Method and system for optimal configuration of main grid reactive power compensation in extreme scenarios, and electronic device

Abstract

The present application relates to a kind of extreme scene main network reactive power compensation optimization configuration method, system, electronic equipment, method is extracted from mass time series section by clustering analysis, and forms optimization boundary data set, including typical and extreme section of extreme low load, new energy large and extreme weather;Respectively, the reactive power voltage sensitivity of 500kV and 220kV bus of target transformer substation to low voltage compensation port is calculated, and the total demand of inductive reactive power supplement of whole station is accurately accounted for by voltage grade;Multi-objective optimization model with customizable parameters is constructed, which considers investment and network loss, matches expansion, replacement or new optimization mode;Parallelly generate two sets of in-station reconstruction schemes of unified capacity expansion and multi-specification mixed expansion, and regional collaborative voltage regulation scheme relying on adjacent station sensitivity sorting, which activates idle reactor and is included in AVC linkage;Finally, full-condition simulation checking is carried out under extreme section, and the optimal configuration is selected by comprehensive total investment, land occupation, voltage out-of-limit elimination rate and other multi-index quantitative weighting.

Description

Technical Field

[0001] This invention relates to the field of power system planning technology, and in particular to a method, system, and electronic equipment for optimizing reactive power compensation configuration in extreme scenarios for the main grid. Background Technology

[0002] As a core node of the regional power grid, the 500kV hub substation typically covers multiple cities and prefectures, with a high proportion of industrial load and load characteristics exhibiting fluctuations during holidays and seasonality. During holidays and summer nighttime off-peak periods, the system load drops sharply. Simultaneously, renewable energy generation output is often high, compounded by the inherent excess capacitive charging power of ultra-high voltage long-distance transmission lines. This causes the 500kV bus voltage to frequently exceed its upper limit, threatening the safe and stable operation of the main grid and posing a severe challenge to the reactive power compensation configuration within the substation.

[0003] Currently, the optimization configuration of reactive power compensation in the main power grid primarily employs a modeling and calculation method based on typical daily load sections. This involves selecting several representative daily load curves to conduct reactive power balance analysis, thereby determining the installed capacity of compensation equipment such as reactors. However, this method has significant limitations in covering the power grid's operating conditions, failing to effectively incorporate low-probability, high-risk scenarios such as extreme off-peak loads and extreme renewable energy surges. This leads to overly optimistic configuration results, resulting in severely insufficient voltage regulation margins under extreme conditions after actual operation, and frequent occurrences of bus voltage exceeding limits. Furthermore, existing optimization methods are largely limited to the expansion and upgrading of equipment at the substation itself, neglecting the collaborative adjustment potential of idle low-voltage reactors and shunt reactors in adjacent substations. This results in high investment costs for project expansion and low overall resource utilization efficiency.

[0004] In addition, the current scheme lacks quantitative comparative analysis of multi-dimensional economic indicators such as land acquisition area, civil construction cost, and annual network loss revenue in the equipment selection process. The selection decision relies heavily on engineering experience, making it difficult to balance the voltage control effect with the project investment benefits. The economic evaluation system is still imperfect.

[0005] In summary, there is currently a lack of a method for optimizing the reactive power compensation configuration of the main grid that takes into account comprehensive coverage of extreme operating conditions, reasonable capacity allocation, inter-station resource synergy, and scientific economic comparison. Summary of the Invention

[0006] The purpose of this invention is to overcome the defects of the existing technology and provide a method, system, and electronic equipment for optimizing the configuration of reactive power compensation in extreme scenarios, so as to solve or partially solve the problems of incomplete coverage of optimization conditions, unreasonable configuration of compensation capacity, low utilization rate of regional reactive power resources, and lack of quantitative basis for scheme selection in the existing methods for optimizing reactive power compensation.

