Electric energy project investment intelligent evaluation method and system based on artificial intelligence

Abstract

The invention provides an electric energy project investment intelligent evaluation method and system based on artificial intelligence. The method comprises the steps of collecting electric energy data of an electric energy project deployed in a target area; on the basis of the load demand quantity of the target area in combination with various energy resources and power generation technical parameters thereof, simulating supply coordination relationships of the various energy resources in different environments, and constructing an energy supply link; operation collaborative planning is carried out based on the power grid power transmission parameters and the energy supply link of the target area, and an integrated collaborative operation scheme is obtained; project calculation is carried out based on the power generation technical parameters of the energy resources in the energy supply link and the integrated cooperative operation scheme, and the energy production amount of each type of energy resources in the full life cycle of the project is obtained; and based on the energy production amount of each energy resource and the corresponding energy economic data, carrying out income measurement and calculation to obtain an economic benefit evaluation index of the electrical energy project. According to the invention, the accuracy of evaluating the economic benefit of the electric energy project is improved.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to an intelligent evaluation method and system for power energy project investment based on artificial intelligence. Background Technology

[0002] With the rapid development of the social economy, the demand for electricity, as a core element supporting production and daily life, continues to grow, and the investment and construction scale of electricity projects is also expanding. Electricity projects are characterized by large investment amounts, long construction periods, and numerous involved stages; any misjudgment in investment decisions can lead to huge economic losses and resource waste. Therefore, in order to improve the accuracy of electricity project investment and reduce investment risks, it is crucial to develop accurate and effective methods for evaluating electricity project investments.

[0003] However, existing investment evaluation methods for power energy projects often focus solely on the project's technical parameters and performance indicators, failing to fully consider the synergistic effects between various energy forms within the power system and the overall integration of the power generation, grid, and load systems. During the evaluation process, the technical analysis lacks consideration of key factors such as multi-energy complementarity and coordinated operation of power generation, grid, and load. This results in evaluations that fail to accurately reflect the project's technical feasibility and stability in actual operation. Deviations in technical feasibility and stability directly lead to inaccurate energy output calculations and distorted cost-benefit accounting, ultimately affecting the accuracy of economic benefit assessments of power energy projects and misleading investment decisions. Summary of the Invention

[0004] This invention provides an intelligent evaluation method and system for power energy project investment based on artificial intelligence, aiming to improve the accuracy of economic benefit assessment of power energy projects.

[0005] In a first aspect, the present invention provides an intelligent evaluation method for power energy project investment based on artificial intelligence, comprising: Collect power energy data from power energy projects deployed in the target area; the power energy data includes various energy resources and their power generation technology parameters; Based on the load demand of the target area and the various energy resources and their power generation technology parameters, the supply coordination relationship of various energy resources under different environments is simulated to construct an energy supply chain path. Based on the power grid transmission parameters of the target area and the energy supply chain, an integrated collaborative operation plan is obtained. Based on the power generation technology parameters of energy resources in the energy supply chain and the integrated collaborative operation scheme, the project calculation is carried out to obtain the energy production of various energy resources throughout the project's entire life cycle. Based on the energy production of various energy resources and their corresponding energy economic data, the economic benefit evaluation indicators of the power energy project are obtained.

[0006] Secondly, the present invention also provides an intelligent evaluation system for power energy project investment based on artificial intelligence, applied to the intelligent evaluation method for power energy project investment based on artificial intelligence as described in the first aspect; the intelligent evaluation system for power energy project investment based on artificial intelligence includes: The data acquisition module is used to collect power energy data from power energy projects deployed in the target area; the power energy data includes various energy resources and their power generation technology parameters; The supply chain construction module is used to simulate the supply coordination relationship of various energy resources under different environments based on the load demand of the target area and the parameters of various energy resources and their power generation technology, and to construct the energy supply chain. The collaborative planning module is used to perform collaborative operation planning based on the power grid transmission parameters of the target area and the energy supply chain routes to obtain an integrated collaborative operation scheme. The project calculation module is used to calculate the project based on the power generation technology parameters of energy resources in the energy supply chain and the integrated collaborative operation scheme, so as to obtain the energy production of various energy resources throughout the project's entire life cycle. The revenue calculation module is used to calculate revenue based on the energy production of various energy resources and their corresponding energy economic data, and to obtain the economic benefit evaluation indicators of the power energy project.

[0007] Thirdly, the present invention also provides an electronic device, comprising: a memory for storing computer software programs; and a processor for reading and executing the computer software programs, thereby realizing the intelligent evaluation method for power energy project investment based on artificial intelligence as described above.

[0008] Fourthly, the present invention also provides a non-transitory computer-readable storage medium storing a computer software program, which, when executed by a processor, implements the intelligent evaluation method for power energy project investment based on artificial intelligence as described above.

[0009] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the intelligent evaluation method for power energy project investment based on artificial intelligence as described above.

