Photovoltaic power distribution network voltage stability control method and system

Abstract

The invention discloses a photovoltaic power distribution network voltage stability control method and system. The method comprises the following steps: acquiring electrical parameters and photovoltaic power parameters of nodes of a power distribution network; according to the electrical parameters and the photovoltaic power parameters, simulation adjustment is carried out on a transformer tap through a particle swarm optimization algorithm, and the optimal transformer tap action and the optimal voltage of a power distribution network node are obtained; acquiring node voltage from the electrical parameters, screening excellent nodes from the nodes of the power distribution network according to the optimal voltage and the node voltage, and generating an equipment action strategy of the excellent nodes; the equipment action strategy comprises a capacitor bank switching action and a photovoltaic inverter reactive power regulation action; and controlling the power distribution network according to the optimal transformer tap action and the equipment action strategy. By adopting the embodiment of the invention, the photovoltaic fitness of the power distribution network can be enhanced, the voltage stability of the power distribution network is improved, and the global optimization of the power distribution network is realized.

Description

Technical Field

[0001] This invention relates to the field of power distribution network control technology, and in particular to a method and system for controlling the voltage stability of a power distribution network incorporating photovoltaics. Background Technology

[0002] As the global energy structure transitions towards a low-carbon model, photovoltaic (PV) power generation, as a clean and renewable energy source, is seeing its penetration rate in power distribution networks continue to increase. However, PV power is significantly affected by environmental factors such as sunlight intensity and temperature, exhibiting marked intermittency and volatility. This poses a severe challenge to the voltage stability of power distribution networks. With large-scale PV grid integration, power distribution networks are prone to voltage exceeding limits and increased volatility under conditions such as sudden changes in sunlight and load fluctuations, seriously impacting power quality and the safe and stable operation of the system.

[0003] Traditional distribution network voltage regulation schemes involve adjusting transformer taps and switching capacitor banks individually. These schemes have limitations such as slow response speed and localized optimization, making it difficult to adapt to the rapid changes in photovoltaic characteristics and resulting in distribution network voltage stability that cannot meet the requirements. Summary of the Invention

[0004] The present invention aims to provide a method and system for controlling the voltage stability of a distribution network with photovoltaics, which can enhance the adaptability of the distribution network to photovoltaics, improve the voltage stability of the distribution network, and achieve global optimization of the distribution network.

[0005] In a first aspect, embodiments of the present invention provide a method for controlling the voltage stability of a distribution network containing photovoltaics, comprising: Collect electrical parameters and photovoltaic power parameters of distribution network nodes; Based on the electrical parameters and the photovoltaic power parameters, the transformer tap changer is simulated and adjusted using a particle swarm optimization algorithm to obtain the optimal transformer tap changer action and the optimal voltage of the distribution network node. The node voltage is obtained from the electrical parameters. Based on the optimal voltage and the node voltage, excellent nodes are selected from the distribution network nodes, and the equipment operation strategy of the excellent nodes is generated. The equipment operation strategy includes capacitor bank switching operation and photovoltaic inverter reactive power regulation operation. The power distribution network is controlled based on the optimal transformer tap changer operation and the equipment operation strategy.

[0006] As an improvement to the above scheme, the electrical parameters include voltage, active power, and reactive power; the photovoltaic power parameters include photovoltaic output power and photovoltaic load power.

[0007] As an improvement to the above scheme, the step of simulating and adjusting the transformer tap changer using a particle swarm optimization algorithm based on the electrical parameters and the photovoltaic power parameters to obtain the optimal transformer tap changer action and the optimal voltage at each distribution network node includes: Initialize the population size and iteration parameters of the particle swarm optimization algorithm, and treat the voltage action of the transformer tap as a particle; Based on the electrical parameters and the photovoltaic power parameters, the current particle swarm is solved for particle-by-particle power flow to obtain the distribution network node voltage corresponding to each particle; Based on the voltage of the distribution network nodes, the Jacobian matrix is ​​obtained, and the minimum singular value of the Jacobian matrix is ​​calculated. Calculate the fitness of each particle in the current particle swarm based on the voltage of the distribution network node and the minimum singular value. Based on the fitness, the current particle swarm is updated until the optimization objective or the preset maximum number of iterations is met, thereby obtaining the optimal transformer tap changer action and the optimal voltage of the distribution network node.

[0008] As an improvement to the above scheme, the step of solving the current particle swarm particle-by-particle power flow based on the electrical parameters and the photovoltaic power parameters to obtain the distribution network node voltage corresponding to each particle includes: The electrical parameters and the photovoltaic power parameters are substituted into the power flow equations; the power flow equations are constructed based on the topology of the distribution network. Substitute each particle in the current particle swarm into the power flow equation and solve it using the Newton-Raphson method to obtain the distribution network node voltage under the transformer tap changer operation corresponding to the current particle swarm.

[0009] As an improvement to the above scheme, the step of obtaining the Jacobian matrix based on the distribution network node voltage and calculating the minimum singular value of the Jacobian matrix includes: Construct the Jacobian matrix based on the voltage of the distribution network nodes; Calculate the product of the transpose of the Jacobian matrix and the Jacobian matrix to obtain a symmetric matrix; Calculate the eigenvalues ​​of the symmetric matrix, and obtain the minimum singular value of the Jacobi matrix based on the eigenvalues.

[0010] As an improvement to the above scheme, the step of calculating the fitness of each particle in the current particle swarm based on the distribution network node voltage and the minimum singularity includes: Based on the voltage of the distribution network nodes, the voltage deviation and network loss of the distribution network corresponding to each particle are obtained; The fitness of each particle in the current particle swarm is obtained by weighted summing of the minimum singular value, the reciprocal of the voltage deviation, and the reciprocal of the grid loss.

