Optimization control method and device suitable for power distribution network, terminal equipment and storage medium

Abstract

The invention discloses an optimization control method and device suitable for a power distribution network, terminal equipment and a storage medium, three function models are constructed for network loss minimization, voltage deviation minimization and reactive compensation equipment output change minimization, and the economy, stability and equipment life of power grid operation are comprehensively considered; the objective of minimizing the output change of the reactive compensation equipment is introduced, so that the finally solved control strategy can consider the influence of the output change of the reactive compensation equipment, frequent switching or adjustment of the compensation equipment can be reduced, reactive compensation can be rationalized when the objective function is solved, and the optimization of the reactive compensation can be realized. The reactive power demand of the system can be balanced more effectively, the transmission of reactive power in a power grid is reduced, and the line loss is further reduced. According to the method, the globally optimal control strategy considering the influence of the output change of the reactive compensation equipment can be obtained, and the energy utilization efficiency of the power grid is improved through reasonable reactive compensation.

Description

Technical Field

[0001] The present invention relates to the technical field of optimization control of distribution networks, and in particular to an optimization control method, device, terminal equipment and storage medium suitable for distribution networks. Background Art

[0002] The load in the distribution network (such as residential electricity, industrial electricity, etc.) is constantly changing, and this change is random and uncertain. In order to cope with this change, it is necessary to adjust the node voltage of the distribution network in real time to avoid excessive voltage deviation that causes equipment damage or system crash, and to adjust the output of reactive compensation equipment in each area in real time to ensure the stability and reliability of power supply. However, when dealing with the ever-changing load, the traditional power grid optimization control method simply collects various distribution network operation data, such as voltage, current, power, etc., so as to maximize system stability or minimize network losses, and then derive the final reactive compensation equipment output control and the voltage setting control strategy of the nodes in the distribution network.

[0003] When optimizing the control of the distribution network and deriving the control strategy, the traditional optimization control method does not consider the impact of the change in the output of the reactive compensation equipment on the overall optimization control of the power grid, and cannot effectively balance the relationship between the system reactive power demand and the output of the compensation equipment, resulting in frequent switching or adjustment of the compensation equipment, which increases the mechanical and electrical stress of the equipment, shortens the equipment life, and may cause system voltage fluctuations and flickers, making it impossible to maintain a stable voltage level, thereby affecting the power quality and power supply reliability of the power grid; in the optimization control of the distribution network, if the impact of the change in the output of the reactive compensation equipment on the actual operation of the power grid is not considered, the global optimal control effect cannot be achieved, resulting in unreasonable reactive power distribution of the system as a whole, which will increase line losses and reduce the energy utilization efficiency of the power grid. Summary of the invention

[0004] The embodiments of the present invention provide an optimization control method, apparatus, terminal device and storage medium applicable to a distribution network, which can obtain a globally optimal control strategy that takes into account the influence of output changes of reactive power compensation equipment, thereby improving the energy utilization efficiency of the power grid, and can effectively solve the problem that the prior art cannot achieve a globally optimal control effect, and that the reactive power distribution is unreasonable, resulting in low energy utilization efficiency of the power grid.

[0005] An embodiment of the present invention provides an optimization control method applicable to a distribution network, comprising:

[0006] According to the voltage data of each node in the distribution network, a first function model with the goal of minimizing network loss is constructed;

[0007] According to the voltage of each node in the distribution network and the corresponding voltage threshold, a second function model with the goal of minimizing voltage deviation is constructed;

[0008] According to the output values ​​of each reactive compensation device in the distribution network at different times, a third function model is constructed with the goal of minimizing the output change of the reactive compensation device;

[0009] According to the first function model, the second function model and the third function model, a corresponding objective function is constructed; wherein the objective function includes the following constraints: node voltage limit constraint and reactive power compensation device output constraint;

[0010] Based on the constraint conditions, the objective function is solved to generate each target node voltage and each target reactive power compensation device output corresponding to the minimum value of the objective function;

[0011] According to each target node voltage and each target reactive power compensation device output, the nodes and reactive power compensation devices in the distribution network are controlled separately.

[0012] Preferably, the step of constructing a first function model with the goal of minimizing network loss according to the voltage data of each node in the distribution network includes:

[0013] Obtaining voltage data of each node in the distribution network; wherein the voltage data includes: the voltage of each node, the conductance value between nodes, the susceptance value between nodes, and the phase angle between nodes;

[0014] According to the voltage of each node, the phase angle of each node, the conductance value between nodes and the susceptance value between nodes, a first function model with the goal of minimizing network loss is constructed.

[0015] Preferably, the objective function includes:

[0016] min F=w1·f1+w2·f2+w3·f3;

[0017]

[0018] Among them, F is the function value corresponding to the objective function, w1, w2, and w3 are different weight coefficients, f1 is the first function model with the goal of minimizing network loss, f2 is the second function model with the goal of minimizing voltage deviation, and f3 is the third function model with the goal of minimizing the output change of reactive compensation equipment. V i 、V j are the voltages at nodes i and j, G ij , B ij are the conductance and susceptance of nodes i and j respectively, θ i ,θ jare the phase angles of node i and node j respectively, N is the number of nodes in the distribution network, V ref is the voltage threshold corresponding to the node, represents the output of the kth reactive power compensation device at time t, and M is the number of reactive power compensation devices in the distribution network.

