A power system multi-energy comprehensive regulation optimization operation method and system

Abstract

The application provides a kind of power system multi-energy comprehensive regulation optimization operation method and system, it is related to power system technical field.In the present application, first, determine the multi-energy system data;Second, using the first semantic mining network, the multi-energy system data corresponding multi-energy system semantic vector is mined;Then, using semantic fusion network, the multi-energy system data corresponding multi-energy system semantic vector and each reference multi-energy operation scheme corresponding multi-energy scheme semantic vector configured in advance is carried out semantic fusion, and the multi-energy system data corresponding multi-energy scheme semantic vector is output;Finally, using the first mapping output network, the multi-energy system data corresponding multi-energy scheme semantic vector is mapped and output, and the target multi-energy operation scheme is obtained.Based on the above, the reliability of the existing multi-energy comprehensive regulation optimization operation can be improved, and the problem of high labor cost can be solved.

Description

Technical Field

[0001] This application relates to the field of power system technology, and more specifically, to a method and system for multi-energy integrated regulation and optimization operation of a power system. Background Technology

[0002] Traditional energy sources (such as coal, natural gas, and nuclear power) can provide stable power output, while renewable energy sources (such as wind, solar, and hydropower) are fluctuating and intermittent, effectively reducing greenhouse gas emissions, but their output is uncertain. The main objectives of multi-energy system regulation and optimization include: minimizing the operating costs of the power system (such as fuel costs and operating costs); ensuring the system can provide sufficient power supply to meet load demand while guaranteeing system stability and security; reducing greenhouse gas emissions, promoting the use of green energy, and reducing dependence on traditional fossil fuels. However, the inventors have found that in existing technologies, schemes for the integrated regulation and optimization of power systems based on multi-energy sources are generally derived from manual analysis by relevant personnel, which leads to low reliability and high labor costs. Summary of the Invention

[0003] In view of this, the purpose of this application is to provide a method and system for multi-energy integrated regulation and optimization operation of power systems, so as to improve the problems of low reliability and high labor cost of multi-energy integrated regulation and optimization operation in the prior art.

[0004] To achieve the above objectives, this application adopts the following technical solution:

[0005] A method for integrated regulation and optimization of power system based on multiple energy sources includes:

[0006] The multi-energy system data is determined, wherein the multi-energy system data includes at least descriptive data of multiple energy subsystems in the target power system and descriptive data of the electricity demand of the electricity consumption area corresponding to the target power system;

[0007] Using a first semantic mining network, the semantic vectors of the multi-energy system corresponding to the multi-energy system data are mined.

[0008] Using a semantic fusion network, the semantic vector of the multi-energy system corresponding to the multi-energy system data and the semantic vector of the multi-energy scheme corresponding to each reference multi-energy operation scheme included in the pre-configured set of reference multi-energy operation schemes are semantically fused to output the semantic vector of the multi-energy scheme corresponding to the multi-energy system data. The semantic vector of the multi-energy scheme corresponding to the reference multi-energy operation scheme is mined using a second semantic mining network.

[0009] Using the first mapping output network, the semantic vector of the multi-energy scheme corresponding to the multi-energy system data is mapped and output to obtain the target multi-energy operation scheme corresponding to the multi-energy system data. The target multi-energy operation scheme serves as the basis for the power generation operation of multiple energy subsystems in the target power system.

[0010] In a preferred embodiment of this application, in the above-mentioned multi-energy integrated regulation and optimization operation method for power systems, the semantic fusion network includes a first number of first connection units and a second number of first semantic fusion units, and the first connection units and the first semantic fusion units are arranged alternately in sequence, and the difference between the first number and the second number is equal to 1.

[0011] The step of using a semantic fusion network to perform semantic fusion on the multi-energy system semantic vector corresponding to the multi-energy system data and the multi-energy scheme semantic vector corresponding to each reference multi-energy operation scheme included in a pre-configured set of reference multi-energy operation schemes, and outputting the multi-energy scheme semantic vector corresponding to the multi-energy system data, includes:

[0012] Using the a-th first connection unit, the multi-energy system input vector of the a-th first connection unit is processed to form the multi-energy system output vector of the a-th first connection unit. The multi-energy system input vector of the first first connection unit includes the multi-energy scheme semantic vector of the reference multi-energy operation scheme, and the multi-energy system output vector of the last first connection unit includes the multi-energy scheme semantic vector corresponding to the multi-energy system data.

[0013] Using the a-th first semantic fusion unit, the multi-energy system input vector of the a-th first semantic fusion unit is processed to form the multi-energy system output vector of the a-th first semantic fusion unit. The multi-energy system input vector of the a-th first semantic fusion unit includes the multi-energy system output vector of the a-th first connection unit and the multi-energy system semantic vector of the multi-energy system data. The multi-energy system input vector of the b-th first connection unit includes the multi-energy system output vector of the a-th first semantic fusion unit. The b-th first connection unit is the first connection unit following the a-th first connection unit.

[0014] In a preferred embodiment of this application, in the aforementioned multi-energy integrated regulation and optimization operation method for power systems, the first connection unit includes a high-dimensional mapping subunit and a first depth mining subunit.

[0015] The step of processing the multi-energy system input vector of the a-th first connection unit to form the multi-energy system output vector of the a-th first connection unit includes:

[0016] Using the high-dimensional mapping subunit of the a-th first connection unit, the multi-energy system input vector of the a-th first connection unit is mapped in a high dimension, and a high-dimensional mapping vector is output.

[0017] Using the first deep mining subunit, the high-dimensional mapping vector is subjected to multi-stage deep mining to form the multi-energy system output vector of the a-th first connection unit.

[0018] In a preferred embodiment of this application, in the above-mentioned multi-energy integrated regulation and optimization operation method for power systems, the first connection unit further includes a second depth mining subunit and a third depth mining subunit;

[0019] The step of using the first deep mining subunit to perform multi-stage deep mining on the high-dimensional mapping vector to form the multi-energy system output vector of the a-th first connection unit includes:

[0020] Using the first deep mining subunit, the second deep mining subunit, and the third deep mining subunit, the high-dimensional mapping vector is subjected to multi-stage deep mining to form the multi-energy system output vector of the a-th first connection unit;

[0021] In each stage of deep mining, the first deep mining subunit mines the multi-energy system input vector of the first deep mining subunit to form the multi-energy system output vector of the first deep mining subunit; the second deep mining subunit mines the semantic vector of the associated multi-energy scheme of the multi-energy system data to form the multi-energy system output vector of the second deep mining subunit; the multi-energy system output vectors of the first and second deep mining subunits are aggregated and then mined by the third deep mining subunit to form the multi-energy system output vector of the third deep mining subunit; and in the next stage of deep mining, the multi-energy system input vector of the first deep mining subunit is determined based on the multi-energy system output vector of the third deep mining subunit and the multi-energy system input vector of the first deep mining subunit.

