Reactive equipment multi-target collaborative optimization configuration method and device based on short-circuit ratio

Abstract

The embodiment of the invention provides a reactive equipment multi-target collaborative optimization configuration method and device based on the short-circuit ratio, and the method comprises the steps: obtaining the related data of a new energy station, carrying out the scene division based on the related data, and determining the scene information; performing load flow calculation and transient simulation on parameter information corresponding to the scene information; based on the parameter information, performing multi-target collaborative optimization on the reactive equipment type and capacity combination through a genetic algorithm, and determining an equipment configuration list; and performing verification based on the equipment configuration list, determining a verification result, and performing configuration adjustment based on the verification result. The reactive configuration problem of the new energy station in a low-short-circuit-ratio scene is solved, high economical efficiency, high adaptability and engineering practicability are achieved, and a better solution is provided for safe and stable operation of a novel electric power system.

Description

Technical Field

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

[0002] With the large-scale access of new energy (wind power, photovoltaic) to the power grid, there are currently problems in new energy power stations such as weak active support ability, low short-circuit ratio of multiple power stations, and abnormal sensitivity to voltage fluctuations. Under a low short-circuit ratio, the grid connection point voltage of new energy power stations is easily affected by disturbances, leading to transient overvoltage or low voltage problems. Moreover, new energy equipment lacks the inertia of synchronous machines and cannot independently support the grid voltage and frequency during faults, exacerbating the risk of system instability. According to the standard requirements, "in areas with a relatively high proportion of new energy grid-connected power generation, new energy power stations should provide necessary inertia and short-circuit capacity support", and "the short-circuit ratio of new energy power stations should reach a reasonable level". Therefore, to solve the problem of low short-circuit ratio (SCR) and limited new energy output, it is necessary to configure reactive power compensation devices in new energy power stations to improve the new energy output capacity. The main reactive power compensation devices in new energy power stations include equipment such as SVG, synchronous condensers, STATCOM, and network-forming energy storage. However, there are still the following deficiencies in the current research on the configuration of reactive power equipment in new energy power stations: 1) Unreasonable configuration of reactive power equipment: Traditional methods use single equipment (such as SVG, STATCOM) or fixed-capacity configuration, ignoring the dynamic changes of SCR and being unable to adapt to the fluctuations of new energy output and grid fault scenarios.

[0003] 2) Insufficient response speed: Conventional synchronous condensers have a slow response (>100ms), and the capacity of SVG / STATCOM is limited, resulting in problems of insufficient response or high cost.

[0004] Therefore, there is an urgent need for a better solution. Summary of the Invention

[0005] In view of this, the embodiments of this specification provide a multi-objective collaborative optimization configuration method for reactive power equipment based on the short-circuit ratio. One or more embodiments of this specification also relate to a multi-objective collaborative optimization configuration device for reactive power equipment based on the short-circuit ratio, a computing device, a computer-readable storage medium, and a computer program to solve the technical defects existing in the prior art.

[0006] According to the first aspect of the embodiments of this specification, a multi-objective collaborative optimization configuration method for reactive power equipment based on the short-circuit ratio is provided, including: Obtain relevant data of a new energy power station, divide scenarios based on the relevant data, and determine scenario information; Perform power flow calculation and transient simulation on the parameter information corresponding to the scenario information; Based on the parameter information, multi-objective collaborative optimization is carried out on the reactive power equipment type and capacity combination through the genetic algorithm to determine the equipment configuration list; Verify based on the equipment configuration list, determine the verification result, and make configuration adjustments based on the verification result.

[0007] In a possible implementation, obtain the relevant data of the new energy power station, perform scenario division based on the relevant data, and determine the scenario information, including: Obtain the rated capacity, grid short-circuit capacity, historical voltage fluctuation data, and fault recording data; Calculate the short-circuit ratio data based on the rated capacity, grid short-circuit capacity, historical voltage fluctuation data, and fault recording data; Perform scenario division based on the short-circuit ratio data to determine the scenario information.

[0008] In a possible implementation, for the parameter information corresponding to the scenario information through power flow calculation and transient simulation, including: Determine the steady-state reactive power demand of the scenario information through power flow calculation; Determine the transient reactive power demand of the scenario information through transient simulation; Determine the total demand based on the steady-state reactive power demand and the transient reactive power demand.

[0009] In a possible implementation, based on the parameter information, multi-objective collaborative optimization is carried out on the reactive power equipment type and capacity combination through the genetic algorithm to determine the equipment configuration list, including: Determine the optimization objective; Combine the discrete variables of the equipment type and the continuous variables of the capacity to construct a hybrid-coded chromosome; Construct a dynamic fitness function based on the optimization objective; Determine the penalty mechanism and constraint weights based on the short-circuit ratio data and the response time; Perform multi-objective collaborative optimization through the genetic algorithm based on the penalty mechanism, constraint weights, hybrid-coded chromosome, and dynamic fitness function to determine the equipment configuration list.

