Integrated energy optimization method and system using automatic dispatching model

Abstract

This invention relates to a comprehensive energy optimization method employing an automatic allocation model, comprising: acquiring energy data; analyzing and processing the acquired energy data; acquiring energy status information; analyzing the energy status; automatically allocating energy based on the analysis results; and adjusting the energy supply based on the allocation results to optimize the energy supply system. This invention also discloses an energy optimization system. By real-time acquisition of response-sensitive and price-sensitive data from energy data, this invention uses a genetic algorithm to calculate optimal values, thereby guiding the allocation of scarce, urgently needed, and other energy sources. This achieves comprehensive energy supply optimization and scientific and rational allocation, reduces energy costs, and improves energy response speed and energy utilization rate.

Description

Technical Field

[0001] This invention belongs to the field of energy optimization, specifically relating to a comprehensive energy optimization method and system employing an automatic allocation model. Background Technology

[0002] Different energy sources have different energy supply effects, and a combination of multiple different types of energy is called integrated energy. Chinese invention application CN114936707A, entitled "A Green and Low-Carbon Integrated Energy System Optimization System," discloses an energy system optimization system including a data acquisition terminal, a processing center, a node acquisition terminal, a node maintenance terminal, a model generation terminal, and an output terminal. However, this optimization system relies on a relatively singular data acquisition channel, resulting in insufficient comprehensiveness and quantity of the final acquired data. Furthermore, it does not separately classify scarce and urgently needed energy sources during optimization, raising questions about its application effectiveness. Summary of the Invention

[0003] The technical problem of this invention is that the existing integrated energy system is affected by the fluctuation of different energy prices and response speed. It does not take into account that different regions have different geographical locations, industrial distribution and concentration, resulting in different energy shortages and urgent needs. This leads to an increase in the overall consumption of the integrated energy system and poor energy utilization.

[0004] The purpose of this invention is to address the above-mentioned problems by providing a comprehensive energy optimization method with an efficient response using an automatic allocation model, thereby reducing the consumption of the comprehensive energy system and improving energy utilization.

[0005] The technical solution of this invention is a comprehensive energy optimization method using an automatic allocation model, comprising the following steps:

[0006] S1. Obtain energy data;

[0007] S2. Analyze and process the acquired energy data;

[0008] S3. Obtain information on the current energy status;

[0009] S4. Analyze the current energy situation;

[0010] S5. Automatically allocate energy based on the analysis results;

[0011] S6. Based on the automatic allocation results of step S5, adjust the energy supply to optimize the energy supply system.

[0012] The energy optimization system of the above-mentioned integrated energy optimization method includes an energy data acquisition module, an energy data processing module, an energy status acquisition module, and an energy status analysis module.

[0013] The energy data acquisition module is used to acquire energy data from various nodes and stations in the integrated energy system.

[0014] The energy data processing module is used to integrate, filter, and remove invalid energy data. The remaining data is decomposed into response speed-sensitive data and price-sensitive data to form an energy data matrix A. The energy data matrix A obtained multiple times is then grouped together.

[0015] The Energy Status Acquisition Module receives historical energy data of the integrated energy system from the Big Data Acquisition Submodule of the Energy Data Acquisition Module, and decomposes it into price-sensitive data and response speed-sensitive data.

[0016] The Energy Status Analysis module is used to analyze and calculate multiple sets of historical energy data to determine the response speed and price of various energy sources.

[0017] Furthermore, the energy data acquisition module includes an active acquisition submodule, a passive acquisition submodule, and a network data acquisition submodule, a Bluetooth data transmission submodule, and an external data transmission submodule that are communicatively connected to the active acquisition submodule, as well as a big data acquisition submodule that is connected to the passive acquisition submodule. The active acquisition submodule is communicatively connected to the passive acquisition submodule.

[0018] The active acquisition submodule is used to acquire and receive energy data from various nodes and stations transmitted in different ways.