[0007] The objective of this invention can be achieved through the following technical solutions: One aspect of the present invention provides a method for optimizing the configuration of reactive power compensation in extreme scenarios of the main network, comprising the following steps: Acquire power grid topology parameters and time-series operation section data, perform power flow calculations, and screen out target substations where the bus voltage exceeds the limit and the reactive power regulation capacity within the station is exhausted. The time-series running section data is clustered and grouped, and the cluster center, typical section and extreme limit-breaking section of each group are extracted. The set of typical section and extreme limit-breaking section of each group is used as the optimization boundary dataset. For the target substation, the reactive voltage sensitivity of the busbars at different voltage levels to the low-voltage reactive power compensation port is calculated respectively. Based on the over-limit amplitude of the busbars under extreme cross-sections and the reactive voltage sensitivity, the required inductive reactive power supplement capacity for each voltage level is determined, and then the total reactive power supplement capacity of the entire substation is obtained by combining them. A multi-objective optimization model is constructed with the goal of minimizing overall cost and network loss. Constraints are set for power flow, voltage, total capacity, and total cost, and the optimization mode is automatically matched according to the current status of the site. Based on the multi-objective optimization model, an in-station reactive power compensation capacity expansion scheme and a regional coordinated voltage regulation scheme are generated. The in-station reactive power compensation capacity expansion scheme is obtained by configuring the number of fixed reactors and their capacity. The regional coordinated voltage regulation scheme selects the idle reactive power resources of high-sensitivity neighboring stations to participate in automatic voltage control linkage voltage regulation based on the reactive power and voltage sensitivity ranking results of neighboring stations. Using the extreme sections in the optimized boundary dataset, all generated schemes are verified by full-condition power flow simulation. Multiple evaluation indicators, including total cost, new reactive power capacity, and voltage over-limit elimination rate, are extracted. The optimal reactive power compensation configuration scheme is determined by quantitative comparison of multiple indicators.

[0008] As a preferred technical solution, the process of screening the target substation includes the following steps: The target substation is defined as a substation where the bus voltage continuously exceeds the upper limit and all inductive reactive power equipment within the station is fully operational with no remaining regulation capacity.

[0009] As a preferred technical solution, the process of constructing the optimized boundary dataset includes the following steps: K-means clustering was used to cluster the effective time-series operating sections. The clustering feature vectors included the total active load of the entire network, the total output of new energy sources, the average bus voltage, and the reactive power of line charging. For each cluster after clustering, the section closest to the cluster center is extracted as the typical section of the cluster center, and sorted in descending order according to the over-limit amplitude of the bus voltage. The first predetermined number of sections are selected as the extreme over-limit sections. All extreme cross sections are categorized into three types of severe scenarios: extreme low load, high renewable energy generation, and extreme weather, which together form the optimized boundary dataset.

[0010] As a preferred technical solution, the calculation process for the total reactive power supplementation capacity of the entire station includes the following steps: For the 500kV bus and 220kV bus of the target substation, calculate the reactive voltage sensitivity of their voltage to the low-voltage reactive power compensation port. Read the maximum over-limit amplitude of the two busbars under extreme cross-section, and calculate the inductive reactive power required to eliminate the overvoltage based on their respective sensitivities. The total reactive power supplementary capacity required for the entire station is obtained by superimposing the inductive reactive power capacity required for the two voltage levels.

[0011] As a preferred technical solution, the objective function of the multi-objective optimization model comprehensively considers the active power loss cost and the equipment cost, and sets constraints including node voltage constraints, reactive power constraints at the junction, total capacity constraints and total investment constraints. The optimization mode is automatically matched to the existing capacity expansion, capacitive reactance replacement or new site deployment mode according to the current site status.

[0012] As a preferred technical solution, the generation process of the on-site reactive power compensation capacity expansion scheme includes the following steps: Keeping the total number of existing reactor installations unchanged, generate separate expansion plans for the entire substation with the same specifications and unified capacity, as well as expansion plans for mixed configurations of reactors with multiple specifications.

[0013] As a preferred technical solution, the generation process of the regional coordinated voltage regulation scheme includes the following steps: Traverse the nearby substations surrounding the target substation, repeatedly calculate the reactive voltage sensitivity of each neighboring station and sort them in descending order. Prioritize selecting cold standby idle reactors at sites with high sensitivity ranking and include them in the regional automatic voltage control linkage voltage regulation resource pool; If the total capacity of all idle reactive power resources is insufficient to eliminate extreme overvoltage at the target substation, then additional reactors should be added at adjacent substations to supplement the capacity.

[0014] As a preferred technical solution, the process of multi-indicator quantitative comparison includes the following steps: For each scheme, the total newly added reactive power capacity, total one-time investment of the project, civil engineering land area, annual loss reduction benefits, annual average reduction in automatic voltage control operations, and construction complexity indicators are statistically analyzed. The overall voltage management effect, project cost, and operation and maintenance difficulty are weighted and scored, and the optimal configuration scheme and the corresponding retrofit equipment capacity, installation location, regional automatic voltage control linkage switching strategy, and annual investment calculation report are output.