[0022] In the first stage of deep mining, the multi-energy system input vector of the first deep mining subunit is the high-dimensional mapping vector;

[0023] In the final stage of deep mining, the multi-energy system output vector of the third deep mining subunit is used as the multi-energy system output vector of the a-th first connection unit.

[0024] In a preferred embodiment of this application, in the aforementioned method for integrated regulation and optimization of a power system using multiple energy sources, the step of processing the multi-energy system input vector of the a-th first semantic fusion unit to form the multi-energy system output vector of the a-th first semantic fusion unit includes:

[0025] Using the a-th first semantic fusion unit, the vector similarity parameters between each local semantic vector in the multi-energy system output vector of the a-th first connection unit and the multi-energy system semantic vector of the multi-energy system data are analyzed;

[0026] Based on the vector similarity parameter between the multi-energy system semantic vector of the multi-energy system data and the multi-energy scheme semantic vector of each of the reference multi-energy operation schemes, the local semantic vectors in the multi-energy system output vector of the a-th first connection unit that do not match the pre-configured first vector similarity parameter conditions are updated to form the multi-energy system output vector of the a-th first semantic fusion unit.

[0027] In a preferred embodiment of this application, in the aforementioned method for integrated regulation and optimization of a power system based on multiple energy sources, the step of updating the local semantic vector in the multi-energy system output vector of the a-th first connection unit that does not match the pre-configured first vector similarity parameter condition, based on the vector similarity parameter between the multi-energy system semantic vector of the multi-energy system data and the multi-energy scheme semantic vector of each reference multi-energy operation scheme, to form the multi-energy system output vector of the a-th first semantic fusion unit, includes:

[0028] Based on the vector similarity parameter between the multi-energy system semantic vector of the multi-energy system data and the multi-energy scheme semantic vector of each of the reference multi-energy operation schemes, the local semantic vector in the multi-energy system output vector of the a-th first connection unit that does not match the pre-configured first vector similarity parameter condition is updated to form the updated multi-energy system output vector.

[0029] Based on the updated multi-energy system output vector and the associated multi-energy scheme semantic vector of the multi-energy system data, the multi-energy system output vector of the a-th first semantic fusion unit is formed.

[0030] In a preferred embodiment of this application, the aforementioned multi-energy integrated regulation and optimization operation method for power systems further includes a training step for an operation scheme determination network. This operation scheme determination network comprises a first semantic mining network, a second semantic mining network, a first mapping output network, and a semantic fusion network. The training step includes:

[0031] At least one first training data combination is extracted, wherein each first training data combination includes a corresponding training multi-energy system data and a multi-energy operation scheme label;

[0032] Using the first semantic mining network, the semantic vector of the multi-energy system corresponding to the training multi-energy system data is mined;

[0033] Using the semantic fusion network, the semantic vector of the multi-energy system corresponding to the training multi-energy system data and the semantic vector of the multi-energy scheme corresponding to each reference multi-energy operation scheme included in the reference multi-energy operation scheme set are semantically fused to output the semantic vector of the multi-energy scheme corresponding to the training multi-energy system data.

[0034] Using the first mapping output network, the semantic vector of the multi-energy scheme corresponding to the training multi-energy system data is mapped and output to obtain the training multi-energy operation scheme corresponding to the training multi-energy system data.

[0035] Based on the difference between the multi-energy operation scheme label corresponding to the training multi-energy system data and the training multi-energy operation scheme corresponding to the training multi-energy system data, the corresponding first training error is obtained.

[0036] Based on the first training error, the network parameters of the operation scheme determination network are updated to form an updated operation scheme determination network.

[0037] In a preferred embodiment of this application, in the above-mentioned multi-energy integrated regulation and optimization operation method for power systems, the semantic fusion network includes a first number of first connection units and a second number of first semantic fusion units, wherein the first connection units and the first semantic fusion units are arranged alternately in sequence, and the difference between the first number and the second number is equal to 1.

[0038] The step of using the semantic fusion network to perform semantic fusion on the multi-energy system semantic vector corresponding to the training multi-energy system data and the multi-energy scheme semantic vector corresponding to each reference multi-energy operation scheme included in the reference multi-energy operation scheme set, and outputting the multi-energy scheme semantic vector corresponding to the training multi-energy system data, includes:

[0039] Using the a-th first connection unit, the multi-energy system input vector of the a-th first connection unit is processed to form the multi-energy system output vector of the a-th first connection unit. The multi-energy system input vector of the first first connection unit includes the multi-energy scheme semantic vector of the reference multi-energy operation scheme, and the multi-energy system output vector of the last first connection unit includes the multi-energy scheme semantic vector corresponding to the training multi-energy system data.

[0040] Using the a-th first semantic fusion unit, the multi-energy system input vector of the a-th first semantic fusion unit is processed to form the multi-energy system output vector of the a-th first semantic fusion unit. The multi-energy system input vector of the a-th first semantic fusion unit includes the multi-energy system output vector of the a-th first connection unit and the multi-energy system semantic vector of the training multi-energy system data. The multi-energy system input vector of the b-th first connection unit includes the multi-energy system output vector of the a-th first semantic fusion unit.

[0041] In a preferred embodiment of this application, in the aforementioned multi-energy integrated regulation and optimization operation method for power systems, the step of updating the network parameters of the operation scheme determination network based on the first training error to form an updated operation scheme determination network includes:

[0042] At least one second training data combination is extracted, wherein each second training data combination includes a corresponding actual multi-energy operation scheme and a multi-energy system data label;

[0043] Using the second semantic mining network, the semantic vector of the multi-energy scheme corresponding to the actual multi-energy operation scheme is mined.

[0044] Using the semantic fusion network, the semantic vector of the multi-energy scheme corresponding to the actual multi-energy operation scheme and the semantic vector of the multi-energy system corresponding to each reference multi-energy system data contained in the reference multi-energy system dataset are semantically fused to output the semantic vector of the multi-energy system corresponding to the actual multi-energy operation scheme. The semantic vector of the multi-energy system of the reference multi-energy system data is formed by mining using the first semantic mining network.

[0045] Using the second mapping output network included in the operation scheme, the semantic vector of the multi-energy system corresponding to the actual multi-energy operation scheme is mapped and output to obtain the multi-energy system generation data corresponding to the actual multi-energy operation scheme.

[0046] Based on the difference between the multi-energy system data label corresponding to the actual multi-energy operation scheme and the multi-energy system generated data corresponding to the actual multi-energy operation scheme, the corresponding second training error is obtained.

[0047] Based on the first training error and the second training error, the network parameters of the operation scheme determination network are updated to form an updated operation scheme determination network.

[0048] This application also provides a multi-energy integrated regulation and optimization operation system for power systems, used to implement the above-described methods, including:

[0049] The description data determination module is used to determine multi-energy system data, wherein the multi-energy system data includes description data of multiple energy subsystems in the target power system and description data of the electricity demand of the electricity consumption area corresponding to the target power system;

[0050] The semantic mining module is used to mine the multi-energy system semantic vector corresponding to the multi-energy system data using the first semantic mining network.