[0010] In a possible implementation, determine the penalty mechanism and constraint weights based on the short-circuit ratio data and the response time, including: In the case where the short-circuit ratio data is less than the first threshold, determine the first penalty mechanism; In the case where the response time is greater than the second threshold, determine the second penalty mechanism; In the case where the short-circuit ratio data is less than the first threshold and the response time is greater than the second threshold, determine the third penalty mechanism; In the case where the short-circuit ratio data is greater than or equal to the first threshold and less than the third threshold, determine the constraint weight.

[0011] In a possible implementation, verification is performed based on the device configuration list to determine the verification result, including: Obtain the fitness data of each generation in the genetic algorithm; Verify the device configuration list based on the fitness data to determine the verification result.

[0012] In a possible implementation, configuration adjustment is performed based on the verification result, including: In the case where the verification result is unqualified, re - perform multi - objective collaborative optimization on the reactive power device type and capacity combination based on parameter information through the genetic algorithm to determine the device configuration list.

[0013] According to the second aspect of the embodiments of the present specification, a multi - objective collaborative optimization configuration device for reactive power devices based on short - circuit ratio is provided, including: A scenario division module, configured to obtain relevant data of a new - energy power station, perform scenario division based on the relevant data, and determine scenario information; A parameter information module, configured to calculate the 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 the reactive power device type and capacity combination based on the parameter information through the genetic algorithm to determine the device configuration list; A configuration adjustment module, configured to verify based on the device configuration list to determine the verification result, and perform configuration adjustment based on the verification result.

[0014] According to the third aspect of the embodiments of the present specification, a computing device is provided, including: A memory and a processor; 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, the steps of the above - mentioned multi - objective collaborative optimization configuration method for reactive power devices based on short - circuit ratio are implemented.

[0015] According to the fourth aspect of the embodiments of the present specification, a computer - readable storage medium is provided, which stores computer - executable instructions. When the instructions are executed by a processor, the steps of the above - mentioned multi - objective collaborative optimization configuration method for reactive power devices based on short - circuit ratio are implemented.

[0016] According to the fifth aspect of the embodiments of the present specification, a computer program is provided. When the computer program is executed on a computer, the computer is made to execute the steps of the above - mentioned multi - objective collaborative optimization configuration method for reactive power devices based on short - circuit ratio.

[0017] The embodiments of this specification provide a multi-objective collaborative optimization configuration method and device for reactive power equipment based on the short-circuit ratio. The multi-objective collaborative optimization configuration method for reactive power equipment based on the short-circuit ratio includes: obtaining relevant data of a new energy power station, dividing scenarios based on the relevant data, and determining scenario information; calculating parameters corresponding to the scenario information through power flow calculation and transient simulation; based on the parameter information, performing multi-objective collaborative optimization on the combination of reactive power equipment types and capacities through a genetic algorithm to determine an equipment configuration list; verifying based on the equipment configuration list to determine a verification result, and making configuration adjustments based on the verification result. It solves the problem of reactive power configuration in new energy power stations under low short-circuit ratio scenarios, 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 of the Drawings

[0018] Figure 1 is a flowchart of a multi-objective collaborative optimization configuration method for reactive power equipment based on the short-circuit ratio provided by an embodiment of this specification; Figure 2 is another flowchart of a multi-objective collaborative optimization configuration method for reactive power equipment based on the short-circuit ratio provided by an embodiment of this specification; Figure 3 is a schematic structural diagram of a multi-objective collaborative optimization configuration device for reactive power equipment based on the short-circuit ratio provided by an embodiment of this specification; Figure 4 is a structural block diagram of a computing device provided by an embodiment of this specification. Detailed Embodiments

[0019] Many specific details are set forth in the following description in order to provide a thorough understanding of this specification. However, this specification can be implemented in many other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of this specification. Therefore, this specification is not limited by the specific embodiments disclosed below.

[0020] The terms used in one or more embodiments of this specification are for the purpose of describing specific embodiments only and are not intended to limit one or more embodiments of this specification. The singular forms "a" and "the" 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 dictates otherwise. It should also be understood that the term "and / or" used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more of the associated listed items.

[0021] It should be understood that although the terms first, second, etc. may be used in one or more embodiments of this specification to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of one or more embodiments of this specification, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining".

[0022] In this specification, a multi-objective collaborative optimization configuration method for reactive power equipment based on the short-circuit ratio is provided. This specification also relates to a multi-objective collaborative optimization configuration device for reactive power equipment based on the short-circuit ratio, a computing device, and a computer-readable storage medium, which will be described in detail one by one in the following embodiments.

[0023] See Figure 1 , Figure 1 shows a flowchart of a multi-objective collaborative optimization configuration method for reactive power equipment based on the short-circuit ratio according to an embodiment of this specification, which specifically includes the following steps.

[0024] Step 101: Obtain relevant data of the new energy power station, perform scenario division based on the relevant data, and determine scenario information.

[0025] In practical applications, see Figure 2 , SCR calculation and scenario division: Based on the rated capacity of the new energy power station , the short-circuit capacity of the power grid , historical voltage fluctuation data, fault recording data and other parameters, calculate the SCR of the new energy power station, and perform scenario division according to the value of SCR.

