New energy dispatching strategy deduction method and system based on hierarchical grading system

Abstract

The present application relates to the technical field of regulation and management, and particularly relates to a new energy dispatching strategy deduction method and system based on a hierarchical system. The method comprises the following steps: constructing a hierarchical regulation and management architecture system corresponding to a macro regulation and control layer, a regional coordination layer and a local control layer, and collecting real-time operation data, meteorological data and power load data and uploading them to the macro regulation and control layer; performing sharp peak denoising and normalization on the operation data, meteorological data and power load data, and simulating and deducing different new energy operation dispatching simulation conditions; performing dispatching evaluation index analysis and hierarchical evaluation analysis on the corresponding new energy power system to obtain a corresponding new energy dispatching strategy comprehensive score; and using the macro regulation and control layer to generate corresponding new energy power generation dispatching control instructions and hierarchically issue them to the local control layer to execute the corresponding new energy dispatching optimization strategy. The present application can realize efficient deduction and accurate analysis of new energy dispatching strategies.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of regulation and management, and particularly relates to a new energy dispatching strategy deduction method and system based on a hierarchical system. BACKGROUND

[0002] New energy power generation has the characteristics of intermittency, volatility and randomness. For example, solar energy is affected by light intensity and time, and wind energy is affected by wind speed and wind direction, which brings great challenges to the stable operation and dispatching of the power system. The traditional power dispatching strategy is mainly aimed at conventional energy power generation and is difficult to adapt to the complex working conditions after large-scale access of new energy. However, the existing new energy dispatching strategy analysis method is mostly based on simple mathematical models and experience judgments. When facing the uncertainty of new energy power generation, it lacks a deep understanding of the characteristics of new energy power generation and the operation rules of the power system, and cannot comprehensively and accurately evaluate the effect of the dispatching strategy, thereby affecting the safe and stable operation of the power system. SUMMARY

[0003] Therefore, it is necessary to provide a new energy dispatching strategy deduction method and system based on a hierarchical system to solve at least one of the above technical problems.

[0004] To achieve the above-mentioned purpose, a new energy dispatching strategy deduction method based on a hierarchical system comprises the following steps:

[0005] Step S1: constructing a hierarchical regulation and control architecture system corresponding to a macro regulation and control layer, a regional coordination layer and a local control layer;

[0006] Step S2: collecting the operation data, meteorological data and power load data of the new energy power generation equipment in real time through the local control layer in the hierarchical regulation and control architecture system, and uploading them to the data regulation and control processing center corresponding to the macro regulation and control layer; using the data regulation and control processing center to carry out spike denoising and normalization on the operation data, meteorological data and power load data of the new energy power generation equipment, to obtain corresponding new energy power generation operation data, new energy meteorological data and new energy power load data in the same order of magnitude scale;

[0007] Step S3: simulating and deducing different new energy operation dispatching simulation conditions based on the corresponding new energy power generation operation data, new energy meteorological data and new energy power load data in the same order of magnitude scale, which include the new energy dispatching strategy corresponding to different new energy equipment operation states, meteorological conditions and power load change conditions; based on different new energy operation dispatching simulation conditions, analyzing the dispatching evaluation indexes of the corresponding new energy power system, to obtain the power safety index, power reliability index and power carbon emission reduction index of the power system under different operation dispatching conditions;

[0008] Step S4: hierarchical evaluation and analysis of the power safety index, power reliability index and power carbon emission reduction index corresponding to the power system under different operation scheduling conditions are performed to obtain the comprehensive score of the new energy scheduling strategy corresponding to the power system under different operation scheduling conditions; based on the comprehensive score of the new energy scheduling strategy corresponding to the power system under different operation scheduling conditions, the macro-control layer response is used to generate corresponding new energy power generation scheduling control instructions, which are conveyed to the regional coordination layer to formulate corresponding new energy power generation scheduling plans according to the corresponding new energy power generation conditions in the region and are issued to the local control layer to execute the corresponding new energy scheduling optimization strategy.

[0009] Further, step S1 includes the following steps:

[0010] Step S11: a macro-control layer is designed by the new energy power system management department to be responsible for formulating the overall target and control instructions of new energy power generation scheduling from a global perspective;

[0011] Step S12: a regional coordination layer is designed by the regional power dispatching center to respond to the control instructions of the macro-control layer and formulate the new energy scheduling plan according to the new energy generation quota and scheduling principles of each region;

[0012] Step S13: a local control layer is designed by the new energy power station, meteorological monitoring station and power user end to monitor the operation data, meteorological data and power load data of the new energy power generation equipment in real time, and execute the new energy scheduling plan issued by the regional coordination layer to adjust the operation parameters of the new energy power generation equipment;

[0013] Step S14: an information interaction and cooperation network corresponding to each level between the macro-control layer, the regional coordination layer and the local control layer is established by using a communication network to construct a hierarchical control architecture system.

[0014] Further, step S2 includes the following steps:

[0015] Step S21: the operation data of the new energy power generation equipment, including power generation, power generation power and equipment operation state, are collected in real time by the new energy power station corresponding to the local control layer in the hierarchical control architecture system;

[0016] Step S22: the meteorological data of the new energy power generation equipment, including the light intensity, wind speed and temperature of the equipment area, are collected in real time by the meteorological monitoring station corresponding to the local control layer in the hierarchical control architecture system;

[0017] Step S23: the power load data of the new energy power generation equipment, including the power load size and power load change trend, are collected in real time by the power user end corresponding to the local control layer in the hierarchical control architecture system;

[0018] Step S24: The operation data, meteorological data and power load data corresponding to the new energy power generation equipment are uploaded from the local control layer to the data control processing center of the macro control layer;

[0019] Step S25: The operation data, meteorological data and power load data corresponding to the new energy power generation equipment are subjected to spike denoising and normalization by the data control processing center, so as to obtain the corresponding new energy power generation operation data, new energy meteorological data and new energy power load data in the same order of magnitude scale.

[0020] Further, step S25 includes the following steps:

[0021] Step S251: The operation data, meteorological data and power load data corresponding to the new energy power generation equipment are subjected to multi-element feature depth analysis by the data control processing center, so as to analyze the long-term and short-term operation fluctuation trend characteristics of the new energy power generation equipment by decomposing the parameters corresponding to the operation data into trend items, periodic items and random items using time series decomposition method, analyze the spatial distribution characteristics of different meteorological elements by expanding the discrete meteorological data into continuous regional meteorological field using spatial interpolation technology, and analyze the power load change mode characteristics of the new energy power generation equipment through the power load data, so as to obtain a new energy equipment multi-element depth feature set.

[0022] Step S252: Based on the new energy equipment multi-element depth feature set and combined with the physical principles and power system operation rules corresponding to the new energy power generation equipment, the operation data, meteorological data and power load data corresponding to the new energy power generation equipment are subjected to abnormality comparison and identification, so as to obtain operation abnormal data, meteorological abnormal data and power abnormal data corresponding to the new energy power generation equipment.

[0023] Step S253: The operation abnormal data, meteorological abnormal data and power abnormal data corresponding to the new energy power generation equipment are subjected to spike denoising processing, so as to highlight the data characteristics corresponding to the abnormal spike noise by decomposing the corresponding abnormal data into different frequency subbands using wavelet transform, remove the wavelet coefficients corresponding to the abnormal spike noise by adopting the opening and closing operation in morphological filtering, and reconstruct the corresponding abnormal data by inverse wavelet transform, so as to obtain new energy power generation denoising data, new energy meteorological denoising data and new energy power load denoising data.

[0024] Step S254: The new energy power generation denoising data, new energy meteorological denoising data and new energy power load denoising data are subjected to normalization processing, so as to obtain the corresponding new energy power generation operation data, new energy meteorological data and new energy power load data in the same order of magnitude scale.

[0025] Further, step S3 includes the following steps:

[0026] Step S31: Different new energy operation scheduling simulation conditions are designed by simulating and deducing corresponding new energy power generation operation data, new energy meteorological data and new energy power load data at the same order of magnitude scale, including new energy scheduling strategies corresponding to different new energy equipment operation states, meteorological conditions and power load change conditions;

[0027] Step S32: Based on different new energy operation scheduling simulation conditions, power safety evaluation index analysis is performed on the corresponding new energy power system, and the corresponding power safety indexes of the power system under different operation scheduling conditions are obtained, including power operation frequency deviation and power operation line overload rate;

[0028] Step S33: Based on different new energy operation scheduling simulation conditions, the corresponding outage time and outage frequency of the new energy power system under the corresponding operation scheduling conditions are obtained, and the corresponding power reliability indexes of the power system under different operation scheduling conditions are obtained;

[0029] Step S34: Based on different new energy operation scheduling simulation conditions, real-time power carbon emission monitoring is performed on the corresponding new energy power system, and the corresponding real-time carbon emission of the power system under different operation scheduling conditions is obtained;

[0030] Step S35: The real-time carbon emission of the power system under different operation scheduling conditions is compared with the corresponding historical carbon emission, and the power carbon emission reduction amount index of the power system under different operation scheduling conditions is obtained.

[0031] Further, step S32 includes the following steps:

[0032] Step S321: Based on different new energy operation scheduling simulation conditions and combining with the phasor measurement unit, the power operation phasor of the corresponding new energy power system is measured, and the power operation phasor of the power system under different operation scheduling conditions is obtained, including voltage phasor and current phasor;

[0033] Step S322: Based on the power operation phasor of the power system under different operation scheduling conditions, the operation frequency of the corresponding new energy power system is calculated, and the actual power operation frequency of the power system under different operation scheduling conditions is obtained;

[0034] Step S323: The corresponding rated frequency is obtained by the new energy power system, and the frequency deviation of the actual power operation frequency of the power system under different operation scheduling conditions is calculated based on the rated frequency, and the power operation frequency deviation of the power system under different operation scheduling conditions is obtained;

[0035] Step S324: Based on different new energy operation scheduling simulation conditions, the line overload rate of the corresponding new energy power system is evaluated, and the corresponding power operation line overload rate of the power system under different operation scheduling conditions is obtained.

[0036] Further, step S324 includes the following steps:

[0037] Based on different new energy operation scheduling simulation conditions, the corresponding new energy power system is disturbed and transiently simulated to generate the corresponding power operation disturbance transient process of the power system under different operation scheduling conditions.