[0011] As an improvement to the above solution, the step of obtaining the node voltage from the electrical parameters, selecting excellent nodes from the distribution network nodes based on the optimal voltage and the node voltage, and generating equipment action strategies for the excellent nodes includes: Obtain the node voltage from the electrical parameters, and calculate the voltage deviation score, power balance score, and singular value score for each distribution network node based on the optimal voltage and the node voltage; According to the preset weights, the voltage deviation score, power balance score and singular value score are weighted and summed to obtain the comprehensive score of each distribution network node. Based on the comprehensive score, outstanding nodes are selected from the distribution network nodes; The device action strategy for generating the excellent nodes.

[0012] As an improvement to the above solution, the device action strategy for generating the excellent node includes: The switching actions of capacitor banks and the reactive power regulation actions of photovoltaic inverters at the excellent nodes are simulated and adjusted. During the simulation and adjustment process, the minimum singular value of the power flow of the distribution network is calculated as the voltage stability of the distribution network. With the goal of improving voltage stability, the optimal simulation adjustment result is obtained, and the corresponding device action strategy for the excellent node is generated.

[0013] As an improvement to the above scheme, the step of controlling the distribution network based on the optimal transformer tap changer operation and the equipment operation strategy includes: The optimal transformer tap changer action and the equipment action strategy are used to control the distribution network and collect the real-time distribution network voltage after control. The real-time distribution network voltage is compared with the optimal voltage. If the deviation is greater than a preset deviation threshold, the optimal transformer tap changer action and the equipment action strategy are regenerated.

[0014] Secondly, embodiments of the present invention also provide a voltage stability control system for a distribution network incorporating photovoltaics, comprising: The parameter acquisition module is used to collect electrical parameters and photovoltaic power parameters of the distribution network nodes; The transformer simulation adjustment module is used to simulate and adjust the transformer taps according to the electrical parameters and the photovoltaic power parameters using a particle swarm optimization algorithm to obtain the optimal transformer tap action and the optimal voltage of the distribution network node. The node action simulation module is used to obtain the node voltage from the electrical parameters, select excellent nodes from the distribution network nodes based on the optimal voltage and the node voltage, and generate the equipment action strategy for the excellent nodes; the equipment action strategy includes capacitor bank switching actions and photovoltaic inverter reactive power regulation actions; The distribution network control module is used to control the distribution network according to the optimal transformer tap changer action and the equipment action strategy.

[0015] Compared with existing technologies, this invention discloses a voltage stability control method and system for a photovoltaic (PV) distribution network. This method collects electrical parameters and PV power parameters of distribution network nodes. Based on these parameters, it simulates and adjusts transformer taps using a particle swarm optimization algorithm to obtain optimal transformer tap actions and optimal voltages at distribution network nodes. It then obtains node voltages from the electrical parameters, selects superior nodes based on the optimal voltages and node voltages, and generates equipment action strategies for these superior nodes. These equipment action strategies include capacitor bank switching actions and PV inverter reactive power regulation actions. Finally, it controls the distribution network based on the optimal transformer tap actions and the equipment action strategies. Using this invention, the adaptability of the distribution network to PV is enhanced, the voltage stability of the distribution network is improved, and global optimization of the distribution network is achieved. Attached Figure Description

[0016] Figure 1 This is a schematic flowchart of a photovoltaic-based power distribution network voltage stability control method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of a photovoltaic-integrated power distribution network voltage stability control system provided in an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] In the description and claims, it should be understood that the terms "first," "second," etc., used in the description and claims are only for the purpose of distinguishing the description of the same technical features, and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated, nor necessarily the order of description or chronological order. The terms are interchangeable where appropriate. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature.

[0019] This invention provides a method for controlling voltage stability in a photovoltaic power distribution network. Please refer to [link to relevant documentation]. Figure 1In this embodiment, the voltage stability control method for photovoltaic-integrated distribution networks is specifically executed through steps S1 to S4: S1. Collect electrical parameters and photovoltaic power parameters of the distribution network nodes.

[0020] S2. Based on the electrical parameters and the photovoltaic power parameters, the transformer tap changer is simulated and adjusted using a particle swarm optimization algorithm to obtain the optimal transformer tap changer action and the optimal voltage of the distribution network node.

[0021] S3. Obtain the node voltage from the electrical parameters, select excellent nodes from the distribution network nodes based on the optimal voltage and the node voltage, and generate the equipment operation strategy for the excellent nodes; the equipment operation strategy includes capacitor bank switching operation and photovoltaic inverter reactive power regulation operation.

[0022] S4. Control the power distribution network according to the optimal transformer tap changer action and the equipment action strategy.

[0023] In this embodiment of the invention, a distribution network node refers to the connection point of various electrical equipment, lines or loads in the distribution network, and the electrical parameters reflect the real-time operating status and system characteristics of the distribution network.

[0024] In distribution networks containing photovoltaic (PV) power, the fluctuation of PV output is greatly affected by the environment. By collecting PV power parameters, the control actions of the distribution network can be precisely matched with the characteristics of PV, so as to achieve the coordinated control goal of efficient PV absorption and stable distribution network voltage.

[0025] It should be noted that, in this embodiment of the invention, the particle swarm optimization algorithm optimizes transformer taps. The particle swarm can efficiently search for the tap action that optimizes the stability of the distribution network, and simultaneously obtain the corresponding optimal voltage. Preferably, the tap action refers to the tap position of the transformer, with different tap positions corresponding to different transformer tap voltages.

