A power system full voltage grade distributed power supply carrying capacity evaluation method based on time sequence power flow calculation

Abstract

The application discloses a kind of power system full voltage grade distributed power bearing capacity evaluation method based on time sequence power flow calculation, each electrical equipment is hierarchically divided by obtaining power grid equipment type and topological connection relationship, construct full voltage grade relationship architecture based on hierarchical division result, obtain the load output data and power output data of each grade in full voltage grade relationship architecture and carry out time sequence power flow calculation, based on time sequence power flow calculation result, comprehensive bearing capacity evaluation processing is carried out, the bearing capacity evaluation result of each hierarchical electrical equipment is processed in two-way collaborative constraint, according to the constraint result, the final bearing capacity evaluation result of each hierarchical electrical equipment after constraint is output, the final bearing capacity evaluation result is compared item by item with preset early warning index, according to the matching corresponding early warning grade power supply access strategy of comparison result, access power supply is processed in hierarchical early warning. With the effect of improving evaluation precision, efficiency and practicability.

Description

Technical Field

[0001] This invention relates to the technical field of power supply capacity assessment, and in particular to a method for assessing the capacity of distributed power sources across all voltage levels in a power system based on time-series power flow calculation. Background Technology

[0002] Currently, with the rapid development of new energy technologies, the penetration rate of distributed power sources (photovoltaics, wind power, etc.) in the power grid continues to rise. The intermittent and random nature of their output, as well as the dynamic changes in load, pose serious challenges to the safe and stable operation of the power grid. Accurately assessing the power grid's carrying capacity for distributed power sources has become a core technical requirement for the large-scale integration of new energy sources and the optimization of power grid planning.

[0003] Existing methods for assessing the carrying capacity of distributed power sources are mostly based on static analysis of static data at a typical moment. However, this approach often ignores the temporal fluctuations in distributed power output, such as diurnal variations in photovoltaic power, random fluctuations in wind power, and dynamic load evolution. This leads to significant discrepancies between the assessment results and the actual operation of the power grid, failing to provide effective data support for decisions regarding dynamic grid integration. Existing assessment methods often focus on single voltage levels, such as 10kV distribution substations or local power grids, resulting in biased assessments. Traditional power flow calculations employ serial processing, which is computationally intensive and slow-converging for complex distribution networks with multiple nodes and constraints. Furthermore, these methods are ill-suited to the computational logic of different voltage levels and the real-time assessment requirements of dynamic renewable energy integration. Moreover, existing assessment methods often provide single numerical values, requiring manual feasibility and risk level analysis, making them unsuitable for direct application in project approval and grid planning. In short, these existing technologies suffer from low assessment accuracy, narrow coverage, and an inability to accurately reflect the actual carrying capacity of the power grid for distributed power sources. Summary of the Invention

[0004] To address the lack of an assessment system in existing technologies that considers both timing characteristics and the coordination across all voltage levels, resulting in low accuracy, narrow coverage, and an inability to accurately reflect the actual carrying capacity of the power grid for distributed power sources, this application provides a power system distributed power source carrying capacity assessment method based on time-series power flow calculation. This method can comprehensively improve the accuracy, efficiency, and practicality of the assessment through full-chain hierarchical modeling, time-series dynamic calculation, upper and lower level collaborative constraints, and hierarchical early warning design.

[0005] Firstly, the above-mentioned inventive objective of this application is achieved through the following technical solution: A method for assessing the carrying capacity of distributed generation at all voltage levels in a power system based on time-series power flow calculation, the method comprising: Obtain the power grid equipment type and topology connection relationship, divide each electrical equipment into levels according to the power grid equipment type and the topology connection relationship, and construct a full voltage level relationship architecture based on the level division result; The load output data and power output data of each level in the full voltage level relationship architecture are obtained and time-series power flow calculation is performed. Based on the time-series power flow calculation results, a comprehensive carrying capacity assessment is performed. The load-bearing capacity assessment results of electrical equipment at each level are subjected to bidirectional collaborative constraint processing, and the final load-bearing capacity assessment results of electrical equipment at each level after constraint are output based on the constraint results. The final load-bearing capacity assessment results are compared with the preset early warning indicators one by one. Based on the comparison results, the power supply access strategy corresponding to the early warning level is matched, and the access power supply is subjected to graded early warning processing.

[0006] In a preferred embodiment, this application can be further configured as follows: obtaining the power grid equipment type and topology connection relationship, dividing each electrical device into levels according to the power grid equipment type and the topology connection relationship, and constructing a full voltage level relationship architecture based on the level division result, specifically including: Obtain the equipment types and corresponding connection relationships of the power equipment connected to the power system, perform simulation operation on the power system based on the equipment types and connection relationships, and construct the corresponding power distribution network simulation model; The power supply types in the power distribution network simulation model are marked, and the power supply type and the topological connection relationship between the power supply are used to divide the power distribution network simulation model into levels and construct a full voltage level relationship architecture.

[0007] In a preferred embodiment, this application can be further configured as follows: obtaining load output data and power output data for each level in the full voltage level relationship architecture and performing time-series power flow calculations, and performing comprehensive carrying capacity assessment based on the time-series power flow calculation results, specifically includes: Obtain the load output data and power output data for each level in the full voltage level relationship architecture, and construct the time-series load output curve and the time-series renewable energy output curve respectively to obtain the time-series load and renewable energy output curves; The time-series load and renewable energy output curves are iterated in time periods and the node voltage and branch power are calculated to form several time-series power flow sequences carrying output node voltage, branch power, load output and renewable energy output. The time-series load and renewable energy output curves are marked with key sections and matched with corresponding section weights. Based on the section weights and the time-series power flow sequences corresponding to the key sections, a comprehensive carrying capacity assessment is performed.