[0038] According to a certain time interval, the node voltage, line current and power generation power in the power operation disturbance transient process of the power system under different operation scheduling conditions are collected.

[0039] Based on the node voltage, line current and power generation power, the line overload rate of the corresponding new energy power system is evaluated by using the line overload rate calculation formula, and the corresponding power operation line overload rate of the power system under different operation scheduling conditions is obtained.

[0040] Further, the line overload rate calculation formula is specifically:

[0041] ;

[0042] In the formula, is the power operation line overload rate, is the initial time of overload rate calculation, is the termination time of overload rate calculation, is the time variable parameter, is the line current corresponding to the time , is the rated current that the line can carry, is the node voltage corresponding to the time , is the node voltage influence weight, is the power generation power corresponding to the time , is the line transmission capacity, is the power generation power influence weight, is the line nominal voltage, is the correction coefficient of the power operation line overload rate.

[0043] Further, step S4 includes the following steps:

[0044] Step S41: hierarchical evaluation analysis is performed on the power safety index, the power reliability index and the power carbon emission reduction amount index corresponding to the power system under different operation scheduling conditions, to obtain a comprehensive score of the new energy scheduling strategy corresponding to the power system under different operation scheduling conditions;

[0045] Step S42: based on the comprehensive score of the new energy scheduling strategy corresponding to the power system under different operation scheduling conditions, the macro control layer is used to analyze the scheduling optimization target of the corresponding new energy power system, if the comprehensive score of the new energy scheduling strategy corresponding to the power system under different operation scheduling conditions is greater than or equal to a preset score threshold, then the comprehensive score of the scheduling strategy under the corresponding operation scheduling condition is continuously evaluated and monitored; if the comprehensive score of the new energy scheduling strategy corresponding to the power system under different operation scheduling conditions is less than the preset score threshold, then the macro control layer is used to determine the overall optimization target of the corresponding power system scheduling;

[0046] Step S43: the macro control layer is driven to respond to the overall optimization target of the corresponding power system scheduling and generate a corresponding new energy power generation scheduling control instruction;

[0047] Step S44: the new energy power generation scheduling control instruction generated by the control is conveyed to the regional coordination layer to formulate a corresponding new energy power generation scheduling plan according to the corresponding new energy power generation condition of the region, and is hierarchically issued to the local control layer to execute the corresponding new energy scheduling optimization strategy.

[0048] Further, the application also provides a new energy scheduling strategy deduction system based on a hierarchical grading system, which is used to execute the new energy scheduling strategy deduction method based on the hierarchical grading system as described above, and the new energy scheduling strategy deduction system based on the hierarchical grading system comprises:

[0049] A hierarchical grading control architecture design module is used to build a hierarchical grading control architecture system comprising a macro control layer, a regional coordination layer and a local control layer.

[0050] A new energy multi-source data processing module is used to collect the operation data, meteorological data and power load data corresponding to the new energy power generation equipment in real time through the local control layer in the hierarchical grading control architecture system, and upload them to the data control processing center corresponding to the macro control layer; the operation data, meteorological data and power load data corresponding to the new energy power generation equipment are subjected to spike denoising and normalization by the data control processing center, so as to obtain the new energy power generation operation data, new energy meteorological data and new energy power load data corresponding to the same order of magnitude scale;

[0051] The dispatching evaluation index deduction analysis module is used for simulating and deducing different new energy operation dispatching simulation conditions by corresponding new energy power generation operation data, new energy meteorological data and new energy power load data in the same magnitude scale, wherein the new energy dispatching strategies corresponding to different new energy equipment operation states, meteorological conditions and power load change conditions are included; the corresponding new energy power system is subjected to dispatching evaluation index analysis based on different new energy operation dispatching simulation conditions, so that the power safety index, the power reliability index and the power carbon emission reduction amount index corresponding to the power system under different operation dispatching conditions are obtained;

[0052] The new energy dispatching hierarchical issuing control module is used for performing hierarchical evaluation analysis on the power safety index, the power reliability index and the power carbon emission reduction amount index corresponding to the power system under different operation dispatching conditions, so that the new energy dispatching strategy comprehensive score corresponding to the power system under different operation dispatching conditions is obtained; the corresponding new energy power generation dispatching control instruction is generated by using the macro control layer response based on the new energy dispatching strategy comprehensive score corresponding to the power system under different operation dispatching conditions, and is conveyed to the regional coordination layer to formulate the corresponding new energy power generation dispatching plan according to the corresponding new energy power generation condition of the region, and the plan is issued to the local control layer to execute the corresponding new energy dispatching optimization strategy.

[0053] The beneficial effects of the present application are as follows:

[0054] 1、The new energy scheduling strategy deduction method based on the hierarchical system proposed in the application has the beneficial effects compared with the prior art, that is, by constructing a hierarchical control architecture system including a macro-control layer, a regional coordination layer and a local control layer, the macro-control layer serves as a global decision-making layer, responsible for formulating long-term and global new energy power generation strategies, policies and plans, the regional coordination layer combines the actual demand and resource status in the region according to the instructions of the macro layer, carries out medium-term resource scheduling and planning, and provides specific scheduling suggestions and operation instructions for the local control layer, and the local control layer is responsible for real-time control and data collection of specific new energy power generation equipment, so as to ensure the efficiency and stability of the new energy equipment in the operation process, through this hierarchical management system, cross-level and cross-regional information sharing and cooperation can be realized, ensuring that different levels of control systems can effectively respond to changes in power demand, equipment failure and weather changes, while avoiding the problems of low efficiency and decision lag caused by a single control system, thereby improving the overall operation efficiency and flexibility of the new energy power generation system. Secondly, by collecting the operation data, weather data and power load data of the new energy power generation equipment in real time through the local control layer and uploading them to the data control processing center of the macro-control layer, the timeliness and integrity of the data can be effectively guaranteed, and through the peak noise elimination technology, the abnormal fluctuations and noise in the data can be removed, so that the collected data is smoother and conforms to the actual trend, improving the quality and reliability of the data, and the data normalization processing can unify the data of different sources and different magnitudes to the same standard scale, so that various types of data can be directly compared and analyzed, avoiding the processing difficulty caused by the dimensional difference of the data, through these steps, not only the accuracy and consistency of the data can be improved, but also more reliable input data can be provided for subsequent scheduling simulation and decision analysis, thereby improving the scientificity and effectiveness of the whole power system scheduling process.Then, by simulating different new energy operation scheduling simulation conditions under a unified magnitude scale, and combining different device operation states, weather conditions and power load change conditions, more comprehensive and detailed scheduling strategy design can be provided for the new energy power system. The core of this process is to systematically analyze and predict the power system under different operating conditions. By simulating different scenarios, the operating effect and possible problems of the power system are predicted, so as to obtain the optimal scheduling strategy. By evaluating the scheduling strategy under different simulation conditions, multi-dimensional power system performance indicators such as power safety, reliability and carbon emission reduction amount can be obtained. These indicators are crucial for new energy scheduling decision-making. The safety indicator can reflect the fault handling capability of the power system under different conditions. The reliability indicator can evaluate the stability and emergency response capability of the system. The carbon emission reduction amount indicator reflects the contribution of the scheduling strategy to environmental protection goals. Through this process, scientific and quantitative scheduling evaluation can be provided for the power system to ensure that the scheduling strategy can maximize the stability of the power system, so that the generation characteristics of new energy and the operating rules of the power system can be better understood. Finally, by performing hierarchical evaluation analysis on the performance indicators of the power system under different scheduling conditions, not only can the performance of each indicator under different conditions be comprehensively evaluated, but also the new energy scheduling strategy can be comprehensively scored from multiple angles and dimensions. This hierarchical evaluation method can fully consider the complexity and diversity of the power system, ensure that the optimal scheduling scheme is selected under different operating conditions, and the scheduling instructions generated by the macro control layer can accurately transmit the new energy scheduling strategy to the regional coordination layer. The regional coordination layer formulates detailed scheduling plans according to the actual situation of the region and issues them to the local control layer for execution. In this way, the scheduling decision-making process of the entire power system is refined and optimized layer by layer, thereby avoiding the problem of low scheduling efficiency caused by poor information transmission or decision lag. This process can comprehensively and accurately evaluate the effect of the scheduling strategy and promote the development of the power system towards higher safety, reliability and low carbonization.

[0055] 2、The new energy scheduling strategy deduction system based on the hierarchical grading system can realize any new energy scheduling strategy deduction method based on the hierarchical grading system, and can realize the operation between the computer programs running on each module to realize the new energy scheduling strategy deduction method based on the hierarchical grading system. The internal structure of the system cooperates with each other, which can greatly reduce the repeated work and manpower investment, can quickly and effectively provide more accurate and efficient new energy scheduling strategy deduction process based on the hierarchical grading system, and thereby simplifies the operation process of the new energy scheduling strategy deduction system based on the hierarchical grading system. Attached Figure Description

[0056] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0057] Figure 1 This is a flowchart illustrating the steps of the new energy dispatch strategy deduction method based on a hierarchical system according to the present invention.

[0058] Figure 2 for Figure 1 A detailed flowchart of step S1;

[0059] Figure 3 for Figure 1 A detailed flowchart of step S2. Detailed Implementation

[0060] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0061] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0062] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0063] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides a method for deriving new energy dispatching strategies based on a hierarchical system, the method comprising the following steps:

[0064] Step S1: constructing a hierarchical and graded control architecture system including a macro-control layer, a regional coordination layer and a local control layer;

[0065] Step S2: collecting operation data, meteorological data and power load data of the new energy power generation equipment in real time by the local control layer in the hierarchical and graded control architecture system, and uploading them to a data control processing center corresponding to the macro-control layer; using the data control processing center to perform spike denoising and normalization on the operation data, meteorological data and power load data of the new energy power generation equipment, to obtain corresponding new energy power generation operation data, new energy meteorological data and new energy power load data at the same order of magnitude scale;

[0066] Step S3: simulating and deducing different new energy operation scheduling simulation conditions based on the corresponding new energy power generation operation data, new energy meteorological data and new energy power load data at the same order of magnitude scale, wherein the new energy scheduling strategies corresponding to different new energy equipment operation states, meteorological conditions and power load change conditions are included; based on the different new energy operation scheduling simulation conditions, the corresponding new energy power system is scheduled to analyze the scheduling evaluation indexes, to obtain the power safety indexes, power reliability indexes and power carbon emission reduction indexes of the power system under different operation scheduling conditions;

[0067] Step S4: performing hierarchical evaluation analysis on the power safety indexes, power reliability indexes and power carbon emission reduction indexes of the power system under different operation scheduling conditions, to obtain the comprehensive scores of the new energy scheduling strategies of the power system under different operation scheduling conditions; based on the comprehensive scores of the new energy scheduling strategies of the power system under different operation scheduling conditions, the macro-control layer generates corresponding new energy power generation scheduling control instructions, and transmits them to the regional coordination layer to formulate corresponding new energy power generation scheduling plans according to the corresponding new energy power generation conditions in the region, and then issues them to the local control layer to execute corresponding new energy scheduling optimization strategies.