[0026] In some preferred embodiments, the particle swarm optimization algorithm detects parameter changes in each iteration through a pre-built power distribution network model to obtain optimization results.

[0027] It should also be noted that steps S2 and S3 are both simulations of distribution network operations, which aim to select the optimal combination of equipment operations and transformer tapping operations from several possible operation logics, and then apply the optimal combination of operations obtained from the simulation to the actual distribution network.

[0028] In this embodiment of the invention, the best nodes that have the greatest impact on global voltage stability are first selected, and then the device actions of the best nodes are precisely controlled, which makes up for the shortcomings of traditional local optimization methods.

[0029] In the above scheme, by controlling the operation of transformer taps and the operation of equipment at optimal nodes, optimized control can be achieved across the entire distribution network, significantly improving the stability and reliability of distribution network voltage operation and fully ensuring the efficient utilization of photovoltaic energy.

[0030] In a preferred embodiment, the electrical parameters include voltage, active power, and reactive power; the photovoltaic power parameters include photovoltaic output power and photovoltaic load power.

[0031] It should be noted that the photovoltaic power parameters are the parameter data of the photovoltaic power source collected from the photovoltaic power source access point in the distribution network.

[0032] In some preferred embodiments, smart meters and sensors are installed at each node of the distribution network to collect real-time data on voltage, active power, and reactive power at each node, as well as output power and load power data of the photovoltaic power source. The distribution network nodes then transmit the data to the control center through a communication network.

[0033] The electrical parameters and photovoltaic power parameters shown in the embodiments of this invention can accurately capture the operating status of the distribution network and reflect the interaction between photovoltaics and the power grid. In some other preferred embodiments, the electrical parameters may also include current, frequency, or power factor, etc. The selection of electrical parameters and photovoltaic power parameters should be able to meet the subsequent power flow calculation and action control calculation. The specific parameter selection does not affect the beneficial effects produced by this invention.

[0034] In a preferred embodiment, step S2 involves simulating and adjusting the transformer tap changer using a particle swarm optimization algorithm based on the electrical parameters and the photovoltaic power parameters to obtain the optimal transformer tap changer operation and the optimal voltage at each distribution network node. This is then executed via steps S21-S25. S21. Initialize the population size and iteration parameters of the particle swarm optimization algorithm, and treat the voltage action of the transformer tap as particles.

[0035] S22. Based on the electrical parameters and the photovoltaic power parameters, perform a particle-by-particle power flow solution on the current particle swarm to obtain the distribution network node voltage corresponding to each particle.

[0036] S23. Based on the voltage of the distribution network nodes, obtain the Jacobian matrix and calculate the minimum singular value of the Jacobian matrix.

[0037] S24. Calculate the fitness of each particle in the current particle swarm based on the voltage of the distribution network node and the minimum singular value.

[0038] S25. Update the current particle swarm according to the fitness until the optimization objective or the preset maximum number of iterations is met, and obtain the optimal transformer tap changer action and the optimal voltage of the distribution network node.

[0039] It should be noted that treating the voltage action of the transformer tap as a particle means that each particle in the particle group corresponds to a specific tap adjustment scheme, ensuring that the particle is completely matched with the actual controlled object.

[0040] In some preferred embodiments, the iteration parameters include an iteration learning factor and a maximum number of iterations. During each iteration, the velocity and position of all particles in the current particle swarm are updated using the iteration learning factor.

[0041] The process of solving for each particle is the process of screening candidate regulation schemes. Based on electrical parameters and photovoltaic power parameters, the power grid operation scenario corresponding to each particle can be constructed. Then, the power flow solution of the power grid operation scenario can be performed to simulate the actual state of power transmission in the power grid under the tap changer action, so as to obtain the optimal particle.

[0042] Distribution network power flow typically exhibits a nonlinear behavior. In this embodiment of the invention, the Jacobian matrix is ​​used to linearize it. The sparse matrix technique can reduce the amount of computation and quantify the impact of node voltage changes on power balance. The stability of the distribution network is characterized by the minimum singular value.

[0043] Fitness is used in particle optimization algorithms to comprehensively evaluate the merits of each particle. In this embodiment of the invention, fitness is calculated by the distribution network node voltage and the minimum singular value, which can comprehensively evaluate the stability of the distribution network and other demand indicators.

[0044] It should be noted that, in some preferred embodiments, the optimal voltage is the optimal voltage of each distribution network node under the optimal transformer tap operation, and the number of optimal voltages corresponds to the number of distribution network nodes; in other preferred embodiments, the optimal voltage is the average voltage of each distribution network node under the optimal transformer tap operation, and the degree to which each node deviates from the average value can be measured by the optimal voltage.

[0045] In summary, the optimal voltage is associated with the optimal transformer tap changer operation, and its direct or indirect correspondence with the voltage of each distribution network node is applicable in the embodiments of the present invention without affecting the beneficial effects produced by the embodiments of the present invention.

[0046] For example, the optimal voltage is expressed as ;in, Let be the voltage of the i-th distribution network node. N represents the set voltage of the distribution network node, and N represents the number of distribution network nodes.

[0047] The above scheme combines particle swarm optimization algorithm with minimum singular value index. It can efficiently solve the optimal voltage of transformer taps through particle swarm optimization algorithm, and evaluate voltage stability by using minimum singular value quantification. This achieves closed-loop synergy between optimization solution and stability assessment, and breaks through the application limitations of single algorithm or index.

[0048] Further, preferably, step S22, solving the current particle swarm particle power flow on a particle-by-particle basis according to the electrical parameters and the photovoltaic power parameters to obtain the distribution network node voltage corresponding to each particle, includes: The electrical parameters and the photovoltaic power parameters are substituted into the power flow equations; the power flow equations are constructed based on the topology of the distribution network. Substitute each particle in the current particle swarm into the power flow equation and solve it using the Newton-Raphson method to obtain the distribution network node voltage under the transformer tap changer operation corresponding to the current particle swarm.