[0019] The passive acquisition submodule is used to receive and store energy data from the active acquisition submodule and the big data acquisition submodule.

[0020] The network data acquisition submodule is used to acquire energy data from each node and station and transmit it to the active acquisition submodule.

[0021] The Bluetooth data transmission submodule is used to acquire energy data from nodes and stations that require wireless data transmission when wired transmission is inconvenient, and then transmit the data to the active acquisition submodule.

[0022] The external data transmission submodule is used to obtain the response speed and price of different energy sources and transmit them to the active acquisition submodule.

[0023] The big data acquisition submodule is used to acquire historical energy data from various nodes and stations and transmit it to the passive acquisition submodule.

[0024] Furthermore, the energy data processing module includes an energy data integration submodule, a data overall filtering submodule, an invalid data removal submodule, an energy data classification submodule, and an energy data reordering submodule, which are connected in sequence.

[0025] The energy data integration submodule is used to integrate the acquired energy data and convert the simultaneously transmitted data into a matrix.

[0026] The overall data filtering submodule is used to filter the acquired energy data and determine the energy data matrix A by comparing it with historical data, so as to avoid calculation errors caused by excessive or missing data.

[0027] The invalid data removal submodule is used to remove invalid data from the acquired energy data. It determines the energy data matrix A by comparing it with historical data, thus avoiding calculation errors caused by large deviations between the data transmitted when the system malfunctions and the data during normal operation.

[0028] The energy data classification submodule is used to decompose the data in the energy data matrix A into two columns of column vector data: response speed sensitive data and price sensitive data.

[0029] The Energy Data Reordering submodule is used to arrange the acquired energy data into multiple groups.

[0030] Furthermore, the energy status acquisition module includes an energy application status acquisition submodule, an energy consumption ranking submodule, a scarce energy type acquisition submodule, an urgently needed energy type acquisition submodule, and a data fast storage submodule, which are connected in sequence via communication.

[0031] The Energy Application Status Acquisition Submodule is used to acquire the actual energy consumption status of each node and station.

[0032] The energy consumption sorting submodule is used to sort different energy consumption levels, energy response speeds, and prices.

[0033] The scarce energy type acquisition submodule determines whether an energy source is price-sensitive based on the price-sensitive data in the energy data matrix A, and further determines whether the purchase quantity of the energy exceeds twice the energy storage quantity. If both the determination results are yes, the energy source is marked as a scarce energy type to solve the problem of scarce energy that needs to be purchased in large quantities.

[0034] The "Energy Type Acquisition Submodule" determines whether the response time of an energy source is lower than the average response time based on the response speed-sensitive data in the energy data matrix A. If the result is yes, the energy source is marked as an energy source of urgent need to solve the problem of energy sources with slow response speeds but in urgent need.

[0035] The fast data storage submodule is used to accelerate energy data processing and save data processing time.

[0036] Furthermore, the energy status analysis module includes a scarce energy consumption analysis submodule, an urgent energy consumption analysis submodule, a scarce energy replenishment calculation submodule, an urgent energy replenishment calculation submodule, and a data integration and statistics submodule, which are connected in sequence via communication.

[0037] The scarce energy consumption analysis submodule is used to perform statistical analysis on the consumption of scarce energy types in different time periods and regions, as well as price fluctuations and regional price differences, based on the acquired historical energy data. The statistical analysis results are then presented to the user in the form of graphs and tables.

[0038] The Urgent Energy Consumption Analysis submodule is used to statistically analyze the regional differences in the consumption and response speed of the urgent energy types in different time periods and regions based on the acquired historical energy data, and to display the statistical analysis results to users in the form of graphs and tables.

[0039] The scarce energy replenishment calculation submodule is used to analyze the acquired historical energy data and constrain the price-sensitive data in this column to a value of 0 when the value is negative.