[0068] In the embodiment of the application, please refer to Figure 1 The figure shows the step flowchart of the new energy scheduling strategy deduction method based on the hierarchical and graded system, and in this example, the new energy scheduling strategy deduction method based on the hierarchical and graded system includes the following steps:

[0069] Step S1: constructing a hierarchical and graded control architecture system including a macro-control layer, a regional coordination layer and a local control layer;

[0070] In the embodiment of the present application, the first task of constructing a hierarchical regulation architecture is to set the function and responsibility division between different levels. In the macro regulation layer, a data regulation processing center should be built to undertake the task of overall data collection, processing and decision-making. The center collects data from the regional coordination layer and the local control layer through various information transmission channels. In the regional coordination layer, a regional scheduling system is designed to be responsible for unified scheduling planning of new energy power generation facilities in each region according to the instructions of the macro regulation layer, and to monitor the power flow and load change in the region. The local control layer directly participates in the actual operation and control of the equipment to ensure that each new energy power generation equipment operates according to the scheduling instructions. Each device collects the operating state and real-time data of the device through its embedded control system or SCADA system in real time and reports to the upper level. This hierarchical architecture ensures efficient and stable operation of the new energy power generation system under multi-level cooperation, supporting intelligent and high-precision scheduling decisions.

[0071] Step S2: collecting the operating data of the new energy power generation equipment, the meteorological data and the power load data in real time through the local control layer in the hierarchical regulation architecture, and uploading them to the corresponding data regulation processing center in the macro regulation layer; using the data regulation processing center to perform spike denoising and normalization on the operating data of the new energy power generation equipment, the meteorological data and the power load data to obtain corresponding new energy power generation operating data, new energy meteorological data and new energy power load data in the same order of magnitude scale;

[0072] In the embodiment of the present application, the local control layer first acquires multiple data sources through the real-time data acquisition system connected with the new energy power generation equipment, including but not limited to equipment operating state data, meteorological data and power load data. The equipment operating state data includes power generation power, equipment health status, etc. The meteorological data involves temperature, wind speed, radiation intensity and other weather factors affecting new energy power generation. The power load data reflects the demand of the power grid for the new energy power generation system. After all the data are collected, they are uploaded to the data regulation processing center in the macro regulation layer through a special communication protocol (such as Modbus, DNP3, etc.). After receiving the data, the data regulation processing center first performs data cleaning to eliminate outliers, and then uses mathematical algorithms (such as spike denoising technology) to smooth the data to reduce noise caused by short-term fluctuations. Subsequently, the data are normalized to enable data from different sources to be compared and analyzed in the same order of magnitude scale. For example, the values of various data are converted to the interval of 0 to 1 by standardization method to eliminate the interference caused by different original data dimensions. Finally, the corresponding new energy power generation operating data, new energy meteorological data and new energy power load data in the same order of magnitude scale are obtained.

[0073] Step S3: Different new energy operation scheduling simulation conditions are designed by simulating and deducing the corresponding new energy power generation operation data, new energy meteorological data and new energy power load data at the same order of magnitude, including the new energy scheduling strategy corresponding to different new energy equipment operation states, weather conditions and power load change conditions; based on different new energy operation scheduling simulation conditions, the corresponding new energy power system is scheduled to analyze the scheduling evaluation indexes, and the corresponding power system safety index, power reliability index and power carbon emission reduction index under different operation scheduling conditions are obtained.

[0074] In the embodiment of the application, based on the processed standardized data, the next task is to simulate and deduce the operation of the new energy power generation system under different scheduling conditions. First, different scheduling conditions are set in the simulation environment, including but not limited to different equipment operation states (such as changes in device power output), different weather conditions (such as changes in wind speed, illumination, etc.), and power load fluctuations. For each simulation condition, the behavior of the new energy power system is modeled, considering factors such as power generation, load regulation and grid stability, and multiple rounds of simulation calculations are performed. Appropriate scheduling algorithms (such as heuristic optimization algorithms, genetic algorithms, etc.) are used to calculate the optimal scheduling strategy under specific conditions. After completing the simulation and deduction, a series of scheduling evaluations are performed for each different scheduling strategy, including the safety, reliability and carbon emission reduction of the power system. Safety evaluation usually checks whether the grid has collapsed or overloaded under simulation conditions. Reliability evaluation considers the impact of device failure rate and load fluctuation. Carbon emission reduction is calculated based on the replacement benefit of new energy generation and the emission of traditional energy. Finally, the power safety index, power reliability index and power carbon emission reduction index corresponding to the power system under different operation scheduling conditions are obtained.

[0075] Step S4: Hierarchical evaluation analysis is performed on the power safety index, power reliability index and power carbon emission reduction index corresponding to the power system under different operation scheduling conditions to obtain the comprehensive score of the new energy scheduling strategy corresponding to the power system under different operation scheduling conditions. Based on the comprehensive score of the new energy scheduling strategy corresponding to the power system under different operation scheduling conditions, the corresponding new energy power generation scheduling control instructions are generated by the macro control layer response, and are conveyed to the regional coordination layer to formulate the corresponding new energy power generation scheduling plan according to the corresponding new energy power generation conditions in the region, and are issued to the local control layer to execute the corresponding new energy scheduling optimization strategy.

[0076] In this embodiment of the invention, based on the simulation results, the next step is to comprehensively evaluate the performance of the power system under different dispatching conditions. A multi-dimensional hierarchical evaluation method is used to weight and calculate three evaluation indicators: safety, reliability, and carbon emission reduction. This yields a comprehensive score for the new energy dispatching strategy corresponding to the power system under different operating and dispatching conditions. This score provides a clear decision-making basis for the macro-control layer. In this way, dispatching decisions not only consider the safety and reliability of the power system but also fully consider environmental benefits. Based on the comprehensive score, the macro-control layer generates new energy power generation dispatching and control instructions and transmits these instructions to the regional coordination layer. At the regional coordination layer, based on the actual operating conditions and dispatching needs of new energy power generation facilities within the region, specific dispatching plans are refined and adjusted, and further distributed to the local control layer. The local control layer is responsible for executing specific dispatching optimization strategies, ensuring that the dispatching plan can be efficiently and accurately implemented in the operation and control of specific equipment, thereby achieving coordinated operation and optimized dispatching of the entire system.

[0077] Furthermore, step S1 includes the following steps:

[0078] Step S11: The new energy power system management department designs a macro-control layer to be responsible for formulating the overall objectives and control instructions for new energy power generation dispatch from a global perspective;

[0079] Step S12: Design a regional coordination layer through the regional power dispatch center to respond to the corresponding control instructions of the macro-control layer and formulate corresponding new energy dispatch plans based on the new energy power generation quotas and dispatch principles of each region.

[0080] Step S13: Design a local control layer through new energy power plants, meteorological monitoring stations and power users to monitor the operation data, meteorological data and power load data of new energy power generation equipment in real time, and execute the new energy dispatch plan issued by the regional coordination layer to adjust the operation parameters of new energy power generation equipment.

[0081] Step S14: Utilize communication networks to establish corresponding information exchange and coordination networks among the macro-control layer, regional coordination layer, and local control layer, in order to construct a corresponding hierarchical control architecture.

[0082] As an embodiment of the present invention, reference is made to... Figure 2 As shown, Figure 1 A detailed flowchart of step S1 is shown below. In this embodiment, step S1 includes the following steps:

[0083] Step S11: The new energy power system management department designs a macro-control layer to be responsible for formulating the overall objectives and control instructions for new energy power generation dispatch from a global perspective;

[0084] In the embodiment of the present application, in the hierarchical structure design of the new energy power system management department, the main task of the macro-control layer is to formulate the overall target and control instruction of new energy power generation dispatching from the global perspective. To achieve this goal, first of all, an advanced optimization algorithm, such as a scheduling optimization based on a genetic algorithm, is used to establish a balance model of new energy power generation and power demand. Through the integration of a weather prediction system, the trend of weather changes and the power generation potential of new energy power generation equipment are obtained in advance, and the dispatching target that meets the current weather conditions and load demand is formulated. According to the power system load demand prediction model and the power market transaction mechanism, combined with the national and local new energy development plan, the macro-control layer will formulate the new energy power generation quota and dispatching instruction of each region. These instructions will be adjusted based on the dynamically changing demand, and the dispatching platform will issue control instructions to the subordinate regional coordination layer to ensure the optimal operation of the whole system new energy power generation.

[0085] Step S12: Design a regional coordination layer through a regional power dispatching center to respond to the control instructions from the macro-control layer and formulate corresponding new energy dispatching plans according to the corresponding new energy generation quotas of each region and dispatching principles;

[0086] In the embodiment of the present application, the regional power dispatching center is designed as a regional coordination layer to respond to the instructions from the macro-control layer and formulate specific new energy generation dispatching plans based on this. Specifically, the regional dispatching center processes the new energy generation capacity, load demand, and weather data in the region, and uses a multi-objective optimization algorithm (such as a particle swarm optimization algorithm) to compile the dispatching plan. This algorithm will dispatch the output power of new energy power generation equipment according to the new energy generation quota of each region, the operating characteristics of the generation equipment, and the demand load, while ensuring the stability and safety of the power grid. Through an information integration platform, real-time aggregation of various types of power generation equipment, weather monitoring data, user demand, and other information in the region is performed, and intelligent algorithms are used for data analysis to form detailed new energy generation dispatching plans. The plans are transmitted to the subordinate local control layer through a communication network for execution.

