Distributed energy efficient access power supply network device

Abstract

The invention discloses a distributed energy efficient access power supply network device, and relates to the technical field of distributed energy access power grids, and the device comprises a distributed energy end, a 5G communication module, a power grid access end and an intelligent analysis and prediction platform. Efficient real-time communication between the distributed energy and the power grid is achieved through 5G communication, timely data interaction is guaranteed, the accurate operation state of the distributed energy can be obtained at the first time, deep analysis and high-precision prediction are carried out on distributed energy supply and power grid demands through the artificial intelligence technology, and on the basis of the accurate prediction result, the real-time performance of the distributed energy is improved. The energy scheduling strategy can realize refined operation, avoids blindness of traditional empirical scheduling, accurately adjusts the access power and the access sequence of the distributed energy according to the predicted generating capacity of the distributed energy and the change of the power grid demand, realizes efficient distribution of the energy, reduces energy waste, and improves the energy utilization rate. And the economical efficiency and the reliability of power grid operation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed energy access to a power grid, and in particular to a device for efficiently accessing a distributed energy to a power supply grid. Background Art

[0002] As the world's awareness of the crisis of non-renewable resources such as coal and oil increases, distributed energy technology has developed rapidly. Distributed energy is based on the research of new energy technologies, such as solar photovoltaic power generation and wind power generation. These clean energy sources have the advantages of being renewable, environmentally friendly, and low-carbon, and have become an important trend in future energy development. Distributed energy power generation does not use coal, oil, natural gas and other energy sources, but instead uses a large amount of new recyclable and renewable energy that meets national energy-saving and emission reduction standards. At the same time, distributed energy systems can perform outstandingly in the co-production effect of cold energy, heat energy, and electric energy, reducing the consumption problem caused by the transmission of electric energy. With the adjustment of the energy structure, the proportion of new energy in power supply has gradually increased. As an important part of new energy, distributed energy needs to be efficiently connected to the power supply grid to meet power demand.

[0003] With the growing global demand for clean energy, distributed energy has been widely developed and applied. However, in the process of connecting distributed energy to the power grid, the existing technology still has certain defects. In terms of communication, traditional communication technology has problems such as low data transmission rate, poor real-time performance and insufficient stability. The power grid cannot obtain the operating status of distributed energy in time, which leads to energy scheduling lag and inability to quickly adjust access power and other operations according to actual conditions. In terms of energy analysis and management, there is a lack of effective data analysis and prediction methods. When scheduling energy, it can only rely on experience, which is prone to energy waste or insufficient power supply during peak hours, thus affecting the stability and reliability of power grid operation. Therefore, it is necessary to propose a device for efficiently connecting distributed energy to the power grid to solve the problems in the existing technology. Summary of the invention

[0004] The purpose of the present invention is to make up for the deficiencies of the prior art and to provide a device for efficiently accessing a distributed energy source to a power supply network, which can solve the problems of low communication efficiency and insufficient intelligence in energy scheduling and management during the prior art of accessing distributed energy to the power grid.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a device for efficiently accessing a distributed energy source to a power supply network, the device comprising a distributed energy source terminal, a 5G communication module, a power grid access terminal and an intelligent analysis and prediction platform, wherein:

[0006] The distributed energy terminal includes at least one distributed energy generation device, and each distributed energy generation device is equipped with a sensor, the sensor is used to collect the operating parameters of the distributed energy, the operating parameters include power generation power, voltage, current, frequency, equipment temperature, solar power generation equipment includes ambient light intensity, photovoltaic panel temperature, wind power generation equipment includes wind speed and wind direction;

[0007] The 5G communication modules are installed at the distributed energy end and the grid access end respectively, and are used to realize data transmission between the distributed energy end and the grid access end and the intelligent analysis and prediction platform. The 5G communication module adopts a customized 5G communication protocol. The data frame structure of the customized 5G communication protocol is as follows: the data frame is defined to consist of a frame header, a data field and a frame tail, and the frame header length is L h , the frame tail length is L t , the length of the data field is L d , where L h =[a×log2N+b] bytes, L t =c bytes, L d =M bytes, N is the number of distributed energy devices, a, b, c are preset constants, M is the number of bytes determined according to the actual amount of collected data, and the encryption algorithm of the data frame during transmission is: define the original data as x, the encrypted data as y, the encryption algorithm is y=(x+k1)×(k2+sin(x)), where k1 and k2 are encryption keys, and k1 and k2 are dynamically generated according to the unique identification and timestamp of the device;

[0008] The grid access terminal includes an access device and a grid control device. The access device is used to connect the electric energy generated by the distributed energy to the grid and adjust the access process in real time according to the distributed energy operation data transmitted by the 5G communication module. The grid control device is used to monitor the overall operation status of the grid.

[0009] The intelligent analysis and prediction platform includes a data acquisition and preprocessing module, an artificial intelligence analysis and prediction module and a decision support module. The data acquisition and preprocessing module is used to receive data from the 5G communication module and clean and normalize the data. The artificial intelligence analysis and prediction module is used to analyze the supply of distributed energy and predict the demand of the power grid. The distributed energy supply analysis algorithm adopted by the artificial intelligence analysis and prediction module is: the distributed energy is defined as solar power generation, and its power generation prediction value is P solar , light intensity is I, photovoltaic panel temperature is T, air quality index is AQI, cloud thickness is C, solar azimuth is θ, and the algorithm formula is: Among them, k3, k4, k5, and k6 are coefficients obtained through historical data training. For wind power generation, the power generation prediction value is defined as Puind , wind speed is v, wind direction is α, terrain feature parameter is H, air density is ρ, and the algorithm formula is: P wind =k7×v 3 ×sin(α)×(1+k8×H)×ρ, where k7 and k8 are coefficients obtained through historical data training;

[0010] The power grid demand prediction algorithm adopted by the artificial intelligence analysis and prediction module is: the power demand prediction value of the power grid at time t is defined as P demand (t), the historical electricity consumption data is E hist (ti), i = 1, 2, ..., n, economic development data is Eco(t), population mobility data is Pop(t), industrial production plan data is Ind(t), and large-scale activity arrangement data is Act(t). The algorithm formula is: Among them, k9, k 10 , k 11 , k 12 , k 13 is the coefficient obtained through historical data training, ω i is the weight coefficient of historical electricity consumption data;

[0011] The decision support module is used to provide decision support for energy scheduling and management based on the results of the artificial intelligence analysis and prediction module.