[0019] Preferably, the objective function further includes: a power balance constraint;

[0020] The node voltage limit constraint is:

[0021] V min ≤V i ≤V max ;

[0022] Among them, V min is the minimum node voltage limit, V max is the maximum limit value of the node voltage;

[0023] The output constraint of the reactive power compensation equipment is:

[0024]

[0025] Among them, Q C,min is the minimum output threshold of the reactive power compensation equipment, Q C,max It is the maximum output threshold of the reactive power compensation equipment.

[0026] The power balance constraint is:

[0027] P i =V i ∑ j∈N V j (G ij cosθ ij +B ij sinθ ij );

[0028] Q i =V i ∑ j∈N V j (G ij sinθ ij -B ij cosθ ij );

[0029] Among them, P i , Q i are the active injection power and reactive injection power of node i, respectively, ij is the phase angle difference between node i and node j.

[0030] Preferably, solving the objective function based on the constraint condition to generate each target node voltage and each target reactive power compensation device output corresponding to the minimum objective function value includes:

[0031] dividing the distribution network into a plurality of target areas;

[0032] The node voltage and reactive power compensation equipment output corresponding to each target area are used as decision variables;

[0033] Repeat the following decision variable determination operation until the current number of iterations is equal to the preset iteration number threshold, and output the target decision variables corresponding to each target area, and each output target decision variable satisfies the node voltage limit constraint, the reactive compensation equipment output constraint, and the power balance constraint:

[0034] Get the current iteration number;

[0035] When the current number of iterations is less than the iteration number threshold, for each target area, based on the candidate decision variables obtained last time and the target decision variables, the decision variables of the target function are locally solved to generate the candidate decision variables corresponding to the minimum value of the target function; wherein, initially, the decision variables of the target function are directly locally solved to generate the candidate decision variables corresponding to the minimum value of the target function;

[0036] For all target regions, the decision variables of the target function are globally solved according to the function values ​​of each decision variable and each candidate decision variable in the target function value, and each target decision variable corresponding to the minimum target function value is generated; wherein each target decision variable corresponds to a target region;

[0037] The current iteration number value is added to the preset increment to obtain the updated iteration number, and the updated iteration number is used as the iteration number when the decision variable determination operation is performed next time.

[0038] Preferably, the decision variable corresponding to each target area is a group of particles;

[0039] For each target area, based on the candidate decision variables and the target decision variables obtained last time, the decision variables of the target function are locally solved to generate the candidate decision variables corresponding to the minimum value of the target function, including:

[0040] For each particle, based on the particle historical position, particle historical velocity, population historical position and population historical function value obtained last time, an updated particle position of the particle is obtained; wherein, at the initial time, each particle of the population is initialized to obtain the particle historical position and particle historical velocity of each particle, as well as the population historical position and population historical function value of the population;

[0041] According to the updated particle position of the particle, a candidate particle corresponding to the minimum objective function value is generated.

[0042] Preferably, for all target areas, according to each decision variable and the function value of each candidate decision variable in the target function value, the decision variables of the target function are globally solved to generate each target decision variable corresponding to the minimum target function value, including:

[0043] For each particle, obtaining an updated particle velocity of the particle based on the updated particle position;

[0044] Obtain a particle function value corresponding to each candidate particle in the objective function value;

[0045] For all particles, according to the updated particle position of each particle, the updated particle velocity of each particle and the particle function value of each candidate particle, the updated population history position and the updated population history function value are obtained;

[0046] Based on the updated historical positions of the population, the decision variables of the objective function are globally solved to generate several target particles corresponding to the minimum value of the objective function.

[0047] Based on the above method embodiments, the present invention provides corresponding device embodiments.

[0048] An embodiment of the present invention provides an optimization control device suitable for a distribution network, comprising: a function model construction module, an objective function construction module, an objective function solving module and a distribution network control module;

[0049] The function model building module is used to build a first function model with the goal of minimizing network loss according to the voltage data of each node in the distribution network;

[0050] According to the voltage of each node in the distribution network and the corresponding voltage threshold, a second function model with the goal of minimizing voltage deviation is constructed;

[0051] According to the output values ​​of each reactive compensation device in the distribution network at different times, a third function model is constructed with the goal of minimizing the output change of the reactive compensation device;

[0052] The objective function construction module is used to construct a corresponding objective function according to the first function model, the second function model and the third function model; wherein the objective function includes the following constraints: node voltage limit constraint and reactive compensation equipment output constraint;

[0053] The objective function solving module is used to solve the objective function based on the constraint conditions to generate each target node voltage and each target reactive power compensation device output corresponding to the minimum objective function value;

[0054] The distribution network control module is used to control the nodes and reactive compensation equipment in the distribution network respectively according to the target node voltages and the target reactive compensation equipment outputs.

[0055] Based on the above method embodiments, the present invention provides corresponding terminal device embodiments.

[0056] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, an optimization control method applicable to a distribution network as described in the above-mentioned embodiment of the invention is implemented.

[0057] Based on the above method embodiments, the present invention provides corresponding storage medium item embodiments.

[0058] Another embodiment of the present invention provides a storage medium, wherein the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute an optimization control method applicable to a distribution network as described in the above-mentioned embodiment of the invention.

