Multi-scene self-adaptive micro-distribution collaborative planning method and multi-scene self-adaptive micro-distribution collaborative planning system

Abstract

The invention discloses a multi-scene self-adaptive power distribution and micro-grid collaborative planning method and system, and the method comprises the steps: carrying out the unified processing of the data of a power distribution network and a micro-grid, and generating a typical scene list; analyzing the output of the distributed power supply, carrying out space-time coupling on the output of the distributed power supply, an adjustable load and stored energy, and superposing a grid constraint to form a scene constraint parameter; defining an equipment list and an access point, completing access check and generating time sequence simulation input; and finally, running multi-scene time sequence simulation, screening schemes according to voltage out-of-limit, line load rate and abandoned electric quantity indexes, and outputting a planning result through parameter backfilling and closed-loop updating. According to the method, the data aperture is unified, the constraint consistency is improved, the power abandoning risk is remarkably reduced, the accuracy of equipment configuration and grid-connected consumption is improved, increment recalculation and closed-loop iteration are supported, and the method is suitable for distribution and micro collaborative planning of multiple operation modes.

Description

Technical Field

[0001] This invention belongs to the field of matching micro-cooperative planning technology, and particularly relates to a multi-scenario adaptive matching micro-cooperative planning method and system. Background Technology

[0002] In the field of power supply and distribution technology, existing schemes for collaborative planning of distribution networks and microgrids typically involve offline analysis based on historical operating data and equipment ledgers. They rely on typical scenario assumptions and static boundaries for device selection and time-series simulation. However, these schemes suffer from limitations such as difficulty in unifying the data definitions of distribution networks and microgrids, difficulty in stably reusing typical scenario list structures and scenario constraint parameter structures across grids, and difficulty in forming a continuous mapping between equipment configuration variable sets and time-series verification input structures across multiple scenarios. Existing methods often rely on empirical rules to decompose scenarios and perform single verifications. This approach is frequently implemented when grid boundary identification is incomplete or distributed power source type analysis and historical output extraction are insufficient. In scenarios with both line load rate and node voltage constraints, and frequent changes in interconnection interface status, fluctuations in voltage limit exceedance and power curtailment indices are common, and unclear coupling between the candidate equipment configuration set and operating mode can lead to difficulties in achieving a stable planning result structure. For the joint processing of existing datasets, typical scenario list structures, and scenario constraint parameter structures, existing technologies generally suffer from fragmentation in areas such as field dictionary unification, time alignment, scenario coupling, network structure constraint overlay, and parameter backfilling. This makes it difficult to form a consistent process of acquisition-alignment-judgment-control-recording in application scenarios with multi-scenario operation and closed-loop revision. As a result, the definitions of equipment type lists and access point lists are disconnected from network structure access verification, the time-series verification input structure has a weak closed loop with the scenario operation record set, and the configuration parameter revision package does not provide sufficient support for the planning result structure. Summary of the Invention

[0003] This invention provides a multi-scenario adaptive micro-collaborative planning method and system to solve the technical problems of disconnect between the definition of the equipment type list and access point list and the network access verification, weak closed loop between the timing verification input structure and the scenario operation record set, and insufficient support of the configuration parameter revision package for the planning result structure.

[0004] In a first aspect, the present invention provides a multi-scenario adaptive micro-collaborative planning method, comprising: Acquire distribution network data and microgrid data, and perform time base correction, time zone normalization and event segmentation processing on the distribution network data and the microgrid data to generate a typical scenario list structure; Based on the typical scenario list structure, distributed power source type parsing and historical output extraction are performed, and access point location and time window intersection processing are performed in combination with adjustable load and energy storage ledger to generate a scenario constraint parameter structure superimposed with node voltage, line current, sectional switch operability and interconnection interface capacity. Based on the scenario constraint parameter structure, the equipment type list and access point list are defined, the network access verification is implemented to form a feasible capacity range, and the timing simulation input orchestration and load complementarity strategy serialization processing are performed to generate the timing verification input structure. Based on the aforementioned timing verification input structure, multi-scenario timing simulation is performed. Schemes are selected based on voltage over-limit, line load rate, and abandoned power indicators. Parameters for distributed power supply capacity, energy storage power, grid connection switch type, and measurement and control communication capability are backfilled to generate a configuration parameter revision package. After closed-loop update, the planning result is obtained.

[0005] Secondly, the present invention provides a multi-scenario adaptive micro-collaborative planning system, comprising: The acquisition module is configured to acquire distribution network data and microgrid data, and perform time base correction, time zone normalization and event segmentation processing on the distribution network data and the microgrid data to generate a typical scenario list structure. The generation module is configured to perform distributed power source type parsing and historical output extraction based on the typical scenario list structure, and combine adjustable load and energy storage ledger to perform access point location and time window intersection processing to generate a scenario constraint parameter structure superimposed with node voltage, line current, sectional switch operability and interconnection interface capacity. The processing module is configured to define the device type list and access point list based on the scenario constraint parameter structure, perform network access verification to form a capacity feasible range, and perform time-series simulation input orchestration and load complementarity strategy serialization processing to generate a time-series verification input structure. The update module is configured to perform multi-scenario time-series simulation based on the aforementioned time-series verification input structure. It selects schemes based on voltage over-limit, line load rate, and abandoned power indicators, and backfills parameters for distributed power capacity, energy storage power, grid connection switch type, and measurement and control communication capability to generate a configuration parameter revision package. After closed-loop update, the planning result is obtained.

[0006] Thirdly, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the multi-scenario adaptive micro-cooperative planning method of any embodiment of the present invention.

[0007] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the steps of the multi-scenario adaptive micro-cooperative planning method of any embodiment of the present invention.

[0008] This application presents a multi-scenario adaptive distribution and microgrid collaborative planning method and system. By constructing a full-process method from data standardization, scenario-based constraint generation, equipment configuration verification to time-series simulation closed-loop optimization, it achieves a significant improvement in the accuracy and efficiency of collaborative planning between distribution networks and microgrids in multiple scenarios. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 A flowchart illustrating a multi-scenario adaptive micro-cooperative planning method provided in an embodiment of the present invention; Figure 2 This is a structural block diagram of a multi-scenario adaptive micro-collaborative planning system provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0012] Please see Figure 1 The flowchart of a multi-scenario adaptive micro-cooperative planning method of this application is shown.

[0013] like Figure 1 As shown, the multi-scenario adaptive micro-collaborative planning method specifically includes the following steps: Step S101: Obtain distribution network data and microgrid data, and perform time base correction, time zone normalization and event segmentation processing on the distribution network data and the microgrid data to generate a typical scenario list structure.

[0014] In this step, distribution network data and microgrid data are acquired, and time base correction based on unified clock service is performed, cross-system time zone normalization is performed, sampling step size resampling and missing intervals are marked but not valued, event window truncation and operation mode label segmentation are performed to generate a typical scenario list structure containing unified time zone and scale, missing and event labels.

[0015] Distribution network data includes feeder profiles, transformer substation profiles, node topology, line parameters, transformer parameters, protection configurations, switch status, online monitoring records, fault waveform summaries, operation and maintenance records, and asset code mapping. Microgrid data includes distributed power source profiles, energy storage device profiles, interface protection settings, micro-source operation records, power and voltage / current curves, energy management strategy records, and local load details.

[0016] Step S102: Based on the typical scenario list structure, perform distributed power source type parsing and historical output extraction, and combine adjustable load and energy storage ledger to perform access point location and time window intersection processing to generate a scenario constraint parameter structure superimposed with node voltage, line current, sectional switch operability and interconnection interface capacity.

[0017] In this step, based on the typical scenario list structure, distributed power source type parsing and historical output extraction are performed. Available time periods are extracted according to data quality and maintenance and fault marking, and upper and lower capacity boundaries, source descriptions, and fluctuation level labels are given. Access point location and time window intersection processing with adjustable load and energy storage ledger are performed to generate a scenario constraint parameter structure with superimposed node voltage, line current, sectional switch operability, and interconnection interface capacity.

[0018] Step S103: Based on the scenario constraint parameter structure, define the equipment type list and access point list, perform network access verification to form a feasible capacity range, and perform timing simulation input orchestration and load complementarity strategy serialization processing to generate a timing verification input structure.

[0019] In this step, the scenario constraint parameter structure is obtained, the equipment type list and access point list are defined, and the grid access is verified according to four criteria: node voltage, line current, sectionalizing switch operability, and interconnection interface capacity. The upper and lower bounds of the capacity are then defined as feasible ranges and reasons for non-selection. The timing simulation input orchestration and load complementarity strategy serialization processing are performed under a unified time scale to generate the timing verification input structure.