[0012] Furthermore, when the distributed energy end sensor collects data, for the solar power generation equipment:

[0013] The light intensity sensor adopts a multi-probe structure, including at least three probes at different angles, and the material used for the probe is a new photoelectric material whose light absorption efficiency reaches a preset threshold in a specific band. The relationship between the light absorption efficiency η of the material and the wavelength λ satisfies Where λ0 is the wavelength when the light absorption efficiency of the material is the highest, k 14 and k 15 is the material characteristic constant;

[0014] Determine k 14 and k 15 The process is as follows: First, in a laboratory environment, the wavelength scanning test of the new optoelectronic material is carried out, and the light absorption efficiency η is measured at different wavelengths λ to obtain a series of (λ,η) data points. Then, the least squares method is used to fit these data points. The fitting function is: Through the fitting process, determine the k that minimizes the fitting error 14 and k 15 value;

[0015] The photovoltaic panel temperature sensor is installed by attaching, attached to the back of the photovoltaic panel and in close contact with the photovoltaic panel. The sensor uses a micro-nano structured thermosensitive material. The relationship between the resistance R of the thermosensitive material and the temperature T is: R = R0×(1+k 16 ×T+k 17 ×T 2 ), where R0 is the resistance at the reference temperature, k 16 and k 17 is the characteristic parameter of the thermosensitive material;

[0016] Determine k 16 and k 17 The method is as follows: place the sensor with micro-nano structure thermosensitive material in an environmental box where the temperature can be precisely controlled, gradually change the ambient temperature T from low temperature to high temperature, and measure the corresponding resistance R value at the same time to obtain multiple sets of (T, R) data, and perform quadratic polynomial fitting on these data, that is, R = R0×(1+k 16 ×T+k 17 ×T 2 ), k is determined by the fitting algorithm 16 and k 17 The value of .

[0017] Furthermore, the installation position of the 5G communication module at the distributed energy terminal is optimized as follows:

[0018] Considering the electromagnetic radiation characteristics of distributed energy generation equipment and the electromagnetic interference of the surrounding environment, the electromagnetic radiation intensity distribution function of distributed energy generation equipment is defined as E(x, y, z), where (x, y, z) is the spatial coordinate, the electromagnetic interference intensity distribution function of the surrounding environment is I(x, y, z), the installation position coordinates of the 5G communication module are (X, Y, Z), and the signal reception strength function of the 5G communication module is S(X, Y, Z). The relationship is: where k 18 and k 19 is a constant related to the hardware characteristics of the 5G communication module;

[0019] Determine k 18 and k 19 The process is as follows: In the laboratory, a standard electromagnetic radiation source is used to simulate the electromagnetic radiation of distributed energy generation equipment, and electromagnetic radiation E of different intensities is set. At the same time, electromagnetic interference I of different intensities is applied around. 5G communication modules are placed at different locations, and their actual signal reception strength S is measured. By performing multiple linear regression analysis on multiple groups of (E, I, S) data, k is obtained. 18 and k 19 The value of

[0020] By measuring and analyzing the electromagnetic radiation intensity of distributed energy generation equipment and the electromagnetic interference intensity of the surrounding environment, the coordinates (X, Y, Z) that maximize S (X, Y, Z) are determined as the optimal installation position for the 5G communication module.

[0021] Furthermore, the power electronic conversion device in the access device at the grid access end adopts a new topological structure, which is composed of multiple power semiconductor devices and passive components, as follows:

[0022] Define the number of power semiconductor devices as n and the number of passive components as m. In this topology, the on-resistance of the power semiconductor device is R on (i), i = 1, 2, ..., n, the equivalent impedance of the passive component is Z eq (j), j = 1, 2, ..., m, the voltage conversion ratio is K, and the input voltage is V in , the output voltage is V out , and the relationship satisfies:

[0023] Method for determining K value: Build a test platform in the laboratory and input a known stable voltage V to the power electronic conversion device of the new topology structure. in , measure the output voltage V out , in different R on (i) and Z eq (j) Perform multiple measurements under the combination, and use the formula The K value is calculated.

[0024] Furthermore, the grid control device at the grid access end adopts a control algorithm based on a multi-agent system, which is as follows:

[0025] Define that there are p nodes in the power grid, each node is an intelligent agent, and the state variable of the kth intelligent agent is x k , k = 1, 2, ..., p, the coupling weight between agents is w ij , i,j=1,2,…,p, represents the degree of mutual influence between the ith agent and the jth agent, and the control input is u k , the dynamic equation of the system is: Among them A k is the state transfer matrix of the kth agent;

[0026] Determine the coupling weight w ij The process is as follows: First, under the normal operation of the power grid, the state variables x of each node are k Long-term monitoring is performed to obtain status data at different times. Then, the coupling weight w is defined according to the physical connection relationship and power flow characteristics of the power grid. ijThen, the monitored state data is substituted into the dynamic equation, and w is continuously adjusted through the least squares algorithm. ij The value of w is chosen to minimize the error between the state variables calculated according to the dynamic equation and the state variables actually monitored, and finally determine the appropriate w ij value.