[0026] In a possible implementation manner, obtaining relevant data of the new energy power station, performing scenario division based on the relevant data, and determining scenario information includes: obtaining the rated capacity, the short-circuit capacity of the power grid, historical voltage fluctuation data, and fault recording data; calculating short-circuit ratio data based on the rated capacity, the short-circuit capacity of the power grid, historical voltage fluctuation data, and fault recording data; performing scenario division based on the short-circuit ratio data to determine scenario information.

[0027] In practical applications, input data: the rated capacity of the new energy power station , the short-circuit capacity of the power grid , historical voltage fluctuation data, fault recording data.

[0028] Calculate SCR: ; Scenario classification: If SCR < 2: extremely weak power grid scenario; If 2 ≤ SCR < 3: weak power grid scenario; If SCR ≥ 3: Strong power grid scenario; Output: SCR level label (extremely weak / weak / strong).

[0029] Step 102: Parameter information corresponding to the scenario information through power flow calculation and transient simulation.

[0030] In practical applications, dynamic reactive power demand assessment and quantification: Analyze voltage fluctuations, transient overvoltage / undervoltage problems under different SCRs through power flow calculation and transient simulation (such as PSCAD / BPA), and quantify dynamic and steady-state reactive power demands.

[0031] In a possible implementation, the parameter information corresponding to the scenario information through power flow calculation and transient simulation includes: 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; determining the total demand based on the steady-state reactive power demand and the transient reactive power demand.

[0032] In practical applications, to determine the reactive power demand of the system, during the research process, the reactive power demand of the system is divided into steady-state reactive power demand and transient reactive power demand: Steady-state reactive power demand calculation: Obtain the voltage deviation based on the power flow calculation results ; Calculate the steady-state reactive power compensation amount ; Transient reactive power demand calculation: Extract the voltage change rate through transient simulation (such as PSCAD) ; Calculate the transient reactive power increment ; Total demand integration: ; Among them, is the dynamic reactive power capacity required by the new energy power station; is the voltage deviation (the difference between the actual voltage and the nominal value); is the reactive power change amount; is the transient voltage change rate, is the weight coefficient related to the short-circuit ratio (SCR).

[0033] The first term ( ) reflects the sensitivity of the steady-state voltage deviation to the reactive power demand, and the steady-state reactive power to be compensated; The second term ( ) reflects the additional demand for dynamic reactive power by the transient voltage change rate, and is used to suppress rapid voltage fluctuations.

[0034] Weight coefficient Adjust in segments through SCR (e.g., the lower the SCR, the higher the weight); Output: Dynamic reactive power demand curve .

[0035] Step 103: Based on the parameter information, perform multi-objective collaborative optimization on the combination of reactive power equipment types and capacities through the genetic algorithm to determine the equipment configuration list.

[0036] In practical applications, for multi-device selection and capacity optimization: The candidate set of reactive power equipment includes synchronous condensers, STATCOMs, network-forming energy storage, and network-forming SVG. Use the genetic algorithm to perform multi-objective collaborative optimization on the combination of reactive power equipment types and capacities. By introducing the genetic algorithm, minimize the total cost of reactive power equipment (equipment investment + operation and maintenance costs) and minimize voltage deviation as the upper-layer optimization objective function. At the same time, considering the system voltage stability, use the equipment dynamic response time less than 1 s, dynamic SCR ≥ threshold, and voltage fluctuation ≤ ±5% as constraint conditions to form a multi-device collaborative optimization model, and output the optimal equipment configuration list (type, capacity, location).

[0037] In a possible implementation, based on the parameter information, perform multi-objective collaborative optimization on the combination of reactive power equipment types and capacities through the genetic algorithm to determine the equipment configuration list, including: determining the optimization objective; combining discrete variables of equipment types and continuous variables of capacities to construct a hybrid-encoded chromosome; constructing a dynamic fitness function based on the optimization objective; determining the penalty mechanism and constraint weights based on the short-circuit ratio data and response time; performing multi-objective collaborative optimization through the genetic algorithm based on the penalty mechanism, constraint weights, hybrid-encoded chromosome, and dynamic fitness function to determine the equipment configuration list.

[0038] In practical applications, use the genetic algorithm to perform multi-objective collaborative optimization on the combination of reactive power equipment types and capacities to ensure: Minimize the total cost of reactive power equipment: New energy projects (such as wind farms, photovoltaic power stations) usually require huge initial investments, and the cost of reactive power compensation equipment (such as synchronous condensers, STATCOMs, energy storage systems) accounts for a relatively high proportion. Through optimized configuration, dynamically screen the combination of equipment types and capacities, eliminate redundant configurations, and avoid "over-design".

[0039] Maximize voltage stability (minimize voltage deviation): New energy power stations lack the inertia of synchronous machines, and the voltage drops faster during faults. Whether the voltage of the system can return 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 collaboration, and algorithm optimization, achieve the leap of reactive power equipment from "passive compensation" to "active support", and maximize the system voltage stability.

