A digital power analysis method and device for coordinating multiple production factors

Abstract

The present application discloses a digital power analysis method and device for coordinating multiple production factors. Production factor data are extracted from the power data on the power plant side and the power grid side respectively. Data vectors are constructed based on power analysis demand information using the extracted characteristic production factor data; key control production factor data associated with the power analysis demand information are determined from the extracted control production factor data. Artificial intelligence technology is used to construct a functional operator with a mapping function as the core to construct a power analysis intelligent body. The power analysis intelligent body is called to generate an analysis response result for a power analysis request. The present application realizes power analysis of multiple production factors in coordination, more accurately captures the connection between data and data, and thereby obtains more accurate and transferable analysis response results. This solution can form high-value data assets in the power system. The analysis value of power data is enhanced.

Description

Technical Field

[0001] The present application relates to the field of electric power production technology, and in particular to a digital electric power analysis method and device that coordinates multiple production factors. Background Art

[0002] With the continuous improvement of the automation, information and intelligence levels of the power system, the monitoring and control of the power system operation status can be realized, and the reliability and safety of power supply have been improved. With the continuous development of the global economy and the increasing demand for stable supply of electric energy in various countries, international cooperation in the power industry has also received increasing attention. The trend of power globalization has further strengthened the significance of power data.

[0003] The operation of the power system is inseparable from the analysis of power data. The current power analysis method in the power system is generally an isolated analysis method. This isolated analysis method makes it difficult to systematically and globally grasp the connection between data and data, and it is also difficult to transform the results of the analysis and apply them to other analysis directions. In turn, it weakens the analytical value of power data and often leads to a waste of resources. Summary of the invention

[0004] Based on the above problems, the present application provides a digital power analysis method and device that coordinates multiple production factors. The purpose is to more accurately capture the connection between data by collaboratively analyzing multiple production factors, thereby enhancing the analytical value of power data.

[0005] The embodiments of the present application disclose the following technical solutions:

[0006] In a first aspect, the present application provides a digital power analysis method for coordinating multiple production factors, the method comprising:

[0007] Extracting characteristic production factor data corresponding to the characteristic production factor group from the power data on the power plant side; and extracting control production factor data corresponding to the control production factor group from the power data on the power grid side; the characteristic production factor group includes characteristic production factors of multiple levels; the control production factor group includes control production factors of multiple aspects;

[0008] Based on the power analysis demand information, construct a target data vector using the extracted characteristic production factor data, and determine key control production factor data associated with the power analysis demand information from the extracted control production factor data;

[0009] Based on the target data vector and the key control production factor data, an artificial intelligence algorithm is used to learn and obtain a mapping function between the target data vector and the key control production factor data, a functional operator with the mapping function as the core is constructed, and a power analysis intelligent agent is constructed based on multiple functional operators; the power analysis intelligent agent corresponds to the power analysis demand information;

[0010] receiving power analysis requests;

[0011] If the requirement information carried in the power analysis request matches the power analysis requirement information, the power analysis agent is called to generate an analysis response result to the power analysis request.

[0012] In an optional implementation, the step of constructing a target data vector based on the power analysis demand information and using the extracted characteristic production factor data includes:

[0013] Based on the power analysis demand information, determining a target characteristic production factor associated with the power analysis demand information from the characteristic production factor group;

[0014] Determining target characteristic production factor data corresponding to the target characteristic production factor from the extracted characteristic production factor data;

[0015] Based on the construction rules of data vectors, the target data vector is constructed using the target characteristic production factor data.

[0016] In an optional implementation, the characteristic production factor group includes primary characteristic production factors, secondary characteristic production factors and tertiary characteristic production factors corresponding to a plurality of different power generation types; wherein the primary characteristic production factors are natural characteristic production factors, the secondary characteristic production factors are single system characteristic production factors, and the tertiary characteristic production factors are plant system characteristic production factors; the natural characteristic production factors are production factors that directly reflect natural characteristics; the single system characteristic production factors are production factors involving a single system in a power system; and the plant system characteristic production factors are production factors involving the entire system in a power system;

[0017] The step of extracting characteristic production factor data corresponding to the characteristic production factor group from the power data on the power plant side includes:

[0018] Extracting primary characteristic production factor data, secondary characteristic production factor data and tertiary characteristic production factor data corresponding to thermal power generation from the power data of thermal power plants;

[0019] Extracting primary characteristic production factor data, secondary characteristic production factor data and tertiary characteristic production factor data corresponding to photovoltaic power generation from the power data of the photovoltaic power plant;

[0020] Extracting primary characteristic production factor data, secondary characteristic production factor data and tertiary characteristic production factor data corresponding to hydropower generation from the power data of the hydropower plant;

[0021] From the electricity data of the wind power plant, the primary characteristic production factor data, the secondary characteristic production factor data and the tertiary characteristic production factor data corresponding to wind power generation are extracted.

[0022] In an optional implementation, the multiple aspects of the controlled production factors include: electricity price, inertia and frequency, active and reactive power, electricity carbon emission, safety and stability, and grid connection and disconnection;

[0023] The step of extracting the control production factor data corresponding to the control production factor group from the power data on the power grid side includes:

[0024] From the power data on the grid side, electricity price data, inertia and frequency data, power data, electricity carbon emission data, safety and stability index data and grid connection and disconnection impact data are extracted and converted into digital vector representations respectively.

[0025] In an optional implementation, the method further includes:

[0026] Parsing the power analysis demand information to obtain a plurality of sub-demand information and a logical dependency relationship or a numerical conversion relationship between the plurality of sub-demand information; the plurality of sub-demand information includes: a first sub-demand information and a second sub-demand information;

[0027] The method of constructing a target data vector based on the power analysis demand information by using the extracted characteristic production factor data, and determining key control production factor data associated with the power analysis demand information from the extracted control production factor data, includes:

[0028] constructing a first target data vector based on the first sub-demand information and determining first key control production factor data associated with the first sub-demand information; and constructing a second target data vector based on the second sub-demand information and determining second key control production factor data associated with the second sub-demand information;

[0029] Based on the target data vector and the key control production factor data, an artificial intelligence algorithm is used to learn and obtain a mapping function between the target data vector and the key control production factor data, a functional operator with the mapping function as the core is constructed, and a power analysis intelligent agent is constructed based on multiple functional operators, including:

[0030] Based on the first target data vector and the first key control production factor data, an artificial intelligence algorithm is used to learn a first operator; and based on the second target data vector and the second key control production factor data, an artificial intelligence algorithm is used to learn a second operator;

[0031] Based on the logical dependency relationship or the numerical conversion relationship, constructing a transfer function relationship between the first operator and the second operator;

[0032] Based on the transfer function relationship, the first operator and the second operator, constructing a power analysis agent;

[0033] The calling of the power analysis agent to generate an analysis response result to the power analysis request is specifically:

[0034] The power analysis agent is called to trigger the first operator and the second operator to work based on the transfer function relationship to generate a final analysis response result to the power analysis request.

[0035] In an optional implementation, the power analysis agent is configured with a power plant side interface, a grid side interface and an agent interface; the method further includes:

[0036] The power analysis agent sends the analysis response result to the power plant through the power plant side interface; or,

[0037] The power analysis agent sends the analysis response result to the power grid through the power grid side interface; or,

[0038] The power analysis agent sends the analysis response result to other power analysis agents through the agent interface.

[0039] In an optional implementation, the power analysis agent is configured with a power plant side interface, a grid side interface and an agent interface; the method further includes:

[0040] The power analysis agent generates a first control instruction for the power plant, a second control instruction for the power grid, or a third control instruction for other agents based on the analysis response result;

[0041] The method further comprises:

[0042] The power analysis agent sends the first control instruction to the power plant through the power plant side interface; or,

[0043] The power analysis agent sends the second control instruction to the power grid through the power grid side interface; or,

[0044] The power analysis agent sends the third control instruction to other agents through the agent interface.

[0045] In an optional implementation, the constructed functional operator is an electricity price operator, and the electricity price operator is used to calculate the electricity price; the calling of the power analysis agent to generate an analysis response result to the power analysis request includes:

[0046] Calling the power analysis agent to obtain the power price calculation result of the power plant through the power price operator;

[0047] Verifying the electricity price calculation result through a financial verification model on the power grid side;

[0048] If the electricity price calculation result is verified, then further selecting an electricity price calculation function that is adapted and feasible on the grid side from the dispatch strategy library, and loading the electricity price calculation function into the electricity price operator to perform function update on the electricity price operator;

[0049] The electricity price calculation result on the grid side is obtained through the updated electricity price operator.

[0050] In an optional implementation, the financial verification model includes: a first verification condition and a second verification condition;

[0051] The first verification condition is: the actual output of various types of power plants ≥ the power grid;

[0052] The second verification condition is: the sum of the actual output of various types of power plants, the power grid electricity price at the corresponding time and the product of the power generation time is ≤ the product of the average power grid price and the power grid electricity;

[0053] If the electricity price calculation result satisfies the first verification condition and the second verification condition, it is determined that the electricity price calculation result has been verified; if the electricity price calculation result does not satisfy any of the first verification condition and the second verification condition, it is determined that the electricity price calculation result has not been verified.

[0054] In an optional implementation, after loading the electricity price calculation function into the electricity price operator to perform function update on the electricity price operator, the method further includes:

[0055] The grid side records the applied transaction power and power price reported by the power plant side to the grid side;

[0056] Based on the power grid side, judging whether the grid is stable, so as to determine whether it is necessary to start the virtual power plant function;

[0057] If it is determined that the virtual power plant function does not need to be activated, the grid side sends a dispatch instruction of clearing electricity prices and electricity quantities to various types of power plants based on the electricity price calculation result of the grid side;

[0058] If it is determined that the virtual power plant function needs to be started, the virtual power plant function is started to call upon its storage capacity.

