Multi-objective coordinated optimization configuration method and device for reactive power equipment based on short circuit ratio

Abstract

The embodiment of the specification provides a short-circuit ratio based multi-objective collaborative optimization configuration method and device for reactive power equipment, wherein the short-circuit ratio based multi-objective collaborative optimization configuration method for reactive power equipment comprises the following steps: obtaining relevant data of a new energy station, performing scene division based on the relevant data, and determining scene information; performing parameter information corresponding to the scene information through power flow calculation and transient simulation; performing multi-objective collaborative optimization on the combination of the type and capacity of the reactive power equipment through a genetic algorithm based on the parameter information, determining an equipment configuration list, performing verification based on the equipment configuration list, determining a verification result, and performing configuration adjustment based on the verification result. The short-circuit ratio based multi-objective collaborative optimization configuration method for reactive power equipment solves the problem of reactive power configuration of the new energy station in a low short-circuit ratio scene, has high economy, strong adaptability and engineering practicability, and provides a better solution for the safe and stable operation of a new power system.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of power control technology, and in particular to a multi-objective collaborative optimization configuration method for reactive power equipment based on short-circuit ratio. Background Technology

[0002] With the large-scale grid connection of new energy sources (wind power and photovoltaics), current new energy power plants face problems such as weak active support capabilities, low short-circuit ratios in many plants, and abnormal sensitivity to voltage fluctuations. Under low short-circuit ratios, the voltage at the grid connection point of new energy power plants is easily affected by disturbances, leading to transient overvoltage or undervoltage problems. Furthermore, new energy equipment lacks synchronous inertia, and cannot autonomously support the grid voltage frequency during faults, exacerbating the risk of system instability. According to standards, "in areas with a high proportion of grid-connected new energy power generation, new energy power plants should provide necessary inertia and short-circuit capacity support," and "the short-circuit ratio of new energy power plants should reach a reasonable level." Therefore, to address the problem of low short-circuit ratios (SCR) and limited new energy transmission, reactive power compensation devices need to be configured at new energy power plants to improve their transmission capacity. Reactive power compensation devices at new energy power plants mainly include SVG, synchronous condensers, STATCOM, and grid-connected energy storage. However, current research on the configuration of reactive power equipment at new energy power plants still has the following shortcomings:

[0003] 1) Inappropriate configuration of reactive power equipment: Traditional methods use single equipment (such as SVG, STATCOM) or fixed capacity configuration, ignoring dynamic changes in SCR, and cannot adapt to the fluctuation of new energy output and grid fault scenarios.

[0004] 2) Insufficient response speed: Conventional synchronous condensers have slow response (>100ms), and SVG / STATCOM has limited capacity, resulting in insufficient response or excessive cost.

[0005] Therefore, a better solution is urgently needed. Summary of the Invention

[0006] In view of this, embodiments of this specification provide a method for multi-objective cooperative optimization configuration of reactive power equipment based on short-circuit ratio. One or more embodiments of this specification also relate to a device for multi-objective cooperative optimization configuration of reactive power equipment based on short-circuit ratio, a computing device, a computer-readable storage medium, and a computer program, to address the technical deficiencies existing in the prior art.

[0007] According to a first aspect of the embodiments of this specification, a multi-objective cooperative optimization configuration method for reactive power equipment based on short-circuit ratio is provided, comprising:

[0008] Acquire relevant data from new energy power stations, divide scenarios based on the relevant data, and determine scenario information;

[0009] The parameter information corresponding to the scene information is obtained through power flow calculation and transient simulation;

[0010] Based on parameter information, a genetic algorithm is used to perform multi-objective collaborative optimization of reactive power equipment type and capacity combination to determine the equipment configuration list.

[0011] Verification is performed based on the device configuration list, the verification results are determined, and configuration adjustments are made based on the verification results.

[0012] In one possible implementation, relevant data from new energy power plants is acquired, and based on this data, scenarios are segmented to determine scenario information, including:

[0013] Acquire rated capacity, grid short-circuit capacity, historical voltage fluctuation data, and fault recording data;

[0014] Short-circuit ratio data is calculated based on rated capacity, grid short-circuit capacity, historical voltage fluctuation data, and fault recording data.

[0015] Scenarios are segmented based on short-circuit ratio data to determine scenario information.

[0016] In one possible implementation, the parameter information corresponding to the scene information is obtained through power flow calculation and transient simulation, including:

[0017] Determine the steady-state reactive power demand of the scenario information through power flow calculation;

[0018] Transient reactive power demand for scenario information is determined through transient simulation.

[0019] The total demand is determined based on steady-state reactive power demand and transient reactive power demand.

[0020] In one possible implementation, based on parameter information, a genetic algorithm is used to perform multi-objective collaborative optimization of reactive power equipment types and capacity combinations to determine the equipment configuration list, including:

[0021] Define the optimization objective;

[0022] By combining discrete variables of equipment type with continuous variables of capacity, a hybrid coding chromosome is constructed;

[0023] Construct a dynamic fitness function based on the optimization objective;

[0024] The penalty mechanism and constraint weights are determined based on short-circuit ratio data and response time.

