time alignment. This cross-sectional data stream can more realistically represent the operating state of the regional microgrid under the same unified time reference, providing reliable input for subsequent data quality assessment and optimization modeling.

[0041] During the data quality processing phase, the system performs reasonable boundary and continuity checks on the time-aligned cross-sectional data and assigns seven data quality labels according to the Quality Flag. If the data passes the check, it is marked as Label 1, i.e., a valid measured value "Good," indicating that the data is in a healthy state and can be directly imported into the usable sequence for subsequent model use. If the system detects a communication link disconnection or fails to receive data from the corresponding device within a preset time, the data status is marked as Label 2, i.e., a communication timeout value "Timeout." For local missing data caused by short-term communication interruptions, the system can further trigger an interpolation mechanism to generate Label 3, i.e., an interpolated value "Interpolated," based on historical data or data from adjacent time points to maintain the continuity of the short-time series. If a device's data has missing features, but the system can still extrapolate based on the digital twin model, state estimation model, or related data from related devices, Label 4, i.e., an estimated value "Estimated," is generated. If operation and maintenance personnel force-write certain data based on on-site inspections, temporary adjustments, or manual confirmation results, the data is marked as Label 5, i.e., a manually set value "Manual."

[0042] Furthermore, for data unsuitable for subsequent optimization models, the system employs isolation or interception mechanisms. If the timestamp corresponding to the data experiences an abnormal jump, the deviation between the device clock and the NTP timing reference exceeds a preset range, or the data arrival order fails to meet the unified time-scale alignment requirements, it is marked as identifier 6, i.e., the timing anomaly value TimeSyncErr. This type of data can be intercepted and discarded to prevent contamination of subsequent models and enters the anomaly isolation area for fault tracing. If the data exhibits significant spikes or abrupt changes, exceeds the device's physical boundaries, exceeds the reasonable operating range, or has a severe discrepancy with the continuity of data from adjacent times, it is marked as identifier 7, i.e., the out-of-limit value. This type of out-of-limit value also does not directly participate in subsequent optimization calculations but enters the anomaly isolation area to prevent incorrect operating settings from being generated in the unified solution model for planning, design, and operation plans due to abnormal sampling values.

[0043] After the aforementioned cleaning and seven-category quality labeling, the system incorporates valid measured values, compliant interpolated values, acceptable estimated values, and confirmed manually assigned values ​​into the usable sequence, forming a cleaned and labeled high-quality variable-resolution sample sequence. This sample sequence not only contains the running values ​​themselves but also retains a unified timestamp and data quality markers, enabling edge computing devices to prioritize the use of high-reliability data during subsequent segmentation processing, fluctuation discrimination, and multi-granularity aggregation, and to adopt different usage strategies for data of different quality levels.

[0044] At the model layer, the cleaned and labeled high-quality variable-resolution sample sequences can be loaded into the online rolling optimization engine or the unified solution model for planning, design, and operation. The system can solve for the next lead time window at preset intervals, such as every 15 minutes, generating short-term granular operating setpoints. The online rolling optimization engine shown in the figure can use mixed-integer nonlinear programming (MINLP) or equivalent optimization models to calculate setpoints for energy storage output, photovoltaic curtailment, adjustable load regulation, and grid connection point switching power. The closed-loop correction control commands obtained from the solution can be output to the control execution link to generate short-term granular operating setpoints, correct current plan deviations, and issue equipment control. The changes in operating status, feedback quantities, and abnormal sampling results of the next cycle after equipment execution flow back to the perception layer and data quality management process, thus forming a closed loop of data acquisition, time alignment, quality labeling, model optimization, control execution, and feedback correction.

[0045] Through the above implementation method, the system does not simply collect field data and directly participate in optimization. Instead, it first uses NTP global time synchronization to ensure the consistency of multi-source data in the time dimension, then uses the Quality Flag seven-class classification rule to identify the reliability of the data, and isolates high-risk data such as time synchronization anomalies and out-of-limit values. Finally, only data that meets the quality requirements is input into the subsequent variable time resolution aggregation and online rolling optimization process. This can significantly reduce the impact of communication interruptions, clock drift, data glitches, manual data setting, and estimation errors on the operation plan, and improve the reliability of the baseline operation plan and the revised short-term operation plan.

[0046] In one feasible implementation, the above-mentioned segmentation, fluctuation discrimination, and multi-granularity aggregation of the original running dataset to generate a variable time resolution input sequence includes: Extract source-side power, load-side power, grid-connected point switching power, and environmental sequence from the above original operational dataset, and divide the continuous time axis into multiple basic time windows; Fluctuation analysis is performed on the net load changes, distributed power output changes, or energy storage charging and discharging changes within each of the above-mentioned basic time windows. Based on the first and second thresholds, the above-mentioned basic time windows are divided into high volatility range, medium volatility range and low volatility range; The basic time resolution data is retained for the high fluctuation range, the first aggregation granularity processing is performed for the medium fluctuation range, and the second aggregation granularity processing is performed for the low fluctuation range to obtain the variable time resolution input sequence.

[0047] For example, such as Figure 3 As shown, this embodiment employs the processing logic of inputting raw time-series data and segmenting the basic time window, scoring comprehensive fluctuation characteristics and judging by the engine, identifying interval fluctuation levels, and performing multi-granular reconstruction and sequence generation. This embodiment is mainly used to compress and reconstruct long-cycle operation data of regional microgrids without losing key short-term fluctuation characteristics. This allows the unified solution model for subsequent planning, design, and operation plans to both perceive rapid changes during periods of high fluctuation and avoid the problem of excessively large solution scale caused by high-resolution modeling throughout the entire time period.

[0048] Specifically, during the raw time-series data input stage, the variable time resolution data aggregation unit 202b in the edge computing device 102 first extracts source-side power, load-side power, grid-connected point exchange power, and environmental sequence from the raw operating dataset under a unified time scale. The source-side power can include distributed photovoltaic power output, wind power output, and gas or biogas generator output. The load-side power can include conventional loads, electric vehicle charging loads, and adjustable loads. The grid-connected point exchange power reflects the power interaction status between the regional microgrid and the external power grid. The environmental sequence can include external variables affecting renewable energy output, such as irradiance, wind speed, and temperature. Figure 3 The example illustrates that the original operational data sequence can be high-frequency sampling points such as photovoltaic and load data at the 1-minute level. After completing unified time alignment and cleaning, the system divides the continuous time axis into multiple basic time windows T_k, for example, using 1 hour as a basic analysis window, so that each basic time window contains a set of continuous power and environmental data.

[0049] In the comprehensive fluctuation characteristic scoring stage, the system sends data from each basic time window into the fluctuation assessment process. For each basic time window, the system can first calculate the power change characteristics between adjacent sampling points within the window. For example, based on the difference between the power at the current moment and the power at the previous sampling moment, combined with the sampling time interval, the absolute slope R(t) is calculated to characterize the rate of power change within the window. If, within a certain time window, photovoltaic output rapidly decreases due to cloud cover, the net load suddenly increases due to concentrated charging load access, or energy storage undergoes a short-term charging and discharging switch, the absolute slope within that window will increase significantly. Subsequently, the system further calculates the comprehensive fluctuation characteristic value V_k for that basic time window. This comprehensive fluctuation characteristic value can be composed of the maximum ramp characteristic and statistical variance within the window. For example, the maximum absolute slope and power variance can be weighted and combined to reflect both short-term peaks or rapid ramps and the overall fluctuation degree of the power sequence within the entire window. Therefore, compared to using only the average value or a single rate of change, this comprehensive fluctuation characteristic value can more accurately distinguish between severe fluctuations, general fluctuations, and stable operating periods.