[0020] It should be noted that the operation mode and time window set are obtained from the scenario constraint parameter structure, and the equipment type spectrum is filtered by voltage level, short circuit capability, protocol and time synchronization capability. The scenario-access point mapping index is established and the device type list and access point list are defined by writing mode tags for isolated network and power limiting mode. The non-useful communication dependency level and unavailability description are written into the access point record to obtain the set of device configuration variables. Key configuration items and access points are extracted from the equipment configuration variable set. The grid access is checked line by line according to four criteria: node voltage, line current, interconnection interface capacity, and switch operation limit. Based on the superposition of power supply, energy storage boundary and line current carrying capacity, upper, middle, lower or power feasible ranges are formed and capacity boundary determination is retained for reasons of non-selection, generating a set of equipment configuration candidates. Candidate entries are obtained from the equipment configuration candidate set. The timing simulation input is arranged by constructing simulation units according to time windows, reusing a unified time scale and only slicing and referencing historical curves without modifying them, and removing those that do not meet the phase balance / short circuit capability and marking non-useful external coordination prompts. The load complementarity strategy is generated by serializing interruptible, peak shifting, energy storage charging and discharging, and grid-connected switching operations within a single time window and recording the minimum duration and recovery time. The timing verification input structure is generated.

[0021] The process of obtaining the set of operating modes and time windows from the scenario constraint parameter structure also includes: in the key configuration item extraction stage, locating the equipment categories that have actual adjustment value and change necessity within the scenario time window, and maintaining the correspondence between mode labels and priority labels.

[0022] Key configuration items include distributed power supply capacity level, energy storage rated capacity level and power level, grid connection switch type level, measurement and control terminal sampling and control capability level, protection device configuration level and metering device accuracy level.

[0023] Step S104: Based on the timing verification input structure, perform multi-scenario timing simulation, screen schemes according to voltage over-limit, line load rate and abandoned power indicators, and backfill parameters of distributed power capacity, energy storage power, grid connection switch type and measurement and control communication capability to generate configuration parameter revision package, and obtain planning results after closed-loop update.

[0024] In this step, based on the aforementioned timing verification input structure, multi-scenario timing simulation processing is performed to obtain a scenario running record set; Voltage over-limit indicators, line load rate indicators, and abandoned power indicators are extracted from the scenario operation record set, and scheme screening and parameter backfilling are performed to generate the configuration parameter revision package. The configuration parameter revision package is updated in a closed loop and the scenario recalculation is triggered to generate the planning result.

[0025] In summary, the method presented in this application significantly improves the accuracy and efficiency of collaborative planning for distribution networks and microgrids across multiple scenarios by constructing a comprehensive approach encompassing data standardization, scenario-based constraint generation, equipment configuration verification, and time-series simulation closed-loop optimization. First, a consistent data standard system is established through a unified field dictionary and time alignment processing, fundamentally eliminating the discrepancies in data interpretation caused by heterogeneous multi-source data and providing a reliable data foundation for accurate planning. Second, it innovatively couples the output potential of distributed power sources with demand-side response capabilities within a unified time window and pre-adds real-time grid constraints such as node voltage and line current, ensuring high electrical feasibility of the planning scheme from the outset. This effectively avoids repeated subsequent verifications and enables adaptive operation under various modes, including grid-connected, islanded, and power-limited systems. Ultimately, a complete closed-loop chain was formed, from simulation operation, indicator selection, parameter backfilling to incremental recalculation. This enabled the planning results to be continuously iterated and optimized based on simulation feedback, significantly reducing the risk of power curtailment and voltage over-limit probability in the planning scheme, improving the accuracy of equipment configuration and grid connection, and providing a traceable, iterative, and adaptive efficient solution for distribution and micro-distribution collaborative planning in complex scenarios.

[0026] In one specific embodiment, a flowchart of a multi-scenario adaptive micro-collaborative planning method is shown, which may include at least steps S100-S400: Step S100: Obtain distribution network data and microgrid data, perform time base correction based on unified clock service, cross-system time zone normalization, sampling step size resampling and missing interval only marking without valuation, event window truncation and operation mode label segmentation processing, and generate a typical scenario list structure containing unified time zone and scale, missing and event labels; Step S200: Based on the typical scenario list structure, perform distributed power source type parsing and historical output extraction, extract available time periods according to data quality and maintenance, fault marking and give capacity upper and lower boundaries and source descriptions, fluctuation level labels, perform access point location and time window intersection processing with adjustable load and energy storage ledger, and generate a scenario constraint parameter structure with superimposed node voltage, line current, sectional switch operability and interconnection interface capacity. Step S300: Obtain the scenario constraint parameter structure, define the equipment type list and access point list, implement the grid access verification according to four criteria: node voltage, line current, sectional switch operability, and interconnection interface capacity, and form the upper and lower bounds of the capacity feasible range and the reasons for not being selectable. Perform time-series simulation input orchestration and load complementarity strategy serialization processing under a unified time scale to generate the time-series verification input structure. Step S400: Based on the time-series verification input structure, perform multi-scenario time-series simulation, screen schemes according to voltage over-limit, line load rate and abandoned power indicators, and backfill the recommended and disabled interval parameters of distributed power capacity, energy storage power, grid connection switch type and measurement and control communication capability, generate configuration parameter revision package containing entry source and binding index, and mark only the entries that need external coordination to obtain the planning result structure.

[0027] Step S100 includes at least steps S110-S130: Step S110: Obtain distribution network data and microgrid data, perform field dictionary unification and time alignment processing to obtain the current status dataset; Specifically, the data generated during the project preparation phase at the distribution automation master station, substation monitoring terminal, super substation control system, and microgrid local controller are used as inputs. The distribution network data is structured operational and equipment information covering both the main grid and distribution sides, including feeder files, transformer area files, node topology, line parameters, transformer parameters, protection configurations, switch status, online monitoring records, fault waveform summaries, maintenance records, and asset code mappings. The microgrid data is a local data set within the microgrid, including distributed power source files, energy storage device files, interface protection settings, micro-source operation records, power and voltage / current curves, energy management strategy records, and local load details. For the aforementioned multi-source heterogeneous inputs, the data access layer's acquisition and adaptation process is executed. The field communication protocol adapter is invoked to complete protocol parsing, registering data source identifiers, acquisition frequencies, and security credentials with the gateways of various manufacturers' equipment. Breakpoint resume and data integrity checks are triggered, and connection events and missing segment intervals are recorded, forming a raw aggregated stream that can be consumed by subsequent field processing modules.

[0028] Furthermore, to address the field differences between the distribution network data and microgrid data, a unified field dictionary processing flow is established. This unified field dictionary refers to mapping fields expressing the same meaning from different systems, manufacturers, and versions to a unified standard field set, a unified unit system, and a unified value domain. The field mapping rule management component is run, loading preset terminology entries, unit conversion rules, and value specifications. For equipment file objects, standardization is achieved for name, model, capacity, rated parameters, installation location, asset code, voltage level, and connection relationship. For operation record objects, standardization is achieved for units, sampling period, time zone, data precision, and missing codes. For event objects, standardization is achieved for event type, trigger source, duration interval, level, and recovery flag. For ambiguous, duplicate, and conflicting fields that appear during the field dictionary unification process, a manual verification task is triggered, generating a verification list and handling suggestions. Simultaneously, a comparison record of the original field values ​​and the converted field values ​​is retained, forming a unified field dictionary log for easy retrospective review in subsequent scenario verification and compliance audits.

[0029] Understandably, after unifying the field dictionary, time alignment processing is performed to address the time dimension differences between distribution network data and microgrid data. This time alignment processing includes time base correction, cross-system time zone normalization, sampling step size resampling, intermittent missing data interpolation marking, and event window truncation. A reference time series provided by a unified clock service is loaded to correct timestamp offsets and daylight saving time settings from different systems. For data with different sampling granularities (second-level, high-frequency, and minute-level), resampling and aggregation are performed according to the target time series step size to generate a consistent time scale. Missing data intervals are marked with interpolation and data quality level codes. No value estimation is performed; only the missing time range and corresponding equipment identifier are recorded to avoid affecting subsequent typical day selection and constraint generation. For data during emergencies, event window truncation is performed to generate operational segments with start and end marks. For outage intervals covered by maintenance plans, they are marked as non-representative periods and prohibited from entering the typical day candidate set. The aforementioned time alignment process, when processing high-frequency voltage and current curves, also requires phase correspondence and sampling boundary clipping for records from different phases and sampling synchronization levels to ensure that three-phase quantities are stored side-by-side at the same time scale. When processing microgrid local controller records, it is necessary to add operating mode labels to segments based on the microgrid's time-dependent islanded and grid-connected mode switching records for subsequent scenario integration. After all processing is completed, a current status dataset is output. This dataset includes a standard field set, unified unit system, unified time zone, unified time scale, missing interval markers, event window segments, and operating mode labels, and is registered in the data directory as input that can be directly consumed by the current status dataset in step S120. Thus, step S110 clarifies the input source, processing link, abnormal records, and output products, and the output field name is "current status dataset," which is subsequently called by the current status dataset in step S120. At the cross-main step level, the current status dataset serves as one of the basic data sources for step S200, providing a consistent foundation for power output and demand-side capacity assessment.