[0015] Another aspect of the present invention provides a main grid reactive power compensation optimization configuration system for extreme scenarios, used to implement the aforementioned main grid reactive power compensation optimization configuration method, the system comprising: The data access unit is used to read power grid topology parameters and time-series operating section data, perform power flow calculations in batches, and mark target substations. The clustering analysis unit is used to cluster and group time-series running cross-section data, automatically extract the cluster center typical cross-sections and extreme over-limit cross-sections, and form an optimization boundary dataset. The sensitivity calculation unit is used to calculate the reactive voltage sensitivity of different voltage level buses in the target substation separately, and determine the required inductive reactive power supplement capacity based on the over-limit values ​​of each bus level. The optimized modeling and solving unit is used to construct a multi-objective function that minimizes both overall investment and network loss, sets various operational constraints, and automatically matches the optimization mode based on the current site status. The scheme generation unit is used to generate in-station renovation schemes, including unified capacity expansion schemes and multi-specification mixed capacity expansion schemes, as well as regional coordinated voltage regulation schemes based on the sensitivity ranking of neighboring stations. The simulation verification and optimization unit is used to import the extreme sections in the optimization boundary dataset to perform full-condition power flow simulation, automatically calculate multiple evaluation indicators of each scheme, and comprehensively select the optimal modification scheme.

[0016] In another aspect, an electronic device is provided, including one or more processors, a memory, and one or more programs stored in the memory, said one or more programs including instructions for executing the aforementioned main grid reactive power compensation optimization configuration method.

[0017] Compared with the prior art, the present invention has at least one of the following beneficial effects: (1) Comprehensive coverage of optimized operating conditions: Existing technologies have problems such as incomplete coverage of optimized operating conditions and insufficient compensation capacity configuration in extreme scenarios. In this regard, the present invention adopts extreme section extraction based on K-means clustering, automatically collecting three types of severe scenario sections from massive time-series operation data: extreme low load, large-scale generation of new energy, and extreme weather, to form an optimized boundary dataset. This ensures that the basic boundary of reactive power compensation optimization calculation fully covers all kinds of potential high-risk operating conditions, avoiding the risk of voltage exceeding limits and insufficient compensation capacity configuration due to omission of operating scenarios.

[0018] (2) Reduce the waste or inadequacy of compensation capacity: For the problems of rough calculation of reactance supplement capacity and deviation from the actual over-limit requirements of substation level in existing methods, this invention adopts the 500kV / 220kV dual voltage level layered sensitivity calculation technology to calculate the reactive voltage sensitivity of the two busbars to the low voltage compensation port respectively, and then accurately quantifies the required inductive reactive supplement capacity of each level and calculates the total demand. The calculation results are consistent with the actual over-limit degree of different voltage levels of the target substation, avoiding the waste or inadequacy of compensation capacity caused by single estimation, and making the equipment selection capacity configuration more reasonable.

[0019] (3) Improve the reactive power utilization efficiency of regional power grid assets: In response to the problems of idle reactive power resources in the region, large investment and low resource utilization rate of new construction in the substation, this invention adopts the optimization technology of dual-path parallel expansion of the substation and neighbor station collaboration. While generating multi-specification substation renovation schemes, it revitalizes the cold standby idle reactors of surrounding substations based on the sensitivity ranking of neighbor stations, incorporates them into the automatic voltage control (AVC) linkage voltage regulation resource pool to explore and utilize the existing idle reactive power resources, reduces the investment, land occupation and engineering workload of new equipment in the target substation, and improves the reactive power utilization efficiency of the entire regional power grid assets.

[0020] (4) Strong engineering adaptability and versatility: For the problem of non-universality and poor applicability of the scheme model when the SVG / fixed reactor ratio, cost and investment limit change, the present invention adopts the open multi-objective optimization model building technology with customizable parameters. In the objective function of minimizing cost and network loss, the custom interface of parameters such as SVG / fixed reactor ratio, equipment unit price, single unit capacity and total investment limit is opened, so that the optimization model can flexibly adapt to the differentiated transformation standards and cost levels of the power grid in different regions, avoiding model reconstruction caused by standard changes, and has strong engineering adaptability and versatility. Attached Figure Description

[0021] Figure 1 This is a flowchart of the main network reactive power compensation optimization configuration method for extreme scenarios in the embodiment; Figure 2 This is a schematic diagram of the main network reactive power compensation optimization configuration system in an extreme scenario, as shown in the embodiment. Figure 3 This is a schematic diagram of the electronic device in the embodiment. Detailed Implementation

[0022] 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 should fall within the scope of protection of the present invention.