[0027] Furthermore, the data collection and preprocessing module of the intelligent analysis and prediction platform adopts a data outlier detection method based on cluster analysis and density peak algorithm when performing data cleaning, which is as follows:

[0028] Define the collected data point set as D = {x1, x2, ..., x N}, where x i represents the i-th data point, i = 1, 2, ..., N, firstly cluster the data points, and calculate the distance metric d(x i ,x j ), using an adaptive distance metric function where ∥x i -x j ∥ represents the Euclidean distance, μ is the average distance of the data point set D, k 20 and k 21 is the adaptive coefficient;

[0029] Determine k 20 and k 21 Method: Randomly select a part of known normal data points as the training set, and calculate the Euclidean distance between these data points ∥x i -x j ∥, substitute these distance data into the distance metric function, and through the iterative optimization algorithm, make the distance metric between normal data points in the training set reflect the similarity of the data as accurately as possible. In the optimization process, k is continuously adjusted 20 and k 21 The value of k is finally determined 20 and k 21 ;

[0030] According to the clustering results, the center and density of each cluster are determined, and the local density ρ(x i ) and the relative distance δ(x i ), the local density and relative distance of the data points meet certain conditions, namely ρ(x i )<ρ threshold And δ(x i )>δ threshold , where ρ threshold and δ thresholdis the preset threshold, the data point is determined to be an outlier and is removed.

[0031] Furthermore, when analyzing the distributed energy supply situation, the artificial intelligence analysis and prediction module of the intelligent analysis and prediction platform considers factors such as the type, humidity, particle size, fermentation and combustion efficiency of biomass for biomass power generation equipment, and its power generation prediction algorithm is as follows:

[0032] Define the predicted value of biomass power generation as P liio , the type parameter of biomass is G lio , humidity is H lio , the particle size is G lio , the fermentation and combustion efficiency is E bio , the algorithm formula is: lino =k 22 ×C lio ×(1-k 23 ×H lio )×(1+k 24 ×G lio )×E lino , where k 22 , k 23 , k 24 It is the coefficient obtained by training the historical operation data of biomass power generation equipment;

[0033] Determine k 22 , k 23 , k 24 The process is as follows: First, the historical operation data of biomass power generation equipment is collected, including the actual power generation under different biomass types, humidity, particle size and corresponding fermentation and combustion efficiency. The number of collected data samples is defined as n. For the i-th sample (i = 1, 2, ..., n), its biomass type parameter is C bioi , humidity is H bioi , the particle size is G b ioi, fermentation and combustion efficiency is E b ioi, the actual power generation is P l ioi, build the objective function The objective function J is minimized by the optimization algorithm to obtain the k that minimizes J. 22 , k 23 , k 24 value.

[0034] Furthermore, when predicting grid demand, the artificial intelligence analysis and prediction module of the intelligent analysis and prediction platform further considers the impact of seasonal changes on residents' electricity consumption habits and introduces seasonal coefficients. The grid demand prediction algorithm is improved as follows:

[0035] Define the seasonal coefficient as s(t), where t represents time. For different seasons, the value of s(t) is different. In the power grid demand forecasting algorithm,

[0036] The process of determining the seasonal coefficient s(t): Collect historical electricity consumption data for many years and classify the data according to seasons into spring, summer, autumn and winter. For each season, calculate the average daily electricity consumption E in that season. seasonavg , and then calculate the average daily electricity consumption E throughout the year yearavg , then the seasonal coefficient s(t) in the corresponding season is

[0037] Furthermore, the decision support module of the intelligent analysis and prediction platform considers the electrical distance between the distributed energy access point and the grid load center when generating energy scheduling and management decisions. Its decision algorithm is as follows:

[0038] Define the electrical distance between the distributed energy access point and the grid load center as L elec , the dispatchable power of distributed energy is P dis , the power shortage of the power grid load center is ΔP, the decision variable is d, when ΔP>0 and P d When is>0, where k 25 is the decision coefficient;

[0039] Determine k 25 The process: First, conduct a simulation experiment and set different ΔP, P dis and L elec For each set of values, a better decision variable d is determined according to the operation requirements of the power grid and actual experience. optimal Then, through linear regression and other methods, according to multiple groups (ΔP, P dis ,L elec ,d optimal ) data, solve for k 25 The value of As close as possible optimal .

[0040] Furthermore, the intelligent analysis and prediction platform adopts a data processing acceleration method based on quantum computing principles when performing data analysis and prediction, as follows:

[0041] The data processing task can be decomposed into the operation of q quantum bits. The state of each quantum bit is represented by |ψ>, the quantum gate operation matrix is ​​U, and the data processing function is F. In the quantum computing environment, the data processing result is: Where t represents the actual data processing time, t0 represents the theoretical optimal data processing time, and k 26 and k 27 is a constant related to the characteristics of quantum computing hardware;

[0042] Determine k 26 and k 27 The process is as follows: Use specific quantum computing hardware to process a series of data processing tasks with known processing time. For each task, record the actual data processing time t. At the same time, determine the theoretical optimal data processing time t0 based on the complexity of the task and quantum computing theory. Substitute the processed quantum state into the data processing function F to obtain the function value. By fitting and analyzing multiple groups of (t, t0, F) data, determine k 26 and k 27 The value of .

[0043] Compared with the existing technology, the distributed energy efficient access to the power supply network device has the following beneficial effects:

[0044] The present invention realizes efficient real-time communication between distributed energy and power grid through 5G communication, ensures timely data interaction, can obtain the precise operating status of distributed energy at the first time, and conducts in-depth analysis and high-precision prediction of distributed energy supply and power grid demand through artificial intelligence technology. Based on accurate prediction results, the energy scheduling strategy can realize refined operation, avoiding the blindness of traditional empirical scheduling, and accurately adjust the access power and access sequence of distributed energy according to the predicted power generation of distributed energy and changes in power grid demand, thereby realizing efficient energy allocation, reducing energy waste, and improving the economy and reliability of power grid operation.

[0045] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 The figure is a structural diagram of a device for efficiently connecting distributed energy to a power supply grid. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. 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.

[0049] Embodiment 1

[0050] Near a small industrial park, there is a distributed energy device consisting of a solar photovoltaic power station and a wind farm, which needs to be connected to the local power supply grid to provide power for the industrial park and surrounding areas.

