An optimal configuration method and system for reactive power compensation of AC-DC hybrid system

Abstract

This invention discloses a method and system for reactive power compensation optimization configuration applicable to AC / DC hybrid systems, belonging to the field of power system and automation technology. The method includes: acquiring historical operating data of an ultra-high voltage AC / DC hybrid transmission system; constructing parameter correlation relationships for the AC / DC hybrid system using the historical operating data; constructing a reactive power compensation optimization configuration model based on the reactive power correlation relationships of the AC / DC hybrid system; and solving the reactive power compensation optimization configuration model using a multi-objective particle swarm optimization algorithm according to the constraints of the AC / DC hybrid system to obtain the equipment configuration parameters of the AC / DC hybrid system. This invention fully utilizes the transmission capacity of AC / DC hybrid systems and has significant theoretical and engineering value for ensuring the safe operation of energy systems and promoting the consumption of clean energy.

Description

Technical Field

[0001] This invention belongs to the field of power system and automation technology, specifically relating to a reactive power compensation optimization configuration method and system applicable to AC / DC hybrid systems. Background Technology

[0002] Ultra-high voltage (UHV) AC / DC hybrid power transmission systems offer advantages in long-distance, large-capacity power transmission and are currently a major component of inter-regional power grids. However, with the continuous increase in the scale of UHV AC / DC power grid feeds, hybrid AC / DC systems still face challenges in safe and stable operation. A reasonable reactive power compensation configuration can effectively support the AC bus voltage. Therefore, how to fully utilize the dynamic transmission potential of transmission and transformation equipment, rationally configure reactive power compensation equipment, and fully tap the transmission capacity of hybrid AC / DC power transmission systems is of great theoretical and engineering value for ensuring the safe operation of energy systems and promoting the consumption of clean energy.

[0003] In existing technologies, the two objectives of system reactive power loss and voltage stability are transformed into a single objective for optimization. An improved particle swarm optimization algorithm is used to solve the optimization model to determine the reactive power compensation capacity. The weighted method transforms voltage deviation and system active power loss into a single objective, and the fish swarm algorithm is used to optimize the steady-state reactive power of the system. However, the issue of improving the transmission capacity of the UHV AC / DC hybrid transmission system is ignored. Summary of the Invention

[0004] The purpose of this invention is to provide a reactive power compensation optimization configuration method and system applicable to AC / DC hybrid systems. By solving the reactive power compensation equipment optimization configuration model through a multi-objective particle swarm optimization algorithm, the capacity of the reactive power compensation equipment is optimized, the apparent current of the transmission lines is reduced, and the transmission capacity of the transmission channels is improved.

[0005] To achieve the above objectives, the present invention is implemented using the following technical solution.

[0006] On one hand, the present invention provides a reactive power compensation optimization configuration method applicable to AC / DC hybrid systems, comprising:

[0007] Obtain historical operating data of the UHV AC / DC hybrid power transmission system;

[0008] Using the historical operating data, construct the parameter correlation relationship of the AC / DC hybrid system;

[0009] Based on the reactive power correlation of the AC / DC hybrid system, a reactive power compensation optimization configuration model is constructed.

[0010] The reactive power compensation optimization configuration model is solved using the multi-objective particle swarm optimization algorithm based on the obtained constraints of the AC / DC hybrid system, and the equipment configuration parameters of the AC / DC hybrid system are obtained.

[0011] Optionally, the historical transportation data includes maximum transmission power, unit capacity investment cost, unit power operating cost, unit capacity disposal conversion revenue, configured capacity, and node voltage amplitude;

[0012] Using the historical operating data, the parameter correlations of the AC / DC hybrid system are determined, including:

[0013] The transmission capacity correlation of the AC / DC hybrid system is constructed based on the maximum transmission power.

[0014] Based on the unit capacity investment cost, unit power operating cost, unit capacity disposal conversion revenue, and configured capacity, construct the investment cost relationship of the AC / DC hybrid system;

[0015] A voltage deviation correlation is constructed based on the node voltage amplitude.

[0016] Optionally, the investment cost relationship of the AC / DC hybrid system can be constructed based on the unit capacity investment cost, unit power operating cost, unit capacity disposal discount revenue, and configured capacity, including:

[0017] An investment cost relationship is constructed based on the unit capacity investment cost and the configured capacity; the expression for the investment cost relationship is:

[0018] ;

[0019] A relationship between operation and maintenance costs is established based on the unit power operating cost and the configured capacity; the expression for the relationship between operation and maintenance costs is:

[0020] ;

[0021] A processing revenue correlation is constructed based on the unit capacity disposal discount revenue and the configured capacity; the expression for the processing revenue correlation is:

[0022] ;

[0023] In the formula, For investment costs, For maintenance costs, In order to handle the revenue, , , , , , These are the unit capacity investment cost, unit power operating cost, and unit capacity disposal revenue of the static var generator, respectively. , , The unit capacity investment cost, unit power operating cost, and unit capacity disposal revenue of conventional reactive power compensation equipment; , The configuration capacity for static var generators and conventional reactive power compensation equipment; , The service life of static var generators and conventional reactive power compensation equipment; It is the present value factor. ; Let t be the discount rate for funds, and t represent time, where T is the period.