[0059] The following beneficial effects are achieved by implementing the present invention:

[0060] The embodiments of the present invention provide an optimization control method, device, terminal device and storage medium suitable for distribution networks. The present invention constructs three function models for minimizing network losses, minimizing voltage deviations and minimizing output changes of reactive compensation equipment, respectively. Thus, by optimizing these three objectives simultaneously, the economy, stability and equipment life of the power grid operation can be more comprehensively considered. Moreover, when solving the objective function, the present invention introduces the goal of minimizing the output change of reactive compensation equipment, so that the control strategy finally solved can take into account the influence of the output change of reactive compensation equipment, thereby reducing the frequent switching or adjustment of the compensation equipment, reducing the mechanical and electrical stress of the equipment, and extending the life of the equipment. At the same time, it can also make it possible to rationalize reactive compensation when solving the objective function, so that the reactive demand of the system can be more effectively balanced, the transmission of reactive power in the power grid can be reduced, and then the line loss can be reduced. Compared with the prior art, the present invention can integrate the above three function models into one objective function, and take into account the node voltage limit and the output constraint of the reactive compensation equipment. By solving the objective function, a globally optimal control strategy that takes into account the impact of the output change of the reactive compensation equipment can be obtained. Through multi-objective optimization, refined reactive compensation control and voltage control, the voltage level of the distribution network can be maintained stable, and through reasonable reactive compensation, the energy utilization efficiency of the power grid is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a flow chart of an optimization control method applicable to a distribution network provided by an embodiment of the present invention.

[0062] Figure 2 It is a schematic diagram of the objective function solution process provided by an embodiment of the present invention.

[0063] Figure 3 It is a structural schematic diagram of an optimization control device applicable to a distribution network provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] like Figure 1 FIG. 1 is a flow chart of an optimization control method applicable to a distribution network provided by an embodiment of the present invention. The optimization control method applicable to a distribution network includes:

[0066] Step S1: constructing a first function model with the goal of minimizing network loss according to the voltage data of each node in the distribution network;

[0067] According to the voltage of each node in the distribution network and the corresponding voltage threshold, a second function model with the goal of minimizing voltage deviation is constructed;

[0068] According to the output values ​​of each reactive compensation device in the distribution network at different times, a third function model is constructed with the goal of minimizing the output change of the reactive compensation device;

[0069] Step S2: constructing a corresponding objective function according to the first function model, the second function model and the third function model; wherein the objective function includes the following constraints: node voltage limit constraint and reactive power compensation equipment output constraint;

[0070] Step S3: Solving the objective function based on the constraint conditions to generate the target node voltages and target reactive power compensation device outputs corresponding to the minimum value of the objective function;

[0071] Step S4: Control the nodes and reactive compensation devices in the distribution network respectively according to the target node voltages and the target reactive compensation device outputs.

[0072] For step S1, in a preferred embodiment, the present invention considers the following three optimization objectives:

[0073] f1: Minimize network loss;

[0074] f2: voltage deviation minimization;

[0075] f3: Minimize the output change of reactive power compensation equipment;

[0076] The present invention first constructs a first function model, a second function model and a third function model. Specifically, the first function model f1 aiming at minimizing network loss can be constructed according to the voltage data of each node in the distribution network.

[0077] Specifically, according to the voltage data of each node in the distribution network, a first function model with the goal of minimizing network loss is constructed, including:

[0078] Obtaining voltage data of each node in the distribution network; wherein the voltage data includes: the voltage of each node, the conductance value between nodes, the susceptance value between nodes, and the phase angle between nodes;

[0079] According to the voltage of each node, the phase angle of each node, the conductance value between nodes and the susceptance value between nodes, the first function model f1 with the goal of minimizing network loss is constructed. The formula is as follows:

[0080]

[0081] Furthermore, according to the voltage of each node in the distribution network and the corresponding voltage threshold, a second function model f2 with the goal of minimizing the voltage deviation is constructed, and the formula is as follows:

[0082] f2=min∑ i∈N (V i -V ref ) 2 ;

[0083] Furthermore, according to the output values ​​corresponding to each reactive compensation device in the distribution network at different times, a third function model f3 is constructed with the goal of minimizing the output change of the reactive compensation device. The formula is as follows:

[0084]

[0085] Among them, f1 is the first function model with the goal of minimizing network loss, f2 is the second function model with the goal of minimizing voltage deviation, and f3 is the third function model with the goal of minimizing the output change of reactive compensation equipment. i 、V j are the voltages at nodes i and j, G ij , B ij are the conductance and susceptance of nodes i and j respectively, θ i ,θ j are the phase angles of node i and node j respectively, N is the number of nodes in the distribution network, V ref is the voltage threshold corresponding to the node, represents the output of the kth reactive power compensation device at time t, and M is the number of reactive power compensation devices in the distribution network.

[0086] Indicatively, the three function models of the present invention are respectively optimized for the key performance indicators in the operation of the distribution network. The first function model focuses on reducing the loss in the process of power transmission and distribution, which can improve the economy; the second function model ensures that the voltage of each node is stable within a reasonable range, ensuring the power supply quality and equipment safety; the third function model aims to reduce the frequent adjustment of reactive compensation equipment, extend the life of the equipment and reduce unnecessary energy loss. Combining these three models, the comprehensive optimization of the operating status of the distribution network can be achieved.