[0051] Each photovoltaic panel in a solar photovoltaic power station is equipped with a sensor, in which the light intensity sensor adopts a multi-probe structure, and the probe adopts a light absorption efficiency that meets Characteristic of new optoelectronic materials, through the laboratory determined k 14 and k 15 The value can accurately collect the light intensity. The photovoltaic panel temperature sensor is installed in an attached manner. The relationship between the resistance and temperature of the internal thermistor material is R = R0 × (1 + k 16 ×T+k 17 ×T 2 ), can accurately obtain the temperature of the photovoltaic panel, and collect operating parameters such as power generation power, voltage, and current.

[0052] Each wind turbine in the wind farm is also equipped with a sensor to collect data such as wind speed, wind direction, power generation, and frequency. For the prediction of wind power generation, the wind speed υ and wind direction α obtained by the sensor are combined with the terrain characteristic parameter H (obtained by digital modeling of the local terrain) and air density ρ, and the formula P is used. w ind=k7×v 3 ×sin(α)×(1+k8×H)×ρ is used to analyze wind power generation, where k7 and k8 are trained based on the historical operating data of wind turbines.

[0053] For the installation location of the 5G communication module at the distributed energy end, considering the electromagnetic radiation characteristics of the solar photovoltaic power station and wind farm and the electromagnetic interference of the surrounding environment, the formula is used To determine the best installation position, by measuring and analyzing the electromagnetic radiation intensity E(x,y,z) and the electromagnetic interference intensity I(x,y,z), find the coordinates X,Y,Z) that make the signal reception intensity function S(X,Y,Z) reach the maximum value. For example, in a solar photovoltaic power station, after measurement and calculation, it is determined that the 5G communication module will be installed in the top corner of the power station control room, where the electromagnetic interference is relatively small and the electromagnetic radiation near the photovoltaic panels can be better received; in a wind farm, the 5G communication module is installed in the middle of the wind turbine tower, which can balance the electromagnetic radiation reception and interference avoidance.

[0054] Adopting customized 5G communication protocol, the data frame structure is based on L h =[a×log2N+b] bytes (frame header), L t =c bytes (frame tail), L d =M bytes (data domain), taking the distributed energy system as an example, N is the total number of solar photovoltaic panels and wind turbines, a, b, c are preset constants, and M is determined according to the actual amount of data collected. During data transmission, the original data x is encrypted according to the encryption algorithm y=(x+k1)×(k2+sin(x)), where k1 and k2 are dynamically generated according to the unique device identifier and timestamp. In this way, the data collected by the sensors at the distributed energy end can be transmitted to the grid access end and the intelligent analysis and prediction platform in a high-speed, low-latency, safe and reliable manner.

[0055] The access device at the grid access end adopts a power electronic conversion device with a new topology structure. The topology structure consists of multiple power semiconductor devices and passive components. Its input voltage V in After conversion, the output voltage V out satisfy By building a test platform in the laboratory to determine the K value, the voltage and frequency parameters of the electricity generated by solar and wind energy are adjusted to meet the grid access standards during the process of distributed energy access to the grid. For example, when the output voltage of the solar photovoltaic power station fluctuates, the access device adjusts the on-resistance R of the internal power semiconductor device according to the real-time data transmitted by the 5G communication module. on (i) and the equivalent impedance Z of the passive components eq (j) to achieve stable control of the output voltage.

[0056] The power grid control device adopts a control algorithm based on a multi-agent system. There are p nodes in the power grid, each node acts as an agent, and the state variable of the kth agent is x k , the coupling weight between agents is w ij Through long-term monitoring of node data under normal operation of the power grid and optimization algorithm, the control input is u kThe system dynamic equation is When distributed energy is connected, the grid control equipment quickly and accurately adjusts grid voltage, frequency and other parameters based on the status of each node and the interaction between intelligent agents. For example, when the output power of a wind farm suddenly increases, the parameters of related nodes in the grid are quickly adjusted through coordination between intelligent agents to avoid voltage fluctuations.

[0057] The data acquisition and preprocessing module receives data from the 5G communication module. When cleaning the data, it uses a data outlier detection method based on cluster analysis and density peak algorithm to collect data points D = {x1, x2, ..., x N}, through the adaptive distance metric function Perform cluster analysis, where k 20 and k 21 The normal data point training set is randomly selected and iteratively optimized, and the local density ρ(x i ) and the relative distance δ(x i ), and eliminate those that satisfy ρ(x i )<ρ threshold And δ(x i )>δ threshold Conditional data points to ensure data quality.

[0058] The AI ​​analysis and prediction module analyzes the distributed energy supply and predicts grid demand. For solar power generation, the formula Predicted power generation, where k3-k6 are obtained through historical data training. For biomass power generation (assuming that the park has small biomass power generation equipment), consider the biomass type C bio 、Humidity bio , Granularity G bio and fermentation or combustion efficiency E hio , through the formula P bio =k 22 ×C bio ×(1-k 23 ×H bio )×(1+k 24 ×G bio )×E bio Forecasted power generation, k 22 -k 24 Determined by training with historical operating data of biomass power generation equipment.

[0059] In terms of grid demand forecasting, let the power demand forecast value of the grid at time t be P demand (t), combined with historical electricity consumption data E hist(ti), economic development data Eco(t), population mobility data Pop(t), industrial production plan data Ind(t), large-scale event arrangement data Act(t), through the formula Make a prediction, where k9-k 13 is the coefficient obtained through training, ω i is the weight coefficient of historical electricity consumption data. In addition, considering the impact of seasonal changes on residents’ electricity consumption habits, the seasonal coefficient s(t) is introduced. s(t) is obtained by statistically analyzing many years of historical electricity consumption data by season, which further optimizes the grid demand forecast.

