Power distribution scheduling strategy method and system based on artificial bee colony algorithm

Abstract

The invention relates to a power distribution scheduling strategy method and system based on an artificial bee colony algorithm. The method comprises the following specific steps: S1, demand power distribution node active power prediction; s2, active power output and fluctuation prediction of distributed photovoltaic energy nodes; s3, constructing a power grid topology model; and S4, distributed photovoltaic energy node artificial bee colony power distribution scheduling. The invention provides a power distribution scheduling strategy method based on an artificial bee colony algorithm, and the method comprises the steps: setting a bee colony with a proper scale, carrying out the iterative search, randomly generating the active power of a centralized power generation end and the reactive power value in a power grid, enabling the fitness value of the bee colony to be the highest through the optimization of the active power and the reactive power, and achieving the optimal power distribution. Therefore, an optimal distributed photovoltaic energy node is selected, intelligent regulation and control of a photovoltaic load are realized, a matching relation between photovoltaic power generation and the load is dynamically adjusted, regional autonomous consumption is realized, the utilization efficiency of photovoltaic power generation is improved, the overall circuit loss and voltage fluctuation are remarkably reduced, and the stability and efficiency of a power system are improved.

Description

Technical Field

[0001] The present invention belongs to the field of power distribution dispatching strategies, and specifically relates to a power distribution dispatching strategy method and system based on an artificial bee colony algorithm. Background Art

[0002] With the continuous growth of electricity demand in modern society and the diversified development of energy structure, the application of distributed energy, especially photovoltaic power generation, in power systems is becoming more and more extensive. Distributed photovoltaic power generation, as a clean and renewable energy, has the advantages of flexible installation, green environmental protection, and reduced transmission loss, and has received extensive attention and application. However, the intermittent and volatile nature of distributed photovoltaic power generation has brought new challenges to the stability and scheduling of the power grid.

[0003] In the traditional power system, the distribution network mainly relies on centralized power plants to provide a stable power supply. Although this centralized power generation method can ensure the continuous supply of electricity, it has problems such as high risk of single point failure and large transmission loss. Centralized power plants are usually located far away from the load center, and electricity needs to be transmitted over long distances, resulting in increased losses during the transmission process. In addition, centralized power generation relies on fossil fuels and produces a large amount of carbon dioxide emissions, which is not conducive to environmental protection.

[0004] The introduction of distributed photovoltaic power generation can reduce the pressure of centralized power generation, reduce transmission losses, and reduce environmental pollution. However, distributed photovoltaic power generation is greatly affected by factors such as weather and sunshine, and its output power is highly intermittent and volatile, which brings new challenges to the stability and scheduling of the power grid.

[0005] Existing power dispatch methods usually rely on prediction and dispatch models for centralized power generation, which lack effective response strategies for the volatility of distributed photovoltaic power generation. Specifically, traditional prediction models are often unable to accurately capture the volatility of distributed photovoltaic power generation, resulting in large prediction errors and affecting the accuracy of dispatch decisions. These models are usually based on historical data and statistical methods, and cannot fully consider dynamic factors such as weather changes and sunshine intensity. They also have a slow response speed to real-time changes, making it difficult to adjust distribution strategies in a timely manner to cope with the rapid changes in distributed photovoltaic power generation. Traditional dispatch systems usually adopt centralized control methods, and the information transmission and decision-making process are cumbersome, and cannot quickly respond to the dynamic changes of distributed photovoltaic power generation;

[0006] Therefore, there is an urgent need for an optimization method that can comprehensively utilize the real-time data of distributed photovoltaic power generation to improve the stability and efficiency of the power system. Summary of the invention

[0007] In view of the above problems, the present invention proposes a distribution scheduling strategy method and system based on artificial bee colony algorithm. By setting a bee colony of corresponding size and performing iterative search, the active power of distributed photovoltaic power generation nodes and other distributed energy nodes and the reactive power value in the power grid are randomly generated, so that the bee colony fitness value is the highest, thereby selecting the optimal distributed energy node, reducing the overall circuit loss and voltage jump. This method effectively utilizes the real-time data of distributed photovoltaic power generation and improves the stability and efficiency of the power system.