[0050] In the interval fluctuation level discrimination stage, the system compares the comprehensive fluctuation characteristic value V_k of each basic time window with the preset first threshold TH1 and second threshold TH2, and classifies the fluctuation level according to the dual threshold logic. The first threshold TH1 can be used as the high fluctuation discrimination threshold, and the second threshold TH2 can be used as the low fluctuation discrimination threshold, with the first threshold being greater than the second threshold. When V_k is greater than or equal to the first threshold TH1, it indicates that there are situations such as photovoltaic sudden changes, load surges, rapid energy storage operations, or drastic changes in grid-connected power within the basic time window, and the system classifies it as a high fluctuation interval. When V_k is less than the second threshold TH2, it indicates that the basic time window is relatively stable overall, and the system classifies it as a low fluctuation interval. When V_k is between the second threshold TH2 and the first threshold TH1, it indicates that the window has some fluctuation but is not a drastic fluctuation, and the system classifies it as a medium fluctuation interval. Through this dual threshold discrimination method, the system can filter out the intervals that truly require high time precision description, while identifying stable intervals that can be compressed with larger granularity.

[0051] During the multi-granularity reconstruction and sequence generation stage, the system triggers different data reconstruction strategies based on different fluctuation levels. For high-fluctuation intervals, the system triggers a fine-grained strategy, not performing aggregation processing and retaining the basic time resolution data, such as retaining 1-minute-level data points, so that the subsequent unified solution model can accurately characterize short-term ramp-ups, power spikes, net load mutations, and rapid charging and discharging behavior of energy storage. For medium-fluctuation intervals, the system triggers a medium-granularity strategy, performing the first aggregation granularity processing, such as aggregating several minutes of data into 15-minute-level medium time blocks through averaging, summation, or representative value extraction, reducing the number of model time points while retaining the main trend of change in this interval. For low-fluctuation intervals, the system triggers a coarse-grained strategy, performing the second aggregation granularity processing, such as aggregating the entire basic time window into 60-minute-level coarse time blocks at once, thereby significantly reducing the number of time units entering the optimization model during stable periods.

[0052] During the aggregation process, the system does not simply delete the original sampling points, but retains information such as time period identifiers, representative values, and cumulative energy for each aggregated time interval. The time period identifier indicates the start and end times and duration of the aggregated interval, enabling it to correspond with time units in the unified solution model for subsequent planning, design, and operation plans. The representative value characterizes the typical power level within the interval, such as average power, peak power, or a rule-determined representative power. The cumulative energy ensures the consistency of energy relationships before and after aggregation, avoiding distortion of electricity, charging / discharging energy, or grid-connected energy exchange due to time compression. The resulting variable time resolution input sequence simultaneously contains fine-grained, medium-grained, and coarse-grained grid samples.

[0053] Through the above implementation methods, during periods of high fluctuation such as sudden drops in photovoltaic output, concentrated access of electric vehicles, and rapid charging and discharging of energy storage, the system retains a high time resolution to avoid averaging out key dynamic characteristics. During periods of relatively stable load and renewable energy output, the system uses a lower time resolution for compression, reducing the number of time units in the optimization model. Thus, in subsequent step S140, when constructing the unified solution model for planning, design, and operation, more refined constraints on power balance, ramp-up, charging / discharging mutual exclusion, or the number of actions can be applied to high-fluctuation intervals, while constraints on energy balance, state transition, and connection capacity boundaries can be applied to low-fluctuation intervals. This balances high-fidelity time-series modeling with model solveability, improving the engineering applicability of the planning and design results and the baseline operation plan.

[0054] In one feasible implementation, each time interval in the above-mentioned variable time resolution input sequence retains the corresponding time period length identifier, representative value, cumulative energy value, and source data start and end time information, and each time interval in the above-mentioned variable time resolution input sequence corresponds to the corresponding time unit in the unified solution model of planning, design and operation plan.

[0055] For example, such as Figure 3 and Figure 4 As shown, after identifying and granularly aggregating the high-fluctuation, medium-fluctuation, and low-fluctuation intervals, the system does not simply output a set of average power data. Instead, for each time interval in the variable time resolution input sequence, it retains the complete time and energy attributes that can support the subsequent unified solution model.

[0056] Specifically, each time interval is configured with a corresponding time period length identifier, which indicates the actual duration covered by that time interval on the original time axis. For example, high-fluctuation intervals can retain time lengths in the 1-minute range, medium-fluctuation intervals can be aggregated into 15-minute ranges, and low-fluctuation intervals can be aggregated into 60-minute ranges. By retaining this time period length identifier, the subsequent unified solution model for planning, design, and operation plans can calculate power balance, energy balance, energy storage state of charge transition, grid-connected power, and equipment operating costs according to the actual duration, instead of mistakenly treating all time points as equally spaced time points.

[0057] Each time interval also retains a representative value. This representative value can be used to characterize the typical levels of source-side power, load-side power, grid-connected power exchange, or environmental parameters within that time interval. For example, for a low-fluctuation interval where photovoltaic output is relatively stable, the average power can be used as the representative value; for a medium-fluctuation interval with some variation, a weighted average, typical sampled value, or a representative value reflecting the interval's trend can be used; for a high-fluctuation interval, since the system retains basic time-resolution data, its representative value can directly correspond to the original high-frequency sampled value. Through representative values, the unified solution model can obtain the main operating status of each time interval while reducing the number of time nodes.

[0058] Each time interval also retains a cumulative energy value. This cumulative energy value represents the energy result obtained by integrating power over time within that time interval, such as load power consumption, photovoltaic power generation, energy storage charging and discharging energy, or grid-connected exchange power. Since different time intervals have different lengths, relying solely on representative power may lead to statistical distortion of energy. Therefore, retaining the cumulative energy value ensures that the energy relationship before and after aggregation with varying time resolution remains essentially consistent. This avoids energy deviations caused by time compression when calculating changes in energy storage state of charge, electricity purchase costs, power generation revenue, or tie-line power boundaries in subsequent models.

[0059] Each time interval also retains the start and end time information of the source data. This information is used to record the start and end times of the original data corresponding to the aggregated time interval, allowing the aggregated time interval to be traced back to the original operating data. Thus, when subsequent baseline operating plans deviate, or when the operating status corresponding to a certain control command needs to be verified, the system can trace back to the original sampled data based on the source data start and end time information to determine whether there are any issues such as sudden photovoltaic drops, sudden load increases, communication anomalies, data interpolation, or changes in equipment status within that time interval. This improves the interpretability of the data processing process and the ability to trace faults.

[0060] Furthermore, each time interval in the variable time resolution input sequence corresponds to a specific time unit in the unified solution model for planning, design, and operation. In other words, the variable time resolution input sequence is not only a data compression result but also directly determines the time discretization structure of the subsequent model. For high-fluctuation intervals, the corresponding model time units are shorter, allowing for the application of more refined power balance constraints, ramp-up constraints, charge / discharge mutual exclusion constraints, start / stop constraints, or action count constraints to reflect the transient adjustment capability of the equipment under rapidly changing scenarios. For low-fluctuation intervals, the corresponding model time units are longer, allowing for the application of more focused energy balance constraints, state transition constraints, connection capacity boundary constraints, and cycle boundary constraints to reduce model size while maintaining the continuity of long-term operational relationships.