[0008] In a preferred embodiment, this application can be further configured as follows: marking key sections of the time-series load and renewable energy output curves and matching corresponding section weights, and performing comprehensive carrying capacity assessment based on the section weights and the time-series power flow sequences corresponding to the key sections, specifically including: The time-series load and new energy output curves are marked with key sections according to the set section type, and weights are assigned according to the criticality of the section type to obtain the section weight of each key section. Based on the cross-sectional weights, the time-series power flow sequence of each key cross-section is weighted, and the comprehensive bearing capacity assessment is performed based on the multi-cross-sectional weight calculation results of the entire time series to obtain the bearing capacity assessment results of the electrical equipment.

[0009] In a preferred embodiment, this application can be further configured as follows: the load-bearing capacity assessment results of electrical equipment at each level are subjected to bidirectional collaborative constraint processing, and the final load-bearing capacity assessment results of electrical equipment at each level after constraint are output based on the constraint results, specifically including: Calculate the theoretical access capacity for each level, and constrain the load-bearing capacity assessment results of the lower level from the bottom up based on the hierarchical order of the full voltage level relationship architecture and the theoretical access capacity of the higher level. Obtain the equipment hardware condition constraints at each level, and according to the hierarchical order of the full voltage level relationship architecture, constrain the load-bearing capacity evaluation results of the lower level from top to bottom through the equipment hardware condition constraints of the higher level. Under the dual constraints of bottom-up and top-down constraints, the final load-bearing capacity assessment results of electrical equipment at each level after bidirectional collaborative constraints are output.

[0010] In a preferred embodiment, this application can be further configured as follows: the calculation of the theoretical access capacity for each level, based on the hierarchical order of the full voltage level relationship architecture, constrains the load-bearing capacity assessment results of the lower level from the bottom up using the theoretical access capacity of the higher level, specifically including: Calculate the sum of the theoretical access capacity of all lower-level units, and determine whether the sum of the theoretical access capacity of all lower-level units is greater than the theoretical access capacity of the higher-level unit. If so, calculate the capacity allocation ratio, and under the constraint of the theoretical remaining accessible capacity at the next higher level, redistribute all theoretical accessible capacity at the next lower level according to the capacity allocation ratio. If not, it will operate according to the theoretical access capacity of each lower level.

[0011] In a preferred embodiment, this application can be further configured as follows: comparing the final load-bearing capacity assessment result with preset early warning indicators item by item, matching the power supply access strategy corresponding to the early warning level based on the comparison result, and performing graded early warning processing on the access power supply, specifically including: The constraint satisfaction rate, bearing capacity margin, and risk accumulation times in the final bearing capacity assessment results are compared with the preset early warning indicators one by one, and the corresponding level of electrical equipment is marked with an early warning level based on the comparison results. The power access strategy preset by the marked warning level is invoked to perform graded warning processing on the electrical equipment, and the final load-bearing capacity assessment result and warning processing decision of each level of electrical equipment are output based on the graded warning results.

[0012] Secondly, the above-mentioned inventive objective of this application is achieved through the following technical solutions: A power system distributed generation capacity assessment system based on time-series power flow calculation across all voltage levels, the system comprising: The full voltage level architecture construction module is used to obtain the grid equipment type and topology connection relationship, divide each electrical equipment into levels according to the grid equipment type and topology connection relationship, and construct the full voltage level relationship architecture based on the level division result; The load-bearing capacity classification assessment module is used to acquire the load output data and power output data of each level in the full voltage level relationship architecture and perform time-series power flow calculation, and perform comprehensive load-bearing capacity assessment based on the time-series power flow calculation results; The load-bearing capacity assessment result constraint module is used to perform bidirectional collaborative constraint processing on the load-bearing capacity assessment results of electrical equipment at each level, and outputs the final load-bearing capacity assessment results of electrical equipment at each level after constraint based on the constraint results; The graded early warning module is used to compare the final load-bearing capacity assessment result with the preset early warning indicators one by one, match the power access strategy corresponding to the early warning level according to the comparison result, and perform graded early warning processing on the access power supply.

[0013] Thirdly, the above-mentioned objectives of this application are achieved through the following technical solutions: A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described method for assessing the carrying capacity of distributed power sources across all voltage levels of a power system based on time-series power flow calculation.

[0014] Fourthly, the above-mentioned objectives of this application are achieved through the following technical solutions: A computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for assessing the carrying capacity of distributed power sources across all voltage levels in a power system based on time-series power flow calculation.

[0015] In summary, this application includes at least one of the following beneficial technical effects: 1. High assessment accuracy: The time-series power flow calculation method is adopted, which fully considers the dynamic changes in the output of distributed power sources and loads, thus improving the accuracy of the assessment results; 2. Good hierarchical coordination: A complete full voltage level assessment system has been established, which fully considers the mutual influence and constraints of the bearing capacity between different voltage levels, and realizes hierarchical and coordinated assessment. 3. High computational efficiency: Parallel computing is used to process the carrying capacity calculation of substations at different voltage levels, combined with efficient power flow calculation methods, which significantly improves computational efficiency; 4. Intuitive Results: The three-tiered early warning mechanism of green, yellow, and red makes the assessment results intuitive and clear, facilitating quick understanding and application by decision-makers; 5. High practicality: The evaluation results can be directly applied to the formulation of distributed power source access schemes and power grid planning decisions, providing technical support for the rational development and utilization of new energy sources. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0017] Figure 1 This is a flowchart illustrating the implementation of the power system full voltage level distributed power source carrying capacity assessment method in this embodiment.