[0008] To achieve the above object, the technical solution adopted by the present invention is:

[0009] A distribution scheduling strategy method based on artificial bee colony algorithm, the specific steps are as follows:

[0010] S1: Active power prediction of demand distribution nodes;

[0011] To predict the active power value and trend of the demand distribution node, it is necessary to consider the timely transient distributed energy access, and to predict and feedback the active power value and trend of the demand distribution node, that is, to predict the active power value and trend of the demand distribution node within minutes, where the active power trend of the demand distribution node is the active power of the demand distribution node 30 seconds ago minus the active power of the current demand distribution node. Separate models are set for different regions, and normalized general characteristics are not considered;

[0012] The smart meter is used to read the active power of the real-time power consumption data of the demand distribution node every second in kilowatt-hours and record it as W. p , read the weather conditions of the nearby weather station, including the real-time temperature T, humidity S and wind speed F of the area, and use one-hot encoding to record the time scale at this time as a D vector, which includes hours, minutes, day of the week and whether it is a holiday. The scale value that the specific minute is close to is the scale value as the mark value, the day of the week is marked as Monday to Sunday, and whether it is a holiday is marked as yes or no;

[0013] S2: Active power output and volatility prediction of distributed photovoltaic energy nodes;

[0014] Step S2 predicts the active power output and volatility of distributed photovoltaic energy nodes;

[0015] Firstly, a distributed solar photovoltaic prediction time series model near the demand distribution node is established to predict the distributed solar photovoltaic active power change trend value and the specific solar photovoltaic active power value. The distributed solar photovoltaic active power change trend value is the difference between the real-time solar photovoltaic active power and the distributed solar photovoltaic active power 30 seconds ago.

[0016] Among them, the distributed solar photovoltaic prediction timing model is expressed as follows:

[0017] Solar tend ,Solar energy =Rnn(Soir,T,S,F,G,Clc)

[0018] Among them, Solar tend Indicates the trend value of solar photovoltaic active power change. energy represents the energy of solar energy in the next second, Rnn represents recurrent neural network, Soir represents real-time sunshine intensity, Clc represents real-time cloud cover, G represents solar radiation intensity, Clc represents real-time cloud cover;

[0019] S3: Construction of power grid topology model;

[0020] Determine the location of the demand distribution node loc x , and confirm the location of each distributed energy source loc f , and then the impedance R of the power grid is approximately calculated by the impedance calculation formula i :

[0021] R i =R line *L+j*X line *L

[0022] Among them, R i is the grid impedance, R line is the line resistance, j is the imaginary unit, X line is the line reactance, L is the line length from the distributed energy node to the demand distribution node;

[0023] S4: artificial swarm power distribution scheduling of distributed energy nodes;

[0024] An artificial bee colony algorithm based on which distributed energy nodes can be dispatched is proposed.

[0025] Furthermore, the active power prediction process of the demand distribution node in step S1 includes enhancing the dynamic load factor formula, integrating the time series characteristics, and considering the active power usage fluctuation of the demand distribution node in a short period of time:

[0026]

[0027] Among them, DIF enhaced To enhance the dynamic load factor, p avg Ask the average active power within 30 seconds in real time, p maxis the maximum active power within 30 seconds in real time, t is the time of the day in real time, in hours, T is the total number of hours in a day, which is 24, ε and δ are weight factors, G is the solar radiation intensity, P pv is the output power of the photovoltaic power generation system, ΔP pv is the change in the output power of the photovoltaic power generation system (i.e., the current P pv Subtract the P from 30 seconds ago pv ), γ, ρ and θ are weight factors of photovoltaic power generation;

[0028] Furthermore, the active power prediction process of the demand distribution node in step S1 includes characterizing the instantaneous efficiency of the transformer. When the ratio of the instantaneous output power to the input power of the transformer is low, although there is no direct active power demand at the demand distribution node, the overall energy consumption of the power grid will increase, thereby requiring more active power:

[0029]

[0030] Among them, Tran effic is the instantaneous efficiency of the transformer, p in (t),p out (t) are the input power and output power at time t, p' in (t) and p' out (t) is the instantaneous change rate of input power and output power, where the change rate sampling time interval is 30 seconds.

[0031] Furthermore, in the step S1, the Transformer network is used to predict the real-time active power value and trend of the demand distribution node in the next second, wherein the Transformer network is represented as:

[0032] Acticep tend , Acticep z =Transformer(W p ,T,S,F,D,DIF enhaced ,P pv ,ΔP pv ,Tran effic )

[0033] Among them, Acticep tend Indicates active power trend, Acticep z Indicates the active power value of the demand distribution node in the next second. Transformer is a sequential network architecture. pv is the output power of the photovoltaic power generation system, ΔP pv The change in output power of the photovoltaic power generation system;

[0034] Furthermore, in the step S2, a wind energy prediction time series model near the demand distribution node is established in the process of predicting the active power output and volatility of the distributed energy nodes to predict the active power change trend value of the wind energy and the specific active power value of the wind energy, wherein the active power change trend value of the wind energy is specifically expressed as the difference between the active power value of the wind energy and the active power of the wind energy 30 seconds ago;

[0035] Wind tend ,Wind energy =Rnn(F,T,Ap,Wd)

[0036] Among them, Wind tend is the trend value of wind energy active power change, Wind energy is the active power value of wind energy in the next second, Ap is the air pressure, and Wd is the wind direction.