[0060] The decision support module considers the electrical distance L between the distributed energy access point and the grid load center when generating energy scheduling and management decisions. elec , when there is a power shortage ΔP in the power grid load center and the distributed energy has dispatchable power P dis When the decision variable Determine the energy scheduling strategy, where k 25 Through simulation experiments and linear regression methods, it is determined that, for example, when the power consumption of the industrial park increases during the daytime working hours (ΔP>0), and the solar photovoltaic power station has sufficient dispatchable power (P dis >0), the decision support module calculates the appropriate dispatching amount according to the electrical distance, giving priority to dispatching the electricity of photovoltaic panels that are closer to the industrial park, reducing the power transmission loss and improving energy utilization efficiency.

[0061] When performing data analysis and prediction, the intelligent analysis and prediction platform adopts a data processing acceleration method based on the principle of quantum computing. The data processing task can be decomposed into q quantum bit operations. The state of each quantum bit is represented by |ψ>, the quantum gate operation matrix is ​​U, and the data processing function is F. In the quantum computing environment, the data processing result is: where k 26 and k 27 By processing and fitting data processing tasks with known processing time on quantum computing hardware, this method accelerates data processing speed, enabling the platform to quickly provide decision support for energy scheduling and management.

[0062] The effects brought by this embodiment are as follows:

[0063] Through the implementation of this device, distributed energy can be efficiently and stably connected to the power supply grid. The 5G communication module ensures real-time data transmission, enabling the power grid to grasp the status of the energy end in a timely manner, avoiding untimely or unstable energy access due to communication delays. For example, when the power of solar and wind power generation fluctuates, the grid access end can make quick adjustments to ensure smooth access to electricity, reduce energy waste, and improve energy utilization.

[0064] The intelligent analysis and prediction platform uses advanced algorithms to accurately analyze and predict distributed energy supply and grid demand. In the industrial park scenario, it can reasonably arrange energy dispatch according to the characteristics of different energy sources and the actual needs of the grid. For example, taking into account seasonal factors and the production plans of industrial park enterprises, it can accurately allocate the supply of energy such as solar energy, wind energy and biomass energy to ensure that the electricity demand of park enterprises and the lives of surrounding residents can be met during peak electricity consumption. During low electricity consumption, energy access can be reasonably reduced to avoid impact on the grid, thereby achieving optimal energy allocation and stable operation of the grid.

[0065] The innovative design of each part of the device improves the overall reliability. The optimized installation of the 5G communication module and the secure communication protocol ensure the reliability of data transmission. The new power electronic conversion equipment and intelligent control algorithm at the grid access end effectively deal with the grid fluctuation problem caused by distributed energy access. The data cleaning method and precise prediction algorithm ensure the accuracy of decision-making and avoid grid failures caused by erroneous data or inaccurate predictions. For example, in the face of complex weather changes and changes in the power load in the park, the entire device can operate stably, reducing the occurrence of power outages and ensuring the normal production order of the industrial park and the living electricity consumption of surrounding residents.

[0066] Embodiment 2

[0067] In a remote rural area, there are multiple small distributed energy facilities, including several user solar photovoltaic power generation systems, small wind turbines and a small hydropower plant. These distributed energy sources need to be connected to the local rural power supply grid to provide power to local residents.

[0068] The photovoltaic panels of the user's solar photovoltaic power generation system are equipped with sensors. The light intensity sensor is based on Collect illumination data, its k 14 and k 15 It is determined by testing under local common lighting conditions. The photovoltaic panel temperature sensor is closely attached to the back of the photovoltaic panel. The relationship between the resistance of the thermistor material and the temperature is R = R0 × (1 + k 16 ×T+k 17 ×T 2 ), k 16 and k 17It is calibrated based on temperature tests under local climate conditions, and simultaneously collects data such as power generation and voltage for each household.

[0069] Sensors on small wind turbines collect wind speed, wind direction and power generation data. For wind power generation prediction, the collected wind speed υ and wind direction α are combined with the local topographic characteristic parameters H (obtained through topographic mapping and analysis of the surrounding villages) and air density ρ. wind =k7×v 3 ×sin(α)×1+k8×H)×ρ to calculate the wind power generation, where k7 and k8 are trained based on the long-term operation data of the wind turbine in a rural environment.

[0070] Sensors at small hydropower plants collect operating parameters such as water flow velocity, water level, power generation, and frequency. For hydropower prediction, a specific power generation prediction model (similar to other energy prediction models) can be established based on data such as water flow velocity and water level, combined with the equipment characteristics and historical operating data of the hydropower plant.

[0071] Considering the electromagnetic characteristics of distributed energy facilities in rural environments and the surrounding electromagnetic interference, the location of the 5G communication module is determined. In the user's solar photovoltaic power generation system, the 5G communication module is usually installed under the eaves of the house. This location is calculated It is determined that it can better receive the electromagnetic radiation of the photovoltaic system while avoiding electromagnetic interference from residents' lives. For small wind turbines, the 5G communication module is installed in the control box at the bottom of the wind turbine. This position can ensure signal reception while reducing damage to the module in the wild environment.

[0072] Using a customized 5G communication protocol, the data frame structure is determined by the number of distributed energy facilities in rural areas, N, and then the frame header L is determined. h =[a×log2N+b] bytes, frame tail L t =c bytes and data field L d =M bytes. When transmitting data, the data is encrypted according to the encryption algorithm y=(x+k1)×(k2+sin(x)) to ensure the security and reliability of data transmission in a rural environment, so that the data can be accurately transmitted to the grid access terminal and the intelligent analysis and prediction platform.

[0073] The access device at the grid access end adopts a new topology power electronic conversion device to convert and regulate the power of rural distributed energy access to the grid. in Converted to output voltage V out When, according to The K value is determined by testing the rural grid access conditions. For example, when the voltage of the user's solar power generation system fluctuates, the access device can make timely adjustments based on the data transmitted by 5G communication.

[0074] The power grid control device uses a control algorithm based on a multi-agent system, treating the nodes in the rural power grid as agents, and its state variables x k , coupling weight w ij According to the actual connection and operation of the rural power grid, when the distributed energy access causes grid fluctuations, It can realize rapid adjustment of grid voltage, frequency and other parameters. For example, when wind power generation increases at night, the grid control equipment can reasonably distribute electric energy to avoid excessive voltage affecting residents' electrical equipment.