[0023] Example 1 To address the shortcomings of existing technologies, such as incomplete coverage of operating conditions, unreasonable capacity configuration, low resource utilization, and lack of quantitative basis for scheme selection, this embodiment provides a method for optimizing the reactive power compensation configuration of the main grid in extreme scenarios. It relies on clustering to extract full-scenario cross-sections, performs hierarchical sensitivity for accurate capacity calculation, combines in-site capacity expansion with neighboring site collaboration for parallel optimization of dual schemes, and uses multi-index quantitative optimization to select the best configuration, taking into account both voltage management effectiveness and engineering costs.

[0024] See Figure 1 The method includes the following steps: Step S1, Data Access and Initial Screening: Import the power grid model and historical cross sections, and screen the target hub substations for power flow.

[0025] In a preferred but non-limiting embodiment of the present invention, step S1 specifically includes: Step S101: Batch import regional main network CIM static topology model files and year-round DT format real-time running section time series data, and perform steady-state power flow calculation.

[0026] Step S102: Select substations whose bus voltage continuously exceeds the upper limit, all inductive reactive power equipment in the station is fully engaged, and there is no remaining regulation capacity, and mark them as target hub substations to be optimized.

[0027] Step S2, Cross-section clustering and extreme condition extraction: Typical cross-sections and the top 5 extreme over-limit cross-sections are extracted by clustering.

[0028] In a preferred but non-limiting embodiment of the present invention, step S2 specifically includes: Step S201: The K-means clustering algorithm is used to cluster all valid historical DT sections. The clustering feature vector includes five key electrical quantities: total active load of the whole network, total output of new energy sources, average voltage of 500kV bus, average voltage of 220kV bus, and reactive power of line charging. The operating condition clusters are automatically divided.

[0029] Step S202: For each cluster group, calculate the Euclidean distance of each section within the group, and extract the section closest to the cluster center as the typical operating section; at the same time, sort the sections in each group from high to low according to the bus voltage over-limit amplitude, and select the top five sections with the most severe over-limit as extreme over-limit sections.

[0030] Step S203: Collect all extreme cross sections and automatically classify them into three typical severe scenarios: extreme low load under conditions such as holidays / nighttime light load, large-scale new energy generation under conditions such as wind and solar full power generation, and extreme weather under conditions such as rain / evening peak light load superimposed with long-line capacitive reactive power excess. Use all typical cross sections and extreme cross sections as the constraint boundary conditions for subsequent optimization calculations.

[0031] Step S3, graded sensitivity calculation and reactive power deficit calculation: calculate voltage reactive power sensitivity by voltage level and calculate the total inductive reactive power supplement capacity of the entire station.

[0032] In a preferred but non-limiting embodiment of the present invention, step S3 specifically includes: Step S301: For the target substations selected in step S1, calculate the voltage reactive power sensitivity of the 500kV and 220kV busbars respectively. The specific calculation formula is as follows: Node voltage change / Reactive power output change of reactive power compensation device, denoted as S. U U is the node voltage, Q is the reactive power compensation capacity at this point, and the general form of the formula is: , Step S302: Read the maximum over-limit amplitude of the two busbars under extreme cross-section, and calculate the inductive reactive power capacity required to eliminate overvoltage in reverse; superimpose the reactive power demand of the two voltage levels to obtain the total reactive power supplementary demand capacity of the entire station.

[0033] Step S4, Multi-objective optimization model construction: Adaptively match three types of optimization paths to construct a multi-constraint model that minimizes cost and network loss.

[0034] In a preferred but non-limiting embodiment of the present invention, step S4 specifically includes: Step S401: Construct a multi-objective optimization model. The objective function comprehensively considers multiple factors such as active power loss costs and equipment costs. By adjusting the weights and values ​​of each factor, a balance is struck between the economical operation of the distribution network and the investment cost of the compensation equipment. Various constraints are set to ensure that the optimization results conform to the actual operating rules of the distribution network, such as ensuring power balance, maintaining voltage stability, limiting reactive power, and limiting the capacity of the compensation equipment. A suitable optimization algorithm is used to solve the model. Under the constraints, the optimal combination of compensation equipment installation location and capacity is found, which maximizes the objective function. The objective function is as follows: In the formula For active network loss, This is the weighting coefficient for equipment costs. The cost of the equipment is directly proportional to its capacity. The cost is for equipment installation, and the last item in the above formula is for adjustment costs. , These are the sets of balanced nodes. For the weighting coefficient of the switching operation, , The first j The current actual operating compensation susceptance of the capacitor bank and the initial reference operating susceptance of the capacitor.