[0030] Step S120: Extract grid division information, electricity consumption curves, and equipment ledgers from the current dataset, perform grid boundary identification and typical day screening, and generate a preliminary draft of the scene boundary. Specifically, using the current dataset output in step S110 as the sole input, the spatial elements of the geographic information system and the topology of the distribution network nodes are first loaded into the grid partitioning information extraction process. The affiliation between transformer substations and feeders, switch locations and connection point locations, transferability relationships, and operating mode constraints are analyzed to decompose a candidate set of grid units. The grid partitioning information includes grid unit identifiers, boundary node sets, a list of included equipment, a set of connected energy users, and a description of the connection interfaces with adjacent grids. A topology verification subprocess is run to check the closure and uniqueness of the boundary node sets for feeder open-loop or closed-loop operation modes. If inconsistencies are found between historical records and online status, a status verification task is triggered, recording the anomaly number and its impact range. In this round of grid identification, a strategy based on online status is used to write temporary boundaries. Subsequently, the equipment ledger verification process is initiated, reading the equipment ledger from the current dataset and verifying the consistency of equipment status, rated capacity, protection settings, and access nodes. When duplicate asset codes or missing capacity fields are found, they are recorded and marked as items requiring supplementation in the equipment ledger verification log, without interrupting subsequent electricity consumption curve extraction.

[0031] In the electricity consumption curve processing stage, periodic load curves, time-of-use power curves, node voltage curves, and power factor curves are extracted from the current dataset for each grid cell's user set and key node set, and holiday, weather category, and maintenance markers are added. A consistency check of the operating curves is performed, removing abnormal peaks and zero-value segments caused by obvious transmission gaps, while retaining the marked missing interval information without numerical padding, ensuring that subsequent typical day screening is based solely on real, usable samples. For load curves within the microgrid, classification labels derived from electric loads, production loads, and peak-shifting loads are further decomposed, based on joint discrimination from the microgrid local controller and user-side metering data, and the classification labels are written into the curve metadata. Simultaneously, timestamps of energy storage device charging / discharging events and distributed power source start / stop events from the equipment ledger are loaded, marking the corresponding self-consumption and grid-connected interaction status on the electricity consumption curves, forming operating mode characteristics that can be used for typical day screening.

[0032] After completing grid boundary identification and curve labeling, the typical day screening process begins. This screening covers year-round or multi-seasonal data, grouping curve samples based on weekdays, weekends, and holidays, combined with meteorological categories, energy consumption structures, production shifts, school opening and closing times, and peak tourist seasons. For each group, coverage and missing percentage checks are performed. If the missing percentage exceeds a preset threshold, the group is marked as non-representative, retaining its information but not participating in the typical day candidate pool. For groups entering the candidate pool, several representative dates are selected from the high-quality samples of segmented power curves and node voltage curves as candidate dates, along with associated operating mode, maintenance plan, and major event markers. In grid units with isolated microgrid operation, typical day candidates should cover both grid-connected and isolated operation modes; in grid units with strong renewable energy penetration, typical day candidates should cover multiple combinations of high, medium, and low output. If there are timestamp alignment residuals between candidate dates from different data sources, a candidate correction procedure is triggered, backtracking to the time alignment log of step S110. The corresponding samples are then processed for consistency before returning to the filtering process. After the process is completed, a preliminary draft of the scenario boundary is generated. This draft includes a set of grid cells, a set of boundary nodes, a description of communication interfaces, a list of typical day candidates, operation mode markers, and a list of key loads and key nodes. This output is registered as the output field name "Preliminary Draft of Scenario Boundary," which is directly consumed by the preliminary draft of the scenario boundary in step S130. Simultaneously, at the cross-main step level, the preliminary draft of the scenario boundary defines the space and operation mode for the resource potential and response capability assessment in subsequent step S200, and provides a boundary frame for the device configuration range and access point list in step S300.

[0033] Step S130: Integrate and number the initial draft of the scene boundaries to generate a typical scene list structure; Specifically, taking the initial draft of the scenario boundary output in step S120 as input, the first step in the scenario integration process is to construct operable scenario units based on the grid unit set and the communication interface description. Grid units with similar electrical coupling and operating modes are merged to form scenario units, while grid units with significantly different operating modes or constraints are separated into independent scenario units. During scenario integration, a typical day candidate list and operating mode markers are used to bind a set of candidate dates and corresponding grid-connected, isolated, or power-limiting constraint tags to each scenario unit. In areas with cross-feeder communication, merging is prioritized according to the commonly used communication status under the operating mode. If the communication point is in an infrequently used state, it is marked as a spare combination in the scenario integration record and is not included in the basic scenario. The above processing also synchronizes the constraints on key equipment in the equipment ledger, including interface protection settings, sectional switch operability, voltage limiting strategies, and current limiting strategies, so that the scenario units have complete boundaries and control prerequisites.

[0034] Further, the process proceeds to the scenario numbering stage. The scenario number serves as a unique identifier throughout the project lifecycle, employing a segmented coding rule. The coding segment includes a region segment, a grid segment, an operation mode segment, a typical day segment, and a version segment. The region segment corresponds to an administrative or scheduling division, the grid segment to a set of grid units, the operation mode segment to grid connection, isolated grid, or power limitation, the typical day segment to a set of candidate dates, and the version segment to maintain historical traceability after scenario changes. During code generation, the numbering process checks the consistency between the code and the actual scenario structure against the scenario integration record. If a scenario unit conflicts with multiple codes, a numbering reordering is triggered, and the reason for the reordering and the old-new mapping are recorded in the numbering log. After numbering is completed, a scenario record card is generated for each scenario unit. The scenario record card includes the scenario code, scenario name, included grid units, communication interface, operation mode, typical day candidate set, key equipment constraints, and a data source summary. For areas with high penetration of new energy and prominent demand-side response, the scenario record card will also include an emphasis segment to highlight the relevant constraints in subsequent evaluation processes.

[0035] In the scenario list generation process, typical scenario list structures are formed by summarizing the scenario record cards according to their numbers. These typical scenario list structures serve as standard input objects for subsequent resource potential and response capability assessments, including scenario codes, scenario names, grid ranges, communication interfaces, operating modes, typical day sets, key node sets, key load sets, key equipment constraints, and data quality levels. To enhance traceability, the typical scenario list structure references the summary numbers of the unified log and time-aligned log fields from step S110, enabling rapid backtracking when boundary anomalies are encountered in subsequent assessments. After generation, the typical scenario list structure is registered as the output field name "Typical Scenario List Structure," directly consumed by the typical scenario list structure in step S210 at the step level. Simultaneously, at the cross-main step level, this structure connects to the power output potential assessment and demand-side response capability quantification in step S200, and prepares to reference the grid range and communication interfaces through the equipment configuration and timing verification in step S300, permeating subsequent multi-scenario operation and closed-loop revisions. At this point, the current dataset from step S110 has been fully consumed by step S120, and the initial draft of the scenario boundaries from step S120 has been fully consumed by this section, forming a typical scenario list structure that can directly drive evaluation and configuration.

[0036] In summary, the technical effects of this step are as follows: by unifying the field dictionary and aligning it with time, the current status data is expressed in a standardized manner in both spatial and temporal dimensions; by identifying grid boundaries and filtering typical days, the operational boundaries and representative time periods are clearly defined; and by integrating and numbering scenarios, a standardized list structure of typical scenarios is formed that can be directly referenced in subsequent evaluations and configurations.

[0037] Step S200 includes at least steps S210-S230: Step S210: Obtain the typical scenario list structure, perform distributed power type parsing and historical output extraction processing to obtain the power output potential set; Specifically, the typical scenario list structure output in step S130 is used as the sole input. The scenario code, grid range, communication interface, operating mode, typical day set, key node set, key load set, and key equipment constraints are read from this structure, and then loaded into the scenario context of the resource assessment task. Distributed power source type resolution expands on the distributed power source files and microgrid local controller records within the grid range. In this embodiment, the distributed power source type refers to the power category based on primary energy and grid connection interface, including photovoltaic power generation, wind power generation, gas power generation, oil power generation, biomass power generation, and other conventional small power sources that meet integration conditions. It also includes the charging and discharging of energy storage devices coupled with the above power sources as a source of output regulation, but these are not included in the distributed power source type set. The type resolution process calls the equipment model, access voltage, rated capacity, inverter parameters, grid connection point location, and protection configuration from the equipment ledger. Combined with the operating mode and communication interface in the typical scenario list structure, a mapping relationship from type to scenario is established. For scenarios with an isolated grid or power-limited operating mode, a corresponding grid connection interface status label is added. To eliminate duplicate registrations of the same power source from different data sources, the type parsing performs asset code deduplication and access point uniqueness checks during the import phase. If the same asset code corresponds to different access points or different asset codes correspond to the same access point, a data consistency flag is triggered, conflict entries are recorded, and the subsequent output extraction of that power source is temporarily listed as pending review and will not participate in this round of potential aggregation.