[0059] In an optional implementation, the method further includes:

[0060] The power plant receives the clearing electricity price given by the grid;

[0061] Combining the power plant cost and the clearing electricity price, verification is performed using a financial verification model on the power plant side;

[0062] If the verification result indicates that the verification is passed, the different power supply points of the same power plant type will be further judged based on the power consumption and electricity price to determine whether they can be cleared;

[0063] If all power supply points can be cleared, the power will be cleared.

[0064] In an optional implementation, the financial verification model on the power plant side includes: a third verification condition;

[0065] The third verification condition is: the sum of the products of the actual output of the power plant, the power price of the power grid at the corresponding time and the power generation time>the product of the power plant cost and the actual output of the power plant.

[0066] The second aspect of the present application provides a digital power analysis device for coordinating multiple production factors, the device comprising:

[0067] The data extraction module is used to extract characteristic production factor data corresponding to the characteristic production factor group from the power data on the power plant side; and to extract control production factor data corresponding to the control production factor group from the power data on the power grid side; the characteristic production factor group includes characteristic production factors of multiple levels; the control production factor group includes control production factors of multiple aspects;

[0068] A vector construction module, for constructing a target data vector using the extracted characteristic production factor data based on power analysis demand information;

[0069] A data determination module, used to determine key control production factor data associated with the power analysis demand information from the extracted control production factor data;

[0070] An operator construction module is used to use an artificial intelligence algorithm to learn a mapping function between the target data vector and the key control production factor data based on the target data vector and the key control production factor data, construct a functional operator with the mapping function as the core, and construct an electric power analysis intelligent body based on multiple functional operators; the electric power analysis intelligent body corresponds to the electric power analysis demand information;

[0071] A request receiving module, used for receiving a power analysis request;

[0072] The power analysis module is used to call the power analysis agent to generate an analysis response result to the power analysis request if the requirement information carried in the power analysis request matches the power analysis requirement information.

[0073] Compared with the prior art, this application has the following beneficial effects:

[0074] In the technical solution of the present application, in order to realize digital power analysis, an intelligent body is first constructed. To this end, production factor data are extracted from the power data on the power plant side and the power grid side, respectively. Among them, the data of characteristic production factors are extracted from the power plant side; and the data of control production factors are extracted from the power grid side. Thus, the coordination of characteristic production factors on the power plant side and control production factors on the power grid side is achieved. On the basis of the extracted relevant production factor data, in order to construct an intelligent body with specific power analysis functions, artificial intelligence technology is further used, and sufficient preparations are made for the use of this technology: based on specific power analysis demand information, the extracted characteristic production factor data is used to construct a data vector; and the control production factor data associated with the power analysis demand information is determined from the extracted control production factor data, and it is regarded as the key control production factor data.

[0075] On the basis of the target data vector and the key control production factor data, the mapping function between the target data vector and the key control production factor data is learned by using the artificial intelligence algorithm, and a functional operator with the mapping function as the core is constructed, and a power analysis agent is constructed based on multiple functional operators. At this point, the power analysis agent has the corresponding functional operator, so it has the function of power analysis and can respond to the above-mentioned power analysis demand information. Thereafter, if a power analysis request is received, and the demand information carried therein matches the power analysis demand information, it means that the power analysis agent can be called, and the function of the power analysis agent is used to generate the analysis response result of the power analysis request. In the technical solution of the present application, since the construction of the power analysis agent depends on the relevant production factor data on the power plant side and the power grid side, and the characteristic production factor group includes multiple levels of characteristic production factors, and the control production factor group includes multiple aspects of control production factors, it is equivalent to realizing the coordination of multiple levels of characteristic production factors and multiple aspects of control production factors. The power analysis agent constructed based on this effectively utilizes the diversified information of power data and can realize the power analysis of coordinated multiple production factors. The power analysis agent can more accurately capture the connection between data and data, and thus obtain more accurate and transferable analysis response results. It can also be understood that this solution can form high-value data assets in the power system. In this way, the analytical value of power data is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0077] Figure 1 A schematic diagram of a power supply scenario provided in an embodiment of the present application;

[0078] Figure 2 A schematic diagram of a production factor division architecture provided in an embodiment of the present application;

[0079] Figure 3 A flowchart of a digital power analysis method for coordinating multiple production factors provided in an embodiment of the present application;

[0080] Figure 4 A schematic diagram of the relationship between an agent, an operator, a function, and production factor data provided in an embodiment of the present application;

[0081] Figure 5 A flowchart for implementing a power analysis intelligent entity provided in an embodiment of the present application;

[0082] Figure 6 A schematic diagram of the relationship between another agent, operator, function, and production factor data provided in an embodiment of the present application;

[0083] Figure 7 It is the grid side flow chart based on the electricity price operator;

[0084] Figure 8 It is the flow chart of the power plant side based on the electricity price operator;

[0085] Fig. 9 It is the signaling diagram between the power plant side, the grid side and the intelligent agent;

[0086] Fig.10 A schematic diagram of the structure of a digital power analysis device that coordinates multiple production factors provided in an embodiment of the present application. DETAILED DESCRIPTION

[0087] As described above, the current power system generally analyzes power data in isolation. For example, when analyzing a certain indicator, only a very small number of data directly related to the indicator are considered, and the influence of some other factors is also ignored, resulting in insufficient accuracy of power analysis results, or too single content, and low value of analysis results. In this case, it is easy to cause ineffective waste of power resources.

[0088] In order to solve the above problems, the inventors in this application propose a digital power analysis method and related devices that coordinate multiple production factors. In this method. The characteristic production factor data on the power plant side and the control production factor data on the power grid side are coordinated, and based on this, a functional operator of a mapping function containing the relationship between the two is constructed, and a power analysis intelligent body is built accordingly. When faced with corresponding power analysis needs, the power analysis intelligent body can be called to complete the analysis response. This method solves the problems of incomplete existing analysis and overly single data considerations. It can more accurately and sensitively capture the connection between data and data in the power system, and form data assets for use in a variety of analysis links. Thereby, it can assist relevant personnel to dispatch and control in the power system more efficiently.

[0089] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0090] Figure 1A schematic diagram of a power supply scenario provided in an embodiment of the present application. Figure 1 As shown, in actual applications, various types of power plants may communicate with the grid side and transmit power. These different types of power plants can be collectively referred to as the power plant side. In the embodiment of the present application, the power plant side can report its own data to the grid side and provide timely feedback; the grid side can perform scheduling on the power plant side, such as peak load regulation, frequency regulation, etc.

[0091] Based on the influence of various factors such as power generation cost, performance of generator sets, uncertainty of power generation of different types of power plants, and changes in load demand, the power plant side often needs to reach a "consensus" with the power grid side and perform corresponding actions according to the instructions of the power grid side. Taking a thermal power plant as an example, the thermal power plant can further increase the heat stored in the thermal power plant or further release the heat stored in the thermal power plant according to the instructions of the power grid side; or the thermal power plant can adjust the following mode of the motor-machine-furnace according to the requirements of the load. Taking a wind power plant as an example, the fan can add or subtract loads and adjust the chamfer of the fan according to the instructions of the power grid side. Taking a photovoltaic power plant as an example, the angle of the thyristor can be controlled according to the dispatching instructions of the power grid side for the load, thereby changing the power generation. Taking a hydropower plant as an example, the water volume and water use cost can be calculated according to the dispatching curve based on the power grid side, thereby controlling the power generation of the hydro turbine.

[0092] Combination Figure 1 It can be seen that in the actual power production process, the grid side and the power plant side need to be closely dispatched so as to achieve better and more stable power supply and maintain the cost demands of both parties. In combination with the above-mentioned practical needs, the digital power analysis method and related devices for coordinated multi-production factors proposed in this application have a relatively broad application prospect and have the potential to be used in power systems for a long time. Through the coordinated and digital analysis of multiple production factors, the accuracy and reliability of power analysis in the current power system can be improved. Through the coordination of multiple production factors, the barriers between the data on the power plant side and the grid side are broken, and the connection between the data of multiple production factors is captured through artificial intelligence technology, so as to realize digital power analysis in the field of power systems in a more intelligent and automated way.

[0093] For ease of understanding, the following Figure 2 Introduce the production factor division structure in the technical solution of this application. Figure 2 A schematic diagram of a production factor division architecture provided for an embodiment of the present application. In the embodiment of the present application, the production factors are divided according to the power plant side and the power grid side. The basis for dividing the production factors on the power plant side is called the characteristic production factor group; the basis for dividing the production factors on the power grid side is called the control production factor group.

[0094] In the embodiment of the present application, the characteristic production factor group includes characteristic production factors of multiple levels. Specifically, these characteristic production factors can be subdivided into: primary characteristic production factors, secondary characteristic production factors and tertiary characteristic production factors. Among them, the primary characteristic production factors are natural characteristic production factors (or called: natural indicators), which are production factors that can directly reflect natural characteristics. The secondary characteristic production factors are single-system characteristic production factors (or called: single-system indicators), which are production factors involving monomer systems in the power system. The tertiary characteristic production factors are plant system characteristic production factors (or called: plant system indicators), which are production factors involving the entire system in the power system.

[0095] Combination Figure 1 As shown, there are various types of power plants, such as thermal power plants, photovoltaic power plants, wind power plants, hydropower plants, etc., which use different power generation methods, and the natural resources used and the internal system structures are also different. In order to more accurately realize digital power analysis, in the technical solution of this application, for various types of power generation, the first-level characteristic production factors, second-level characteristic production factors and tertiary characteristic production factors can be subdivided. Therefore, for each type of power plant, the data of the characteristic production factors of the corresponding level can be subsequently extracted.