[0024] Optionally, the objective function expression of the reactive power compensation optimization configuration model is:

[0025] ;

[0026] ;

[0027] ;

[0028] ;

[0029] In the formula, The objective function for the reactive power compensation optimization configuration model. For the transmission capacity correlation, For investment cost related relationships, The correlation is related to voltage deviation. U represents the weight of node bi; i For nodes voltage amplitude, is the maximum transmission power, N is the total number of nodes, and min(*) is the minimum value function.

[0030] Optionally, the constraints of the AC / DC hybrid system include power balance constraints, investment cost constraints, power constraints of new energy units, node voltage constraints, and reactive power compensation equipment constraints.

[0031] Optionally, the calculation expression for the power balance constraint is as follows:

[0032] ;

[0033] ;

[0034] In the formula, , These represent the active power output and active power load of the generator set at node i, respectively. , , , Representing nodes respectively The reactive power output of the generator set, reactive load, reactive power compensation capacity of the static var generator and conventional reactive power compensation equipment; , , These represent the nodes of the admittance matrix. The conductance, susceptance, and phase angle difference of the node voltage between node j and node j; express The voltage amplitude of the node, N is the total number of nodes, c is the number of conventional reactive power compensation equipment nodes, N c This represents the total number of conventional reactive power compensation equipment nodes.

[0035] The formula for calculating investment cost constraints is:

[0036] ;

[0037] In the formula, This indicates the maximum investment amount for static var generators and conventional reactive power compensation equipment. To constrain investment costs;

[0038] The calculation expression for the power constraint of new energy units is:

[0039] ;

[0040] In the formula, , These represent the maximum output values ​​for wind power and solar power, respectively. , This refers to the actual output value of wind power and photovoltaic power.

[0041] The calculation expression for nodal voltage constraints is as follows:

[0042] ;

[0043] In the formula, , and Representing nodes respectively The lower limit of the voltage, the upper limit of the voltage, and the node voltage at that point;

[0044] The calculation expression for the constraints of the reactive power compensation equipment is as follows:

[0045] ;

[0046] ;

[0047] In the formula, , , , These represent the maximum and minimum configured capacity, maximum and minimum operating power of the static var generator, respectively. , , , These represent the maximum and minimum configured capacity, maximum and minimum operating power of conventional reactive power compensation equipment. , , , Let t represent the configured capacity of the static var generator, the configured capacity of the conventional reactive power compensation equipment, the operating power of the static var generator, and the operating power of the conventional reactive power compensation equipment.

[0048] Optionally, a multi-objective particle swarm optimization algorithm is used to solve the reactive power compensation optimization configuration model based on the obtained AC / DC hybrid system constraints, thereby obtaining the equipment configuration parameters of the AC / DC hybrid system, including:

[0049] Based on the historical operating data, the constraints of the AC / DC hybrid system, and the preset parameters of the multi-objective particle swarm optimization algorithm, a pre-configured population for the optimal configuration of the AC / DC hybrid system is constructed.

[0050] Based on the pre-configured population, an initial population satisfying the constraints of the AC / DC hybrid system is obtained through initial iterations using non-dominated sorting, selection, crossover, and mutation operations.

[0051] Based on the initial population, the optimized population is obtained by iterative optimization through the crowding distance sorting mechanism.

[0052] Based on the optimized population, the optimal individual is obtained by calculating the distance of the population from the reference point;

[0053] The optimal individual is used to solve the reactive power compensation optimization configuration model to obtain the equipment configuration parameters of the AC / DC hybrid system.

[0054] Optionally, based on the initial population, an optimized population is obtained through iterative optimization using a crowding distance sorting mechanism, including:

[0055] Step S1: Based on the initial population, offspring individuals are generated through crossover and mutation operations, and parent individuals and offspring individuals are merged to generate an iterative population;

[0056] Step S2: Based on the iterative population, the non-dominant levels of the population are quickly sorted using the crowding distance sorting mechanism, and the individual crowding degree is calculated. Local search boundaries and sparse regions are constructed using the AC / DC hybrid system constraints.

