A voltage stability control method and system for photovoltaic integrated distribution network

Abstract

The application discloses a kind of photovoltaic containing distribution network voltage stability control method and system, by collecting the electrical parameter and photovoltaic power parameter of distribution network node;According to the electrical parameter and the photovoltaic power parameter, the optimal transformer tap action and the optimal voltage of distribution network node are obtained by simulating adjustment to transformer tap through particle swarm optimization algorithm;From the electrical parameter, obtain node voltage, according to the optimal voltage and the node voltage, from distribution network node, excellent node is screened, and the equipment action strategy of the excellent node is generated;The equipment action strategy includes capacitor group switching action and photovoltaic inverter reactive power regulation action;According to the optimal transformer tap action and the equipment action strategy, control is carried out to distribution network. By adopting the embodiment of the present application, the adaptability of distribution network to photovoltaic can be enhanced, the voltage stability of distribution network can be improved, and the global optimization of distribution network can be realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution network control, and particularly relates to a power distribution network voltage stability control method and system containing photovoltaic. BACKGROUND

[0002] With the transformation of global energy structure to low carbonization, photovoltaic power generation as a clean and renewable energy, its penetration rate in the power distribution network continues to improve. Photovoltaic power supply is affected by environmental factors such as light intensity and temperature, and has significant intermittency and volatility, which makes the voltage stability of the power distribution network face serious challenges. When a large amount of photovoltaic is connected, the power distribution network is prone to voltage out-of-limit and voltage fluctuation under the conditions of light mutation and load fluctuation, which seriously affects the power supply quality and safe and stable operation of the system.

[0003] The traditional voltage regulation scheme of the power distribution network is to adjust the transformer tap separately and switch the capacitor bank, which has the limitations of slow response speed and local optimization, and is difficult to adapt to the rapid change characteristics of photovoltaic, resulting in that the voltage stability of the power distribution network is difficult to match the demand. SUMMARY

[0004] The present application aims to provide a power distribution network voltage stability control method and system containing photovoltaic, which can enhance the adaptability of the power distribution network to photovoltaic, improve the voltage stability of the power distribution network, and realize the global optimization of the power distribution network.

[0005] In the first aspect, the present application provides a power distribution network voltage stability control method containing photovoltaic, comprising:

[0006] Collecting electrical parameters and photovoltaic power parameters of nodes in the power distribution network;

[0007] According to the electrical parameters and the photovoltaic power parameters, the transformer tap is simulated and adjusted by a particle swarm optimization algorithm to obtain optimal transformer tap action and optimal voltage of nodes in the power distribution network;

[0008] The node voltage is obtained from the electrical parameters, and according to the optimal voltage and the node voltage, excellent nodes are screened from the nodes in the power distribution network, and a device action strategy of the excellent nodes is generated; the device action strategy includes capacitor bank switching action and photovoltaic inverter reactive power regulation action;

[0009] According to the optimal transformer tap action and the device action strategy, the power distribution network is controlled.

[0010] As an improvement of 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.

[0011] As an improvement of the above scheme, the transformer tapping is simulated and adjusted by a particle swarm optimization algorithm according to the electrical parameters and the photovoltaic power parameters, to obtain optimal transformer tapping action and optimal voltage of each distribution network node, comprising:

[0012] Initialize the population size and iteration parameters of the particle swarm optimization algorithm, and take the voltage action of the transformer tapping as a particle;

[0013] According to the electrical parameters and the photovoltaic power parameters, the current particle swarm is solved by particle-by-particle power flow, to obtain the distribution network node voltage corresponding to each particle;

[0014] According to the distribution network node voltage, the Jacobi matrix is obtained, and the minimum singular value of the Jacobi matrix is calculated;

[0015] According to the distribution network node voltage and the minimum singular value, the fitness of each particle under the current particle swarm is calculated;

[0016] According to the fitness, the current particle swarm is updated until the optimization target or the maximum iteration number is met, to obtain the optimal transformer tapping action and the optimal voltage of the distribution network node.

[0017] As an improvement of the above scheme, the transformer tapping is simulated and adjusted by a particle swarm optimization algorithm according to the electrical parameters and the photovoltaic power parameters, to obtain optimal transformer tapping action and optimal voltage of each distribution network node, comprising:

[0018] The electrical parameters and the photovoltaic power parameters are substituted into the power flow equation; the power flow equation is constructed according to the topology structure of the distribution network;

[0019] Each particle in the current particle swarm is substituted into the power flow equation, and the Newton-Raphson method is used for solving, to obtain the distribution network node voltage under the transformer tapping action corresponding to the current particle swarm.

[0020] As an improvement of the above scheme, the transformer tapping is simulated and adjusted by a particle swarm optimization algorithm according to the electrical parameters and the photovoltaic power parameters, to obtain optimal transformer tapping action and optimal voltage of each distribution network node, comprising:

[0021] According to the distribution network node voltage, the Jacobi matrix is constructed;

[0022] The product of the transpose matrix of the Jacobi matrix and the Jacobi matrix is calculated, to obtain a symmetric matrix;

[0023] The eigenvalue of the symmetric matrix is calculated, and the minimum singular value of the Jacobi matrix is obtained according to the eigenvalue.

[0024] 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:

[0025] 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;

[0026] 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.

[0027] 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:

[0028] 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;

[0029] 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.

[0030] Based on the comprehensive score, excellent nodes are selected from the distribution network nodes;

[0031] The device action strategy for generating the excellent nodes.

[0032] As an improvement to the above solution, the device action strategy for generating the excellent node includes:

[0033] 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.

[0034] 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.