[0037] Furthermore, the specific steps of the S4 distributed energy node artificial bee colony power distribution scheduling are as follows:

[0038] Step A1: Distributed energy fluctuation threshold processing;

[0039] In this step, a distributed energy fluctuation threshold function is first proposed to consider all distributed energy near the demand distribution node. When the volatility is too high, the scheduling is directly abandoned:

[0040]

[0041] Among them, C i is the judgment value of the i-th distributed energy node. When the value is 0, its supply right is cancelled, and when it is 1, its supply is considered. Cv i is the active power change trend value of the i-th distributed energy node, Cv max is the upper limit of the active power change trend of the distributed energy node;

[0042] Step A2: Artificial bee colony algorithm power optimization;

[0043] In this step, we first determine an artificial bee colony algorithm power optimization formulation and propose a dynamic matching response function:

[0044]

[0045] Among them, Fle i is the dynamic demand matching value of the ith distributed energy node, Ct i is the real-time active power of the i-th distributed energy node;

[0046] Based on the dynamic matching response function above, the distributed energy nodes with negative dynamic demand matching values ​​are directly used as backup supply energy, and the distributed energy nodes with positive dynamic demand matching values ​​close to 0 are considered as energy response nodes;

[0047] Step A3: Artificial bee colony algorithm reactive power setting;

[0048] In the above step A2, the specific supply point of the distributed energy node is determined, and the active power of the centralized power generation end and the reactive power in the power grid are optimized by using the artificial bee colony algorithm, thereby optimizing the voltage and reducing the overall circuit power loss;

[0049] In the artificial bee colony algorithm, the solution includes the active power of the centralized power generation terminal and the reactive power value in the power grid:

[0050] x=(p centeral ,Q grid )

[0051] Where x is the solution of reactive power optimization of artificial bee colony algorithm, p centeral is the active power of the centralized power generation terminal, Q grid is the reactive power value in the power grid;

[0052] According to the solution of reactive power optimization of artificial bee colony algorithm, the voltage of demand distribution node is approximately calculated:

[0053]

[0054] Among them, V t is the adjusted voltage of the demand distribution node, I is the demand distribution current;

[0055] Based on this, in this step, a fitness function is proposed to give the target value of the artificial bee colony algorithm:

[0056]

[0057] Among them, Fit is the fitness of the bee colony, R i is the grid impedance, I is the current value, V t is the voltage adjusted for the demand distribution node, V ideal The ideal voltage of the demand distribution node;

[0058] Step A4: The artificial bee colony algorithm finally finds the optimal solution.

[0059] A distribution dispatching strategy system based on artificial bee colony algorithm, including the following modules:

[0060] Data acquisition module: The data acquisition module is used to install sensors in the distributed photovoltaic power generation system to collect the output power of the photovoltaic power generation system in real time, including the current, voltage and power data of the photovoltaic panels. At the same time, the real-time weather data, including temperature, humidity, wind speed and solar radiation intensity, are obtained through the meteorological station. These data are used to analyze and predict the output of photovoltaic power generation and are connected with other modules to ensure the accuracy and real-time nature of the data.

[0061] Data preprocessing module: The data preprocessing module is used to process the collected photovoltaic power output power data and weather data, normalize the power data, calculate the power change trend, denoise, standardize and extract features of the weather data, generate environmental feature data for model training, and ensure the consistency and comparability of the data for subsequent model use;

[0062] Prediction model module: The prediction model module includes enhanced dynamic load factor calculation and Transformer prediction model. By calculating the enhanced dynamic load factor, the real-time fluctuation of photovoltaic power generation, weather factors and changes in power demand are comprehensively considered to generate features for load prediction. The Transformer network model is used to make real-time predictions on the active power value and trend of the demand distribution node. The input data includes pre-processed power data, environmental data and enhanced dynamic load factor.