[0025] Based on a penalty mechanism, constraint weights, hybrid encoded chromosomes, and dynamic fitness functions, a genetic algorithm is used to perform multi-objective collaborative optimization to determine the equipment configuration list.

[0026] In one possible implementation, the penalty mechanism and constraint weights are determined based on the short-circuit ratio data and response time, including:

[0027] If the short-circuit ratio is less than the first threshold, a first penalty mechanism is determined.

[0028] If the response time exceeds the second threshold, a second penalty mechanism is determined.

[0029] If the short-circuit ratio is less than the first threshold and the response time is greater than the second threshold, a third penalty mechanism is determined.

[0030] The constraint weights are determined when the short-circuit ratio data is greater than or equal to the first threshold and less than the third threshold.

[0031] In one possible implementation, verification is performed based on a device configuration list to determine the verification result, including:

[0032] Obtain fitness data for each generation in the genetic algorithm;

[0033] The device configuration list is validated based on fitness data to determine the validation results.

[0034] In one possible implementation, configuration adjustments are made based on the verification results, including:

[0035] If the verification result is not up to standard, the multi-objective collaborative optimization of reactive power equipment type and capacity combination is carried out again based on parameter information and genetic algorithm to determine the equipment configuration list.

[0036] According to a second aspect of the embodiments of this specification, a multi-objective collaborative optimization configuration device for reactive power equipment based on short-circuit ratio is provided, comprising:

[0037] The scenario segmentation module is configured to acquire relevant data from new energy power stations, segment scenarios based on the relevant data, and determine scenario information.

[0038] The parameter information module is configured to process the parameter information corresponding to the scene information through power flow calculation and transient simulation.

[0039] The target optimization module is configured to perform multi-objective collaborative optimization of reactive power equipment type and capacity combination based on parameter information and through genetic algorithm to determine the equipment configuration list.

[0040] The configuration adjustment module is configured to perform verification based on the device configuration list, determine the verification results, and perform configuration adjustments based on the verification results.

[0041] According to a third aspect of the embodiments of this specification, a computing device is provided, comprising:

[0042] Memory and processor;

[0043] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the above-mentioned multi-objective collaborative optimization configuration method for reactive power equipment based on short-circuit ratio.

[0044] According to a fourth aspect of the embodiments of this specification, a computer-readable storage medium is provided that stores computer-executable instructions, which, when executed by a processor, implement the steps of the above-described method for multi-objective cooperative optimization configuration of reactive power equipment based on short-circuit ratio.

[0045] According to a fifth aspect of the embodiments of this specification, a computer program is provided, wherein when the computer program is executed in a computer, the computer is instructed to perform the steps of the above-described method for multi-objective cooperative optimization configuration of reactive power equipment based on short-circuit ratio.

[0046] This specification provides a method and apparatus for multi-objective collaborative optimization configuration of reactive power equipment based on short-circuit ratio. The method includes: acquiring relevant data from renewable energy power plants; dividing the system into scenarios based on the data to determine scenario information; performing power flow calculations and transient simulations to analyze the parameters corresponding to the scenario information; using a genetic algorithm to perform multi-objective collaborative optimization of reactive power equipment types and capacity combinations based on the parameter information to determine an equipment configuration list; verifying the equipment configuration list to determine the verification results; and adjusting the configuration based on the verification results. This method solves the reactive power configuration problem of renewable energy power plants in low short-circuit ratio scenarios, combining high economic efficiency, strong adaptability, and engineering practicality, providing a better solution for the safe and stable operation of new power systems. Attached Figure Description

[0047] Figure 1 This is a flowchart of a multi-objective collaborative optimization configuration method for reactive power equipment based on short-circuit ratio, provided in one embodiment of this specification;

[0048] Figure 2 This is another flowchart of a multi-objective collaborative optimization configuration method for reactive power equipment based on short-circuit ratio, provided in one embodiment of this specification;

[0049] Figure 3 This is a schematic diagram of a multi-objective collaborative optimization configuration device for reactive power equipment based on short-circuit ratio, provided in one embodiment of this specification.

[0050] Figure 4 This is a structural block diagram of a computing device provided in one embodiment of this specification. Detailed Implementation

[0051] Many specific details are set forth in the following description to provide a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0052] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of this specification. The singular forms “a” and “the” as used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0053] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this specification, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0054] This specification provides a method for multi-objective collaborative optimization configuration of reactive power equipment based on short-circuit ratio. This specification also relates to a device for multi-objective collaborative optimization configuration of reactive power equipment based on short-circuit ratio, a computing device, and a computer-readable storage medium, which will be described in detail in the following embodiments.

[0055] See Figure 1 , Figure 1 A flowchart is shown of a multi-objective collaborative optimization configuration method for reactive power equipment based on short-circuit ratio according to an embodiment of this specification, which specifically includes the following steps.

[0056] Step 101: Obtain relevant data from new energy power stations, divide scenarios based on the relevant data, and determine scenario information.

[0057] In practical applications, see Figure 2 SCR Calculation and Scenario Classification: Based on the Rated Capacity of New Energy Power Stations Grid short-circuit capacity Parameters such as historical voltage fluctuation data and fault recording data are used to calculate the SCR of new energy power stations, and the scenarios are divided according to the SCR value.