[0087] Step S13: Design a local control layer through new energy power stations, weather monitoring stations, and power user terminals to monitor the operating data, weather data, and power load data of new energy power generation equipment in real time, and execute the new energy dispatching plan issued by the regional coordination layer to adjust the operating parameters of new energy power generation equipment;

[0088] In the embodiment of the present application, by cooperating with the new energy power station, the meteorological monitoring station and the power user terminal in the local control layer, the optimization scheduling of the new energy power generation equipment is realized, each new energy power station accurately controls the power generation equipment through the embedded control system, and the operation data, power generation efficiency, environmental meteorological data and power load information of the equipment are monitored in real time, based on the scheduling plan received from the regional coordination layer, the local control layer adjusts the output power, wind speed, light and other operation parameters of the new energy power generation equipment according to the real-time monitored power demand, equipment state and meteorological data, in the specific operation process, the local control system will collect the operation state and meteorological information of the equipment in real time through the sensor, and predict the possible power generation of the equipment in the future short time through the model, based on the data and the scheduling instruction, the local control layer can adjust the fan speed, the angle of the solar panel and the output power of the generator set in real time to ensure the smooth operation of the power grid.

[0089] Step S14: an information interaction and cooperation network corresponding to each level between the macro-control layer, the regional coordination layer and the local control layer is established by using a communication network, so as to construct a corresponding hierarchical control architecture system.

[0090] In the embodiment of the present application, in order to realize efficient information exchange and cooperation between the macro-control layer, the regional coordination layer and the local control layer, an information interaction and cooperation network needs to be constructed, which uses communication technologies such as optical fiber communication and 5G network to ensure that the information transmission between the three layers is fast and stable, first, a standardized communication protocol is established between the macro-control layer, the regional coordination layer and the local control layer to ensure real-time updating and accurate transmission of data, in the network architecture design, distributed data storage technology is adopted to collect and store the system state of different levels in real time, data sharing and processing are carried out through the cloud computing platform, the scheduling instruction, power generation data and meteorological forecast information of each level are transmitted through encryption technology to ensure the safety and confidentiality of the information, in the cooperation process, each level adjusts and optimizes according to the real-time data feedback to ensure the overall coordination and sustainability of the new energy scheduling, the redundant design of the network ensures that the system can be quickly switched when a fault occurs, avoiding information loss or miscommunication in the scheduling, and finally a corresponding hierarchical control architecture system is constructed.

[0091] Further, step S2 includes the following steps:

[0092] Step S21: the operation data of the new energy power generation equipment is collected in real time by the local control layer of the hierarchical control architecture system through the corresponding new energy power station, including power generation, power generation power and equipment operation state;

[0093] Step S22: Real-time collection of meteorological data corresponding to the new energy power generation equipment by the local control layer corresponding to the meteorological monitoring station in the hierarchical control architecture, including the light intensity, wind speed and temperature corresponding to the power generation equipment area;

[0094] Step S23: Real-time collection of power load data corresponding to the new energy power generation equipment by the local control layer corresponding to the power user end in the hierarchical control architecture, including the power load size and power load change trend;

[0095] Step S24: Cross-layer uploading of the operation data, meteorological data and power load data corresponding to the new energy power generation equipment from the local control layer to the data control processing center corresponding to the macro control layer;

[0096] Step S25: Peak denoising and normalization of the operation data, meteorological data and power load data corresponding to the new energy power generation equipment by the data control processing center, to obtain the corresponding new energy power generation operation data, new energy meteorological data and new energy power load data in the same order of magnitude.

[0097] As an embodiment of the present application, referring to Figure 3 , it is Figure 1 the detailed step flowchart of step S2 in the embodiment, step S2 in the embodiment includes the following steps:

[0098] Step S21: Real-time collection of operation data corresponding to the new energy power generation equipment by the local control layer corresponding to the new energy power station in the hierarchical control architecture, including the power generation, power generation power and equipment operation state;

[0099] In the embodiment of the present application, the operation data of the new energy power generation equipment is collected in real time by the local control layer in the hierarchical control architecture, and the specific operation process is as follows: first, the real-time power generation and power generation power data of the new energy power generation equipment (such as a wind turbine generator set or a solar photovoltaic panel) are monitored by the internal sensors and collection devices of the equipment, for the wind power generation equipment, a wind speed meter and a power meter are equipped, which can monitor the output power and operation state of the generator set in real time, and record electrical parameters such as voltage and current, in the solar photovoltaic power generation system, the power generation power of the photovoltaic module is collected by using the power monitoring device built-in the photovoltaic module, the operation state includes whether the equipment is normally running or whether there is a fault, all these data are collected, processed and transmitted to the next control layer in real time by the edge computing device of the local control layer, and finally the operation data corresponding to the new energy power generation equipment is obtained, including the power generation, power generation power and equipment operation state.

[0100] Step S22: Real-time collection of meteorological data corresponding to the new energy power generation equipment by the local control layer in the hierarchical regulation architecture system, including the light intensity, wind speed and temperature corresponding to the power generation equipment region;

[0101] In the embodiment of the present application, the meteorological data of the region where the new energy power generation equipment is located is collected in real time by the local control layer in the hierarchical regulation architecture system. The specific operation is that the meteorological monitoring station is deployed inside the local control layer, equipped with meteorological sensors (such as light intensity sensor, anemometer, thermometer, etc.) to obtain data related to the meteorological environment of the region where the new energy power generation equipment is located in real time, for example, the intensity of sunlight is measured in real time by using the light sensor to provide meteorological support for the output of photovoltaic power generation, the anemometer monitors the wind speed change to ensure the meteorological conditions required for the operation of the wind power generation equipment, and the thermometer records the environmental temperature, especially the influence on the operation of the equipment under extreme temperature conditions. All these data are transmitted in real time to the central data platform through the sensor network of the local control layer, and finally the meteorological data corresponding to the new energy power generation equipment is obtained, including the light intensity, wind speed and temperature corresponding to the power generation equipment region.

[0102] Step S23: Real-time collection of power load data corresponding to the new energy power generation equipment by the power user end corresponding to the local control layer in the hierarchical regulation architecture system, including the power load size and power load change trend;

[0103] In the embodiment of the present application, the power load data of the power user end is collected in real time by using the local control layer in the hierarchical regulation architecture system. The specific operation is that the power load monitoring system in the local control layer is directly connected with the smart meter of the power user. The smart meter has high-precision data acquisition function and can record the power consumption and change trend of the user end in real time. The power load monitoring system obtains the load data of the user end according to the required period, including instantaneous power, cumulative load, load change rate and other important information, and uploads them to the dispatching system through a safe communication protocol. In order to ensure the accuracy of the load data, multi-channel data verification and data encryption technology is adopted to avoid data loss or tampering in communication. Through the collection and uploading of these power load data, the power load data corresponding to the new energy power generation equipment is finally obtained, including the power load size and power load change trend.

[0104] Step S24: Cross-layer uploading of the operation data, meteorological data and power load data corresponding to the new energy power generation equipment from the local control layer to the data regulation and processing center corresponding to the macroscopic regulation layer;

[0105] In the embodiment of the present application, the operation data, meteorological data and power load data of the new energy power generation equipment are uploaded from the local control layer to the data control processing center of the macro control layer across the hierarchy, and the specific operation is that each local control layer arranges and uploads the collected data to the upper control center through a stable network transmission protocol (such as MQTT protocol, HTTP protocol, etc.), and in the data uploading process, the data will be encrypted to ensure the safety and privacy protection of data transmission. The data includes the real-time power generation, power generation power, meteorological data (such as light intensity, wind speed, temperature, etc.) and power load data of each power generation unit. After these data are collected, they are processed by a cloud computing platform or an edge computing node, integrated into a unified standard format, and then subjected to further data analysis and processing.

[0106] Step S25: The operation data, meteorological data and power load data corresponding to the new energy power generation equipment are subjected to peak denoising and normalization by the data control processing center, to obtain the corresponding new energy power generation operation data, new energy meteorological data and new energy power load data in the same order of magnitude.

[0107] In the embodiment of the present application, the operation data, meteorological data and power load data of the new energy power generation equipment are subjected to peak denoising and normalization by the data control processing center, and the specific operation is as follows: first, the dispatching processing center performs peak denoising processing on the uploaded original data, removes abnormal data points by using a signal processing algorithm (such as moving average method or wavelet transform method), and ensures the smoothness and consistency of the data. Then, the normalization processing converts various data to the same order of magnitude, for example, the operation data (such as power generation power) is subjected to standardization processing to make the data value between 0 and 1, so as to be uniformly compared and analyzed with the meteorological data and power load data. The meteorological data and power load data are also processed by the same normalization method to ensure that they have consistent dimensions. Finally, the corresponding new energy power generation operation data, new energy meteorological data and new energy power load data in the same order of magnitude are obtained.

[0108] Further, step S25 includes the following steps:

[0109] Step S251: The operation data, meteorological data and power load data corresponding to the new energy power generation equipment are subjected to multi-element feature depth analysis by the data control processing center, to analyze the long-term and short-term operation fluctuation trend characteristics of the new energy power generation equipment by decomposing the parameters corresponding to the operation data into trend items, periodic items and random items by using a time series decomposition method, analyze the spatial distribution characteristics of different meteorological elements by expanding the discrete meteorological data into a continuous regional meteorological field by using a spatial interpolation technique, and analyze the power load change mode characteristics of the new energy power generation equipment by the power load data, to obtain a multi-element depth feature set of the new energy equipment.