[0063] Grid topology module: The grid topology module is used to determine the locations of demand distribution nodes and distributed photovoltaic power generation nodes, as well as the locations of other distributed energy nodes, to establish a grid topology model, and to accurately simulate the operation status of the grid by comprehensively considering the impact of photovoltaic power generation nodes and other distributed energy nodes through impedance calculation;

[0064] Distribution scheduling module: The distribution scheduling module is used to evaluate the power fluctuation of distributed photovoltaic power generation nodes. When the fluctuation is too large, its supply qualification is cancelled, and the artificial bee colony algorithm is used to optimize the power. The optimal distributed energy node is determined by dynamically matching the response function to improve the efficiency of the power system. At the same time, the reactive power in the power grid is optimized, and the active power of the centralized power generation end and the distributed photovoltaic power generation node is combined to reduce the power grid loss and voltage fluctuation.

[0065] Feedback and optimization module: The feedback and optimization module will feed back the prediction results and scheduling plans to the control system in real time, dynamically adjust the power distribution strategy, ensure the stability and reliability of power supply, and continuously optimize the artificial bee colony algorithm and scheduling strategy based on feedback data to improve the overall efficiency and stability of the system and ensure the optimized operation of the power system.

[0066] Beneficial effects:

[0067] (1) This solution can more accurately capture the volatility of photovoltaic power generation by real-time collection and processing of the output power and related environmental data of distributed photovoltaic power generation systems, enhance the application of dynamic load factors and Transformer network models, and significantly improve the prediction accuracy of the active power value and trend of demand distribution nodes. The artificial bee colony algorithm is used for distribution scheduling, which can quickly respond to real-time changes in distributed photovoltaic power generation, dynamically adjust the distribution strategy, and ensure the stability and continuity of power supply.

[0068] (2) This solution effectively improves the overall scheduling optimization effect by comprehensively considering the output data of different types of distributed energy and using multimodal data fusion technology. The application of artificial bee colony algorithm enables the system to find the optimal distributed energy node in a dynamic environment, reduce the overall loss and voltage fluctuation of the power grid, and improve the operation efficiency of the power grid. The introduction of distributed photovoltaic power generation system reduces the long-distance transmission loss of electricity and further improves energy utilization.

[0069] (3) This solution reduces power waste and equipment loss caused by improper scheduling through accurate load forecasting and optimized distribution scheduling strategies. The effective use of distributed photovoltaic power generation systems reduces dependence on centralized power generation, reduces power generation and transmission costs, and thus reduces the overall operating cost of the power system. This not only improves economic benefits, but also conforms to the development concept of green environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 It is the overall flow chart of the algorithm; Figure 2 This is the algorithm power optimization flow chart of this method. DETAILED DESCRIPTION

[0071] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments:

[0072] The present invention proposes a flow chart such as Figure 1 The algorithm power optimization flow chart is as follows Figure 2 A distribution scheduling strategy method and system based on artificial bee colony algorithm is shown, and the specific implementation method is as follows;

[0073] The specific implementation method is as follows;

[0074] S1: Active power prediction of demand distribution nodes;

[0075] In this step, the active power value and its trend of the demand distribution node are predicted, but the difference is that this application needs to consider the timely and transient distributed photovoltaic energy access, so this application needs to be able to predict and feedback the active power value and its trend of the demand distribution node in real time and quickly, that is, to predict the active power value and its trend of the demand distribution node within minutes, where the active power trend of the demand distribution node is the active power of the demand distribution node 30 seconds ago minus the active power of the current demand distribution node. Therefore, this application sets up separate models for different areas for consideration, and does not consider normalized general characteristics, such as the population size of the area, which will not affect the demand distribution area within minutes;

[0076] Therefore, based on the above application, it is proposed to use smart meters to read the active power of the real-time power consumption data of the demand distribution node every second, in kilowatt-hours, and record it as W p , read the weather conditions of the nearby meteorological station, including the real-time temperature T, humidity S, wind speed value F and solar radiation intensity G in the area, and record the output power of the nearby distributed photovoltaic power generation system in kilowatt-hours, which is recorded as P pv In addition, the time scale at this time is recorded as a D vector using one-hot encoding. The vector includes hours, minutes, days of the week, and whether it is a holiday. Specifically, the hours are marked as 0-23, and the minutes are 00, 15, 30, and 45. The scale value that the specific minute is close to is used as the mark value. The days of the week are marked as Monday to Sunday, and whether it is a holiday is marked as yes or no.

[0077] Calculate the output power trend of the photovoltaic power generation system, that is, P 30 seconds ago pv Subtract the current P pv The value of .