[0096] The following is an illustrative introduction to several primary characteristic production factors in combination with different types of power generation. Taking photovoltaic power generation as an example, the primary characteristic production factors may include: photovoltaic light intensity. Taking hydropower generation as an example, the primary characteristic production factors may include: precipitation (water inflow), etc. Taking thermal power generation as an example, the primary characteristic production factors may include: the lower calorific value of coal, coal consumption, heat consumption, etc. Taking wind power generation as an example, the primary characteristic production factors may include: wind speed, etc.

[0097] For thermal power plants, a single system can be a boiler system, an electrical system, etc. For hydropower plants, a single system can be a turbine system, a switching system, a butterfly valve layer system, etc. For photovoltaic power plants, a single system can be a photovoltaic panel area, a substation area, etc. In addition, these different power plants also share a transmission and transformation system. Correspondingly, the secondary characteristic production factors can be production factors that reflect a single system, such as furnace efficiency and steam turbine efficiency. Tertiary characteristic production factors can be production factors that reflect the overall situation of multiple systems, such as photovoltaic efficiency, hydropower efficiency, thermal power unit efficiency, and power plant thermal efficiency (specific heat index). From Figure 2 It can be seen that the mining level of data features is gradually deepened from the first-level characteristic production factors, the second-level characteristic production factors, and the third-level characteristic production factors. It can be seen that in the embodiment of the present application, relying on the division structure of the characteristic production factors in the characteristic production factor group, the data of natural characteristics, single system characteristics and plant system characteristics can be collaboratively extracted, and digital power analysis can be realized from the characteristics of multiple levels.

[0098] In the embodiment of the present application, the control production factor group includes multiple aspects of control production factors. Control production factors are production factors that reflect the performance of the power grid, which are composed of some key physical quantities in the power system. Control production factors help to provide an effective theoretical basis for power system control. In the embodiment of the present application, for example, the control production factors may include but are not limited to the following six aspects: (1) electricity price; (2) inertia and frequency; (3) active and reactive power; (4) carbon emission; (5) safety and stability; (6) power grid connection and disconnection. It should be noted that the six aspects of control production factors in the above examples are only defined from a macro perspective. When implemented specifically, each aspect of the control production factor can be further subdivided or derived into more specific indicators. The control production factors are not limited in quantity and content here. In the embodiment of the present application, relying on the division structure of the control production factors in the control production factor group, data on multiple aspects such as electricity price, inertia and frequency, active and reactive power, carbon emission, safety and stability, and power grid connection and disconnection can be extracted collaboratively, and digital power analysis can be realized from multiple aspects of power grid concern.

[0099] The digital power analysis method for coordinating multiple production factors provided in the embodiment of the present application is described in detail below with reference to the accompanying drawings. Figure 3 A flowchart of a digital power analysis method for coordinating multiple production factors provided in an embodiment of the present application. Steps S301 to S303 introduce the construction process of an important tool (power analysis agent) for realizing digital power analysis, and steps S304 to S305 introduce the application process of the power analysis agent.

[0100] like Figure 3 As shown in the figure, the digital power analysis method for coordinating multiple production factors includes:

[0101] S301. Extract characteristic production factor data corresponding to characteristic production factor groups from power data on the power plant side; and extract control production factor data corresponding to control production factor groups from power data on the power grid side.

[0102] As mentioned above, the technical solution of this application pre-plans characteristic production factor groups and control production factor groups. Among them, the characteristic production factor group includes characteristic production factors of multiple levels, such as first-level characteristic production factors, second-level characteristic production factors and third-level characteristic production factors of various different power generation types. The control production factor group includes multiple aspects of control production factors, such as electricity price, inertia and frequency, active and reactive power, electric carbon emissions, safety and stability, and grid connection and disconnection. On the basis of the above-mentioned characteristic production factor group and control production factor group, in this step, the production factor data is extracted in combination with the power data on the power plant side and the grid side.

[0103] In an optional implementation, characteristic production factor data corresponding to the characteristic production factor group are extracted from the power data on the power plant side, including: extracting the first-level characteristic production factor data, the second-level characteristic production factor data and the third-level characteristic production factor data corresponding to thermal power generation from the power data of the thermal power plant; extracting the first-level characteristic production factor data, the second-level characteristic production factor data and the third-level characteristic production factor data corresponding to photovoltaic power generation from the power data of the photovoltaic power plant; extracting the first-level characteristic production factor data, the second-level characteristic production factor data and the third-level characteristic production factor data corresponding to hydropower generation from the power data of the hydropower plant; and extracting the first-level characteristic production factor data, the second-level characteristic production factor data and the third-level characteristic production factor data corresponding to wind power generation from the power data of the wind power plant.

[0104] In an optional implementation, control production factor data corresponding to the production factor group associated with control is extracted from the power data on the grid side to form a data vector to express a specific function, such as power system stability, including: extracting electricity price data, inertia and frequency data, power data, electricity carbon emission data, safety and stability index data and grid connection and disconnection impact data from the power data on the grid side.

[0105] As an example, the electricity price data may include electricity prices at different times and with different load demands.

[0106] Inertia and frequency data can include system inertia and frequency. The relationship between system inertia and frequency is an important aspect in power system stability analysis. System inertia usually refers to the rotational inertia of the generator sets in the power system, which reflects the system's resistance to frequency changes. The following are some key points that outline the connection between system inertia and frequency. Inertia support: When there is a large load change in the power system or the sudden addition or withdrawal of generator sets, the system inertia can slow down the change rate of the system frequency. This inertia support helps maintain the frequency stability of the power system. Frequency change rate: The size of the system inertia directly affects the frequency change rate. The larger the inertia, the smaller the rate of change of the system frequency for a given power imbalance. A smaller frequency change rate helps avoid underfrequency load shedding or other stability control measures triggered by too fast frequency changes.

[0107] Voltage and active and reactive power data can include power at different voltages, for example in the form of (voltage, active power, reactive power). In power systems, voltage and frequency have no direct relationship, but they are related to power (active and reactive, respectively), which creates an indirect relationship. When reactive power is insufficient, the voltage drops, and when reactive power is excessive, the voltage rises; when active power is insufficient, the frequency drops, and when active power is excessive, the frequency rises.

[0108] Electricity carbon emissions data may include carbon footprint, such as the amount of carbon dioxide emitted per kilowatt-hour of electricity generated.

[0109] Safety and stability index data can include the mean time between failures (MTBF) of the power grid. MTBF is an important indicator for measuring the reliability of products (especially electronic products). It indicates the average working time between two adjacent failures of the product under specified conditions and within a specified time range.

[0110] The grid connection and disconnection image data may include the degree of influence of each network on other networks.

[0111] In specific implementation, after extracting the control production factor data, it can be further converted into a digital vector representation to facilitate network recognition, calculation and processing. The converted digital vector representation can contain data of one aspect of the control production factor, or data of multiple aspects of the control production factor. The dimension and data source of the digital vector representation can be set according to demand and are not limited here.

[0112] S302: Based on the power analysis demand information, the target data vector is constructed using the extracted characteristic production factor data, and key control production factor data associated with the power analysis demand information is determined from the extracted control production factor data.

[0113] In an embodiment of the present application, an electric power analysis intelligent body is constructed based on the electric power analysis needs and in combination with the characteristic production factor data and control production factor data extracted in the previous step. It can be understood that multi-level and multi-faceted production factor data are extracted in the previous step, but some of these data may be highly relevant to the electric power analysis needs, and these data are helpful in building an electric power analysis intelligent body with the ability to meet specific electric power analysis needs. However, some of these extracted data may have a very weak correlation with the electric power analysis needs. For example, the electric power analysis needs are related to the electricity price. At this time, the electric carbon emission data in the data is of little significance for the calculation and analysis of the electricity price. To this end, in order to avoid the negative and adverse effects of redundant and massive data on the construction of the electric power analysis intelligent body, the present application extracts and refines the massive data obtained in S301 through this step S302, so as to facilitate the accurate capture of the connection between the data.

[0114] In the embodiment of the present application, on the basis of the extracted characteristic production factor data, based on the specific demand reflected in the power analysis demand information, and the connection between the specific demand and the characteristic production factor, one or more characteristic production factor data with a high correlation with the specific demand (as the target characteristic production factor) are further extracted from the extracted characteristic production factor data, and then a data vector is constructed. For the convenience of distinction, the data vector is referred to as the target data vector. When constructing the target data vector, the vector can be constructed based on certain construction rules. For example, the dimension of the vector and the level or name of the characteristic production factor corresponding to each dimension can be set in the construction rule.

[0115] Table 1 shows some characteristic production factor data extracted from the power data of thermal power plants. In Table 1, SHRW represents the weighted specific heat index, in kJ / (kW·h). Ea represents the power supply (or power generation), in kW·h. EP represents the power charge, in US dollars. SHRcc represents the specific heat index, in kJ / (kW·h). U represents the coal price, in USD / t. As an example, U=69 USD / ton. E represents the unit power generation heat consumption rate, that is, the heat consumption of the unit per kilowatt-hour of electricity, in kcal / kWh. N represents the specific heat index, specifically the heat required per kilowatt-hour of electricity, in kcal / kWh, and AUX represents the plant power consumption rate. ECRm is the electricity charge per kilowatt-hour, in USD / (kW·h). ECRm represents the power charge ratio, that is, the coal cost per kilowatt-hour of electricity produced, in USD / (kW·h).

[0116] Table 1

[0117]

[0118] If the specific demand reflected in the power analysis demand information is to calculate the electricity price, multiple target data vectors can be extracted based on the characteristic production factor data shown in Table 1. For example, each row of data in Table 1 is extracted to construct a target data vector with the structure of (operating condition, SHRW, ECRm, Ea, CERm, EP, actual coal consumption per kWh, actual coal cost per kWh, overall coal cost). For example, using the second row of data in Table 1 to construct target data vector 1 is (100%, 2138.4, 0.0293, 9267053600 0.0311, 287772056.8, 450.0942, 0.0311, 287802236), and using the third row of data in Table 1 to construct target data vector 2 is (95%, 2152.69, 0.0295, 8803700920, 0.0311, 273383454, 453.5357, 0.0313, 275502693). And so on.