[0051] The semantic fusion module is used to perform semantic fusion on the semantic vector of the multi-energy system corresponding to the multi-energy system data and the semantic vector of the multi-energy scheme corresponding to each reference multi-energy operation scheme included in the pre-configured set of reference multi-energy operation schemes using a semantic fusion network, and output the semantic vector of the multi-energy scheme corresponding to the multi-energy system data, wherein the semantic vector of the multi-energy scheme corresponding to the reference multi-energy operation scheme is mined using a second semantic mining network.

[0052] The operation scheme output module is used to map and output the semantic vector of the multi-energy scheme corresponding to the multi-energy system data using the first mapping output network, so as to obtain the target multi-energy operation scheme corresponding to the multi-energy system data. The target multi-energy operation scheme serves as the basis for the power generation operation of multiple energy subsystems in the target power system.

[0053] The method and system for integrated regulation and optimization of power system multi-energy systems provided in this application firstly determine the multi-energy system data; secondly, using a first semantic mining network, the multi-energy system semantic vectors corresponding to the multi-energy system data are mined; then, using a semantic fusion network, the semantic vectors corresponding to the multi-energy system data and the semantic vectors corresponding to each pre-configured reference multi-energy operation scheme are semantically fused to output the semantic vectors of the multi-energy schemes corresponding to the multi-energy system data; finally, using a first mapping output network, the semantic vectors of the multi-energy schemes corresponding to the multi-energy system data are mapped and output to obtain the target multi-energy operation scheme. Based on the above, after mining the semantic vectors of multi-energy systems corresponding to multi-energy system data, the semantic vectors of multi-energy schemes corresponding to reference multi-energy operation schemes are semantically fused. This results in the formation of semantic vectors of multi-energy schemes corresponding to multi-energy system data that not only represent the semantic information of the multi-energy system data but also carry the semantic information of the semantic space in which the operation scheme resides. Thus, when generating a target multi-energy operation scheme based on the semantic vectors of multi-energy schemes corresponding to multi-energy system data, the reliability of the generated target multi-energy operation scheme can be higher because it carries the semantic information of the semantic space in which the operation scheme resides. This improves the problems of low reliability and high manpower cost in the existing technology of multi-energy integrated regulation and optimization operation. Attached Figure Description

[0054] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings.

[0055] Figure 1 A structural block diagram of an electronic device provided in an embodiment of this application.

[0056] Figure 2 This is a flowchart illustrating a multi-energy integrated regulation and optimization operation method for a power system, provided as an embodiment of this application.

[0057] Figure 3 This is a schematic diagram of a semantic fusion network provided in an embodiment of this application.

[0058] Figure 4 This is a block diagram of a multi-energy integrated regulation and optimization operation system for a power system, provided as an embodiment of this application. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0060] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0061] Example 1

[0062] like Figure 1 As shown in the figure, this application provides an electronic device. The electronic device may include a memory, a processor, and a multi-energy integrated regulation and optimization operation system for a power system.

[0063] In detail, the memory and the processor are electrically connected directly or indirectly to enable data transmission or interaction. For example, the memory and the processor can be electrically connected via one or more communication buses or signal lines. The power system multi-energy integrated regulation and optimization operation system includes at least one software functional module stored in the memory in the form of software or firmware. The processor is used to execute executable computer programs stored in the memory, such as the software functional modules and computer programs included in the power system multi-energy integrated regulation and optimization operation system, to implement the power system multi-energy integrated regulation and optimization operation method provided in the embodiments of this application.

[0064] Optionally, the memory may be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc.

[0065] Furthermore, the processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), a system on chip (SoC), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0066] Understandable. Figure 1 The structure shown is for illustrative purposes only; the electronic device may also include components that are more advanced than those shown. Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown may include, for example, a communication unit for exchanging information with other devices.

[0067] Combination Figure 2 This application also provides a method for integrated regulation and optimization operation of a power system using multiple energy sources, applicable to the aforementioned electronic equipment. The method steps defined in the process of this integrated regulation and optimization operation method can be implemented by the electronic equipment. The following will describe... Figure 2 The specific process shown will be explained in detail.

[0068] Step S110: Determine the multi-energy system data.

[0069] In this embodiment, the electronic device can determine multi-energy system data. This multi-energy system data includes at least descriptive data (such as the output power range of the generator units) of various energy subsystems within the target power system (e.g., hydroelectric power generation systems, thermal power generation systems with stable output, and wind power generation systems, solar power generation systems with unstable output) and descriptive data of the electricity demand of the corresponding power consumption area (e.g., electricity consumption, electricity stability requirements). For example, some data in the multi-energy system data could be: "The power output range of the hydroelectric power generation system is 50MW to 200MW, the power output range of the thermal power generation system is 100MW to 500MW, the power output range of the wind power generation system is 0MW to 150MW, the power output range of the solar power generation system is 0 to 50MW, the electricity demand may reach 500MW, and a stable power supply is required." Additionally, it may include information such as the generation cost of each energy subsystem. In a more detailed example, it may also include reservoir water level and water flow rate into the turbine (m³ / s). 3 The data include water flow rate (m / s), fossil fuel reserves, estimated wind speed, and estimated solar radiation intensity and duration.

[0070] Step S120: Using the first semantic mining network, the multi-energy system semantic vector corresponding to the multi-energy system data is mined.

[0071] In this embodiment, after determining the multi-energy system data, the electronic device can utilize a first semantic mining network to mine the multi-energy system semantic vector corresponding to the multi-energy system data, that is, to mine the semantic features in the multi-energy system data and represent them as vectors. Specifically, the first semantic mining network may include a word embedding network for performing word embedding processing on the multi-energy system data to form corresponding multi-energy system semantic vectors. For example, for "the power output range of the hydroelectric power generation system is 50MW to 200MW", the resulting vector is:

[0072] "Hydraulic": [0.32, -0.12, 0.45, 0.27, -0.14, ...];

[0073] "Power Generation": [0.29, 0.15, -0.34, 0.48, -0.12, ...];

[0074] “Subsystem”: [0.12, -0.26, 0.54, -0.17, 0.13, ...];

[0075] "Power": [0.40, 0.07, 0.22, 0.13, -0.20, ...];

[0076] Output: [0.33, -0.12, -0.46, 0.08, 0.24, ...];

[0077] "Interval": [0.25, 0.09, 0.17, -0.35, 0.17, ...];

[0078] "50MW": [0.15, 0.22, -0.13, 0.31, 0.54,...];

[0079] "To": [0.07, -0.02, 0.01, 0.23, -0.25, ...];

[0080] "200MW": [0.30, 0.17, 0.08, 0.40, 0.46, ...].

[0081] In addition, after obtaining the word embedding vectors, multiple word embedding vectors can be concatenated. Then, the concatenated vectors are processed through a self-attention network and a feedforward neural network to obtain the final semantic vector of the multi-energy system.