[0038] In the historical output extraction process, the historical output refers to the continuous power and energy records from the distribution automation master station, microgrid local controller, and user-side advanced metering infrastructure within the typical daily set coverage period of the typical scenario list structure, mapped to the corresponding distributed power source according to the grid range and access point of the scenario context. Output extraction is first aligned based on the sampling period and timestamp reference in the equipment file, using the time alignment rules established in step S110, without requiring further unit conversion. During the extraction process, the marked periods for outages, maintenance, and faults are trimmed, retaining the status identifier and removing unrepresentative short periods from the continuous sequence; for output breakpoints caused by inverter faults, communication interruptions, or data loss, breakpoint location and breakpoint length statistics are performed, and these are appended to the output segment metadata of the power source as quality tags. For weather-driven power sources such as photovoltaic and wind power, historical output extraction simultaneously reads category labels from meteorological observations or reanalysis data, mapping labels such as sunny, cloudy, overcast, low wind, and high wind to dates in the typical day set, forming a traceable output segment association. For controllable power sources such as gas-fired and oil-fired power, historical output extraction simultaneously reads start-stop command records and output limit records, writing the number of start-stops, duration, and power limit range into segment metadata, forming a source of evidence for subsequent capacity upper and lower boundaries. For access points with multiple units in parallel, the extraction process aggregates at the access point level while retaining the independent output segments of each unit to meet the need for unit-level flexibility when coupling subsequent scenarios. After completing type parsing and output extraction, the continuous segments of each power source under each typical day candidate are classified by quality. Low-quality segments are not deleted but are marked as for reference only. All valid segments enter the potential archiving process, and the archived items include power source identifier, access point, type, typical date, operating mode, output curve segment, quality level, and additional labels. After archiving is completed, the available output ranges of each power source in each scenario are aggregated to form a power output potential set. The power output potential set is used as the output field name "Power Output Potential Set" in this subsection and is directly consumed by the power output potential set in step S220. At the same time, it provides the original output boundary reference for the device configuration and timing verification preparation in step S300 at the cross-main step level.

[0039] Step S220: Extract available time periods, lower and upper capacity limits from the power output potential pool, match adjustable loads with energy storage ledgers and identify time windows to generate a demand-side response capability set; Specifically, the power output potential set output in step S210 is used as input. First, in the time-segmentation structuring process, output segments for each scenario and power type are extracted. In this embodiment, the available time segment refers to the continuous time interval within the output segment that meets the data quality level requirements and is not covered by maintenance or fault markers. The extraction logic follows the time scale of a typical day set, breaking and re-identifying segments that span days or scenarios. Simultaneously with the extraction of available time segments, based on the statistical distribution and operating mode labels of the output segments, a lower capacity limit and an upper capacity limit are given. In this step, the lower capacity limit refers to the minimum effective output that the power supply can stably maintain under typical scenario conditions and access point constraints. The upper capacity limit refers to the maximum allowable output that does not impact local protection or trigger reverse power risks under the same conditions. To improve the engineering usability of the boundary description, the upper and lower capacity boundaries are accompanied by source descriptions, labeled as historical segment statistics, equipment nameplate limitations, or scheduling mode limitations. Simultaneously, fluctuation level labels are added to power supplies with unstable boundaries. For access points with multiple units in parallel, merged upper and lower capacity boundaries are given during capacity aggregation, while the boundaries of each unit are retained for subsequent availability determination.

[0040] After establishing the power supply boundary, the process of matching adjustable loads with energy storage records begins. Adjustable loads refer to loads that can be shifted, reduced in load, or switched without violating user-side operating conditions and contractual agreements. These include air conditioning and ventilation loads, flexible loads in production processes, charging loads, and other loads with interruptible or peak-shifting characteristics. Energy storage records refer to the equipment files and operating records of energy storage facilities, including rated capacity, charging and discharging power, conversion device parameters, interface protection settings, cycle life records, and actual charging and discharging events. The matching process reads the equipment records and electricity consumption curve metadata compiled in steps S110 and S120, and locates the correspondence between adjustable loads, energy storage devices, and access points within the grid range defined by the typical scenario list structure. For adjustable loads, based on historical load curves and production or service period records, interruptible time slots, peak-shifting time slots, and non-interventional time slots are identified, and the minimum intervention cycle and intervention recovery interval are given. For energy storage devices, combined with charging and discharging events and interface protection settings, charging time slots, discharging time slots, and inoperable time slots are identified, and the available duration and start / end limits for a single charging and discharging cycle are given. Based on this, time window identification is performed, and the intersection of the available time periods on the power supply side and the operable time slots on the load and energy storage sides is calculated to obtain a set of interoperable time windows within the scenario. The upper and lower boundaries of power supply capacity, the interruptible scale of the load, and the operable scale of energy storage are added to each time window. For scenarios operating in islanded mode, the transfer capacity provided by the interconnection interface is marked as inactive. For scenarios operating in power-limited mode, the upper limit of power supply capacity is reduced according to the power-limited range, and power-limited indicators are added to both the load and energy storage time windows. All identified time windows and their associated scales are written into a temporary structure and then aggregated after anomaly checks. Anomaly checks include marking and recording situations such as overlapping time windows, excessively large time gaps, and excessively short inoperable windows, without affecting the entry of time windows that have passed verification into the database. After aggregation, a demand-side response capability set is formed. This demand-side response capability set is a structured expression of the load interruptibility scale, load peak shifting scale, energy storage charging scale, energy storage discharging scale, and the upper and lower boundaries of the corresponding power supply capacity for each time window within the scenario, and is registered as the output field name "Demand-Side Response Capability Set". This output is directly consumed by the demand-side response capability set in step S230, and is also connected to the timing verification input arrangement in step S300 at the cross-main step level.

[0041] Step S230: Perform scenario coupling and grid constraint superposition processing on the power output potential set and the demand-side response capability set to generate a scenario constraint parameter structure; Specifically, the power output potential set output in step S210 and the demand-side response capability set output in step S220 are used as inputs. First, in the scenario coupling process, the two types of inputs are aligned according to the scenario coding of a typical scenario list structure, ensuring that power entries and time window entries under each scenario correspond one-to-one in terms of access point, operating mode, and typical day dimension. Scenario coupling synchronously maps the available time periods and upper and lower capacity boundaries of the power side to each time window of the demand-side response capability set. Within each time window, a linkage configuration unit is generated. The linkage configuration unit includes power type, access point, upper and lower capacity boundaries, interruptible load scale, peak-shifting load scale, chargeable energy storage scale, releaseable energy storage scale, and operating mode label. For time windows with multiple units operating in parallel or multiple types of loads participating simultaneously, the linkage configuration unit is recorded independently and constrained and converged in subsequent overlay stages using electrical connection relationships. For power output segments marked as low quality, the coupling process marks their linkage configuration units as reference only and reduces their weight when constraints are superimposed. For scenarios where the operating mode changes, the coupling process generates segmented linkage configuration units before and after the change, and adds communication interface status change records at the boundary time.

[0042] In the process of overlaying network constraints, the key equipment constraints in the typical scenario list structure and the line parameters, node voltage limits, switch operation restrictions, protection setting range and interconnection interface capacity limits within the grid range are read, and an electrical feasibility determination is performed for each linkage configuration unit. Electrical feasibility assessment includes four types of checks: node voltage constraints, line current constraints, sectionalizing switch operability constraints, and interconnection interface capacity constraints. Node voltage constraint checks determine the upper and lower limits of the voltage at critical nodes within each time window. If there is a risk of exceeding the limit after applying the capacity upper limit, the power supply capacity upper limit is reduced to a safe level within that time window, and the source of the reduction is recorded. Line current constraint checks determine whether the load rate exceeds the limit within the grid range and on adjacent interconnection lines. If it does, the load peak shifting capacity and energy storage capacity are reduced, along with the reasons for the reduction. Sectionalizing switch operability constraint checks check the operability and operating intervals of switches for time windows requiring switching operating modes or segmented operation. If these are not met, the time window is marked as unexecutable. Interconnection interface capacity constraint checks check the interconnection interface capacity and normal operating status for linkage configuration units involving power flow across interfaces. If a non-normal operating status is required, it is marked as requiring upstream coordination. For configurations with superimposed constraints, a consistency check is performed. The check items include the balance between upper and lower capacity boundaries and load and energy storage scale within the time window, the consistency between operating mode labels and communication interface status, and the simultaneous satisfaction of key equipment constraints and control strategies. For items that fail the check, the process is automatically reduced or split in the scenario coupling process, and the check log is retained for subsequent traceability.

[0043] After completing constraint overlay and consistency verification, the parameter structuring process is initiated to generate a scenario constraint parameter structure. This scenario constraint parameter structure organizes linked configuration units at the scenario dimension, including scenario encoding, operating mode, communication interface status, time window set, upper and lower boundaries of power capacity for each time window, interruptible load scale, peak-shifting load scale, chargeable energy storage scale, dischargeable energy storage scale, node voltage constraints, line current constraints, switch operation restrictions, and interface capacity restrictions, along with a source summary and quality tag. This structure, as the output field name "Scenario Constraint Parameter Structure" in this subsection, is directly consumed by the scenario constraint parameter structure in step S310 at the step level. Simultaneously, it is used for equipment configuration and timing verification preparation in step S300 at the cross-main step level, providing constraints and boundary boxes for subsequent timing simulation operation and scheme selection.