[0049] It should be noted that power flow equations are mathematical models describing the relationship between power transmission and voltage distribution in a distribution network, and their construction depends on the distribution network topology. Generally, it involves determining the node types based on the node connections in the distribution network, then calculating the admittance matrix based on the topology, and finally constructing the power flow equations using Kirchhoff's laws. The specific process for constructing power flow equations will not be elaborated here.

[0050] In the process of solving power flow using the Newton-Raphson method, the initial node voltage is used as the starting point for iteration. The power imbalance is calculated, the Jacobian matrix is ​​constructed for sparsity optimization, and the correction equation is solved. The process is iterated repeatedly until the power imbalance meets the requirements. Then, the distribution network node voltage corresponding to the particle can be obtained, and the correspondence between the transformer tapping action and the node voltage of the entire distribution network is formed.

[0051] The Newton-Raphson method has a fast convergence speed, which can quickly handle multiple solutions for particle swarm optimization; at the same time, it has high solution accuracy, which can support the reliability of optimization results.

[0052] Preferably, step S23, obtaining the Jacobian matrix based on the distribution network node voltages and calculating the minimum singular value of the Jacobian matrix, includes: Construct the Jacobian matrix based on the voltage of the distribution network nodes; Calculate the product of the transpose of the Jacobian matrix and the Jacobian matrix to obtain a symmetric matrix; Calculate the eigenvalues ​​of the symmetric matrix, and obtain the minimum singular value of the Jacobi matrix based on the eigenvalues.

[0053] In some preferred embodiments, the power flow equations are linearized based on the distribution network node voltages to obtain a Jacobian matrix comprising orthogonal and diagonal matrices. The minimum singular value is then obtained by performing idiomatic value decomposition on the Jacobian matrix to represent the distribution network stability.

[0054] For example, the Jacobian matrix is ​​represented as Where U and V are orthogonal matrices, satisfying , , and It is the identity matrix. Let J be a diagonal matrix, and let J be the singular values ​​of the Jacobian matrix J. The smallest singular value is the smallest element on the diagonal of the diagonal matrix.

[0055] Taking a 3-node distribution network as an example, due to the presence of a slack node, its Jacobian matrix has a dimension of 2×2, and is expressed as follows: .

[0056] According to the singular value decomposition formula, calculate the product of the transpose of the Jacobian matrix and the Jacobian matrix. Solving for the given information yields the following results. eigenvalues and Then the singular values ​​are represented as , The minimum singular value of the Jacobi matrix is .

[0057] In a preferred embodiment of the present invention, if the minimum singular value of a node is less than a preset singular value threshold, the node is considered to have poor voltage stability and is at risk of voltage instability.

[0058] Preferably, in actual complex power distribution networks, specialized power system analysis software, such as MATLAB's Power System Toolbox, PSCAD / EMTDC, etc., is typically used to quickly and accurately calculate the minimum singular value by utilizing their built-in matrix operations and singular value decomposition functions, thereby improving computational efficiency and accuracy.

[0059] Preferably, step S24, calculating the fitness of each particle in the current particle swarm based on the distribution network node voltage and the minimum singularity, includes: Based on the voltage of the distribution network nodes, the voltage deviation and network loss of the distribution network corresponding to each particle are obtained; The fitness of each particle in the current particle swarm is obtained by weighted summing of the minimum singular value, the reciprocal of the voltage deviation, and the reciprocal of the grid loss.

[0060] In some preferred embodiments, the voltage deviation of the distribution network is calculated by the difference between the voltage of the distribution network nodes and the rated voltage; the power loss of the distribution network is measured by the active power loss of the lines and the active power loss of the transformers during the power flow calculation.

[0061] It should be noted that the logic of the particle swarm optimization algorithm is to find the particle with the highest fitness. The smaller the voltage deviation and grid loss characteristics, the better; the larger the minimum singular value, that is, the grid stability characteristic, the better. Therefore, in the embodiment of the present invention, the fitness calculation takes the reciprocal of the voltage deviation and grid loss to ensure that the larger the fitness, the better the overall performance of the particle, which is in line with the optimization logic of the particle swarm optimization algorithm.

[0062] Preferably, the fitness function is expressed as Fitness = 0.4 × (1 / voltage deviation) + 0.3 × minimum singular value + 0.3 × (1 / grid loss).

[0063] It is understandable that the weights of each item in the fitness function can be adjusted based on the actual application scenario, and the weight values ​​do not affect the beneficial effects produced by the embodiments of the present invention.

[0064] As a preferred implementation, step S3 involves obtaining the node voltage from the electrical parameters, selecting excellent nodes from the distribution network nodes based on the optimal voltage and the node voltage, and generating equipment operation strategies for the excellent nodes, including: Obtain the node voltage from the electrical parameters, and calculate the voltage deviation score, power balance score, and singular value score for each distribution network node based on the optimal voltage and the node voltage; According to the preset weights, the voltage deviation score, power balance score and singular value score are weighted and summed to obtain the comprehensive score of each distribution network node. Based on the comprehensive score, outstanding nodes are selected from the distribution network nodes; The device action strategy for generating the excellent nodes.

[0065] In some preferred embodiments, the degree of deviation between the optimal voltage and the node voltage can be calculated, and a voltage deviation score can be calculated based on this deviation. The optimal target active power and target reactive power can be derived from the optimal voltage to calculate the actual power balance rate and obtain a power balance score. Based on the node voltages, the sensitivity of each node to stability can be obtained, and singularity scores can be calculated.