[0082] Step S130: Using a semantic fusion network, perform semantic fusion on the multi-energy system semantic vector corresponding to the multi-energy system data and the multi-energy scheme semantic vector corresponding to each reference multi-energy operation scheme included in the pre-configured reference multi-energy operation scheme set, and output the multi-energy scheme semantic vector corresponding to the multi-energy system data.

[0083] In this embodiment, after mining the multi-energy system semantic vector, the electronic device can utilize a semantic fusion network to semantically fuse the multi-energy system semantic vector corresponding to the multi-energy system data with the multi-energy scheme semantic vector corresponding to each reference multi-energy operation scheme included in a pre-configured set of reference multi-energy operation schemes. The resulting output is the multi-energy scheme semantic vector corresponding to the multi-energy system data. That is, the multi-energy scheme semantic vector corresponding to the multi-energy system data carries both the semantic information represented by the multi-energy system semantic vector corresponding to the multi-energy system data and the semantic information represented by the multi-energy scheme semantic vector corresponding to each reference multi-energy operation scheme. The multi-energy scheme semantic vector corresponding to the reference multi-energy operation scheme is mined using a second semantic mining network. Furthermore, the reference multi-energy operation scheme can be a historical operation scheme of the target power system or other power systems; it can be the whole or a part, such as the operation scheme of a certain energy subsystem, as long as it can incorporate the semantic information of the operation scheme semantic space to improve the reliability of subsequent mapping output.

[0084] Step S140: Using the first mapping output network, the semantic vector of the multi-energy scheme corresponding to the multi-energy system data is mapped and output to obtain the target multi-energy operation scheme corresponding to the multi-energy system data.

[0085] In this embodiment, after forming the semantic vector of the multi-energy scheme corresponding to the multi-energy system data, the electronic device can use a first mapping output network to map and output the semantic vector of the multi-energy scheme corresponding to the multi-energy system data, thereby obtaining the target multi-energy operation scheme corresponding to the multi-energy system data. The target multi-energy operation scheme serves as the basis for the power generation operation of various energy subsystems in the target power system. For example, in a hydroelectric power generation system, the generator's excitation current is 400A and the excitation voltage is 200V; in a thermal power generation system, the generator's excitation current is 500A and the excitation voltage is 250V. Exemplarily, the first mapping output network can be a decoder neural network, thus sequentially generating each word in the target multi-energy operation scheme corresponding to the multi-energy system data.

[0086] Based on the above, after mining the semantic vectors of multi-energy systems corresponding to multi-energy system data, the semantic vectors of multi-energy schemes corresponding to reference multi-energy operation schemes are semantically fused. This results in the formation of semantic vectors of multi-energy schemes corresponding to multi-energy system data that not only represent the semantic information of the multi-energy system data but also carry the semantic information of the semantic space in which the operation scheme resides. Thus, when generating a target multi-energy operation scheme based on the semantic vectors of multi-energy schemes corresponding to multi-energy system data, the reliability of the generated target multi-energy operation scheme can be higher because it carries the semantic information of the semantic space in which the operation scheme resides. This improves the problems of low reliability and high manpower cost in the existing technology of multi-energy integrated regulation and optimization operation.

[0087] It should be noted that for steps S120 and S140, the semantic mining and mapping output can refer to the relevant existing technologies, which will not be elaborated here. In the embodiments of this application (as described below), the semantic fusion in step S130 will be explained in detail.

[0088] For example, in an alternative implementation, in order to ensure that the multi-energy system semantic vector corresponding to the multi-energy system data and the multi-energy scheme semantic vector corresponding to each reference multi-energy operation scheme included in the pre-configured reference multi-energy operation scheme set can be fully integrated, and to guarantee that the obtained multi-energy scheme semantic vector corresponding to the multi-energy system data has better semantic representation capability, combined with Figure 3 The semantic fusion network includes a first number of first connection units and a second number of first semantic fusion units, and the first connection units and the first semantic fusion units are arranged alternately in sequence. The difference between the first number and the second number is equal to 1. The above step S130 may further include steps S131 and S132, the specific contents of which are as follows.

[0089] Step S131: Using the a-th first connection unit, process the multi-energy system input vector of the a-th first connection unit to form the multi-energy system output vector of the a-th first connection unit.

[0090] In this embodiment, the multi-energy system input vector of the a-th first connection unit can be processed to form the multi-energy system output vector of the a-th first connection unit. The multi-energy system input vector of the first first connection unit includes the multi-energy scheme semantic vector of the reference multi-energy operation scheme, and the multi-energy system output vector of the last first connection unit includes the multi-energy scheme semantic vector corresponding to the multi-energy system data. That is, the multi-energy scheme semantic vector of the reference multi-energy operation scheme serves as the input of the first first connection unit, and the multi-energy scheme semantic vector corresponding to the multi-energy system data serves as the output of the last first connection unit.

[0091] Step S132: Using the a-th first semantic fusion unit, process the multi-energy system input vector of the a-th first semantic fusion unit to form the multi-energy system output vector of the a-th first semantic fusion unit.

[0092] In this embodiment, after forming the multi-energy system output vector of the a-th first connection unit, the multi-energy system input vector of the a-th first semantic fusion unit can be processed using the a-th first semantic fusion unit to form the multi-energy system output vector of the a-th first semantic fusion unit. The multi-energy system input vector of the a-th first semantic fusion unit includes the multi-energy system output vector of the a-th first connection unit and the multi-energy system semantic vector of the multi-energy system data. The multi-energy system input vector of the b-th first connection unit includes the multi-energy system output vector of the a-th first semantic fusion unit. The b-th first connection unit is the next first connection unit after the a-th first connection unit. Thus, an arrangement of first first connection unit, first first semantic fusion unit, second first connection unit, second first semantic fusion unit, ..., penultimate first connection unit, last first semantic fusion unit, and last first connection unit can be formed.

[0093] It is understood that in step S131 above, the specific processing method of the first connection unit is not limited and can be selected according to actual needs. For example, in an alternative implementation, in order to make the semantic representation capability of the multi-energy system output vector formed by the first connection unit better, the first connection unit may include a high-dimensional mapping subunit and a first deep mining subunit. Based on this, step S131 above may further include steps S131a and S131b, and the specific contents of each step are as follows.

[0094] Step S131a: Using the high-dimensional mapping subunit of the a-th first connection unit, the multi-energy system input vector of the a-th first connection unit is mapped in a high dimension, and a high-dimensional mapping vector is output.

[0095] In this embodiment, the high-dimensional mapping subunit of the a-th first connection unit can be used to perform high-dimensional mapping on the multi-energy system input vector of the a-th first connection unit, and output a high-dimensional mapping vector. For example, the data m (equal to the number of reference multi-energy operation schemes) × 2560 can be adjusted to m × 160 × 16. The specific dimensions can be configured according to actual needs, such as making the multi-energy scheme semantic vector of the reference multi-energy operation scheme match the multi-energy scheme semantic vector corresponding to the multi-energy system data.