[0040] A two - layer optimization structure is constructed to optimize the selection and capacity of multiple devices from two aspects: minimizing the total cost of reactive power devices and maximizing the system voltage stability. In each optimization iteration, first, a preliminary solution is obtained through optimizing the total cost of reactive power devices, and then the solution is corrected and evaluated using voltage stability verification. This method enables the final solution to not only have a relatively low total cost of reactive power devices but also meet the requirements of the power grid for voltage stability, thus ensuring the rapid recovery of the system voltage when a fault occurs. The specific steps are as follows.

[0041] In a possible implementation, a penalty mechanism and constraint weights are determined based on short - circuit ratio data and response time, including: when the short - circuit ratio data is less than the first threshold, the first penalty mechanism is determined; when the response time is greater than the second threshold, the second penalty mechanism is determined; when the short - circuit ratio data is less than the first threshold and the response time is greater than the second threshold, the third penalty mechanism is determined; when the short - circuit ratio data is greater than or equal to the first threshold and less than the third threshold, the constraint weights are determined.

[0042] In practical applications, device variable modeling and hybrid coding: The discrete variables of device types and the continuous variables of capacity are combined to construct a hybrid - coded chromosome, where: (1) The set of candidate devices is {synchronous condenser, STATCOM, network - forming energy storage, network - forming SVG}, and each device type is encoded with an integer: 0: Not configured; 1: Synchronous condenser; 2: STATCOM; 3: Network - forming energy storage; 4: Network - forming SVG.

[0043] Device type coding rule: The synchronous condenser and the network - forming energy storage are mutually exclusive in the same scheme to avoid functional redundancy; SVG and STATCOM are allowed to co - exist, but the total capacity does not exceed 30% of the rated capacity of the substation.

[0044] (2) The device capacity is segmented - coded according to the preset engineering range, and the capacity step is dynamically associated with the device type. The capacity range of each type of device needs to be set according to the actual engineering situation. For example: Synchronous condenser: 10 MVar ~ 100 MVar (step 10 MVar); STATCOM: 5 MVar ~ 50 MVar (step 5 MVar); Network - forming energy storage: 10 MW / 20 MWh ~ 50 MW / 100 MWh; Network - forming SVG: 5 MVar ~ 30 MVar (step 5 MVar); Capacity coding rule: The capacity of the synchronous condenser is discretized in integer multiples of 10 MVar; the capacity of the network-forming energy storage needs to meet the energy-power ratio (E / P) ≥ 2 hours.

[0045] (3)Chromosome coding: Adopt hybrid coding (integer + real number): Gene structure: Each chromosome represents a device configuration scheme, and the gene is divided into two parts: 1. Type gene: An integer sequence with a length of 4, indicating whether a certain type of device is configured (0 or 1). For example, [1,0,1,1] means configuring a synchronous condenser, not configuring STATCOM, configuring energy storage and SVG.

[0046] 2. Capacity gene: The capacity value of the corresponding device. For example, [30 MVar, -, 20 MW / 40 MWh, 15 MVar].

[0047] Construction of dynamic fitness function: 1. With the minimization of the total cost and the maximization of voltage stability as the dual objectives, design a weighted fitness function: (1) In the formula, —Total cost of reactive power equipment (including investment and operation and maintenance costs); —Voltage amplitude of the th node; —Voltage reference value (nominal voltage); —Voltage stability weight system (dynamically adjusted according to the short-circuit ratio SCR, a trade-off between economy and stability); —Constraint violation penalty weight system; —Constraint violation penalty term (such as SCR not meeting the standard or response timeout); —Minimize the sum of voltage deviations, which directly reflects the voltage deviation degree of the key nodes of the new energy power station in the whole network. The smaller the value, the more stable the system.

[0048] Among them, the total cost Calculation formula:

[0049] In the formula, —Device type index (1. Synchronous condenser, 2. STATCOM, 3. Network-forming energy storage, 4. Network-forming SVG); —The configured capacity of the th type of device, MVar; —The th type of device's unit capacity investment cost, yuan / kVar; —The Operation and maintenance cost per unit capacity of the device, yuan / kVar / year; Constraint-driven penalty function fusion: Introduce a dynamic penalty function linked to the SCR level and response time, and impose an exponential penalty on configuration schemes that do not meet the grid strength (SCR≥2.5) or dynamic response (recovery time < 1 second).

[0050] Among them, the penalty function:

[0051] In the formula, SCR—dynamic short-circuit ratio, which needs to meet SCR≥2.5; response time: the time from the occurrence of a fault to full-capacity output of the device, in seconds.

[0052] The specific penalty mechanism includes: 1. Hierarchical penalty mechanism: If SCR < 2.5, the penalty term is 10 times the additional cost function value; If the voltage recovery time > 1 second, the penalty term is 5 times the additional cost function value; If both the SCR and response time constraints are violated, the penalty terms are superimposed.

[0053] 2. Weight adaptive adjustment: When SCR is at the critical value (2.5≤SCR<3), the voltage stability weight Increases to 1.5 times to give priority to ensuring voltage stability.

[0054] Genetic optimization and scenario adaptation: Adopt the elite retention strategy and adaptive crossover and mutation probabilities, and screen the optimal configuration through multiple generations of iteration; limit the device selection range according to the SCR level. For example, when SCR < 2, a synchronous condenser or network-forming energy storage must be forcibly included.