[0087] For step S2, in a preferred embodiment, the present invention can construct a corresponding objective function based on the first function model, the second function model and the third function model; wherein the objective function includes the following constraints: node voltage limit constraint, reactive compensation equipment output constraint and power balance constraint;

[0088] The objective function includes:

[0089] min F=w1·f1+w2·f2+w3·f3;

[0090] Among them, F is the function value corresponding to the objective function, w1, w2, w3 are different weight coefficients, f1 is the first function model with the goal of minimizing network loss, f2 is the second function model with the goal of minimizing voltage deviation, and f3 is the third function model with the goal of minimizing the output change of reactive compensation equipment. Among them, w1+w2+w3=1.

[0091] Through the above objective function, the present invention can ensure that the voltage level in the distribution network fluctuates within the allowable range by minimizing the voltage deviation, avoiding damage to the equipment caused by excessively high or low voltage, thereby improving the stability of the system. At the same time, reducing the frequent adjustment of reactive power compensation equipment also helps to maintain the stability of the system voltage and reduce voltage fluctuations and flicker.

[0092] Minimizing network losses is directly related to the effective use of electricity and reducing waste. By optimizing the control strategy, line losses can be reduced, energy efficiency can be improved, and operating costs can be reduced. In addition, reducing the number of adjustments and stresses on reactive power compensation equipment can also reduce equipment maintenance costs and replacement frequency.

[0093] By constructing the objective function, the voltage level can be stabilized and reactive power distribution can be rationalized when solving the optimal control strategy, which can improve the reliability of power supply and reduce power outages and faults caused by unstable voltage or insufficient reactive power. The weights and constraints of each function model proposed in the present invention can adapt to different operating scenarios and demand changes.

[0094] In addition to the optimization target, the objective function also includes node voltage limit constraints, reactive compensation equipment output constraints, and power balance constraints.

[0095] The node voltage limit constraint is:

[0096] V min ≤V i ≤V max ;

[0097] Among them, V min is the minimum node voltage limit, V max is the maximum limit value of the node voltage;

[0098] The output constraint of the reactive power compensation equipment is:

[0099]

[0100] Among them, Q C,minis the minimum output threshold of the reactive power compensation equipment, Q C,max It is the maximum output threshold of the reactive power compensation equipment.

[0101] The power balance constraint is:

[0102] P i =V i ∑ j∈N V j (G ij cosθ ij +B ij sinθ ij );

[0103] Q i =V i ∑ j∈N V j (G ij sinθ ij -B ij cosθ ij );

[0104] Among them, P i , Q i are the active injection power and reactive injection power of node i, respectively, ij is the phase angle difference between node i and node j.

[0105] The above constraints ensure the feasibility and safety of the optimization control strategy in practical applications and avoid control failure or system instability caused by violation of physical laws or equipment limitations.

[0106] For step S3, in a preferred embodiment, by constructing the objective function and solving it based on various constraints, the corresponding target node voltages and target reactive power compensation equipment outputs when the objective function value is minimum can be generated. The optimal solution not only meets the requirements of the optimization target, but also fully considers the constraints of actual operation, so it can significantly improve the operation efficiency and optimization effect of the distribution network.

[0107] In a preferred embodiment, based on the constraint conditions, the objective function is solved to generate each target node voltage and each target reactive power compensation device output corresponding to the minimum objective function value, including:

[0108] dividing the distribution network into a plurality of target areas;

[0109] The node voltage and reactive power compensation equipment output corresponding to each target area are used as decision variables;

[0110] Repeat the following decision variable determination operation until the current number of iterations is equal to the preset iteration number threshold, and output the target decision variables corresponding to each target area, and each output target decision variable satisfies the node voltage limit constraint, the reactive compensation equipment output constraint, and the power balance constraint:

[0111] Get the current iteration number;

[0112] When the current number of iterations is less than the iteration number threshold, for each target area, based on the candidate decision variables obtained last time and the target decision variables, the decision variables of the target function are locally solved to generate the candidate decision variables corresponding to the minimum value of the target function; wherein, initially, the decision variables of the target function are directly locally solved to generate the candidate decision variables corresponding to the minimum value of the target function;

[0113] For all target regions, the decision variables of the target function are globally solved according to the function values ​​of each decision variable and each candidate decision variable in the target function value, and each target decision variable corresponding to the minimum target function value is generated; wherein each target decision variable corresponds to a target region;

[0114] The current iteration number value is added to the preset increment to obtain the updated iteration number, and the updated iteration number is used as the iteration number when the decision variable determination operation is performed next time.

[0115] Among them, the decision variables corresponding to each target area can be regarded as particles of a group. When seeking local optimization and global optimization, particle optimization can be achieved through agent division and particle group construction.

[0116] Specifically, when performing local optimization, for each target area, based on the candidate decision variables and the target decision variables obtained last time, the decision variables of the objective function are locally solved to generate the candidate decision variables corresponding to the minimum value of the objective function, specifically:

[0117] For each particle, based on the particle historical position, particle historical velocity, population historical position and population historical function value obtained last time, an updated particle position of the particle is obtained; wherein, at the initial time, each particle of the population is initialized to obtain the particle historical position and particle historical velocity of each particle, as well as the population historical position and population historical function value of the population;

[0118] According to the updated particle position of the particle, a candidate particle corresponding to the minimum objective function value is generated.