[0096] Step S131b: Using the first deep mining subunit, the high-dimensional mapping vector is subjected to multi-stage deep mining to form the multi-energy system output vector of the a-th first connection unit.

[0097] In this embodiment of the application, after obtaining the high-dimensional mapping vector, the first deep mining subunit can be used to perform multi-stage deep mining on the high-dimensional mapping vector to form the multi-energy system output vector of the a-th first connection unit. In this way, the semantic information of the multi-energy system output vector can be enriched, and the reliability of subsequent analysis can be improved.

[0098] It is understood that in step S131b above, the specific method of performing multi-stage deep mining is not limited and can be selected according to actual needs. For example, in an alternative implementation, in order to improve the reliability of deep mining, the first connection unit further includes a second deep mining subunit and a third deep mining subunit. Based on this, step S131b above can further include the following specific implementation details:

[0099] The first deep mining subunit, the second deep mining subunit, and the third deep mining subunit can be used to perform multi-stage deep mining on the high-dimensional mapping vector to form the multi-energy system output vector of the a-th first connection unit.

[0100] In each stage of deep mining, the first deep mining subunit mines the multi-energy system input vector of the first deep mining subunit (including self-attention processing, nonlinear activation processing, and fully connected processing) to form the multi-energy system output vector of the first deep mining subunit. The second deep mining subunit mines the semantic vector of the associated multi-energy scheme of the multi-energy system data (e.g., obtained by semantic mining of the operation scheme of similar power systems of the target power system through a second semantic mining network) (including self-attention processing, nonlinear activation processing, and fully connected processing) to form the multi-energy system output vector of the second deep mining subunit. The multi-energy system output vectors of the first and second deep mining subunits are then aggregated (e.g., the multi-energy system output vector of the second deep mining subunit can be combined with the multi-energy system output vector of the first deep mining subunit). The multi-energy system output vector of the mining subunit is multiplied by its transpose to obtain the corresponding similarity parameter. This similarity parameter is then multiplied by the multi-energy system output vector of the first deep mining subunit to obtain the vector aggregation result. Mining is then performed using the third deep mining subunit (including self-attention processing, nonlinear activation processing, and fully connected processing) to form the multi-energy system output vector of the third deep mining subunit. Furthermore, the multi-energy system input vector of the first deep mining subunit in the next deep mining stage is determined based on the multi-energy system output vector of the third deep mining subunit and the multi-energy system input vector of the first deep mining subunit (for example, the multi-energy system output vector of the third deep mining subunit and the multi-energy system input vector of the first deep mining subunit can be added together as the multi-energy system input vector of the first deep mining subunit in the next deep mining stage).

[0101] In addition, in the first stage of deep mining, the multi-energy system input vector of the first deep mining subunit is the high-dimensional mapping vector; and in the last stage of deep mining, the multi-energy system output vector of the third deep mining subunit is the multi-energy system output vector of the a-th first connection unit; the specific number of multiple stages is also not limited and can be selected according to actual needs, such as 2, 3, 4, 5, 6, etc.

[0102] It is understood that in step S132 above, the specific method of processing by the first semantic fusion unit is not limited and can be selected according to actual needs. For example, in an alternative implementation, in order to ensure the reliability of the output vector of the formed multi-energy system, step S132 above can further include steps S132a and S132b, the specific contents of which are as follows.

[0103] Step S132a: Using the a-th first semantic fusion unit, analyze the vector similarity parameters between each local semantic vector in the multi-energy system output vector of the a-th first connection unit and the multi-energy system semantic vector of the multi-energy system data.

[0104] In this embodiment, the vector similarity parameter between each local semantic vector in the multi-energy system output vector of the a-th first connection unit and the multi-energy system semantic vector of the multi-energy system data can be analyzed using the a-th first semantic fusion unit. For example, each local semantic vector in the multi-energy system output vector of the first connection unit corresponds one-to-one with each reference multi-energy operation scheme. Thus, the cosine similarity between each local semantic vector and the multi-energy system semantic vector of the multi-energy system data can be calculated separately.

[0105] Step S132b: Based on the vector similarity parameter between the multi-energy system semantic vector of the multi-energy system data and the multi-energy scheme semantic vector of each reference multi-energy operation scheme, update the local semantic vector in the multi-energy system output vector of the a-th first connection unit that does not match the pre-configured first vector similarity parameter condition, and form the multi-energy system output vector of the a-th first semantic fusion unit.

[0106] In this embodiment of the application, after obtaining the vector similarity parameters, the local semantic vectors in the multi-energy system output vector of the a-th first connection unit that do not match the pre-configured first vector similarity parameter conditions can be updated based on the vector similarity parameters (such as the cosine similarity between vectors) between the multi-energy system semantic vector of the multi-energy system data and the multi-energy scheme semantic vector of each reference multi-energy operation scheme, so as to form the multi-energy system output vector of the a-th first semantic fusion unit, thereby improving the reliability of the multi-energy system output vector.

[0107] It is understood that the specific method for updating the local semantic vector in step S132b above is not limited and can be selected according to actual needs. For example, in an alternative implementation, in order to further improve the reliability of the multi-energy system output vector, step S132b above may further include the following specific implementation:

[0108] First, based on the vector similarity parameters between the multi-energy system semantic vector of the multi-energy system data and the multi-energy scheme semantic vector of each of the reference multi-energy operation schemes, the local semantic vectors in the multi-energy system output vector of the a-th first connection unit that do not match the pre-configured first vector similarity parameter conditions are updated to form an updated multi-energy system output vector. For example, the vector similarity parameters between each local semantic vector in the multi-energy system output vector of the a-th first connection unit and the multi-energy system semantic vector of the multi-energy system data are analyzed. For instance, the local semantic vector 1 corresponding to reference multi-energy operation scheme 1 and the multi-energy system semantic vector of the multi-energy system data are obtained. The vector similarity parameter 1 between the semantic vectors of the system is used as a reference for the vector similarity parameter 2 between the local semantic vector 2 corresponding to the multi-energy operation scheme 2 and the multi-energy system semantic vector of the multi-energy system data. If the vector similarity parameter 1 is greater than the threshold, it is considered to match the pre-configured first vector similarity parameter condition. If the vector similarity parameter 2 is not greater than the threshold, it is considered not to match the pre-configured first vector similarity parameter condition. Thus, the reliability of the local semantic vector 2 is considered to be low. Therefore, it can be directly replaced with the multi-energy scheme semantic vector of the reference multi-energy operation scheme with the largest vector similarity parameter to the multi-energy system semantic vector of the multi-energy system data, thereby forming the updated multi-energy system output vector.

[0109] Secondly, based on the updated multi-energy system output vector and the associated multi-energy scheme semantic vector of the multi-energy system data (such as camera operation or weighted average calculation), the multi-energy system output vector of the a-th first semantic fusion unit is formed.