[0058] In one possible implementation, relevant data from new energy power plants are acquired, and scenario segmentation is performed based on the relevant data to determine scenario information. This includes: acquiring rated capacity, grid short-circuit capacity, historical voltage fluctuation data, and fault recording data; calculating short-circuit ratio data based on rated capacity, grid short-circuit capacity, historical voltage fluctuation data, and fault recording data; and segmenting scenarios based on the short-circuit ratio data to determine scenario information.

[0059] In practical applications, the input data is: the rated capacity of the new energy power station. Grid short-circuit capacity Historical voltage fluctuation data and fault recording data.

[0060] Calculate SCR: ;

[0061] Scenario classification: If SCR < 2: Extremely weak power grid scenario;

[0062] If 2≤SCR<3: weak power grid scenario;

[0063] If SCR≥3: Strong power grid scenario;

[0064] Output: SCR level label (very weak / weak / strong).

[0065] Step 102: Analyze the parameter information corresponding to the scene information through power flow calculation and transient simulation.

[0066] In practical applications, dynamic reactive power demand assessment and quantification involves analyzing voltage fluctuations and transient overvoltage / undervoltage issues under different SCRs through power flow calculations and transient simulations (such as PSCAD / BPA) to quantify dynamic and steady-state reactive power demand.

[0067] In one possible implementation, the parameter information corresponding to the scenario information is determined through power flow calculation and transient simulation, including: determining the steady-state reactive power demand of the scenario information through power flow calculation; determining the transient reactive power demand of the scenario information through transient simulation; and determining the total demand based on the steady-state reactive power demand and the transient reactive power demand.

[0068] In practical applications, the reactive power demand of a system is determined. During the research process, the reactive power demand of the system is divided into steady-state reactive power demand and transient reactive power demand:

[0069] Steady-state reactive power demand calculation:

[0070] Voltage deviation is calculated based on power flow calculation results. ;

[0071] Calculate steady-state reactive power compensation ;

[0072] Transient reactive power demand calculation:

[0073] Extracting voltage change rate using transient simulation (such as PSCAD) ;

[0074] Calculate transient reactive power increment ;

[0075] Total demand integration:

[0076] ;

[0077] in, The dynamic reactive power capacity required for new energy power plants; Voltage deviation (the difference between the actual voltage and the nominal value); This represents the change in reactive power. The transient voltage change rate, The weighting coefficient is related to the short-circuit ratio (SCR).

[0078] The first item ( ): Reflects the sensitivity of steady-state voltage deviation to reactive power demand, and the amount of steady-state reactive power that needs to be compensated;

[0079] The second item ( ): Reflects the additional demand for dynamic reactive power by the transient voltage change rate, and is used to suppress rapid voltage fluctuations.

[0080] Weighting coefficient Adjust the SCR in segments (e.g., the lower the SCR, the better). (The higher the weight)

[0081] Output: Dynamic reactive power demand curve .

[0082] Step 103: Based on the parameter information, perform multi-objective collaborative optimization of reactive power equipment type and capacity combination using a genetic algorithm to determine the equipment configuration list.

[0083] In practical applications, multi-device selection and capacity optimization are employed: the candidate set of reactive power equipment includes synchronous condensers, STATCOM, grid-type energy storage, and grid-type SVG. A genetic algorithm is used for multi-objective collaborative optimization of reactive power equipment type and capacity combinations. By introducing the genetic algorithm, minimizing the total cost of reactive power equipment (equipment investment + operation and maintenance cost) and minimizing voltage deviation are used as the upper-level optimization objective functions. Simultaneously considering system voltage stability, constraints such as equipment dynamic response time less than 1 second, dynamic SCR ≥ threshold, and voltage fluctuation ≤ ±5% are used to construct a multi-device collaborative optimization model, outputting an optimal equipment configuration list (type, capacity, location).

[0084] In one possible implementation, based on parameter information, a genetic algorithm is used to perform multi-objective collaborative optimization of reactive power equipment type and capacity combination to determine the equipment configuration list. This includes: determining the optimization objective; combining discrete variables of equipment type with continuous variables of capacity to construct a hybrid coding chromosome; constructing a dynamic fitness function based on the optimization objective; determining the penalty mechanism and constraint weights based on short-circuit ratio data and response time; and performing multi-objective collaborative optimization using a genetic algorithm based on the penalty mechanism, constraint weights, hybrid coding chromosome, and dynamic fitness function to determine the equipment configuration list.

[0085] In practical applications, genetic algorithms are used to perform multi-objective collaborative optimization of reactive power equipment types and capacity combinations to ensure:

[0086] Minimizing the total cost of reactive power equipment: New energy projects (such as wind farms and photovoltaic power plants) typically require huge initial investments, and the cost of reactive power compensation equipment (such as synchronous condensers, STATCOM, and energy storage systems) accounts for a high proportion of the total cost. By optimizing the configuration, dynamically selecting equipment types and capacity combinations, eliminating redundant configurations, and avoiding "over-design" can be achieved.

[0087] Maximizing voltage stability (minimizing voltage deviation): New energy power plants lack the inertia of synchronous machines, leading to accelerated voltage drops during faults. Whether the system voltage can recover to a stable state after a fault (such as a short circuit) is crucial for the safe operation of the power system. Through dynamic modeling, equipment coordination, and algorithm optimization, the reactive power equipment can transition from "passive compensation" to "active support," thereby maximizing system voltage stability.