[0119] When performing global optimization, for all target areas, according to the function values ​​of each decision variable and each candidate decision variable in the target function value, the decision variables of the target function are globally solved to generate the target decision variables corresponding to the minimum target function value. The specific process is as follows:

[0120] For each particle, obtaining an updated particle velocity of the particle based on the updated particle position;

[0121] Obtain a particle function value corresponding to each candidate particle in the objective function value;

[0122] For all particles, according to the updated particle position of each particle, the updated particle velocity of each particle and the particle function value of each candidate particle, the updated population history position and the updated population history function value are obtained;

[0123] Based on the updated historical positions of the population, the decision variables of the objective function are globally solved to generate several target particles corresponding to the minimum value of the objective function.

[0124] It is understandable that if Figure 2 As shown in the solution flow chart, the present invention can divide the distribution network into K control areas, each area as an intelligent agent. The basis for division includes: electrical connection relationship, geographical proximity and load characteristic similarity; and each intelligent agent is responsible for local optimization in its area. Dividing the distribution network into multiple control areas (intelligent agents can reduce the complexity of the problem and improve the calculation efficiency; it can also adapt to the distributed characteristics of the distribution network and enhance the scalability of the system; allowing different areas to adopt different optimization strategies, thereby improving flexibility.

[0125] Since each region is an intelligent agent, a particle swarm can be constructed for each intelligent agent k;

[0126] Then there is a particle group X k ={x k1 ,x k2 ,…,x kN}, where N is the number of particles, and each particle x ki represents a complete set of control variables, corresponding to the control strategy of a target area (i.e., intelligent agent), then:

[0127]

[0128] Among them, Q C,kj (j=1,2,…,M) represents the output of M reactive compensation devices in the intelligent agent k area;

[0129] Represents the voltage values ​​of P nodes in the intelligent agent k area.

[0130] For each agent k, particle x ki Represents a set of controllable variables, including the output Q of the reactive power compensation device C,k And the voltage optimization target value of the node When solving locally, each agent k independently performs the following steps: a) Initialize the particle swarm X k ; b) Calculate the fitness value of each particle (objective function F value); c) Update the individual optimal position p ki and the global optimal position g k ; d) Use the following formula to update the particle velocity and position; Repeat steps bd until the maximum number of iterations is reached or the convergence condition is met.

[0131] The particle update process is as follows:

[0132]

[0133] Among them, ω is the inertia weight, usually 0.7 to 0.9; c1 and c2 are acceleration constants, usually 2; r1 and r2 are random numbers between [0,1]; is the individual optimal position of particle i; is the global optimal position of agent k; is the attention weight, calculated by scaled dot-product attention:

[0134]

[0135] In the process of repeatedly executing the decision variable determination operation, the present invention first performs local optimization on each particle, and based on the updated particle position, updated particle velocity and particle function value of each particle, the optimal position and function value of the population can be updated, that is, the updated population historical position and the updated population historical function value are obtained as parameters for the next iterative solution.

[0136] Based on the updated historical position of the population, the decision variables of the objective function can be globally solved to generate several target particles corresponding to the minimum value of the objective function. If the current number of iterations is equal to the preset iteration number threshold, the solution in the target particles corresponding to each target area is output, that is, the target node voltage and the target reactive compensation equipment output corresponding to each target area.

[0137] Specifically, each agent performs collaborative optimization through ADMM (alternating direction multiplier method), and then:

[0138] a) Each agent k solves a local optimization problem:

[0139]

[0140] stx k ∈X k ;

[0141] b) Update the global variable z:

[0142]

[0143] c) Update the dual variable u:

[0144] u k =u k +x k -z

[0145] Repeat steps ac until convergence;

[0146] In the ADMM process, x k Represents the local variables of agent k (including ), z represents the global consistency variable, u k Represents the dual variable. The process of updating the global variable z is actually to coordinate the local optimization results of each intelligent agent to make them consistent.

[0147] After the algorithm converges, the optimal control strategy is output, including:

[0148]

[0149] The optimal output of each reactive compensation device can be extracted from it And the voltage optimization value of each key node

[0150] The present invention realizes the distributed collaborative optimization of large-scale distribution networks, which not only ensures the efficiency of local optimization, but also achieves global consistency and optimality. Compared with traditional centralized optimization, it has better scalability and computational efficiency, and is particularly suitable for handling large-scale and complex distribution network optimization problems.

[0151] The present invention divides the distribution network into multiple agents, and enables each agent to perform local optimization using Attention-PSO; coordinates the optimization results of each agent through the ADMM method, iterates the above process until convergence, and improves computational efficiency and scalability; the Attention mechanism enhances the adaptability and convergence speed of the algorithm, and ADMM coordination ensures global optimality; the entire process adapts to the distributed characteristics of the distribution network and is more in line with actual application requirements. Schematically, the present invention can more effectively handle large-scale and complex distribution network optimization problems, and is superior to traditional methods in both optimization effect and computational efficiency.

[0152] For step S4, in a preferred embodiment, the present invention is based on the optimal solution obtained, that is, each target node voltage and each target reactive compensation device output, and can control the nodes and reactive compensation devices in the distribution network separately based on the target node voltage and the target reactive compensation device output.

[0153] The present invention obtains the optimal solution by solving the objective function, which can accurately determine the voltage setting value of each target node and the output value of each target reactive compensation device, and can ensure that the distribution network operates in the optimal state, meeting the requirements of economy, voltage stability, equipment life, etc. Therefore, it can minimize network losses, reduce voltage deviations, and reduce the frequent adjustment of reactive compensation equipment, making the operation of the distribution network more efficient and reliable.