[0110] It should be noted that, in order to ensure that the above-mentioned networks have reliable processing capabilities, the power system multi-energy integrated regulation and optimization operation method further includes a training step for an operation scheme determination network. The operation scheme determination network includes the first semantic mining network, the second semantic mining network, the first mapping output network, and the semantic fusion network. In detail, the training step for the operation scheme determination network may include steps S151, S152, S153, S154, S155, and S156, and the specific contents of each step are as follows.

[0111] Step S151: Extract at least one first training data combination.

[0112] In this embodiment of the application, at least one first training data combination can be extracted. Each first training data combination includes a corresponding training multi-energy system data (such as multi-energy system data) and a multi-energy operation scheme label (i.e., the actual operation scheme).

[0113] Step S152: Using the first semantic mining network, the multi-energy system semantic vector corresponding to the training multi-energy system data is mined, as in step S120.

[0114] Step S153: Using the semantic fusion network, perform semantic fusion on the multi-energy system semantic vector corresponding to the training multi-energy system data and the multi-energy scheme semantic vector corresponding to each reference multi-energy operation scheme included in the reference multi-energy operation scheme set, and output the multi-energy scheme semantic vector corresponding to the training multi-energy system data, as in step S130.

[0115] Step S154: Using the first mapping output network, the semantic vector of the multi-energy scheme corresponding to the training multi-energy system data is mapped and output to obtain the training multi-energy operation scheme (i.e. the generated operation scheme) corresponding to the training multi-energy system data, as in step S140.

[0116] Step S155: Based on the difference between the multi-energy operation scheme label corresponding to the training multi-energy system data and the training multi-energy operation scheme corresponding to the training multi-energy system data, the corresponding first training error is obtained (the specific error calculation method can be selected according to the requirements).

[0117] Step S156: Based on the first training error, update the network parameters of the operation scheme determination network to form an updated operation scheme determination network. For example, the network parameters of the operation scheme determination network can be updated along the direction of reducing the first training error until the error converges or the number of updates reaches a threshold to obtain the updated operation scheme determination network.

[0118] It is understood that, in an alternative implementation, the semantic fusion network includes a first number of first connection units and a second number of first semantic fusion units, which are arranged alternately in sequence, and the difference between the first number and the second number is equal to 1, as described above. Based on this, step S153 can further include the following implementation:

[0119] First, the multi-energy system input vector of the a-th first connection unit can be processed to form the multi-energy system output vector of the a-th first connection unit. The multi-energy system input vector of the first first connection unit includes the multi-energy scheme semantic vector of the reference multi-energy operation scheme, and the multi-energy system output vector of the last first connection unit includes the multi-energy scheme semantic vector corresponding to the training multi-energy system data.

[0120] Secondly, using the a-th first semantic fusion unit, the multi-energy system input vector of the a-th first semantic fusion unit is processed to form the multi-energy system output vector of the a-th first semantic fusion unit. The multi-energy system input vector of the a-th first semantic fusion unit includes the multi-energy system output vector of the a-th first connection unit and the multi-energy system semantic vector of the training multi-energy system data. The multi-energy system input vector of the b-th first connection unit includes the multi-energy system output vector of the a-th first semantic fusion unit.

[0121] It is understood that in step S156 above, the specific method of updating based on the first training error is not limited and can be selected according to actual needs. For example, in an alternative implementation, in order to improve the reliability of training, step S156 above may further include the following implementation:

[0122] First, at least one second training data combination can be extracted, wherein each second training data combination includes a corresponding actual multi-energy operation scheme (i.e., the actual operation scheme) and a multi-energy system data label;

[0123] Secondly, the second semantic mining network can be used to mine the semantic vector of the multi-energy scheme corresponding to the actual multi-energy operation scheme;

[0124] Then, the semantic fusion network can be used to perform semantic fusion on the semantic vector of the multi-energy scheme corresponding to the actual multi-energy operation scheme and the semantic vector of the multi-energy system corresponding to each reference multi-energy system data contained in the reference multi-energy system dataset, and output the semantic vector of the multi-energy system corresponding to the actual multi-energy operation scheme. The semantic vector of the multi-energy system of the reference multi-energy system data (which may be local data of a certain energy subsystem or description data of electricity demand, etc.) is formed by mining using the first semantic mining network.

[0125] Then, the second mapping output network included in the network can be determined by the operation scheme to map and output the semantic vector of the multi-energy system corresponding to the actual multi-energy operation scheme, so as to obtain the multi-energy system generation data corresponding to the actual multi-energy operation scheme.

[0126] Furthermore, based on the difference between the multi-energy system data label corresponding to the actual multi-energy operation scheme and the multi-energy system generated data corresponding to the actual multi-energy operation scheme, the corresponding second training error is obtained (the specific error calculation method can be selected according to actual needs);

[0127] Finally, the network parameters of the operation scheme determination network can be updated based on the first training error and the second training error to form an updated operation scheme determination network. For example, the first training error and the second training error can be summed to obtain the total error, and then the network parameters can be updated in the direction of reducing the total error. Alternatively, the network parameters can be updated based on the first training error and the second training error respectively.

[0128] Combination Figure 4 This application also provides a power system multi-energy integrated regulation and optimization operation system applicable to the aforementioned electronic equipment. The power system multi-energy integrated regulation and optimization operation system may include a descriptive data determination module, a semantic mining module, a semantic fusion module, and an operation plan output module.

[0129] The description data determination module is used to determine multi-energy system data, wherein the multi-energy system data includes description data of various energy subsystems in the target power system and description data of the electricity demand of the corresponding electricity consumption area of ​​the target power system. In this embodiment of the application, the description data determination module can be used to perform... Figure 2 The relevant content regarding the description data determination module in step S110 shown can be found in the previous description of step S110.

[0130] The semantic mining module is used to mine multi-energy system semantic vectors corresponding to the multi-energy system data using a first semantic mining network. In this embodiment, the semantic mining module can be used to execute... Figure 2 The relevant content regarding the semantic mining module in step S120 shown can be found in the previous description of step S120.

[0131] The semantic fusion module is used to perform semantic fusion on the multi-energy system semantic vector corresponding to the multi-energy system data and the multi-energy scheme semantic vector corresponding to each reference multi-energy operation scheme included in the pre-configured set of reference multi-energy operation schemes using a semantic fusion network, and outputs the multi-energy scheme semantic vector corresponding to the multi-energy system data. The multi-energy scheme semantic vector corresponding to the reference multi-energy operation scheme is mined using a second semantic mining network. In this embodiment, the semantic fusion module can be used to perform... Figure 2 The relevant content regarding the semantic fusion module in step S130 shown can be found in the previous description of step S130.