[0018] Figure 2 This is a flowchart illustrating the implementation of step S10 of the power system full voltage level distributed power source carrying capacity assessment method in this embodiment.

[0019] Figure 3 This is a flowchart illustrating the implementation of step S20 of the power system full voltage level distributed power source carrying capacity assessment method in this embodiment.

[0020] Figure 4 This is a flowchart illustrating the implementation of step S203 of the power system full voltage level distributed power source carrying capacity assessment method in this embodiment.

[0021] Figure 5This is a flowchart illustrating the implementation of step S30 of the power system full voltage level distributed power source carrying capacity assessment method in this embodiment.

[0022] Figure 6 This is a flowchart illustrating the implementation of step S301 of the power system full voltage level distributed power source carrying capacity assessment method in this embodiment.

[0023] Figure 7 This is a flowchart illustrating the implementation of step S40 of the power system full voltage level distributed power source carrying capacity assessment method in this embodiment.

[0024] Figure 8 This is a structural block diagram of the power system full voltage level distributed power supply capacity assessment system in this embodiment.

[0025] Figure 9 This is a schematic diagram of the internal structure of a computer device used to implement a method for assessing the carrying capacity of distributed power sources across all voltage levels in a power system. Detailed Implementation

[0026] 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, not all, of the embodiments of the present invention. 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.

[0027] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0028] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0029] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0030] In one embodiment, a computational framework of "time-series data-driven - time-segmented iterative solution" is adopted. Combining the differences in time-series characteristics between load and renewable energy, an adaptive step-size power flow algorithm is used to achieve full-cycle, multi-time-segment power flow calculation, ultimately outputting continuous time-series load and renewable energy output data. For example... Figure 1 As shown, this application discloses a method for assessing the carrying capacity of distributed generation at all voltage levels in a power system based on time-series power flow calculation, which specifically includes the following steps: S10: Obtain the power grid equipment type and topology connection relationship, divide each electrical equipment into levels according to the power grid equipment type and topology connection relationship, and construct the full voltage level relationship architecture based on the level division results.

[0031] Specifically, such as Figure 2 As shown, step S10 includes: S101: Obtain the equipment type and corresponding connection relationship of the power equipment connected to the power system, simulate the operation of the power system based on the equipment type and connection relationship, and construct the corresponding power distribution network simulation model.

[0032] Specifically, an integrated main and distribution network modeling approach is adopted. Utilizing the automated modeling (component addition, pin binding, parameter modification, etc.) API interface provided by the distribution network simulation platform, the system reads the vector graphic (SVG) and equipment information files (XML) describing the power grid topology from the Geographic-Information-System (GIS) to automatically generate the system network structure. The system automatically identifies the equipment type of each electrical node in the SVG diagram, such as circuit breakers, transformers, feeder loads, and power sources; and parses the XML file to clarify the topological connections between each electrical node. Based on the identified equipment types and wiring methods, corresponding simulation components are generated and placed in their corresponding positions on the drawing for simulation operation. Based on the component information provided in the XML, the connection points between the distribution network and the main network are determined. In the corresponding substation, the corresponding components are modified to connect the distribution network to the main network, resulting in a distribution network simulation model corresponding to the power system.

[0033] S102: Mark the power supply types in the power distribution network simulation model, and based on the power supply types and the topological connection relationships between power supplies, divide the power distribution network simulation model into levels and construct a full voltage level relationship architecture.

[0034] Specifically, the power supply types in the distribution network simulation model are labeled, including 220kV substations, 110kV substations, 35kV substations, medium-voltage lines, distribution transformers, and distributed power sources. The topological connections between power sources are defined as follows: a 220kV substation supplies power to a 110kV substation, a 110kV substation receives 220kV power and supplies power to the 35kV and below grid, and so on. The distribution network simulation model is hierarchically divided according to power supply type, constructing a 6-level full voltage level relationship architecture covering "distributed power source - distribution transformer - medium-voltage line - substation main transformer," clarifying the electrical connections and attribution relationships of each device, laying the foundation for cross-level collaborative evaluation. The full voltage level relationship architecture is defined as follows: Tier 1: 220kV substation main transformer, serving as the backbone power source of the power grid; Level 2: 110kV substation main transformer, receiving 220kV power and supplying power to 35kV and below power grids; Level 3: 35kV substation main transformer, which realizes voltage level conversion and power distribution; Level 4: Medium voltage lines (10kV), responsible for distributing electrical energy from substations to various distribution substations; Level 5: Distribution transformers in the distribution area, which directly provide power supply to users; Level 6: Distributed power sources, including new energy power generation equipment such as photovoltaic and wind power; For situations where there are no distributed power sources, the proportion of distributed power sources in the load of the distribution transformer area can be processed according to the parameter. If the proportion is not 0, an equivalent distributed power source with a capacity of not 0 is generated. If the proportion is 0, a virtual distributed power source with a capacity of 0 is generated to ensure the integrity and consistency of the hierarchical relationship.