[0154] Since the control strategy is based on the optimal solution, it can adapt to the changes in demand of the distribution network under different operating conditions. For example, when the grid load changes, the objective function can be re-solved and the control strategy can be adjusted to adapt to the new operating conditions.

[0155] In a preferred embodiment, the present invention can be applied to a distribution network autonomous collaborative optimization control system based on digital twins, which includes a data perception layer 1, a digital twin layer 2, a collaborative optimization layer 3 and a control execution layer 4.

[0156] In schematic form, the collaborative optimization layer 3 can implement a multi-agent collaborative optimization method based on an improved spatiotemporal attention mechanism particle swarm algorithm (Attention-PSO) for distribution network control involving multiple coupled objectives such as loss reduction, load balancing, and improving voltage qualification rate, and the characteristics of the distribution units being dispersed and requiring distributed collaboration. That is, the objective function mentioned above is solved to generate the target node voltages and the outputs of the target reactive compensation devices corresponding to the minimum value of the objective function.

[0157] The control execution layer 4 can perform specific control actions based on the received target node voltages and target reactive compensation device outputs: issuing power adjustment instructions to the reactive compensation devices and updating the node voltage target values ​​to the distribution automation system.

[0158] The data perception layer 1 is the data source of the system, which is composed of distributed IoT sensors and smart meters, mainly including smart three-phase meters, micro-meteorological station sensors, distribution line status online monitoring terminals, etc. The above devices are connected to the data perception layer through various communication interfaces to collect multi-source heterogeneous status data of distribution network operation in real time, providing data support for upper-level analysis and optimization.

[0159] In the process of autonomous collaborative optimization and control of the distribution network, two types of data are mainly involved: 1) Distribution network operation monitoring data, including time series data such as power load, voltage and current, power factor, and data on influencing factors such as ambient temperature and humidity. These data are collected by smart meters and various sensors in the data perception layer, and after multi-scale adaptive coding compression, they are uploaded to the digital twin layer for analysis and visualization. 2) Distribution equipment ledger information, including static data such as nameplate parameters, geographic coordinates, and topological connection relationships of equipment such as transformers, feeders, and switches. These data serve as the basis of the physical information model of the distribution network, and are used to build an intelligent association model of all-factor information of the distribution network to provide data support for collaborative optimization.

[0160] In the process of data preprocessing, routine processing such as data cleaning, normalization, and feature selection is required. At the same time, in view of the periodic characteristics of distribution network operation, signal processing technologies such as wavelet transform and empirical mode decomposition are used to extract the multi-scale time-frequency characteristics of data, explore the inherent dynamic mode of data, and provide richer decision-making information for optimization control.

[0161] Considering the pressure that massive monitoring data brings to communication and storage, the present invention can also integrate a multi-scale adaptive coding module in the data perception layer. Suppose the original monitoring data is X = {x1, x2, ..., x N}, multi-scale adaptive coding is performed in the following steps:

[0162] First, perform wavelet packet decomposition of X at scale J to obtain the subband coefficients C at each scale. j , as shown in the following formula. Where WPD(·) represents the wavelet packet decomposition function.

[0163] C j =WPD(X,j),j=1,2,...,J;

[0164] The selection of J value needs to balance the data compression rate and reconstruction accuracy. The larger J is, the higher the compression rate is, but the reconstruction error also increases. Usually J=3-5 is taken based on experience. In this embodiment, J=3 is taken, and an ideal compression effect is obtained while ensuring the reconstruction accuracy.

[0165] Then, for each scale subband coefficient C j Perform adaptive quantization. Quantization threshold T j As shown in the following formula, λ is the threshold control factor, which can be set in the range of 0.5 to 0.8 according to the characteristics of the data to balance data redundancy and information fidelity. In this example, λ=0.75.

[0166] Then we have: T j =λ·max(|C j |);

[0167] Cj The absolute value is less than T j The coefficients are set to zero to obtain the quantized coding coefficients Q j Adaptive quantization can significantly reduce data redundancy. Finally, the quantized coding coefficient Q j Run-length coding and Huffman coding are performed as shown in the following formula to obtain the compressed coded data stream E:

[0168] E=Huff(RLE(Q j ));

[0169] Among them, RLE(·) and Huff(·) represent run-length encoding and Huffman coding respectively. Run-length encoding improves the compression rate by compactly representing repeated coefficients, while Huffman coding reduces data entropy through variable-length coding. The codeword lengths of the two codes are 8 bits and 12 bits respectively, achieving a balance between encoding and decoding complexity and compression efficiency.

[0170] The digital twin layer 2 is responsible for building a virtual model of the distribution network, which is a real-time mapping of the physical distribution network in the information space. It is based on the physical information model (PIM) of the distribution network, integrates all-dimensional data such as parameters, topological connections, and real-time status of various distribution equipment, keeps synchronization and interacts with the physical world, and provides digital support for applications such as perception, analysis, prediction, and control of the distribution network.

[0171] In order to enhance the digital twin model's ability to express the multi-attribute information of power distribution equipment, this layer designs an intelligent association model for all-factor information of equipment. First, the knowledge graph technology is used to construct a semantic library of equipment attributes and relationships, that is, KG = (V, R), where V is the equipment entity set and R is the relationship predicate set between entities. The triplet in the graph is in the form of (head entity, relationship, tail entity), such as (transformer A, connection, busbar B), etc. The knowledge graph is stored in the form of RDF triples, which can easily express the semantic connection between devices.