[0057] Step S3: Based on the local search boundary and sparse region, calculate the crowding degree of the reactive power compensation device population through the non-dominated layer hierarchical principle, and construct the updated initial population.

[0058] Repeat steps S1 to S3 until the preset number of iterations is met to obtain the optimized population.

[0059] Optionally, the formula for calculating the crossover probability in the offspring individuals generated through the crossover mutation operation is as follows:

[0060] ;

[0061] Where g is the current generation number; For the maximum number of generations, To preset the upper limit of crossover probability, To preset the lower bound of the crossover probability, The crossover probability of the crossover operation;

[0062] The formula for calculating the mutation probability is:

[0063] ;

[0064] in, This is a preset lower limit for the mutation probability. To preset the upper limit of mutation probability, represents the mutation probability of the mutation operation.

[0065] Secondly, the present invention provides a reactive power compensation optimization configuration system suitable for AC / DC hybrid systems, comprising:

[0066] The data acquisition module is used to acquire historical operating data of the UHV AC / DC hybrid power transmission system.

[0067] The reactive power compensation optimization module is used to construct the parameter correlation relationship of the AC / DC hybrid system using the historical operating data, and to construct a reactive power compensation optimization configuration model based on the reactive power correlation relationship of the AC / DC hybrid system.

[0068] The multi-objective particle swarm optimization (MPS) algorithm solution module is used to solve the reactive power compensation optimization configuration model based on the obtained AC / DC hybrid system constraints using the multi-objective particle swarm optimization algorithm, and obtain the equipment configuration parameters of the AC / DC hybrid system.

[0069] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0070] This invention utilizes a multi-objective particle swarm optimization algorithm to solve the reactive power compensation optimization configuration model, achieving capacity optimization for static var generators and traditional reactive power compensation equipment. This reduces the apparent current of transmission lines, enhances the transmission capacity of transmission channels, and lowers the overall cost of reactive power compensation. Furthermore, by constructing parameter correlations using historical operating data, the accuracy of the constructed reactive power compensation optimization configuration model is improved. This invention rationally configures reactive power compensation equipment and fully leverages the transmission capacity of AC / DC hybrid transmission systems, possessing significant theoretical and engineering value for ensuring the safe operation of energy systems and promoting the consumption of clean energy.

[0071] This invention improves the global search capability of the reactive power compensation optimization configuration model through crossover mutation operations, effectively achieving comprehensive optimization of reactive power loss, voltage stability, and transmission capacity. By utilizing the constraints of the AC / DC hybrid system, the practical performance of the solution results of the reactive power compensation optimization configuration model is guaranteed, effectively taking into account multiple optimization objectives in the model solution. Attached Figure Description

[0072] Figure 1 The diagram shows the solution steps of the NSGA-Ⅲ-RLS algorithm applied in this invention.

[0073] Figure 2 The diagram shows the topology of an ultra-high voltage AC / DC hybrid power transmission system.

[0074] Figure 3 The diagram shown is a typical daytime solar power output.

[0075] Figure 4 The diagram shown illustrates a typical daily load demand.

[0076] Figure 5 The figure shown is a schematic diagram of the typical daily transport capacity variation curve. Detailed Implementation

[0077] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0078] The term "and / or" simply describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0079] Example 1

[0080] This embodiment introduces a reactive power compensation optimization configuration method applicable to AC / DC hybrid systems, specifically including:

[0081] Obtain historical operating data of the UHV AC / DC hybrid transmission system; historical operating data includes the maximum transmission power of the AC / DC hybrid system, investment cost per unit capacity, operating cost per unit power, disposal revenue per unit capacity, configured capacity, and node voltage amplitude.

[0082] Based on historical operating data, by extracting characteristic information from the historical operating data, parameter correlations of the AC / DC hybrid system are constructed to describe the correlations between transmission capacity, investment cost, and voltage deviation, including:

[0083] Construct the transmission capacity correlation of AC / DC hybrid systems based on maximum transmission power;

[0084] The relationship between investment costs of AC / DC hybrid systems is constructed based on unit capacity investment cost, unit power operating cost, unit capacity disposal conversion revenue, and configured capacity.

[0085] A voltage deviation correlation is constructed based on the node voltage amplitude.

[0086] Based on the reactive power correlation of AC / DC hybrid systems, a reactive power compensation optimization configuration model is constructed.

[0087] The multi-objective particle swarm optimization (PSO) algorithm is used to solve the reactive power compensation optimization configuration model based on the constraints of the AC / DC hybrid system, thereby obtaining the equipment configuration parameters of the AC / DC hybrid system. The multi-objective PSO algorithm can achieve collaborative optimization of multiple objectives.