[0035] As an improvement to the above scheme, the step of controlling the distribution network based on the optimal transformer tap changer action and the equipment action strategy includes:

[0036] 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.

[0037] 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.

[0038] In a second aspect, the embodiment of the present application also provides a voltage stability control system for a power distribution network containing photovoltaic, comprising:

[0039] a parameter collection module, configured to collect electrical parameters and photovoltaic power parameters of nodes of the power distribution network;

[0040] a transformer simulation adjustment module, configured to simulate adjustment of a transformer tap according to the electrical parameters and the photovoltaic power parameters by using a particle swarm optimization algorithm, so as to obtain optimal transformer tap action and optimal voltage of the nodes of the power distribution network;

[0041] a node action simulation module, configured to obtain node voltage from the electrical parameters, filter excellent nodes from the nodes of the power distribution network according to the optimal voltage and the node voltage, and generate device action strategy of the excellent nodes; the device action strategy comprises capacitor bank switching action and photovoltaic inverter reactive power regulation action;

[0042] a power distribution network control module, configured to control the power distribution network according to the optimal transformer tap action and the device action strategy.

[0043] Compared with the prior art, the voltage stability control method and system for the power distribution network containing photovoltaic disclosed by the present application can collect electrical parameters and photovoltaic power parameters of nodes of the power distribution network, simulate adjustment of a transformer tap according to the electrical parameters and the photovoltaic power parameters by using a particle swarm optimization algorithm, so as to obtain optimal transformer tap action and optimal voltage of the nodes of the power distribution network, obtain node voltage from the electrical parameters, filter excellent nodes from the nodes of the power distribution network according to the optimal voltage and the node voltage, and generate device action strategy of the excellent nodes; the device action strategy comprises capacitor bank switching action and photovoltaic inverter reactive power regulation action; and the power distribution network is controlled according to the optimal transformer tap action and the device action strategy. By using the embodiment of the present application, the adaptability of the power distribution network to photovoltaic can be enhanced, the voltage stability of the power distribution network can be improved, and global optimization of the power distribution network can be realized. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 is a step flowchart of a voltage stability control method for a power distribution network containing photovoltaic provided by the embodiment of the present application;

[0045] Figure 2 is a structural schematic diagram of a voltage stability control system for a power distribution network containing photovoltaic provided by the embodiment of the present application. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0047] In the description and claims of the specification, it is to be understood that the terms first, second, etc. merely denote different technical features described in the specification and claims, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. The terms are interchangeable under appropriate circumstances. Therefore, the features defined as "first" and "second" can be explicitly or implicitly included at least one of the features.

[0048] The embodiment of the present application provides a voltage stability control method for a power distribution network containing photovoltaic. Figure 1 In the embodiment, the voltage stability control method for the power distribution network containing photovoltaic is specifically executed through steps S1 to S4.

[0049] S1, collecting electrical parameters and photovoltaic power parameters of a power distribution network node.

[0050] S2, simulating and adjusting a transformer tap through a particle swarm optimization algorithm according to the electrical parameters and the photovoltaic power parameters, to obtain optimal transformer tap action and optimal voltage of the power distribution network node.

[0051] S3, obtaining node voltage from the electrical parameters, screening excellent nodes from the power distribution network node according to the optimal voltage and the node voltage, and generating a device action strategy of the excellent nodes; the device action strategy includes capacitor bank switching action and photovoltaic inverter reactive power regulation action.

[0052] S4, controlling the power distribution network according to the optimal transformer tap action and the device action strategy.

[0053] In the embodiment of the present application, the power distribution network node refers to the connection and intersection point of various electrical equipment, lines or loads in the power distribution network, and the electrical parameters reflect the real-time operation state and system characteristics of the power distribution network.

[0054] In the power distribution network containing photovoltaic, the fluctuation of photovoltaic output is greatly affected by the environment. By collecting photovoltaic power parameters, the power distribution network control action can be accurately matched with the photovoltaic characteristics, to realize the cooperative control target of efficient photovoltaic consumption and voltage stability of the power distribution network.

[0055] It should be noted that in the embodiment of the present application, the optimization object of the particle swarm optimization algorithm is the transformer tap, and the particle swarm can efficiently search for the tap action that optimizes the stability of the power distribution network, and the corresponding optimal voltage is obtained. Preferably, the tap action is the gear of the transformer tap, and different gears correspond to different transformer tap voltages.

[0056] In some preferred embodiments, each iteration of the particle swarm optimization algorithm is performed by parameter change detection through a pre-constructed power distribution network model to obtain the optimization result.

[0057] It should be further noted that steps S2 and S3 are both simulation of power distribution network actions, aiming to select the optimal combination of device actions and transformer tap actions from a number of possible action logics, and then apply the optimal action combination obtained by simulation to the actual power distribution network.

[0058] In the embodiment of the present application, 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.

[0059] In the above scheme, by controlling the transformer tap action and the device action of the best nodes, optimization control can be achieved in the global range of the power distribution network, and the stability and reliability of the voltage operation of the power distribution network can be significantly improved, thereby fully guaranteeing the efficient use of photovoltaic energy.

[0060] As a preferred implementation, the electrical parameters include voltage, active power and reactive power; and the photovoltaic power parameters include photovoltaic output power and photovoltaic load power.

[0061] It should be noted that the photovoltaic power parameters are parameter data of the photovoltaic power source collected from the photovoltaic power source access point in the power distribution network.

[0062] In some preferred embodiments, by installing smart meters and sensors on each node of the power distribution network, real-time collection of voltage, active power, and reactive power data of each node is realized, and at the same time, output power and load power data of the photovoltaic power source are collected, and the power distribution network nodes transmit the data to the control center through a communication network.