[0040] An energy shortage calculation submodule is urgently needed to analyze the acquired historical energy data and constrain the response speed-sensitive data in this column to a minimum value when it is less than a certain limit.

[0041] The data integration and statistics submodule is used to integrate and statistically analyze the response speed and energy prices of different types of energy based on the region.

[0042] Preferably, a genetic algorithm is used to allocate energy, and the allocation model is as follows:

[0043]

[0044] In the formula, A is the energy data matrix; A new This is the encoded energy data matrix; min(A) and max(A) represent the minimum and maximum values ​​in the energy data, respectively.

[0045] The energy response speed and energy price in the energy data reflect the impact of the energy in the data on the integrated energy system. The faster the energy response speed and the lower the energy price, the more beneficial it is to the operation and cost control of the integrated energy system.

[0046] The formula for calculating energy data fitness is as follows:

[0047]

[0048] In the formula F b This indicates the adaptability of energy data as reflected in the energy data; v i Indicates energy response speed; m iIndicates the price of energy; a i1 A represents new The column vector data in the first column is response speed sensitive data; a i2 A represents new The column vector data in the second column is price-sensitive data.

[0049] A new The data items in the data are used as genes for crossover in the genetic algorithm:

[0050]

[0051] In the formula a m,x A represents new The data in the m-th row and x-th column; a n,x A represents new The data in the nth row and xth column; x takes the value 1 or 2; p represents the crossover probability, when or When p = 0.9, p = 0.6 in other cases.

[0052] For A new Mutate the data items in the data:

[0053]

[0054] In the formula a i,x A represents new The data in the i-th row and x-th column; x takes the value 1 or 2; a max a min They represent data item a respectively i,x The upper and lower bounds of r; r is a random value, r∈[0,1].

[0055] Multiple energy data strings are selected, and the energy data matrices within them are cross-crossed and mutated. The fitness F of the energy data is then calculated. b The optimal energy data fitness of the same type of energy in the same region is selected, and the optimal energy data matrix obtained by mutation corresponding to the optimal energy data fitness is obtained.

[0056] The optimization results differ depending on the energy supply nodes and stations. In resource-rich areas, additional alternative energy sources can be added to address energy shortages and urgent needs. In resource-scarce areas, energy sources with faster response times and relatively abundant reserves can be sourced from other regions. By increasing the input of alternative energy sources to address energy shortages, the cost of purchasing these energy sources can be reduced. By allocating energy from other regions, the response speed of urgently needed energy sources can be adjusted and increased. This ensures that the values ​​of the data items in the energy data matrix are close to or equal to the values ​​in the energy data matrix corresponding to the calculated optimal energy data fitness, thereby achieving efficient response and scientific and rational allocation of comprehensive energy resources.

[0057] Preferably, the energy allocation method adopted in this invention specifically includes:

[0058] 1) For energy in urgent need, provide readily available and readily available alternative energy sources with fast local and cross-regional allocation capabilities and large reserves, and take measures to improve the transmission methods of energy in urgent need and increase the response speed; build power stations in areas with abundant energy in urgent need; and set the allocation priority of energy in urgent need as level one.

[0059] 2) For scarce energy resources, increase the types of alternative energy sources and the reserves of alternative energy resources; set the priority of the allocation of scarce energy resources as level two;

[0060] 3) Other energy sources, taking into account energy costs and transmission speed, should be appropriately allocated across regions; energy storage capacity should be rationally allocated to achieve a balance between energy prices and response speed; the types of energy included in comprehensive energy should be increased; and the allocation priority of energy sources other than urgently needed energy and scarce energy should be set at three levels.

[0061] Compared with the prior art, the beneficial effects of the present invention include:

[0062] 1) This invention collects response speed-sensitive data and price-sensitive data from energy data in real time, and uses a genetic algorithm to calculate the optimal values. This guides the allocation of scarce energy, urgently needed energy and other energy sources, thereby achieving comprehensive energy supply optimization and scientific and rational allocation, reducing energy costs, and improving energy response speed and energy utilization.