[0172] Based on the knowledge graph KG, we further learn each device entity v i The corresponding attention embedding vector h i , as shown in formula (4). Attention() represents the attention mechanism function, which is used to learn the importance weight of the entity in the KG. Attention embedding aggregates the structural and semantic information of the entity in the graph and can be used as a low-dimensional semantic representation of the entity.

[0173] h i =Attention(v i ,KG) (4)

[0174] Finally, the multivariate attributes of the distribution equipment are integrated into the attention embedding matrix H = {h1,h2,...,h M}, which can be used for efficient association analysis and semantic retrieval. Taking the distribution network of this embodiment as an example, a knowledge graph containing 326 device entities and 1107 associated edges is constructed, and the attention embedding dimension is taken as 64, which reflects rich and accurate semantic association information.

[0175] In addition, the present invention can also use a three-dimensional visualization engine to fuse and render the physical model of the distribution network and geographic information to generate a realistic digital twin scene of the distribution network. Visual modeling adopts the CIM / E standard, the three-dimensional engine uses Unity3D, and the geographic information comes from the AutoNavi Map API. The twin model is synchronized with the operating status of the physical distribution network in real time through data drive, providing a platform for visual monitoring and interactive analysis of the distribution network operation.

[0176] like Figure 3 As shown, based on the above-mentioned various embodiments of the optimization control method applicable to the distribution network, the present invention provides a corresponding device embodiment;

[0177] An embodiment of the present invention provides an optimization control device suitable for a distribution network, comprising: a function model construction module, an objective function construction module, an objective function solving module and a distribution network control module;

[0178] The function model building module is used to build a first function model with the goal of minimizing network loss according to the voltage data of each node in the distribution network;

[0179] According to the voltage of each node in the distribution network and the corresponding voltage threshold, a second function model with the goal of minimizing voltage deviation is constructed;

[0180] According to the output values ​​of each reactive compensation device in the distribution network at different times, a third function model is constructed with the goal of minimizing the output change of the reactive compensation device;

[0181] The objective function construction module is used to construct a corresponding objective function according to the first function model, the second function model and the third function model; wherein the objective function includes the following constraints: node voltage limit constraint and reactive compensation equipment output constraint;

[0182] The objective function solving module is used to solve the objective function based on the constraint conditions to generate each target node voltage and each target reactive power compensation device output corresponding to the minimum objective function value;

[0183] The distribution network control module is used to control the nodes and reactive compensation equipment in the distribution network respectively according to the target node voltages and the target reactive compensation equipment outputs.

[0184] It should be noted that the device embodiments described above are merely schematic, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without paying creative labor.

[0185] Those skilled in the art can clearly understand that, for the sake of convenience and simplicity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0186] Based on the above-mentioned various embodiments of the optimization control method applicable to the distribution network, the present invention provides corresponding embodiments of terminal equipment items.

[0187] An embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, an optimization control method applicable to a distribution network as described in any method embodiment of the present invention is implemented.

[0188] The terminal device may be a computing terminal device such as a desktop computer, a notebook, a palm computer, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0189] The processor may be a central processing unit (CPU), or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0190] The memory can be used to store the computer program, and the processor realizes various functions of the terminal device by running or executing the computer program stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile phone, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Med i aCard, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device or other volatile solid-state storage device.

[0191] Based on the above-mentioned various embodiments of the optimization control method applicable to the distribution network, the present invention provides corresponding storage medium item embodiments.

[0192] An embodiment of the present invention provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute an optimization control method applicable to a distribution network as described in any method embodiment of the present invention.

[0193] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0194] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. An optimization control method applicable to a distribution network, characterized in that: include: According to the voltage data of each node in the distribution network, a first function model with the goal of minimizing network loss is constructed; According to the voltage of each node in the distribution network and the corresponding voltage threshold, a second function model with the goal of minimizing voltage deviation is constructed; According to the output values ​​of each reactive compensation device in the distribution network at different times, a third function model is constructed with the goal of minimizing the output change of the reactive compensation device; According to the first function model, the second function model and the third function model, a corresponding objective function is constructed; wherein the objective function includes the following constraints: node voltage limit constraint and reactive power compensation device output constraint; Based on the constraint conditions, the objective function is solved to generate each target node voltage and each target reactive power compensation device output corresponding to the minimum value of the objective function; According to each target node voltage and each target reactive compensation device output, the nodes and reactive compensation devices in the distribution network are controlled separately.

2. An optimization control method applicable to a distribution network as claimed in claim 1, characterized in that: The first function model with the goal of minimizing network loss is constructed according to the voltage data of each node in the distribution network, including: Obtaining voltage data of each node in the distribution network; wherein the voltage data includes: the voltage of each node, the conductance value between nodes, the susceptance value between nodes, and the phase angle between nodes; According to the voltage of each node, the phase angle of each node, the conductance value between nodes and the susceptance value between nodes, a first function model with the goal of minimizing network loss is constructed.