[0078] Furthermore, this application proposes an enhanced dynamic load factor formula that integrates time series characteristics and considers the fluctuation of active power usage of demand distribution nodes in a short period of time:

[0079]

[0080] Among them, DIF enhaced To enhance the dynamic load factor, p avg Ask the average active power within 30 seconds in real time, p max is the maximum active power within 30 seconds in real time, t is the time of the day in real time, in hours, T is the total number of hours in a day, which is 24, ε and δ are weight factors, G is the solar radiation intensity, P pv is the output power of the photovoltaic power generation system, ΔP pv is the change in the output power of the photovoltaic power generation system (i.e., the current Ppv Subtract the P from 30 seconds ago pv ), γ, ρ and θ are weight factors of photovoltaic power generation;

[0081] Furthermore, for example, through statistical analysis, ε and δ in a residential area, the active power at each time node every day in a certain summer is normalized, and linear regression is performed on all active power values, and it is obtained that ε is 0.3 and δ is 0.5, that is, the overall time is at noon and evening, and the active power demand is higher at this time, which amplifies the active power fluctuation value of the demand distribution node.

[0082] Furthermore, this application characterizes the instantaneous efficiency of the transformer. When the ratio of the instantaneous output power to the input power of the transformer is low, although there is no direct active power demand at the demand distribution node, the overall energy consumption of the power grid will increase, and more active power will be required:

[0083]

[0084] Among them, Tran effic is the instantaneous efficiency of the transformer, p in (t),p out (t) are the input power and output power at time t, p' in (t) and p' out (r) is the instantaneous rate of change of input power and output power, where the sampling interval of the rate of change is 30 seconds;

[0085] Based on the above, this application uses the Transformer network to predict the real-time active power value and its trend of the demand distribution node in the next second, where the Transformer network is expressed as:

[0086] Acticep tend , Acticep z =Transformer(W p ,T,S,F,D,DIF enhaced ,P pv ,ΔP pv ,Tran effic )

[0087] Among them, Acticep tend Indicates active power trend, Acticep z Indicates the active power value of the demand distribution node in the next second. Transformer is a sequential network architecture. pv is the output power of the photovoltaic power generation system, ΔP pv The change in output power of the photovoltaic power generation system;

[0088] S2: Active power output and volatility prediction of distributed photovoltaic energy nodes;

[0089] In step S1, this application predicts the active power and its trend of the demand distribution node. Further, in this step, it is necessary to predict the active power and volatility of the distributed photovoltaic power generation nodes and other distributed energy nodes;

[0090] There are usually many distributed energy nodes near the demand distribution nodes, including solar photovoltaic power generation, wind power generation, small-scale hydropower generation, etc. Among them, solar photovoltaic power generation has great intermittency and instability due to changes in sunshine intensity and weather, while changes in wind speed in wind power generation will also cause large fluctuations in its active power provision capacity.

[0091] Based on the above, in this step, a distributed solar photovoltaic prediction timing model near the demand distribution node is first established to predict the distributed solar photovoltaic active power change trend value and the specific solar photovoltaic active power value, where the distributed solar photovoltaic active power change trend value is the difference between the real-time solar photovoltaic active power and the distributed solar photovoltaic active power 30 seconds ago;

[0092] Among them, the distributed solar photovoltaic prediction timing model is expressed as follows:

[0093] Solar tend ,Solar energy =Rnn(Soir,T,S,F,G,Clc)

[0094] Among them, Solar tend Indicates the trend value of solar photovoltaic active power change. energy represents the energy of solar energy in the next second, Rnn represents the recurrent neural network, Soir represents the real-time sunshine intensity, Clc represents the real-time cloud cover, and G represents the solar radiation intensity;

[0095] Furthermore, a wind energy prediction timing model near the demand distribution node is established to predict the active power change trend value of wind energy and the specific active power value of wind energy, wherein the active power change trend value of wind energy can be specifically expressed as the difference between the wind energy active power value and the wind energy active power 30 seconds ago.

[0096] Wind tend ,Wind energy =Rnn(F,T,Ap,Wd)

[0097] Among them, Wind tend is the trend value of wind energy active power change, Wind energyis the active power value of wind energy in the next second, Ap is the air pressure, and Wd is the wind direction;

[0098] S3: Construction of power grid topology model;

[0099] Determine the location of the demand distribution node loc x , and confirm the location of each distributed photovoltaic power generation node and other distributed energy nodes f , and then the impedance R of the power grid is approximately calculated by the impedance calculation formula i :

[0100] R i =R line *L+j*X line *L

[0101] Among them, R i is the grid impedance, R line is the line resistance, j is the imaginary unit, X line is the line reactance, L is the line length from the distributed energy node to the demand distribution node;

[0102] S4: artificial swarm power distribution scheduling of distributed photovoltaic energy nodes;