[0063] 2) This invention effectively solves the supply problems of urgently needed and scarce energy by adopting a scientific and reasonable energy allocation strategy and setting different allocation priorities, making energy supply more secure and highlighting the overall advantages of integrated energy.

[0064] 3) This invention acquires energy data through various methods, expanding the data acquisition channels, effectively increasing the total amount and comprehensiveness of data acquisition, facilitating subsequent energy optimization and allocation, realizing comprehensive energy optimization, and promoting further big data analysis of energy.

[0065] 4) The comprehensive energy optimization method and energy optimization system of the present invention are easy to execute by a computer, realizing the automation of energy allocation and optimization. Attached Figure Description

[0066] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0067] Figure 1 This is a flowchart illustrating the comprehensive energy optimization method according to an embodiment of the present invention.

[0068] Figure 2 This is a schematic diagram of the energy optimization system according to an embodiment of the present invention.

[0069] Figure 3 This is a schematic diagram of the energy data acquisition module according to an embodiment of the present invention.

[0070] Figure 4 This is a schematic diagram of the energy data processing module according to an embodiment of the present invention.

[0071] Figure 5 This is a schematic diagram of the energy status acquisition module according to an embodiment of the present invention.

[0072] Figure 6 This is a schematic diagram of the energy status analysis module according to an embodiment of the present invention. Detailed Implementation

[0073] like Figure 1 As shown, the integrated energy optimization method using an automatic allocation model includes the following steps:

[0074] S1. Obtain energy data;

[0075] S2. Analyze and process the acquired energy data;

[0076] S3. Obtain information on the current energy status;

[0077] S4. Analyze the current energy situation;

[0078] S5. Automatically allocate energy based on the analysis results;

[0079] S6. Based on the automatic allocation results of step S5, adjust the energy supply to optimize the energy supply system.

[0080] In step S5, the automatic allocation of energy is carried out using a genetic algorithm. The allocation model is as follows:

[0081]

[0082] In the formula, A is the energy data matrix; A new The encoded energy data matrix; min(A) and max(A) represent the minimum and maximum values ​​in the energy data, respectively;

[0083] The faster the energy response speed and the lower the energy price, the more beneficial it is to the operation and cost control of the integrated energy system.

[0084] The formula for calculating energy data fitness is as follows:

[0085]

[0086] In the formula F b This indicates the fitness of the energy data reflected in the energy data string; v i Indicates energy response speed; m i Indicates the price of energy; a i1 A represents new The column vector data in the first column of the array; a i2 A represents new The column vector data in the second column;

[0087] A new =[a i1 a i2 ]

[0088] A new The data items in the data are used as genes for crossover in the genetic algorithm:

[0089]

[0090] In the formula a m(1,2) A represents new The data in the first or second column of the m-th row; a n(1,2) A represents new The data in the first or second column of the nth row; when the index (1,2) appears, if 1 is selected, then all calculation formulas will select A. new In the first column of the calculation, if you select 2, then all calculation formulas will select A. new The second column in the calculation; p represents the crossover probability, when or When p = 0.9, p = 0.6 in other cases.

[0091] For A newMutate the data items in the data:

[0092]

[0093] In the formula ai(1 ,2) A represents new The data in the first or second column of the i-th row; a max a min They represent data item a respectively i(1,2) The upper and lower bounds; r is a random value, r∈[0,1];

[0094] Select F from the data strings in group b b The data string corresponding to the maximum value is used to allocate energy through an automatic allocation system to make it as close as possible to this set of data, thereby achieving efficient response and scientific allocation of the integrated energy system.

