Collaborative scheduling method of power grid energy side based on carbon verification data mining algorithm

In the process of the collaborative scheduling of the grid energy-consuming side, the voltage value is low, and the utilization rate of collaborative scheduling resources of the grid energy-consuming side is poor. Therefore, a collaborative scheduling method for the grid energy-consuming side based on a carbon verification data mining algorithm is proposed. Based on the structure of the data management system, the related data structure lookup formula is set to determine the collaborative scheduling task data of the power grid energy side. Under the carbon verification data mining algorithm, the power grid topology structure is constructed, the scheduling target parameters are processed, and the coordinated scheduling of the power grid energy side is realized. The experimental results show that this method can effectively improve the voltage value of each node, and the voltage value of all nodes can reach above the qualified value and improve the utilization rate and energy-saving performance, which has a certain effectiveness.


Introduction
With the increasing global energy demand, people pay more and more attention to environmental protection, and the coordinated dispatching of the power grid energy side has become a key issue in optimizing the operation of power systems and improving energy utilization efficiency.As an effective technical means, the carbon verification data mining algorithm can help realize the coordinated dispatching of power grid energy consumption side through the analysis and mining of power grid energy consumption data [1].Traditional power system scheduling methods mainly focus on the supply side, ignoring the potential optimization space of the energy side [2][3].However, with the advancement of energy transformation, the power system should be more flexible to meet the challenges of renewable energy fluctuation and load change.Therefore, the cooperative scheduling of the energy consumption side of the power grid has become the key to solving these problems.
On this basis, this paper proposes a collaborative scheduling method of the power grid energy side based on a carbon verification data mining algorithm.Carbon verification data refers to the data for measuring and tracking factors related to energy consumption or emissions in the power grid.These data include power supply energy type, energy consumption, carbon dioxide emissions, and so on.By mining and analyzing these data, we can reveal the potential optimization strategy and scheduling mode of the power grid energy consumption side, to realize the coordinated scheduling of power grid energy consumption.By mining carbon verification data, the dynamic coordination between supply and demand of the power grid can be realized, the supply pressure can be reduced, and the environmental problems caused by energy consumption and carbon dioxide emission can be reduced.

Collaborative scheduling task data acquisition of grid energy side
Collaborative scheduling of the power grid energy consumption side refers to organically connecting the power grid and energy consumption side by means of information technology to realize a dynamic balance of energy supply and demand and optimal scheduling [4].Among them, data acquisition is the basis of collaborative scheduling, and the data of energy users are collected through smart meters, sensors, and other equipment, and transmitted to the dispatching center through the communication mode under the carbon verification data mining algorithm [5].To improve the cooperative scheduling ability of the power grid energy side, this paper preliminarily obtains the cooperative scheduling task data of the power grid energy side, centrally adjusts the data existence mode and storage space, and carries out integrated data processing according to the relevant storage principles [6].We organize the data information collection mode.When observing the collaborative scheduling task on the energy side of the power grid, we select the corresponding matching data for internal data matching operation and a more suitable data management system according to the matched data correlation degree.The data management system structure is shown in Figure 1.In Figure 1, the real-time monitoring operation of the task data of the energy-consuming side of the power grid is continuously strengthened, so that the collected data is kept in the state required by the system, and the data structure is searched.It is assumed that the data development mode is M , and the power grid load data set is input to obtain nodes with a high matching degree with the target node parameters.The related data structure search formula is set as follows: where  represents the overall data storage mode data;  represents the overall amount of data;  indicates the existence status of relevant parameters.To strengthen the data collection intensity of the central system, we improve the processing operation of the internal system, calculate the expected data quantity in each storage space, add the data that meets the system requirements to the scheduling task data set, filter the data parameters that are inconsistent with the system requirements, and gradually adjust the data proportion in each storage space.
where  represents the power demand of the energy consumption side; DF A stands for energy-using side characteristics.According to the processing of the internal storage space of data, the centralized storage of collected data is realized, the storage system is integrated, and the integrity of data is guaranteed, to realize the data acquisition operation of collaborative scheduling tasks on the energy side of the power grid.

Parameter processing of scheduling target under carbon verification data mining algorithm
The carbon verification data mining algorithm is a method to analyze and process carbon verification data by using data mining technology.Carbon verification refers to the process of monitoring and evaluating greenhouse gas emissions, aiming at quantifying and managing greenhouse gas emissions to support measures to mitigate climate change.Under the carbon verification data mining algorithm, the scheduling target parameter processing is to realize the monitoring, analysis, and optimal management of carbon emissions.The collaborative scheduling data of the energy consumption side of the power grid is roughly divided into two parts: nodes and links.Nodes usually have two ways, among which one is an internet computer and the other is a power processor, and links are the line connecting the two nodes.The scheduling data network usually transmits data from high to low.The scheduling parameters of the dynamic critical path of the collaborative scheduling data balance of the energy-consuming side are closely related to the topology of the power grid.In this scenario, the topology of the power grid is shown in Figure 2. As can be seen from Figure 2, the power grid topology of the base station covers many resources with different sources and structures and realizes the interconnection of power grid dispatching information.It is assumed that a control parameter is Q .Under the carbon verification data mining algorithm, the power grid load parameter is guaranteed to be minimum.At this time, the corresponding time delay parameters can be selected for the dynamic critical path scheduling parameters of collaborative scheduling data balance of the power grid on the energy-consuming side.In the process of dispatching, the corresponding delay parameters can be selected to balance the dynamic critical path of collaborative dispatching on the energy consumption side of the power grid.It is assumed that the length of each idle period is 12 , , , n L L L in turn, and the information ( ) n DL of the n th control data node is: where K J represents the data attribute of collaborative scheduling of the energy consumption side of the power grid; L G stands for load strength; C S represents the trust value obtained from the recent query of the collaborative dispatching data of the energy-consuming side of the power grid.After adjusting the data attributes, load intensity, and trust value, we optimize the scheduling target parameters.
( ) T  stands for the total grid load balancing time.According to the parameters obtained above, the time scale characteristics of information flow are extracted, and the scheduling target parameter processing under the carbon verification data mining algorithm is completed.