[0035] Constraints: (1) Nodal power flow balance equation. in, , , The first i The active power injected by the generator at the first node, the ... i Active power consumed by the load of each node, node i Flow to adjacent nodes j The active power of the line, , , , These are the real part of the voltage, the imaginary part of the voltage, the node voltage, and reactive power compensation, respectively. , , The first i The reactive power injected by the generator at the first node, the reactive power injected by the generator at the second node, the reactive power injected by the generator at the first ... i The reactive power absorbed by the load at each node, the node i With neighboring nodes j Line reactive power It is a set of nodes.

[0036] (2) Voltage constraints, including feeder outgoing nodes, high and low voltage sides of distribution transformers and user voltage. in, , , They are nodes i Allowable operating voltage lower limit, node i Allowable operating voltage limit, node i Current actual operating voltage, This refers to the set of PQ type nodes in the distribution network.

[0037] (3) Reactive power constraint at the gate. in, , , , The first wReactive power lower limit at each metering point, slack variable, metering point w Actual reactive power, the first w The upper limit of reactive power at each metering point. This is the set of checkpoint measurement points.

[0038] (4) Power supply constraints. in, , , The first i The lower limit of reactive power output of the power supply, the first i The actual reactive power generated by the generator, the first i The maximum reactive power output of the power supply unit. It refers to the collection of all power sources within the system that have reactive power regulation capabilities.

[0039] (5) Capacitor constraint. in, , , The first i The lower limit of the input capacity of the capacitor bank, the first i The actual compensation capacity of the capacitor bank, the first i The upper limit of the input capacity of the capacitor bank, This is the set of all parallel capacitors in the system.

[0040] (6) Total capacity constraint: Set the total capacity according to the conditions. in, , The first i The current actual reactive power compensation capacity of the capacitor bank and the upper limit of the total capacitor capacity allowed by the system. This is the set of all switchable parallel capacitors.

[0041] (7) Total cost constraint: The total investment is set according to the conditions. in, , They were respectively in the second i The individual construction investment cost required for building a capacitor of corresponding capacity at each node, and the upper limit of the total investment for capacitor renovation given by the project.

[0042] Step S5, dual-path optimization scheme generation: two expansion schemes within the station and a near-area AVC coordinated voltage regulation scheme are solved in parallel.

[0043] In a preferred but non-limiting embodiment of the present invention, S5 specifically includes: Step S501, solve the station expansion scheme: Keep the total number of existing reactors unchanged, and output two alternative engineering schemes: Scheme 1 is a station expansion scheme with the same specifications and unified capacity; Scheme 2 is a mixed configuration expansion scheme with multiple specifications of reactors.

[0044] Step S502, Solving the regional coordinated voltage regulation scheme: Traverse all 220kV and 500kV near-area substations around the target station, repeat step S3 to calculate the reactive power-voltage sensitivity of each neighboring station and sort them in descending order; prioritize the inclusion of cold standby idle reactors of stations with higher sensitivity into the regional AVC linkage voltage regulation resource pool; if the total capacity of the standby reactors is insufficient to eliminate extreme overvoltages of the master station, add a small number of reactors to the neighboring stations to supplement the capacity.

[0045] Step S503: Completely record the equipment capacity, number of bays, floor area, total investment, annual network loss reduction, and annual AVC action frequency changes for the two in-station expansion schemes and one regional coordinated voltage regulation scheme.

[0046] Step S6, Scheme Verification and Optimization: Extreme cross-section simulation verification, multi-dimensional quantitative comparison to determine the optimal configuration scheme.

[0047] In a preferred but non-limiting embodiment of the present invention, step S6 specifically includes: In step S601, all extreme cross sections extracted in step S2 are used as simulation boundaries. Power flow simulation verification is carried out on the three alternative schemes generated in step S5 in sequence, and the over-limit elimination rate of 500kV and 220kV bus voltage is calculated for each scheme.

[0048] Step S602, quantitatively extract the evaluation indicators for each scheme: total new reactive power capacity, total one-time investment of the project, civil engineering land area, annual loss reduction benefit, annual average reduction in AVC operation, and construction complexity. Among them, construction complexity is quantitatively determined by the types of reactor specifications and the number of equipment foundation models.