[0055] Furthermore, the basic workflow of the genetic algorithm can be divided into the following steps: 1. Initialize the population: Population size: Preset the optional set of device types according to the SCR level. For example, when SCR < 2, the gene positions of the synchronous condenser or energy storage must be forcibly included in the initial population, and it can be set to 50 - 200 individuals (adjusted according to computing resources).

[0056] Random generation rule: Device type gene: Limit the range of optional devices according to the preset scenario (such as SCR level). For example, when SCR < 2, a synchronous condenser or energy storage must be included.

[0057] Capacity gene: Randomly generate within the preset range according to the Monte Carlo method. For example, the capacity of the synchronous condenser is a random value among 10 MVar, 20 MVar,..., 100 MVar.

[0058] 2. Selection operation Roulette wheel selection: Allocate selection probabilities according to the fitness value ratio.

[0059] Elite retention: Retain the top 10% of the best individuals in each generation and directly pass them on to the next generation.

[0060] 3. Crossover operation Type gene: Use single-point crossover for the device type gene to ensure that the offspring inherit feasible configuration combinations. For example: Parent 1: [1,0,1,1] Parent 2: [0,1,1,0] Crossover point: 2 Offspring 1: [1,0 | 1,0] Offspring 2: [0,1 | 1,1] Capacity gene: Use non-uniform mutation for the capacity gene, and the mutation step size decreases with the number of iterations, taking into account both global search and local convergence. For example:

[0061] 4. Mutation operation Type gene: Randomly flip a bit (0 ↔ 1), with a mutation probability of 1% - 5%.

[0062] Capacity gene: Gaussian perturbation or random reset, for example:

[0063] 5. Termination conditions Maximum number of iterations: 100 - 500 generations.

[0064] Convergence threshold: If the change rate of the optimal fitness < 1% for 20 consecutive generations, then terminate.

[0065] Furthermore, the key parameters and optimization techniques of the algorithm:

[0066] 2. Acceleration strategy Parallel computing: Perform parallel power flow calculations and transient simulations on the population individuals.

[0067] Surrogate model: Use a neural network surrogate model to replace part of the power flow calculation. The input is the device configuration parameters, and the output is the voltage deviation and the predicted value of SCR, reducing the simulation time. The surrogate model is iteratively updated every 50 generations, and the training data comes from the simulation results of the historical optimization process.

[0068] Step 104: Verify based on the device configuration list, determine the verification result, and perform configuration adjustment based on the verification result.

[0069] In practical applications, simulation verification and configuration adjustment: After optimization is completed, verify the correctness and superiority of the control technology and determine whether the standards are met.

[0070] In a possible implementation, verification is performed based on the device configuration list to determine the verification result, including: obtaining the fitness data of each generation in the genetic algorithm; verifying the device configuration list based on the fitness data to determine the verification result.

[0071] In practical applications, output the final optimization result: Output the final optimal device configuration list, including the type, capacity, location, etc. of the reactive power devices to be configured; Program verification and result analysis: At the end of the program, display the minimum fitness value of each generation through a curve graph to verify the optimization process of the genetic algorithm. The optimization effect of the algorithm can be judged by observing whether the fitness curve shows an obvious decline.

[0072] In a possible implementation, configuration adjustment is performed based on the verification result, including: in the case where the verification result is not up to the standard, re-perform multi-objective collaborative optimization of the reactive power device type and capacity combination based on the parameter information through the genetic algorithm to determine the device configuration list.

[0073] In practical applications, if the voltage recovery is not up to the standard, return to step 103 for re-optimization; if the economy is insufficient, adjust the weight coefficient . Finally, output the final configuration plan.

[0074] The embodiments of this specification provide a method and device for multi-objective collaborative optimization configuration of reactive power devices based on the short-circuit ratio. The method for multi-objective collaborative optimization configuration of reactive power devices based on the short-circuit ratio includes: obtaining relevant data of a new energy power station, performing scenario division based on the relevant data to determine scenario information; calculating the parameters corresponding to the scenario information through power flow calculation and transient simulation; based on the parameter information, performing multi-objective collaborative optimization of the reactive power device type and capacity combination through the genetic algorithm to determine the device configuration list; verifying based on the device configuration list to determine the verification result, and performing configuration adjustment based on the verification result. It solves the problem of reactive power configuration in new energy power stations under low short-circuit ratio scenarios, and has high economy, strong adaptability and engineering practicability, providing a better solution for the safe and stable operation of a new power system.

[0075] Furthermore, this solution effectively improves the short-circuit ratio of new energy power stations by establishing a multi-objective collaborative optimization model and combining a hierarchical matching strategy for the short-circuit ratio (SCR), dynamically adjusting the capacity and location of equipment such as synchronous condensers, STATCOMs, and network-forming energy storage. Considering both economy and technical constraints, with the total equipment investment and operation and maintenance costs minimized as the objective function, combined with the voltage stability weight factor (α) and the constraint penalty function (β), the economy and technical requirements are balanced. Through multi-objective optimization, hybrid coding algorithms, and hierarchical control strategies, the problem of reactive power configuration in new energy power stations in low short-circuit ratio scenarios is solved, with high economy, strong adaptability, and engineering practicability, providing an innovative solution for the safe and stable operation of new power systems.