[0088] A two-layer optimization structure was constructed to optimize the selection and capacity of multiple devices from two aspects: minimizing the total cost of reactive power equipment and maximizing system voltage stability. In each optimization iteration, a preliminary solution is first obtained through optimization of the total cost of reactive power equipment, and then the solution is corrected and evaluated using voltage stability verification. This method ensures that the final solution not only has a lower total cost of reactive power equipment but also meets the grid's requirements for voltage stability, thereby guaranteeing rapid system voltage recovery in the event of a fault. The specific steps are as follows.

[0089] In one possible implementation, the penalty mechanism and constraint weights are determined based on the short-circuit ratio data and the response time, including: determining a first penalty mechanism when the short-circuit ratio data is less than a first threshold; determining a second penalty mechanism when the response time is greater than a second threshold; determining a third penalty mechanism when the short-circuit ratio data is less than the first threshold and the response time is greater than the second threshold; and determining constraint weights when the short-circuit ratio data is greater than or equal to the first threshold and less than the third threshold.

[0090] In practical applications, equipment variable modeling and hybrid coding are used.

[0091] By combining discrete variables of equipment type with continuous variables of capacity, a hybrid coding chromosome is constructed, wherein:

[0092] (1) The candidate device set is {synchronous condenser, STATCOM, grid-type energy storage, grid-type SVG}, and each device type is encoded by an integer:

[0093] 0: No configuration;

[0094] 1: Adjust the camera;

[0095] 2: STATCOM;

[0096] 3: Grid-based energy storage;

[0097] 4: Mesh-based SVG.

[0098] Equipment type coding rules: Synchronous condensers and grid-type energy storage are mutually exclusive in the same scheme to avoid functional redundancy; SVG and STATCOM are allowed to coexist, but the total capacity shall not exceed 30% of the rated capacity of the station.

[0099] (2) Equipment capacity is segmented and coded according to the preset project scope, and the capacity step size is dynamically linked to the equipment type. The capacity range for each type of equipment needs to be set according to the actual project, for example:

[0100] Camera adjustment: 10MVar~100MVar (step size 10MVar);

[0101] STATCOM: 5MVar~50MVar (step size 5MVar);

[0102] Grid-based energy storage: 10MW / 20MWh~50MW / 100MWh;

[0103] Mesh-type SVG: 5MVar~30MVar (step size 5MVar);

[0104] Capacity coding rules: The capacity of synchronous condensers is discretized in integer multiples of 10MVar; the capacity of grid-type energy storage must meet the energy-to-power ratio (E / P) ≥ 2 hours.

[0105] (3) Chromosome coding:

[0106] Mixed encoding (integer + real number):

[0107] Gene structure: Each chromosome represents a device configuration scheme; the gene is divided into two parts:

[0108] 1. Type gene: An integer sequence of length 4, indicating whether a certain type of device is configured (0 or 1). For example, [1,0,1,1] indicates that a synchronous condenser is configured, STATCOM is not configured, and energy storage and SVG are configured.

[0109] 2. Capacity Gene: The capacity value corresponding to the equipment, for example [30MVar,-,20MW / 40MWh,15MVar].

[0110] Construction of dynamic fitness function:

[0111] 1. Design a weighted fitness function with the dual objectives of minimizing total cost and maximizing voltage stability:

[0112] (1)

[0113] In the formula, —Total cost of reactive power equipment (including investment and operation and maintenance costs); —No. Voltage amplitude at each node; —Voltage reference value (nominal voltage); —Voltage stability weighting system (dynamically adjusted based on short-circuit ratio SCR, balancing economy and stability); —Constrain the penalty weighting system for violations; —Constraining penalties for violations (such as SCR failure or response timeout); —Minimizing the total voltage deviation directly reflects the degree of voltage deviation at key nodes of the entire new energy power station network. The smaller the value, the more stable the system.

[0114] Of which, total cost Calculation formula:

[0115]

[0116] In the formula, —Equipment type index (1. Synchronous condenser, 2. STATCOM, 3. Grid-type energy storage, 4. Grid-type SVG).

[0117] —No. The configuration capacity of this type of device, MVar; —No. Unit capacity investment cost of this type of equipment, in yuan / kVar;

[0118] —No. The unit capacity operation and maintenance cost of this type of equipment, in yuan / kVar / year;

[0119] Constraint-driven penalty function fusion:

[0120] A dynamic penalty function linked to the SCR level and response time is introduced to impose an exponential penalty on configuration schemes that do not meet the grid strength (SCR≥2.5) or dynamic response (recovery time<1 second).

[0121] Where the penalty function is:

[0122]

[0123] In the formula, SCR is the dynamic short-circuit ratio, which must meet the requirement of SCR≥2.5; response time is the time from the occurrence of a fault to full capacity output of the equipment, in seconds.

[0124] Specific penalty mechanisms include:

[0125] 1. Tiered penalty mechanism:

[0126] If SCR < 2.5, the penalty term adds 10 times the cost function value;

[0127] If the voltage recovery time is greater than 1 second, a penalty term is added, which is 5 times the cost function value.