[0035] The hierarchical division rules in this embodiment include: for equipment with a clear main transformer affiliation, parameters are accurately divided according to the substation to which the main transformer belongs; for equipment without a clear affiliation (such as scattered distributed power sources and cross-regional lines), an innovative dual-dimensional automatic aggregation mechanism of "geographical location + electrical connection" is adopted to achieve dynamic verification of equipment affiliation through power grid topology correlation analysis; multiple main transformers on the same bus are regarded as belonging to the same substation and are uniformly named according to the bus key to ensure the consistency of hierarchical relationships.

[0036] S20: Obtain load output data and power output data for each level in the full voltage level relationship architecture and perform time-series power flow calculations. Based on the time-series power flow calculation results, perform comprehensive carrying capacity assessment.

[0037] Specifically, such as Figure 3 As shown, step S20 includes: S201: Obtain the load output data and power output data of each level in the full voltage level relationship architecture, construct the time-series load output curve and the time-series renewable energy output curve respectively, and obtain the time-series load and renewable energy output curves.

[0038] Specifically, load processing data and power output data for each level in the full voltage level relationship architecture are obtained through historical load data of the power system. The load output data and power output data are corrected in combination with specific application scenarios to construct time-series load output curves and time-series renewable energy output curves, forming a comprehensive time-series load and renewable energy output curve.

[0039] S202: Iterate the time-series load and renewable energy output curves in different time periods and calculate the node voltage and branch power to form several time-series power flow sequences carrying the output node voltage, branch power, load output, and renewable energy output.

[0040] Specifically, in this embodiment, a minimum step size of 1 hour is used to iterate the time-series load and renewable energy output curves for the entire assessment period in different time periods. For example, if the assessment period is 1 year, 12 time-series power flow sequences are obtained. The output node voltage, branch power, load output, and renewable energy output data for each time period are calculated to form multiple time-series power flow sequences with specific step sizes carrying output node voltage, branch power, load output, and renewable energy output. In this embodiment, the time-series power flow sequences can also be hierarchically divided according to the full voltage level relationship architecture, including distribution areas, medium voltage lines, and various substations.

[0041] S203: Mark key sections of the time-series load and renewable energy output curves and match corresponding section weights. Based on the section weights and the time-series power flow sequences corresponding to the key sections, perform comprehensive carrying capacity assessment.

[0042] Specifically, such as Figure 4 As shown, step S203 includes: S2031: Mark key sections of the time-series load and new energy output curves according to the set section type, assign weights according to the criticality of the section type, and obtain the section weight of each key section.

[0043] Specifically, the cross-section types include: Category I cross-sections mainly consisting of peak load cross-sections (daily load top 3 times), peak renewable energy output cross-sections (PV noon / wind power gust times), and load-renewable energy reverse peak cross-sections (load trough + renewable energy peak); Category II cross-sections mainly consisting of flat load cross-sections (daily load 50%-80% range) and flat renewable energy output cross-sections; and Category III cross-sections mainly consisting of trough load cross-sections and trough renewable energy output cross-sections. Based on the defined cross-section types, the time-series load and renewable energy output curves are marked with cross-sections of each type. Weights are assigned according to the criticality of the cross-section type; for example, Category I cross-sections have a weight of 0.4, Category II cross-sections have a weight of 0.3, and Category III cross-sections have a weight of 0.3. The sum of the weights for all types is 1, thus obtaining the cross-section weight for each critical cross-section.

[0044] S2032: Based on the section weight, the time-series power flow sequence of each key section is weighted, and the comprehensive bearing capacity assessment is performed based on the multi-section weight calculation results of the entire time series to obtain the bearing capacity assessment results of the electrical equipment.

[0045] Specifically, based on the section weight, the time-series power flow sequence of each key section is weighted and calculated. For example, each parameter of the time-series power flow sequence is multiplied by the corresponding section weight. The remaining accessible capacity is calculated based on the time-series power flow sequence after the addition of section weight. The remaining accessible capacity is used to characterize the carrying capacity of electrical equipment for comprehensive carrying capacity assessment, and the carrying capacity assessment result of electrical equipment is obtained.

[0046] The formula for calculating the remaining available access capacity is as follows: (1) in, Indicates the remaining available access capacity. Indicates the cross-sectional weight. The maximum reverse load rate during the evaluation period. Indicates the rated capacity of the transformer. This represents the equipment operating margin coefficient, which is typically set to 0.8 in this embodiment.

[0047] The reverse load rate calculation expression in this embodiment is as follows: (2) in, This represents the output power of distributed power sources (kW). The equivalent electrical load (kW) at any given moment is represented by the maximum load within the sampling evaluation period. Indicates the actual operating limit (kW) of the transformer or line.

[0048] S30: Perform bidirectional collaborative constraint processing on the load-bearing capacity assessment results of electrical equipment at each level, and output the final load-bearing capacity assessment results of electrical equipment at each level after constraint based on the constraint results.

[0049] Specifically, such as Figure 5 As shown, step S30 includes: S301: Calculate the theoretical access capacity for each level. Based on the hierarchical order of the full voltage level relationship architecture, use the theoretical access capacity of the higher level to constrain the load-bearing capacity assessment results of the lower level from the bottom up.

[0050] Specifically, the theoretical access capacity for each level is calculated using the formulas (1) and (2) above, such as... Figure 6 As shown, step S301 includes: S3011: Calculate the sum of the theoretical access capacity of all lower-level units and determine whether the sum of the theoretical access capacity of all lower-level units is greater than the theoretical access capacity of the higher-level unit.