[0088] In summary, this embodiment solves the reactive power compensation optimization configuration model using a multi-objective particle swarm optimization algorithm, achieving capacity optimization of the static var generator and traditional reactive power compensation equipment. This reduces the apparent current of the transmission lines, thereby improving the transmission capacity of the transmission channels. This embodiment rationally configures the reactive power compensation equipment, fully exploits the transmission capacity of the AC / DC hybrid transmission system, and effectively reduces overall costs. It has significant theoretical and engineering value for ensuring the safe operation of energy systems and promoting the consumption of clean energy.

[0089] Example 2

[0090] Based on the same inventive concept as Embodiment 1, this embodiment introduces a reactive power compensation optimization configuration method suitable for AC / DC hybrid systems, including:

[0091] Obtain historical operating data of the UHV AC / DC hybrid transmission system; historical transportation data includes maximum transmission power, investment cost per unit capacity, operating cost per unit power, disposal revenue per unit capacity, configured capacity, and node voltage amplitude;

[0092] Using historical operating data, determine the parameter correlations of the AC / DC hybrid system, including:

[0093] Construct the transmission capacity correlation of AC / DC hybrid systems based on maximum transmission power;

[0094] The investment cost relationship of AC / DC hybrid systems is constructed based on unit capacity investment cost, unit power operating cost, unit capacity disposal proceeds, and configured capacity, including:

[0095] The relationship between investment costs is constructed based on the unit capacity investment cost and the allocated capacity; the expression for the investment cost relationship is:

[0096] ;

[0097] The relationship between operation and maintenance costs is established based on the unit power operating cost and the configured capacity; the expression for the relationship between operation and maintenance costs is:

[0098] ;

[0099] The processing revenue relationship is constructed based on the discounted revenue per unit capacity and the allocated capacity; the expression for the processing revenue relationship is:

[0100] ;

[0101] In the formula, For investment costs, For maintenance costs, In order to handle the revenue, , , , , , These are the unit capacity investment cost, unit power operating cost, and unit capacity disposal revenue of the static var generator, respectively. , , The unit capacity investment cost, unit power operating cost, and unit capacity disposal revenue of conventional reactive power compensation equipment; , The configuration capacity for static var generators and conventional reactive power compensation equipment; , The service life of static var generators and conventional reactive power compensation equipment; It is the present value factor. ; Let t be the discount rate for funds, and t represent time, where T is the period.

[0102] A voltage deviation correlation is constructed based on the node voltage amplitude.

[0103] By utilizing historical operating data, the parameter correlation relationships of the AC / DC hybrid system are constructed;

[0104] Based on the reactive power correlation of AC / DC hybrid systems, a reactive power compensation optimization configuration model is constructed; the objective function expression of the reactive power compensation optimization configuration model is:

[0105] ;

[0106] ;

[0107] ;

[0108] ;

[0109] In the formula, The objective function for the reactive power compensation optimization configuration model. For the transmission capacity correlation, For investment cost related relationships, The correlation is related to voltage deviation. U represents the weight of node bi; i For nodes voltage amplitude, is the maximum transmission power, N is the total number of nodes, and min(*) is the minimum value function.

[0110] like Figure 1 As shown, the reactive power compensation optimization configuration model is solved using the multi-objective particle swarm optimization algorithm based on the constraints of the AC / DC hybrid system, yielding the equipment configuration parameters of the AC / DC hybrid system, including:

[0111] A pre-configured population for optimizing the configuration of the AC / DC hybrid system is constructed based on historical operating data, constraints of the AC / DC hybrid system, and preset parameters of the multi-objective particle swarm optimization algorithm.

[0112] Based on the pre-configured population, an initial population that satisfies the constraints of the AC / DC hybrid system is obtained through initial iteration using non-dominated sorting, selection, crossover, and mutation operations.

[0113] Based on the initial population, the optimized population is obtained through iterative optimization using the crowding distance sorting mechanism.

[0114] Based on the optimized population, the optimal individual is obtained by calculating the distance of the population from the reference point;

[0115] The optimal individual solution reactive power compensation optimization configuration model is used to obtain the equipment configuration parameters of the AC / DC hybrid system.

[0116] The optimized population is obtained through iterative optimization based on the initial population and the crowding distance sorting mechanism, including:

[0117] Step S1: Based on the initial population, offspring individuals are generated through crossover and mutation operations, and parent individuals and offspring individuals are merged to generate an iterative population;

[0118] Step S2: Based on the iterative population, the non-dominant levels of the population are quickly sorted through the crowding distance sorting mechanism, and the individual crowding degree is calculated. The local search boundary and sparse region are constructed through the AC / DC hybrid system constraints.