[0061] Through the above method, this embodiment achieves a direct mapping from variable time resolution data processing results to a unified solution model time unit. This mapping relationship allows subsequent models to no longer use a fixed time granularity for year-round or long-term modeling, but instead adaptively set the model time unit length according to the degree of fluctuation in different time intervals. Therefore, key dynamic features can be preserved during periods of high fluctuation, while the model size can be compressed during stable periods. Furthermore, the consistency of time relationships, power relationships, energy relationships, and data traceability relationships is ensured through time period length identifiers, representative values, cumulative energy values, and source data start and end time information, thus providing a reliable data foundation for solving planning and design results and baseline operation plans.

[0062] In one feasible implementation, the above-mentioned unified solution model for planning, design, and operation is constructed and solved based on the variable time resolution input sequence to obtain the planning and design results and the baseline operation plan, including: By combining equipment investment parameters, operation and maintenance parameters, electricity price parameters, fuel cost parameters, grid connection constraint parameters, and equipment efficiency parameters, a unified solution model for the above planning, design, and operation plan is constructed. The decision-making objects include distributed photovoltaic capacity, wind power capacity, energy storage power capacity, energy storage capacity, biogas or gas generator capacity, grid connection point power exchange boundary, and equipment operation setpoints within each time unit. This ensures that the time units in the unified solution model for the above planning, design, and operation plan correspond one-to-one with the time intervals in the above variable time resolution input sequence, and that the duration of the above time units is consistent with the time period granularity of the corresponding time intervals. Power balance constraints, ramping constraints, charge / discharge mutual exclusion constraints, start / stop constraints, or action count constraints are applied to short-time granular time units, while energy balance constraints, state transition constraints, connection capacity boundary constraints, and periodic boundary constraints are applied to long-time granular time units.

[0063] Exemplary, exemplary, such as Figure 4 As shown, based on the generated variable time resolution input sequence, the planning and design and online rolling optimization unit 202c in the edge computing device 102 further constructs a unified solution model for planning, design, and operation plans, and obtains the equipment capacity configuration results and baseline operation plan through a mathematical programming solver. The aforementioned variable time resolution input sequence includes multiple time units τ, each with a duration of Δtτ. High fluctuation intervals correspond to smaller Δtτ, while low fluctuation intervals correspond to larger Δtτ. Therefore, the time units in the unified solution model are not of fixed duration, but correspond one-to-one with the corresponding time intervals in the aforementioned variable time resolution input sequence. This allows the model to maintain high computational accuracy during high fluctuation periods and reduce the number of variables and constraints during low fluctuation periods.

[0064] During model construction, the system incorporates both planning and design variables and operational decision variables into a unified solution framework. The planning and design variables include distributed photovoltaic (PV) capacity, wind power capacity, energy storage power capacity, energy storage energy capacity, biogas or gas generator capacity, and grid connection point power exchange boundaries. The operational decision variables include PV output, wind power output, generator output, energy storage charging power, energy storage discharging power, grid connection point power exchange, equipment start-up and shutdown status, and adjustable load regulation within each time unit τ. By solving for both capacity planning and operational control variables in a unified manner, the problem of mismatch between capacity configuration and actual operational boundaries that occurs when equipment capacity is determined first and then operational scheduling is performed separately can be avoided.

[0065] Regarding the objective function, the unified solution model for planning, design, and operation plans described above aims to minimize the system's annualized total cost, which includes equipment investment cost (CAPEX) and operating cost (OPEX). For example, the objective function can be expressed as: ; in, This represents the system's annualized total cost; This represents a set of candidate devices, such as photovoltaic equipment, wind power equipment, energy storage equipment, biogas or gas generator sets, etc. Indicates equipment Configuration capacity decision variables; Indicates equipment The unit capacity investment cost; Indicates the discount rate; Indicates the project's lifespan; This represents the capital recovery factor, used to convert one-time investment costs into annualized investment costs. Represents a set of time units with varying time resolution; Represents any one of these time units; Representing time units The actual duration; Representing time units Fuel costs within the country; Representing time units The cost of purchasing or selling electricity within the country; Representing time units The objective function described above allows the model to comprehensively balance equipment investment costs, fuel consumption costs, grid switching costs, and operation and maintenance costs.

[0066] Regarding constraints, due to different time units Because they have different time granularities, the system sets different forms of balance constraints for short-time and long-time granularity units. For short-time granularity units in the high-fluctuation range, due to their time step size... For smaller loads, the system focuses on applying transient power balance constraints to ensure that the equipment can respond to short-term, rapid fluctuations. For example, the power balance constraint can be expressed as: ; Among them, Gen represents a collection of power generation equipment, including distributed photovoltaic, wind power, biogas or gas generator sets, etc. Indicates power generation equipment In time unit Internal output power; Representing time units The internal grid connection point's switching power, when it takes a positive value, can indicate that electricity is purchased from the external grid, and when it takes a negative value, it can indicate that electricity is fed into the external grid. Representing time units The load power demand within; Indicates the energy storage system in time unit Internal charging power; Indicates the energy storage system in time unit The formula represents the power balance between the power output of the generator and the power exchanged with the grid to meet the electricity demand of the load and the charging and discharging demand of energy storage.

[0067] For long-granularity time units in low-fluctuation ranges, due to their time step size With a larger size, the system can avoid establishing fine-grained constraints for every short-term transient fluctuation, and instead impose integral energy balance constraints to reduce model dimensionality. For example, the energy balance constraint can be expressed as: ; in, Indicates power generation equipment In time unit The electrical energy generated internally; Representing time units Electrical energy exchanged through grid connection points; Representing time units The electrical energy consumed by the internal load; Representing time units The electrical energy absorbed by the internal energy storage system for charging; Representing time units The electrical energy released by the internal energy storage system. The above energy balance constraints are applicable to time intervals with small fluctuations, and can reduce the number of high-precision power balance constraints while ensuring the overall supply and demand relationship is met.

[0068] For an energy storage system (ESS), continuous state transition constraints for the state of charge (SOC) are also established. This is because each time unit... Actual duration Unlike other energy storage systems, the State of Charge (SOC) cannot be updated using a fixed time step; instead, it should be calculated based on the actual duration of the corresponding time unit. For example, the SOC state transition constraint can be expressed as: ; in, Representing time units The state of charge of the energy storage system at the end; This indicates the state of charge of the energy storage system at the end of the previous time unit; Indicates energy storage charging efficiency; Indicates the energy storage discharge efficiency; Representing time units Internal energy storage charging power; Representing time units Internal energy storage discharge power; Represents the decision variable for the energy storage capacity to be optimized; Representing time units The actual duration. Through this constraint, the model can accurately characterize the changes in the energy state of energy storage at different time granularities, avoiding the erroneous treatment of different time units such as 1 minute, 15 minutes or 1 hour as the same time step.

[0069] Meanwhile, to ensure the safe operation of the energy storage system, SOC boundary constraints are also applied: ; in, This indicates the minimum permissible state of charge for an energy storage system. This indicates the maximum permissible state of charge (SOC) of the energy storage system. This constraint is used to prevent the energy storage system from being overcharged or over-discharged, thereby ensuring the lifespan and operational safety of the energy storage device.