[0076] Furthermore, in one embodiment, To verify the correctness and superiority of this optimization configuration method, for a certain actual new energy collection and transmission system, in this case, the #1 main transformer section I bus of the wind power step-up substation (#1 step-up substation) collects 20 MW of wind power, section II bus collects 27.5 MW of wind power, the #1 main transformer section I bus of the #2 step-up substation collects 20 MW of wind power, and section II bus collects 27.5 MW of wind power; the #1 main transformer section I bus of the wind-solar step-up substation (#2 step-up substation) collects 27.5 MW of wind power, section II bus collects 27.5 MW of wind power, the #1 main transformer section I bus of the #2 step-up substation collects 15.2 MW of photovoltaic power, and section II bus collects 35.2 MW of photovoltaic power.

[0077] An electromechanical transient simulation model of this system is established on the PSD-BPA power system analysis software platform. The short-circuit ratios and short-circuit capacities of multiple power stations at the busbars of each grid connection point of the system are evaluated, and the results are shown in Table 3.

[0078] Table 3 Short-circuit ratio calculation results

[0079] It can be seen from the calculation results that the short-circuit ratio of the new energy grid connection point is less than 3 in this mode, belonging to a weak grid. And there are the following problems: during the fault, the voltage drops to 0.3 p.u., and the recovery time is as long as 2 seconds, triggering the wind turbines to trip off the grid; the steady-state voltage fluctuation range reaches ±8%, exceeding the national standard limit of ±5%; the existing configuration only includes 2×20 MVar SVG, which cannot meet the dynamic reactive power demand.

[0080] The configuration solution of the present invention: (1) Objectives: Increase the SCR to ≥2.5, the voltage recovery time <0.5 s, and the total cost ≤ 10 million yuan.

[0081] (2) Equipment selection and capacity:

[0082] (3) Layout: The STATCOM and energy storage are deployed on the 220 kV bus of the step-up substation; the SVG is dispersedly arranged at the end of the 35 kV collector line.

[0083] (4) Simulation steps 1. Initial state verification: Simulate a three-phase short-circuit fault, record that the voltage drops to 0.3 pu and recovers to 0.85 pu after 2 seconds.

[0084] 2. Optimization algorithm execution: Adopt the genetic algorithm (population size 100, iteration 200 generations), with the total cost and voltage deviation as the dual objectives.

[0085] Constraint conditions: SCR ≥ 2.5, equipment response time < 1 s.

[0086] 3. Transient simulation verification: Inject the optimized equipment parameters and repeat the short-circuit fault test.

[0087] 4. Economic evaluation: Calculate the equipment investment and the operation and maintenance cost for 10 years.

[0088] 5. Effect comparison

[0089] Corresponding to the above method embodiments, this specification also provides embodiments of a multi-objective collaborative optimization configuration device for reactive power equipment based on the short-circuit ratio. Figure 3 The structural schematic diagram of a multi-objective collaborative optimization configuration device for reactive power equipment based on the short-circuit ratio provided by an embodiment of this specification is shown. As Figure 3 shown, the device includes: A scenario division module 301, configured to obtain relevant data of the new energy power station, perform scenario division based on the relevant data, and determine scenario information; A parameter information module 302, configured to calculate the parameter information corresponding to the scenario information through power flow calculation and transient simulation; A target optimization module 303, configured to perform multi-objective collaborative optimization on the combination of reactive power equipment types and capacities based on the parameter information through the genetic algorithm, and determine the equipment configuration list; A configuration adjustment module 304, configured to verify based on the equipment configuration list, determine the verification result, and perform configuration adjustment based on the verification result.

[0090] In a possible implementation manner, obtaining relevant data of the new energy power station, performing scenario division based on the relevant data, and determining scenario information includes: Obtain the rated capacity, grid short-circuit capacity, historical voltage fluctuation data, and fault recording data; Calculate the short-circuit ratio data based on the rated capacity, grid short-circuit capacity, historical voltage fluctuation data, and fault recording data; Perform scenario division based on the short-circuit ratio data to determine the scenario information.

[0091] In a possible implementation, for the parameter information corresponding to the scenario information through power flow calculation and transient simulation, including: Determine the steady-state reactive power demand of the scenario information through power flow calculation; Determine the transient reactive power demand of the scenario information through transient simulation; Determine the total demand based on the steady-state reactive power demand and the transient reactive power demand.

[0092] In a possible implementation, based on the parameter information, perform multi-objective collaborative optimization on the combination of reactive power equipment types and capacities through a genetic algorithm to determine the equipment configuration list, including: Determine the optimization objectives; Combine the discrete variables of equipment types and the continuous variables of capacities to construct a hybrid-coded chromosome; Construct a dynamic fitness function based on the optimization objectives; Determine the penalty mechanism and constraint weights based on the short-circuit ratio data and response time; Perform multi-objective collaborative optimization through a genetic algorithm based on the penalty mechanism, constraint weights, hybrid-coded chromosome, and dynamic fitness function to determine the equipment configuration list.