[0119] Step S3: Based on the local search boundary and sparse region, calculate the crowding degree of the reactive power compensation equipment population through the non-dominated layer hierarchical principle, and construct the updated initial population.

[0120] Repeat steps S1 to S3 until the preset number of iterations is met to obtain the optimized population.

[0121] The specific formula for calculating the crossover probability in offspring generated through crossover mutation is as follows:

[0122] ;

[0123] Where g is the current generation number; For the maximum number of generations, To preset the upper limit of crossover probability, To preset the lower bound of the crossover probability, The crossover probability of the crossover operation;

[0124] The formula for calculating the mutation probability is:

[0125] ;

[0126] in, This is a preset lower limit for the mutation probability. To preset the upper limit of mutation probability, represents the mutation probability of the mutation operation.

[0127] Constraints on AC / DC hybrid systems include power balance constraints, investment cost constraints, power constraints of new energy units, node voltage constraints, and reactive power compensation equipment constraints.

[0128] The calculation expression for the power balance constraint is:

[0129] ;

[0130] ;

[0131] In the formula, , These represent the active power output and active power load of the generator set at node i, respectively. , , , Representing nodes respectively The reactive power output of the generator set, reactive load, reactive power compensation capacity of the static var generator and conventional reactive power compensation equipment; , , These represent the nodes of the admittance matrix. The conductance, susceptance, and phase angle difference of the node voltage between node j and node j; express The voltage amplitude of the node, N is the total number of nodes, c is the number of conventional reactive power compensation equipment nodes, N c This represents the total number of conventional reactive power compensation equipment nodes.

[0132] The formula for calculating investment cost constraints is:

[0133] ;

[0134] In the formula, This indicates the maximum investment amount for static var generators and conventional reactive power compensation equipment. To constrain investment costs;

[0135] The calculation expression for the power constraint of new energy units is as follows:

[0136] ;

[0137] In the formula, , These represent the maximum output values ​​for wind power and solar power, respectively. , This refers to the actual output value of wind power and photovoltaic power.

[0138] The expression for calculating the node voltage constraint is:

[0139] ;

[0140] In the formula, , and Representing nodes respectively The lower limit of the voltage, the upper limit of the voltage, and the node voltage at that point;

[0141] The calculation expression for the constraints of reactive power compensation equipment is:

[0142] ;

[0143] ;

[0144] In the formula, , , , These represent the maximum and minimum configured capacity, maximum and minimum operating power of the static var generator, respectively. , , , These represent the maximum and minimum configured capacity, maximum and minimum operating power of conventional reactive power compensation equipment. , , , Let t represent the configured capacity of the static var generator, the configured capacity of the conventional reactive power compensation equipment, the operating power of the static var generator, and the operating power of the conventional reactive power compensation equipment.

[0145] Example 3

[0146] Based on the same inventive concept as other embodiments, this embodiment introduces a reactive power compensation optimization configuration system suitable for AC / DC hybrid systems, including:

[0147] The data acquisition module is used to acquire historical operating data of the UHV AC / DC hybrid power transmission system.

[0148] The reactive power compensation optimization module is used to construct the parameter correlation relationship of the AC / DC hybrid system using the historical operating data, and to construct a reactive power compensation optimization configuration model based on the reactive power correlation relationship of the AC / DC hybrid system.

[0149] The multi-objective particle swarm optimization (MPS) algorithm solution module is used to solve the reactive power compensation optimization configuration model based on the obtained AC / DC hybrid system constraints using the multi-objective particle swarm optimization algorithm, and obtain the equipment configuration parameters of the AC / DC hybrid system.

[0150] Step 1: Input relevant operating data of the UHV AC / DC hybrid transmission system, set relevant constraints for decision variables and relevant algorithm parameters, generate an initial population with iteration number k=0, and randomly generate an initial population for optimizing the reactive power compensation equipment configuration of the UHV AC / DC hybrid transmission system considering the improvement of transmission capacity; (hereinafter referred to as the initial population)

[0151] Step 2: The initial population is used to generate a first-generation population that meets the requirements through a fast non-dominated sorting, selection, crossover, and mutation algorithm.

[0152] Step 3: The first generation of the population generates a new generation by merging the parent and offspring individuals.