[0103] In step S1, this application predicts the active power value and its trend of the demand distribution node, and in step S2, completes the active power change trend value and specific active power value generated by the distributed photovoltaic power generation node and other distributed energy nodes. Based on this, this step proposes to perform power dispatch of distributed energy nodes based on the artificial bee colony algorithm;

[0104] Wherein, step S4 comprises the following sub-steps:

[0105] Step A1: Distributed energy fluctuation threshold processing;

[0106] In this step, a distributed energy fluctuation threshold function is first proposed to consider all distributed photovoltaic power generation nodes and other distributed energy nodes near the demand distribution node. When the volatility is too high, the scheduling is directly abandoned:

[0107]

[0108] Among them, C i is the judgment value of the i-th distributed energy node. When the value is 0, its supply right is cancelled, and when it is 1, its supply is considered. Cv i is the active power change trend value of the i-th distributed energy node, Cv max It is the upper limit of the active power change trend of the distributed energy node.

[0109] Step A2: Artificial bee colony algorithm power optimization

[0110] In this step, we first determine an artificial bee colony algorithm power optimization formulation and propose a dynamic matching response function:

[0111]

[0112] Among them, Fle i is the dynamic demand matching value of the ith distributed energy node, Ct i is the real-time active power of the i-th distributed energy node;

[0113] Based on the dynamic matching response function above, the distributed energy nodes with negative dynamic demand matching values ​​are directly used as backup energy supply nodes, and the distributed energy nodes with positive dynamic demand matching values ​​close to 0 are considered as energy response nodes.

[0114] Step A3: Artificial bee colony algorithm reactive power setting

[0115] In the above step A2, this application determines the specific supply point of the distributed energy node. Based on this, this application uses an artificial bee colony algorithm to optimize the active power of the centralized power generation end and the reactive power in the power grid, optimize the voltage and reduce the overall circuit power loss;

[0116] In the artificial bee colony algorithm, the solution includes the active power of the centralized power generation terminal and the reactive power value in the power grid:

[0117] x=(p centeral ,Q grid )

[0118] Where x is the solution of reactive power optimization of artificial bee colony algorithm, p centeral is the active power of the centralized power generation terminal, Q grid is the reactive power value in the power grid;

[0119] Furthermore, the voltage of the demand distribution node is approximately calculated based on the solution of reactive power optimization of the artificial bee colony algorithm:

[0120]

[0121] Among them, V t is the adjusted voltage of the demand distribution node, I is the demand distribution current;

[0122] Based on this, in this step, a fitness function is proposed to give the target value of the artificial bee colony algorithm:

[0123]

[0124] Among them, Fit is the fitness of the bee colony, R i is the grid impedance, I is the current value, V t is the voltage adjusted for the demand distribution node, V ideal The ideal voltage of the demand distribution node;

[0125] Step A4: The artificial bee colony algorithm finally finds the optimal solution.

[0126] Based on the above, this application proposes a swarm size of 30, and performs an iterative search to randomly generate active power at the generator-type power generation end and reactive power values ​​in the power grid, so that the swarm fitness value is the highest, and then the optimal distributed energy node is selected to reduce the overall circuit loss and voltage jump.

[0127] The above description is only a preferred embodiment of the present invention and does not constitute any other form of limitation to the present invention. Any modification or equivalent change made based on the technical essence of the present invention still falls within the scope of protection required by the present invention.