[0063] Through the electrical parameters and photovoltaic power parameters shown in the embodiment of the present application, the operating state of the power distribution network can be accurately captured, and the interaction between photovoltaic and power grid can be reflected. In other preferred embodiments, the electrical parameters can also include current, frequency or power factor, etc., and the selection of electrical parameters and photovoltaic power parameters should be able to meet the subsequent power flow calculation and action control calculation, and the selection of specific parameters does not affect the beneficial effects produced by the present application.

[0064] As a preferred embodiment, in step S2, the transformer taps are simulated and adjusted by a particle swarm optimization algorithm according to the electrical parameters and the photovoltaic power parameters, to obtain optimal transformer tap actions and optimal voltages of the nodes of the power distribution network, which are implemented through steps S21-S25.

[0065] S21, initialize the population size and iteration parameters of the particle swarm optimization algorithm, and take the voltage actions of the transformer taps as particles.

[0066] S22, perform particle-by-particle power flow calculation on the current particle swarm according to the electrical parameters and the photovoltaic power parameters, to obtain the voltages of the nodes of the power distribution network corresponding to each particle.

[0067] S23, obtain the Jacobian matrix according to the voltages of the nodes of the power distribution network, and calculate the minimum singular value of the Jacobian matrix.

[0068] S24, calculate the fitness of each particle under the current particle swarm according to the voltages of the nodes of the power distribution network and the minimum singular value.

[0069] S25, update the current particle swarm according to the fitness until the optimization target is met or the maximum number of iterations is reached, to obtain the optimal transformer tap actions and the optimal voltages of the nodes of the power distribution network.

[0070] It should be noted that taking the voltage actions of the transformer taps as particles means that each particle in the particle swarm corresponds to a specific tap adjustment scheme, ensuring that the particles are completely matched with the actual control objects.

[0071] In some preferred embodiments, the iteration parameters include an iteration learning factor and a maximum number of iterations, and in each iteration process, the speeds and positions of all particles in the current particle swarm are updated through the iteration learning factor.

[0072] The particle-by-particle calculation process is the process of screening candidate adjustment schemes, and according to the electrical parameters and the photovoltaic power parameters, the power grid operation scenarios corresponding to each particle can be constructed, and the power flow calculation on the power grid operation scenarios can simulate the actual state of power transmission in the power grid under the tap action, to obtain the optimal particle.

[0073] The power flow of the power distribution network usually presents a nonlinear trend, and in the embodiments of the present application, the Jacobian matrix is used for linearization, which can reduce the calculation amount on the one hand and quantify the impact of node voltage changes on power balance on the other hand, and the minimum singular value represents the stability of the power distribution network.

[0074] The fitness is used for comprehensively evaluating the advantages and disadvantages of each particle in the particle optimization algorithm, and the fitness is calculated by the node voltage of the power distribution network and the minimum singular value in the embodiment of the application, so that the stability of the power distribution network and other various demand indexes can be comprehensively evaluated.

[0075] It should be noted that, in some preferred embodiments, the optimal voltage is the optimal voltage of each node of the power distribution network under the optimal transformer tap action, and the number of optimal voltages corresponds to the number of nodes of the power distribution network; in other preferred embodiments, the optimal voltage is the average voltage of each node of the power distribution network under the optimal transformer tap action, and the optimal voltage can be used to measure the degree of deviation of each node from the average value.

[0076] Generally, the optimal voltage is associated with the optimal transformer tap action, and the direct or indirect correspondence between the optimal voltage and the voltage of each node of the power distribution network can be applied in the embodiment of the application, and does not affect the beneficial effects produced by the embodiment of the application.

[0077] For example, the optimal voltage is represented as ; wherein, is the voltage of the i th node of the power distribution network, is the set voltage of the node of the power distribution network, and N is the number of nodes of the power distribution network.

[0078] In the above scheme, the particle swarm optimization algorithm is combined with the minimum singular value index, the optimal voltage of the transformer tap is efficiently solved by the particle swarm algorithm, and the voltage stability is quantitatively evaluated by the minimum singular value, so that the closed-loop cooperation of optimization solution and stability evaluation is realized, and the application limitation of a single algorithm or index is broken through.

[0079] Further, preferably, in step S22, the power flow of each particle in the current particle swarm is solved according to the electrical parameters and the photovoltaic power parameters, to obtain the node voltage of the power distribution network corresponding to each particle, including:

[0080] The electrical parameters and the photovoltaic power parameters are substituted into the power flow equation; the power flow equation is obtained according to the topological structure of the power distribution network;

[0081] Each particle in the current particle swarm is substituted into the power flow equation, and the Newton-Raphson method is used for solving, to obtain the node voltage of the power distribution network under the transformer tap action corresponding to the current particle swarm.

[0082] It should be noted that the power flow equation is a mathematical model for describing the relationship between power transmission and voltage distribution of the power distribution network, and its construction depends on the topological structure of the power distribution network. Generally, the node connection relationship of the power distribution network is determined, the node type is determined, and then the admittance matrix is calculated based on the topological structure, and the power flow equation is constructed by combining the Kirchhoff law. The specific construction process of the power flow equation will not be described here.

[0083] In the process of solving power flow by Newton-Raphson method, the initial node voltage is taken as the iteration starting point, the power imbalance is calculated, the Jacobian matrix is constructed for sparse optimization, the correction equation is solved, and the power imbalance is repeatedly iterated until it meets the demand, so as to obtain the node voltage of the distribution network corresponding to the particle and form the corresponding relationship between the transformer tap action and the node voltage of the whole network.