[0095] The energy allocation strategy adopted in this embodiment is as follows:

[0096] 1) Allocation of Urgently Needed Energy: Due to the nature of urgently needed energy, its slow response speed leads to significant transmission delays, causing a decrease in the efficiency of the integrated energy system and increased consumption. To address this, the response speed can be improved by modifying transmission methods, or by allocating locally or across regions energy resources with fast response speeds and abundant resources to meet the demand for urgently needed energy. Power stations should be constructed near areas with abundant urgently needed energy resources. This approach meets the demand for urgently needed energy, improves energy utilization, and achieves the highest priority in the integrated energy system allocation.

[0097] 2) Allocation of Scarce Energy: Due to the nature of scarcity energy, its demand is large and it needs to be purchased in bulk. To reduce dependence on a single or a few energy sources, the types of alternative energy sources and the reserves of alternative energy sources should be increased. Meeting the demand for scarcity energy reduces energy costs, and its priority in the overall energy system allocation is second only to urgently needed energy.

[0098] 3) Allocation of non-scarce and non-urgent energy: While meeting the demands of 1) and 2) as much as possible, energy costs and transmission issues should be considered, and allocation should be carried out according to the principle of automatic allocation. This involves increasing the variety of energy sources in the integrated energy system, rationally allocating energy storage capacity, and enabling long-distance inter-regional transmission. Other non-scarce and non-urgent energy sources have the lowest priority in the allocation of the integrated energy system.

[0099] The energy optimization system employing the aforementioned comprehensive energy optimization method includes an energy data acquisition module, an energy data processing module, an energy status acquisition module, and an energy status analysis module, such as... Figure 2 As shown.

[0100] The energy data acquisition module is used to acquire energy data from various nodes and stations in the integrated energy system.

[0101] like Figure 3 As shown, the energy data acquisition module includes an active acquisition submodule, a passive acquisition submodule, and a network data acquisition submodule, a Bluetooth data transmission submodule, and an external data transmission submodule that are communicatively connected to the active acquisition submodule. It also includes a big data acquisition submodule that is connected to the passive acquisition submodule. The active acquisition submodule and the passive acquisition submodule are communicatively connected.

[0102] The active acquisition submodule is used to acquire and receive energy data from various nodes and stations transmitted in different ways.

[0103] The passive acquisition submodule is used to receive and store energy data from the active acquisition submodule and the big data acquisition submodule.

[0104] The network data acquisition submodule is used to acquire energy data from each node and station and transmit it to the active acquisition submodule.

[0105] The Bluetooth data transmission submodule is used to acquire energy data from nodes and stations that require wireless data transmission when wired transmission is inconvenient, and then transmit the data to the active acquisition submodule.

[0106] The external data transmission submodule is used to obtain the response speed and price of different energy sources and transmit them to the active acquisition submodule.

[0107] The big data acquisition submodule is used to acquire historical energy data from various nodes and stations and transmit it to the passive acquisition submodule.

[0108] The energy data processing module is used to integrate, filter, and remove invalid energy data, and decompose the remaining data into a... i1 a i2 This forms an energy data matrix A, and the energy data matrix A obtained multiple times is arranged into group b.

[0109] like Figure 4 As shown, the energy data processing module includes an energy data integration submodule, a data overall filtering submodule, an invalid data removal submodule, an energy data classification submodule, and an energy data reordering submodule, which are connected in sequence.

[0110] The energy data integration submodule is used to integrate the acquired energy data and convert the simultaneously transmitted data into a matrix.

[0111] The overall data filtering submodule is used to filter the acquired energy data and determine the energy data matrix A by comparing it with historical data, so as to avoid calculation errors caused by excessive or missing data.

[0112] The invalid data removal submodule is used to remove invalid data from the acquired energy data. It determines the energy data matrix A by comparing it with historical data, thus avoiding calculation errors caused by large deviations between the data transmitted when the system malfunctions and the data during normal operation.

[0113] The energy data classification submodule is used to decompose the data in the energy data matrix A into two columns of column vector data.

[0114] The Energy Data Reordering submodule is used to arrange the acquired energy data into group b.