[0070] For adjustable generator sets, the system can also apply ramp constraints in high-fluctuation ranges or high-resolution time units to limit the variation in generator output between adjacent time units. For example, a generator ramp constraint can be expressed as: ; in, Indicates the generator set in time unit Internal output power; This indicates the output power of the generator set in the previous time unit; This indicates the maximum permissible uphill climbing rate of the generator set; This indicates the maximum permissible downhill / climbing rate of the generator set; Representing time units The actual duration. Since the climbing limit is multiplied by the time unit duration, therefore when... When the value is small, the generator set can tolerate smaller output variations between adjacent time units, allowing for a more accurate assessment of the unit's ability to track high-frequency fluctuations; when... When the output is large, a wider range of adjustments are allowed to adapt to the energy dispatching needs of long-term granular ranges.

[0071] Furthermore, the system can apply corresponding operational boundary constraints based on equipment type. For generator sets, constraints are applied for upper and lower output limits, start-stop mutual exclusion, minimum start-up time, minimum downtime, and number of actions. For energy storage systems, constraints are applied for upper and lower charging and discharging power limits, charging and discharging mutual exclusion constraints, and SOC upper and lower limit constraints. For grid connection points, constraints are applied for upper limits of purchased power, upper limits of grid-connected power, and interconnection capacity boundaries. For adjustable loads, constraints are applied for adjustable capacity, adjustment duration, and user comfort boundaries. For short-time granularity time units, the model focuses on constraining power balance, ramp-up capability, charging and discharging mutual exclusion, start-stop states, and number of actions to reflect the equipment's response to rapid fluctuations. For long-time granularity time units, the model focuses on constraining energy balance, SOC state transitions, interconnection capacity boundaries, and cycle boundaries to reduce model complexity and maintain long-term operational continuity.

[0072] After constructing the objective function, decision variables, and constraints, the system forms a unified solution model for planning, design, and operation. This model can be modeled as a mixed-integer programming model based on discrete variables such as equipment start-up / shutdown states and mutually exclusive charging / discharging states; after linearization of the cost function and equipment efficiency relationship, it can also be converted into a mixed-integer linear programming model and solved using a mathematical programming solver. This mathematical programming solver can employ branch and bound, cutting plane, or heuristic search methods to obtain the optimal solution or feasible near-optimal solution that satisfies the constraints.

[0073] After the solution is completed, the system outputs two types of results: the first type is the planning and design results, including photovoltaic capacity, wind power capacity, energy storage power capacity, energy storage capacity, biogas or gas generator capacity, and grid connection point power exchange boundaries; the second type is the baseline operation plan, including energy storage charging and discharging power, generator output, grid connection point power exchange, adjustable load adjustment, and related start-up and shutdown status within each time unit. Since the above baseline operation plan adopts the time structure of the variable time resolution input sequence, it is itself a variable resolution operation plan, that is, high fluctuation ranges correspond to denser operation setpoints, and low fluctuation ranges correspond to sparser operation setpoints.

[0074] In this embodiment, the time granularity information in the variable time resolution input sequence is directly passed to the unified solution model through the above method, so that the objective function, power balance constraint, energy balance constraint, energy storage SOC state transition constraint, and generator ramping constraint can all be adjusted according to the actual duration of different time units. The calculations are performed. This preserves the key dynamic characteristics of high-fluctuation periods while reducing the number of variables and constraints during low-fluctuation periods, avoiding the dimensionality curse caused by uniformly modeling all 8760 hours of the year with minute-level precision. This achieves a balance between the accuracy of planning and design, the executability of the operational plan, and the solvability of the optimization model.

[0075] In one feasible implementation, the generation of structured control command objects based on the aforementioned baseline operation plan, real-time status, equipment availability information, and online adjustment results includes: The structured control command object described above is encapsulated using a self-describing message format; Write the device object identifier, writable control semantic attribute label, target set value, unit, command generation time, planned execution time, effective time window, priority, source plan identifier, idempotency check field and expected readback attribute identifier into the above structured control command object; The structured control command object is sent to the edge semantic gateway so that the edge semantic gateway can query the semantic attribute-communication interface mapping table based on the device object identifier and the writable control semantic attribute tag.

[0076] For example, such as Figure 1 , Figure 2 and Figure 5 .in, Figure 1 This shows the position of this step in the overall method flow. Figure 2 The structural relationship between the edge computing device 102, the edge semantic gateway 103, the field terminal equipment, and the metering acquisition device 105 is shown. Figure 5 This further illustrates the complete process of structured control command objects, from upper-layer semantic representation to edge semantic gateway table lookup and transformation, and then to lower-layer communication message assembly and execution readback.

[0077] In this embodiment, the control instruction set generation module 202d in the edge computing device 102 can generate a structured control command object based on the baseline operation plan, real-time status, device availability information, and online adjustment results. This structured control command object can be encapsulated using XML, JSON, or an equivalent self-describing message format. As shown in the upper right part of the figure, the structured control command object can include a target device object identifier, writable control semantic attributes, target setpoint, priority, effective time window, and anti-duplication verification information. For example, the target device object identifier can be ESS_PCS_01, indicating that the target device is a certain energy storage converter; the writable control semantic attribute can be Active PowerSet Point, representing the active power setpoint; the target setpoint and priority can be 600.0kW and High; the effective time window and anti-duplication verification field can include TTL=500ms and Token=0xA1. Through the above encapsulation method, the control instructions do not directly reflect the underlying register address, but are expressed in the form of device objects and semantic attributes, thereby decoupling the upper-layer operation plan from the physical communication interfaces of different devices.

[0078] After the structured control command object is generated, the control instruction set generation module 202d sends it to the edge semantic gateway 103. Upon receiving the object, the edge semantic gateway 103 enters the "edge semantic gateway table lookup and parameter conversion engine" processing procedure shown in the diagram through the underlying control message parsing module 203b. Specifically, the edge semantic gateway 103 first parses JSON, XML, or equivalent self-descriptive objects, extracting the device object identifier and writable control semantic attribute tags. Then, using Device_ID and Attr as matching conditions, it queries the semantic attribute-communication interface mapping table in the standardized device instance library and mapping table storage area 203a. The query results may include parameters such as the target device's register address, communication protocol, function code, data type, scaling factor, and byte order. For example, for ActivePowerSetPoint of ESS_PCS_01, the mapping table may return register address 40102, communication protocol Modbus RTU, write function code 0x10, and scaling factor Scale=10.

[0079] After completing the table lookup, the edge semantic gateway 103 further performs parameter conversion. Since the target setpoint in the upper-layer structured control command object is typically expressed in engineering quantities, such as 600.0 kW, while the lower-layer device registers usually require integer or hexadecimal data, the system needs to perform conversion based on the scaling factor and data type. As shown in the figure, when the target value is 600.0 and the scaling factor is 10, the system can convert the engineering quantity 600.0 into the register write value 6000, and further convert it into the corresponding hexadecimal data 0x1770. Through this conversion process, it can be ensured that the upper-layer semantic setpoint is consistent with the actual recognizable data format of the lower-layer device.

[0080] Subsequently, the underlying control message parsing module 203b enters... Figure 5 The lower middle section shows the "lower-level heterogeneous communication message assembly and execution readback" stage. The system combines the register address, function code, communication address, data length, and converted hexadecimal data obtained from the lookup table to generate a low-level communication message executable by the target device. For example, for Modbus RTU devices, the communication message may include fields such as device address, function code, register address, written data, and checksum; for other types of devices, the message can be encapsulated according to the corresponding RS485, CAN, or Ethernet communication link requirements. Thus, the "energy storage active power setpoint 600.0kW" originally generated by the operation plan is converted into a specific physical communication message that the target energy storage device can recognize and execute.