[0110] In the embodiments of the present application, by using the data regulation processing center, a large amount of data generated in the operation process of the new energy power generation equipment is collected, including the real-time operation state of the equipment, meteorological conditions (such as temperature, humidity, wind speed, etc.) and power load data (including load fluctuation, maximum load, minimum load, etc.), so as to process the operation data by using the time series decomposition method, decompose each parameter into trend item, periodic item and random item, respectively reveal the long-term change trend, periodic fluctuation and short-term abnormal fluctuation of the data, and this decomposition process helps to understand the operation characteristics and fluctuation mode of the new energy power generation equipment. Then, through the spatial interpolation technology (such as Kriging interpolation method or inverse distance weighted method), the discrete meteorological data is processed and expanded into a continuous regional meteorological field to show the spatial distribution law of different meteorological elements, and through the analysis of the time sequence change in the power load data, appropriate clustering algorithm and frequency analysis method are used to identify the change mode of the power load, and these information is integrated with other data to form a multi-depth feature set, which provides a comprehensive understanding of the long-term and short-term fluctuation trend of the new energy equipment, the spatial distribution of meteorological elements and the power load mode, and finally obtains the multi-depth feature set of the new energy equipment.

[0111] Step S252: Based on the multi-depth feature set of the new energy equipment and combined with the physical principle of the new energy power generation equipment and the operation rule of the power system, the operation data, meteorological data and power load data corresponding to the new energy power generation equipment are compared and identified for abnormality, to obtain the operation abnormal data, meteorological abnormal data and power abnormal data corresponding to the new energy power generation equipment;

[0112] In the embodiments of the present application, based on the previously obtained multi-depth feature set, combined with the physical principle of the new energy power generation equipment and the operation rule of the power system, the operation data, meteorological data and power load data of the equipment are compared and identified for abnormality. In this step, first, according to the known physical model and the operation principle of the power system, an abnormality detection framework is constructed. For the equipment operation data, by setting a threshold based on historical data or using a machine learning algorithm (such as an abnormality detection algorithm) to analyze the data, for the meteorological data, the deviation between the predicted value and the actual observation value is compared, and the abnormal meteorological event is identified through deviation analysis, and the abnormality of the power load data can be identified by combining the deviation between the real-time load data and the predicted value with the load prediction model, and the load fluctuation abnormality is identified. After the above data is compared, the abnormal points in the operation data, the abnormal events in the meteorological data and the abnormal fluctuations in the power load can be identified, and a comprehensive abnormality identification result is obtained, and finally the operation abnormal data, meteorological abnormal data and power abnormal data corresponding to the new energy power generation equipment are obtained.

[0113] Step S253: The operation abnormal data, meteorological abnormal data and power abnormal data corresponding to the new energy power generation equipment are subjected to spike denoising processing, so as to decompose the corresponding abnormal data into different frequency subbands by wavelet transform to highlight the data characteristics corresponding to the abnormal spike noise, and remove the wavelet coefficients corresponding to the abnormal spike noise by using the opening and closing operation in the morphological filtering, and the corresponding abnormal data is reconstructed by inverse wavelet transform, so as to obtain new energy power generation denoising data, new energy meteorological denoising data and new energy power load denoising data.

[0114] In the embodiment of the application, by performing spike denoising processing on the previously obtained abnormal data, firstly, for the abnormal data (operation, meteorological and power load data), the wavelet transform is used to decompose it into different frequency subbands, the wavelet transform can separate the high-frequency noise component and the low-frequency trend component in the data, and it is particularly sensitive to spike noise, then the morphological filtering method is used to perform opening and closing operation, which can effectively remove the high-frequency component in the data caused by spike noise and retain the true characteristics of the data, the opening operation can eliminate isolated noise points in a small range, and the closing operation can smooth the data and remove fluctuations, through these operations, the abnormal data fluctuations caused by spike noise can be eliminated, the key trend and periodic characteristics are retained, and the processed data is reconstructed by inverse wavelet transform, so as to obtain the new energy power generation data, meteorological data and power load data after denoising, and finally obtain the new energy power generation denoising data, new energy meteorological denoising data and new energy power load denoising data.

[0115] Step S254: The new energy power generation denoising data, new energy meteorological denoising data and new energy power load denoising data are subjected to normalization processing, so as to obtain the corresponding new energy power generation operation data, new energy meteorological data and new energy power load data in the same order of magnitude scale.

[0116] In the embodiment of the present application, by normalizing the previously obtained denoised data, the purpose of normalization is to uniformly convert different scale data (for example, power generation equipment operation data, meteorological data and power load data) to the same order of magnitude, eliminate the influence of dimensional difference on subsequent analysis, and the specific operation method is to scale each data according to its minimum value and maximum value by using standardization or Min-Max Scaling, so as to ensure that the value of each data is in a unified interval (such as [0, 1] interval), and the standardization method adjusts the data to a distribution with a mean value of zero and a standard deviation of one. Regardless of which method is used, the normalized data will have the same order of magnitude, which is convenient for multi-dimensional comparison and further modeling analysis. After this processing, the dimensional problems of the operation data of the new energy power generation equipment, the meteorological data and the power load data are solved, ensuring that the data is in the same scale for subsequent analysis, and finally the corresponding new energy power generation operation data, new energy meteorological data and new energy power load data in the same order of magnitude are obtained.

[0117] Further, step S3 includes the following steps:

[0118] Step S31: simulating and deducing different new energy operation scheduling simulation conditions by corresponding new energy power generation operation data, new energy meteorological data and new energy power load data in the same order of magnitude, including different new energy equipment operation states, meteorological conditions and power load change conditions corresponding to new energy scheduling strategies;

[0119] In the embodiment of the present application, by collecting the historical operation data of the new energy power generation system, including the power generation capacity data of various new energy equipment such as wind power and solar energy, and collecting the meteorological data (such as wind speed, solar radiation, etc.) and power load data related to these new energy, these data need to be standardized according to time sequence to ensure that they are in the same order of magnitude, which is convenient for subsequent simulation and deduction. Then, based on the historical data and the prediction of future meteorological conditions, a simulation model of the new energy power generation system is established, and different scheduling scenarios are deduced through this model. Different new energy equipment operation states (such as full load operation, partial load operation and shutdown, etc.), meteorological conditions (such as sunny, cloudy, wind speed change, etc.) and power load fluctuation (peak load and trough load, etc.) are set. During the model running process, different new energy scheduling strategies are formulated according to these conditions, and simulation is performed in multiple time periods to simulate the power generation, load balancing and operation scheduling scheme under each condition. Finally, different new energy operation scheduling simulation conditions are obtained, including different new energy equipment operation states, meteorological conditions and power load change conditions corresponding to new energy scheduling strategies.

[0120] Step S32: Based on different new energy operation scheduling simulation conditions, the power safety evaluation index of the corresponding new energy power system is analyzed, and the power safety index corresponding to the power system under different operation scheduling conditions is obtained, including power operation frequency deviation and power line overload rate;

[0121] In the embodiment of the present application, the safety of the power system under different scheduling conditions is evaluated based on the simulation deduction results obtained by previous analysis. In the evaluation process, first, the power frequency deviation and line overload under each scheduling scheme are calculated according to the operation rules and device capacity limits of the power system. The power frequency deviation refers to the difference between the actual frequency and the standard frequency in the power grid operation. A large frequency deviation will cause damage to power equipment and affect system stability. The line overload rate reflects the load of the power line under different operating conditions. When the overload rate exceeds the standard, the power equipment is at risk of overheating or damage. In the safety evaluation process, a special power simulation analysis software (such as MATLAB / Simulink, PSS / E, etc.) can be used for simulation to simulate the frequency deviation and line overload of the power system under different scheduling conditions. The safety index under each scheduling scheme is analyzed and calculated, and finally the power safety index corresponding to the power system under different operation scheduling conditions is obtained.

[0122] Step S33: Based on different new energy operation scheduling simulation conditions, the power system under corresponding operation scheduling conditions is obtained, and the power reliability index corresponding to the power system under different operation scheduling conditions is obtained.

[0123] In the embodiment of the present application, the reliability of the power system under different operation scheduling conditions is analyzed, and the outage time and frequency are evaluated. First, according to the simulation results obtained previously, the power supply interruption situations under each scheduling scheme are identified. These interruptions are caused by factors such as equipment failure, high power load, insufficient energy supply, etc. The power system reliability evaluation model is used to calculate the system reliability index under each scheduling condition, including outage time and outage frequency. Through historical load data and simulation data, combined with the maintenance and backup mechanism of the power system, possible power outage events and their occurrence frequency are determined. For example, a reliability analysis tool (such as RAPSim, Reliability Toolbox, etc.) can be used for real-time simulation to obtain the failure rate, outage time and outage frequency of the power system under different operation scheduling conditions. Finally, the power reliability index corresponding to the power system under different operation scheduling conditions is obtained.

[0124] Step S34: Based on different new energy operation scheduling simulation conditions, the power carbon emission of the corresponding new energy power system is monitored in real time, and the real-time carbon emission of the power system under different operation scheduling conditions is obtained.

[0125] In the embodiment of the present application, by monitoring the carbon emissions of the power system in different operation scheduling conditions in real time, the carbon emissions of various devices (such as wind power and solar power generation devices) are quantitatively calculated according to the power generation capacity and operation state of the new energy power system. First, carbon emission data related to the power system are collected, which are usually derived from the energy consumption of power generation devices (such as carbon emissions of coal-fired power generation) and the carbon emission reduction effect of new energy power generation devices. Then, by establishing a carbon emission monitoring model, the new energy power generation efficiency is adjusted in combination with meteorological data (such as wind speed, solar radiation, etc.). Through the real-time data acquisition system (such as the SCADA system) and the carbon emission calculation model, the carbon emissions under different scheduling conditions are calculated in real time. The tools involved in this process may include carbon emission monitoring software (such as Greenhouse Gas Protocol tools, EPA calculation tools, etc.), and power system data acquisition and analysis platform. By comparing the real-time carbon emissions under different operation conditions, the real-time carbon emissions of the power system corresponding to different operation scheduling conditions are obtained.

[0126] Step S35: Comparing the real-time carbon emissions of the power system corresponding to different operation scheduling conditions with the corresponding historical carbon emissions, the power carbon emission reduction amount index of the power system corresponding to different operation scheduling conditions is obtained.