[0075] The data collection and preprocessing module processes the distributed energy data in rural areas. When cleaning the data, a data outlier detection method based on cluster analysis and density peak algorithm is used. For the collected data point set D = {x1, x2, ..., x N},pass Perform cluster analysis, k 20 and k 21 It is determined based on the historical normal data of rural power grid operation, and the density peak algorithm is used to eliminate abnormal data to ensure data quality.

[0076] The AI ​​analysis and prediction module analyzes the distributed energy supply situation. For rural solar power generation, based on the collected data and To predict power generation, k3-k6 are trained based on historical data of solar power generation in rural environments. For power generation prediction of small hydropower plants, factors such as water flow velocity and water level are considered to establish a suitable prediction model.

[0077] In terms of grid demand forecasting, we combine data such as the electricity consumption patterns of rural residents (such as different electricity consumption characteristics during busy and slack seasons), population mobility (such as young people working away from home, which leads to reduced electricity consumption), and production plans of local small handicraft workshops. Make a prediction, k9-k 13 It is determined through training with historical data of rural power grids. At the same time, seasonal factors are taken into account, such as the introduction of a seasonal coefficient s(t) for peak electricity consumption in summer (due to the use of electrical appliances such as air conditioners). s(t) is obtained based on statistical analysis of seasonal electricity consumption differences in rural areas over the years to optimize grid demand forecasts.

[0078] The decision support module considers the electrical distance L between the distributed energy access point and the rural grid load center (such as concentrated residential area) when generating energy scheduling and management decisions. elec , when there is a power shortage ΔP in the rural power grid load center and the distributed energy has dispatchable power P dis When, through Determine the scheduling strategy, k 25 It is determined based on the simulation of the actual operation of the rural power grid. For example, when the electricity demand in the village center increases at night and the nearby users' solar power generation systems have surplus power, electricity from nearby locations is preferentially dispatched to reduce transmission losses.

[0079] The intelligent analysis and prediction platform adopts a data processing acceleration method based on the principle of quantum computing. It assumes that the data processing task can be decomposed into q quantum bit operations. Accelerate data processing, k 26 and k 27 Based on the quantum computing hardware tests in the rural power grid data processing scenario, it can quickly provide decision support for rural energy dispatch and management.

[0080] The effects brought by this embodiment are as follows:

[0081] In rural scenarios, this device enables efficient access to distributed energy. 5G communication ensures the real-time nature of data, allowing the power grid to respond promptly to fluctuations in rural distributed energy. For example, when weather changes cause unstable solar or wind power generation, the power grid can adjust quickly, ensuring the stability of power supply to rural residents and reducing power outages caused by unstable energy access.

[0082] The intelligent analysis and prediction platform combines the characteristics of rural areas to accurately analyze energy supply and demand, and reasonably dispatches distributed energy according to the living and production electricity consumption patterns of rural residents. For example, during the busy farming season, priority is given to ensuring the energy supply of agricultural power equipment such as irrigation; on winter nights, it reasonably allocates users' solar energy and electricity from small hydropower plants to ensure the electricity needs of residents' heating equipment, thus achieving the optimal allocation of rural energy and improving energy utilization efficiency.

[0083] The innovative design of each part of the device adapts to the rural environment. The reasonable installation and safe transmission of the 5G communication module ensure the reliable transmission of data in the complex rural environment. The equipment and control algorithms at the grid access end effectively deal with the grid fluctuation problems caused by distributed energy access in rural areas. Data processing and prediction algorithms ensure the accuracy of decision-making. For example, when facing severe weather and changes in residents' electricity usage habits in rural areas, the entire device can operate stably, ensuring the normal living electricity needs of rural residents and the electricity needs of small production workshops, and promoting the stable development of the rural economy.

[0084] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the same elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A device for efficiently connecting distributed energy to a power supply network, characterized in that: The device includes a distributed energy terminal, a 5G communication module, a grid access terminal and an intelligent analysis and prediction platform, among which: The distributed energy terminal includes at least one distributed energy generation device, and each distributed energy generation device is equipped with a sensor, the sensor is used to collect the operating parameters of the distributed energy, the operating parameters include power generation power, voltage, current, frequency, equipment temperature, solar power generation equipment includes ambient light intensity, photovoltaic panel temperature, wind power generation equipment includes wind speed and wind direction; The 5G communication modules are installed at the distributed energy end and the grid access end respectively, and are used to realize data transmission between the distributed energy end and the grid access end and the intelligent analysis and prediction platform. The 5G communication module adopts a customized 5G communication protocol. The data frame structure of the customized 5G communication protocol is as follows: the data frame is defined to consist of a frame header, a data field and a frame tail, and the frame header length is L h , the frame tail length is L t , the data field length is L d , where L h =[a×log2N+b] bytes, L t = C bytes, L d =M bytes, N is the number of distributed energy devices, a, b, c are preset constants, M is the number of bytes determined according to the actual amount of collected data, and the encryption algorithm of the data frame during transmission is: define the original data as x, the encrypted data as y, the encryption algorithm is y=(x+k1)×(k2+sin(x)), where k1 and k2 are encryption keys, and k1 and k2 are dynamically generated according to the unique identification and timestamp of the device; The grid access terminal includes an access device and a grid control device. The access device is used to connect the electric energy generated by the distributed energy to the grid and adjust the access process in real time according to the distributed energy operation data transmitted by the 5G communication module. The grid control device is used to monitor the overall operation status of the grid. The intelligent analysis and prediction platform includes a data acquisition and preprocessing module, an artificial intelligence analysis and prediction module and a decision support module. The data acquisition and preprocessing module is used to receive data from the 5G communication module and clean and normalize the data. The artificial intelligence analysis and prediction module is used to analyze the supply of distributed energy and predict the demand of the power grid. The distributed energy supply analysis algorithm adopted by the artificial intelligence analysis and prediction module is: the distributed energy is defined as solar power generation, and its power generation prediction value is P solar , light intensity is I, photovoltaic panel temperature is T, air quality index is AQI, cloud thickness is C, solar azimuth is θ, and the algorithm formula is: Among them, k3, k4, k5, and k6 are coefficients obtained through historical data training. For wind power generation, the power generation prediction value is defined as P uind , wind speed is v, wind direction is α, terrain feature parameter is H, air density is ρ, and the algorithm formula is: P wind =k7×v 3 ×sin(α)×(1+k8×H)×ρ, where k7 and k8 are coefficients obtained through historical data training; The power grid demand prediction algorithm adopted by the artificial intelligence analysis and prediction module is: the power demand prediction value of the power grid at time t is defined as P demand (t), the historical electricity consumption data is E hist (ti), i = 1, 2, ..., n, economic development data is Eco(t), population mobility data is Pop(t), industrial production plan data is Ind(t), and large-scale activity arrangement data is Act(t). The algorithm formula is: Among them, k9, k 10 , k 11 , k 12 , k 13 is the coefficient obtained through historical data training, ω i is the weight coefficient of historical electricity consumption data; The decision support module is used to provide decision support for energy scheduling and management based on the results of the artificial intelligence analysis and prediction module.