[0128] If both SCR and response time constraints are violated simultaneously, the penalties will be applied cumulatively.

[0129] 2. Adaptive weight adjustment:

[0130] When the SCR is at the critical value (2.5≤SCR<3), the voltage stability weight... Increased to 1.5 times, prioritizing voltage stability.

[0131] Genetic optimization and scene adaptation:

[0132] An elite retention strategy and adaptive crossover mutation probability are adopted to select the optimal configuration through multiple generations of iteration; the range of equipment selection is limited according to the SCR level, for example, when SCR<2, synchronous condensers or grid-type energy storage are mandatory.

[0133] Furthermore, the basic workflow of a genetic algorithm can be divided into the following steps:

[0134] 1. Initialize the population:

[0135] Population size: The preset equipment type can be selected based on the SCR level. For example, when SCR<2, the initial population is forced to include the gene loci of the condenser or energy storage, and can be set to 50~200 individuals (adjusted according to computing resources).

[0136] Random generation rules:

[0137] Equipment type gene: Limit the range of selectable equipment according to preset scenarios (such as SCR level) (for example, when SCR<2, synchronous condenser or energy storage must be included).

[0138] Capacity gene: Randomly generated within a preset range using the Monte Carlo method, for example, a random value among 10MVar, 20MVar, ..., 100MVar for adjusting camera capacity.

[0139] 2. Select Operation

[0140] Roulette wheel selection: The selection probability is allocated proportionally to the fitness value.

[0141] Elite retention: The top 10% of individuals from each generation are retained and directly enter the next generation.

[0142] 3. Cross operations

[0143] Type Gene: Single-point crossover is used for device type genes to ensure feasible configuration combinations for offspring inheritance. For example:

[0144] Parent 1: [1,0,1,1] Parent 2: [0,1,1,0]

[0145] Intersection: 2

[0146] Child 1: [1,0 | 1,0] Child 2: [0,1 | 1,1]

[0147] Capacity gene: Non-uniform mutation is used for the capacity gene, and the variable asynchronous length decreases with the number of iterations, balancing global search and local convergence. For example:

[0148]

[0149] 4. Mutation operation

[0150] Type gene: randomly flip a certain position (0 1) The mutation probability is 1%~5%.

[0151] Capacity gene: Gaussian perturbation or random reset, for example:

[0152] 5. Termination Conditions

[0153] Maximum number of iterations: 100~500 generations.

[0154] Convergence threshold: If the rate of change of the optimal fitness is less than 1% for 20 consecutive generations, then the process terminates.

[0155] Furthermore, key algorithm parameters and optimization techniques:

[0156]

[0157] 2. Acceleration Strategy

[0158] Parallel computing: Performing parallel power flow calculations and transient simulations on individual populations.

[0159] Proxy Model: A neural network proxy model is used to replace part of the power flow calculation. The input is the equipment configuration parameters, and the output is the voltage deviation. This includes SCR prediction values, reducing simulation time. The surrogate model is updated every 50 generations, with training data derived from simulation results of historical optimization processes.

[0160] Step 104: Verify based on the device configuration list, determine the verification results, and adjust the configuration based on the verification results.

[0161] In practical applications, simulation verification and configuration adjustment: After optimization, the correctness and superiority of the control technology are verified, and it is determined whether the standard is met.

[0162] In one possible implementation, verification is performed based on the device configuration list to determine the verification result, including: obtaining fitness data for each generation in the genetic algorithm; verifying the device configuration list based on the fitness data to determine the verification result.

[0163] In practical applications, the final optimization result is output: a final optimal equipment configuration list is provided, including the type, capacity, and location of the reactive power equipment to be configured. Program verification and result analysis: at the end of the program, the minimum fitness value for each generation is displayed using a graph, thus verifying the optimization process of the genetic algorithm. The optimization effect of the algorithm can be judged by observing whether the fitness curve shows a significant decrease.

[0164] In one possible implementation, configuration adjustments are made based on the verification results, including: if the verification results are not up to standard, multi-objective collaborative optimization is performed again based on parameter information and a genetic algorithm to determine the equipment configuration list.

[0165] In practical applications, if the voltage recovery fails to meet the target, return to step 103 for re-optimization; if the economic efficiency is insufficient, adjust the weighting coefficients. Finally, the final configuration scheme is output.

[0166] This specification provides a method and apparatus for multi-objective collaborative optimization configuration of reactive power equipment based on short-circuit ratio. The method includes: acquiring relevant data from renewable energy power plants; dividing the system into scenarios based on the data to determine scenario information; performing power flow calculations and transient simulations to analyze the parameters corresponding to the scenario information; using a genetic algorithm to perform multi-objective collaborative optimization of reactive power equipment types and capacity combinations based on the parameter information to determine an equipment configuration list; verifying the equipment configuration list to determine the verification results; and adjusting the configuration based on the verification results. This method solves the reactive power configuration problem of renewable energy power plants in low short-circuit ratio scenarios, combining high economic efficiency, strong adaptability, and engineering practicality, providing a better solution for the safe and stable operation of new power systems.