3. The optimization control method applicable to a distribution network as claimed in claim 2, characterized in that: The objective function includes: min F=w1·f1+w2·f2+w3·f3; Among them, F is the function value corresponding to the objective function, w1, w2, and w3 are different weight coefficients, f1 is the first function model with the goal of minimizing network loss, f2 is the second function model with the goal of minimizing voltage deviation, and f3 is the third function model with the goal of minimizing the output change of reactive compensation equipment. V i 、V j are the voltages at nodes i and j, G ij , B ij are the conductance and susceptance of nodes i and j respectively, θ i ,θ j are the phase angles of node i and node j respectively, N is the number of nodes in the distribution network, V ref is the voltage threshold corresponding to the node, represents the output of the kth reactive power compensation device at time t, and M is the number of reactive power compensation devices in the distribution network.

4. The optimization control method applicable to a distribution network as claimed in claim 3, characterized in that: The objective function also includes: a power balance constraint; The node voltage limit constraint is: In min ≤V i ≤V max ; Among them, V min is the minimum node voltage limit, V max is the maximum limit value of the node voltage; The output constraint of the reactive power compensation equipment is: Among them, Q C,min is the minimum output threshold of the reactive power compensation equipment, Q C,max It is the maximum output threshold of the reactive power compensation equipment; The power balance constraint is: P i =V i ∑ j∈N V j (G ij cosθ ij +B ij sinθ ij ); Q i =V i ∑ j∈N V j (G ij sinθ ij -B ij cosθ ij ); Among them, P i , Q i are the active injection power and reactive injection power of node i, respectively, ij is the phase angle difference between node i and node j.

5. The optimization control method applicable to a distribution network as claimed in claim 4, characterized in that: The objective function is solved based on the constraint condition to generate each target node voltage and each target reactive power compensation device output corresponding to the minimum objective function value, including: dividing the distribution network into a plurality of target areas; The node voltage and reactive power compensation equipment output corresponding to each target area are used as decision variables; Repeat the following decision variable determination operation until the current number of iterations is equal to the preset iteration number threshold, and output the target decision variables corresponding to each target area, and each output target decision variable satisfies the node voltage limit constraint, the reactive compensation equipment output constraint, and the power balance constraint: Get the current iteration number; When the current number of iterations is less than the iteration number threshold, for each target area, based on the candidate decision variables obtained last time and the target decision variables, the decision variables of the target function are locally solved to generate the candidate decision variables corresponding to the minimum value of the target function; wherein, initially, the decision variables of the target function are directly locally solved to generate the candidate decision variables corresponding to the minimum value of the target function; For all target regions, the decision variables of the target function are globally solved according to the function values ​​of each decision variable and each candidate decision variable in the target function value, and each target decision variable corresponding to the minimum target function value is generated; wherein each target decision variable corresponds to a target region; The current iteration number value is added to the preset increment to obtain the updated iteration number, and the updated iteration number is used as the iteration number when the decision variable determination operation is performed next time.

6. The optimization control method applicable to a distribution network as claimed in claim 5, characterized in that: The decision variable corresponding to each target area is a swarm of particles; For each target area, based on the candidate decision variables and the target decision variables obtained last time, the decision variables of the target function are locally solved to generate the candidate decision variables corresponding to the minimum value of the target function, including: For each particle, based on the particle historical position, particle historical velocity, population historical position and population historical function value obtained last time, an updated particle position of the particle is obtained; wherein, at the initial time, each particle of the population is initialized to obtain the particle historical position and particle historical velocity of each particle, as well as the population historical position and population historical function value of the population; According to the updated particle position of the particle, a candidate particle corresponding to the minimum objective function value is generated.

7. The optimization control method applicable to a distribution network according to claim 6, characterized in that: For all target areas, according to each decision variable and the function value of each candidate decision variable in the target function value, the decision variables of the target function are globally solved to generate each target decision variable corresponding to the minimum target function value, including: For each particle, obtaining an updated particle velocity of the particle based on the updated particle position; Obtain a particle function value corresponding to each candidate particle in the objective function value; For all particles, according to the updated particle position of each particle, the updated particle velocity of each particle and the particle function value of each candidate particle, the updated population history position and the updated population history function value are obtained; Based on the updated historical positions of the population, the decision variables of the objective function are globally solved to generate several target particles corresponding to the minimum value of the objective function.

8. An optimization control device suitable for a distribution network, characterized in that: include: Function model building module, objective function building module, objective function solving module and distribution network control module; The function model building module is used to build a first function model with the goal of minimizing network loss according to the voltage data of each node in the distribution network; According to the voltage of each node in the distribution network and the corresponding voltage threshold, a second function model with the goal of minimizing voltage deviation is constructed; According to the output values ​​of each reactive compensation device in the distribution network at different times, a third function model is constructed with the goal of minimizing the output change of the reactive compensation device; The objective function construction module is used to construct a corresponding objective function according to the first function model, the second function model and the third function model; wherein the objective function includes the following constraints: node voltage limit constraint and reactive compensation equipment output constraint; The objective function solving module is used to solve the objective function based on the constraint conditions to generate each target node voltage and each target reactive power compensation device output corresponding to the minimum objective function value; The distribution network control module is used to control the nodes and reactive compensation equipment in the distribution network respectively according to the target node voltages and the target reactive compensation equipment outputs.

9. A terminal device, characterized in that: It comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, an optimization control method applicable to a distribution network as described in any one of claims 1 to 7 is implemented.

10. A storage medium, characterized in that: The storage medium includes a stored computer program, wherein when the computer program is executed, the device where the storage medium is located is controlled to execute an optimization control method applicable to a distribution network as described in any one of claims 1 to 7.

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