[0153] Step four involves rapidly sorting the non-dominated levels of reactive power compensation equipment within the UHV AC / DC hybrid transmission system based on the congestion distance sorting mechanism, then calculating the individual congestion degree to generate boundary and sparse region centers. Using transmission capacity, overall cost, and voltage deviation as direction vectors for optimal configuration, a local search boundary and sparse region are constructed. A further local search generates a local solution set, and the congestion degree of the reactive power compensation equipment is calculated according to the non-dominated layer hierarchical principle. The selected solution set is then... Individuals merge with individuals in the non-dominant layer of the population of the reactive power compensation equipment of the UHV AC / DC hybrid transmission system, and the new generation of population self-iterates to form a new generation of population that meets the requirements;

[0154] Step 5: The new generation population calculates the distance of each population to the reference point, selects the best individual based on the reference point, and determines whether the maximum number of iterations is met. If it is met, the configuration result of the reactive power compensation equipment of the UHV AC / DC hybrid transmission system that meets the multi-objective conditions is output; otherwise, steps 3 to 4 are repeated.

[0155] Output the configuration results of reactive power compensation equipment for the UHV AC / DC hybrid power transmission system that meet multiple objective conditions.

[0156] For renewable energy transmission systems with a high proportion of wind and solar power inputs, based on actual wind power, solar power, and load operation data of a certain region's UHV AC / DC hybrid transmission system, a system was constructed as follows: Figure 2 The topology of the UHV AC / DC hybrid transmission system shown is used to simulate and verify the optimized configuration model of reactive power compensation equipment for the UHV AC / DC hybrid transmission system that takes into account the improvement of transmission capacity proposed in this embodiment.

[0157] like Figure 2 As shown, A, B, and C are new energy collection stations, and D is a converter station; the transmission distance is 600km; the new energy power is stepped up by two transformers and then transmitted to the receiving-end power grid via overhead lines. The models of the transformers and overhead lines are as follows, and the relevant parameters are shown in Table 1:

[0158] type parameter type parameter 55℃ 70℃ 65℃ R（20℃） 0.045Ω / km R 20 R（70℃） 0.054Ω / km x 1.0 unit weight 1919kg / km y 1.6 Static current carrying 1100A 140℃

[0159] The busbar has a rated voltage of ±800kV, a current of 5kA, and a rated transmission power of 7500MW; the total installed capacity of wind power and photovoltaic power is 3500MW and 4800MW respectively, with a maximum load of 50MW; the basic parameters of the static var generator and conventional reactive power compensation equipment are shown in Table 2.

[0160] Construction cost (ten thousand yuan / MVar) 80 50 Operation and maintenance costs (ten thousand yuan / MVar) 0.5 1.2 efficiency(%) 99.2 98.5 Discount rate (%) 12 8 Disposal Conversion Factor 0.09 0.085 Service life (years) 25 25

[0161] Typical daily power generation curves and typical daily load demand curves for wind and solar power are as follows: Figures 3-4 As shown.

[0162] To verify the effectiveness of the proposed optimized configuration model for reactive power compensation equipment in an ultra-high voltage AC / DC hybrid power transmission system that takes into account transmission capacity improvement, two scenarios were set up:

[0163] Scenario 1: Optimization configuration model of traditional reactive power compensation equipment without considering the impact of the dynamic heat capacity of transformers and transmission lines on the transmission capacity of UHV AC / DC hybrid transmission system.

[0164] Scenario 2: This embodiment is a model for optimizing the configuration of reactive power compensation equipment in an ultra-high voltage AC / DC hybrid power transmission system that takes into account the improvement of transmission capacity.

[0165] Table 3 shows the optimization results of the capacity configuration of reactive power compensation equipment in the UHV AC / DC hybrid power transmission system under the two scenarios:

[0166] type Scene 1 Scene 2 Static var generator capacity (MVar) 350 420 Traditional reactive power compensation equipment capacity (MVar) 480 350

[0167] It is known that the total voltage deviation of each node in the UHV AC / DC hybrid transmission system without reactive power compensation equipment is 25.63 pu. Table 4 shows the investment cost of the UHV AC / DC hybrid transmission system and the total voltage deviation of each node after connecting reactive power compensation equipment in both Scenario 1 and Scenario 2.

[0168] type Scene 1 Scene 2 <![CDATA[comprehensive cost (10 4 ten thousand yuan)]]> 5.96 5.32 Total voltage deviation (pu) 11.72 4.45

[0169] The transport capacity curves for typical intraday delivery routes in two scenarios are as follows: Figure 5 As shown.