[0119] In addition, in the specific implementation, the corresponding relationship between the control production factors and the power analysis demand information can be pre-constructed. Furthermore, according to the specific power analysis demand information, the corresponding control production factors can be timely and efficiently determined as key control production factors, and based on this, the key control production factor data associated with the power analysis demand information can be determined from the extracted massive control production factor data. For the extracted key control production factor data, similarly, it can also be converted into a digital vector representation.

[0120] S303. Based on the target data vector and the key control production factor data, use the artificial intelligence algorithm to learn the mapping function between the target data vector and the key control production factor data, construct a functional operator with the mapping function as the core, and construct an electric power analysis intelligent body based on multiple functional operators.

[0121] Figure 4 A schematic diagram of the relationship between an agent, an operator, a function, and production factor data provided in an embodiment of the present application. Figure 4 The implementation process of this step is described below. Figure 4 As shown in , in the embodiment of the present application, the target data vector is obtained based on the characteristic production factor data, and the key control production factor data is determined based on the control production factor data. Next, it is necessary to rely on the artificial intelligence algorithm to learn based on the target data vector and the key control production factor data, and infer and analyze the potential data connection between the characteristic production factor data and the key control production factor data behind the target data vector. Through the application of the artificial intelligence algorithm, the connection between the two can be expressed through a mapping function.

[0122] In the embodiment of the present application, it is proposed to construct a functional operator with a mapping function as the core. Figure 4 In the example, taking agent 1 as an example, it contains multiple functional operators, namely: operator 1, operator 2 and operator 3. A mapping function is loaded in each functional operator. It can be understood that the mapping functions loaded in different functional operators are different. This is because the power analysis demand information driving the construction of each functional operator may be different, and thus the characteristic production factor data and control production factor data used may also be different. By executing this step, the constructed power analysis agent corresponds to the aforementioned power analysis demand information. For example, according to the power analysis demand information 1, agent 1 is finally constructed; according to the power analysis agent 2, agent 2 is finally constructed.

[0123] In the embodiments of the present application, the artificial intelligence algorithms used may be diverse. For example, all open source artificial intelligence algorithms currently available on the market can be based on the above technical concepts, and can be used to infer and analyze the intrinsic relationship between the target data vector and the key control production factor data, thereby obtaining an accurate mapping function. The specific type of artificial intelligence algorithm used is not limited here.

[0124] Combination Figure 4 It can be seen that in the embodiment of the present application, an agent can include one or more functional operators. Taking agent 1 as an example, it includes 3 functional operators. Functional operators in the same agent can be independent of each other. In addition, functional operators in the same agent can also have technical or logical connections.

[0125] exist Figure 4 In addition to Agent 1, other agents are also shown, such as Figure 4 The intelligent agents 2, N, etc. shown in the figure. After these intelligent agents are successively constructed, an automated device that integrates the wisdom of multiple intelligent agents is formed. Therefore, the device can also be regarded as a "robot". It has rich knowledge related to the power system, and by using the functional operators in each intelligent agent, it can solve various power system analysis problems. With the increasing expansion of international cooperation in the power industry, the construction method of the intelligent agent and the architecture of the "robot" proposed in the technical solution of this application are also breakthrough technologies for coping with international power cooperation.

[0126] The construction of the power analysis agent is completed through the above S301~S303. When a power analysis request is received, the corresponding power analysis agent work can be matched by identifying the demand information in the power analysis request, as shown in the description of S304~S305 for details.

[0127] S304: Receive a power analysis request.

[0128] In actual applications, the power analysis request can be initiated by the power plant side, the power grid side, or any other third party other than the power plant side and the power grid side. As long as the relevant subject or device that initiates the power analysis request has the authority or qualification to start the power analysis agent for data analysis, the execution device (such as a server or terminal) of the digital power analysis method for coordinating multiple production factors proposed in this scheme can respond to the power analysis request and analyze the matching of the demand information carried in the request with one or more constructed power analysis agents. Obviously, a power analysis agent with a higher matching degree has a greater probability of providing a power analysis function that meets the demand information.

[0129] S305: If the demand information carried in the power analysis request matches the power analysis demand information, the power analysis agent is called to generate an analysis response result to the power analysis request.

[0130] It is understandable that if the analysis determines that the demand information carried in the power analysis request matches a certain power analysis agent that has been constructed, the agent is called, and the functional operator inside the agent is used to respond to the power analysis request. For example, if the demand information carried in the power analysis request indicates the calculation of the electricity price, the electricity price operator (whose function is to calculate the electricity price) summarized by the power analysis agent is called, and the electricity price calculation is performed based on the data currently provided to the operator that is necessary for the relevant calculation.

[0131] In the technical solution of the present application, in order to realize digital power analysis, an intelligent body is first constructed. To this end, production factor data are extracted from the power data on the power plant side and the power grid side, respectively. Among them, the data of characteristic production factors are extracted from the power plant side; and the data of control production factors are extracted from the power grid side. Thus, the coordination of characteristic production factors on the power plant side and control production factors on the power grid side is achieved. On the basis of the extracted relevant production factor data, in order to construct an intelligent body with specific power analysis functions, artificial intelligence technology is further used, and sufficient preparations are made for the use of this technology: based on specific power analysis demand information, the extracted characteristic production factor data is used to construct a data vector; and the control production factor data associated with the power analysis demand information is determined from the extracted control production factor data, and it is regarded as the key control production factor data.

[0132] On the basis of the target data vector and the key control production factor data, the mapping function between the target data vector and the key control production factor data is learned by using the artificial intelligence algorithm, and a functional operator with the mapping function as the core is constructed, and a power analysis agent is constructed based on multiple functional operators. At this point, the power analysis agent has the corresponding functional operator, so it has the function of power analysis and can respond to the above-mentioned power analysis demand information. Thereafter, if a power analysis request is received, and the demand information carried therein matches the power analysis demand information, it means that the power analysis agent can be called, and the function of the power analysis agent is used to generate the analysis response result of the power analysis request. In the technical solution of the present application, since the construction of the power analysis agent depends on the relevant production factor data on the power plant side and the power grid side, and the characteristic production factor group includes multiple levels of characteristic production factors, and the control production factor group includes multiple aspects of control production factors, it is equivalent to realizing the coordination of multiple levels of characteristic production factors and multiple aspects of control production factors. The power analysis agent constructed based on this effectively utilizes the diversified information of power data and can realize the power analysis of coordinated multiple production factors. The power analysis agent can more accurately capture the connection between data and data, and thus obtain more accurate and transferable analysis response results. It can also be understood that this solution can form high-value data assets in the power system. In this way, the analytical value of power data is improved.

[0133] In practical applications, it is possible that a single operator cannot meet a relatively large analysis goal. In this case, a possible solution is to call multiple operators to meet an analysis goal. This type of situation is introduced below.

[0134] To deal with the above problems, the power analysis demand information can be parsed in the initial stage of constructing the power analysis intelligent body to obtain multiple sub-demand information and the logical dependency relationship or digital conversion relationship between the multiple sub-demand information. By parsing and disassembling the power analysis demand information, functional operators can be built according to the sub-demand information in a targeted manner. Assume that by parsing the power analysis demand information, multiple sub-demand information is obtained, including the first sub-demand information and the second sub-demand information.

[0135] Figure 5 This is a flowchart for building an intelligent power analysis agent according to an embodiment of the present application. Figure 5 As shown, in a specific implementation, the digital power analysis method for coordinating multiple production factors provided in the embodiment of the present application further includes:

[0136] A first target data vector is constructed based on the first sub-demand information and a first key control production factor data associated with the first sub-demand information is determined; and a second target data vector is constructed based on the second sub-demand information and a second key control production factor data associated with the second sub-demand information is determined.

[0137] for Figure 5 In the specific example shown, based on the target data vector and the key control production factor data, an artificial intelligence algorithm is used to learn the mapping function between the target data vector and the key control production factor data, a functional operator with the mapping function as the core is constructed, and a power analysis intelligent agent is constructed based on multiple functional operators, which may specifically include:

[0138] Based on the first target data vector and the first key control production factor data, an artificial intelligence algorithm is used to learn the first operator; and based on the second target data vector and the second key control production factor data, an artificial intelligence algorithm is used to learn the second operator; based on a logical dependency relationship or a numerical conversion relationship, a transfer function relationship between the first operator and the second operator is constructed; based on the transfer function relationship, the first operator and the second operator, an electric power analysis intelligent entity is constructed.

[0139] So far, a multi-operator power analysis agent including the first operator and the second operator has been constructed. Based on this, when a power analysis request is received, and the demand information carried therein also matches the first sub-demand information and the second sub-demand information, the power analysis agent can be called to trigger the first operator and the second operator based on the transfer function relationship to generate the final analysis response result to the power analysis request. In this way, the collaborative work of multiple functional operators in the power analysis agent is realized. By constructing multiple functional operators with logical dependencies or numerical conversion relationships, and constructing a power analysis agent accordingly, the power analysis agent can handle more complex and diversified power data analysis tasks.

[0140] As mentioned above, the transfer function relationship between the first operator and the second operator is constructed based on the logical dependency relationship or the numerical conversion relationship. Therefore, in the intelligent agent, different operators also have corresponding logical dependencies or numerical conversion relationships. The following are several dependencies that exist in the power system: reactive power and voltage, heat consumption and coal consumption, system inertia and frequency, electricity price and coal price, etc.

[0141] For example, the numerical conversion relationship between different operators may mean that different operators have basically the same functions, but there are differences at the transaction level or pricing level. In order to meet the interoperability requirements of transactions or pricing, it is necessary to call operators to perform digital conversion at the transaction level or pricing level. As an example, operator A calculates the electricity price of the power grid of country A, and operator B calculates the electricity price of the power grid of country B. When it is necessary to convert the electricity prices of countries A and B, it is necessary to operate according to the digital conversion relationship.