[0051] Specifically, when evaluating medium-voltage lines, according to the full voltage level relationship framework, the total theoretical access capacity of two levels, namely distribution transformers and distributed power sources, is calculated. The total theoretical access capacity of the lower level is compared with the theoretical access capacity of the medium-voltage lines. Based on the comparison results, it is determined whether the total theoretical access capacity of all lower levels is greater than the theoretical access capacity of the higher level.

[0052] S3012: If so, calculate the capacity allocation ratio and, under the constraint of the theoretical remaining accessible capacity at the next higher level, redistribute all theoretical accessible capacities at the next lower level according to the capacity allocation ratio.

[0053] If so, it means the total expected capacity of the lower-level devices exceeds the carrying capacity of the corresponding upper-level devices. In other words, the carrying capacity of the upper-level devices cannot meet the access capacity requirements of all lower-level devices. Therefore, the capacity allocation ratio is the ratio of the remaining capacity of this level to the sum of the theoretical capacities of all lower-level devices. In this embodiment, the capacity allocation ratio is less than 1. After multiplying the theoretical remaining accessible capacity of the higher-level device by the capacity allocation ratio as a constraint, the total accessible capacity of all devices at the lower level is determined. This redistribution ensures that the sum of the actual capacities of all lower-level devices is less than or equal to their own remaining capacities, preventing over-limitation.

[0054] S3013: If not, then operate according to the theoretical access capacity of each lower level.

[0055] Specifically, if not, it means that the total capacity of the lower levels is less than or equal to the remaining capacity of this level, and the capacity demand of the lower levels is within the range that this level can accept. In this case, all lower-level devices can retain the theoretical capacity calculated for the corresponding level and operate according to the theoretical access capacity of each lower level.

[0056] In one embodiment, the theoretically achievable capacity constraints for each level of equipment from the distributed power source to the 220kV main transformer are as follows: The remaining connectable capacity of the distribution transformer in the area is calculated by considering constraints such as load factor, voltage deviation, and power factor, using formulas (1) and (2). Under the constraints of line reverse load factor ≤80% (thermal stability constraint), voltage deviation ≤±7% (GB / T12325 standard), and short-circuit capacity not exceeding the limit, the remaining connectable capacity of the medium-voltage line is calculated to ensure the safe operation of the line after the distributed power source is connected. The calculation results of the remaining connectable capacity of the medium-voltage line are used to verify the remaining connectable capacity of the distribution transformer in the area, thereby constraining the load-bearing capacity assessment results of the distribution transformer in the area from the bottom up.

[0057] In this embodiment, the remaining connectable capacity of 35kV, 110kV, and 220kV substations is calculated in parallel. The minimum value of the parallel evaluation results of equipment carrying capacity assessment, short-circuit current assessment, and voltage deviation assessment is selected as the theoretical connectable capacity of the substation. The theoretical connectable capacity of this level is then calculated using the theoretical connectable capacity of the previous level, thus implementing bottom-up constraints through multi-dimensional comprehensive evaluation and constraints. This avoids the one-sidedness of a single dimension.

[0058] Specifically, the formula "Main transformer carrying capacity = Typical moment main transformer load + Main transformer capacity × 0.8" is used to accurately calculate the permissible photovoltaic / wind power output under reverse non-overload conditions, thereby assessing the equipment carrying capacity; the short-circuit current margin of the low-voltage and high-voltage busbars is checked separately to avoid short-circuit current exceeding the limit due to distributed power source access, and short-circuit current assessment is performed; according to GB / T 12325 standard, the voltage deviation of each node after distributed power source access is calculated to ensure that it does not exceed the allowable range, and voltage deviation assessment is performed.

[0059] S302: Obtain the hardware constraints of each level of equipment, and constrain the load-bearing capacity assessment results of the lower level from top to bottom by using the hardware constraints of the higher level of equipment.

[0060] Specifically, the hardware constraints of each level of equipment are obtained. According to the hierarchical order of the full voltage level relationship architecture, the carrying capacity assessment results of the lower level are constrained by the hardware constraints of the higher level of equipment. For example, the hardware constraints of the tabletop distribution transformer layer constrain the carrying capacity assessment results of the distributed power source, the hardware constraints of the medium voltage line constrain the carrying capacity assessment results of the tabletop distribution transformer, and so on.

[0061] Specifically, as the highest voltage level, the available capacity of a 220kV substation directly affects the grid connection capacity of its downstream substations. When calculating the remaining available capacity of a 220kV substation, the following must be considered: the transmission capacity constraints of the upstream grid (500kV), the capacity constraints of the 220kV main transformer, the bus load constraints, and equipment margin requirements.

[0062] The calculation of the remaining open capacity of a 110kV substation needs to take into account the constraints of the 220kV substation. The specific calculation method is as follows: First, calculate the access capacity of the 110kV substation without upper-level constraints, and then compare it with the capacity limit allocated to the 110kV substation by the 220kV substation. Take the smaller value of the two as the remaining open capacity of the 110kV substation.

[0063] The calculation of the remaining available capacity of a 35kV substation needs to take into account the constraints of the 110kV substation. The calculation method is similar to that of the 110kV substation: first calculate the available capacity of the 35kV substation itself, then compare it with the capacity limit allocated by the superior 110kV substation, and take the smaller value as the final remaining available capacity.

[0064] The calculation of the remaining available capacity of medium-voltage lines and transformer substations needs to consider the constraints of higher levels step by step. The final remaining available capacity is: the access capacity considering the network structure constraints and the access capacity without considering the network structure constraints. The smaller of the two values ​​is taken as the final result.