[0084] The convergence speed of Newton-Raphson method is fast, which can quickly process the multi-group solving demand of particle swarm; at the same time, the solving precision is high, which can support the reliability of the optimization result.

[0085] Preferably, in step S23, the Jacobian matrix is obtained according to the node voltage of the distribution network, and the minimum singular value of the Jacobian matrix is calculated, including:

[0086] The Jacobian matrix is constructed according to the node voltage of the distribution network;

[0087] The product of the transpose matrix of the Jacobian matrix and the Jacobian matrix is calculated to obtain a symmetric matrix;

[0088] The eigenvalue of the symmetric matrix is calculated, and the minimum singular value of the Jacobian matrix is obtained according to the eigenvalue.

[0089] In some preferred embodiments, the power flow equation is linearized according to the node voltage of the distribution network, and the Jacobian matrix including an orthogonal matrix and a diagonal matrix can be obtained. Then, the minimum singular value can be obtained by singular value decomposition of the Jacobian matrix to represent the stability of the distribution network.

[0090] Exemplarily, the Jacobian matrix is represented as , wherein U and V are orthogonal matrices, satisfying , , and is a unit matrix, is a diagonal matrix, the singular values of the Jacobian matrix J are on the diagonal, and the minimum singular value is the smallest element on the diagonal.

[0091] Taking a 3-node distribution network as an example, since there is a balanced node, the dimension of the Jacobian matrix is 2x2, and the Jacobian matrix is represented as .

[0092] According to the singular value decomposition formula, the product of the transpose matrix of the Jacobian matrix and the Jacobian matrix is calculated , and the eigenvalues of are and . Then the singular value is represented as , , the minimum singular value of the Jacobi matrix is .

[0093] In the preferred embodiment of the present application, if the minimum singular value of a node is less than the preset singular value threshold, it is considered that the voltage stability of the node is poor and there is a risk of voltage instability.

[0094] Preferably, in actual complex power distribution networks, the minimum singular value is usually calculated quickly and accurately by means of professional power system analysis software such as MATLAB power system toolbox, PSCAD / EMTDC, etc., using the built-in matrix operation and singular value decomposition functions to improve the calculation efficiency and accuracy.

[0095] Preferably, step S24, according to the node voltage of the power distribution network and the minimum singular value, the fitness of each particle under the current particle swarm is calculated, comprising:

[0096] According to the node voltage of the power distribution network, the voltage deviation and the grid loss of the power distribution network corresponding to each particle are obtained;

[0097] The minimum singular value, the reciprocal of the voltage deviation and the reciprocal of the grid loss are weighted and summed to obtain the fitness of each particle under the current particle swarm.

[0098] In some preferred embodiments, the voltage deviation of the power distribution network is calculated by the difference between the node voltage of the power distribution network and the rated voltage; the grid loss of the power distribution network is measured by the line active loss and the transformer active loss in the process of power flow calculation.

[0099] It should be noted that the logic of the particle swarm optimization algorithm is to find the particle with the maximum fitness, and the characteristics of the voltage deviation and the grid loss are better the smaller; the minimum singular value, i.e. the stability of the power grid, is better the larger, so in the embodiments of the present application, the fitness calculation takes the reciprocal of the voltage deviation and the grid loss to ensure that the larger the fitness represents the better the comprehensive performance of the particle, which adapts to the optimization logic of the particle swarm optimization algorithm.

[0100] Preferably, the fitness function is represented as Fitness=0.4×(1 / voltage deviation)+0.3×minimum singular value+0.3×(1 / grid loss).

[0101] It can be understood that the weights of each term in the above 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 application.

[0102] As a preferred implementation, step S3, the node voltage is obtained from the electrical parameters, the excellent nodes are screened from the power distribution network nodes according to the optimal voltage and the node voltage, and the device action strategy of the excellent nodes is generated, which comprises:

[0103] The node voltage is obtained from the electrical parameters, and the voltage deviation score, the power balance score and the singular value score of each power distribution network node are calculated according to the optimal voltage and the node voltage;

[0104] According to the preset weight, the voltage deviation score, the power balance score and the singular value score are weighted and summed to obtain the comprehensive score of each power distribution network node;

[0105] According to the comprehensive score, the excellent nodes are screened from the power distribution network nodes;

[0106] The device action strategy of the excellent nodes is generated.

[0107] In some preferred embodiments, according to the optimal voltage and the node voltage, the deviation degree between them can be calculated, and the voltage deviation score is calculated according to the deviation degree. Based on the optimal voltage, the optimal target active power and target reactive power can be derived to calculate the actual power balance rate to obtain the power balance score. Based on the node voltage, the sensitivity of each node to stability can be obtained to calculate the singular value score.

[0108] The voltage deviation score reflects the degree of coincidence between the simulated voltage and the optimal voltage in the execution of each action, the power balance score reflects the matching degree of the supply and demand of the active power and the reactive power of the node, and the singular value score reflects the voltage stability of the node.

[0109] Exemplarily, the comprehensive score of each power distribution network node is calculated by Score=w1*singular value score+w2*voltage deviation score+w3*power balance score; wherein w1, w2 and w3 are preset weights.

[0110] In some preferred embodiments, the power distribution network nodes are sorted according to the comprehensive score, and the excellent nodes are selected according to a preset proportion. In other preferred embodiments, the comprehensive score of the power distribution network node is compared with a preset score threshold, and if the comprehensive score is greater than the preset score threshold, it is regarded as an excellent node.