[0081] Furthermore, after the message is sent, the system does not use "message sent" as the final success indicator. Instead, it reads the feedback register or status bit within the effective time window to confirm execution. As shown in the figure, after the communication message is sent and executed, the system triggers a readback mechanism within the effective time window to read the readback confirmation value or status bit of the target device and compares the readback value with the target set value. If the status bit displays normally, and the feedback set value is consistent with the target set value or within the allowable deviation range, a command execution success record is generated. If the readback result is inconsistent with the target set value, or the device does not return confirmation information within the effective time window, an execution exception record can be generated, such as device rejection, communication timeout, missing readback, or execution deviation exceeding the limit.

[0082] Through the above implementation method, the structured control command object maintains a unified and clear semantic expression at the upper layer, while the edge semantic gateway 103 completes the automatic table lookup, numerical scaling, hexadecimal conversion, communication message assembly, and execution readback confirmation of semantic attributes to physical communication interfaces. Thus, the baseline operating plan or online adjustment results output by the planning and design and online rolling optimization unit 202c do not need to directly concern themselves with the differences in register addresses, function codes, and byte order of equipment from different manufacturers. When field equipment is replaced, expanded, or the communication protocol changes, only the mapping relationship between the standardized equipment instance library and the mapping table storage area 203a needs to be maintained to continue using the upper-layer structured control command generation logic. Therefore, this embodiment can achieve a closed-loop conversion from "running setpoints" to "device-executable communication messages" and then to "execution result confirmation," improving the accuracy, scalability, and traceability of heterogeneous equipment control issuance.

[0083] In one feasible implementation, the above-mentioned parsing and conversion of the semantic attribute-communication interface mapping table into a communication message executable by the target device includes: Based on the device object identifier and the writable control semantic attribute tag, obtain the communication protocol, communication address, function code, register address, data type, scaling factor, byte order and writing method corresponding to the target device; The target setting value is formatted according to the scaling factor and data type mentioned above. When the target device is a Modbus type device, select either single register write message or multi register write message according to the register word length and write method corresponding to the target parameters. When the target device has remote-to-local switching requirements or write-before-enable requirements, control enable sub-commands and set value write sub-commands are generated in a preset order to form the underlying control message queue corresponding to the above communication message.

[0084] For example, such as Figure 5 As shown in this embodiment, the underlying control message parsing module 203b in the edge semantic gateway 103 can be used to perform table lookup and parameter conversion in conjunction with the standardized device instance library and mapping table storage area 203a.

[0085] In this embodiment, after the control instruction set generation module 202d in the edge computing device 102 generates a structured control command object, the structured control command object is sent to the edge semantic gateway 103. This object typically carries information such as a device object identifier, a writable control semantic attribute tag, a target setpoint, unit, planned execution time, effective time window, priority, idempotency check field, and expected readback attribute identifier. For example, the structured control command object may contain the target device object identifier "ESS_PCS_01", the writable control semantic attribute tag "ActivePowerSetPoint", the target setpoint "600.0kW", and the effective time window and anti-duplication check field. At this time, the object still belongs to the upper-layer semantic control command and cannot be directly executed by field devices such as energy storage converters, photovoltaic inverters, and charging pile controllers.

[0086] After receiving a structured control command object, the edge semantic gateway 103 first queries the semantic attribute-communication interface mapping table based on the device object identifier and writable control semantic attribute tag. This mapping table can be stored in the standardized device instance library and the mapping table storage area 203a, used to record the correspondence between upper-layer semantic attributes and lower-layer communication interfaces. By looking up the table, the edge semantic gateway 103 can obtain communication parameters such as the communication protocol, communication address, function code, register address, data type, scaling factor, byte order, and write method corresponding to the target device. For example, for the active power setpoint of a certain energy storage converter, the mapping table can return that its communication protocol is Modbus RTU, the communication address is the corresponding device station address, the function code is the multi-register write function code, the register address is the target power setting register, the data type is integer or floating-point, the scaling factor is 10, and it specifies whether big-endian or little-endian byte order is used.

[0087] After obtaining the aforementioned communication parameters, the underlying control message parsing module 203b further performs format conversion on the target setpoint based on the scaling factor and data type. Since the target setpoint in the upper-level structured control command object is typically expressed as an engineering quantity, such as 600.0kW, while field device registers usually need to receive integer, floating-point, or hexadecimal encoded values, it is necessary to convert the engineering quantity into a data format recognizable by the device. For example, when the target setpoint is 600.0kW and the scaling factor is 10, it can be converted into a register write value of 6000, and then converted into the corresponding hexadecimal data field according to the data type and byte order. If the target device requires a specific byte order, the system also needs to arrange the high and low bytes according to the byte order rules in the mapping table to avoid the device parsing incorrect setpoints due to incorrect byte order.

[0088] For Modbus-type devices, the edge semantic gateway 103 can also select between single-register write messages and multi-register write messages based on the register word length and write method corresponding to the target parameters. When the target parameter occupies only one register and the device supports single-register writing, a single-register write message can be generated; when the target parameter occupies multiple registers, such as 32-bit integers, floating-point numbers, or data structures containing multiple control fields, a multi-register write message can be generated. The message can include the device address, function code, register start address, number of registers to be written, data content, and checksum fields. For devices using RS485, CAN, Ethernet, or other communication methods, the converted data fields can also be encapsulated into corresponding underlying communication messages according to the relevant communication protocols.

[0089] Furthermore, for target devices requiring remote-to-local switching or pre-write enable, the system does not directly generate a single setpoint write message. Instead, it generates control enable sub-commands and setpoint write sub-commands in a preset order, forming a lower-level control message queue corresponding to the communication message. For example, some energy storage converters or generator sets need to confirm that the device is in remote control mode before receiving the power setpoint, or write the control enable word, unlock word, and mode switching word first; only when the control enable condition is met is writing the target power setpoint allowed. Therefore, the edge semantic gateway 103 can first generate a remote control switching sub-command or a pre-write enable sub-command according to the mapping table, then generate the target setpoint write sub-command, and arrange them in a lower-level control message queue in a preset order. This avoids the device refusing to execute the target setpoint due to control mode mismatch or unmet enable conditions.

[0090] Through the above implementation, the upper-layer planning and design and online rolling optimization unit 202c only needs to output operating setting values ​​with unified semantics, without needing to directly perceive differences in register addresses, function codes, scaling rules, and byte order of devices from different manufacturers; the edge semantic gateway 103 completes protocol adaptation, value conversion, and message assembly through a semantic attribute-communication interface mapping table. When field devices are replaced, expanded, or communication protocols change, typically only the communication interface parameters in the mapping table need to be updated, without modifying the upper-layer optimization model and control command generation logic, thereby improving the reliability, maintainability, and scalability of heterogeneous device access and control execution.

[0091] In one feasible implementation, the boundary validity check is performed before the communication message is sent, and a readback confirmation is performed after the message is sent, and a command execution result record is generated, including: Before the above communication messages are sent, the rated power boundary, energy storage state of charge boundary, current operating mode of the equipment, interlocking conditions, grid connection point switching power boundary and command priority conflict relationship are verified. After the verification is successful, the above communication message is sent to the target device, and the readback confirmation point or status point is read within the preset valid time window; Compare the consistency between the equipment's returned results, feedback settings, actual execution values, and target settings; Based on the consistency comparison results, a command execution result record associated with the above structured control command object is generated. The command execution result record includes at least the following: execution success, device rejection, communication timeout, readback missing, and execution deviation exceeding the limit. Feedback data with a unified time scale and whose data quality indicators meet preset conditions will be used to update short-term forecast deviations, equipment availability status, energy storage adjustability margin, and grid connection point power exchange status, so as to further optimize the equipment operation settings in subsequent rolling windows.