[0065] S303: Under the dual constraints of bottom-up and top-down constraints, output the final load-bearing capacity assessment results of electrical equipment at each level after bidirectional coordinated constraints.

[0066] Specifically, based on the bottom-up constraints and top-down dual constraints of the load-bearing capacity assessment results, the final load-bearing capacity assessment results of electrical equipment at each level that meet the dual constraints are output.

[0067] S40: Compare the final load-bearing capacity assessment results with the preset early warning indicators one by one, match the power access strategy corresponding to the early warning level according to the comparison results, and carry out graded early warning processing for the access power supply.

[0068] Specifically, such as Figure 7 As shown, step S40 includes: S401: The constraint satisfaction rate, bearing capacity margin, and risk accumulation times in the final bearing capacity assessment results are compared with the preset early warning indicators one by one, and the corresponding level of electrical equipment is marked with an early warning level based on the comparison results.

[0069] Specifically, the constraint satisfaction rate, bearing capacity margin, and risk accumulation count in the final bearing capacity assessment results are compared item by item with the preset early warning indicators. Based on the comparison results, the electrical equipment is marked with the corresponding early warning level. The warning levels and warning indicators in this embodiment are shown in Table 1: Table 1 In this embodiment, the constraint satisfaction rate is calculated as the number of cross sections satisfying all constraints (load rate, voltage deviation, short-circuit current) throughout the entire cycle / total number of cross sections × 100%; the load capacity margin is calculated as (constraint limit - actual maximum value) / constraint limit × 100% (e.g., load rate margin = (80% - actual maximum load rate) / 80% × 100%); the cumulative risk count is obtained by statistically analyzing the cumulative number of constraints exceeding the limit (e.g., voltage deviation > 7%) throughout the entire cycle. In this embodiment, the primary constraints are voltage deviation and short-circuit current margin, and the secondary constraint is load rate.

[0070] S402: Invoke the preset power access strategy of the marked warning level to perform graded warning processing on electrical equipment, and output the final load-bearing capacity assessment result and warning processing decision of each level of electrical equipment based on the graded warning results.

[0071] Specifically, the system matches the marked warning level with the corresponding applicable scenario, and then matches the power access strategy based on the matched scenario. For example, in the scenario of "light grid load and strong renewable energy absorption capacity", distributed power access is recommended; in the scenario of "stable grid operation and critical local constraints", it is necessary to confirm the access project and conduct a special analysis; in the scenario of "some cross-section constraints exceed the standard and there is local risk" or "the grid is heavily loaded and the systemic risk is high", the access of new distributed power projects is suspended. The system performs graded warning processing on electrical equipment based on the matching results of the matched power access strategy, and finally outputs the final load-bearing capacity assessment results and warning processing decisions for each level of electrical equipment.

[0072] The final carrying capacity assessment results for each level include a comparative analysis of carrying capacity classification considering higher-level constraints and carrying capacity classification without considering higher-level constraints. The final carrying capacity is the maximum connectable capacity after constraints, which can provide data support for decisions such as the approval of distributed power generation projects and adjustments to power grid planning.

[0073] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0074] In one embodiment, a power system full-voltage-level distributed generation capacity assessment system based on time-series power flow calculation is provided. This power system full-voltage-level distributed generation capacity assessment system based on time-series power flow calculation corresponds one-to-one with the power system full-voltage-level distributed generation capacity assessment method based on time-series power flow calculation in the above embodiments. Figure 8 As shown, the power system distributed generation capacity assessment system based on time-series power flow calculation includes a full-voltage-level architecture construction module, a capacity-level assessment module, a capacity assessment result constraint module, and a graded early warning module. Detailed descriptions of each functional module are as follows: The full voltage level architecture construction module is used to obtain the grid equipment type and topology connection relationship, divide each electrical equipment into levels according to the grid equipment type and topology connection relationship, and construct the full voltage level relationship architecture based on the level division results.

[0075] The load capacity classification assessment module is used to acquire load output data and power output data for each level in the full voltage level relationship architecture and perform time-series power flow calculations. Based on the time-series power flow calculation results, a comprehensive load capacity assessment is performed.

[0076] The load-bearing capacity assessment result constraint module is used to perform bidirectional collaborative constraint processing on the load-bearing capacity assessment results of electrical equipment at each level, and outputs the final load-bearing capacity assessment results of electrical equipment at each level after constraint based on the constraint results.

[0077] The graded early warning module is used to compare the final load-bearing capacity assessment results with the preset early warning indicators one by one, and match the power access strategy corresponding to the early warning level according to the comparison results, and perform graded early warning processing on the access power.

[0078] Preferably, the full voltage level architecture building module specifically includes: The model building submodule is used to obtain the equipment types and corresponding connection relationships of the power equipment connected to the power system, perform simulation operation on the power system based on the equipment types and connection relationships, and build the corresponding power distribution network simulation model.

[0079] The architecture building submodule is used to mark the power source types in the power distribution network simulation model, and to divide the power distribution network simulation model into levels based on the power source types and the topological connection relationships between the power sources, thus constructing a full voltage level relationship architecture.

[0080] Preferably, the load-bearing capacity grading assessment module specifically includes: The curve construction submodule is used to obtain load output data and power output data for each level in the full voltage level relationship architecture, and construct time-series load output curves and time-series renewable energy output curves respectively, thus obtaining the time-series load and renewable energy output curves.