Claims

1. A power distribution dispatching strategy method based on artificial bee colony algorithm, characterized in that: The specific steps are as follows: S1: Active power prediction of demand distribution nodes; To predict the active power value and trend of the demand distribution node, it is necessary to consider the timely transient distributed energy access, and to predict and feedback the active power value and trend of the demand distribution node, that is, to predict the active power value and trend of the demand distribution node within minutes, where the active power trend of the demand distribution node is the active power of the demand distribution node 30 seconds ago minus the active power of the current demand distribution node. Separate models are set for different regions, and normalized general characteristics are not considered; Smart meters are used to read the active power of real-time power consumption data of demand distribution nodes every second in kilowatt-hours and record it as Wp. The weather conditions of nearby meteorological stations are read, including the real-time temperature T, humidity S and wind speed F of the area. Unique hot encoding is used to record the time scale at this time as a vector D, which includes hours, minutes, day of the week, and whether it is a holiday. The scale value to which the specific minute is close is the scale value as the mark value, the day of the week is marked as Monday to Sunday, and whether it is a holiday is marked as yes or no; S2: Active power output and volatility prediction of distributed photovoltaic energy nodes; Step S2 predicts the active power output and volatility of distributed photovoltaic energy nodes; Firstly, a distributed solar photovoltaic prediction time series model near the demand distribution node is established to predict the distributed solar photovoltaic active power change trend value and the specific solar photovoltaic active power value. The distributed solar photovoltaic active power change trend value is the difference between the real-time solar photovoltaic active power and the distributed solar photovoltaic active power 30 seconds ago. Among them, the distributed solar photovoltaic prediction timing model is expressed as follows: Supply tend ,Solar energy =Rnn(Soir,T,S,F,G,Clc) Among them, Solar tend Indicates the trend value of solar photovoltaic active power change. energy represents the energy of solar energy in the next second, Rnn represents recurrent neural network, Soir represents real-time sunshine intensity, Clc represents real-time cloud cover, G represents solar radiation intensity, and Clc represents real-time cloud cover; S3: Construction of power grid topology model; Determine the location of the demand distribution node loc x , and confirm the location of each distributed energy source loc f , and then the impedance R of the power grid is approximately calculated by the impedance calculation formula i : R i =R line *L+j*X line *L Among them, R i is the grid impedance, R line is the line resistance, j is the imaginary unit, X line is the line reactance, L is the line length from the distributed energy node to the demand distribution node; S4: artificial swarm power distribution scheduling of distributed energy nodes; An artificial bee colony algorithm based on which distributed energy nodes can be dispatched is proposed.

2. A power distribution scheduling strategy method based on artificial bee colony algorithm according to claim 1, characterized in that: The active power prediction process of the demand distribution node in step S1 includes enhancing the dynamic load factor formula, integrating the time series characteristics, and considering the active power usage fluctuation of the demand distribution node in a short period of time: Among them, DIF enhaced To enhance the dynamic load factor, p avg Ask the average active power within 30 seconds in real time, p max is the maximum active power within 30 seconds in real time, t is the time of the day in real time, in hours, T is the total number of hours in a day, which is 24, ε and δ are weight factors, G is the solar radiation intensity, P pv is the output power of the photovoltaic power generation system, ΔP pv is the change in the output power of the photovoltaic power generation system (i.e., the current P pv Subtract the P from 30 seconds ago pv ), γ, ρ and θ are the weight factors of photovoltaic power generation.

3. The power distribution scheduling strategy method based on artificial bee colony algorithm according to claim 1 is characterized in that: The step S1 of predicting the active power of the demand distribution node includes characterizing the instantaneous efficiency of the transformer. When the ratio of the instantaneous output power to the input power of the transformer is low, although there is no direct active power demand at the demand distribution node, the overall energy consumption of the power grid will increase, thereby requiring more active power: Among them, Tran effic is the instantaneous efficiency of the transformer, p in (t), p out (t) are the input power and output power at time t, p' in (t) and p′ out (t) is the instantaneous change rate of input power and output power, where the change rate sampling time interval is 30 seconds.

4. A power distribution dispatching strategy method based on artificial bee colony algorithm according to claim 2 or 3, characterized in that: In the step S1, the Transformer network is used to predict the real-time active power value and trend of the demand distribution node in the next second during the active power prediction process of the demand distribution node, wherein the Transformer network is represented as: Acticep tend ,Acticep z =Transfomer(W p ,T,S,F,D,DIF enhaced ,P pv ,ΔP pv ,Tran effic ) Among them, Acticepten d represents the active power trend, Acticepz represents the active power value of the demand distribution node in the next second, Transfomer is the sequential network architecture, P pv is the output power of the photovoltaic power generation system, ΔP pv It is the change in output power of the photovoltaic power generation system.

5. The method for power distribution scheduling strategy based on artificial bee colony algorithm according to claim 1 is characterized in that: In the step S2, a wind energy prediction time series model near the demand distribution node is established in the process of predicting the active power output and volatility of the distributed energy nodes to predict the active power change trend value of the wind energy and the specific active power value of the wind energy, wherein the active power change trend value of the wind energy is specifically expressed as the difference between the active power value of the wind energy and the active power of the wind energy 30 seconds ago; Wind tend ,Wind energy =Rnn(F,T,Ap,Wd) Among them, Wind tend is the trend value of wind energy active power change, Wind energy is the active power value of wind energy in the next second, Ap is the air pressure, and Wd is the wind direction.