[0092] For example, such as Figure 6 and Figure 7 As shown, this embodiment is mainly used to solve the problem that although the structured control commands have been converted into executable communication messages for the target device, they may still be unexecutable or have execution risks under the conditions of the current state of the device, power boundary, energy storage state of charge, grid connection constraints or command priority. At the same time, it is used to feed back the effective feedback data after execution to the subsequent rolling optimization process.

[0093] Specifically, after the edge semantic gateway 103 generates a communication message based on the semantic attribute-communication interface mapping table, the system does not immediately send the communication message to the field terminal equipment and metering acquisition device 105. Instead, it first enters the pre-write boundary legality verification process shown in the figure. This verification process is equivalent to a security firewall set up before the physical issuance of control commands, used to comprehensively judge the target set value, priority, and planned execution time in the structured control command object. The system can sequentially perform rated power boundary verification, energy storage SOC boundary verification, equipment operating mode verification, physical interlocking condition verification, grid-connected switching power verification, and priority conflict verification. Through the above multi-level verification, the system can identify in advance whether the target set value exceeds the rated capacity of the equipment, whether the energy storage is in the allowed charging and discharging state of charge range, whether the equipment is in a remote control or executable state, whether the relevant contactors or protection interlocks meet the execution conditions, whether the grid-connected switching power will exceed the limit due to the command, and whether the command conflicts with higher priority protection commands, manual intervention commands, or existing scheduling commands before the communication message is actually written to the equipment register.

[0094] For example, when the target command is an energy storage discharge command, the system can first determine whether the discharge power exceeds the rated power of the energy storage converter, then determine whether the current SOC is higher than the allowable discharge lower limit, then determine whether the equipment is in remote control mode and not in a fault, interlocked, or maintenance state, and further determine whether the discharge action will cause the backfeed to the grid connection point to exceed the allowable boundary. If any check fails, the system will no longer send communication messages, but will transfer the command to the command execution result recording stage, forming a record containing the reason for failure. For example, if the target power exceeds the rated range, it can be recorded as "over-limit rejection"; if the equipment is in local control or maintenance state, it can be recorded as "mode error" or "equipment rejection"; if the interlocking condition is not met, it can be recorded as "interlock not met"; if it conflicts with a high-priority command, it can be recorded as "conflict prevention". The above records can be associated with and saved with the structured control command object so that maintenance personnel can trace the reason why the command was not executed.

[0095] After all pre-write boundary validity checks pass, the system enters the physical delivery and execution phase of the communication message. The edge semantic gateway 103, based on the aforementioned semantic mapping results, sends the underlying communication message corresponding to the target device to the target device via RS485, CAN, Ethernet, or other communication links. This communication message may include the device address, function code, register address, write data, and verification fields, and the validity period of this delivery can be determined based on the valid time window field in the structured control command object. If the communication message is successfully written to the target device within the valid time window, the system enters the post-write readback confirmation process; if the communication link does not respond within a preset time, a communication timeout execution result can be directly generated.

[0096] During the write-back confirmation phase, the system reads the target device's feedback setpoint, actual execution value, execution confirmation bit, or operating status according to the readback confirmation point or status point configured in the semantic attribute-communication interface mapping table. The system then compares the consistency between the device's returned result, the feedback setpoint, the actual execution value, and the target setpoint. If the device returns normally, the feedback setpoint matches the target setpoint, the actual execution value is within the allowable deviation range, and the status point indicates that the command has taken effect, a successful execution record is generated. If the device does not return confirmation information within the valid time window, a readback missing or communication timeout record is generated. If the device provides feedback but the actual execution value deviates from the target setpoint beyond the allowable range, an execution deviation exceeding the limit record is generated. If the device returns a rejection code, protection status, or fault status, a device rejection record is generated. Thus, the system can distinguish between "message has been sent," "message has been written," "device has responded," and "device has actually executed successfully," avoiding the misconception that successful communication transmission equates to successful control execution.

[0097] Furthermore, the command execution result records are not only used for log storage, but also serve as an important basis for subsequent control strategy adjustments and online rolling optimization. Each command execution result record can be associated with the structured control command object, target device object identifier, target setpoint, planned execution time, effective time window, readback confirmation point, device feedback value, and anomaly cause, thus forming a complete command closed-loop link. When a device repeatedly experiences device rejection, communication timeout, or execution deviation exceeding limits, the system can update the device's availability status accordingly and reduce its adjustability or temporarily exclude the device in subsequent rolling optimizations to avoid continuing to assign control tasks to unavailable devices.

[0098] Meanwhile, through unified time synchronization and data quality labeling logic, the field feedback data after command execution still needs to undergo unified time scale alignment and Quality Flag processing before entering the subsequent rolling optimization process. Specifically, the network time synchronization unit 104 can continue to perform time alignment on the feedback data of devices such as photovoltaic inverters, smart meters, and energy storage BMS based on the NTP time synchronization reference. The data acquisition and quality management unit 202a in the edge computing device 102 performs reasonable boundary checks and continuity checks on the returned data, and distinguishes between valid measured values, communication timeout values, interpolated values, estimated values, manually set values, time synchronization anomalies, and out-of-limit values ​​according to their quality status. For valid measured values, they can be directly imported into the available sequence; for missing values ​​caused by short-term communication interruptions, interpolated values ​​can be formed when conditions are met; for data calculated based on the model, they can be marked as estimated values; for time synchronization anomalies and out-of-limit values, they can be intercepted or isolated to prevent them from directly polluting the subsequent optimization model.

[0099] After time alignment and quality labeling, the feedback data can be cleaned and labeled to form a high-quality variable-resolution sample sequence, which is then fed into the online rolling optimization engine. The planning and design and online rolling optimization unit 202c can only call feedback data under a unified time scale and whose data quality flags meet preset conditions to update short-term prediction deviations, equipment availability status, energy storage adjustability margin, and grid connection point power exchange status. For example, when the execution result shows that the actual discharge power of energy storage is lower than the target set value, the system can combine the readback feedback value to correct the energy storage adjustability margin. When the actual exchange power of the grid connection point is close to the boundary, the system can adjust the set values ​​of energy storage, generator sets, and adjustable loads in the next rolling window. When a device experiences a communication timeout or timing anomaly, the system can reduce the credibility of the device's feedback data in the rolling optimization, or temporarily exclude it from the solution as an adjustable resource.

[0100] Therefore, the system can further optimize the device operation settings within the subsequent rolling window according to a preset rolling cycle, and generate a revised short-term operation plan. This short-term operation plan then enters the structured control command object generation, semantic attribute mapping, communication message conversion, pre-write boundary legality verification, and post-write readback confirmation process, thus forming a closed-loop control mechanism of "control command issuance - device execution feedback - data quality identification - status update - rolling re-optimization". Through the above implementation method, this embodiment can not only block control commands that exceed limits, conflict, or do not meet the execution conditions before the communication message is issued, but also determine whether the device has truly executed successfully through readback confirmation after issuance, and continuously revise the subsequent operation plan using feedback data that meets quality requirements, thereby improving the safety, reliability, and traceability of the operation control of the distributed integrated energy system.