[0066] The voltage deviation score reflects the degree of agreement between the simulated voltage and the optimal voltage under the scenario of performing each action; the power balance score reflects the supply and demand matching degree of active and reactive power at the node; and the singular value score reflects the node voltage stability.

[0067] For example, the comprehensive score of each distribution network node is calculated by Score = w1 * singular value score + w2 * voltage deviation score + w3 * power balance score; where w1, w2 and w3 are preset weights.

[0068] In some preferred embodiments, distribution network nodes are ranked according to the comprehensive score, and outstanding nodes are selected according to a preset ratio. In other preferred embodiments, the comprehensive score of a distribution network node is compared with a preset score threshold; if the comprehensive score is greater than the preset score threshold, it is considered an outstanding node.

[0069] Further, preferably, the device action strategy for generating the excellent node includes: The switching actions of capacitor banks and the reactive power regulation actions of photovoltaic inverters at the excellent nodes are simulated and adjusted. During the simulation and adjustment process, the minimum singular value of the power flow of the distribution network is calculated as the voltage stability of the distribution network. With the goal of improving voltage stability, the optimal simulation adjustment result is obtained, and the corresponding device action strategy for the excellent node is generated.

[0070] In this embodiment of the invention, excellent nodes are first selected. The selected excellent nodes have a strong positive impact on the overall distribution network. Then, equipment action strategies are generated for the excellent nodes. It should be noted that the equipment action strategies may include the equipment actions of all or some of the excellent nodes, or they may maintain the equipment actions of the excellent nodes.

[0071] By simulating and adjusting the capacitor bank switching actions and photovoltaic inverter reactive power regulation actions of the excellent nodes, the optimal equipment action strategy with overall performance can be selected from several combinations of simulated actions, thus supporting the overall safe operation of the distribution network.

[0072] As a preferred implementation, step S4, controlling the distribution network according to the optimal transformer tap changer operation and the equipment operation strategy, includes: The optimal transformer tap changer action and the equipment action strategy are used to control the distribution network and collect the real-time distribution network voltage after control. The real-time distribution network voltage is compared with the optimal voltage. If the deviation is greater than a preset deviation threshold, the optimal transformer tap changer action and the equipment action strategy are regenerated.

[0073] In a preferred embodiment of the present invention, the voltage and power parameters of the distribution network after real-time monitoring and control are compared with the optimal voltage of multiple distribution network nodes. If the deviation is between 5% and 8%, the optimal voltage is recalculated, the control strategy and controller parameters are adjusted and optimized, and control is performed again to ensure that the voltage stability of the distribution network meets the requirements.

[0074] This invention provides three preferred embodiments of the photovoltaic-integrated distribution network voltage stability control method.

[0075] In Example 1, a 10kV distribution network is used, comprising 8 nodes (Node 1-Node 8), 2 photovoltaic power source access points (PV1, PV2), 1 on-load tap-changing transformer (installed at Node 1), and a total line length of 12km. Component parameters are as follows: The wiring uses LGJ-185 conductors, with a resistance of 0.17Ω / km and a reactance of 0.34Ω / km per unit length. Transformer: Model S11-6300 / 10, turns ratio adjustment range ±2.5% (5 levels in total, 0.625% per level); Photovoltaic power supply: PV1 rated power 1.5MW, conversion efficiency 97%; PV2 rated power 1MW, conversion efficiency 96%, reactive power adjustment range [-500kvar, 500kvar].

[0076] Smart meters (accuracy class 0.5S) and voltage sensors are installed at 8 nodes to collect voltage (sampling frequency 100Hz), active power, and reactive power data in real time. Photovoltaic output power and load power are collected through the inverter communication interface and load-side sensors, and the data is transmitted to the control center via a fiber optic network with a transmission delay of ≤30ms.

[0077] The particle swarm size was initialized to N=40, and the maximum number of iterations was T=80. Then, the optimal tap position of the transformer was obtained by solving the particle swarm optimization algorithm as +1 tap (turn ratio 10.125kV / 0.4kV), and the corresponding optimal average voltage of each node was 10.2kV.

[0078] By constructing the Jacobian matrix (dimension 14×14) of an 8-node distribution network, and performing singular value decomposition, the minimum singular value is found to be 0.9, corresponding to Node5, which is less than the threshold of 1, and is therefore identified as a node with poor voltage stability.

[0079] Next, the voltage deviation score, power balance score, and singular value score are calculated. Using preset weights w1=0.5, w2=0.3, and w3=0.2, a comprehensive score is calculated, resulting in Node1, Node2, and Node7 as the outstanding nodes. Optimized control of the equipment actions at these outstanding nodes leads to the following capacitor bank switching actions: two 100kvar capacitors are switched on at Node2; and the photovoltaic inverter reactive power regulation actions are: PV1 injects 300kvar reactive power, and PV2 injects 200kvar reactive power.

[0080] After control, the voltage of each node was monitored, and the average value was 10.18kV, with a deviation from the optimal voltage of 0.2% (less than 5%), which meets the requirements. After continuous operation for 24 hours, the minimum singularity of Node 5 increased to 1.1, and the voltage stability met the standard.

[0081] In Example 2, a 380V low-voltage distribution network is used, comprising 15 nodes, 3 photovoltaic access points (PV1-PV3, rooftop photovoltaic), and 1 distribution transformer (10kV / 0.4kV). Component parameters are as follows: Line: BV-50mm² copper cable, resistance per unit length 0.39Ω / km, reactance 0.07Ω / km; Photovoltaic power supply: single unit rated power 50kW, conversion efficiency 95%, reactive power regulation range [-20kvar, 20kvar]; Load: Residential load accounts for 70%, commercial load accounts for 30%, and the total capacity is 150kVA.