[0081] The sequence segmentation submodule is used to iterate the time-series load and renewable energy output curves in different time periods and calculate the node voltage and branch power to form several time-series power flow sequences carrying the output node voltage, branch power, load output, and renewable energy output.

[0082] The carrying capacity assessment submodule is used to mark key sections of the time-series load and renewable energy output curves and match the corresponding section weights. Based on the section weights and the time-series power flow sequences corresponding to the key sections, a comprehensive carrying capacity assessment is performed.

[0083] Preferably, the load-bearing capacity assessment submodule specifically includes: The weighting unit is used to mark key sections of the time-series load and new energy output curves according to the set section type, and assign weights according to the criticality of the section type to obtain the section weight of each key section.

[0084] The load-bearing capacity assessment unit is used to calculate the weight of the time-series power flow sequence of each key section according to the section weight, and to perform a comprehensive load-bearing capacity assessment based on the multi-section weight calculation results of the entire time series, so as to obtain the load-bearing capacity assessment result of the electrical equipment.

[0085] Preferably, the bearing capacity assessment result constraint module specifically includes: The bottom-up constraint submodule is used to calculate the theoretical access capacity of each level. Based on the hierarchical order of the full voltage level relationship architecture, the load-bearing capacity assessment results of the lower level are constrained from the bottom up by the theoretical access capacity of the higher level.

[0086] The top-down constraint submodule is used to obtain the equipment hardware condition constraints at each level. Based on the hierarchical order of the full voltage level relationship architecture, it applies top-down constraints to the load-bearing capacity assessment results of the lower level through the equipment hardware condition constraints of the higher level.

[0087] Under the dual constraints of bottom-up and top-down constraints, the final load-bearing capacity assessment results of electrical equipment at each level after bidirectional collaborative constraints are output.

[0088] Preferably, the bottom-up constraint submodule specifically includes: The lower-level capacity over-limit judgment unit is used to calculate the sum of the theoretically accessible capacity of all lower-level units and determine whether the sum of the theoretically accessible capacity of all lower-level units is greater than the theoretically accessible capacity of the higher-level unit.

[0089] The capacity redistribution unit is used to calculate the capacity allocation ratio if the condition is met, and redistribute all theoretically available access capacity at the lower level according to the capacity allocation ratio, under the constraint of the theoretical remaining available access capacity at the higher level.

[0090] The capacity confirmation unit is used to determine whether to operate according to the theoretically available capacity of each lower level if not.

[0091] Preferably, the tiered early warning module specifically includes: The rating marking submodule is used to compare the constraint satisfaction rate, bearing capacity margin and risk accumulation times in the final bearing capacity assessment results with the preset warning indicators one by one, and mark the corresponding level of electrical equipment with warning level based on the comparison results.

[0092] The graded early warning submodule is used to call the preset power access strategy of the marked early warning level to perform graded early warning processing on electrical equipment, and output the final load-bearing capacity assessment result and early warning processing decision of each level of electrical equipment based on the graded early warning result.

[0093] Specific limitations regarding the power system full-voltage-level distributed generation capacity assessment system based on time-series power flow calculation can be found in the limitations of the power system full-voltage-level distributed generation capacity assessment method based on time-series power flow calculation mentioned above, and will not be repeated here. Each module in the aforementioned power system full-voltage-level distributed generation capacity assessment system based on time-series power flow calculation can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0094] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores data on the carrying capacity of distributed generation sources across all voltage levels of the power system. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a method for assessing the carrying capacity of distributed generation sources across all voltage levels of the power system based on time-series power flow calculation.

[0095] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements a method for assessing the carrying capacity of distributed power sources across all voltage levels in a power system based on time-series power flow calculation.

[0096] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application of the technical solution and the constraints involved. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0097] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical functional division. In actual implementation, there may be other division methods, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored.

[0098] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0099] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0100] 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 or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for assessing the carrying capacity of distributed generation at all voltage levels in a power system based on time-series power flow calculation, characterized in that, The method includes: Obtain the power grid equipment type and topology connection relationship, divide each electrical equipment into levels according to the power grid equipment type and the topology connection relationship, and construct a full voltage level relationship architecture based on the level division result; The load output data and power output data of each level in the full voltage level relationship architecture are obtained and time-series power flow calculation is performed. Based on the time-series power flow calculation results, a comprehensive carrying capacity assessment is performed. The load-bearing capacity assessment results of electrical equipment at each level are subjected to bidirectional collaborative constraint processing, and the final load-bearing capacity assessment results of electrical equipment at each level after constraint are output based on the constraint results. The final load-bearing capacity assessment results are compared with the preset early warning indicators one by one. Based on the comparison results, the power supply access strategy corresponding to the early warning level is matched, and the access power supply is subjected to graded early warning processing.

2. The method for assessing the carrying capacity of distributed generation at all voltage levels in a power system based on time-series power flow calculation as described in claim 1, characterized in that, The process of acquiring power grid equipment types and topology connections, classifying each electrical device into hierarchical levels based on these data, and constructing a full voltage level relationship architecture based on the hierarchical classification results specifically includes: Obtain the equipment types and corresponding connection relationships of the power equipment connected to the power system, perform simulation operation on the power system based on the equipment types and connection relationships, and construct a power distribution network simulation model corresponding to the power system; The power supply types in the power distribution network simulation model are marked, and the power supply type and the topological connection relationship between the power supply are used to divide the power distribution network simulation model into levels and construct a full voltage level relationship architecture.