[0111] Further, preferably, the generation of the device action strategy of the excellent nodes comprises:

[0112] The capacitor group switching action and the photovoltaic inverter reactive power regulation action of the excellent nodes are simulated to regulate, and the minimum singular value of the power distribution network power flow is calculated during the simulation to regulate as the voltage stability of the power distribution network;

[0113] An optimal simulation adjustment result is obtained to improve the voltage stability, and a device action strategy corresponding to the excellent node is generated.

[0114] In the embodiment of the application, the excellent nodes are screened first, and the excellent nodes screened have strong positive influence on the global distribution network, and then a device action strategy for the excellent nodes is generated. It should be noted that the device action strategy may include device actions of all or part of the excellent nodes, or may maintain the device actions of the excellent nodes.

[0115] Through simulation adjustment of the capacitor group switching action and the photovoltaic inverter reactive power regulation action of the excellent nodes, the device action strategy with the best overall performance can be selected from a plurality of simulation action combinations, thereby supporting the overall operation safety of the distribution network.

[0116] As a preferred embodiment, step S4 controls the distribution network according to the optimal transformer tap action and the device action strategy, including:

[0117] The optimal transformer tap action and the device action strategy are used to control the distribution network, and real-time distribution network voltage after control is collected.

[0118] The real-time distribution network voltage is compared with the optimal voltage, and if the deviation is greater than a preset deviation threshold, the optimal transformer tap action and the device action strategy are regenerated.

[0119] In the preferred embodiment of the application, the voltage and power parameters of the distribution network after control are monitored in real time, the voltage average value of a plurality of distribution network nodes is compared with the optimal voltage, and if the deviation is 5%-8%, the optimal voltage is recalculated, the optimization control strategy and the controller parameters are adjusted, and control is performed again to ensure that the voltage stability of the distribution network meets the requirements.

[0120] The application provides three preferred embodiments of the voltage stability control method of the distribution network containing photovoltaic.

[0121] In the first embodiment, the distribution network is a 10kV distribution network, the distribution network includes 8 nodes (Node1-Node8), 2 photovoltaic power supply access points (PV1, PV2), 1 on-load voltage regulating transformer (installed at Node1), and the total length of the line is 12km. The element parameters are as follows:

[0122] Line: LGJ-185 wire is used, the unit length resistance is 0.17Ω / km, and the reactance is 0.34Ω / km;

[0123] Transformer: model S11-6300 / 10, transformer ratio adjustment range ±2.5% (total of 5 grades, each grade 0.625%);

[0124] Photovoltaic power: PV1 rated power 1.5 MW, conversion efficiency 97%; PV2 rated power 1 MW, conversion efficiency 96%, reactive power regulation range [-500 kvar, 500 kvar].

[0125] Intelligent electric meters (accuracy 0.5S level) and voltage sensors are installed at 8 nodes to collect voltage (sampling frequency 100 Hz), 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 through the optical fiber network with a transmission delay ≤30 ms.

[0126] The particle swarm size N=40 and the maximum iteration number T=80 are initialized. Then the optimal tap position of the transformer is obtained by the particle swarm optimization algorithm as +1 tap (transformer ratio 10.125 kV / 0.4 kV), and the optimal voltage average value of each node is 10.2 kV.

[0127] The Jacobian matrix (dimension 14x14) of the 8-node distribution network is constructed, and the minimum singular value is obtained by singular value decomposition as 0.9, which is less than the threshold value 1, and Node5 is determined as a node with poor voltage stability.

[0128] Then the voltage deviation score, power balance score, and singular value score are calculated, and the comprehensive score is calculated by the preset weights w1=0.5, w2=0.3, and w3=0.2, and the excellent nodes are Node1, Node2, and Node7. The final capacitor bank switching action is to put in 2 groups of 100 kvar capacitors at Node2, and the photovoltaic inverter reactive power regulation action is to inject 300 kvar reactive power by PV1 and 200 kvar reactive power by PV2.

[0129] After control, the average voltage of each node is monitored as 10.18 kV, with a voltage deviation of 0.2%<5%, which meets the requirements. After 24 hours of continuous operation, the minimum singular value of Node5 is improved to 1.1, and the voltage stability meets the requirements.

[0130] In Example Two, a 380V low-voltage distribution network is used, which includes 15 nodes, 3 photovoltaic access points (PV1-PV3, rooftop photovoltaic), and 1 distribution transformer (10 kV / 0.4 kV). The element parameters are as follows:

[0131] Line: BV-50 mm² copper cable, unit length resistance 0.39 Ω / km, reactance 0.07 Ω / km;

[0132] Photovoltaic power: single rated power 50 kW, conversion efficiency 95%, reactive power regulation range [-20 kvar, 20 kvar].

[0133] Load: 70% residential load and 30% commercial load, total capacity 150 kVA.

[0134] Data is collected by smart meters (accuracy level 1.0) and wireless sensor networks (ZigBee protocol) with a sampling frequency of 50 Hz and a data transmission delay of ≤100 ms.

[0135] The particle swarm size N is initialized to 30 and the maximum number of iterations T is set to 50. Then, the optimal tap position of the transformer is obtained by the particle swarm optimization algorithm as -1 tap (transformer ratio 10 kV / 0.38 kV). The voltage deviation and the photovoltaic reactive power adjustment are optimized by the fitness function, and the optimal average voltage is 390 V.

[0136] A Jacobian matrix with a dimension of 28x28 is constructed, and the minimum singular value is 0.8, corresponding to Node 12, which is less than the threshold value 1, indicating that Node 12 is a node with poor voltage stability.

[0137] Then, the voltage deviation score, power balance score, and singular value score are calculated. By setting the weights w1=0.6, w2=0.3, and w3=0.1, the comprehensive score is calculated, and the excellent nodes are Node 3, Node 6, and Node 9. The final capacitor bank switching action is to switch 1 group of 5 kvar capacitors at Node 9, and the photovoltaic inverter reactive power adjustment action is to absorb 15 kvar, 10 kvar, and 15 kvar of reactive power at PV1, PV2, and PV3, respectively.