[0044] In summary, the technical effects of this step are as follows: through type parsing and output extraction, the power supply capacity is restored to a set of potential that can be called upon; through boundary extraction and time window identification, the operability of the load and energy storage sides is clearly expressed in the time dimension; through scenario coupling and grid constraint superposition, a scenario constraint parameter structure that is consistent with the scenario and can be directly entered into equipment configuration and timing simulation is formed.

[0045] Step S300 includes at least steps S310-S330: Step S310: Obtain the scenario constraint parameter structure, perform device type list and access point list definition processing, and obtain the device configuration variable set; Specifically, the scenario constraint parameter structure output in step S230 is used as input. The scenario code, operating mode, interconnection interface status, time window set, upper and lower boundaries of power capacity, interruptible load scale, peak-shifting load scale, chargeable energy storage scale, releaseable energy storage scale, node voltage constraint, line current constraint, switch operation restriction, and interface capacity restriction corresponding to the scenario are read and loaded as boundary conditions for this step. In this embodiment, the equipment type list refers to a structured set of equipment categories and model families corresponding to the distribution network and microgrid, covering distributed power supply equipment, energy storage devices, inverters and converters, grid-connected switches and ring network switches, measurement and control and communication terminals, protection devices and metering devices, etc., and each type of equipment is associated with equipment files, rated parameters, environmental adaptability range, and operation and maintenance strategy number. In this embodiment, the access point list refers to a list of electrical nodes that can be used for equipment access or retrofit access, including unique node identifiers, belonging grid units, phase balance attributes, short-circuit capacity range, adjacent node interconnection relationships, and switch operability attributes. To ensure consistency in terminology and data, the grid range and key node set in the scenario constraint parameter structure are first mapped in the scenario-level loading process to establish an access point candidate set. The communication interface status is then projected onto the access point candidate set to form a scenario-access point mapping index. For scenarios with isolated network or power limitation labels in their operating mode, a mode label is added to the mapping index to serve as the effective condition for subsequent derived access rules.

[0046] In the equipment type list construction phase, equipment ledgers and technical standard entries are retrieved, and combined with the aforementioned node voltage constraints, line current constraints, and interface capacity limitations, a subset of equipment that meets the constraints of voltage level, short-circuit level, and installation space is selected. Specifically, for distributed power equipment, based on power type and capacity range, model families corresponding to the voltage level and grid connection strategy of the scenario are selected, and grid connection point protection configuration and grid connection switch type are added as auxiliary attributes; for energy storage devices, based on rated energy storage capacity and power range, converter level, and interface protection settings, model levels matching the energy storage charging and discharging scale of the scenario are selected, and temperature and altitude adaptation tags are written for different electrochemical systems or mechanical energy storage subcategories; for measurement and control and communication terminals, based on the protocol family and security strategy of the local distribution automation system and microgrid local controller, model entries that support existing grid protocols and have remote maintenance capabilities are selected, and sampling period and time synchronization capability are written into the attribute set; for protection devices and metering devices, the protection setting range and metering accuracy level requirements are read, and model entries that match the key nodes in the scenario are selected. During the above screening process, if there are missing fields or duplicate entries in the equipment ledger, record the missing and duplicate lists, and temporarily separate such equipment from the optional items. They will be merged again after the external ledger is supplemented, which will not affect the generation of the list definition in this round.

[0047] In the access point list definition stage, based on the scenario-access point mapping index, the short-circuit capacity range, phase balance attributes, and adjacent node connection relationships of each access point candidate are checked one by one. Access points with current-limited operation or infrequent connection status labels are marked. For ring network nodes with multiple connection ports, the access ports are sorted according to the commonly used operation mode, and the dependency level on infrequent status is marked in the record. For scenarios with an isolated operation mode, all cross-regional connection interfaces are marked as unusable, and the corresponding unusable description is given in the access point list. For scenarios with a power-limited operation mode, the capacity reduction information in the scenario constraint parameter structure is used, and this information is bound to the corresponding access port record in the access point list for consumption in subsequent capacity boundary determination. Through the bidirectional constraints of the equipment type list and the access point list, the equipment-access point matching stage is entered, generating candidate binding relationships between equipment and access points. The binding relationship records the equipment category, equipment model family, access voltage level, estimated layout method, on-site construction level, and grid connection switch requirements. When multiple device categories can satisfy the same access point, a candidate list is generated in the order of voltage level matching priority, interface protection compatibility priority, and space constraint satisfaction priority, and a priority tag is attached to the binding record. After the above processing is completed, a device configuration variable set is formed. In this embodiment, the device configuration variable set refers to a set of variable structures that can be used for subsequent verification and simulation orchestration, including a device type list, an access point list, device-access point binding relationships, mode tags, priority tags, and reference indexes for scenario constraints. This structure is registered as the output field name "device configuration variable set" and is directly consumed as the device configuration variable set in step S320. At the same time, at the cross-main step level, the device configuration variable set provides a static variable framework for parameter backfilling in the multi-scenario operation and closed-loop revision in step S400.

[0048] Step S320: Extract key configuration items and access points from the set of device configuration variables, perform network access verification and capacity boundary determination, and generate a candidate set of device configurations; Specifically, taking the set of device configuration variables output in step S310 as input, the key configuration item extraction stage first locates the device categories with actual adjustment value and necessity for change within the scenario time window based on the device type list and the device-access point binding relationship. In this embodiment, key configuration items refer to configuration objects that have a significant impact on scenario power flow distribution and voltage constraints and can be supplemented or replaced in the planning stage, including distributed power supply capacity levels, energy storage rated capacity levels and power levels, grid-connected switch type levels, measurement and control terminal sampling and control capability levels, protection device configuration levels, and metering device accuracy levels. During extraction, the correspondence between the method label and priority label is maintained on each type of configuration object. Items that are not yet confirmed due to missing records are kept in a pending state and excluded in subsequent verification. On the access point side, based on the unique identifier of the node in the access point list, a subset of access points with spatial and operational feasibility is extracted. In this embodiment, spatial feasibility means that the on-site layout conditions meet the equipment installation requirements, and operational feasibility means that the access point is not restricted by the prohibited use mark under the scenario operation mode.

[0049] In the network access verification phase, for each key configuration item and access point binding relationship, electrical feasibility is determined item by item according to node voltage constraints, line current constraints, tie interface capacity limits, and switch operation constraints. Specifically, the node voltage constraint determination performs boundary checks on possible node voltage changes that may be introduced within a typical scenario time window after installation, based on the access voltage level and the set of key nodes; the line current constraint determination performs an acceptability check on possible current increments that may be introduced after installation, based on the current carrying capacity of the line segment where the access point is located and the current carrying capacity of adjacent segments; the tie interface capacity limit determination performs a capacity margin check on the possibility of power flow crossing interfaces after installation, based on the interface capacity limits of the scenario; and the switch operation constraint determination performs a timing operation reachability check on the configuration that requires adding or modifying switches, based on the operability records and operation interval requirements of the sectional switches. When any constraint check shows that it is infeasible, the binding relationship is marked as unselectable, and the constraint category and triggering conditions are recorded and stored in the verification log; when the boundary conditions are critical, the binding relationship is marked as a low-priority candidate, and the constraint source is retained for reference in the subsequent strategy orchestration phase. After completing the network access verification, the capacity boundary determination stage begins. In this embodiment, capacity boundary determination refers to forming feasible ranges for the capacity or power levels of key configuration items based on the upper and lower boundaries of power capacity and the demand-side scale boundaries in the scenario constraint parameter structure. For distributed power capacity levels, the feasibility and infeasibility marks of the upper, middle, and lower capacity levels at the access point are given based on the superposition of the scenario's upper power capacity limit, voltage, and line constraints. For energy storage rated capacity and power levels, the feasible ranges for charging and discharging power and capacity are formed based on the superposition of the energy storage's chargeable and dischargeable scale and line current carrying constraints. For grid-connected switch type levels, the type compatibility mark is given based on short-circuit capability and operational limitations. For measurement and control terminal sampling and control capability levels, the availability mark is given based on the existing network protocol and time synchronization capability. For protection device configuration levels and metering device accuracy levels, the matching mark is given based on the setting range and metering level. After the capacity boundary determination is completed, the configuration objects that have been verified and have clear capacity or power levels are summarized and bound to the access points to form a set of entries that can be directly used by policy orchestration.

[0050] Understandably, the process begins with the generation of the device configuration candidate set, based on the entries obtained from network access verification and capacity boundary determination. In this embodiment, the device configuration candidate set refers to the list of candidate configurations generated for subsequent timing simulation input orchestration and load complementarity strategy generation. It includes configuration object identifiers, access point identifiers, capacity or power levels, mode labels, priority labels, and scenario constraint reference indexes. In scenarios where multiple candidates can be satisfied simultaneously, candidate entries are sorted by priority labels, and parallel candidate relationships are retained in the record. In scenarios requiring modifications, corresponding change prompts are added to candidate entries, such as grid connection switch replacement or communication terminal upgrades, and related entries are recorded. After generation, it is registered as the output field name "device configuration candidate set" and directly consumed as the device configuration candidate set in step S330. Simultaneously, at the cross-main step level, the device configuration candidate set provides a backfillable and replaceable configuration entry point for multi-scenario operation and closed-loop revision in step S400.