[0049] Step S603 involves a multi-dimensional weighted score based on the overall voltage management effect, project cost, operation and maintenance difficulty, and construction period. The system automatically outputs the optimal reactive power compensation configuration scheme and simultaneously outputs the capacity of the modified equipment, installation location, regional AVC linkage switching strategy, and annual investment calculation report.

[0050] The following example, using the reactive power optimization configuration of a 500kV substation A in a certain region, illustrates the method.

[0051] Step S1, Data Access and Initial Screening: Import the regional main grid CIM model and massive DT real-time cross-sections throughout the year. Power flow calculations show that during the Spring Festival and summer off-peak periods, the highest voltage of the 220kV bus at station A is 238.232kV and the highest voltage of the 500kV bus is 520.378kV, both exceeding the upper limit of the regulations. All six original 60Mvar reactors in the station have been put into operation and all capacitors have been deactivated, leaving no residual reactive power regulation capacity. Therefore, Far East Station was identified as the target station for optimization.

[0052] Step S2, Cross-section clustering and extreme operating condition extraction: The cross-sections of Station A throughout the year are clustered into two major clusters, Category 0 and Category 1. One typical cross-section of the cluster center and the top 5 extreme over-limit cross-sections are extracted from each cluster, for a total of 2 typical and 10 extreme cross-sections. The operating conditions cover light load during the Spring Festival, low load in the early morning of summer, and concentrated high load of new energy sources, which serve as optimization boundaries.

[0053] Step S3, graded sensitivity calculation and reactive power deficit calculation: The reactive power sensitivity of the 220kV side is calculated to be 0.010728, and the required additional reactor for this level is 95Mvar; the reactive power sensitivity of the 500kV side is 0.00257, and the required reactor for this level is 30Mvar. The total calculation shows that a total of 95Mvar of inductive reactive power needs to be added for the entire station.

[0054] Step S4, Multi-objective optimization model construction: Input the unit price of 75Mvar and 90Mvar reactors and the unit price of civil engineering land occupation, set the total investment and single-station land occupation constraints, set the SVG configuration ratio to 0%, and use fixed reactors for all; Station A is an existing capacity expansion scenario, and the original equipment expansion optimization mode is selected.

[0055] Step S5, generation of dual-path optimization scheme: 1. Station Expansion: Keeping the number of reactors unchanged at 6, Option 1: All 6 units are expanded to 75Mvar, adding 90Mvar capacity, with a total investment of 10.005 million yuan and an area of ​​270㎡; Option 2: 4 units of 75Mvar + 2 units of 90Mvar, adding 120Mvar, with a total investment of 10.576 million yuan and an area of ​​284㎡.

[0056] 2. Regional Coordination Scheme: The sensitivity of station B in the near area is much higher than that of stations C and D. The existing 120Mvar low-voltage reactor of station B is activated in conjunction with the newly added 85Mvar reactor, and the voltage regulation of station A is achieved by relying on AVC linkage switching.

[0057] Step S6, Scheme Verification and Optimization: Ten clustered extreme cross-section simulations were used for verification: Scheme 1 achieved a 92% voltage over-limit elimination rate, an annual loss reduction benefit of 400,000 yuan, and a 25% reduction in AVC frequency; Scheme 2 achieved a 99% over-limit elimination rate and an annual loss reduction benefit of 450,000 yuan, but required an additional 14 square meters of land and had a higher cost; the collaborative scheme could solve the over-limit problem but required modification of adjacent station equipment. Considering land occupation, construction difficulty, and investment recovery period, Scheme 1, i.e., a unified expansion of 6×75Mvar, was selected as the final configuration scheme.

[0058] Example 2 Based on Example 1, this example provides a main network reactive power compensation optimization configuration system for extreme scenarios, used in the main network reactive power compensation optimization configuration method of Example 1. (See [link to example 1]). Figure 2 The system includes: The data access unit reads the CIM power grid topology parameters and DT time-series section data, performs batch power flow calculations, automatically marks target substations where the bus voltage exceeds the upper limit and all reactive power in the station is fully utilized with no adjustment margin, and completes the basic data screening.

[0059] Clustering analysis unit: It clusters and groups massive time-series cross sections, and automatically outputs the typical cross sections of the cluster center and the most severely extreme cross sections that exceed the limits for each group. It classifies and collects data from three scenarios: extreme load, extreme new energy, and extreme weather, forming an optimized boundary dataset.

[0060] Sensitivity calculation unit: The reactive voltage sensitivity of the target station's 500kV and 220kV busbars to the low-voltage compensation port is calculated separately. Based on the over-limit values ​​of each busbar, the required additional inductive reactive capacity is calculated separately, and the total reactor capacity expansion requirement of the entire station is obtained by combining them.