[0127] In the embodiment of the present application, by comparing the difference between the real-time carbon emissions of the power system under different operation scheduling conditions and the historical carbon emissions, the carbon emission reduction amount of the power system under different scheduling conditions is calculated. First, the historical carbon emission data are obtained, which are derived from the carbon emissions of traditional power generation methods (such as coal-fired power plants, gas-fired power plants, etc.) in the same time period. Next, the historical carbon emissions are compared with the obtained real-time carbon emissions to calculate the carbon emission reduction amount. In this process, the carbon emission reduction amount calculation formula (such as emission factor method, life cycle analysis method, etc.) can be used to combine the power generation of new energy and the carbon emissions of traditional energy to obtain the final reduction amount. Finally, the power carbon emission reduction amount index of the power system corresponding to different operation scheduling conditions is obtained.

[0128] Further, step S32 includes the following steps:

[0129] Step S321: Based on different new energy operation scheduling simulation conditions and in combination with the corresponding new energy power system, the power operation phasor measurement is performed by the phasor measurement unit to obtain the power operation phasor corresponding to the power system under different operation scheduling conditions, including voltage phasor and current phasor.

[0130] In the embodiment of the present application, by determining the working state of the new energy power system under each scheduling condition, setting different scheduling parameters (such as the proportion of wind power and photovoltaic power generation), and using power system simulation software (such as PSCAD, DIgSILENT PowerFactory, etc.), the operation of the power system under different scheduling conditions is simulated. In the simulation process, the phasor measurement unit (PMU) is used to monitor each link of the power system in real time, and the voltage phasor and current phasor of each node in the system are obtained. The specific operation is to arrange sensors on each key node and line of the system through the phasor measurement unit, measure and record the phasor data of the voltage and current of each node. These data reflect the operating state of the power system, including important parameters such as voltage amplitude, phase angle, frequency, etc. Finally, the corresponding power operation phasor of the power system under different operating scheduling conditions is obtained, which includes voltage phasor and current phasor.

[0131] Step S322: Based on the corresponding power operation phasor of the power system under different operating scheduling conditions, the operating frequency of the corresponding new energy power system is calculated, and the actual operating frequency of the power system corresponding to different operating scheduling conditions is obtained.

[0132] In the embodiment of the present application, the actual operating frequency of the system is calculated based on the power operation phasor data of the power system under different operating scheduling conditions. The specific steps are as follows: first, according to the previously obtained voltage phasor and current phasor, the operating frequency data of each node and line is extracted in the simulation software combined with the working state of the power system. In the power system, the frequency is determined by the change rate of the voltage phasor and current phasor of each node. Therefore, according to the voltage phasor and current phasor of each node, the frequency calculation formula is used to calculate the frequency. This frequency data reflects the actual dynamic operating frequency of the power system, and can be used to evaluate and analyze the stability of the power system according to different operating scheduling conditions. Finally, the actual operating frequency of the power system corresponding to different operating scheduling conditions is obtained.

[0133] Step S323: The corresponding rated frequency is obtained by the new energy power system, and the frequency deviation of the actual operating frequency of the power system corresponding to different operating scheduling conditions is calculated based on the rated frequency, and the operating frequency deviation of the power system corresponding to different operating scheduling conditions is obtained.

[0134] In the embodiment of the present application, the frequency deviation is calculated according to the rated frequency of the new energy power system, and the rated frequency generally refers to the frequency (such as 50 Hz or 60 Hz) specified by the power system under standard operating conditions. In this step, the actual operating frequency of the power system under each simulation condition is obtained and compared with the rated frequency. Specifically, the rated frequency of the power system under each dispatching condition is first confirmed, which is determined by the corresponding calibration value. Then, the difference between the actual operating frequency and the rated frequency is calculated to obtain the frequency deviation. The deviation value can be calculated by the formula: frequency deviation = (actual frequency - rated frequency). If the actual frequency is greater than the rated frequency, the system has the risk of overload or instability. If it is lower than the rated frequency, the system has the condition of insufficient load or slow response. Finally, the power operating frequency deviation of the power system corresponding to different operating dispatching conditions is obtained.

[0135] Step S324: Based on different new energy operating dispatching simulation conditions, the line overload rate of the corresponding new energy power system is evaluated to obtain the power operating line overload rate of the power system corresponding to different operating dispatching conditions.

[0136] In the embodiment of the present application, when evaluating the line overload rate of the power system under different new energy operating dispatching simulation conditions, the key transmission lines and substations in the power system need to be identified first to determine the capacity limit of the line. Based on the previously obtained power operating phasor data, the ratio of the actual load of each transmission line to its rated load is calculated in combination with the load condition of the system, so as to obtain the overload rate of the line. Specifically, in the simulation software, the load flow under each dispatching condition is simulated, and the load condition of each line is calculated by algorithm. If the load of a certain line exceeds the specified threshold of the design capacity, the line will be determined to be in an overload state. According to different operating dispatching conditions, the overload rate of each line can be obtained. These data help to judge whether the dispatching strategy causes line overload. Finally, the power operating line overload rate of the power system corresponding to different operating dispatching conditions is obtained.

[0137] Further, step S324 includes the following steps:

[0138] The disturbance transient simulation of the corresponding new energy power system is performed based on different new energy operating dispatching simulation conditions to generate the power operating disturbance transient process of the power system corresponding to different operating dispatching conditions.

[0139] In the embodiment of the present application, by simulating the disturbance transient of the new energy power system under different new energy operation scheduling simulation conditions, the specific operation is as follows: a typical new energy power system architecture is selected, the scheduling mode of wind power generation, solar power generation and traditional energy generation inside the architecture is considered, a mathematical model of the power system and a power system simulation platform are constructed, different disturbance conditions are set, such as load fluctuation, generator fault, wind speed change, solar irradiance change, etc., the transient response of the power system to the disturbance is simulated, a power system simulation software such as PSSE (Power System Simulator for Engineering) is used as the simulation tool, according to the given operation scheduling strategy such as centralized scheduling or distributed scheduling, the corresponding power system disturbance transient process is generated, during each simulation run, the scheduling strategy is gradually changed according to the actual power generation and load demand, the transient response of the power system such as voltage fluctuation and frequency change is obtained through simulation, and finally the corresponding power operation disturbance transient process of the power system under different operation scheduling conditions is generated.

[0140] Preferably, the node voltage, line current and power generation power in the power operation disturbance transient process of the power system corresponding to different operation scheduling conditions are collected at a certain time interval.

[0141] In the embodiment of the present application, by setting a fixed time interval, a data acquisition system is used to collect specific key parameters from the simulated power system disturbance transient process, specifically, the node voltage, line current and power generation power are the main power system parameters collected, in this process, the simulation software such as PSSE or MATLAB is configured with an acquisition module, the time interval of data acquisition is set (for example, 0.5 seconds or 1 second), the node voltage (reflecting system stability), line current (reflecting line load condition) and generator output power (reflecting new energy power supply capacity) at each time are recorded in real time during simulation, the time span of data acquisition should be determined according to the characteristics of system response time, for example, longer system transient response requires longer data recording time, the collected data is stored in a database, and finally the node voltage, line current and power generation power are obtained.

[0142] Preferably, the corresponding new energy power system is evaluated by line overload rate calculation formula based on the node voltage, line current and power generation power, and the power operation line overload rate of the power system corresponding to different operation scheduling conditions is obtained.

[0143] In the embodiment of the present application, a suitable line overload rate calculation formula is constructed by combining time variable parameter, node voltage, line current, power generation, line can bear corresponding rated current, node voltage influence weight, line transmission capacity, power generation influence weight, line nominal voltage and related parameters to perform overload rate evaluation calculation. According to line current data of the system under different dispatching conditions, the overload rate of each line is calculated, and finally the corresponding power operation line overload rate of the power system under different operation dispatching conditions is obtained. In addition, the line overload rate calculation formula can also use any overload evaluation algorithm in the art to replace the process of line overload rate evaluation calculation, and is not limited to the line overload rate calculation formula.

[0144] Further, the line overload rate calculation formula is specifically:

[0145] ;

[0146] In the formula, is the power operation line overload rate, is the initial time of overload rate calculation, is the termination time of overload rate calculation, is the time variable parameter, is the line current corresponding to the time , is the rated current that the line can bear, is the node voltage corresponding to the time , is the node voltage influence weight, is the power generation corresponding to the time , is the line transmission capacity, is the power generation influence weight, is the line nominal voltage, is the correction coefficient of the power operation line overload rate.

[0147] This invention, through the use of a specific mathematical model and verification, derives a formula for calculating line overload rate, used to assess the line overload rate of corresponding new energy power systems. This formula, by incorporating a time function, dynamically evaluates the load situation of the power system at different time points. Specifically, based on changes in line current, node voltage, and power generation at different time points, it can more realistically reflect the instantaneous state of the power system during operation. This dynamic assessment helps determine whether a line is overloaded, providing a more detailed basis for scheduling and control. The formula considers multiple important factors, such as line current, voltage, power generation, and their respective weighting parameters. This allows the formula to comprehensively assess the impact of node voltage changes and power generation fluctuations on the power system load, ensuring a comprehensive understanding of the power system. This comprehensive analysis helps optimize grid dispatch and avoid overload risks caused by single factors. By calculating the line overload rate, potentially overloaded lines or nodes in the power system can be identified in a timely manner. This provides grid dispatchers with an early warning mechanism, enabling them to take timely measures, such as adjusting power generation and grid load, to ensure the safe and stable operation of the power system. With the increasing proportion of renewable energy power, the operation of power systems has become more complex. The introduction of this formula can support more precise intelligent dispatching systems, enabling real-time monitoring and adjustment of the power system's operating status through automated algorithms. This calculation formula allows for flexible responses in real-time operation, avoiding unnecessary risks. In summary, this formula fully considers the overload rate of power lines. Overload rate calculation initial time Overload rate calculation termination time Time variable parameter In time Line current at time The rated current that the line can carry. In time Node voltage at time 1 Node voltage affects weights In time Power generation at time Line transmission capacity Power generation affects weight Line nominal voltage Correction factor for overload rate of power lines According to the overload rate of power lines The interrelationships between the above parameters constitute a functional relationship:

[0148] ;

[0149] The formula can realize the line overload rate evaluation process of the corresponding new energy power system, and the introduction of the correction coefficient of the power operation line overload rate can adjust according to the error in the calculation process, thereby improving the accuracy and applicability of the line overload rate calculation formula.