[0051] Step S330: Perform time-series simulation input orchestration and load complementarity strategy generation processing on the candidate equipment configuration set to generate a time-series verification input structure. Specifically, the candidate equipment configuration set output in step S320 is used as input. First, in the time-series simulation input orchestration stage, the configuration object identifier, access point identifier, capacity or power level, mode label, and priority label in the candidate entries are read and aligned with the time window set, upper and lower boundaries of power capacity, interruptible load scale, peak shifting load scale, chargeable energy storage scale, and releaseable energy storage scale in the scenario constraint parameter structure output in step S230. A simulation unit is constructed for each time window. In this embodiment, a simulation unit refers to a combined data block containing all candidate configurations and constraints within a single time window, recording the scenario code, interconnection interface status, node voltage constraints, line current constraints, and switching operation restrictions for that time window. For candidate entries with different mode labels, a branching approach is used to establish parallel simulation units within the same time window to compare different modes during time-series simulation processing. To avoid data ambiguity, the time alignment rules of step S110 are reused during the compilation process. Historical curves are not numerically modified; only time scale references and slicing are performed. The phase balance attributes and short-circuit capability ranges of the access points are reviewed. Candidate entries that do not meet these attributes or ranges are removed from the simulation unit corresponding to that time window and recorded. For candidate entries requiring non-frequent connection states, external coordination prompts are marked on the simulation unit for identification in subsequent screening stages.

[0052] In the load complementarity strategy generation stage, for each simulation unit, the candidate set of equipment configurations is referenced within the constraint framework of load interruptibility scale, load peak shifting scale, and energy storage charging and discharging scale to generate corresponding load complementarity strategy fragments. In this embodiment, the load complementarity strategy refers to the sequential start-up, shutdown, or power allocation arrangement of load and energy storage around the candidate configuration within a single time window. This includes the interruption sequence of interruptible loads, the peak shifting sequence of peak-shaving loads, the switching sequence of energy storage charging and discharging, and the operation sequence of grid-connected switches. The operation interval, minimum duration, and recovery time requirements are marked on the sequence. For simulation units involving islanded grids, the load complementarity strategy fragment does not reference cross-regional interconnection capabilities; for simulation units involving power limiting, a power limiting reduction indicator is added to the strategy fragment. In cases where multiple candidate entries compete for the same operable resource, they are arranged according to priority tags, and unarranged entries and conflict reasons are reserved for subsequent screening. After the strategy is generated, the device set, access point set, capacity and power level, serialized load and energy storage operation, node and line constraints and communication interface status of each simulation unit are packaged into simulation input blocks, and a simulation input index is established. The index records the scenario code, time window number and candidate configuration fingerprint, which facilitates the location and reuse of the time-series simulation operation stage.

[0053] Furthermore, in the anomaly and consistency handling stage, the simulation input blocks undergo integrity and consistency checks. In this embodiment, the integrity check refers to whether the simulation input block simultaneously contains the necessary fields from the equipment side, load side, energy storage side, and constraint side. The consistency check refers to whether the correspondence between the equipment capacity and power level and the upper and lower boundaries of the scenario's power capacity, and between line current constraints and node voltage constraints, is valid. For incomplete or inconsistent simulation input blocks, correction suggestions are provided, prioritizing automatic correction by reducing candidate capacity levels, extending operation intervals, or deleting interruptible load segments. If the consistency requirements are still not met, the simulation input block is marked for exclusion, and an exclusion flag is added to the index. The simulation input blocks processed as described above are summarized and registered as a timing verification input structure. In this embodiment, the timing verification input structure refers to a standardized input set used to drive multi-scenario timing simulation operation, including simulation input index, scenario code, time window number, equipment configuration and access point configuration, serialized load complementarity strategy, and constraint field references. The structure is registered as the output field name timing verification input structure and is directly consumed by the timing verification input structure of step S410 at the step level. At the same time, at the cross-main step level, the timing verification input structure provides a reusable simulation entry point for the multi-scenario operation and closed-loop revision of step S400, and forms a closed-loop mapping with the parameter backfilling process of step S420.

[0054] In summary, the technical effects of this step are as follows: By defining the equipment type list and access point list, equipment configuration variables are expressed in a structured manner; by verifying the grid access and determining the capacity boundary, candidate configurations are constrained within the electrically feasible range; and by orchestrating the timing simulation input and generating the load complementarity strategy, a timing verification input structure that can directly drive multi-scenario simulations is formed.

[0055] Step S400 includes at least steps S410-S430: Step S410: Obtain the timing verification input structure, perform multi-scenario timing simulation processing, and obtain the scenario running record set; Specifically, the timing verification input structure output in step S330 is used as the sole input. The simulation input index, scene code, time window number, device configuration, access point configuration, serialized load complementarity strategy, and constraint field references are read from it. A running task queue for the scene and time window is then established at the simulation scheduling layer. To avoid data discrepancies, this step uses the time scale and field dictionary from the preceding data processing, without performing any conversion on the input values. Field path parsing and referencing are only completed during task loading, restoring the device set, load set, energy storage set, and constraint set for each simulation unit. Specifically, in the task loading phase, all time windows under the same scenario are first sorted according to the simulation input index, and parallel branches are established for simulation units with mode label branches. Then, the distributed power supply capacity, energy storage power, grid-connected switch type, and measurement and control and communication terminal capabilities in the equipment configuration are mapped to electrical connection relationships and access point attributes to construct an operational image on the equipment side. Next, the interruptible load interruption sequence, peak-shifting load peak-shifting sequence, energy storage charging and discharging switching sequence, and grid-connected switch operation sequence in the load complementarity strategy are injected into the operational image in the start and end order within the time window, forming a complete operation and response timeline. In the constraint injection phase, the constraint field references are read, and node voltage constraints, line current constraints, sectionalizing switch operability restrictions, and interconnection interface capacity restrictions are loaded item by item. External coordination prompts are set in the metadata for simulation units that require non-use interconnection states. For input omissions due to ledger updates or equipment replacement, they are recorded as input anomalies, and the simulation unit is marked as pending rollback and will not be included in this round of calculation.

[0056] Furthermore, in the multi-scenario time-series simulation operation, the operating clock is advanced according to the start and end scales of the time window, and state changes on the equipment side, load side, and energy storage side are triggered according to the serialized operation schedule. Specifically, distributed power sources enter or exit grid-connected states according to capacity level and grid-connected switch type within the time window; energy storage enters charging, discharging, or idle states according to the switching sequence within the time window; interruptible loads execute start-stop according to the interruption sequence within the time window; and peak-shifting loads execute power migration according to the peak-shifting sequence within the time window. Each state change triggers an electrical balance calculation and constraint verification in the simulator actuator, and records the instantaneous values ​​of node voltage, line current, and interconnection interface load rate, as well as the resulting power or load abandonment. For sequences that need to span multiple adjacent time windows, this step determines the operation interval and recovery time at the time window boundary. If the interval and continuity requirements of the sequence are not met, the subsequent sequence is marked as not executed and the reason for non-execution is written. For simulation units labeled with parallel mode, they are advanced in parallel within the same time window, and each forms an independent operation record segment at the end of the simulation. For simulation units with external coordination prompts, this step does not actively modify the input; it only writes the prompt information and the corresponding time into the record segment to maintain the connection with the upstream scheduling process. In the event of a short-term unavailability of the access point, communication anomalies, or temporary maintenance, the simulation actuator triggers an emergency rollback, suspending the affected sequences and recording the rollback time and type, without making any linkage adjustments to other unaffected sequences.

[0057] Understandably, during the simulation process, this step generates operation record segments for each time window, including start and end scales, equipment status trajectories, load and energy storage operation trajectories, node voltage trajectories, line current trajectories, interconnection interface load rate trajectories, and power curtailment records. All segments are then aggregated according to scenario codes to form a scenario operation record set. To ensure traceability in subsequent screening processes, each record in the scenario operation record set is accompanied by a simulation input index and candidate configuration fingerprint, and references are established with the corresponding method labels and external coordination prompts. After consistency and integrity checks, segments that pass are directly aggregated; segments that fail are retained after recording the reasons for failure and do not participate in the next screening step. The final output scenario operation record set is registered as the output field name "Scenario Operation Record Set" and is directly consumed by the scenario operation record set in step S420. Simultaneously, at the cross-main step level, the scenario operation record set can be referenced by the planning review and simulation platform verification processes to compare the operation trajectories and constraint triggering conditions of different equipment configurations.