[0132] The operation plan output module is used to map and output the semantic vector of the multi-energy scheme corresponding to the multi-energy system data using a first mapping output network, thereby obtaining the target multi-energy operation plan corresponding to the multi-energy system data. The target multi-energy operation plan serves as the basis for the power generation operation of multiple energy subsystems in the target power system. In this embodiment, the operation plan output module can be used to execute... Figure 2 For details regarding step S140, please refer to the preceding description of step S140 for information about the output module of the running scheme.

[0133] In summary, the multi-energy integrated regulation and optimization operation method and system for power systems provided in this application firstly determines the multi-energy system data; secondly, it uses a first semantic mining network to mine the multi-energy system semantic vectors corresponding to the multi-energy system data; then, it uses a semantic fusion network to perform semantic fusion on the multi-energy system semantic vectors corresponding to the multi-energy system data and the multi-energy scheme semantic vectors corresponding to each pre-configured reference multi-energy operation scheme, outputting the multi-energy scheme semantic vectors corresponding to the multi-energy system data; finally, it uses a first mapping output network to map and output the multi-energy scheme semantic vectors corresponding to the multi-energy system data, obtaining the target multi-energy operation scheme. Based on the above, after mining the semantic vectors of multi-energy systems corresponding to multi-energy system data, the semantic vectors of multi-energy schemes corresponding to reference multi-energy operation schemes are semantically fused. This results in the formation of semantic vectors of multi-energy schemes corresponding to multi-energy system data that not only represent the semantic information of the multi-energy system data but also carry the semantic information of the semantic space in which the operation scheme resides. Thus, when generating a target multi-energy operation scheme based on the semantic vectors of multi-energy schemes corresponding to multi-energy system data, the reliability of the generated target multi-energy operation scheme can be higher because it carries the semantic information of the semantic space in which the operation scheme resides. This improves the problems of low reliability and high manpower cost in the existing technology of multi-energy integrated regulation and optimization operation.

[0134] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus and method embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0135] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0136] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, electronic device, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks. It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. In the absence of further restrictions, an element defined by the phrase "comprising a..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0137] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for integrated regulation and optimized operation of a power system using multiple energy sources, characterized in that, include: The multi-energy system data is determined, wherein the multi-energy system data includes at least descriptive data of multiple energy subsystems in the target power system and descriptive data of the electricity demand of the electricity consumption area corresponding to the target power system; Using a first semantic mining network, the semantic vectors of the multi-energy system corresponding to the multi-energy system data are mined. Using a semantic fusion network, the semantic vector of the multi-energy system corresponding to the multi-energy system data and the semantic vector of the multi-energy scheme corresponding to each reference multi-energy operation scheme included in the pre-configured set of reference multi-energy operation schemes are semantically fused to output the semantic vector of the multi-energy scheme corresponding to the multi-energy system data. The semantic vector of the multi-energy scheme corresponding to the reference multi-energy operation scheme is mined using a second semantic mining network. Using a first mapping output network, the semantic vectors of the multi-energy schemes corresponding to the multi-energy system data are mapped and output to obtain the target multi-energy operation scheme corresponding to the multi-energy system data. The target multi-energy operation scheme serves as the basis for the power generation operation of multiple energy subsystems in the target power system. The semantic fusion network includes a first number of first connection units and a second number of first semantic fusion units, and the first connection units and the first semantic fusion units are arranged alternately in sequence. The difference between the first number and the second number is equal to 1. The step of using a semantic fusion network to perform semantic fusion on the multi-energy system semantic vector corresponding to the multi-energy system data and the multi-energy scheme semantic vector corresponding to each reference multi-energy operation scheme included in a pre-configured set of reference multi-energy operation schemes, and outputting the multi-energy scheme semantic vector corresponding to the multi-energy system data, includes: Using the a-th first connection unit, the multi-energy system input vector of the a-th first connection unit is processed to form the multi-energy system output vector of the a-th first connection unit. The multi-energy system input vector of the first first connection unit includes the multi-energy scheme semantic vector of the reference multi-energy operation scheme, and the multi-energy system output vector of the last first connection unit includes the multi-energy scheme semantic vector corresponding to the multi-energy system data. Using the a-th first semantic fusion unit, the multi-energy system input vector of the a-th first semantic fusion unit is processed to form the multi-energy system output vector of the a-th first semantic fusion unit. The multi-energy system input vector of the a-th first semantic fusion unit includes the multi-energy system output vector of the a-th first connection unit and the multi-energy system semantic vector of the multi-energy system data. The multi-energy system input vector of the b-th first connection unit includes the multi-energy system output vector of the a-th first semantic fusion unit. The b-th first connection unit is the first connection unit following the a-th first connection unit. The step of processing the multi-energy system input vector of the a-th first semantic fusion unit to form the multi-energy system output vector of the a-th first semantic fusion unit includes: Using the a-th first semantic fusion unit, the vector similarity parameters between each local semantic vector in the multi-energy system output vector of the a-th first connection unit and the multi-energy system semantic vector of the multi-energy system data are analyzed; Based on the vector similarity parameter between the multi-energy system semantic vector of the multi-energy system data and the multi-energy scheme semantic vector of each of the reference multi-energy operation schemes, the local semantic vectors in the multi-energy system output vector of the a-th first connection unit that do not match the pre-configured first vector similarity parameter conditions are updated to form the multi-energy system output vector of the a-th first semantic fusion unit.

2. The method for integrated regulation and optimized operation of a power system with multiple energy sources according to claim 1, characterized in that, The first connection unit includes a high-dimensional mapping subunit and a first depth mining subunit; The step of processing the multi-energy system input vector of the a-th first connection unit to form the multi-energy system output vector of the a-th first connection unit includes: Using the high-dimensional mapping subunit of the a-th first connection unit, the multi-energy system input vector of the a-th first connection unit is mapped in a high dimension, and a high-dimensional mapping vector is output. Using the first deep mining subunit, the high-dimensional mapping vector is subjected to multi-stage deep mining to form the multi-energy system output vector of the a-th first connection unit.

3. The method for integrated regulation and optimized operation of a power system with multiple energy sources according to claim 2, characterized in that, The first connection unit further includes a second depth mining subunit and a third depth mining subunit; The step of using the first deep mining subunit to perform multi-stage deep mining on the high-dimensional mapping vector to form the multi-energy system output vector of the a-th first connection unit includes: Using the first deep mining subunit, the second deep mining subunit, and the third deep mining subunit, the high-dimensional mapping vector is subjected to multi-stage deep mining to form the multi-energy system output vector of the a-th first connection unit; In each stage of deep mining, the first deep mining subunit mines the multi-energy system input vector of the first deep mining subunit to form the multi-energy system output vector of the first deep mining subunit; the second deep mining subunit mines the semantic vector of the associated multi-energy scheme of the multi-energy system data to form the multi-energy system output vector of the second deep mining subunit; the multi-energy system output vectors of the first and second deep mining subunits are aggregated and then mined by the third deep mining subunit to form the multi-energy system output vector of the third deep mining subunit; and in the next stage of deep mining, the multi-energy system input vector of the first deep mining subunit is determined based on the multi-energy system output vector of the third deep mining subunit and the multi-energy system input vector of the first deep mining subunit. In the first stage of deep mining, the multi-energy system input vector of the first deep mining subunit is the high-dimensional mapping vector; In the final stage of deep mining, the multi-energy system output vector of the third deep mining subunit is used as the multi-energy system output vector of the a-th first connection unit.