[0142] In the embodiment of the present application, each power analysis agent is configured with multiple types of interfaces to complete communication or data exchange with other subjects. As an example, the power analysis agent is configured with a power plant side interface, a grid side interface, and an agent interface. Among them, the power plant side interface is used for the agent to connect with the power plant; the grid side interface is used for the agent to connect with the grid; and the agent interface is used for the agent to connect with other agents.

[0143] In one possible implementation, the power analysis agent may be specifically configured with multiple power plant side interfaces, and different power plant side interfaces are used to connect to different types of power plants, or different power plants of the same type. This application does not limit the number of power plant side interfaces configured for a power analysis agent.

[0144] In a possible implementation, the power analysis agent may be specifically configured with multiple agent interfaces, and different agent interfaces are used for the agent to connect to different other agents. This application does not limit the number of agent interfaces configured for a power analysis agent.

[0145] As mentioned above, by calling the power analysis agent, an analysis response result to the power analysis request can be generated. Based on the above introduction to the interface configured on the power analysis agent, in the technical solution of this application, the power analysis agent can send the analysis response result to the power plant through the power plant side interface; the power analysis agent can send the analysis response result to the power grid through the power grid side interface; the power analysis agent can send the analysis response result to other power analysis agents through the agent interface. It can be seen that through the configuration of the above multiple types of interfaces, the power analysis agent has a way to communicate and transmit the analysis results with various external objects through the interface.

[0146] In addition, in an embodiment of the present application, there may be a scenario in which, after the agent obtains the analysis response result, it is necessary to send a control instruction to the outside world. With the help of the configuration of various types of interfaces, the power analysis agent can transmit control instructions. In an example implementation, the power analysis agent generates a first control instruction for the power plant, a second control instruction for the power grid, or a third control instruction for other agents based on the analysis response result. It should be noted that the above first control instruction, second control instruction and third control instruction are generated according to specific control requirements. The power analysis agent can send the first control instruction to the power plant through the power plant side interface; or, send the second control instruction to the power grid through the power grid side interface; or, send the third control instruction to other agents through the agent interface.

[0147] Figure 6 Another schematic diagram of the relationship between an agent, an operator, a function, and production factor data provided in an embodiment of the present application. Figure 6 On the left side, objects of the same level as the concepts of data, function, operator, agent, etc. in the technical solution of this application are shown from the perspective of organisms; Figure 6 On the right side, the concepts of data, functions, operators, intelligent agents, etc. mentioned in the digital power analysis method for coordinating multiple production factors proposed in this application are displayed. Figure 6 The left and right sides are connected by an arrow marked with the word "symbiosis", which reflects the similarity of the roles or functions of the concepts or objects on the left and right sides.

[0148] Combination Figure 6 As shown on the left, the construction of proteins depends on mitochondria, the construction of cells depends on proteins, and the operation of physiological functions such as human joints (or organs) depends on cells. A complete human body cannot be separated from diverse and multifunctional joints and organs. It is understandable that mitochondria provide key biological characteristics, such as the genetic material deoxyribonucleic acid (DNA) in mitochondria. DNA carries the genetic information necessary for the synthesis of RNA and proteins and is an essential biological macromolecule for the development and normal operation of organisms. The performance of human joints (or organs) is also inseparable from the genetic information carried by DNA. Figure 6 The data, functions, operators, agents and robots shown on the right also have a similar progressive relationship. The formation of the mapping function is inseparable from the extraction of characteristic production factor data and the extraction of control production factor data. The mapping function depends on these data, the operator uses the function as the core, and the agent contains one or more operators to realize the analysis function of the power system data. The robot finally formed has the ability to handle complex and diverse power analysis needs because of its diverse agents. And the foundation of all this depends on Figure 2 The production factor division framework is shown.

[0149] In an optional implementation, the functional operator constructed by the digital power analysis method for coordinating multiple production factors provided in the embodiment of the present application is an electricity price operator. The electricity price operator is used to calculate the electricity price. In actual applications, the power plant side needs to calculate the electricity price and then provide the electricity price to the power plant side. If the power plant side and the power grid side reach an agreement on the electricity price later, electricity trading can be carried out.

[0150] Combine the following Figure 7 and Figure 8 From the perspectives of the grid side and the power plant side, the grid side process and the power plant side process based on the electricity price operator are introduced respectively.

[0151] like Figure 7 As shown in the figure, the grid-side process based on the electricity price operator is shown. First, it is necessary to extract the key control production factor data related to the electricity price calculation from the power data on the grid side. Optionally, these key control production factor data can be represented by digital vectors. Next, the grid side also needs to obtain the target data vector obtained by feature extraction based on the power data on the power plant side. It can be understood that this type of data vector is also related to the electricity price calculation. In one example, the performance efficiency related data vector A of unit A is expressed as (coal consumption 1, heat rate 1, unit efficiency 1), and the performance efficiency related data vector B of unit B is (coal consumption 2, heat rate 2, unit efficiency 2). These data vectors are aligned and statistically obtained to obtain the comprehensive production factor data vector (coal consumption 1+coal consumption 2, heat rate 1+heat rate 2, unit efficiency 1+unit efficiency 2). Next, based on the data vectors extracted from the power plant side and the grid side above, the operator function computing power group is obtained by using the artificial intelligence algorithm. The operator function computing power group includes many functional operators with functions as the core. Having these operator function computing power groups can also be understood as building an intelligent power analysis entity.

[0152] Taking electricity price calculation as an example, the constructed operator function computing power group can be called the power grid load and electricity price function computing power group. In addition, considering other possible power analysis needs, it is also possible to establish a power grid inertia frequency computing power group, an active and reactive function computing power group, a power grid carbon footprint computing power group, a power grid voltage and current function computing power group, a power grid safety and stability computing power group, a power grid connection and decoupling computing power group, etc. Among them, the active and reactive function computing power group includes functions used to reflect the performance of active and reactive power in the power grid; the power grid inertia function computing power group includes functions used to reflect the performance of stable frequency conversion of the power grid; the power grid load and electricity price function computing power group includes functions used to calculate the relationship between the power grid electricity price and the power plant power generation load; the power grid safety and stability computing power group includes functions used to calculate the power grid step and flow; the power grid carbon footprint computing power group includes functions used to calculate the carbon consumption in the production of hydropower, thermal power, photovoltaic, wind power and other equipment in the power grid.

[0153] Taking the power grid load and electricity price function computing power group as an example, the mapping function as the core of the electricity price operator can be expressed as: CERm=U*N / E(1-AUX). CERm is the grid compensation coefficient for 1 kWh of electricity payment. U represents the coal price, which is also the fuel cost subsidy, in US dollars / ton. E represents the unit power generation heat consumption rate, that is, the unit's heat consumption per kWh, in kcal / kWh. N represents the specific heat index, in kcal / kWh. AUX represents the plant power consumption rate. This formula reflects the connection between the grid compensation coefficient for 1 kWh of electricity payment and characteristic production factors such as coal price, unit power generation heat consumption, specific heat index, and plant efficiency.

[0154] After obtaining the electricity price operator by using the artificial intelligence algorithm, when calling the intelligent agent containing the operator, the electricity price calculation result on the power plant side is obtained through the electricity price operator. It should be noted that since the construction of this operator depends on the power data on the power plant side, the electricity price calculation result actually reflects more of the power plant's own cost considerations and profit needs. The subsequent power grid side needs to further consider the electricity price calculation result.

[0155] like Figure 7 As shown in , after obtaining the electricity price calculation result on the power plant side through the electricity price operator, the electricity price calculation result is verified through the financial verification model on the power grid side. The main purpose of verifying the electricity price calculation result through the financial verification model on the power grid side is to ensure the balance and stability of the power plant and the power grid. In one example, the financial verification model on the power grid side includes: a first verification condition and a second verification condition; the first verification condition is: the actual output of various types of power plants ≥ the power grid electricity; the second verification condition is: the sum of the products of the actual output of various types of power plants, the power grid electricity price at the corresponding time and the power generation time ≤ the product of the average power grid price and the power grid electricity. If the electricity price calculation result meets the first verification condition and the second verification condition, it is determined that the electricity price calculation result has been verified; if the electricity price calculation result does not meet any of the first verification condition and the second verification condition, it is determined that the electricity price calculation result has not been verified.

[0156] The expression of the first verification condition is:

[0157] Q 火电 +M 风电 +Q 水电 +M 光伏 ≥N 电量

[0158] The expression of the second verification condition is:

[0159] Σ(H*Q 火电 *N 各时刻电网电价 )+ΣM 风电 *N 各时刻电网电价 +Σ(H*Q 水电 *N各时刻电网电价 )+ΣM 光伏 *N 各时刻电网电价 ≤P 电网均价 *N 电量

[0160] In the above expression, Q 火电 、M 风电 , Q 水电 、M 光伏 are the actual outputs of thermal power plants, wind power plants, hydropower plants and photovoltaic power plants respectively. 电量 is the power of the power grid. H represents the power generation time. 各时刻电网电价 is the grid electricity price at the corresponding time. 电网均价 Represents the average grid price.

[0161] For calculation, thermal power and hydropower are both physical power generation with controllable resources, which are deterministic; while photovoltaic and wind power cannot control the wind and light, and their natural elements are highly uncertain. Here, the processing is expressed differently, distinguished by Q and M. This shows the difference in data abstraction between the two types of production factors. The production factors of thermal and hydropower generation are highly deterministic and weakly abstract, represented by Q; while the production factors of wind and photovoltaic generation are highly uncertain and highly abstract, and require a higher-order data operator function M to represent.