[0082] Data is collected using smart meters (accuracy class 1.0) and wireless sensor networks (ZigBee protocol), with a sampling frequency of 50Hz and a data transmission delay of ≤100ms.

[0083] The particle swarm size was initialized to N=30, and the maximum number of iterations was T=50. Then, the optimal tap position of the transformer was obtained by solving the problem using the particle swarm optimization algorithm, which was found to be -1 tap (transformation ratio 10kV / 0.38kV). The voltage deviation and photovoltaic reactive power regulation were optimized by focusing on the fitness function, and the optimal average voltage was obtained as 390V.

[0084] A Jacobi matrix with dimension 28×28 is constructed, and the minimum singular value is 0.8, corresponding to Node12. Since it is less than the threshold of 1, it is determined to be a node with poor voltage stability.

[0085] Next, the voltage deviation score, power balance score, and singular value score are calculated. Using preset weights w1=0.6, w2=0.3, and w3=0.1, a comprehensive score is calculated, resulting in Node3, Node6, and Node9 as the best nodes. Optimizing the equipment actions of these best nodes, the final capacitor bank switching action is to switch one 5kvar capacitor at Node9, and the photovoltaic inverter reactive power regulation action is for PV1-PV3 to absorb 15kvar, 10kvar, and 15kvar reactive power respectively.

[0086] After control, the average voltage was 389V, with a deviation of 0.26% from the optimal voltage, and the minimum singularity of Node12 increased to 1.05. During peak load (18:00-20:00), the voltage deviation increased to 6%, triggering a recalculation of the optimal voltage. After adjusting the photovoltaic reactive power output, the deviation decreased to 3%.

[0087] Example 3 illustrates a 10kV distribution network with photovoltaic (PV) power supply. The network comprises 10 nodes, 3 PV power supply connection points, 2 on-load tap-changing transformers, and a total line length of approximately 15km. Component parameters are as follows: The wiring uses LGJ-120 conductors, with a resistance of 0.27Ω / km and a reactance of 0.36Ω / km per unit length.

[0088] Transformer: Model S11-5000 / 10, turns ratio adjustment range ±5%, tap position 9 (each tap adjusts by 0.625%).

[0089] Photovoltaic power supply: single unit rated power 1000kW, conversion efficiency 96%, reactive power regulation range [-500kvar, 500kvar].

[0090] Smart meters and voltage / power sensors are installed at 10 nodes in the distribution network, with a sampling frequency of 100Hz, to collect real-time data on voltage, active power, and reactive power at each node. Simultaneously, output power and load power data from three photovoltaic power sources are also collected. The data is transmitted to the control center via a fiber optic communication network with a transmission delay of ≤50ms.

[0091] The particle swarm size was initialized to N=50, and the maximum number of iterations was T=100. Then, the optimal tap positions of the transformers were obtained by using the particle swarm optimization algorithm: transformer 1 was increased by 2 taps (+1.25%), and transformer 2 was decreased by 1 tap (-0.625%). The voltage deviation and photovoltaic reactive power regulation were optimized by focusing on the fitness function, and the optimal average voltage was obtained as 390V.

[0092] Construct an 18×18 Jacobi matrix, perform singular value decomposition, and obtain the minimum singular value. The results of the minimum singular value calculation for nodes 1-10 are shown in Table 1.

[0093] Table 1 It can be seen that the minimum singular values ​​of nodes 3, 6, and 10 (0.9, 0.8, and 0.7) are less than the threshold 1, which indicates that they are nodes with poor voltage stability and are at risk of instability.

[0094] Next, the voltage deviation score, power balance score, and singular value score are calculated. Using preset weights w1=0.5, w2=0.3, and w3=0.2, a comprehensive score is calculated. Nodes 1, 4, 7, and 8, representing the top 30% of the scores, are designated as excellent nodes. Through optimized control of the equipment actions of these excellent nodes, the final capacitor bank switching actions are as follows: Node 1 connects one 100kvar capacitor, and Node 4 disconnects one 50kvar capacitor. The photovoltaic inverter reactive power regulation actions are as follows: photovoltaic access points 2 and 3 inject 200kvar and 300kvar reactive power, respectively.

[0095] After control, the voltage of each node was monitored in real time. The average voltage of the 10 nodes was calculated to be 10.15kV, with a deviation of 0.49% from the optimal voltage of 10.2kV, which is less than 5% and meets the requirements. After continuous monitoring for 24 hours, the distribution network voltage remained stable within the range of 10.1kV-10.3kV, and the minimum singular value of each node was greater than 1, indicating good voltage stability.

[0096] Under the distribution network conditions of Embodiment 1, Embodiment 2 and Embodiment 3 above, the traditional transformer tap changer adjustment method was used to control the distribution network. The average value of the control results under the three distribution network conditions was taken and compared with the above embodiments. The comparison results are shown in Table 2.

[0097] Table 2 It can be seen that the photovoltaic-integrated distribution network voltage stability control method described in this embodiment of the invention is significantly superior to traditional methods in terms of voltage stability, network loss control, and response speed, effectively verifying the feasibility and advancement of the invention.

[0098] The photovoltaic-integrated distribution network voltage stability control method provided by this invention can achieve optimized control covering the entire distribution network by controlling the transformer tap changer operation and the equipment operation of excellent nodes, significantly improving the stability and reliability of the distribution network voltage operation and fully ensuring the efficient utilization of photovoltaic energy.