[0058] Step S420: Extract voltage over-limit indicators, line load rate indicators, and abandoned power indicators from the scenario operation record set, perform scheme screening and parameter backfilling processing, and generate configuration parameter revision package; Specifically, using the scenario operation record set output in step S410 as the sole input, in the indicator extraction stage, for each scenario and each time window, the node voltage trajectory, line current trajectory, and interconnection interface load rate trajectory are read to determine whether the node voltage exceeds the upper or lower limit, whether the line current exceeds the allowable load rate, and whether the interconnection interface load rate exceeds the capacity limit. The corresponding time, duration, and involved devices and access points are then summarized to form voltage over-limit indicators, line load rate indicators, and interface over-limit notes. In the power curtailment extraction, the time, scale, and source device identifier in the power curtailment records are read to form power curtailment indicators and power curtailment item descriptions corresponding to the scenario and time window. To maintain consistency with the input, indicator extraction follows a time-series scale, without smoothing or interpolating the trajectory; only abnormal segments are marked to indicate the record quality level. After the above indicators are formed, the scheme selection stage begins. In this embodiment, scheme selection refers to sorting and filtering candidate configurations according to method labels and priority labels, prioritizing the elimination of candidates that cause repeated constraint triggering or a significant increase in abandoned power, and retaining candidates whose indicator performance is stable in most time windows and has few conflicts with external coordination prompts. For situations where there are multiple parallel method labels in the same scenario, this step performs screening within their respective candidate sets and records the reasons for elimination and retention.

[0059] Furthermore, in the parameter backfilling process, the candidate configurations and corresponding performance indicators selected through scheme screening are backfilled to the equipment configuration layer and the scenario constraint layer. Specifically, the equipment configuration layer backfilling includes recommended and prohibited ranges for distributed power supply capacity levels, energy storage power levels, grid-connected switch types, and measurement and control and communication terminal capabilities; the scenario constraint layer backfilling includes suggested revisions to node voltage constraints, line current constraints, and interconnection interface capacity limits for individual time windows, as well as suggestions for adjusting power-limited sections. To maintain the correspondence with the ledger, the parameter backfilling adds access point identifiers and equipment model families to the entries, establishes a binding with the simulation input index and candidate configuration fingerprint, and marks the source as the current round of simulation screening. For entries with external coordination prompts, the parameter backfilling only provides suggestions and does not directly modify the source constraint references. During the backfilling process, records that have not passed the consistency check in the previous step are not included in the backfilling; for boundary records, the optional range is given in the suggestions and marked as low priority, so that they can be called as needed in the next round of simulation. After backfilling is completed, the process proceeds to comprehensive assembly, where the recommended and suggested items are aggregated according to scenario coding to form a structured set that can be used by upstream steps. In this embodiment, this set is defined as a configuration parameter revision package, which includes device-side parameter suggestions, constraint-side parameter suggestions, time window-level strategy adjustment suggestions, and item source indexes. It is registered as the output field name configuration parameter revision package and is directly consumed by the configuration parameter revision package in step S430. At the same time, at the cross-main step level, there is a field-level mapping relationship between the configuration parameter revision package and the device configuration variable set in step S300, which triggers incremental changes for device configuration and timing verification preparation after the closed-loop update.

[0060] Step S430: Perform closed-loop update and scenario recalculation trigger on the configuration parameter revision package to generate the planning result structure; Specifically, the configuration parameter revision package output in step S420 is used as the sole input. During the closed-loop update phase, the entries within the package are first verified for legality. The verification objects include the completeness of fields for equipment-side parameter suggestions, constraint-side parameter suggestions, and strategy adjustment suggestions; their correspondence with existing equipment ledgers; and their consistency with the references in the scenario constraint parameter structure. Entries that pass verification are entered into the mapping application. In this embodiment, the mapping application refers to writing the suggested entries back to the corresponding fields in the equipment configuration variable set and the scenario constraint parameter structure, and establishing version increment records for entries involving changes in equipment model families or access point attributes. Specifically, for suggestions on distributed power supply capacity levels and energy storage power levels, this step updates the selectable status and priority labels of the corresponding capacity or power level in the equipment configuration variable set; for suggestions on grid-connected switch types and measurement and control and communication terminal capabilities, upgrade and replacement suggestions are marked in the equipment type list and equipment-access point binding relationship, and the on-site construction level is added; for suggestions on node voltage constraints, line current constraints, and interconnection interface capacity limitations, the constraint parameters of the corresponding time window are updated in the scenario constraint parameter structure, and the revision source and revision time are added to the entries. For suggestions that require external coordination, the closed-loop update phase only marks them and does not write them back directly. They are applied only after the upstream system completes the authorization.

[0061] Furthermore, during the scenario recalculation triggering phase, the closed-loop updated set of device configuration variables and scenario constraint parameter structure will be repackaged into a new timing verification input structure. To control the recalculation scope, this step filters the affected scenario codes and time window numbers according to the impact range of the entries in the configuration parameter revision package, constructs an incremental recalculation list, and generates a new fingerprint on the simulation input index to mark the differences from the previous round. For scenarios involving device replacement or significant capacity adjustments, the incremental recalculation list will add records of adjacent time windows and related communication interfaces to assess the cascading impact; for scenarios involving only policy adjustments, the incremental recalculation list is limited to the corresponding time window. After construction, this step sends a scheduling request to the multi-scenario timing simulation run processing, writes it into the task queue, and waits for the return results of the new round of scenario run record set. For scenarios that cannot be recalculated immediately due to upstream ledger updates or incomplete external coordination, the incremental recalculation list remains suspended, and the suspension reason and review node are registered in the version increment record, and the recalculation will be triggered again when the conditions are met. Meanwhile, for scenarios where the closed-loop update has been completed in this round and does not require immediate recalculation, this step summarizes the current snapshots of their device configuration, constraint configuration, and policy configuration, and proceeds to the result assembly stage.

[0062] Understandably, in the result assembly stage, the key fields after the closed-loop update are mapped to the candidate configurations retained in this round of screening to form an externally referable planning result structure. In this embodiment, the planning result structure refers to structured data output according to the scenario dimension, including scenario codes, grid ranges, communication interface status, equipment configuration lists, access point configuration lists, capacity and power level settings, strategy configuration summaries, and constraint parameter snapshots, along with version increment records and simulation input index mappings to support project implementation and subsequent review. For scenarios in a suspended state, this step sets a pending review mark in the planning result structure and points it to its incremental recalculation list to avoid confusion with finalized scenarios. The final generated planning result structure is registered as the output field name planning result structure. On the one hand, it flows back to the equipment configuration variable set in step S310 at the step level, serving as a reference for the next round of equipment type list and access point list definition processing; on the other hand, it is referenced at the cross-main step level for construction scheme comparison and investment calculation stages, achieving data continuity from simulation operation to planning finalization.

[0063] In summary, the technical effects of this step are as follows: Through multi-scenario time-series simulation, a traceable running trajectory and constraint trigger records are obtained; through indicator extraction, scheme selection, and parameter backfilling, a structured configuration parameter revision package is formed; through closed-loop updates and scenario recalculation triggers, a planning result structure that can be referenced externally is produced, driving the next round of incremental verification.

[0064] Please see Figure 2 The diagram shows a structural block diagram of a multi-scenario adaptive micro-cooperative planning system according to this application.

[0065] like Figure 2 As shown, the micro-collaborative planning system 200 includes an acquisition module 210, a generation module 220, a processing module 230, and an update module 240.

[0066] The acquisition module 210 is configured to acquire distribution network data and microgrid data, and perform time base correction, time zone normalization, and event segmentation processing on the distribution network data and the microgrid data to generate a typical scenario list structure; the generation module 220 is configured to perform distributed power source type parsing and historical output extraction based on the typical scenario list structure, and perform access point location and time window intersection processing in conjunction with adjustable load and energy storage ledger to generate a scenario constraint parameter structure superimposed with node voltage, line current, sectionalizing switch operability, and interconnection interface capacity; the processing module 230 is configured to... The system defines the scenario constraint parameter structure, defines the equipment type list and access point list, performs network access verification to form a feasible capacity range, and performs time-series simulation input orchestration and load complementarity strategy serialization processing to generate a time-series verification input structure. The update module 240 is configured to perform multi-scenario time-series simulation based on the time-series verification input structure, selects schemes based on voltage over-limit, line load rate and abandoned power indicators, and backfills parameters for distributed power capacity, energy storage power, grid connection switch type and measurement and control communication capability to generate a configuration parameter revision package. After closed-loop update, the planning result is obtained.

[0067] It should be understood that Figure 2 The modules and references described in the document Figure 1 The steps described in the text correspond to those in the method described above. Therefore, the operations, features, and corresponding technical effects described above also apply to the method described in the text. Figure 2 The various modules in the document will not be described in detail here.