[0138] After control, the average voltage is 389 V, the optimal voltage deviation is 0.26%, and the minimum singular value of Node 12 is increased to 1.05. When the load peak occurs (18:00-20:00), the voltage deviation increases to 6%, triggering the recalculation of the optimal voltage. After adjusting the photovoltaic reactive power output, the deviation decreases to 3%.

[0139] In Example Three, a 10 kV distribution network with photovoltaic power is used, which includes 10 nodes, 3 photovoltaic power supply points, 2 on-load voltage regulating transformers, and a total line length of about 15 km. The element parameters are as follows:

[0140] Line: LGJ-120 conductors are used, with a unit length resistance of 0.27 Ω / km and a reactance of 0.36 Ω / km.

[0141] Transformer: Model S11-5000 / 10, with a ratio adjustment range of ±5% and 9 taps (each tap adjusts 0.625%).

[0142] Photovoltaic power supply: single rated power 1000kW, conversion efficiency 96%, reactive power regulation range [-500kvar, 500kvar].

[0143] Intelligent electric meters and voltage and power sensors are installed at 10 nodes of the power distribution network, with a sampling frequency of 100Hz, real-time collection of voltage, active power and reactive power data of each node, and simultaneous collection of output power and load power data of 3 photovoltaic power supplies. The data is transmitted to the control center through the optical fiber communication network, with a transmission delay ≤50ms.

[0144] Initialize the particle swarm size N=50 and the maximum number of iterations T=100. Then, the optimal tap position of the transformer is obtained by solving the particle swarm optimization algorithm as transformer 1 up 2 gears (+1.25%), transformer 2 down 1 gear (-0.625%), the voltage deviation and photovoltaic reactive power regulation are optimized through the fitness function, and the optimal voltage average is 390V.

[0145] The Jacobian matrix with a dimension of 18x18 is constructed, and singular value decomposition is performed to obtain the minimum singular value The minimum singular value calculation results of nodes 1-10 are shown in Table 1.

[0146] Table 1

[0147]

[0148] It can be seen that the minimum singular values (0.9, 0.8, 0.7) of nodes 3, 6 and 10 are less than the threshold value 1, which are determined as poor voltage stability nodes with instability risk.

[0149] Then, the voltage deviation score, power balance score and singular value score are calculated, and the comprehensive score is calculated through the preset weights w1=0.5, w2=0.3, w3=0.2. The top 30% of the nodes Node1, Node4, Node7 and Node8 are selected as excellent nodes. Through the optimization control of the device action of the excellent nodes, the final capacitor bank switching action is that Node1 is put into 1 group of 100kvar capacitors, Node4 is removed from 1 group of 50kvar capacitors, and the photovoltaic inverter reactive power regulation action is that 200kvar and 300kvar reactive power are injected into photovoltaic access points 2 and 3 respectively.

[0150] After control, the voltage of each node is monitored in real time, and the average voltage of the 10 nodes is calculated as 10.15kV, with a deviation of 0.49% from the optimal voltage 10.2kV, which is less than 5% and meets the requirements. Continuous monitoring for 24 hours, the voltage of the power distribution network is stable in the range of 10.1kV-10.3kV, and the minimum singular value of each node is greater than 1, with good voltage stability.

[0151] Under the power distribution network conditions of the above-mentioned embodiment one, embodiment two and embodiment three, the control of the power distribution network is respectively carried out by using the traditional transformer tap adjusting method, and the average value of the control results under the three power distribution network conditions is compared with the above-mentioned embodiment, and the comparison result is shown in Table 2.

[0152] Table 2

[0153]

[0154] It can be seen that the voltage stability control method of the power distribution network containing photovoltaic described in the embodiment of the present application is significantly superior to the traditional method in voltage stability, network loss control and response speed, etc., effectively verifying the feasibility and advancement of the present application.

[0155] By using the voltage stability control method of the power distribution network containing photovoltaic provided by the embodiment of the present application, the optimization control can be realized by controlling the transformer tap action and the device action of the excellent node to cover the whole range of the power distribution network, and the stability and reliability of the voltage operation of the power distribution network are significantly improved, and the efficient use of photovoltaic energy is fully ensured.

[0156] The embodiment of the present application provides a voltage stability control system of a power distribution network containing photovoltaic. Please refer to Figure 2 , the voltage stability control system of the power distribution network containing photovoltaic includes a parameter acquisition module 11, a transformer simulation adjusting module 12, a node action simulation module 13 and a power distribution network control module 14, wherein:

[0157] The parameter acquisition module 11 is used to acquire the electrical parameters and photovoltaic power parameters of the power distribution network node;

[0158] The transformer simulation adjusting module 12 is used to simulate and adjust the transformer tap by using the particle swarm optimization algorithm according to the electrical parameters and the photovoltaic power parameters, so as to obtain the optimal transformer tap action and the optimal voltage of the power distribution network node;

[0159] The node action simulation module 13 is used to obtain the node voltage from the electrical parameters, select the excellent node from the power distribution network node according to the optimal voltage and the node voltage, and generate the device action strategy of the excellent node; the device action strategy includes the capacitor bank switching action and the photovoltaic inverter reactive power regulation action;

[0160] The power distribution network control module 14 is used to control the power distribution network according to the optimal transformer tap action and the device action strategy.

[0161] As a preferred embodiment, the electrical parameters include voltage, active power and reactive power; and the photovoltaic power parameters include photovoltaic output power and photovoltaic load power.