[0170] 4.3. As shown in Tables 3 and 4, compared with Scenario 1, the capacity of the traditional reactive power compensation equipment in Scenario 2 proposed in this embodiment is reduced by 20%, and the capacity of the static var generator is increased by 27.08%. Although the investment cost is only reduced by 1.73%, the comprehensive cost within a typical day is reduced by 10.72%. Furthermore, the voltage situation of the UHV AC / DC hybrid transmission system after the reactive power compensation equipment is connected is improved. Compared with Scenario 1, the total voltage deviation of each node in the system is reduced by 62.03% in Scenario 2. In addition, through... Figure 5 The curves showing the variation of transmission capacity of the UHV AC / DC hybrid transmission system with transmission distance reveal that, compared to Scenario 1, Scenario 2, considering the impact of the dynamic heat capacity of the transformers and transmission lines on transmission capacity, can improve the transmission capacity of the UHV AC / DC hybrid transmission system by optimizing the reactive power compensation equipment and reducing the apparent current on the lines. The optimized configuration model for reactive power compensation equipment in the UHV AC / DC hybrid transmission system proposed in this embodiment, which considers the improvement in transmission capacity, can effectively reduce the configuration cost of reactive power compensation equipment and the voltage deviation of the system, thereby improving the transmission capacity and operational economy of the transmission channel.

[0171] In summary, the present invention rationally configures reactive power compensation equipment and fully utilizes the transmission capacity of AC / DC hybrid power transmission systems, which has significant theoretical and engineering value for ensuring the safe operation of energy systems and promoting the consumption of clean energy.

[0172] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0173] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0174] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0175] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0176] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for optimizing reactive power compensation configuration in AC / DC hybrid systems, characterized in that, include: Obtain historical operating data of the UHV AC / DC hybrid power transmission system; Using the historical operating data, construct the parameter correlation relationship of the AC / DC hybrid system; Based on the reactive power correlation of the AC / DC hybrid system, a reactive power compensation optimization configuration model is constructed. The reactive power compensation optimization configuration model is solved using the multi-objective particle swarm optimization algorithm based on the constraints of the AC / DC hybrid system, and the equipment configuration parameters of the AC / DC hybrid system are obtained.

2. The method for optimizing reactive power compensation configuration in AC / DC hybrid systems according to claim 1, characterized in that, The historical transportation data includes maximum transmission power, unit capacity investment cost, unit power operating cost, unit capacity disposal conversion revenue, configured capacity, and node voltage amplitude; Using the historical operating data, the parameter correlations of the AC / DC hybrid system are determined, including: The transmission capacity correlation of the AC / DC hybrid system is constructed based on the maximum transmission power. Based on the unit capacity investment cost, unit power operating cost, unit capacity disposal conversion revenue, and configured capacity, construct the investment cost relationship of the AC / DC hybrid system; A voltage deviation correlation is constructed based on the node voltage amplitude.

3. The method for optimizing reactive power compensation configuration in AC / DC hybrid systems according to claim 2, characterized in that, Based on the aforementioned unit capacity investment cost, unit power operating cost, unit capacity disposal discount revenue, and configured capacity, the investment cost relationship of the AC / DC hybrid system is constructed, including: An investment cost relationship is constructed based on the unit capacity investment cost and the configured capacity; the expression for the investment cost relationship is: ； A relationship between operation and maintenance costs is established based on the unit power operating cost and the configured capacity; the expression for the relationship between operation and maintenance costs is: ； A processing revenue correlation is constructed based on the unit capacity disposal discount revenue and the configured capacity; the expression for the processing revenue correlation is: ； In the formula, For investment costs, For maintenance costs, In order to handle the revenue, , , , , , These are the unit capacity investment cost, unit power operating cost, and unit capacity disposal revenue of the static var generator, respectively. , , The unit capacity investment cost, unit power operating cost, and unit capacity disposal revenue of conventional reactive power compensation equipment; , The configuration capacity for static var generators and conventional reactive power compensation equipment; , The service life of static var generators and conventional reactive power compensation equipment; It is the present value factor. ; Let t be the discount rate for funds, and t represent time, where T is the period.

4. The method for optimizing reactive power compensation configuration in AC / DC hybrid systems according to claim 3, characterized in that, The objective function expression of the reactive power compensation optimization configuration model is: ； ； ； ； In the formula, The objective function for the reactive power compensation optimization configuration model. For the transmission capacity correlation, For investment cost related relationships, The correlation is related to voltage deviation. U represents the weight of node bi; i For nodes voltage amplitude, is the maximum transmission power, N is the total number of nodes, and min(*) is the minimum value function.

5. The method for optimizing reactive power compensation configuration in AC / DC hybrid systems according to claim 1, characterized in that, The constraints of the AC / DC hybrid system include power balance constraints, investment cost constraints, power constraints of new energy units, node voltage constraints, and reactive power compensation equipment constraints.