[0162] If the electricity price calculation result is verified, the electricity price calculation function that is suitable and feasible on the grid side is further selected from the dispatching strategy library. The dispatching strategy library can be responsible for a special operator that provides a variety of calculation functions. As mentioned above, the construction of the constructed electricity price operator depends on the power data on the power plant side. Therefore, the electricity price calculation result actually reflects more of the power plant's own cost considerations and profit requirements. The subsequent grid side needs to further consider this electricity price calculation result. The specific consideration method is to use the dispatching strategy library. The grid side also needs to select an electricity price calculation function that is suitable for the grid side and technically feasible from the dispatching strategy library, and then load the electricity price calculation function into the electricity price operator to update the function of the electricity price operator; the electricity price calculation result on the grid side is obtained through the updated electricity price operator. In this way, the grid side not only refers to the electricity price calculation result on the power plant side, but also updates the electricity price operator based on its own cost and profit requirements. In this process, the collaborative calculation of electricity prices between the power plant side and the grid side is realized.

[0163] If a grid-adaptive and technically feasible electricity price calculation function cannot be selected from the dispatch strategy library, it is necessary to deeply learn the grid energy consumption average efficiency operator, average electricity price operator and other important production factor operators, return the learning results as samples, and use artificial intelligence algorithms to learn the electricity price operator.

[0164] In an optional implementation, after the electricity price calculation function is loaded into the electricity price operator to perform function update on the electricity price operator, the digital power analysis method for coordinating multiple production factors provided in the embodiment of the present application further includes:

[0165] The grid side collects the transaction volume and electricity price reported by the power plant side to the grid side. Prepare to trade between the power plant side and the grid side. At this time, the grid side completes the electricity price calculation function for the power plant side. Next, based on the grid side, it determines whether the network is stable to determine whether the virtual power plant function needs to be started. The purpose of starting the virtual power plant function is to call the storage capacity, and then give the power of each power generation and clearing according to the cost optimization principle, and finally issue instructions in advance based on artificial intelligence calculations. The grid calls the storage capacity of the virtual power plant. When there is a power shortage, the electricity price is high, and the virtual power plant (battery) is called to discharge; when there is a surplus of electricity, the electricity price is low, and the virtual power plant (battery) is charged.

[0166] In the technical solution of the present application, the power grid side can record the respective clearing curves and dispatching curves under different loads or in emergency situations as input data for deep learning, continuously verify, learn and imitate the laws and characteristics of the power grid, and use them as a reference for issuing instructions next time.

[0167] If it is determined that the virtual power plant function does not need to be started, the grid side sends dispatch instructions for clearing electricity prices and electricity to various types of power plants based on the electricity price calculation results on the grid side. If it is determined that the virtual power plant function needs to be started, the virtual power plant function is started to call storage capacity. And the operator of the virtual power plant function is combined and returned again, and the steps of obtaining the electricity price operator are obtained using the artificial intelligence algorithm.

[0168] like Figure 8 As shown in the figure, the power plant side process based on the electricity price operator is demonstrated. First, various characteristic production factor data on the power plant side are collected to form a data vector. Then, the electricity price operator is trained in combination with the key control production factor data on the power grid side. The power plant side receives the clearing electricity price given by the power grid side; combined with the power plant cost and the clearing electricity price, the financial verification model on the power plant side is used for verification. In an optional implementation, the financial verification model on the power plant side includes: a third verification condition; the third verification condition is: the sum of the product of the actual output of the power plant, the power grid electricity price at the corresponding moment and the power generation time> the product of the power plant cost and the actual output of the power plant. Taking a thermal power plant as an example, the expression of the third verification condition is:

[0169] Σ(H*Q 火电 *N 各时刻电网电价 )>Σ(H*Q 火电 *P 电厂成本 )

[0170] In the expression of the third verification condition above, Q 火电is the actual output of the thermal power plant. H represents the duration of power generation. 各时刻电网电价 is the grid electricity price at the corresponding moment. Artificial intelligence is used to convert the grid electricity price N at each moment 各时刻电网电价 As data input, the power plant cost P under different loads calculated by this power plant system is used. 电厂成本 The corresponding formula is substituted into the formula. The expression of this verification condition reflects that the actual output of thermal power generation production factors is accumulated and calculated as the power grid settlement electricity fee, which fluctuates with the power grid, but the cost production factors of a single power plant change with the load rate, and finally map the cost production factors of the power plant.

[0171] The same power plant type may have several different power supply points. Figure 8 As shown in , if the verification result indicates that the verification is passed, it means that the verification conditions are met as a whole. Then, it is necessary to further determine whether different power sources of the same power plant type can be cleared based on the power volume and electricity price; if each power source can be cleared, the clearing power volume is executed. Subsequently, the corresponding action of the load adjustment of the thermal power plant unit. If there is a situation where the power source cannot be cleared, it is also necessary to calculate the production cost of the power plant or adjust the clearing electricity price of the power plant, and after verification, it is determined again whether each power source can be cleared.

[0172] The first verification condition, the second verification condition and the third verification condition mentioned above are mathematical logics, and all express some physical constraints in the power system. These verification conditions make the power system stable.

[0173] Fig. 9 It is a signaling diagram between the power plant side, the grid side and the intelligent agent. Fig. 9 The significance of peak-shaving in the power system is to find power plants that are suitable for peak-shaving electricity prices within the electricity price range specified by the power grid, so that the revenue and expenditure of the power grid remain balanced and within the budget. At the same time, the clearing electricity and prices of various types of power plants are estimated, and clearing is done from two buses or algorithms. That is, each power plant can provide the technical clearing point of the electricity and clearing curve under the basic electricity price. After the total electricity price of the power grid is distributed to various types of power plants, the minimum value that satisfies the above inequality is the power plant electricity price and electricity clearing point.

[0174] When digital power analysis is required, the measured data is first digitized, and then (based on the power data on the power plant side to be analyzed and / or the power data on the grid side to be analyzed), data cleaning and data composition are performed to facilitate subsequent parallel calculations. The output results of the calculations are generalized into operators (functions) based on the data model. This operator (function) completes a specific function. After the intelligent agent completes the analysis using the above operator, the entire intelligent agent logic process is completed, and the established mapping function output is used to generate the power analysis results, or the operator output is brought into a new intelligent agent nested iteration. The operator is the most important part of the intelligent agent, which realizes specific functions of the power system, such as frequency operator, active and reactive operator, and electricity price operator. The electricity price operator is: the operator is a function. The intelligent agent is a process or logic that contains many operators to realize many functions.

[0175] After the production factor data is digitized, data parallel calculation is performed. Due to the characteristics of GPU, after the data vectors of the same type are aligned, the data can be quickly calculated in parallel. At the same time, the use of neural network algorithms will lose some characteristics of its symbolic artificial intelligence algorithms, but the data tools of graph neural networks are sufficient to retain and calculate the data features at the input and transmission layers under the self-attention mechanism under the probabilistic method, reflecting the data-driven characteristics. In order to make up for the loss of symbolic artificial intelligence and to make it easier to perform functional calculations and control of intelligent agents, the concept of operators is introduced at the output, and multiple operators are placed in the logic of the intelligent agent, so that the intelligent agent obtains the corresponding functions. The capabilities of digital power analysis also revolve around the specific capabilities of these operators, making the intelligent agent the basic unit for power data analysis in the power system.

[0176] Based on the digital power analysis method for coordinating multiple production factors introduced in the aforementioned embodiment, accordingly, the embodiment of the present application also provides a digital power analysis device for coordinating multiple production factors. Fig.10 Figure 1 is a schematic diagram of the structure of the device. Fig.10 As shown, the digital power analysis device for coordinating multiple production factors includes:

[0177] The data extraction module 1001 is used to extract characteristic production factor data corresponding to the characteristic production factor group from the power data on the power plant side; and to extract control production factor data corresponding to the control production factor group from the power data on the power grid side; the characteristic production factor group includes characteristic production factors of multiple levels; the control production factor group includes control production factors of multiple aspects;

[0178] A vector construction module 1002 is used to construct a target data vector using the extracted characteristic production factor data based on the power analysis demand information;

[0179] A data determination module 1003, configured to determine key control production factor data associated with the power analysis demand information from the extracted control production factor data;

[0180] The operator construction module 1004 is used to use an artificial intelligence algorithm to learn a mapping function between the target data vector and the key control production factor data based on the target data vector and the key control production factor data, construct a functional operator with the mapping function as the core, and construct a power analysis intelligent agent based on multiple functional operators; the power analysis intelligent agent corresponds to the power analysis demand information;

[0181] A request receiving module 1005, for receiving a power analysis request;

[0182] The power analysis module 1006 is configured to call the power analysis agent to generate an analysis response result to the power analysis request if the requirement information carried in the power analysis request matches the power analysis requirement information.

[0183] In an optional implementation, the vector construction module 1002 is specifically used for:

[0184] Based on the power analysis demand information, determining a target characteristic production factor associated with the power analysis demand information from the characteristic production factor group;

[0185] Determining target characteristic production factor data corresponding to the target characteristic production factor from the extracted characteristic production factor data;

[0186] Based on the construction rules of data vectors, the target data vector is constructed using the target characteristic production factor data.

[0187] In an optional implementation, the characteristic production factor group includes primary characteristic production factors, secondary characteristic production factors and tertiary characteristic production factors corresponding to a plurality of different power generation types; wherein the primary characteristic production factors are natural characteristic production factors, the secondary characteristic production factors are single system characteristic production factors, and the tertiary characteristic production factors are plant system characteristic production factors; the natural characteristic production factors are production factors that directly reflect natural characteristics; the single system characteristic production factors are production factors involving a single system in a power system; and the plant system characteristic production factors are production factors involving the entire system in a power system;

[0188] The data extraction module 1001 is specifically used for:

[0189] Extracting primary characteristic production factor data, secondary characteristic production factor data and tertiary characteristic production factor data corresponding to thermal power generation from the power data of thermal power plants;

[0190] Extracting primary characteristic production factor data, secondary characteristic production factor data and tertiary characteristic production factor data corresponding to photovoltaic power generation from the power data of the photovoltaic power plant;

[0191] Extracting primary characteristic production factor data, secondary characteristic production factor data and tertiary characteristic production factor data corresponding to hydropower generation from the power data of the hydropower plant;

[0192] From the power data of the wind power plant, the primary characteristic production factor data, the secondary characteristic production factor data and the tertiary characteristic production factor data corresponding to wind power generation are extracted.