[0162] As a preferred implementation, the transformer analog regulation module 12 comprises:

[0163] A particle swarm initialization unit is configured to initialize population size and iteration parameters of a particle swarm optimization algorithm, and take voltage actions of transformer taps as particles.

[0164] A power flow solving unit is configured to perform power flow solving on each particle in the current particle swarm according to the electrical parameters and the photovoltaic power parameters, to obtain node voltages of the power distribution network corresponding to each particle.

[0165] A minimum singular value calculation unit is configured to obtain a Jacobian matrix according to the node voltages of the power distribution network, and calculate a minimum singular value of the Jacobian matrix.

[0166] A fitness calculation unit is configured to calculate fitness of each particle in the current particle swarm according to the node voltages of the power distribution network and the minimum singular value.

[0167] A particle swarm updating unit is configured to update the current particle swarm according to the fitness until an optimization target is met or a preset maximum iteration number is reached, to obtain optimal transformer tap actions and optimal node voltages of the power distribution network.

[0168] Further, preferably, the power flow solving unit is specifically configured to:

[0169] Substitute the electrical parameters and the photovoltaic power parameters into a power flow equation, which is constructed according to a topology of the power distribution network.

[0170] Substitute each particle in the current particle swarm into the power flow equation respectively, and solve the equation by using Newton-Raphson method to obtain node voltages of the power distribution network under the transformer tap actions corresponding to the current particle swarm.

[0171] Preferably, the minimum singular value calculation unit is specifically configured to:

[0172] Construct a Jacobian matrix according to the node voltages of the power distribution network.

[0173] Calculate a product of a transpose matrix of the Jacobian matrix and the Jacobian matrix to obtain a symmetric matrix.

[0174] Calculate eigenvalues of the symmetric matrix, and obtain the minimum singular value of the Jacobian matrix according to the eigenvalues.

[0175] Preferably, the fitness calculation unit is specifically configured to:

[0176] Obtain voltage deviation and power grid loss of the power distribution network corresponding to each particle according to the node voltages of the power distribution network.

[0177] The minimum singular value, the reciprocal of the voltage deviation and the reciprocal of the power grid loss are weighted and summed to obtain the fitness of each particle under the current particle swarm.

[0178] As a preferred embodiment, the node action simulation module 13 comprises:

[0179] The single score calculation unit is configured to obtain a node voltage from the electrical parameters, and calculate a voltage deviation score, a power balance score and a singular value score of each power distribution network node according to the optimal voltage and the node voltage.

[0180] The comprehensive score calculation unit is configured to weight and sum the voltage deviation score, the power balance score and the singular value score according to a preset weight to obtain a comprehensive score of each power distribution network node.

[0181] The excellent node screening unit is configured to screen excellent nodes from the power distribution network nodes according to the comprehensive score.

[0182] The device action strategy generation unit is configured to generate a device action strategy of the excellent nodes.

[0183] Further, preferably, the device action strategy generation unit is specifically configured to:

[0184] The capacitor bank switching action and the photovoltaic inverter reactive power regulation action of the excellent nodes are simulated, and the minimum singular value of the power distribution network power flow is calculated in the simulation process as the voltage stability of the power distribution network.

[0185] The optimal simulation result is obtained by taking the improvement of the voltage stability as a target, and the device action strategy of the excellent nodes is generated.

[0186] As a preferred embodiment, the power distribution network control module 14 is configured to:

[0187] The optimal transformer tap action and the device action strategy are adopted to control the power distribution network, and the real-time power distribution network voltage after the control is collected.

[0188] The real-time power distribution network voltage is compared with the optimal voltage, and if the deviation is greater than a preset deviation threshold, the optimal transformer tap action and the device action strategy are regenerated.

[0189] By adopting the power distribution network voltage stability control system with photovoltaic provided in the embodiments of the present application, the transformer tap action and the device action of the excellent nodes are controlled to realize the optimization control in the global range of the power distribution network, and the stability and reliability of the power distribution network voltage operation are significantly improved, and the efficient utilization of photovoltaic energy is fully ensured.

[0190] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing relevant hardware, and the program can be stored in a computer readable storage medium. When the program is executed, the program can include the processes of the above-mentioned embodiment methods. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM), a random access memory (RAM), or the like.

[0191] The above is the preferred embodiment of the present application. It should be pointed out that, for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which are also considered within the scope of protection of the present application.

Claims

1. A method for voltage stability control of a power distribution grid containing photovoltaics, characterized by, The method comprises the following steps: Collecting electrical parameters and photovoltaic power parameters of nodes in a power distribution network; According to the electrical parameters and the photovoltaic power parameters, the transformer taps are simulated and adjusted by a particle swarm optimization algorithm to obtain optimal transformer tap actions and optimal voltages of nodes in the power distribution network; From the electrical parameters, node voltages are obtained, and according to the optimal voltages and the node voltages, excellent nodes are screened from the nodes in the power distribution network, and a device action strategy of the excellent nodes is generated; the device action strategy comprises capacitor bank switching actions and photovoltaic inverter reactive power regulation actions; According to the optimal transformer tap actions and the device action strategy, the power distribution network is controlled; The step of obtaining node voltages from the electrical parameters, and screening excellent nodes from the nodes in the power distribution network according to the optimal voltages and the node voltages, and generating a device action strategy of the excellent nodes, comprises the following steps: From the electrical parameters, node voltages are obtained, and according to the optimal voltages and the node voltages, voltage deviation scores, power balance scores and singular value scores of each node in the power distribution network are calculated; According to a preset weight, the voltage deviation scores, the power balance scores and the singular value scores are weighted and summed to obtain comprehensive scores of each node in the power distribution network; According to the comprehensive scores, excellent nodes are screened from the nodes in the power distribution network; The device action strategy of the excellent nodes is generated.