[0167] Furthermore, this solution establishes a multi-objective collaborative optimization model, combined with a hierarchical matching strategy for short-circuit ratio (SCR), to dynamically adjust the capacity and location of devices such as synchronous condensers, STATCOMs, and grid-connected energy storage, effectively improving the SCR of renewable energy power plants. Balancing economic and technical constraints, the solution uses minimizing equipment investment and total operation and maintenance costs as the objective function, incorporating a voltage stability weighting factor (α) and a constraint penalty function (β) to balance economic efficiency and technical requirements. Through multi-objective optimization, hybrid coding algorithms, and hierarchical control strategies, this solution addresses the reactive power configuration challenge of renewable energy power plants in low SCR scenarios, offering a highly economical, adaptable, and engineering-practical solution that provides an innovative approach to the safe and stable operation of new power systems.

[0168] Furthermore, in one embodiment,

[0169] To verify the correctness and superiority of this optimized configuration method, a practical new energy collection and transmission system was used. In this case, the wind power booster station (#1 booster station) has 20MW of wind power collected by bus I of main transformer #1 and 27.5MW of wind power collected by bus II; the main transformer (#2 booster station) has 20MW of wind power collected by bus I of main transformer #2 and 27.5MW of wind power collected by bus II; the wind and solar booster station (#2 booster station) has 27.5MW of wind power collected by bus I of main transformer #1 and 27.5MW of wind power collected by bus II; the main transformer (#2 booster station) has 15.2MW of photovoltaic power collected by bus I of main transformer #2 and 35.2MW of photovoltaic power collected by bus II.

[0170] An electromechanical transient simulation model of the system was established on the PSD-BPA power system analysis software platform. The short-circuit ratio and short-circuit capacity of multiple substations at each grid connection point bus were evaluated, and the results are shown in Table 3.

[0171] Table 3 Short-circuit ratio calculation results

[0172]

[0173] The calculation results show that the short-circuit ratio at the grid connection point of new energy sources is less than 3 under this method, which is considered a weak grid. Furthermore, the following problems exist: during a fault, the voltage drops to 0.3 pu, with a recovery time as long as 2 seconds, triggering the wind turbine to disconnect from the grid; the steady-state voltage fluctuation range reaches ±8%, exceeding the national standard limit of ±5%; the existing configuration only includes 2×20MVar SVG, which cannot meet the dynamic reactive power demand.

[0174] Configuration scheme of the present invention:

[0175] (1) Objective: Improve SCR to ≥2.5, voltage recovery time <0.5s, and total cost ≤10 million yuan.

[0176] (2) Equipment selection and capacity:

[0177]

[0178] (3) Layout:

[0179] STATCOM and energy storage are deployed on the 220kV busbar of the substation; SVG is distributed at the end of the 35kV collector line.

[0180] (4) Simulation steps

[0181] 1. Initial state verification:

[0182] Simulate a three-phase short-circuit fault, record the voltage drop to 0.3 pu, and recover to 0.85 pu after 2 seconds.

[0183] 2. Optimize algorithm execution:

[0184] A genetic algorithm (population size 100, 200 generations) was used with total cost and voltage deviation as dual objectives.

[0185] Constraints: SCR ≥ 2.5, device response time < 1s.

[0186] 3. Transient simulation verification:

[0187] Inject the optimized equipment parameters and repeat the short-circuit fault test.

[0188] 4. Economic evaluation:

[0189] Calculate the investment in computing equipment and the 10-year operation and maintenance costs.

[0190] 5. Effect Comparison

[0191]

[0192] Corresponding to the above method embodiments, this specification also provides an embodiment of a multi-objective collaborative optimization configuration device for reactive power equipment based on short-circuit ratio. Figure 3 This specification illustrates a schematic diagram of a multi-objective collaborative optimization configuration device for reactive power equipment based on short-circuit ratio, according to one embodiment of this specification. Figure 3 As shown, the device includes:

[0193] The scenario segmentation module 301 is configured to acquire relevant data from new energy power stations, segment scenarios based on the relevant data, and determine scenario information.

[0194] The parameter information module 302 is configured to process the parameter information corresponding to the scene information through power flow calculation and transient simulation.

[0195] The target optimization module 303 is configured to perform multi-objective collaborative optimization of reactive power equipment type and capacity combination based on parameter information and through a genetic algorithm to determine the equipment configuration list.

[0196] The configuration adjustment module 304 is configured to perform verification based on the device configuration list, determine the verification result, and perform configuration adjustments based on the verification result.

[0197] In one possible implementation, relevant data from new energy power plants is acquired, and based on this data, scenarios are segmented to determine scenario information, including:

[0198] Acquire rated capacity, grid short-circuit capacity, historical voltage fluctuation data, and fault recording data;

[0199] Short-circuit ratio data is calculated based on rated capacity, grid short-circuit capacity, historical voltage fluctuation data, and fault recording data.

[0200] Scenarios are segmented based on short-circuit ratio data to determine scenario information.

[0201] In one possible implementation, the parameter information corresponding to the scene information is obtained through power flow calculation and transient simulation, including:

[0202] Determine the steady-state reactive power demand of the scenario information through power flow calculation;

[0203] Transient reactive power demand for scenario information is determined through transient simulation.

[0204] The total demand is determined based on steady-state reactive power demand and transient reactive power demand.