[0061] Optimization Modeling and Solving Unit: Constructs a multi-objective function that combines comprehensive cost with minimum network loss, including multiple constraints such as power flow, voltage, total capacity, and total investment. It provides open interfaces for customizing SVG / fixed reactance ratio, equipment unit price, and single unit capacity parameters, and automatically matches three optimization modes based on the current site status: capacity expansion, capacitive reactance replacement, and new site deployment.

[0062] Scheme generation unit: Fixed number of reactor installation groups, generating two sets of in-station renovation schemes: unified capacity expansion and multi-specification mixed capacity expansion; Based on the sensitivity ranking results of adjacent stations, high-sensitivity substations are selected to form a regional coordinated voltage regulation scheme for switching existing standby reactor AVC and adding a small number of new equipment.

[0063] The simulation verification and optimization unit is used to import the extreme cross-section extracted by clustering to carry out full-condition simulation, automatically calculate the total investment, land occupation, new reactive power capacity, voltage over-limit elimination rate, annual loss reduction benefit, and AVC regulation reduction ratio of each scheme, and comprehensively select the optimal transformation scheme.

[0064] Example 3 Based on the foregoing embodiments, this embodiment provides an electronic device, see [link to previous embodiment]. Figure 3 It includes one or more processors, a memory, and one or more programs stored in the memory, the one or more programs including instructions for executing the main grid reactive power compensation optimization configuration method of Embodiment 1.

[0065] like Figure 3 At the hardware level, the electronic device includes a processor, internal bus, network interface, memory, and non-volatile memory, and may also include other hardware required for the business operations. The processor reads the corresponding computer program from the non-volatile memory into memory and then runs it to achieve the above-mentioned functions. Figure 1 The method described herein. Of course, in addition to software implementation, this invention does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0066] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0067] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0068] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for optimizing the reactive power compensation configuration of the main network in extreme scenarios, characterized in that, Includes the following steps: Acquire power grid topology parameters and time-series operation section data, perform power flow calculations, and screen out target substations where the bus voltage exceeds the limit and the reactive power regulation capacity within the station is exhausted. The time-series running section data is clustered and grouped, and the cluster center, typical section and extreme limit-breaking section of each group are extracted. The set of typical section and extreme limit-breaking section of each group is used as the optimization boundary dataset. For the target substation, the reactive voltage sensitivity of the busbars at different voltage levels to the low-voltage reactive power compensation port is calculated respectively. Based on the over-limit amplitude of the busbars under extreme cross-sections and the reactive voltage sensitivity, the required inductive reactive power supplement capacity for each voltage level is determined, and then the total reactive power supplement capacity of the entire substation is obtained by combining them. A multi-objective optimization model is constructed with the goal of minimizing overall cost and network loss. Constraints are set for power flow, voltage, total capacity, and total cost, and the optimization mode is automatically matched according to the current status of the site. Based on the multi-objective optimization model, an in-station reactive power compensation capacity expansion scheme and a regional coordinated voltage regulation scheme are generated. The in-station reactive power compensation capacity expansion scheme is obtained by configuring the number of fixed reactors and their capacity. The regional coordinated voltage regulation scheme selects the idle reactive power resources of high-sensitivity neighboring stations to participate in automatic voltage control linkage voltage regulation based on the reactive power and voltage sensitivity ranking results of neighboring stations. Using the extreme sections in the optimized boundary dataset, all generated schemes are verified by full-condition power flow simulation. Multiple evaluation indicators, including total cost, new reactive power capacity, and voltage over-limit elimination rate, are extracted. The optimal reactive power compensation configuration scheme is determined by quantitative comparison of multiple indicators.

2. The method for optimizing the reactive power compensation configuration of the main network in extreme scenarios according to claim 1, characterized in that, The process of selecting the target substation includes the following steps: The target substation is defined as a substation where the bus voltage continuously exceeds the upper limit and all inductive reactive power equipment within the station is fully operational with no remaining regulation capacity.

3. The method for optimizing the reactive power compensation configuration of the main network in extreme scenarios according to claim 1, characterized in that, The process of constructing the optimization boundary dataset includes the following steps: K-means clustering was used to cluster the effective time-series operating sections. The clustering feature vectors included the total active load of the entire network, the total output of new energy sources, the average bus voltage, and the reactive power of line charging. For each cluster after clustering, the section closest to the cluster center is extracted as the typical section of the cluster center, and sorted in descending order according to the over-limit amplitude of the bus voltage. The first predetermined number of sections are selected as the extreme over-limit sections. All extreme cross sections are categorized into three types of severe scenarios: extreme low load, high renewable energy generation, and extreme weather, which together form the optimized boundary dataset.