[0101] In summary, this invention establishes a coherent technical link between planning and design, operational optimization, data governance, and on-site execution. In existing distributed integrated energy systems, offline planning and online optimization results typically remain at the level of mathematical variables, making it difficult to directly convert them into physical control commands that heterogeneous field devices can recognize and execute. This invention establishes a writable attribute description mechanism based on a general information model extension and constructs a mapping relationship between device semantic attributes and communication interfaces. This enables capacity variables, operating setpoints, and device objects in the model to form a regular correspondence with the register addresses, function codes, data types, scaling factors, byte order, and readback points of field devices. Consequently, the control setpoints obtained from planning or optimization can be automatically converted into structured communication messages executable on-site, reducing manual point configuration, script conversion, and hard-coding adaptation work, and improving the implementation efficiency when connecting, expanding, and replacing devices from multiple manufacturers and using multiple protocols.

[0102] This invention also effectively alleviates the contradiction between high-precision time-series modeling and the solvability of year-round optimization models. Addressing the problem that existing technologies using fixed low-resolution modeling tend to smooth out key dynamic features such as sudden photovoltaic drops, load surges, rapid charging and discharging of energy storage, and unit ramp-up, while using minute-level year-round modeling leads to a sharp increase in the number of variables and constraints, this invention uses variable-time-resolution aggregation based on time-series fluctuation characteristics. In high-fluctuation intervals, a shorter time granularity is retained to reflect short-term dynamics such as peaks, ramp-ups, and sudden changes in net load; in low-fluctuation intervals, a longer time granularity is used to compress continuous stable data segments, while retaining the actual duration and energy information of each aggregation period. Through this approach, the model can both retain key dynamic features affecting energy storage capacity, reserve demand, and generator unit regulation capabilities, and reduce the number of time units in the year-round optimization process, thereby improving the engineering feasibility of the planning solution.

[0103] This invention further improves the consistency between planning and design results and actual operational boundaries. By constructing a unified solution model for planning, design, and operation plans based on variable time resolution input sequences, it integrates distributed photovoltaic, wind power, energy storage, biogas or gas generator sets, grid connection point exchange power boundaries, and operational setpoints within each time unit into the same data foundation and constraint system for synchronous solution. This ensures that equipment capacity configuration, interconnection capacity boundaries, and baseline operation plans are mutually matched. Therefore, it avoids the problem of capacity configuration being out of sync with actual operational constraints caused by existing technologies that only perform capacity optimization during the planning phase and then separately schedule operations during the operation phase. It also reduces the assessment bias of energy storage power capacity, energy storage energy capacity, adjustable generator set ramp-up capability, and grid connection point power boundaries, thereby improving the executability of planning results and the reliability of engineering implementation.

[0104] Meanwhile, this invention enables the construction of a more reliable state awareness and closed-loop control system. Through a unified timing mechanism and data quality flag mechanism, the system can collect and identify state data from different devices under a unified time scale, distinguishing between measured values, timeout values, interpolated values, estimated values, manually set values, timing anomalies, and limit violations, thus preventing low-quality data from directly entering the prediction, optimization, and control processes. Furthermore, this invention, through structured control command objects, pre-write security verification, and post-write readback confirmation mechanisms, uniformly encapsulates and verifies the target object, time window, priority, unit, idempotency information, and execution feedback of control commands. It no longer simply uses successful communication transmission as the basis for successful device execution, but can identify situations such as device rejection, communication timeout, missing readback, excessive execution deviation, or local logic overwriting, thereby improving the reliability, fault tracing capability, and operational safety of the control closed loop.

[0105] Furthermore, this invention enhances the system's adaptability to on-site disturbances and changes in operating status. By performing online rolling corrections based solely on effective feedback data under a unified time scale, the system can continuously update short-term forecast results and equipment adjustability margins based on real-time load changes, renewable energy output deviations, equipment availability changes, and energy storage state of charge changes, and dynamically adjust the operating setpoints within subsequent time windows. Thus, the baseline operating plan obtained from offline planning is no longer a fixed, static result, but can be continuously corrected during actual operation based on on-site feedback, enabling the system to maintain continuous, stable, and executable coordinated control effects even under disturbances, forecast deviations, and changes in equipment status.

[0106] like Figure 8 As shown, this application proposes a distributed integrated energy variable resolution planning and control system 10, comprising: Construction unit 11 is used to establish standardized equipment instances of field terminal equipment and metering acquisition devices in the regional microgrid, define writable control semantic attributes, and construct a semantic attribute-communication interface mapping table between the above writable control semantic attributes and the communication interface. The acquisition unit 12 is used to acquire the operating data of the above-mentioned field terminal equipment and metering acquisition device through a unified time synchronization mechanism, and to add a unified timestamp and data quality mark to the above-mentioned operating data to form the original operating dataset under a unified time scale. The generation unit 13 is used to perform segmentation, fluctuation discrimination and multi-granularity aggregation on the above-mentioned original running dataset to generate a variable time resolution input sequence. Solver 14 is used to construct and solve a unified solution model for planning, design and operation based on the above variable time resolution input sequence, so as to obtain the planning and design results and the baseline operation plan. The conversion unit 15 is used to generate a structured control command object based on the above-mentioned baseline operation plan, real-time status, equipment availability information and online adjustment results, and to parse and convert it into a communication message that can be executed by the target device according to the above-mentioned semantic attribute-communication interface mapping table. The verification unit 16 is used to perform boundary legality verification before the above communication message is sent, perform readback confirmation after the message is sent, and generate a command execution result record. The optimization unit 17 is used to further optimize the equipment operation settings in the subsequent scrolling window based on feedback data under a unified time scale and where the data quality flags meet preset conditions, and to generate a corrected short-term operation plan.

[0107] Understandably, the system proposed in the second aspect is capable of performing any of the methods described in the first aspect.

[0108] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A distributed integrated energy variable resolution planning and control method, characterized in that, include: Establish standardized equipment instances for field terminal equipment and metering acquisition devices within a regional microgrid, define writable control semantic attributes, and construct a semantic attribute-communication interface mapping table between the writable control semantic attributes and the communication interface. The operation data of the field terminal equipment and metering acquisition device are collected through a unified time synchronization mechanism, and a unified timestamp and data quality flag are added to the operation data to form the original operation dataset under a unified time scale. The original running dataset is segmented, fluctuation is determined, and multi-granularity aggregation is performed to generate a variable time resolution input sequence; Based on the variable time resolution input sequence, a unified solution model for planning, design and operation is constructed and solved to obtain the planning and design results and the baseline operation plan. Based on the baseline operation plan, real-time status, equipment availability information, and online adjustment results, a structured control command object is generated, and then parsed and converted into a communication message executable by the target device according to the semantic attribute-communication interface mapping table. Before the communication message is sent, a boundary validity check is performed; after the message is sent, a readback confirmation is performed, and a command execution result record is generated. Based on feedback data with a unified time scale and data quality indicators meeting preset conditions, the device operation settings in the subsequent scrolling window are further optimized to generate a revised short-term operation plan.