6. The power distribution dispatching strategy method based on artificial bee colony algorithm according to claim 1 is characterized in that: The specific steps of the S4 distributed energy node artificial bee colony power distribution scheduling are as follows: Step A1: Distributed energy fluctuation threshold processing; In this step, a distributed energy fluctuation threshold function is first proposed to consider all distributed energy near the demand distribution node. When the volatility is too high, the scheduling is directly abandoned: Among them, C i is the judgment value of the i-th distributed energy node. When the value is 0, its supply right is cancelled, and when it is 1, its supply is considered. Cv i is the active power change trend value of the i-th distributed energy node, Cv max is the upper limit of the active power change trend of the distributed energy node; Step A2: Artificial bee colony algorithm power optimization; In this step, we first determine an artificial bee colony algorithm power optimization formulation and propose a dynamic matching response function: Among them, Fle i is the dynamic demand matching value of the ith distributed energy node, Ct i is the real-time active power of the i-th distributed energy node; Based on the dynamic matching response function above, the distributed energy nodes with negative dynamic demand matching values ​​are directly used as backup supply energy, and the distributed energy nodes with positive dynamic demand matching values ​​close to 0 are considered as energy response nodes; Step A3: Artificial bee colony algorithm reactive power setting; In the above step A2, the specific supply point of the distributed energy node is determined, and the active power of the centralized power generation end and the reactive power in the power grid are optimized by using the artificial bee colony algorithm, thereby optimizing the voltage and reducing the overall circuit power loss; In the artificial bee colony algorithm, the solution includes the active power of the centralized power generation terminal and the reactive power value in the power grid: x=(p centeral ,Q grid ) Where x is the solution of reactive power optimization of artificial bee colony algorithm, p centeral is the active power of the centralized power generation terminal, Q grid is the reactive power value in the power grid; According to the solution of reactive power optimization of artificial bee colony algorithm, the voltage of demand distribution node is approximately calculated: Among them, V t is the adjusted voltage of the demand distribution node, I is the demand distribution current; Based on this, in this step, a fitness function is proposed to give the target value of the artificial bee colony algorithm: Among them, Fit is the fitness of the bee colony, R i is the grid impedance, I is the current value, V t is the voltage adjusted for the demand distribution node, V ideal The ideal voltage of the demand distribution node; Step A4: The artificial bee colony algorithm finally finds the optimal solution.

7. A power distribution dispatching strategy system based on artificial bee colony algorithm according to any one of claims 1 to 6, characterized in that: Includes the following modules: Data acquisition module: The data acquisition module is used to install sensors in the distributed photovoltaic power generation system to collect the output power of the photovoltaic power generation system in real time, including the current, voltage and power data of the photovoltaic panels. At the same time, the real-time weather data, including temperature, humidity, wind speed and solar radiation intensity, are obtained through the meteorological station. These data are used to analyze and predict the output of photovoltaic power generation and are connected with other modules to ensure the accuracy and real-time nature of the data. Data preprocessing module: The data preprocessing module is used to process the collected photovoltaic power output power data and weather data, normalize the power data, calculate the power change trend, denoise, standardize and extract features of the weather data, generate environmental feature data for model training, and ensure the consistency and comparability of the data for subsequent model use; Prediction model module: The prediction model module includes enhanced dynamic load factor calculation and Transformer prediction model. By calculating the enhanced dynamic load factor, the real-time fluctuation of photovoltaic power generation, weather factors and changes in power demand are comprehensively considered to generate features for load prediction. The Transformer network model is used to make real-time predictions on the active power value and trend of the demand distribution node. The input data includes pre-processed power data, environmental data and enhanced dynamic load factor. Grid topology module: The grid topology module is used to determine the locations of demand distribution nodes and distributed photovoltaic power generation nodes, as well as the locations of other distributed energy nodes, to establish a grid topology model, and to accurately simulate the operation status of the grid by comprehensively considering the impact of photovoltaic power generation nodes and other distributed energy nodes through impedance calculation; Distribution scheduling module: The distribution scheduling module is used to evaluate the power fluctuation of distributed photovoltaic power generation nodes. When the fluctuation is too large, its supply qualification is cancelled, and the artificial bee colony algorithm is used to optimize the power. The optimal distributed energy node is determined by dynamically matching the response function to improve the efficiency of the power system. At the same time, the reactive power in the power grid is optimized, and the active power of the centralized power generation end and the distributed photovoltaic power generation node is combined to reduce the power grid loss and voltage fluctuation. Feedback and optimization module: The feedback and optimization module will feed back the prediction results and scheduling plans to the control system in real time, dynamically adjust the power distribution strategy, ensure the stability and reliability of power supply, and continuously optimize the artificial bee colony algorithm and scheduling strategy based on feedback data to improve the overall efficiency and stability of the system and ensure the optimized operation of the power system.

Images