2. A distributed energy efficient access to a power supply network device according to claim 1, characterized in that: When the distributed energy end sensor collects data, for the solar power generation equipment: The light intensity sensor adopts a multi-probe structure, including at least three probes at different angles, and the material used for the probe is a new photoelectric material whose light absorption efficiency reaches a preset threshold in a specific band. The relationship between the light absorption efficiency η of the material and the wavelength λ satisfies Where λ0 is the wavelength when the light absorption efficiency of the material is the highest, k 14 and k 15 is the material characteristic constant; Determine k 14 and k 15 The process is as follows: First, in a laboratory environment, the wavelength scanning test of the new optoelectronic material is carried out, and the light absorption efficiency η is measured at different wavelengths λ to obtain a series of (λ, η) data points. Then, the least squares method is used to fit these data points. The fitting function is: Through the fitting process, determine the k that minimizes the fitting error 14 and k 15 value; The photovoltaic panel temperature sensor is installed by attaching, attached to the back of the photovoltaic panel and in close contact with the photovoltaic panel. The sensor uses a micro-nano structured thermosensitive material. The relationship between the resistance R of the thermosensitive material and the temperature T is: R = R0×(1+k 16 ×T+k 17 ×T 2 ), where R0 is the resistance at the reference temperature, k 16 and k 17 is the characteristic parameter of the thermosensitive material; Determine k 16 and k 17 The method is as follows: a sensor with a micro-nanostructure thermosensitive material is placed in an environmental box where the temperature can be precisely controlled, and the ambient temperature T is gradually changed from low temperature to high temperature. The corresponding resistance R value is measured at the same time to obtain multiple sets of (TR) data, and a quadratic polynomial fitting is performed on these data, that is, R = R0×(1+k 16 ×T+k 17 ×T 2 ), k is determined by the fitting algorithm 16 and k 17 The value of .

3. The device for efficiently connecting distributed energy to a power supply network according to claim 1, characterized in that: The installation position of the 5G communication module at the distributed energy terminal has been optimized and designed as follows: Considering the electromagnetic radiation characteristics of distributed energy generation equipment and the electromagnetic interference of the surrounding environment, the electromagnetic radiation intensity distribution function of distributed energy generation equipment is defined as E(x, y, z), where (x, y, z) is the spatial coordinate, the electromagnetic interference intensity distribution function of the surrounding environment is I(x, y, z), the installation position coordinates of the 5G communication module are (X, Y, Z), and the signal reception strength function of the 5G communication module is S(X, Y, Z). The relationship is: where k 18 and k 19 is a constant related to the hardware characteristics of the 5G communication module; Determine k 18 and k 19 The process is as follows: In the laboratory, a standard electromagnetic radiation source is used to simulate the electromagnetic radiation of distributed energy generation equipment, and electromagnetic radiation E of different intensities is set. At the same time, electromagnetic interference I of different intensities is applied around. 5G communication modules are placed at different locations, and their actual signal reception strength S is measured. By performing multiple linear regression analysis on multiple groups of (E, I, S) data, k is obtained. 18 and k 19 The value of By measuring and analyzing the electromagnetic radiation intensity of distributed energy generation equipment and the electromagnetic interference intensity of the surrounding environment, the coordinates (X, Y, Z) that maximize S (X, Y, Z) are determined as the optimal installation position for the 5G communication module.

4. The device for efficiently connecting distributed energy to a power supply network according to claim 1, characterized in that: The power electronic conversion device in the access device at the grid access end adopts a new topological structure, which is composed of multiple power semiconductor devices and passive components, as follows: Define the number of power semiconductor devices as n and the number of passive components as m. In this topology, the on-resistance of the power semiconductor device is R on (i), i = 1, 2, ..., n, the equivalent impedance of the passive component is Z eq (j), j = 1, 2, ..., m, the voltage conversion ratio is K, and the input voltage is V in , the output voltage is V out , and the relationship satisfies: Method for determining K value: Build a test platform in the laboratory and input a known stable voltage V to the power electronic conversion device of the new topology structure. in , measure the output voltage V out , in different R on (i) and Z eq (j) Perform multiple measurements under the combination, and use the formula The K value is calculated.

5. The device for efficiently connecting distributed energy to a power supply network according to claim 1, characterized in that: The grid control device at the grid access end adopts a control algorithm based on a multi-agent system, which is as follows: Define that there are p nodes in the power grid, each node is an intelligent agent, and the state variable of the kth intelligent agent is x k , k = 1, 2, ..., p, the coupling weight between agents is w ij , i, j = 1, 2, ..., p, represents the degree of mutual influence between the i-th agent and the j-th agent, and the control input is u k , the dynamic equation of the system is: Where Ak is the state transfer matrix of the kth agent; Determine the coupling weight w ij The process is as follows: First, under the normal operation of the power grid, the state variables x of each node are k Long-term monitoring is performed to obtain status data at different times. Then, the coupling weight w is defined according to the physical connection relationship and power flow characteristics of the power grid. ij Then, the monitored state data is substituted into the dynamic equation, and w is continuously adjusted through the least squares algorithm. ij The value of w is chosen to minimize the error between the state variables calculated according to the dynamic equation and the state variables actually monitored, and finally determine the appropriate w ij value.