Realization of the coordinated scheduling of the energy consumption side of the power grid
The coordinated dispatching of the power grid energy side refers to the efficient operation and sustainable development of the power grid through collaborative management and optimization of energy use in the power system.Its core goal is to maximize the matching between supply and demand of the power grid, improve energy utilization efficiency, and reduce energy consumption and carbon emissions.Using the network-aware iterative equilibrium strategy, the power data are analyzed in advance, and the initial coordinated dispatching data of the power grid energy side are centralized.We centralize the power data storage and processing system obtained from the analysis, construct the initial coordinated dispatching data on the energy side of the power grid, and constantly update the coordinated dispatching data to keep it consistent with the actual situation of the current power grid.The output results of collaborative scheduling on the final energy consumption side of the power grid are as follows: where M Z represents the power grid load correction value; N Z indicates the number of samples measured by the load; L V represents the threshold set by power grid error.
Data mining and forecasting technology are applied to analyze and predict the energy demand of energy users, make scheduling decisions in advance, optimize the scheduling scheme of power resources, and realize the cooperative scheduling of energy users.

Experimental analysis
To verify the performance of grid energy-side collaborative scheduling method based on carbon verification data mining algorithm in practical application, experiments are carried out in the simulation environment of WindowsXP operating system, Matlab 7 development environment, Tomcat 5.5 server, Microsoft SQL 2018 database, and Eclipse development tool.We preprocess the data sets of power grid energy consumption data and carbon verification data to ensure the accuracy and rationality of the data.According to the specific requirements of the carbon verification data mining algorithm, the corresponding algorithm program is written to carry out experiments.The specific steps are as follows: Step 1: In the Matlab 7 environment, we select the power grid module, network node module, and resource scheduling module in the Simulink library, and use the function gensim to generate the power grid.
Step 2: Python is used to compile the weight vector conversion program so that each module can accept the weight vector of large-scale power grid load and obtain the load weighted input column vector value.
Step 3: We set simulation parameters, build a grid topology model, and generate a subsystem.According to the fundamental wave of the power grid, the chaotic iteration of power grid security scheduling is executed, and HDL code is generated.
Step 4: We deploy the generated HDL code on SoC equipment, write the large-scale power grid load weights through visa, form a weighted feasible region protocol, and unify the ARM Compute library.
Step 5: We click the Run command to start the model simulation and carry out a real-time simulation to prove it.
In the experiment, 125 computers in five cabinets are used to build an application scenario of cloud computing.These devices use special switches to form a huge power grid, and the half bandwidth in the cabinets is about 15 GB/s.In the parameter designation link, the degree of power grid is 20 and the number of network nodes is 850.Through 110 tasks, the cloud storage resource scheduling set of a large-scale power grid load database is constructed, and the power grid load data is divided into 15 cells for virtual detection.This operation is realized in the control process, and the cells are used as models for obtaining basic data and signals.Through the collaborative scheduling platform of the power grid energy side, the security scheduling of the power grid is monitored and early-warned, and the experimental parameters are set.The fundamental wave setting of the power grid changes from 48.5 Hz to 49.5 Hz, the experimental fundamental wave frequency is designated as 48.5 Hz, and the experimental sampling frequency is set as 1, 500 Hz.Other parameter configurations of the experiment are shown in Table Under the above experimental environment and experimental parameter settings, the voltage values of each node before and after the scheduling by this method are measured, and the experimental results are shown in Figure 3. Figure 3 shows the voltage results before and after the dispatching of this method, and the dotted line is the minimum standard of qualified voltage.From the experimental results, it can be seen that the voltage value of each node can be effectively improved by using this method, and the voltage value of all nodes can reach above the qualified value, so it shows that this method is effective.
To further verify the effectiveness of the method in this paper, the utilization ratio of coordinated scheduling resources on the energy side of the power grid is used for verification, and the utilization ratio formula is expressed as: 100% E , e E and E are all calculated from the simulation model.Using the above formulas, the utilization rates of the three methods are obtained, and the results are shown in Figure 4.
The method in this paper  As can be seen from Figure 4, the scheduling method studied in this paper has the best effect on the coordinated scheduling of the power grid energy side, which improves the utilization rate and energysaving performance at the same time.The method in this paper processes the scheduling target parameters under the carbon verification data mining algorithm, which is beneficial to realize the optimal allocation of resources based on effective analysis and has good energy-saving benefits.

Conclusion
In this paper, a collaborative scheduling method of the power grid energy side based on a carbon verification data mining algorithm is proposed, and its effectiveness is verified by an example.The following conclusions are drawn: the voltage value of each node can be effectively improved by using this method, and the voltage value of all nodes can reach above the qualified value.At the same time, the utilization rate is improved and the energy-saving performance is improved, which has a certain effectiveness.However, there are some limitations in this study.To apply the carbon verification data mining algorithm, a large number of high-quality power grid energy consumption data are needed.Future research can further improve the collection and management methods of carbon verification data, to improve the quality and real-time performance of data.We explore more data mining algorithms and technologies to improve the accuracy and stability of the model.

Figure 1 .
Figure 1.Structure diagram of data management system.

Figure 2 .
Figure 2. Topological structure diagram of power grid.

Figure 3 .
Figure 3.Comparison diagram of voltage experimental results before and after dispatching.

Figure 4 .
Figure 4. Comparison results of utilization rate under different methods.