[0068] In other embodiments, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the multi-scenario adaptive micro-cooperative planning method in any of the above method embodiments. In one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions, which are configured as follows: Acquire distribution network data and microgrid data, and perform time base correction, time zone normalization and event segmentation processing on the distribution network data and the microgrid data to generate a typical scenario list structure; Based on the typical scenario list structure, distributed power source type parsing and historical output extraction are performed, and access point location and time window intersection processing are performed in combination with adjustable load and energy storage ledger to generate a scenario constraint parameter structure superimposed with node voltage, line current, sectional switch operability and interconnection interface capacity. Based on the scenario constraint parameter structure, the equipment type list and access point list are defined, the network access verification is implemented to form a feasible capacity range, and the timing simulation input orchestration and load complementarity strategy serialization processing are performed to generate the timing verification input structure. Based on the aforementioned timing verification input structure, multi-scenario timing simulation is performed. Schemes are selected based on voltage over-limit, line load rate, and abandoned power indicators. Parameters for distributed power supply capacity, energy storage power, grid connection switch type, and measurement and control communication capability are backfilled to generate a configuration parameter revision package. After closed-loop update, the planning result is obtained.

[0069] Computer-readable storage media may include a stored program area and a stored data area, wherein the stored program area may store an operating system and an application program required for at least one function; the stored data area may store data created based on the use of the multi-scenario adaptive micro-cooperative planning system, etc. Furthermore, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include memory remotely configured relative to the processor, and these remote memories may be connected to the multi-scenario adaptive micro-cooperative planning system via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0070] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 3 As shown, the device includes a processor 310 and a memory 320. The electronic device may also include an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 can be connected via a bus or other means. Figure 3 Taking a bus connection as an example, the memory 320 is the computer-readable storage medium described above. The processor 310 executes various server functions and data processing by running non-volatile software programs, instructions, and modules stored in the memory 320, thereby realizing the multi-scenario adaptive micro-cooperative planning method described in the above embodiment. The input device 330 can receive input digital or character information and generate key signal inputs related to user settings and function control of the multi-scenario adaptive micro-cooperative planning system. The output device 340 may include a display screen or other display device.

[0071] The aforementioned electronic device can execute the method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in the embodiments of the present invention.

[0072] In one implementation, the above-described electronic device is applied to a multi-scenario adaptive micro-collaborative planning system for a client, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: Acquire distribution network data and microgrid data, and perform time base correction, time zone normalization and event segmentation processing on the distribution network data and the microgrid data to generate a typical scenario list structure; Based on the typical scenario list structure, distributed power source type parsing and historical output extraction are performed, and access point location and time window intersection processing are performed in combination with adjustable load and energy storage ledger to generate a scenario constraint parameter structure superimposed with node voltage, line current, sectional switch operability and interconnection interface capacity. Based on the scenario constraint parameter structure, the equipment type list and access point list are defined, the network access verification is implemented to form a feasible capacity range, and the timing simulation input orchestration and load complementarity strategy serialization processing are performed to generate the timing verification input structure. Based on the aforementioned timing verification input structure, multi-scenario timing simulation is performed. Schemes are selected based on voltage over-limit, line load rate, and abandoned power indicators. Parameters for distributed power supply capacity, energy storage power, grid connection switch type, and measurement and control communication capability are backfilled to generate a configuration parameter revision package. After closed-loop update, the planning result is obtained.

[0073] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention 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; and these 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 the present invention.

Claims

1. A multi-scenario adaptive micro-association planning method, characterized in that, The method comprises the following steps: obtaining power distribution network data and micro-grid data, and performing time reference correction, time zone normalization and event segmentation processing on the power distribution network data and the micro-grid data to generate a typical scenario list structure; based on the typical scenario list structure, performing distributed power source type analysis and historical output extraction, and combining adjustable load and energy storage account to perform access point positioning and time window intersection processing to generate a scenario constraint parameter structure superimposed with node voltage, line current, segment switch operability and tie-in interface capacity; based on the scenario constraint parameter structure, defining device type list and access point list, implementing network access checking to form a capacity feasible interval, and performing time sequence simulation input arrangement and load complementary strategy serialization processing to generate a time sequence checking input structure; based on the time sequence checking input structure, performing multi-scenario time sequence simulation running, selecting schemes according to voltage out-of-limit, line load rate and power abandonment quantity indicators, and performing backfill processing on parameters such as distributed power source capacity grade, energy storage power grade, grid-connected switch type and measurement and control communication capability to generate a configuration parameter revision package, and obtaining a planning result after closed-loop updating.

2. The multi-scenario adaptive micro-allocation planning method according to claim 1, wherein, The time reference correction, time zone normalization and event segmentation processing on the power distribution network data and the micro-grid data to generate a typical scenario list structure comprises: performing field dictionary unification and time alignment processing on the obtained power distribution network data and micro-grid data to obtain a present data set; extracting grid division information, power consumption curve and equipment account from the present data set, performing grid boundary identification and typical day screening to generate a scenario boundary preliminary draft; performing scenario integration and numbering processing on the scenario boundary preliminary draft to generate the typical scenario list structure.

3. The multi-scenario adaptive micro-allocation planning method of claim 1, wherein, The distributed power source type analysis and historical output extraction based on the typical scenario list structure, and the combination of adjustable load and energy storage account to perform access point positioning and time window intersection processing to generate a scenario constraint parameter structure superimposed with node voltage, line current, segment switch operability and tie-in interface capacity comprises: based on the typical scenario list structure, performing distributed power source type analysis and historical output extraction processing to obtain a power source output potential set; extracting available time period, capacity lower limit and capacity upper limit from the power source output potential set, performing adjustable load and energy storage account matching and time window identification to generate a demand side response capability set; performing scenario coupling and network restriction superposition processing on the power source output potential set and the demand side response capability set to generate the scenario constraint parameter structure.

4. The multi-scenario adaptive micro-allocation planning method of claim 1, wherein, The device type list and access point list definition based on the scenario constraint parameter structure, the implementation of network access checking to form a capacity feasible interval, and the performance of time sequence simulation input arrangement and load complementary strategy serialization processing to generate a time sequence checking input structure comprises: based on the scenario constraint parameter structure, performing device type list and access point list definition processing to obtain a device configuration variable set; extracting key configuration items and access points from the device configuration variable set, performing network access checking and capacity boundary determination to generate a device configuration candidate set; The device configuration candidate set is subjected to a timing simulation input arrangement and load complementary strategy generation process to generate the timing check input structure.

5. The multi-scenario adaptive micro-allocation planning method of claim 4, wherein, The key configuration items include: distributed power supply capacity grade, energy storage rated capacity and power grade, grid-connected switch type grade, measurement and control and communication terminal sampling and control ability grade, protection device configuration grade and metering device precision grade.

6. The multi-scenario adaptive micro-allocation planning method of claim 4, wherein, The grid access check includes: according to node voltage constraints, line current constraints, tie-in interface capacity limitations and switch operation limitations, performing electrical feasibility determination piece by piece, and recording the constraint category and triggering condition when it is not feasible.

7. The multi-scenario adaptive micro-allocation planning method of claim 1, wherein, Based on the timing check input structure, multi-scenario timing simulation running is performed, scheme screening is performed according to voltage out-of-limit, line load rate and power abandonment amount indicators, and parameter backfilling processing is performed on distributed power supply capacity grade, energy storage power grade, grid-connected switch type and measurement and control communication ability, to generate a configuration parameter revision package, and after closed-loop updating, the planning result is obtained, including: Based on the timing check input structure, multi-scenario timing simulation running is performed, scheme screening is performed according to voltage out-of-limit, line load rate and power abandonment amount indicators, and parameter backfilling processing is performed on distributed power supply capacity grade, energy storage power grade, grid-connected switch type and measurement and control communication ability, to generate a configuration parameter revision package, and after closed-loop updating, the planning result is obtained, including: From the scenario running record set, voltage out-of-limit indicators, line load rate indicators and power abandonment amount indicators are extracted, scheme screening and parameter backfilling processing are performed, and the configuration parameter revision package is generated. The configuration parameter revision package is subjected to closed-loop updating and scenario recalculation triggering to generate the planning result.

8. A multi-scenario adaptive micro-allocation collaborative planning system, characterized in that, The acquisition module is configured to acquire power distribution network data and microgrid data, and perform time reference correction, time zone normalization and event segmentation processing on the power distribution network data and the microgrid data to generate a typical scenario list structure. The generation module is configured to perform distributed power supply type analysis and historical output extraction based on the typical scenario list structure, and combine adjustable load and energy storage account to perform access point positioning and time window intersection processing to generate a scenario constraint parameter structure superimposed with node voltage, line current, segmented switch operability and tie-in interface capacity. The processing module is configured to perform device type list and access point list definition based on the scenario constraint parameter structure, implement grid access check to form a capacity feasible interval, and perform timing simulation input arrangement and load complementary strategy serialization processing to generate a timing check input structure. The update module is configured to perform multi-scenario timing simulation running based on the timing check input structure, perform scheme screening according to voltage out-of-limit, line load rate and power abandonment amount indicators, and perform parameter backfilling processing on distributed power supply capacity grade, energy storage power grade, grid-connected switch type and measurement and control communication ability to generate a configuration parameter revision package, and after closed-loop updating, the planning result is obtained. The at least one processor and the memory connected in communication with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 7.

9. An electronic device, comprising: The program is executed by the processor to implement the method of any one of claims 1 to 7. ​ 10. A computer-readable storage medium having stored thereon a computer program, characterized in that, ​

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