[0099] This invention provides a voltage stability control system for a power distribution network incorporating photovoltaics. Please refer to [link / reference]. Figure 2 The photovoltaic-integrated distribution network voltage stability control system includes a parameter acquisition module 11, a transformer simulation and adjustment module 12, a node action simulation module 13, and a distribution network control module 14, wherein: The parameter acquisition module 11 is used to acquire electrical parameters and photovoltaic power parameters of the distribution network nodes; The transformer simulation adjustment module 12 is used to simulate and adjust the transformer taps according to the electrical parameters and the photovoltaic power parameters using a particle swarm optimization algorithm to obtain the optimal transformer tap action and the optimal voltage of the distribution network node. The node action simulation module 13 is used to obtain the node voltage from the electrical parameters, select excellent nodes from the distribution network nodes based on the optimal voltage and the node voltage, and generate the equipment action strategy for the excellent nodes; the equipment action strategy includes capacitor bank switching action and photovoltaic inverter reactive power regulation action; The distribution network control module 14 is used to control the distribution network according to the optimal transformer tap changer action and the equipment action strategy.

[0100] In a preferred embodiment, the electrical parameters include voltage, active power, and reactive power; the photovoltaic power parameters include photovoltaic output power and photovoltaic load power.

[0101] In a preferred embodiment, the transformer simulation adjustment module 12 includes: The particle swarm initialization unit is used to initialize the population size and iteration parameters of the particle swarm optimization algorithm, taking the voltage action of the transformer tap as a particle. The power flow solving unit is used to solve the power flow of the current particle swarm particle by particle based on the electrical parameters and the photovoltaic power parameters, and to obtain the distribution network node voltage corresponding to each particle. The minimum singular value calculation unit is used to obtain the Jacobian matrix based on the voltage of the distribution network nodes and to calculate the minimum singular value of the Jacobian matrix. The fitness calculation unit is used to calculate the fitness of each particle in the current particle swarm based on the voltage of the distribution network node and the minimum singular value. The particle swarm update unit is used to update the current particle swarm according to the fitness until the optimization objective or the preset maximum number of iterations is met, so as to obtain the optimal transformer tap changer action and the optimal voltage of the distribution network node.

[0102] Further, preferably, the power flow solving unit is specifically used for: The electrical parameters and the photovoltaic power parameters are substituted into the power flow equations; the power flow equations are constructed based on the topology of the distribution network. Substitute each particle in the current particle swarm into the power flow equation and solve it using the Newton-Raphson method to obtain the distribution network node voltage under the transformer tap changer operation corresponding to the current particle swarm.

[0103] Preferably, the minimum singular value calculation unit is specifically used for: Construct the Jacobian matrix based on the voltage of the distribution network nodes; Calculate the product of the transpose of the Jacobian matrix and the Jacobian matrix to obtain a symmetric matrix; Calculate the eigenvalues ​​of the symmetric matrix, and obtain the minimum singular value of the Jacobi matrix based on the eigenvalues.

[0104] Preferably, the fitness calculation unit is specifically used for: Based on the voltage of the distribution network nodes, the voltage deviation and network loss of the distribution network corresponding to each particle are obtained; The fitness of each particle in the current particle swarm is obtained by weighted summing of the minimum singular value, the reciprocal of the voltage deviation, and the reciprocal of the grid loss.

[0105] In a preferred embodiment, the node action simulation module 13 includes: The single-item score calculation unit is used to obtain the node voltage from the electrical parameters, and calculate the voltage deviation score, power balance score and singular value score of each distribution network node based on the optimal voltage and the node voltage; The comprehensive score calculation unit is used to perform a weighted summation of the voltage deviation score, power balance score and singular value score according to preset weights to obtain the comprehensive score of each distribution network node. An excellent node screening unit is used to screen excellent nodes from the distribution network nodes based on the comprehensive score. The device action strategy generation unit is used to generate the device action strategy for the excellent node.

[0106] Further, preferably, the device action strategy generation unit is specifically used for: The switching actions of capacitor banks and the reactive power regulation actions of photovoltaic inverters at the excellent nodes are simulated and adjusted. During the simulation and adjustment process, the minimum singular value of the power flow of the distribution network is calculated as the voltage stability of the distribution network. With the goal of improving voltage stability, the optimal simulation adjustment result is obtained, and the corresponding device action strategy for the excellent node is generated.

[0107] In a preferred embodiment, the power distribution network control module 14 is used for: The optimal transformer tap changer action and the equipment action strategy are used to control the distribution network and collect the real-time distribution network voltage after control. The real-time distribution network voltage is compared with the optimal voltage. If the deviation is greater than a preset deviation threshold, the optimal transformer tap changer action and the equipment action strategy are regenerated.

[0108] The photovoltaic-integrated distribution network voltage stability control system provided by this invention can achieve optimized control covering the entire distribution network by controlling the operation of transformer tap changers and the operation of equipment at key nodes, significantly improving the stability and reliability of distribution network voltage operation and fully ensuring the efficient utilization of photovoltaic energy.

[0109] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0110] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for controlling voltage stability in a photovoltaic-integrated power distribution network, characterized in that, include: Collect electrical parameters and photovoltaic power parameters of distribution network nodes; Based on the electrical parameters and the photovoltaic power parameters, the transformer tap changer is simulated and adjusted using a particle swarm optimization algorithm to obtain the optimal transformer tap changer action and the optimal voltage of the distribution network node. The node voltage is obtained from the electrical parameters. Based on the optimal voltage and the node voltage, excellent nodes are selected from the distribution network nodes, and the equipment operation strategy of the excellent nodes is generated. The equipment operation strategy includes capacitor bank switching operation and photovoltaic inverter reactive power regulation operation. The power distribution network is controlled based on the optimal transformer tap changer operation and the equipment operation strategy.