[0150] Further, step S4 comprises the following steps:

[0151] Step S41: performing hierarchical evaluation and analysis on the power safety index, the power reliability index and the power carbon emission reduction amount index of the power system corresponding to different operation scheduling conditions to obtain a new energy scheduling strategy comprehensive score of the power system corresponding to different operation scheduling conditions;

[0152] In the embodiment of the present application, under different operation scheduling conditions, firstly, the operation data of the power system need to be collected, including but not limited to the operation state of the generator set, the power grid load, the wind and light resource power generation condition, the state of the energy storage facility and the power demand prediction, then, the power safety index (such as the power operation frequency deviation and the power operation line overload rate), the power reliability index (such as the power outage time and the power outage frequency, etc.) and the power carbon emission reduction amount index (such as the carbon emission reduction amount per unit of power generation, etc.) are used to quantitatively evaluate the power system under each operation scheduling condition. Specifically, the safety of the power system can be evaluated by analyzing the voltage, frequency and power fluctuation of the system, while the reliability index needs to be calculated by statistically analyzing the historical fault data, combined with the simulation prediction model, the carbon emission reduction amount is realized by calculating the reduced carbon dioxide emission amount of different new energy power generation methods instead of traditional fossil energy power generation, and the hierarchical analysis method (AHP) or the entropy weight method is used to evaluate the importance of each index, and the comprehensive score of the new energy scheduling strategy under each scheduling condition is obtained, and finally the comprehensive score of the new energy scheduling strategy of the power system corresponding to different operation scheduling conditions is obtained.

[0153] Step S42: based on the new energy scheduling strategy comprehensive score of the power system corresponding to different operation scheduling conditions, the macro control layer is used to analyze the scheduling optimization target of the corresponding new energy power system, if the new energy scheduling strategy comprehensive score of the power system corresponding to different operation scheduling conditions is greater than or equal to the preset score threshold, the corresponding scheduling strategy comprehensive score under the corresponding operation scheduling condition is continuously evaluated; if the new energy scheduling strategy comprehensive score of the power system corresponding to different operation scheduling conditions is less than the preset score threshold, the macro control layer is used to determine the overall optimization target of the corresponding power system scheduling;

[0154] ​In the embodiment of the present application, by using the macro regulation layer to deeply analyze the comprehensive score of the new energy dispatching strategy, if the comprehensive score of the new energy dispatching strategy under different operation and dispatching conditions is greater than or equal to the preset score threshold, the next stage is entered, and the performance of the related strategy under the dispatching condition is continuously evaluated and monitored. Specifically, the preset score threshold can be set according to the actual situation of the power system operation, and is usually adjusted based on historical data, system bearing capacity and sustainable development requirements. If the comprehensive score meets the standard, multidimensional analysis needs to be performed, such as the response capability of new energy power generation under different weather conditions and emergencies, the volatility influence of wind power and solar power generation, etc. If the evaluation result shows that the score does not reach the threshold, the macro regulation layer needs to adjust the dispatching strategy and re-determine the overall optimization target of the power system dispatching, so as to ensure that the system realizes optimal operation under the premise of ensuring power supply safety and reliability, reduces carbon emissions, and finally determines the corresponding overall optimization target of the power system dispatching.

[0155] Step S43: generating corresponding new energy power generation dispatching control instructions by driving the corresponding overall optimization target of the power system dispatching through the macro regulation layer;

[0156] In the embodiment of the present application, the macro regulation layer will drive the overall optimization target of the power system dispatching. In this stage, the optimization target is first determined, such as maximizing the reliability of system operation, minimizing carbon emissions or improving the proportion of new energy power generation, etc. By using optimization algorithms (such as genetic algorithm, particle swarm optimization algorithm, linear programming, etc.), the regulation layer will weigh and calculate between different optimization targets, so as to ensure that the safe operation of the power system is guaranteed and the maximum benefit is realized under the condition of minimum carbon emission. The key of this stage is the construction of the multi-objective optimization model, which can comprehensively consider the power grid load, energy supply fluctuation, energy storage dispatching and other factors, and develop the most reasonable dispatching strategy. According to the optimization result, the macro regulation layer generates new energy power generation dispatching control instructions, which will provide specific operation basis for the subsequent execution layer, so as to ensure that the system can accurately and efficiently execute, and finally generate corresponding new energy power generation dispatching control instructions.

[0157] Step S44: transmitting the new energy power generation dispatching control instructions generated by the regulation to the regional coordination layer to develop corresponding new energy power generation dispatching plans according to the corresponding new energy power generation conditions of the region, and hierarchically issuing to the local control layer to execute corresponding new energy dispatching optimization strategies.

[0158] In the embodiment of the present application, the generated new energy power generation dispatching control instruction will be transmitted to the regional coordination layer, which formulates a new energy power generation dispatching plan suitable for the region according to the new energy power generation conditions (such as real-time output, power generation forecast data, etc.) of wind power generation and photovoltaic power generation in the region. The regional coordination layer adjusts and optimizes the dispatching plan by comprehensively considering the load demand, power exchange demand and local renewable energy generation volatility in the region, using a dispatching optimization model to ensure that the new energy power generation in each region can be deployed according to the optimal strategy. The regional coordination layer adjusts the short-term and long-term dispatching strategy according to the actual situation and prediction data to ensure the reliability and stability of power supply. The dispatching plan is issued to the local control layer according to different priorities through the regional coordination layer. When executed in the local control layer, the local new energy dispatching strategy will be adjusted according to the local power grid structure, energy storage facilities and the state of distributed power sources, so that the dispatching target of the entire power system can be effectively realized, the new energy resources can be maximized, and energy saving and emission reduction can be achieved. Finally, the corresponding new energy dispatching optimization strategy is executed.

[0159] Further, the present application also provides a new energy dispatching strategy deduction system based on a hierarchical grading system, which is used to execute the new energy dispatching strategy deduction method based on a hierarchical grading system as described above. The new energy dispatching strategy deduction system based on a hierarchical grading system comprises:

[0160] A hierarchical grading control architecture design module is used to build a hierarchical grading control architecture system comprising a macro-control layer, a regional coordination layer and a local control layer.

[0161] A new energy multi-source data processing module is used to collect the operation data, meteorological data and power load data of new energy power generation equipment in real time through the local control layer in the hierarchical grading control architecture system, and upload them to the data control processing center corresponding to the macro-control layer. The operation data, meteorological data and power load data of new energy power generation equipment are subjected to spike denoising and normalization by the data control processing center, so as to obtain corresponding new energy power generation operation data, new energy meteorological data and new energy power load data at the same order of magnitude.

[0162] A dispatching evaluation index deduction analysis module is used to simulate and deduce different new energy operation dispatching simulation conditions, including the new energy dispatching strategy corresponding to different new energy equipment operation states, meteorological conditions and power load change conditions, based on the corresponding new energy power system under different new energy operation dispatching simulation conditions. Dispatching evaluation index analysis is performed to obtain the power safety index, power reliability index and power carbon emission reduction index of the power system under different operation dispatching conditions.

[0163] The new energy dispatching hierarchical issuing control module is configured to perform hierarchical evaluation analysis on the power safety index, the power reliability index and the power carbon emission reduction amount index corresponding to the power system under different operation dispatching conditions, to obtain a comprehensive score of the new energy dispatching strategy corresponding to the power system under different operation dispatching conditions; and based on the comprehensive score of the new energy dispatching strategy corresponding to the power system under different operation dispatching conditions, a corresponding new energy power generation dispatching control instruction is generated by using the macro-control layer response, and is conveyed to the regional coordination layer to formulate a corresponding new energy power generation dispatching plan according to the corresponding new energy power generation condition of the region, and the plan is issued to the local control layer to execute the corresponding new energy dispatching optimization strategy.

[0164] Therefore, from any viewpoint, the embodiments should be considered as being exemplary and non-limiting, the scope of the application being defined by the appended claims and not by the above description, and all the changes falling within the meaning and the scope of the equivalent elements of the patent file are therefore intended to be comprised in the present application.