2. A voltage stability control method for a power distribution grid containing photovoltaics as claimed in claim 1, characterized in that, The electrical parameters comprise voltages, active powers and reactive powers; the photovoltaic power parameters comprise photovoltaic output powers and photovoltaic load powers.

3. The voltage stability control method of a power distribution grid containing photovoltaics of claim 1, wherein, The step of simulating and adjusting the transformer taps according to the electrical parameters and the photovoltaic power parameters by the particle swarm optimization algorithm to obtain optimal transformer tap actions and optimal voltages of each node in the power distribution network, comprises the following steps: The population size and iteration parameters of the particle swarm optimization algorithm are initialized, and the voltage actions of the transformer taps are taken as particles; According to the electrical parameters and the photovoltaic power parameters, each particle in the current particle swarm is solved by particle-by-particle power flow to obtain node voltages corresponding to each particle in the power distribution network; According to the node voltages in the power distribution network, a Jacobian matrix is obtained, and the minimum singular value of the Jacobian matrix is calculated; According to the node voltages in the power distribution network and the minimum singular value, the fitness of each particle under the current particle swarm is calculated; According to the fitness, the current particle swarm is updated until the optimization target is met or the preset maximum iteration number is reached, to obtain optimal transformer tap actions and optimal voltages of nodes in the power distribution network.

4. The voltage stability control method of a power distribution grid containing photovoltaics as claimed in claim 3, characterized by, The step of solving the power flow of the current particle swarm according to the electrical parameters and the photovoltaic power parameters to obtain node voltages in the power distribution network corresponding to each particle, comprises the following steps: The electrical parameters and the photovoltaic power parameters are substituted into a power flow equation; the power flow equation is constructed according to the topological structure of the power distribution network; Each particle in the current particle swarm is substituted into the power flow equation, and the Newton-Raphson method is used to solve the power flow equation to obtain node voltages in the power distribution network corresponding to the transformer taps under the current particle swarm.

5. A voltage stability control method for a power distribution grid containing photovoltaics as claimed in claim 3, characterized by, The step of obtaining a Jacobian matrix according to the node voltages in the power distribution network, and calculating the minimum singular value of the Jacobian matrix, comprises the following steps: According to the node voltages in the power distribution network, a Jacobian matrix is constructed; calculating a product of a transposed matrix of the Jacobian matrix and the Jacobian matrix to obtain a symmetric matrix; calculating eigenvalues of the symmetric matrix, and obtaining a minimum singular value of the Jacobian matrix according to the eigenvalues.

6. A voltage stability control method for a power distribution grid containing photovoltaics as claimed in claim 3, characterized by, The calculating the fitness of each particle under the current particle group according to the node voltage of the power distribution network and the minimum singular value comprises: obtaining voltage deviation and power grid loss of the power distribution network corresponding to each particle according to the node voltage of the power distribution network; performing weighted summation on the minimum singular value, reciprocal of the voltage deviation and reciprocal of the power grid loss to obtain the fitness of each particle under the current particle group.

7. The voltage stability control method of a power distribution grid containing photovoltaics of claim 1, wherein, The generating the device action strategy of the excellent node comprises: simulating adjustment of capacitor group switching action and photovoltaic inverter reactive power regulation action of the excellent node, and calculating a minimum singular value of power flow of the power distribution network in the simulation adjustment process as voltage stability of the power distribution network; obtaining optimal simulation adjustment results by taking the voltage stability as a target, and generating the device action strategy of the excellent node corresponding to the optimal simulation adjustment results.

8. The voltage stability control method of a power distribution grid containing photovoltaics of claim 1, wherein, The controlling the power distribution network according to the optimal transformer tap action and the device action strategy comprises: controlling the power distribution network by using the optimal transformer tap action and the device action strategy, and collecting real-time power distribution network voltage after the control; comparing the real-time power distribution network voltage with the optimal voltage, and regenerating the optimal transformer tap action and the device action strategy if the deviation is greater than a preset deviation threshold.

9. A photovoltaic grid code compliant voltage stability control system, comprising: comprise: a parameter collection module configured to collect electrical parameters and photovoltaic power parameters of nodes of a power distribution network; a transformer simulation adjustment module configured to simulate adjustment of transformer taps by using a particle swarm optimization algorithm according to the electrical parameters and the photovoltaic power parameters, to obtain an optimal transformer tap action and an optimal voltage of the nodes of the power distribution network; a node action simulation module configured to obtain node voltages from the electrical parameters, to select excellent nodes from the nodes of the power distribution network according to the optimal voltage and the node voltages, and to generate a device action strategy of the excellent nodes; the device action strategy comprises capacitor group switching action and photovoltaic inverter reactive power regulation action; a power distribution network control module configured to control the power distribution network according to the optimal transformer tap action and the device action strategy. The node action simulation module comprises: a single-item score calculation unit configured to obtain node voltages from the electrical parameters, and to calculate voltage deviation scores, power balance scores and singular value scores of the nodes of the power distribution network according to the optimal voltage and the node voltages; a comprehensive score calculation unit configured to perform weighted summation on the voltage deviation scores, the power balance scores and the singular value scores according to preset weights, to obtain comprehensive scores of the nodes of the power distribution network; an excellent node selection unit configured to select excellent nodes from the nodes of the power distribution network according to the comprehensive scores; a device action strategy generation unit configured to generate the device action strategy of the excellent nodes.

Images