2. The distributed integrated energy variable resolution planning and control method according to claim 1, characterized in that, The establishment of standardized equipment instances for field terminal equipment and metering acquisition devices within a regional microgrid defines writable control semantic attributes and constructs a semantic attribute-communication interface mapping table between the writable control semantic attributes and the communication interface, including: Based on the general information model extension rules, corresponding standardized equipment instances are established for distributed photovoltaic inverters, wind power inverters, energy storage converters, electric vehicle charging facility controllers, biogas or gas generator control units, adjustable load controllers, smart meters, environmental acquisition devices, and grid connection point metering devices. Configure each of the standardized equipment instances with object identifier, equipment type, rated capacity, readable attributes, writable control semantic attributes, unit, range boundary, control mode, status enumeration, and execution confirmation attributes; Establish a correspondence between the writable control semantic attributes and the communication protocol type, station address or IP address, function code, register address, data type, scaling factor, byte order, pre-write enable condition, effective time window, readback confirmation point, and exception return code interpretation rules to form the semantic attribute-communication interface mapping table.

3. The distributed integrated energy variable resolution planning and control method according to claim 1, characterized in that, The process involves collecting operational data from the field terminal equipment and metering acquisition device through a unified time synchronization mechanism, and attaching a unified timestamp and data quality flag to the operational data to form an original operational dataset under a unified time scale, including: Clock synchronization is performed on the energy management master station, edge computing device, edge semantic gateway and time-synchronizable field terminal equipment based on the Network Time Protocol. The operating data of the field terminal equipment and metering acquisition device are collected according to a preset sampling period or event triggering method. The operating data includes active power, reactive power, voltage, current, energy storage state of charge, switch status, grid connection point switching power, environmental parameters and equipment fault status. The collected operational data is written with a unified timestamp and a quality flag is attached. The data quality flag at least distinguishes between valid measured values, communication timeout values, interpolated values, estimated values, manually set values, time synchronization anomalies, and out-of-limit values.

4. The distributed integrated energy variable resolution planning and control method according to claim 1, characterized in that, The step of segmenting, fluctuation discrimination, and multi-granularity aggregation of the original running dataset to generate a variable time resolution input sequence includes: Extract source-side power, load-side power, grid-connected point switching power, and environmental sequence from the original operational dataset, and divide the continuous time axis into multiple basic time windows; Fluctuation judgment is performed on the net load changes, distributed power output changes, or energy storage charging and discharging changes within each of the aforementioned basic time windows; The basic time window is divided into a high-fluctuation interval, a medium-fluctuation interval, and a low-fluctuation interval based on the first threshold and the second threshold. The basic time resolution data is retained for the high fluctuation range, the first aggregation granularity processing is performed for the medium fluctuation range, and the second aggregation granularity processing is performed for the low fluctuation range to obtain the variable time resolution input sequence.

5. The distributed integrated energy variable resolution planning and control method according to claim 4, characterized in that, Each time interval in the variable time resolution input sequence retains the corresponding time period length identifier, representative value, cumulative energy value, and source data start and end time information. Furthermore, each time interval in the variable time resolution input sequence corresponds to the corresponding time unit in the unified solution model for planning, design, and operation.

6. The distributed integrated energy variable resolution planning and control method according to claim 1, characterized in that, The process of constructing and solving a unified solution model for planning, design, and operation based on the variable time resolution input sequence to obtain planning and design results and a baseline operation plan includes: By combining equipment investment parameters, operation and maintenance parameters, electricity price parameters, fuel cost parameters, grid connection constraint parameters, and equipment efficiency parameters, a unified solution model for the planning, design, and operation plan is constructed. The decision-making objects include distributed photovoltaic capacity, wind power capacity, energy storage power capacity, energy storage capacity, biogas or gas generator capacity, grid connection point power exchange boundary, and equipment operation setpoints within each time unit. The time units in the unified solution model for planning, design and operation are made to correspond one-to-one with the time intervals in the variable time resolution input sequence, and the duration of the time unit is made consistent with the time period granularity of the corresponding time interval. Power balance constraints, ramping constraints, charge / discharge mutual exclusion constraints, start / stop constraints, or action count constraints are applied to short-time granular time units, while energy balance constraints, state transition constraints, connection capacity boundary constraints, and periodic boundary constraints are applied to long-time granular time units.

7. The distributed integrated energy variable resolution planning and control method according to claim 1, characterized in that, The generation of structured control command objects based on the baseline operation plan, real-time status, equipment availability information, and online adjustment results includes: The structured control command object is encapsulated using a self-describing message format; Write the device object identifier, writable control semantic attribute label, target set value, unit, command generation time, planned execution time, effective time window, priority, source plan identifier, idempotency check field and expected readback attribute identifier into the structured control command object; The structured control command object is sent to the edge semantic gateway, so that the edge semantic gateway queries the semantic attribute-communication interface mapping table based on the device object identifier and the writable control semantic attribute tag.

8. The distributed integrated energy variable resolution planning and control method according to claim 7, characterized in that, The process of parsing and converting the semantic attribute-communication interface mapping table into a communication message executable by the target device includes: Based on the device object identifier and the writable control semantic attribute tag, obtain the communication protocol, communication address, function code, register address, data type, scaling factor, byte order and writing method corresponding to the target device; The target setting value is formatted according to the scaling factor and the data type; When the target device is a Modbus type device, select a single register write message or a multi-register write message according to the register word length and write method corresponding to the target parameters. When the target device has remote-to-local switching requirements or write-before-enable requirements, control enable sub-commands and set value write sub-commands are generated in a preset order to form the underlying control message queue corresponding to the communication message.

9. The distributed integrated energy variable resolution planning and control method according to claim 1, characterized in that, The process of performing boundary validity verification before sending the communication message, performing readback confirmation after sending, and generating a command execution result record includes: Before the communication message is sent, the rated power boundary, energy storage state of charge boundary, current operating mode of the equipment, interlocking conditions, grid connection point switching power boundary and command priority conflict relationship are verified. After the verification is successful, the communication message is sent to the target device, and the readback confirmation point or status point is read within the preset valid time window; Compare the consistency between the equipment's returned results, feedback settings, actual execution values, and target settings; Based on the consistency comparison results, a command execution result record associated with the structured control command object is generated, wherein the command execution result record includes at least execution success, device rejection, communication timeout, readback missing, and execution deviation exceeding the limit; Feedback data with a unified time scale and whose data quality indicators meet preset conditions will be used to update short-term forecast deviations, equipment availability status, energy storage adjustability margin, and grid connection point power exchange status, so as to further optimize the equipment operation settings in subsequent rolling windows.

10. A distributed integrated energy variable resolution planning and control system, characterized in that, include: The construction unit is used to establish standardized equipment instances of field terminal equipment and metering acquisition devices in the regional microgrid, define writable control semantic attributes, and construct a semantic attribute-communication interface mapping table between the writable control semantic attributes and the communication interface. The data acquisition unit is used to acquire the operating data of the field terminal equipment and metering acquisition device through a unified time synchronization mechanism, and to attach a unified timestamp and data quality flag to the operating data to form the original operating dataset under a unified time scale. The generation unit is used to perform segmentation, fluctuation discrimination, and multi-granularity aggregation on the original running dataset to generate a variable time resolution input sequence. The solution unit is used to construct and solve a unified solution model for planning, design and operation based on the variable time resolution input sequence, so as to obtain the planning and design results and the baseline operation plan. The conversion unit is used to generate structured control command objects based on the baseline operation plan, real-time status, equipment availability information and online adjustment results, and to parse and convert them into communication messages that can be executed by the target device according to the semantic attribute-communication interface mapping table. The verification unit is used to perform boundary legality verification before the communication message is sent, perform readback confirmation after the message is sent, and generate a command execution result record. The optimization unit is used to further optimize the equipment operation settings in the subsequent scrolling window based on feedback data under a unified time scale and whose data quality indicators meet preset conditions, and to generate a revised short-term operation plan.

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