[0115] The Energy Status Acquisition Module receives historical energy data of the integrated energy system from the Big Data Acquisition Submodule of the Energy Data Acquisition Module, and decomposes it into price-sensitive data and response speed-sensitive data.

[0116] like Figure 5 As shown, the energy status acquisition module includes, in sequence, a sub-module for acquiring energy application status, a sub-module for ranking energy consumption, a sub-module for acquiring scarce energy types, a sub-module for acquiring urgently needed energy types, and a sub-module for fast data storage.

[0117] The Energy Application Status Acquisition Submodule is used to acquire the actual energy consumption status of each node and station.

[0118] The energy consumption sorting submodule is used to sort different energy consumption levels, energy response speeds, and prices.

[0119] The scarce energy type acquisition submodule determines whether an energy source is price-sensitive based on the price-sensitive data in the energy data matrix A, and further determines whether the purchase quantity of the energy exceeds twice the energy storage quantity. If both the determination results are yes, the energy source is marked as a scarce energy type to solve the problem of scarce energy that needs to be purchased in large quantities.

[0120] The "Energy Type Acquisition Submodule" determines whether the response time of an energy source is lower than the average response time based on the response speed-sensitive data in the energy data matrix A. If the result is yes, the energy source is marked as an energy source of urgent need to solve the problem of energy sources with slow response speeds but in urgent need.

[0121] The fast data storage submodule is used to accelerate energy data processing and save time.

[0122] The energy status analysis module is used to analyze and calculate multiple sets of historical energy data to determine the energy response rate v. i and energy prices m i .

[0123] like Figure 6As shown, the energy status analysis module includes a scarce energy consumption analysis submodule, an urgent energy consumption analysis submodule, a scarce energy replenishment calculation submodule, an urgent energy replenishment calculation submodule, and a data integration and statistics submodule, which are connected in sequence via communication.

[0124] The scarce energy consumption analysis submodule is used to perform statistical analysis on the consumption of scarce energy types in different time periods and regions, as well as price fluctuations and regional price differences, based on the acquired historical energy data. The statistical analysis results are then presented to the user in the form of graphs and tables.

[0125] The Urgent Energy Consumption Analysis submodule is used to statistically analyze the regional differences in the consumption and response speed of the urgent energy types in different time periods and regions based on the acquired historical energy data, and to display the statistical analysis results to users in the form of graphs and tables.

[0126] The scarce energy replenishment calculation submodule is used to analyze the acquired historical energy data and constrain the price-sensitive data in this column to a value of 0 when the value is negative.

[0127] An energy shortage calculation submodule is urgently needed to analyze the acquired historical energy data and constrain the response speed-sensitive data in this column to a minimum value when it is less than a certain limit.

[0128] The data integration and statistics submodule is used to integrate and statistically analyze the response speed and energy prices of different types of energy based on the region.