[0193] In an optional implementation, the multiple aspects of the controlled production factors include: electricity price, inertia and frequency, active and reactive power, electricity carbon emission, safety and stability, and grid connection and disconnection;

[0194] The step of extracting the control production factor data corresponding to the control production factor group from the power data on the power grid side includes:

[0195] From the power data on the grid side, electricity price data, inertia and frequency data, power data, electricity carbon emission data, safety and stability index data and grid connection and disconnection impact data are extracted and converted into digital vector representations respectively.

[0196] In an optional implementation, the digital power analysis device for coordinating multiple production factors further includes:

[0197] A parsing module, used to parse the power analysis demand information to obtain a plurality of sub-demand information and a logical dependency relationship or a numerical conversion relationship between the plurality of sub-demand information; the plurality of sub-demand information includes: a first sub-demand information and a second sub-demand information;

[0198] The vector construction module 1002 and the data determination module 1003 are each specifically used for:

[0199] constructing a first target data vector based on the first sub-demand information and determining first key control production factor data associated with the first sub-demand information; and constructing a second target data vector based on the second sub-demand information and determining second key control production factor data associated with the second sub-demand information;

[0200] The operator construction module 1004 is specifically used to: based on the first target data vector and the first key control production factor data, use the artificial intelligence algorithm to learn to obtain the first operator; and based on the second target data vector and the second key control production factor data, use the artificial intelligence algorithm to learn to obtain the second operator;

[0201] Based on the logical dependency relationship or the numerical conversion relationship, constructing a transfer function relationship between the first operator and the second operator;

[0202] Based on the transfer function relationship, the first operator and the second operator, constructing a power analysis agent;

[0203] The power analysis module 1006 is specifically used to: call the power analysis agent to trigger the first operator and the second operator to work based on the transfer function relationship, so as to generate a final analysis response result to the power analysis request.

[0204] In an optional implementation, the power analysis agent is configured with a power plant side interface, a grid side interface and an agent interface; the digital power analysis device for coordinating multiple production factors also includes:

[0205] The first sending module is used for: the power analysis agent sends the analysis response result to the power plant through the power plant side interface; or, the power analysis agent sends the analysis response result to the power grid through the power grid side interface; or, the power analysis agent sends the analysis response result to other power analysis agents through the agent interface.

[0206] In an optional implementation, the power analysis agent is configured with a power plant side interface, a grid side interface and an agent interface; the digital power analysis device for coordinating multiple production factors also includes:

[0207] An instruction generation module, used for the power analysis agent to generate a first control instruction for the power plant, a second control instruction for the power grid, or a third control instruction for other agents based on the analysis response result;

[0208] The digital power analysis device for coordinating multiple production factors also includes:

[0209] A second sending module, which is used for the power analysis agent to send the first control instruction to the power plant through the power plant side interface; or, the power analysis agent to send the second control instruction to the power grid through the power grid side interface; or, the power analysis agent to send the third control instruction to other agents through the agent interface.

[0210] In an optional implementation, the constructed functional operator is an electricity price operator, which is used to calculate the electricity price; the power analysis module 1006 is specifically used to:

[0211] Calling the power analysis agent to obtain the power price calculation result of the power plant through the power price operator;

[0212] Verifying the electricity price calculation result through a financial verification model on the power grid side;

[0213] If the electricity price calculation result is verified, then further selecting an electricity price calculation function that is adapted and feasible on the grid side from the dispatch strategy library, and loading the electricity price calculation function into the electricity price operator to perform function update on the electricity price operator;

[0214] The electricity price calculation result on the grid side is obtained through the updated electricity price operator.

[0215] In an optional implementation, the financial verification model includes: a first verification condition and a second verification condition;

[0216] The first verification condition is: the actual output of various types of power plants ≥ the power grid;

[0217] The second verification condition is: the sum of the actual output of various types of power plants, the power grid electricity price at the corresponding time and the product of the power generation time is ≤ the product of the average power grid price and the power grid electricity;

[0218] If the electricity price calculation result satisfies the first verification condition and the second verification condition, it is determined that the electricity price calculation result has been verified; if the electricity price calculation result does not satisfy any of the first verification condition and the second verification condition, it is determined that the electricity price calculation result has not been verified.

[0219] In an optional implementation, after the power analysis module 1006 loads the electricity price calculation function into the electricity price operator to perform function update on the electricity price operator, it is further configured to:

[0220] The grid side records the applied transaction power and power price reported by the power plant side to the grid side;

[0221] Based on the power grid side, judging whether the grid is stable, so as to determine whether it is necessary to start the virtual power plant function;

[0222] If it is determined that the virtual power plant function does not need to be activated, the grid side sends a dispatch instruction of clearing electricity prices and electricity quantities to various types of power plants based on the electricity price calculation result of the grid side;

[0223] If it is determined that the virtual power plant function needs to be started, the virtual power plant function is started to call upon its storage capacity.

[0224] In an optional implementation, the digital power analysis device for coordinating multiple production factors further includes:

[0225] A receiving module is used for the power plant to receive the clearing electricity price given by the power grid;

[0226] The verification module is used to combine the power plant cost and the clearing electricity price, and use the financial verification model on the power plant side to perform verification; if the verification result indicates that the verification is passed, then further determine whether different power supply points of the same power plant type can be cleared based on the electricity volume and electricity price; if all power supply points can be cleared, the clearing electricity volume is executed.

[0227] In an optional implementation, the financial verification model on the power plant side includes: a third verification condition;

[0228] The third verification condition is: the sum of the products of the actual output of the power plant, the power price of the power grid at the corresponding time and the power generation time>the product of the power plant cost and the actual output of the power plant.

[0229] It should be noted that each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device embodiment described above is merely schematic, in which the unit described as a separate component may or may not be physically separated, and the component prompted as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative work.

[0230] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A digital power analysis method for coordinating multiple production factors, characterized in that: include: Extract characteristic production factor data corresponding to the characteristic production factor group from the power plant side's power data; and, extracting control production factor data corresponding to the control production factor group from the power data on the grid side; The characteristic production factor group includes characteristic production factors of multiple levels, which are natural characteristic production factors, single system characteristic production factors and plant system characteristic production factors belonging to different levels, wherein the natural characteristic production factors are production factors that directly reflect natural characteristics, the single system characteristic production factors are production factors involving monomer systems in the power system, and the plant system characteristic production factors are production factors involving the entire system in the power system; wherein, from the natural characteristic production factors to the single system characteristic production factors and then to the plant system characteristic production factors, the mining level of data characteristics is gradually deepened; the control production factor group includes control production factors of multiple aspects; the control production factors of multiple aspects include: electricity price, inertia and frequency, active and reactive power, electric carbon emissions, safety and stability, and grid connection and disconnection; Based on the power analysis demand information, construct a target data vector using the extracted characteristic production factor data, and determine key control production factor data associated with the power analysis demand information from the extracted control production factor data; On the basis of the target data vector and the key control production factor data, an artificial intelligence algorithm is used to learn and obtain a mapping function between the target data vector and the key control production factor data, a functional operator with the mapping function as the core is constructed, and an electric power analysis intelligent body is constructed based on multiple functional operators; the electric power analysis intelligent body corresponds to the electric power analysis demand information; wherein, if the electric power analysis demand information is parsed into multiple sub-demand information with logical dependencies or numerical conversion relationships, the electric power analysis intelligent body constructed based on the electric power analysis demand information includes different functional operators with corresponding logical dependencies or numerical conversion relationships; the transfer function relationship between the different functional operators with corresponding logical dependencies or numerical conversion relationships is constructed based on the logical dependencies or numerical conversion relationships; the logical dependencies include: reactive power and voltage, heat consumption and coal consumption, system inertia and frequency, electricity price and coal price; the numerical conversion relationship includes a numerical conversion relationship at the transaction level or a numerical conversion relationship at the pricing level; receiving power analysis requests; If the demand information carried in the power analysis request matches the power analysis demand information, the power analysis agent is called to generate an analysis response result to the power analysis request; wherein, if the power analysis demand information is parsed into a plurality of sub-demand information having a logical dependency relationship or a numerical conversion relationship, the calling of the power analysis agent to generate an analysis response result to the power analysis request is specifically: Calling the power analysis agent to trigger different functional operators with corresponding logical dependency relationships or numerical conversion relationships based on the transfer function relationship to generate a final analysis response result to the power analysis request; The power analysis agent is one of the following operator function computing groups: Power grid load and electricity price function computing group, power grid inertia frequency computing group, active and reactive power function computing group, power grid carbon footprint computing group, power grid voltage and current function computing group, power grid safety and stability computing group or power grid connection and decoupling computing group; Among them, the power grid load and electricity price function computing power group includes functions for calculating the correlation between the power grid electricity price and the power plant power generation load; the power grid inertia frequency computing power group includes functions for reflecting the performance of power grid stable frequency conversion; the active and reactive function computing power group includes functions for reflecting the performance of active and reactive power in the power grid; the power grid carbon footprint computing power group includes functions for calculating the carbon consumption in the production of hydropower, thermal power, photovoltaic and wind power equipment in the power grid; the power grid safety and stability computing power group includes functions for calculating power grid steps and trends; the power grid connection and decoupling computing power group includes functions for calculating the degree of influence of each network on other networks.