[0205] In one possible implementation, based on parameter information, a genetic algorithm is used to perform multi-objective collaborative optimization of reactive power equipment types and capacity combinations to determine the equipment configuration list, including:

[0206] Define the optimization objective;

[0207] By combining discrete variables of equipment type with continuous variables of capacity, a hybrid coding chromosome is constructed;

[0208] Construct a dynamic fitness function based on the optimization objective;

[0209] The penalty mechanism and constraint weights are determined based on short-circuit ratio data and response time.

[0210] Based on a penalty mechanism, constraint weights, hybrid encoded chromosomes, and dynamic fitness functions, a genetic algorithm is used to perform multi-objective collaborative optimization to determine the equipment configuration list.

[0211] In one possible implementation, the penalty mechanism and constraint weights are determined based on the short-circuit ratio data and response time, including:

[0212] If the short-circuit ratio is less than the first threshold, a first penalty mechanism is determined.

[0213] If the response time exceeds the second threshold, a second penalty mechanism is determined.

[0214] If the short-circuit ratio is less than the first threshold and the response time is greater than the second threshold, a third penalty mechanism is determined.

[0215] The constraint weights are determined when the short-circuit ratio data is greater than or equal to the first threshold and less than the third threshold.

[0216] In one possible implementation, verification is performed based on a device configuration list to determine the verification result, including:

[0217] Obtain fitness data for each generation in the genetic algorithm;

[0218] The device configuration list is validated based on fitness data to determine the validation results.

[0219] In one possible implementation, configuration adjustments are made based on the verification results, including:

[0220] If the verification result is not up to standard, the multi-objective collaborative optimization of reactive power equipment type and capacity combination is carried out again based on parameter information and genetic algorithm to determine the equipment configuration list.

[0221] This specification provides a method and apparatus for multi-objective collaborative optimization configuration of reactive power equipment based on short-circuit ratio. The apparatus includes: acquiring relevant data from renewable energy power plants; dividing the system into scenarios based on the data to determine scenario information; performing power flow calculations and transient simulations on the parameters corresponding to the scenario information; using a genetic algorithm to perform multi-objective collaborative optimization of reactive power equipment types and capacity combinations based on the parameter information to determine an equipment configuration list; verifying the equipment configuration list, determining the verification results, and adjusting the configuration based on the verification results. This method solves the reactive power configuration problem of renewable energy power plants in low short-circuit ratio scenarios, combining high economic efficiency, strong adaptability, and engineering practicality, providing a better solution for the safe and stable operation of new power systems.

[0222] The above is an illustrative scheme of a multi-objective collaborative optimization configuration device for reactive power equipment based on short-circuit ratio according to this embodiment. It should be noted that the technical solution of this multi-objective collaborative optimization configuration device for reactive power equipment based on short-circuit ratio belongs to the same concept as the technical solution of the aforementioned multi-objective collaborative optimization configuration method for reactive power equipment based on short-circuit ratio. Details not described in detail in the technical solution of the multi-objective collaborative optimization configuration device for reactive power equipment based on short-circuit ratio can be found in the description of the aforementioned technical solution of the multi-objective collaborative optimization configuration method for reactive power equipment based on short-circuit ratio.

[0223] Figure 4A structural block diagram of a computing device 400 according to one embodiment of this specification is shown. The components of the computing device 400 include, but are not limited to, a memory 410 and a processor 420. The processor 420 is connected to the memory 410 via a bus 430, and a database 450 is used to store data.

[0224] The computing device 400 also includes an access device 440, which enables the computing device 400 to communicate via one or more networks 460. Examples of these networks include Public Switched Telephone Network (PSTN), Local Area Network (LAN), Wide Area Network (WAN), Personal Area Network (PAN), or combinations of communication networks such as the Internet. The access device 440 may include one or more of any type of wired or wireless network interface (e.g., a network interface card (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) wireless interface, a Wi-MAX (Worldwide Interoperability for Microwave Access) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, or a Near Field Communication (NFC) interface.

[0225] In one embodiment of this specification, the aforementioned components of the computing device 400 and Figure 4 Other components, not shown, can also be connected to each other, for example, via a bus. It should be understood that... Figure 4 The block diagram of the computing device shown is for illustrative purposes only and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0226] The computing device 400 can be any type of stationary or mobile computing device, including mobile computers or mobile computing devices (e.g., tablet computers, personal digital assistants, laptop computers, notebook computers, netbooks, etc.), mobile phones (e.g., smartphones), wearable computing devices (e.g., smartwatches, smart glasses, etc.) or other types of mobile devices, or stationary computing devices such as desktop computers or personal computers (PCs). The computing device 400 can also be a mobile or stationary server.

[0227] The processor 420 executes computer-executable instructions, which, when executed by the processor, implement the steps of the above-described multi-objective cooperative optimization configuration method for reactive power equipment based on short-circuit ratio. The above is an illustrative scheme of a computing device according to this embodiment. It should be noted that the technical solution of this computing device and the technical solution of the above-described multi-objective cooperative optimization configuration method for reactive power equipment based on short-circuit ratio belong to the same concept. Details not described in detail in the technical solution of the computing device can be found in the description of the technical solution of the above-described multi-objective cooperative optimization configuration method for reactive power equipment based on short-circuit ratio.