Claims

1. A comprehensive energy optimization method employing an automatic allocation model, characterized in that, Includes the following steps: S1. Obtain energy data; S2. Analyze and process the acquired energy data; S3. Obtain information on the current energy status; S4. Analyze the current energy situation; S5. Automatically allocate energy based on the analysis results; S6. Based on the automatic allocation results of step S5, adjust the energy supply to optimize the energy supply system. In step S5, the automatic allocation of energy is carried out using a genetic algorithm. The allocation model is as follows: ； (1) In the formula, A is the energy data matrix; A new This is the encoded energy data matrix; min(A) and max(A) represent the minimum and maximum values ​​in the energy data, respectively. The energy response speed and energy price in energy data reflect the impact of energy on the integrated energy system. The faster the energy response speed and the lower the energy price, the more beneficial it is to the operation and cost control of the integrated energy system. The formula for calculating energy data fitness is as follows: ； ； (2) In the formula Fb This indicates the adaptability of the energy data as reflected in the energy data; vi Indicates energy response speed; mi Indicates the price of energy; a i1 represents A new The column vector data in the first column is response speed sensitive data; a i2 represents A new The column vector data in the second column is price-sensitive data; A new The data items in the data are used as genes for crossover in the genetic algorithm: ；(3) In the formula a m,x A represents new The Middle m Line number x Column data; a n,x A represents new The Middle n Line number x Column data; x The value can be 1 or 2; p Indicates the crossover probability; For A new Mutate the data items in the data: ； (4) In the formula a i,x A represents new The Middle i Line number x Column data; x The value can be 1 or 2; a max , a min Representing data items respectively a i,x The upper and lower bounds; r For random values, r [0, 1]; Multiple energy data strings are selected, and the energy data matrices within them are subjected to cross-multiplication and mutation, with the energy data fitness calculated. F b The optimal energy data fitness of the same type of energy in the same region is selected, and the optimal energy data matrix obtained by mutation corresponding to the optimal energy data fitness is obtained. The optimization results differ depending on the energy supply nodes and stations. In resource-rich areas, additional alternative energy sources can be added to address energy shortages and urgent needs. In resource-scarce areas, energy sources with faster response times and relatively abundant reserves can be sourced from other regions. By increasing the input of alternative energy sources to address energy shortages, the cost of purchasing these energy sources can be reduced. By allocating energy from other regions, the response speed of urgently needed energy sources can be adjusted and increased. This ensures that the values ​​of the data items in the energy data matrix are close to or equal to the values ​​in the energy data matrix corresponding to the calculated optimal energy data fitness, thereby achieving efficient response and scientific and rational allocation of comprehensive energy resources.

2. The comprehensive energy optimization method according to claim 1, characterized in that, The energy allocation methods employed in the comprehensive energy optimization method specifically include: 1) For energy in urgent need, provide readily available and readily available alternative energy sources with fast local and cross-regional allocation capabilities and large reserves, and take measures to improve the transmission methods of energy in urgent need and increase the response speed; construct power stations in areas with abundant energy in urgent need; and set the allocation priority of energy in urgent need as level one. 2) For scarce energy resources, increase the types of alternative energy sources and the reserves of alternative energy resources; set the priority of the allocation of scarce energy resources as level two; 3) Other energy sources, taking into account energy costs and transmission speed, should be appropriately allocated across regions; energy storage should be rationally configured to achieve a balance between energy prices and response speed; the types of comprehensive energy sources should be increased; and the allocation priority of energy sources other than urgently needed energy and scarce energy sources should be set at three levels.

3. The energy optimization system of the comprehensive energy optimization method as described in claim 1 or 2, characterized in that, The system includes an energy data acquisition module, an energy data processing module, an energy status acquisition module, and an energy status analysis module; The energy data acquisition module is used to acquire energy data from various nodes and stations in the integrated energy system; The energy data processing module is used to integrate and filter the acquired energy data, remove invalid data, decompose response speed sensitive data and price sensitive data from the retained energy data, form energy data matrix A, and arrange the energy data matrix A acquired multiple times into groups. The Energy Status Acquisition Module is used to receive historical energy data of the integrated energy system from the Big Data Acquisition Submodule in the Energy Data Acquisition Module, and decompose it into price-sensitive data and response speed-sensitive data. The Energy Status Analysis module is used to analyze and calculate multiple sets of historical energy data to determine the response speed and price of various energy sources.

4. The energy optimization system according to claim 3, characterized in that, The energy data acquisition module includes an active acquisition submodule, a passive acquisition submodule, and a network data acquisition submodule, a Bluetooth data transmission submodule, and an external data transmission submodule that are communicatively connected to the active acquisition submodule. It also includes a big data acquisition submodule that is connected to the passive acquisition submodule. The active acquisition submodule and the passive acquisition submodule are communicatively connected. The active acquisition submodule is used to acquire and receive energy data from various nodes and stations transmitted in different ways; The passive acquisition submodule is used to receive and store energy data from the active acquisition submodule and the big data acquisition submodule; The network data acquisition submodule is used to acquire energy data from each node and station and transmit it to the active acquisition submodule; The Bluetooth data transmission submodule is used to acquire energy data from nodes and stations that require wireless data transmission when line transmission is inconvenient, and then transmit the data to the active acquisition submodule. The external data transmission submodule is used to obtain the response speed and price of different energy sources and transmit them to the active acquisition submodule; The big data acquisition submodule is used to acquire historical energy data from various nodes and stations and transmit it to the passive acquisition submodule.