6. The device for efficiently connecting distributed energy to a power supply network according to claim 1, characterized in that: The data collection and preprocessing module of the intelligent analysis and prediction platform adopts a data outlier detection method based on cluster analysis and density peak algorithm when performing data cleaning, as follows: Define the collected data point set as D = {x1, x2, ..., x N }, where x i represents the i-th data point, i = 1, 2, ..., N, firstly cluster the data points, and calculate the distance metric d(x i , x j ), using an adaptive distance metric function where ||x i -x j || represents the Euclidean distance, μ is the average distance of the data point set D, and k 20 and k 21 is the adaptive coefficient; Determine k 20 and k 21 Method: Randomly select a part of known normal data points as the training set, and calculate the Euclidean distance || x between these data points i -x j ||, substitute these distance data into the distance metric function, and through the iterative optimization algorithm, make the distance metric between normal data points in the training set reflect the similarity of the data as accurately as possible. In the optimization process, k is continuously adjusted 20 and k 21 The value of k is finally determined 20 and k 21 ; According to the clustering results, the center and density of each cluster are determined, and the density peak algorithm is used to calculate the local density μ(x i ) and the relative distance δ(x i ), the local density and relative distance of the data points meet certain conditions, that is, P(x i )<ρ threshold And δ(x i )>δ threshold , where ρ threshold and δ threshold is the preset threshold, the data point is determined to be an outlier and is removed.

7. The device for efficiently connecting distributed energy to a power supply network according to claim 1, characterized in that: When analyzing the distributed energy supply situation, the artificial intelligence analysis and prediction module of the intelligent analysis and prediction platform considers factors such as the type, humidity, particle size, fermentation and combustion efficiency of biomass for biomass power generation equipment, and its power generation prediction algorithm is as follows: Define the predicted value of biomass power generation as P liio , the type parameter of biomass is G lio , humidity is H lio , the particle size is G lio , the fermentation and combustion efficiency is E bio , the algorithm formula is: lino =k 22 ×Cli o ×(1-k 23 ×H lio )×(1+k 24 ×G lio )×E lino , where k 22 , k 23 , k 24 It is the coefficient obtained by training the historical operation data of biomass power generation equipment; Determine k 22 , k 23 , k 24 The process is as follows: First, the historical operation data of biomass power generation equipment is collected, including the actual power generation under different biomass types, humidity, particle size and corresponding fermentation and combustion efficiency. The number of collected data samples is defined as n. For the i-th sample (i = 1, 2, ..., n), its biomass type parameter is C bioi , humidity is H bioi , the particle size is G b ioi, fermentation and combustion efficiency is E b ioi, the actual power generation is P l ioi, build the objective function The objective function J is minimized by the optimization algorithm to obtain the k that minimizes J. 22 , k 23 , k 24 value.

8. The device for efficiently connecting distributed energy to a power supply network according to claim 1, characterized in that: When predicting grid demand, the artificial intelligence analysis and prediction module of the intelligent analysis and prediction platform further considers the impact of seasonal changes on residents' electricity consumption habits and introduces seasonal coefficients. The grid demand prediction algorithm is improved as follows: Define the seasonal coefficient as s(t), where t represents time. For different seasons, the value of s(t) is different. In the power grid demand forecasting algorithm, The process of determining the seasonal coefficient s(t): Collect historical electricity consumption data for many years and classify the data according to seasons into spring, summer, autumn and winter. For each season, calculate the average daily electricity consumption E in that season. seasonavg , and then calculate the average daily electricity consumption E throughout the year yearavg , then the seasonal coefficient s(t) in the corresponding season is 9. The device for efficiently connecting distributed energy to a power supply network according to claim 1, characterized in that: The decision support module of the intelligent analysis and prediction platform considers the electrical distance between the distributed energy access point and the grid load center when generating energy dispatch and management decisions. Its decision algorithm is as follows: Define the electrical distance between the distributed energy access point and the grid load center as L elec , the dispatchable power of distributed energy is P dis , the power shortage of the power grid load center is ΔP, the decision variable is d, when ΔP>0 and P d When is>0, where k 25 is the decision coefficient; Determine k 25 The process: First, conduct a simulation experiment and set different ΔP, P dis and L elec For each set of values, a better decision variable d is determined according to the operation requirements of the power grid and actual experience. optimal Then, through linear regression and other methods, according to multiple groups (ΔP, P dis , L elec , d optimal ) data, solve for k 25 The value of As close as possible optimal .

10. The device for efficiently connecting distributed energy to a power supply network according to claim 1, characterized in that: The intelligent analysis and prediction platform adopts a data processing acceleration method based on quantum computing principles when performing data analysis and prediction, as follows: The data processing task can be decomposed into the operation of q quantum bits. The state of each quantum bit is represented by |ψ>, the quantum gate operation matrix is ​​U, and the data processing function is F. In the quantum computing environment, the data processing result is: Where t represents the actual data processing time, t0 represents the theoretical optimal data processing time, and k 26 and k 27 is a constant related to the characteristics of quantum computing hardware; Determine k 26 and k 27 The process is as follows: Use specific quantum computing hardware to process a series of data processing tasks with known processing time. For each task, record the actual data processing time t. At the same time, determine the theoretical optimal data processing time t0 based on the complexity of the task and quantum computing theory. Substitute the processed quantum state into the data processing function F to obtain the function value. By fitting and analyzing multiple groups of (t, t0, F) data, determine k 26 and k 27 The value of .

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