[0165] The above description is merely illustrative of the application and is not intended to limit the scope of the application. Various modifications of the embodiments will be readily apparent to persons skilled in the art, and the generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Therefore, the present application is not to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for deriving new energy dispatching strategy based on hierarchical classification system, characterized in that, Comprise the following steps: Step S1: Constructing a hierarchical and hierarchical control architecture system corresponding to the macro-control layer, the regional coordination layer and the local control layer; Step S2: Real-time collection of operation data, meteorological data and power load data corresponding to the new energy power generation equipment in the local control layer of the hierarchical and hierarchical control architecture system, and uploading to the data control processing center corresponding to the macro-control layer; Using the data control processing center to carry out peak denoising and normalization on the operation data, meteorological data and power load data corresponding to the new energy power generation equipment, to obtain corresponding new energy power generation operation data, new energy meteorological data and new energy power load data under the same order of magnitude scale; Step S3: Simulating and deducing different new energy operation scheduling simulation conditions based on the corresponding new energy power generation operation data, new energy meteorological data and new energy power load data under the same order of magnitude scale, which includes new energy scheduling strategies corresponding to different new energy equipment operation states, meteorological conditions and power load change conditions; Based on different new energy operation scheduling simulation conditions, the corresponding new energy power system is scheduled to analyze the scheduling evaluation index, to obtain the power safety index, power reliability index and power carbon emission reduction index corresponding to the power system under different operation scheduling conditions; wherein, step S3 comprises the following steps: Step S31: Simulating and deducing different new energy operation scheduling simulation conditions based on the corresponding new energy power generation operation data, new energy meteorological data and new energy power load data under the same order of magnitude scale, which includes new energy scheduling strategies corresponding to different new energy equipment operation states, meteorological conditions and power load change conditions; Step S32: Based on different new energy operation scheduling simulation conditions, the corresponding new energy power system is scheduled to analyze the power safety evaluation index, to obtain the power safety index corresponding to the power system under different operation scheduling conditions, which includes power operation frequency deviation and power operation line overload rate; wherein, step S32 comprises the following steps: Step S321: Based on different new energy operation scheduling simulation conditions and combining with the phasor measurement unit, the power operation phasor of the corresponding new energy power system is measured to obtain the power operation phasor of the power system under different operation scheduling conditions, which includes voltage phasor and current phasor; Step S322: Based on the power operation phasor of the power system under different operation scheduling conditions, the operation frequency of the corresponding new energy power system is calculated to obtain the actual power operation frequency of the power system under different operation scheduling conditions; Step S323: Obtain the corresponding rated frequency of the new energy power system, and based on the rated frequency, the frequency deviation of the actual power operation frequency of the power system under different operation scheduling conditions is calculated to obtain the power operation frequency deviation of the power system under different operation scheduling conditions; Step S324: Based on different new energy operation scheduling simulation conditions, the line overload rate of the corresponding new energy power system is evaluated to obtain the power operation line overload rate of the power system under different operation scheduling conditions; wherein, step S324 comprises the following steps: Step S3241: Based on different new energy operation scheduling simulation conditions, the corresponding new energy power system is disturbed and transient simulation is performed to generate the corresponding power operation disturbance transient process of the power system under different operation scheduling conditions; Step S3242: According to a certain time interval, the node voltage, line current and power generation power in the power operation disturbance transient process of the power system under different operation scheduling conditions are collected; Step S3243: Based on the node voltage, line current and power generation power, the line overload rate calculation formula is used to evaluate the corresponding new energy power system to obtain the power operation line overload rate of the power system under different operation scheduling conditions; The line overload rate calculation formula is specifically: ； In the formula, is the power operation line overload rate, is the initial time for overload rate calculation, is the termination time for overload rate calculation, is the time variable parameter, is the line current corresponding to the time is the line current corresponding to the time is the rated current that the line can carry corresponding to the time is the node voltage corresponding to the time is the node voltage corresponding to the time is the node voltage influence weight, is the generated power corresponding to the time is the generated power corresponding to the time is the line transmission capacity, is the generated power influence weight, is the line nominal voltage, is the correction coefficient of the power operation line overload rate; Step S33: Based on different new energy operation scheduling simulation conditions, the corresponding outage time and outage frequency of the new energy power system under the corresponding operation scheduling conditions are obtained to obtain the power reliability index of the power system under different operation scheduling conditions; Step S34: Based on different new energy operation scheduling simulation conditions, the real-time carbon emission of the corresponding new energy power system is monitored to obtain the real-time carbon emission of the power system under different operation scheduling conditions; Step S35: The real-time carbon emission of the power system under different operation scheduling conditions is compared with the corresponding historical carbon emission to obtain the power carbon emission reduction index of the power system under different operation scheduling conditions; Step S4: The power safety index, power reliability index and power carbon emission reduction index of the power system under different operation scheduling conditions are hierarchically evaluated and analyzed to obtain the new energy scheduling strategy comprehensive score of the power system under different operation scheduling conditions; Based on the new energy scheduling strategy comprehensive score of the power system under different operation scheduling conditions, the macro control layer response is used to generate the corresponding new energy generation scheduling control instruction, which is transmitted to the regional coordination layer to formulate the corresponding new energy generation scheduling plan according to the corresponding new energy generation condition of the region and is hierarchically issued to the local control layer to execute the corresponding new energy scheduling optimization strategy; Wherein, step S4 includes the following steps: Step S41: The power safety index, power reliability index and power carbon emission reduction index of the power system under different operation scheduling conditions are hierarchically evaluated and analyzed to obtain the new energy scheduling strategy comprehensive score of the power system under different operation scheduling conditions; Step S42: Based on the new energy scheduling strategy comprehensive score of the power system under different operation scheduling conditions, the macro control layer is used to analyze the scheduling optimization target of the corresponding new energy power system, if the new energy scheduling strategy comprehensive score of the power system under different operation scheduling conditions is greater than or equal to the preset score threshold, then the scheduling strategy comprehensive score under the corresponding operation scheduling condition is continuously evaluated and monitored; If the new energy scheduling strategy comprehensive score of the power system under different operation scheduling conditions is less than the preset score threshold, then the macro control layer is used to determine the overall optimization target of the power system scheduling. Step S43: drive the corresponding power system scheduling overall optimization target and control to generate the corresponding new energy power generation scheduling control instruction through the macro-control layer; Step S44: the new energy power generation scheduling control instruction generated by the control is conveyed to the regional coordination layer to formulate the corresponding new energy power generation scheduling plan according to the corresponding new energy power generation condition of the region, and is issued to the local control layer to execute the corresponding new energy scheduling optimization strategy.

2. The layered hierarchy-based new energy dispatching strategy deduction method according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: design the macro-control layer through the new energy power system management department to be responsible for formulating the overall target and control instruction of new energy power generation scheduling from the global perspective; Step S12: design the regional coordination layer through the regional power dispatching center to respond to the control instruction of the macro-control layer and formulate the corresponding new energy scheduling plan according to the new energy generation quota and scheduling principle of each region; Step S13: design the local control layer through the new energy power station, weather monitoring station and power user end to monitor the running data, weather data and power load data of the new energy power generation equipment in real time, and execute the new energy scheduling plan issued by the regional coordination layer to adjust the running parameters of the new energy power generation equipment; Step S14: use the communication network to establish the corresponding information interaction coordination network between the macro-control layer, the regional coordination layer and the local control layer to build the corresponding hierarchical control architecture system.

3. The layered hierarchy-based new energy dispatching strategy deduction method according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: the local control layer in the hierarchical control architecture system collects the running data of the new energy power generation equipment in real time, including power generation, power generation and equipment running state; Step S22: the local control layer in the hierarchical control architecture system collects the weather data of the new energy power generation equipment in real time through the weather monitoring station corresponding to the local control layer, including the light intensity, wind speed and temperature of the equipment region; Step S23: the local control layer in the hierarchical control architecture system collects the power load data of the new energy power generation equipment in real time through the power user end corresponding to the local control layer, including the power load size and power load change trend; Step S24: upload the running data, weather data and power load data of the new energy power generation equipment from the local control layer to the data control processing center corresponding to the macro-control layer; Step S25: use the data control processing center to carry out peak denoising and normalization on the running data, weather data and power load data of the new energy power generation equipment to obtain the corresponding new energy power generation running data, new energy weather data and new energy power load data in the same order of magnitude scale.

4. The layered hierarchy-based new energy dispatching strategy deduction method according to claim 3, characterized in that, Step S25 includes the following steps: Step S251: The data regulation and processing center is used for multi-element feature deep analysis on the operation data, meteorological data and power load data corresponding to the new energy power generation equipment, a time series decomposition method is used to decompose the parameters corresponding to the operation data into a trend item, a periodic item and a random item to analyze the long-term and short-term operation fluctuation trend characteristics of the new energy power generation equipment, a spatial interpolation technology is used to expand the discrete meteorological data into a continuous regional meteorological field to analyze the distribution characteristics of different meteorological elements in space, and the power load change mode characteristics of the new energy power generation equipment are analyzed through the power load data, and a new energy equipment multi-element deep feature set is obtained; Step S252: Based on the new energy equipment multi-element deep feature set and combined with the physical principle and power system operation law corresponding to the new energy power generation equipment, the operation data, meteorological data and power load data corresponding to the new energy power generation equipment are compared and identified for abnormalities, and operation abnormal data, meteorological abnormal data and power abnormal data corresponding to the new energy power generation equipment are obtained; Step S253: The operation abnormal data, meteorological abnormal data and power abnormal data corresponding to the new energy power generation equipment are subjected to spike denoising processing, the corresponding abnormal data is decomposed into different frequency subbands by using wavelet transform to highlight the data characteristics corresponding to the abnormal spike noise, the wavelet coefficients corresponding to the abnormal spike noise are removed by using the opening and closing operation in morphological filtering, and the corresponding abnormal data is reconstructed by inverse wavelet transform, and new energy power generation denoising data, new energy meteorological denoising data and new energy power load denoising data are obtained; Step S254: The new energy power generation denoising data, new energy meteorological denoising data and new energy power load denoising data are subjected to normalization processing, and the new energy power generation operation data, new energy meteorological data and new energy power load data corresponding to the same order of magnitude scale are obtained.

5. A new energy dispatching strategy deduction system based on a hierarchical classification system, characterized in that, The layered hierarchical new energy scheduling strategy deduction method is used to execute the layered hierarchical new energy scheduling strategy deduction system as claimed in claim 1, which comprises: A layered hierarchical regulation and control architecture design module is used to construct a layered hierarchical regulation and control architecture system comprising a macro regulation and control layer, a regional coordination layer and a local control layer; A new energy multi-source data processing module is used to collect the operation data, meteorological data and power load data corresponding to the new energy power generation equipment in real time through the local control layer in the layered hierarchical regulation and control architecture system, and upload them to the data regulation and processing center corresponding to the macro regulation and control layer; the data regulation and processing center is used for spike denoising and normalization on the operation data, meteorological data and power load data corresponding to the new energy power generation equipment, so as to obtain the new energy power generation operation data, new energy meteorological data and new energy power load data corresponding to the same order of magnitude scale. The dispatch evaluation index deduction analysis module is configured to simulate and deduce different new energy operation dispatch simulation conditions by corresponding new energy power generation operation data, new energy meteorological data and new energy power load data in the same order of magnitude scale, wherein the new energy dispatch strategies corresponding to different new energy equipment operation states, meteorological conditions and power load change conditions are included; the dispatch evaluation index analysis of the corresponding new energy power system is performed based on different new energy operation dispatch simulation conditions, so as to obtain the power safety index, the power reliability index and the power carbon emission reduction amount index of the power system corresponding to different operation dispatch conditions; The new energy dispatch hierarchical issuing control module is configured to perform hierarchical evaluation analysis on the power safety index, the power reliability index and the power carbon emission reduction amount index of the power system corresponding to different operation dispatch conditions, so as to obtain the comprehensive score of the new energy dispatch strategy of the power system corresponding to different operation dispatch conditions; the new energy power generation dispatch control instruction corresponding to different operation dispatch conditions is generated based on the comprehensive score of the new energy dispatch strategy of the power system corresponding to different operation dispatch conditions by using the macro-control layer response, and is conveyed to the regional coordination layer to formulate the new energy power generation dispatch plan corresponding to the new energy power generation state of the region and issue the new energy power generation dispatch plan to the local control layer to execute the corresponding new energy dispatch optimization strategy.

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