4. The multi-energy integrated regulation and optimization operation method for power systems according to claim 1, characterized in that, The step of updating the local semantic vector in the multi-energy system output vector of the a-th first connection unit that does not match the pre-configured first vector similarity parameter between the multi-energy system semantic vector based on the multi-energy system data and the multi-energy scheme semantic vector of each reference multi-energy operation scheme, to form the multi-energy system output vector of the a-th first semantic fusion unit, includes: Based on the vector similarity parameter between the multi-energy system semantic vector of the multi-energy system data and the multi-energy scheme semantic vector of each of the reference multi-energy operation schemes, the local semantic vector in the multi-energy system output vector of the a-th first connection unit that does not match the pre-configured first vector similarity parameter condition is updated to form the updated multi-energy system output vector. Based on the updated multi-energy system output vector and the associated multi-energy scheme semantic vector of the multi-energy system data, the multi-energy system output vector of the a-th first semantic fusion unit is formed.

5. The method for integrated regulation and optimized operation of a power system based on any one of claims 1-4, characterized in that, The power system multi-energy integrated regulation and optimization operation method further includes a training step for an operation scheme determination network. This operation scheme determination network comprises a first semantic mining network, a second semantic mining network, a first mapping output network, and a semantic fusion network. This training step includes: At least one first training data combination is extracted, wherein each first training data combination includes a corresponding training multi-energy system data and a multi-energy operation scheme label; Using the first semantic mining network, the semantic vector of the multi-energy system corresponding to the training multi-energy system data is mined; Using the semantic fusion network, the semantic vector of the multi-energy system corresponding to the training multi-energy system data and the semantic vector of the multi-energy scheme corresponding to each reference multi-energy operation scheme included in the reference multi-energy operation scheme set are semantically fused to output the semantic vector of the multi-energy scheme corresponding to the training multi-energy system data. Using the first mapping output network, the semantic vector of the multi-energy scheme corresponding to the training multi-energy system data is mapped and output to obtain the training multi-energy operation scheme corresponding to the training multi-energy system data. Based on the difference between the multi-energy operation scheme label corresponding to the training multi-energy system data and the training multi-energy operation scheme corresponding to the training multi-energy system data, the corresponding first training error is obtained. Based on the first training error, the network parameters of the operation scheme determination network are updated to form an updated operation scheme determination network.

6. The multi-energy integrated regulation and optimization operation method for power systems according to claim 5, characterized in that, The semantic fusion network includes a first number of first connection units and a second number of first semantic fusion units, wherein the first connection units and the first semantic fusion units are arranged alternately in sequence, and the difference between the first number and the second number is equal to 1. The step of using the semantic fusion network to perform semantic fusion on the multi-energy system semantic vector corresponding to the training multi-energy system data and the multi-energy scheme semantic vector corresponding to each reference multi-energy operation scheme included in the reference multi-energy operation scheme set, and outputting the multi-energy scheme semantic vector corresponding to the training multi-energy system data, includes: Using the a-th first connection unit, the multi-energy system input vector of the a-th first connection unit is processed to form the multi-energy system output vector of the a-th first connection unit. The multi-energy system input vector of the first first connection unit includes the multi-energy scheme semantic vector of the reference multi-energy operation scheme, and the multi-energy system output vector of the last first connection unit includes the multi-energy scheme semantic vector corresponding to the training multi-energy system data. Using the a-th first semantic fusion unit, the multi-energy system input vector of the a-th first semantic fusion unit is processed to form the multi-energy system output vector of the a-th first semantic fusion unit. The multi-energy system input vector of the a-th first semantic fusion unit includes the multi-energy system output vector of the a-th first connection unit and the multi-energy system semantic vector of the training multi-energy system data. The multi-energy system input vector of the b-th first connection unit includes the multi-energy system output vector of the a-th first semantic fusion unit.

7. The multi-energy integrated regulation and optimization operation method for power systems according to claim 5, characterized in that, The step of updating the network parameters of the operation scheme determination network based on the first training error to form an updated operation scheme determination network includes: At least one second training data combination is extracted, wherein each second training data combination includes a corresponding actual multi-energy operation scheme and a multi-energy system data label; Using the second semantic mining network, the semantic vector of the multi-energy scheme corresponding to the actual multi-energy operation scheme is mined. Using the semantic fusion network, the semantic vector of the multi-energy scheme corresponding to the actual multi-energy operation scheme and the semantic vector of the multi-energy system corresponding to each reference multi-energy system data contained in the reference multi-energy system dataset are semantically fused to output the semantic vector of the multi-energy system corresponding to the actual multi-energy operation scheme. The semantic vector of the multi-energy system of the reference multi-energy system data is formed by mining using the first semantic mining network. Using the second mapping output network included in the operation scheme, the semantic vector of the multi-energy system corresponding to the actual multi-energy operation scheme is mapped and output to obtain the multi-energy system generation data corresponding to the actual multi-energy operation scheme. Based on the difference between the multi-energy system data label corresponding to the actual multi-energy operation scheme and the multi-energy system generated data corresponding to the actual multi-energy operation scheme, the corresponding second training error is obtained. Based on the first training error and the second training error, the network parameters of the operation scheme determination network are updated to form an updated operation scheme determination network.

8. A multi-energy integrated regulation and optimization operation system for a power system, characterized in that, For implementing the method according to any one of claims 1 to 7, comprising: The description data determination module is used to determine multi-energy system data, wherein the multi-energy system data includes description data of multiple energy subsystems in the target power system and description data of the electricity demand of the electricity consumption area corresponding to the target power system; The semantic mining module is used to mine the multi-energy system semantic vector corresponding to the multi-energy system data using the first semantic mining network. The semantic fusion module is used to perform semantic fusion on the semantic vector of the multi-energy system corresponding to the multi-energy system data and the semantic vector of the multi-energy scheme corresponding to each reference multi-energy operation scheme included in the pre-configured set of reference multi-energy operation schemes using a semantic fusion network, and output the semantic vector of the multi-energy scheme corresponding to the multi-energy system data, wherein the semantic vector of the multi-energy scheme corresponding to the reference multi-energy operation scheme is mined using a second semantic mining network. The operation scheme output module is used to map and output the semantic vector of the multi-energy scheme corresponding to the multi-energy system data using the first mapping output network, so as to obtain the target multi-energy operation scheme corresponding to the multi-energy system data. The target multi-energy operation scheme serves as the basis for the power generation operation of multiple energy subsystems in the target power system.

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