6. The method for optimizing reactive power compensation configuration in AC / DC hybrid systems according to claim 5, characterized in that, The calculation expression for the power balance constraint is: ; ; In the formula, , These represent the active power output and active power load of the generator set at node i, respectively. , , , Representing nodes respectively The reactive power output of the generator set, reactive load, reactive power compensation capacity of the static var generator and conventional reactive power compensation equipment; , , These represent the nodes of the admittance matrix. The conductance, susceptance, and phase angle difference of the node voltage between node j and node j; express The voltage amplitude of the node, N is the total number of nodes, c is the number of conventional reactive power compensation equipment nodes, N c This represents the total number of conventional reactive power compensation equipment nodes. The formula for calculating investment cost constraints is: ; In the formula, This indicates the maximum investment amount for static var generators and conventional reactive power compensation equipment. To constrain investment costs; The calculation expression for the power constraint of new energy units is as follows: ; In the formula, , These represent the maximum output values ​​for wind power and solar power, respectively. , This refers to the actual output value of wind power and photovoltaic power. The expression for calculating the node voltage constraint is: ; In the formula, , and Representing nodes respectively The lower limit of the voltage, the upper limit of the voltage, and the node voltage at that point; The calculation expression for the constraints of the reactive power compensation equipment is as follows: ; ; In the formula, , , , These represent the maximum and minimum configured capacity, maximum and minimum operating power of the static var generator, respectively. , , , These represent the maximum and minimum configured capacity, maximum and minimum operating power of conventional reactive power compensation equipment. , , , Let t represent the configured capacity of the static var generator, the configured capacity of the conventional reactive power compensation equipment, the operating power of the static var generator, and the operating power of the conventional reactive power compensation equipment.

7. The method for optimizing reactive power compensation configuration in AC / DC hybrid systems according to claim 1, characterized in that, The reactive power compensation optimization configuration model is solved using the multi-objective particle swarm optimization algorithm based on the constraints of the AC / DC hybrid system, yielding the equipment configuration parameters for the AC / DC hybrid system, including: Based on the historical operating data, the constraints of the AC / DC hybrid system, and the preset parameters of the multi-objective particle swarm optimization algorithm, a pre-configured population for the optimal configuration of the AC / DC hybrid system is constructed. Based on the pre-configured population, an initial population satisfying the constraints of the AC / DC hybrid system is obtained through initial iterations using non-dominated sorting, selection, crossover, and mutation operations. Based on the initial population, the optimized population is obtained by iterative optimization through the crowding distance sorting mechanism. Based on the optimized population, the optimal individual is obtained by calculating the distance of the population from the reference point; The optimal individual is used to solve the reactive power compensation optimization configuration model to obtain the equipment configuration parameters of the AC / DC hybrid system.

8. The method for optimizing reactive power compensation configuration in AC / DC hybrid systems according to claim 7, characterized in that, Based on the initial population, an optimized population is obtained through iterative optimization using a crowding distance sorting mechanism, including: Step S1: Based on the initial population, offspring individuals are generated through crossover and mutation operations, and parent individuals and offspring individuals are merged to generate an iterative population; Step S2: Based on the iterative population, the non-dominant levels of the population are quickly sorted using the crowding distance sorting mechanism, and the individual crowding degree is calculated. Local search boundaries and sparse regions are constructed using the AC / DC hybrid system constraints. Step S3: Based on the local search boundary and sparse region, calculate the crowding degree of the reactive power compensation device population through the non-dominated layer hierarchical principle, and construct the updated initial population. Repeat steps S1 to S3 until the preset number of iterations is met to obtain the optimized population.

9. The method for optimizing reactive power compensation configuration in AC / DC hybrid systems according to claim 8, characterized in that, The formula for calculating the crossover probability in the generation of offspring through crossover mutation is as follows: ； Where g is the current generation number; For the maximum number of generations, To preset the upper limit of crossover probability, To preset the lower bound of the crossover probability, The crossover probability of the crossover operation; The formula for calculating the mutation probability is: ； in, This is a preset lower limit for the mutation probability. To preset the upper limit of mutation probability, denoted as the mutation probability of the mutation operation.

10. A reactive power compensation optimization configuration system suitable for AC / DC hybrid systems, characterized in that, include: The data acquisition module is used to acquire historical operating data of the UHV AC / DC hybrid power transmission system. The reactive power compensation optimization module is used to construct the parameter correlation relationship of the AC / DC hybrid system using the historical operating data, and to construct a reactive power compensation optimization configuration model based on the reactive power correlation relationship of the AC / DC hybrid system. The multi-objective particle swarm optimization (MPS) algorithm solution module is used to solve the reactive power compensation optimization configuration model based on the obtained AC / DC hybrid system constraints using the multi-objective particle swarm optimization algorithm, and obtain the equipment configuration parameters of the AC / DC hybrid system.

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