5. The energy optimization system according to claim 3, characterized in that, The energy data processing module includes an energy data integration submodule, a data overall filtering submodule, an invalid data removal submodule, an energy data classification submodule, and an energy data reordering submodule, which are connected in sequence via communication. The energy data integration submodule is used to integrate the acquired energy data and convert the simultaneously transmitted data into a matrix; The overall data filtering submodule is used to filter the acquired energy data and determine the energy data matrix A by comparing it with historical data, so as to avoid calculation errors caused by excessive or missing data. The invalid data removal submodule is used to remove invalid data from the acquired energy data. It determines the energy data matrix A by comparing it with historical data, so as to avoid calculation errors caused by large deviations between the data transmitted when the system malfunctions and the data during normal operation. The energy data classification submodule is used to decompose the data in the energy data matrix A into two columns of column vector data, namely response speed sensitive data and price sensitive data. The Energy Data Reordering submodule is used to arrange the acquired energy data into multiple groups.

6. The energy optimization system according to claim 3, characterized in that, The energy status acquisition module includes, in sequence, an energy application status acquisition submodule, an energy consumption ranking submodule, a scarce energy type acquisition submodule, an urgently needed energy type acquisition submodule, and a data fast storage submodule, which are connected by communication. The Energy Application Status Acquisition Submodule is used to acquire the actual energy consumption of each node and station. The energy consumption sorting submodule is used to sort different energy consumption levels, energy response speeds, and prices. The scarce energy type acquisition submodule determines whether an energy source is price-sensitive based on the price-sensitive data in the energy data matrix A, and further determines whether the purchase quantity of the energy exceeds twice the energy storage quantity. If both the determination results are yes, the energy source is marked as a scarce energy type to solve the problem of scarce energy that needs to be purchased in large quantities. The "Energy Type Acquisition Submodule" determines whether the response time of an energy source is lower than the average response time based on the response speed-sensitive data in the energy data matrix A. If the result is yes, the energy source is marked as an energy source of urgent need to solve the problem of energy sources with slow response speeds but in urgent need. The fast data storage submodule is used to accelerate the processing of energy data.

7. The energy optimization system according to claim 3, characterized in that, The energy status analysis module includes a scarce energy consumption analysis submodule, an urgent energy consumption analysis submodule, a scarce energy replenishment calculation submodule, an urgent energy replenishment calculation submodule, and a data integration and statistics submodule, which are connected in sequence via communication. The scarce energy consumption analysis submodule is used to perform statistical analysis on the consumption of scarce energy types in different time periods and regions, as well as price fluctuations and regional price differences, based on the acquired historical energy data. The statistical analysis results are then presented to the user in the form of graphs and tables. The Urgent Energy Consumption Analysis submodule is used to perform statistical analysis on the consumption of urgent energy types and regional differences in response speed in different time periods and regions based on the acquired historical energy data, and to display the statistical analysis results to users in the form of graphs and tables. The scarce energy replenishment calculation submodule is used to analyze the acquired historical energy data and constrain the price-sensitive data in this column to a value of 0 when the value is negative. An energy deficit calculation submodule is urgently needed to analyze the acquired historical energy data and constrain the response speed-sensitive data in this column to a minimum value when it is less than a certain limit. The data integration and statistics submodule is used to integrate and statistically analyze the response speed and energy prices of different types of energy based on the region.

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