[0228] An embodiment of this specification also provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the above-described method for multi-objective cooperative optimization configuration of reactive power equipment based on short-circuit ratio.

[0229] The above is an illustrative scheme of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of this storage medium belongs to the same concept as the technical solution of the above-described multi-objective cooperative optimization configuration method for reactive power equipment based on short-circuit ratio. For details not described in detail in the technical solution of the storage medium, please refer to the description of the technical solution of the above-described multi-objective cooperative optimization configuration method for reactive power equipment based on short-circuit ratio.

[0230] An embodiment of this specification also provides a computer program, wherein when the computer program is executed in a computer, the computer is instructed to perform the steps of the above-described method for multi-objective cooperative optimization configuration of reactive power equipment based on short-circuit ratio.

[0231] The above is an illustrative scheme of a computer program according to this embodiment. It should be noted that the technical solution of this computer program belongs to the same concept as the technical solution of the above-mentioned multi-objective cooperative optimization configuration method for reactive power equipment based on short-circuit ratio. For details not described in detail in the technical solution of the computer program, please refer to the description of the technical solution of the above-mentioned multi-objective cooperative optimization configuration method for reactive power equipment based on short-circuit ratio.

[0232] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0233] The computer instructions include computer program code, which may be in the form of source code, object code, executable file, or certain intermediate forms. The computer-readable medium may include any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately added to or subtracted according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

[0234] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments in this specification are not limited to the described order of actions, because according to the embodiments in this specification, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments in this specification.

[0235] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0236] The preferred embodiments disclosed above are merely illustrative of this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the embodiments described herein. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the embodiments, thereby enabling those skilled in the art to better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.

Claims

1. A short-circuit ratio-based multi-objective coordinated optimization configuration method for reactive power equipment, characterized in that, The method comprises the following steps: acquiring relevant data of a new energy station, performing scene division based on the relevant data, and determining scene information; performing parameter information corresponding to the scene information through power flow calculation and transient simulation; performing multi-objective collaborative optimization on reactive device type and capacity combination based on the parameter information through a genetic algorithm, and determining a device configuration list; performing verification based on the device configuration list, determining a verification result, and performing configuration adjustment based on the verification result; The method comprises the following steps: acquiring rated capacity, grid short-circuit capacity, historical voltage fluctuation data, and fault recording data; calculating short-circuit ratio data based on the rated capacity, the grid short-circuit capacity, the historical voltage fluctuation data, and the fault recording data; performing scene division based on the short-circuit ratio data, and determining scene information.

2. The method of claim 1, wherein, The method comprises the following steps: determining steady-state reactive power demand of the scene information through power flow calculation; determining transient-state reactive power demand of the scene information through transient simulation; determining total demand based on the steady-state reactive power demand and the transient-state reactive power demand.

3. The method of claim 1, wherein, The method comprises the following steps: determining an optimization target; combining device type discrete variables and capacity continuous variables to construct a hybrid coding chromosome; constructing a dynamic fitness function based on the optimization target; determining a penalty mechanism and a constraint weight based on the short-circuit ratio data and a response time; performing multi-objective collaborative optimization based on the penalty mechanism, the constraint weight, the hybrid coding chromosome, and the dynamic fitness function through the genetic algorithm, and determining a device configuration list.

4. The method of claim 3, wherein, The method comprises the following steps: determining a first penalty mechanism when the short-circuit ratio data is less than a first threshold value; determining a second penalty mechanism when the response time is greater than a second threshold value; determining a third penalty mechanism when the short-circuit ratio data is less than the first threshold value and the response time is greater than the second threshold value; determining a constraint weight when the short-circuit ratio data is greater than or equal to the first threshold value and less than a third threshold value.

5. The method of claim 1, wherein, The method comprises the following steps: acquiring fitness data of each generation in the genetic algorithm; performing verification on the device configuration list based on the fitness data, and determining a verification result.

6. The method of claim 1, wherein, The method comprises the following steps: in the case that the verification result is not up to standard, re-performing the multi-objective collaborative optimization on the reactive device type and capacity combination based on the parameter information through the genetic algorithm, and determining a device configuration list.

7. A short-circuit ratio based multi-objective coordinated optimization configuration device for reactive power equipment, characterized in that, The method comprises the following steps: a scene division module configured to acquire relevant data of a new energy station, perform scene division based on the relevant data, and determine scene information. A parameter information module configured to obtain parameter information corresponding to the scenario information through power flow calculation and transient simulation; A target optimization module configured to perform multi-objective collaborative optimization on reactive device type and capacity combination based on the parameter information through a genetic algorithm, and determine a device configuration list; A configuration adjustment module configured to verify the device configuration list, determine a verification result, and perform configuration adjustment based on the verification result.

8. A computing device, comprising: Comprise: A memory and a processor; The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, and the computer executable instructions, when executed by the processor, implement the steps of the short-circuit ratio based multi-objective collaborative optimization configuration method for reactive devices according to any one of claims 1 to 6.

9. A computer readable storage medium storing computer executable instructions, which, when executed by a processor, implement the steps of the short-circuit ratio based multi-objective collaborative optimization configuration method for reactive devices according to any one of claims 1 to 6.

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