3. The method for assessing the carrying capacity of distributed generation at all voltage levels in a power system based on time-series power flow calculation as described in claim 1, characterized in that, The process of acquiring load output data and power output data for each level in the full voltage level relationship architecture and performing time-series power flow calculations, followed by comprehensive carrying capacity assessment based on the time-series power flow calculation results, specifically includes: Obtain the load output data and power output data for each level in the full voltage level relationship architecture, and construct the time-series load output curve and the time-series renewable energy output curve respectively to obtain the time-series load and renewable energy output curves; The time-series load and renewable energy output curves are iterated in time periods and the node voltage and branch power are calculated to form several time-series power flow sequences carrying output node voltage, branch power, load output and renewable energy output. The time-series load and renewable energy output curves are marked with key sections and matched with corresponding section weights. Based on the section weights and the time-series power flow sequences corresponding to the key sections, a comprehensive carrying capacity assessment is performed.

4. The method for assessing the carrying capacity of distributed generation at all voltage levels in a power system based on time-series power flow calculation as described in claim 3, characterized in that, The process of marking key sections and matching corresponding section weights on the time-series load and renewable energy output curves, and performing comprehensive carrying capacity assessment based on the section weights and the time-series power flow sequences corresponding to the key sections, specifically includes: The time-series load and new energy output curves are marked with key sections according to the set section type, and weights are assigned according to the criticality of the section type to obtain the section weight of each key section. Based on the cross-sectional weights, the time-series power flow sequence of each key cross-section is weighted, and the comprehensive bearing capacity assessment is performed based on the multi-cross-sectional weight calculation results of the entire time series to obtain the bearing capacity assessment results of the electrical equipment.

5. The method for assessing the carrying capacity of distributed generation at all voltage levels in a power system based on time-series power flow calculation as described in claim 1, characterized in that, The load-bearing capacity assessment results of electrical equipment at each level are subjected to bidirectional collaborative constraint processing. Based on the constraint results, the final load-bearing capacity assessment results of electrical equipment at each level after constraint are output, specifically including: Calculate the theoretical access capacity for each level, and constrain the load-bearing capacity assessment results of the lower level from the bottom up based on the hierarchical order of the full voltage level relationship architecture and the theoretical access capacity of the higher level. Obtain the equipment hardware condition constraints at each level, and according to the hierarchical order of the full voltage level relationship architecture, constrain the load-bearing capacity evaluation results of the lower level from top to bottom through the equipment hardware condition constraints of the higher level. Under the dual constraints of bottom-up and top-down constraints, the final load-bearing capacity assessment results of electrical equipment at each level after bidirectional collaborative constraints are output.

6. The method for assessing the carrying capacity of distributed generation at all voltage levels in a power system based on time-series power flow calculation as described in claim 5, characterized in that, The calculation of the theoretical access capacity for each level involves, according to the hierarchical order of the full voltage level relationship architecture, using the theoretical access capacity of the higher level to constrain the load-bearing capacity assessment results of the lower level from the bottom up. Specifically, this includes: Calculate the sum of the theoretical access capacity of all lower-level units, and determine whether the sum of the theoretical access capacity of all lower-level units is greater than the theoretical access capacity of the higher-level unit. If so, calculate the capacity allocation ratio, and under the constraint of the theoretical remaining accessible capacity at the next higher level, redistribute all theoretical accessible capacity at the next lower level according to the capacity allocation ratio. If not, it will operate according to the theoretical access capacity of each lower level.

7. The method for assessing the carrying capacity of distributed generation at all voltage levels in a power system based on time-series power flow calculation as described in claim 1, characterized in that, The step of comparing the final load-bearing capacity assessment result with preset early warning indicators item by item, matching the power supply access strategy corresponding to the early warning level based on the comparison result, and performing graded early warning processing on the accessed power supply specifically includes: The constraint satisfaction rate, bearing capacity margin, and risk accumulation times in the final bearing capacity assessment results are compared with the preset early warning indicators one by one, and the corresponding level of electrical equipment is marked with an early warning level based on the comparison results. The power access strategy preset by the marked warning level is invoked to perform graded warning processing on the electrical equipment, and the final load-bearing capacity assessment result and warning processing decision of each level of electrical equipment are output based on the graded warning results.

8. A power system full-voltage-level distributed generation capacity assessment system based on time-series power flow calculation, characterized in that, The system includes: The full voltage level architecture construction module is used to obtain the grid equipment type and topology connection relationship, divide each electrical equipment into levels according to the grid equipment type and topology connection relationship, and construct the full voltage level relationship architecture based on the level division result; The load-bearing capacity classification assessment module is used to acquire the load output data and power output data of each level in the full voltage level relationship architecture and perform time-series power flow calculation, and perform comprehensive load-bearing capacity assessment based on the time-series power flow calculation results; The load-bearing capacity assessment result constraint module is used to perform bidirectional collaborative constraint processing on the load-bearing capacity assessment results of electrical equipment at each level, and outputs the final load-bearing capacity assessment results of electrical equipment at each level after constraint based on the constraint results; The graded early warning module is used to compare the final load-bearing capacity assessment result with the preset early warning indicators one by one, match the power access strategy corresponding to the early warning level according to the comparison result, and perform graded early warning processing on the access power supply.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for assessing the carrying capacity of distributed power sources at all voltage levels of a power system based on time-series power flow calculation as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for assessing the carrying capacity of distributed power sources at all voltage levels of a power system based on time-series power flow calculation as described in any one of claims 1 to 7.

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