[0093] In a possible implementation, determine the penalty mechanism and constraint weights based on the short-circuit ratio data and response time, including: When the short-circuit ratio data is less than the first threshold, determine the first penalty mechanism; When the response time is greater than the second threshold, determine the second penalty mechanism; When the short-circuit ratio data is less than the first threshold and the response time is greater than the second threshold, determine the third penalty mechanism; When the short-circuit ratio data is greater than or equal to the first threshold and less than the third threshold, determine the constraint weights.

[0094] In a possible implementation, verify based on the equipment configuration list to determine the verification result, including: Obtain the fitness data of each generation in the genetic algorithm; Verify the equipment configuration list based on the fitness data to determine the verification result.

[0095] In a possible implementation, perform configuration adjustment based on the verification result, including: In the case where the verification result does not meet the standard, based on the parameter information, the multi-objective collaborative optimization of the reactive power equipment type and capacity combination is re-performed through the genetic algorithm to determine the equipment configuration list.

[0096] The embodiments of this specification provide a method and device for multi-objective collaborative optimization configuration of reactive power equipment based on the short-circuit ratio. The device for multi-objective collaborative optimization configuration of reactive power equipment based on the short-circuit ratio includes: obtaining relevant data of a new energy power station, performing scenario division based on the relevant data to determine scenario information; calculating parameters corresponding to the scenario information through power flow calculation and transient simulation; based on the parameter information, performing multi-objective collaborative optimization of the reactive power equipment type and capacity combination through the genetic algorithm to determine the equipment configuration list; verifying based on the equipment configuration list to determine the verification result, and performing configuration adjustment based on the verification result. It solves the problem of reactive power configuration in new energy power stations in low short-circuit ratio scenarios, has high economy, strong adaptability and engineering practicability, and provides a better solution for the safe and stable operation of a new power system.

[0097] The above is a schematic solution of a device for multi-objective collaborative optimization configuration of reactive power equipment based on the short-circuit ratio in this embodiment. It should be noted that the technical solution of the device for multi-objective collaborative optimization configuration of reactive power equipment based on the short-circuit ratio belongs to the same concept as the technical solution of the above-mentioned method for multi-objective collaborative optimization configuration of reactive power equipment based on the short-circuit ratio. For the details not described in the technical solution of the device for multi-objective collaborative optimization configuration of reactive power equipment based on the short-circuit ratio, reference can be made to the description of the technical solution of the above-mentioned method for multi-objective collaborative optimization configuration of reactive power equipment based on the short-circuit ratio.

[0098] Figure 4 The structural block diagram of a computing device 400 provided according to an 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 through a bus 430, and a database 450 is used to store data.

[0099] The computing device 400 also includes an access device 440 that enables the computing device 400 to communicate via one or more networks 460. Examples of such networks include the Public Switched Telephone Network (PSTN), Local Area Network (LAN), Wide Area Network (WAN), Personal Area Network (PAN), or a combination 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 Worldwide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, a Near Field Communication (NFC).

[0100] In one embodiment of the present specification, the above components of the computing device 400, as well as Figure 4 other components not shown, may 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 a limitation on the scope of the present specification. Those skilled in the art may add or replace other components as needed.

[0101] The computing device 400 can be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, personal digital assistant, laptop computer, notebook computer, netbook, etc.), a mobile phone (e.g., a smartphone), a wearable computing device (e.g., a smartwatch, smart glasses, etc.) or other types of mobile devices, or a stationary computing device such as a desktop computer or a Personal Computer (PC). The computing device 400 can also be a mobile or stationary server.

[0102] Among them, the processor 420 is used to execute the following computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the above-mentioned multi-objective collaborative optimization configuration method of reactive power equipment based on the short-circuit ratio are implemented. The above is a schematic solution of a computing device in this embodiment. It should be noted that the technical solution of this computing device and the technical solution of the above-mentioned multi-objective collaborative optimization configuration method of reactive power equipment based on the short-circuit ratio belong to the same concept. For the detailed content not described in the technical solution of the computing device, reference can be made to the description of the technical solution of the above-mentioned multi-objective collaborative optimization configuration method of reactive power equipment based on the short-circuit ratio.

[0103] An embodiment of this specification also provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are executed by a processor, the steps of the above-mentioned multi-objective collaborative optimization configuration method of reactive power equipment based on the short-circuit ratio are implemented.

[0104] The above is a schematic solution of a computer-readable storage medium in this embodiment. It should be noted that the technical solution of this storage medium and the technical solution of the above-mentioned multi-objective collaborative optimization configuration method of reactive power equipment based on the short-circuit ratio belong to the same concept. For the detailed content not described in the technical solution of the storage medium, reference can be made to the description of the technical solution of the above-mentioned multi-objective collaborative optimization configuration method of reactive power equipment based on the short-circuit ratio.

[0105] An embodiment of this specification also provides a computer program. Among them, when the computer program is executed on a computer, the computer is made to execute the steps of the above-mentioned multi-objective collaborative optimization configuration method of reactive power equipment based on the short-circuit ratio.