2. The method according to claim 1, characterized in that The step of constructing a target data vector based on the power analysis demand information and using the extracted characteristic production factor data includes: Based on the power analysis demand information, determining a target characteristic production factor associated with the power analysis demand information from the characteristic production factor group; Determining target characteristic production factor data corresponding to the target characteristic production factor from the extracted characteristic production factor data; Based on the construction rules of data vectors, the target data vector is constructed using the target characteristic production factor data.

3. The method according to claim 1, characterized in that The step of extracting characteristic production factor data corresponding to the characteristic production factor group from the power data on the power plant side includes: Extract the natural characteristic production factor data, single system characteristic production factor data and plant system characteristic production factor data corresponding to thermal power generation from the power data of thermal power plants; Extract the natural characteristic production factor data, single system characteristic production factor data and plant system characteristic production factor data corresponding to photovoltaic power generation from the power data of photovoltaic power plants; Extract the natural characteristic production factor data, single system characteristic production factor data and plant system characteristic production factor data corresponding to hydropower generation from the power data of the hydropower plant; From the power data of the wind power plant, the natural characteristic production factor data, single system characteristic production factor data and plant system characteristic production factor data corresponding to wind power generation are extracted.

4. The method according to claim 1, characterized in that: The step of extracting the control production factor data corresponding to the control production factor group from the power data on the power grid side includes: From the power data on the grid side, electricity price data, inertia and frequency data, power data, electricity carbon emission data, safety and stability index data and grid connection and disconnection impact data are extracted and converted into digital vector representations respectively.

5. The method according to claim 1, characterized in that The method further comprises: Parsing the power analysis demand information to obtain a plurality of sub-demand information and a logical dependency relationship or a numerical conversion relationship between the plurality of sub-demand information; the plurality of sub-demand information includes: a first sub-demand information and a second sub-demand information; The method of constructing a target data vector based on the power analysis demand information by using the extracted characteristic production factor data, and determining key control production factor data associated with the power analysis demand information from the extracted control production factor data, includes: constructing a first target data vector based on the first sub-demand information and determining first key control production factor data associated with the first sub-demand information; and constructing a second target data vector based on the second sub-demand information and determining second key control production factor data associated with the second sub-demand information; Based on the target data vector and the key control production factor data, an artificial intelligence algorithm is used to learn and obtain a mapping function between the target data vector and the key control production factor data, a functional operator with the mapping function as the core is constructed, and a power analysis intelligent agent is constructed based on multiple functional operators, including: Based on the first target data vector and the first key control production factor data, an artificial intelligence algorithm is used to learn a first operator; and based on the second target data vector and the second key control production factor data, an artificial intelligence algorithm is used to learn a second operator; Based on the logical dependency relationship or the numerical conversion relationship, constructing a transfer function relationship between the first operator and the second operator; Based on the transfer function relationship, the first operator and the second operator, constructing a power analysis agent; The calling of the power analysis agent to generate an analysis response result to the power analysis request is specifically: The power analysis agent is called to trigger the first operator and the second operator to work based on the transfer function relationship to generate a final analysis response result to the power analysis request.

6. The method according to claim 1, characterized in that The power analysis intelligent agent is configured with a power plant side interface, a grid side interface and an intelligent agent interface; the method further comprises: The power analysis agent sends the analysis response result to the power plant through the power plant side interface; or, The power analysis agent sends the analysis response result to the power grid through the power grid side interface; or, The power analysis agent sends the analysis response result to other power analysis agents through the agent interface.

7. The method according to claim 1, characterized in that The power analysis intelligent agent is configured with a power plant side interface, a grid side interface and an intelligent agent interface; the method further comprises: The power analysis agent generates a first control instruction for the power plant, a second control instruction for the power grid, or a third control instruction for other agents based on the analysis response result; The method further comprises: The power analysis agent sends the first control instruction to the power plant through the power plant side interface; or, The power analysis agent sends the second control instruction to the power grid through the power grid side interface; or, The power analysis agent sends the third control instruction to other agents through the agent interface.

8. The method according to claim 4, characterized in that The constructed functional operator is an electricity price operator, which is used to calculate the electricity price; calling the power analysis agent to generate an analysis response result to the power analysis request includes: Calling the power analysis agent to obtain the power price calculation result of the power plant through the power price operator; Verifying the electricity price calculation result through a financial verification model on the power grid side; If the electricity price calculation result is verified, then further selecting an electricity price calculation function that is adapted and feasible on the grid side from the dispatch strategy library, and loading the electricity price calculation function into the electricity price operator to perform function update on the electricity price operator; The electricity price calculation result on the grid side is obtained through the updated electricity price operator.

9. The method according to claim 8, characterized in that The financial verification model includes: a first verification condition and a second verification condition; The first verification condition is: the actual output of various types of power plants ≥ the power grid; The second verification condition is: the sum of the actual output of various types of power plants, the power grid electricity price at the corresponding time and the product of the power generation time is ≤ the product of the average power grid price and the power grid electricity; If the electricity price calculation result satisfies the first verification condition and the second verification condition, it is determined that the electricity price calculation result has been verified; if the electricity price calculation result does not satisfy any of the first verification condition and the second verification condition, it is determined that the electricity price calculation result has not been verified.

10. The method according to claim 9, characterized in that After loading the electricity price calculation function into the electricity price operator to perform function update on the electricity price operator, the method further includes: The grid side records the applied transaction power and power price reported by the power plant side to the grid side; Based on the power grid side, judging whether the grid is stable, so as to determine whether it is necessary to start the virtual power plant function; If it is determined that the virtual power plant function does not need to be activated, the grid side sends a dispatch instruction of clearing electricity prices and electricity quantities to various types of power plants based on the electricity price calculation result of the grid side; If it is determined that the virtual power plant function needs to be started, the virtual power plant function is started to call upon its storage capacity.

11. The method according to claim 10, characterized in that The method further comprises: The power plant receives the clearing electricity price given by the grid; Combining the power plant cost and the clearing electricity price, verification is performed using a financial verification model on the power plant side; If the verification result indicates that the verification is passed, the different power supply points of the same power plant type will be further judged based on the power consumption and electricity price to determine whether they can be cleared; If all power supply points can be cleared, the power will be cleared.

12. The method according to claim 11, characterized in that The financial verification model on the power plant side includes: the third verification condition; The third verification condition is: the sum of the products of the actual output of the power plant, the power price of the power grid at the corresponding time and the power generation time>the product of the power plant cost and the actual output of the power plant.

13. A digital power analysis device for coordinating multiple production factors, characterized in that: include: A data extraction module is used to extract characteristic production factor data corresponding to the characteristic production factor group from the power data on the power plant side; and, extracting control production factor data corresponding to the control production factor group from the power data on the grid side; The characteristic production factor group includes characteristic production factors of multiple levels, which are natural characteristic production factors, single system characteristic production factors and plant system characteristic production factors belonging to different levels, wherein the natural characteristic production factors are production factors that directly reflect natural characteristics, the single system characteristic production factors are production factors involving monomer systems in the power system, and the plant system characteristic production factors are production factors involving the entire system in the power system; wherein, from the natural characteristic production factors to the single system characteristic production factors and then to the plant system characteristic production factors, the mining level of data characteristics is gradually deepened; the control production factor group includes control production factors of multiple aspects; the control production factors of multiple aspects include: electricity price, inertia and frequency, active and reactive power, electric carbon emissions, safety and stability, and grid connection and disconnection; A vector construction module, for constructing a target data vector using the extracted characteristic production factor data based on power analysis demand information; A data determination module, used to determine key control production factor data associated with the power analysis demand information from the extracted control production factor data; An operator construction module is used to use an artificial intelligence algorithm to learn and obtain a mapping function between the target data vector and the key control production factor data based on the target data vector and the key control production factor data, construct a functional operator with the mapping function as the core, and construct an electric power analysis intelligent body based on multiple functional operators; the electric power analysis intelligent body corresponds to the electric power analysis demand information; wherein, if the electric power analysis demand information is parsed into multiple sub-demand information with logical dependencies or numerical conversion relationships, the electric power analysis intelligent body constructed based on the electric power analysis demand information includes different functional operators with corresponding logical dependencies or numerical conversion relationships; the transfer function relationship between the different functional operators with corresponding logical dependencies or numerical conversion relationships is constructed based on the logical dependencies or numerical conversion relationships; the logical dependencies include: reactive power and voltage, heat consumption and coal consumption, system inertia and frequency, electricity price and coal price; the numerical conversion relationship includes a numerical conversion relationship at the transaction level or a numerical conversion relationship at the pricing level; A request receiving module, used for receiving a power analysis request; A power analysis module, configured to call the power analysis agent to generate an analysis response result to the power analysis request if the demand information carried in the power analysis request matches the power analysis demand information; wherein, if the power analysis demand information is parsed into a plurality of sub-demand information having a logical dependency relationship or a numerical conversion relationship, the power analysis module is specifically configured to: Calling the power analysis agent to trigger different functional operators with corresponding logical dependency relationships or numerical conversion relationships based on the transfer function relationship to generate a final analysis response result to the power analysis request; The power analysis agent is one of the following operator function computing groups: Power grid load and electricity price function computing group, power grid inertia frequency computing group, active and reactive power function computing group, power grid carbon footprint computing group, power grid voltage and current function computing group, power grid safety and stability computing group or power grid connection and decoupling computing group; Among them, the power grid load and electricity price function computing power group includes functions for calculating the correlation between the power grid electricity price and the power plant power generation load; the power grid inertia frequency computing power group includes functions for reflecting the performance of power grid stable frequency conversion; the active and reactive function computing power group includes functions for reflecting the performance of active and reactive power in the power grid; the power grid carbon footprint computing power group includes functions for calculating the carbon consumption in the production of hydropower, thermal power, photovoltaic and wind power equipment in the power grid; the power grid safety and stability computing power group includes functions for calculating power grid steps and trends; the power grid connection and decoupling computing power group includes functions for calculating the degree of influence of each network on other networks.

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