4. The method for optimizing the reactive power compensation configuration of the main network in extreme scenarios according to claim 1, characterized in that, The calculation process for the total reactive power supplementation capacity of the entire station includes the following steps: For the 500kV bus and 220kV bus of the target substation, calculate the reactive voltage sensitivity of their voltage to the low-voltage reactive power compensation port. Read the maximum over-limit amplitude of the two busbars under extreme cross-section, and calculate the inductive reactive power required to eliminate the overvoltage based on their respective sensitivities. The total reactive power supplementary capacity required for the entire station is obtained by superimposing the inductive reactive power capacity required for the two voltage levels.

5. The method for optimizing the reactive power compensation configuration of the main network in extreme scenarios according to claim 1, characterized in that, The objective function of the multi-objective optimization model comprehensively considers the active power loss cost and the equipment cost, and sets constraints including node voltage constraints, reactive power constraints at the junction, total capacity constraints and total investment constraints. The optimization mode is automatically matched to the existing capacity expansion, capacitive reactance replacement or new site deployment mode according to the current site status.

6. The method for optimizing the reactive power compensation configuration of the main network in extreme scenarios according to claim 1, characterized in that, The generation process of the reactive power compensation expansion scheme within the station includes the following steps: Keeping the total number of existing reactor installations unchanged, generate separate expansion plans for the entire substation with the same specifications and unified capacity, as well as expansion plans for mixed configurations of reactors with multiple specifications.

7. The method for optimizing the reactive power compensation configuration of the main network in extreme scenarios according to claim 1, characterized in that, The generation process of the regional coordinated voltage regulation scheme includes the following steps: Traverse the nearby substations surrounding the target substation, repeatedly calculate the reactive voltage sensitivity of each neighboring station and sort them in descending order. Prioritize selecting cold standby idle reactors at sites with high sensitivity ranking and include them in the regional automatic voltage control linkage voltage regulation resource pool; If the total capacity of all idle reactive power resources is insufficient to eliminate extreme overvoltage at the target substation, then additional reactors should be added at adjacent substations to supplement the capacity.

8. The method for optimizing the reactive power compensation configuration of the main network in extreme scenarios according to claim 1, characterized in that, The process of quantitative comparison of multiple indicators includes the following steps: For each scheme, the total newly added reactive power capacity, total one-time investment of the project, civil engineering land area, annual loss reduction benefits, annual average reduction in automatic voltage control operations, and construction complexity indicators are statistically analyzed. The overall voltage management effect, project cost, and operation and maintenance difficulty are weighted and scored, and the optimal configuration scheme and the corresponding retrofit equipment capacity, installation location, regional automatic voltage control linkage switching strategy, and annual investment calculation report are output.

9. A main network reactive power compensation optimization configuration system for extreme scenarios, characterized in that, The system is used to implement the main network reactive power compensation optimization configuration method as described in any one of claims 1-8, and includes: The data access unit is used to read power grid topology parameters and time-series operating section data, perform power flow calculations in batches, and mark target substations. The clustering analysis unit is used to cluster and group time-series running cross-section data, automatically extract the cluster center typical cross-sections and extreme over-limit cross-sections, and form an optimization boundary dataset. The sensitivity calculation unit is used to calculate the reactive voltage sensitivity of different voltage level buses in the target substation separately, and determine the required inductive reactive power supplement capacity based on the over-limit values ​​of each bus level. The optimized modeling and solving unit is used to construct a multi-objective function that minimizes both overall investment and network loss, sets various operational constraints, and automatically matches the optimization mode based on the current site status. The scheme generation unit is used to generate in-station renovation schemes, including unified capacity expansion schemes and multi-specification mixed capacity expansion schemes, as well as regional coordinated voltage regulation schemes based on the sensitivity ranking of neighboring stations. The simulation verification and optimization unit is used to import the extreme sections in the optimization boundary dataset to perform full-condition power flow simulation, automatically calculate multiple evaluation indicators of each scheme, and comprehensively select the optimal modification scheme.

10. An electronic device, characterized in that, It includes one or more processors, a memory, and one or more programs stored in the memory, said one or more programs including instructions for executing the main grid reactive power compensation optimization configuration method as described in any one of claims 1-8.

Images