[0106] The above is a schematic solution of a computer program in this embodiment. It should be noted that the technical solution of this computer program and the technical solution of the above-mentioned multi-objective collaborative optimization configuration method of reactive power equipment based on the short-circuit ratio belong to the same concept. For the detailed content not described in the technical solution of the computer program, reference can be made to the description of the technical solution of the above-mentioned multi-objective collaborative optimization configuration method of reactive power equipment based on the short-circuit ratio.

[0107] The above describes 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 in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the particular order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0108] The computer instructions include computer program code, which may be in the form of source code, object code, executable files, or some intermediate forms, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, mobile hard disks, magnetic disks, optical discs, computer memories, read-only memories (ROM), random access memories (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 increased or decreased according to the requirements of legislation and patent practice within the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0109] It should be noted that for the foregoing method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the embodiments of this specification are not limited by the described action sequence, because according to the embodiments of this specification, certain steps may be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential for the embodiments of this specification.

[0110] In the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0111] The preferred embodiments of this specification disclosed above are only used to help explain this specification. The alternative embodiments do not elaborate on all details and do not limit the invention to only the specific embodiments described. Obviously, many modifications and variations can be made according to the content of the embodiments of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the embodiments of this specification, so that those skilled in the art can understand and utilize this specification well. This specification is only limited by the claims and their full scope and equivalents.

Claims

1. A multi-objective collaborative optimization configuration method for reactive power equipment based on short-circuit ratio, characterized in that Including: Obtain relevant data of the new energy power station, perform scenario division based on the relevant data, and determine scenario information; Perform parameter information corresponding to the scenario information through power flow calculation and transient simulation; Based on the parameter information, perform multi-objective collaborative optimization on the reactive power equipment type and capacity combination through the genetic algorithm to determine the equipment configuration list; Verify based on the equipment configuration list, determine the verification result, and perform configuration adjustment based on the verification result.

2. The method according to claim 1, characterized in that, The obtaining relevant data of the new energy power station, performing scenario division based on the relevant data, and determining scenario information includes: Obtain the rated capacity, grid short-circuit capacity, historical voltage fluctuation data, and fault recording data; Calculate the short-circuit ratio data based on the rated capacity, the grid short-circuit capacity, the historical voltage fluctuation data, and the fault recording data; Perform scenario division based on the short-circuit ratio data to determine scenario information.

3. The method according to claim 1, wherein The performing parameter information corresponding to the scenario information through power flow calculation and transient simulation includes: Determine the steady-state reactive power demand of the scenario information through power flow calculation; Determine the transient reactive power demand of the scenario information through transient simulation; Determine the total demand based on the steady-state reactive power demand and the transient reactive power demand.

4. The method according to claim 2, wherein The performing multi-objective collaborative optimization on the reactive power equipment type and capacity combination through the genetic algorithm based on the parameter information to determine the equipment configuration list includes: Determine the optimization objective; Combine the discrete variables of the equipment type and the continuous variables of the capacity to construct a hybrid-coded chromosome; Construct a dynamic fitness function based on the optimization objective; Determine the penalty mechanism and constraint weight based on the short-circuit ratio data and response time; Perform multi-objective collaborative optimization through the genetic algorithm based on the penalty mechanism, the constraint weight, the hybrid-coded chromosome, and the dynamic fitness function to determine the equipment configuration list.

5. The method according to claim 4, characterized in that, The determining the penalty mechanism and constraint weight based on the short-circuit ratio data and response time includes: Determine the first penalty mechanism when the short-circuit ratio data is less than the first threshold; Determine the second penalty mechanism when the response time is greater than the second threshold; Determine the 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; Determine the constraint weight when the short-circuit ratio data is greater than or equal to the first threshold and less than the third threshold.

6. The method according to claim 1, wherein The verifying based on the equipment configuration list to determine the verification result includes: Obtain the fitness data of each generation in the genetic algorithm; Verify the equipment configuration list based on the fitness data to determine the verification result.

7. The method according to claim 1, characterized in that, The performing configuration adjustment based on the verification result includes: When the verification result is unqualified, re-perform the multi-objective collaborative optimization on the reactive power equipment type and capacity combination through the genetic algorithm based on the parameter information to determine the equipment configuration list.

8. A reactive power device multi-objective collaborative optimization configuration device based on short circuit ratio, characterized in that Including: A scenario division module configured to obtain relevant data of the new energy power station, perform scenario division based on the relevant data, and determine scenario information; A parameter information module configured to perform parameter information corresponding to the scenario information through power flow calculation and transient simulation; The target optimization module is configured to perform multi-objective collaborative optimization on the combination of reactive power equipment types and capacities through a genetic algorithm based on the parameter information, and determine the equipment configuration list; The configuration adjustment module is configured to perform verification based on the equipment configuration list, determine the verification result, and perform configuration adjustment based on the verification result.

9. A computing device, characterized in that, It includes: A memory and a processor; 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, the steps of the reactive power equipment multi-objective collaborative optimization configuration method based on the short-circuit ratio described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are executed by a processor, the steps of the reactive power equipment multi-objective collaborative optimization configuration method based on the short-circuit ratio described in any one of claims 1 to 7 are implemented.

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