2. The method for controlling voltage stability of a photovoltaic-integrated distribution network as described in claim 1, characterized in that, The electrical parameters include voltage, active power, and reactive power; the photovoltaic power parameters include photovoltaic output power and photovoltaic load power.

3. The method for controlling voltage stability of a photovoltaic-integrated distribution network as described in claim 1, characterized in that, The step of simulating and adjusting the transformer tap changer using a particle swarm optimization algorithm based on the electrical parameters and the photovoltaic power parameters to obtain the optimal transformer tap changer action and the optimal voltage at each distribution network node includes: Initialize the population size and iteration parameters of the particle swarm optimization algorithm, and treat the voltage action of the transformer tap as a particle; Based on the electrical parameters and the photovoltaic power parameters, the current particle swarm is solved for particle-by-particle power flow to obtain the distribution network node voltage corresponding to each particle; Based on the voltage of the distribution network nodes, the Jacobian matrix is ​​obtained, and the minimum singular value of the Jacobian matrix is ​​calculated. Calculate the fitness of each particle in the current particle swarm based on the voltage of the distribution network node and the minimum singular value. Based on the fitness, the current particle swarm is updated until the optimization objective or the preset maximum number of iterations is met, thereby obtaining the optimal transformer tap changer action and the optimal voltage of the distribution network node.

4. The method for controlling voltage stability of a photovoltaic-integrated distribution network as described in claim 3, characterized in that, The step of solving the power flow problem for the current particle swarm based on the electrical parameters and the photovoltaic power parameters to obtain the distribution network node voltage corresponding to each particle includes: The electrical parameters and the photovoltaic power parameters are substituted into the power flow equations; the power flow equations are constructed based on the topology of the distribution network. Substitute each particle in the current particle swarm into the power flow equation and solve it using the Newton-Raphson method to obtain the distribution network node voltage under the transformer tap changer operation corresponding to the current particle swarm.

5. The method for controlling voltage stability of a photovoltaic-integrated distribution network as described in claim 3, characterized in that, The step of obtaining the Jacobian matrix based on the distribution network node voltages and calculating the minimum singular value of the Jacobian matrix includes: Construct the Jacobian matrix based on the voltage of the distribution network nodes; Calculate the product of the transpose of the Jacobian matrix and the Jacobian matrix to obtain a symmetric matrix; Calculate the eigenvalues ​​of the symmetric matrix, and obtain the minimum singular value of the Jacobi matrix based on the eigenvalues.

6. The method for controlling voltage stability of a photovoltaic-integrated distribution network as described in claim 3, characterized in that, The step of calculating the fitness of each particle in the current particle swarm based on the distribution network node voltage and the minimum singular value includes: Based on the voltage of the distribution network nodes, the voltage deviation and network loss of the distribution network corresponding to each particle are obtained; The fitness of each particle in the current particle swarm is obtained by weighted summing of the minimum singular value, the reciprocal of the voltage deviation, and the reciprocal of the grid loss.

7. The method for controlling voltage stability of a photovoltaic-integrated distribution network as described in claim 1, characterized in that, The process of obtaining node voltages from the electrical parameters, selecting excellent nodes from the distribution network nodes based on the optimal voltage and the node voltages, and generating equipment action strategies for the excellent nodes includes: Obtain the node voltage from the electrical parameters, and calculate the voltage deviation score, power balance score, and singular value score for each distribution network node based on the optimal voltage and the node voltage; According to preset weights, the voltage deviation score, power balance score, and singular value score are weighted and summed to obtain the comprehensive score of each distribution network node. Based on the comprehensive score, excellent nodes are selected from the distribution network nodes; The device action strategy for generating the excellent nodes.

8. The method for controlling voltage stability of a photovoltaic-integrated distribution network as described in claim 7, characterized in that, The device action strategy for generating the excellent nodes includes: The switching actions of capacitor banks and the reactive power regulation actions of photovoltaic inverters at the excellent nodes are simulated and adjusted. During the simulation and adjustment process, the minimum singular value of the power flow of the distribution network is calculated as the voltage stability of the distribution network. With the goal of improving voltage stability, the optimal simulation adjustment result is obtained, and the corresponding device action strategy for the excellent node is generated.

9. The method for controlling voltage stability of a photovoltaic-integrated distribution network as described in claim 1, characterized in that, The control of the distribution network based on the optimal transformer tap changer operation and the equipment operation strategy includes: The optimal transformer tap changer action and the equipment action strategy are used to control the distribution network and collect the real-time distribution network voltage after control. The real-time distribution network voltage is compared with the optimal voltage. If the deviation is greater than a preset deviation threshold, the optimal transformer tap changer action and the equipment action strategy are regenerated.

10. A photovoltaic-integrated voltage stability control system for a power distribution network, characterized in that, include: The parameter acquisition module is used to collect electrical parameters and photovoltaic power parameters of the distribution network nodes; The transformer simulation adjustment module is used to simulate and adjust the transformer taps according to the electrical parameters and the photovoltaic power parameters using a particle swarm optimization algorithm to obtain the optimal transformer tap action and the optimal voltage of the distribution network node. The node action simulation module is used to obtain the node voltage from the electrical parameters, select excellent nodes from the distribution network nodes based on the optimal voltage and the node voltage, and generate the equipment action strategy for the excellent nodes; the equipment action strategy includes capacitor bank switching actions and photovoltaic inverter reactive power regulation actions; The distribution network control module is used to control the distribution network according to the optimal transformer tap changer action and the equipment action strategy.

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