[0010] The intelligent evaluation method for power energy project investment based on artificial intelligence provided in this invention constructs an energy supply chain based on the technical parameters of various energy resources, clarifies the synergistic effects of multiple energy sources, and solves the problem of singular technical analysis. Then, based on the energy supply chain and grid transmission parameters, an integrated collaborative operation scheme is planned, achieving a holistic technical analysis of the source, grid, and load. Next, based on the energy supply chain and the integrated collaborative operation scheme, combined with the technical parameters of various energy resources, the energy production of various energy resources is calculated, thereby clarifying the output of various energy sources. This provides a comprehensive and more practical assessment of the technical feasibility and stability of actual operation. Finally, based on the energy production and economic data of various energy resources, economic benefit evaluation indicators are calculated throughout the project's entire life cycle, accurately reflecting the project's actual economic value. This solves the technical problem of existing methods where insufficient technical analysis leads to inaccurate evaluation results reflecting the actual operational technical feasibility and stability of the project, thus affecting the scientific nature of investment decisions. This improves the accuracy of economic benefit evaluation for power energy projects, thereby guiding accurate investment decisions. Attached Figure Description

[0011] Figure 1 This is a flowchart illustrating the intelligent evaluation method for power energy project investment based on artificial intelligence provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of the intelligent evaluation system for power energy project investment based on artificial intelligence provided in an embodiment of the present invention; Figure 3 An embodiment diagram of the electronic device provided in this invention; Figure 4 An embodiment diagram of a computer-readable storage medium provided in accordance with the present invention. Detailed Implementation

[0012] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0014] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0015] Optional, see below Figure 1 , Figure 1 This is a flowchart illustrating the intelligent evaluation method for power energy project investment based on artificial intelligence provided by the present invention. In this embodiment, the executing entity of the intelligent evaluation method for power energy project investment based on artificial intelligence is the project evaluation system. Therefore, the intelligent evaluation method for power energy project investment based on artificial intelligence includes: Step 10: Collect power energy data from power energy projects deployed in the target area.

[0016] Optionally, the project evaluation system can identify existing power energy projects within the target area, such as solar power projects, wind power projects, hydropower projects, and thermal power projects.

[0017] Furthermore, the project evaluation system collects power energy data for power energy projects, such as solar, wind, hydro, and thermal power. The power energy data includes various energy resources and their power generation technical parameters, such as the conversion efficiency of solar panels, the rated power and cut-in and cut-out wind speeds of wind turbines, the single-unit capacity and efficiency curve of hydro turbines, and the installed capacity and coal consumption rate of thermal power units.

[0018] In one embodiment, a solar photovoltaic power generation project is deployed in the target area, and therefore the collected power generation technical parameters include the peak power of the photovoltaic modules (e.g., 250 watts / module), conversion efficiency (e.g., 18%), open circuit voltage (e.g., 36 volts), short circuit current (e.g., 8.5 amps), and operating temperature range (e.g., -40°C to 85°C).

[0019] Step 20: Based on the load demand of the target area and the various energy resources and their power generation technology parameters, simulate the supply coordination relationship of various energy resources under different environments, and construct the energy supply chain path.

[0020] Furthermore, the project evaluation system constructs an energy supply map and an energy synergy structure tree based on historical data. The energy supply map represents the energy relationship between the load and supply sides when various energy resources are supplied individually or collaboratively under their power generation technical parameters in different environments. The nodes in the energy synergy structure tree represent various energy resources, and the edges represent the correlation between various energy resources in different environments.

[0021] Furthermore, the project evaluation system, based on the energy supply map and energy synergy structure tree, and combined with the load demand of the target area and various energy resources and their power generation technology parameters, simulates the supply synergy relationship of various energy resources under different environments, and constructs an energy supply chain path, as detailed in steps 201 to 204.

[0022] Step 30: Based on the power grid transmission parameters and energy supply chain routes of the target area, conduct collaborative operation planning to obtain an integrated collaborative operation scheme.

[0023] Furthermore, the project evaluation system collects power grid transmission parameters for the target area, including parameters such as the rated voltage, rated current, transmission capacity, line resistance, line length, rated capacity, and turns ratio of the transmission lines.

[0024] Furthermore, the project evaluation system performs operational coordination planning based on power grid transmission parameters and energy supply chain routes to obtain an integrated coordinated operation scheme, as detailed in steps 301 to 305.

[0025] Step 40: Based on the power generation technology parameters and integrated collaborative operation scheme of energy resources in the energy supply chain, the project calculation is carried out to obtain the energy production of various energy resources throughout the project's entire life cycle.

[0026] Furthermore, the project evaluation system calculates the project based on the power generation technology parameters of energy resources in the energy supply chain and the integrated collaborative operation plan, and obtains the energy production of various energy resources throughout the project's entire life cycle, as detailed in steps 401 to 404.

[0027] Step 50: Calculate the revenue based on the energy production of various energy resources and their corresponding energy economic data to obtain the economic benefit evaluation indicators for the power energy project.

[0028] Furthermore, the project evaluation system collects energy economic data corresponding to various energy resources.

[0029] The energy economic data in this embodiment of the invention includes the grid connection price of various energy sources, the initial investment cost of the project, the operation and maintenance cost, and the fuel cost.

[0030] Furthermore, the project evaluation system calculates the project's total revenue (energy production × grid-connected electricity price) and total cost (initial investment cost + total operation and maintenance cost + total fuel cost, etc.) throughout the project's entire life cycle based on the energy production of various energy resources and their corresponding energy economic data. By calculating indicators such as net present value, internal rate of return, payback period, and total profit, the economic benefits of the power energy project are evaluated, resulting in economic benefit evaluation indicators.

[0031] In one embodiment, the power energy projects deployed in the target area include solar photovoltaic power generation projects, wind power generation projects, and thermal power generation projects.

[0032] Assuming the total energy production of a solar photovoltaic power generation project is 1 billion kWh over its entire life cycle, with a grid-connected electricity price of 0.35 yuan / kWh, the total revenue is 100 billion yuan × 0.35 = 350 million yuan; the initial investment cost is 200 million yuan, and the 25-year operation and maintenance cost is 50 million yuan, for a total cost of 200 million yuan + 50 million yuan = 250 million yuan.

[0033] Assuming the total energy production of the wind power project over its entire life cycle is 500 million kWh, the on-grid electricity price is 0.38 yuan / kWh, the total revenue is 500 million yuan × 0.38 = 190 million yuan; the initial investment cost is 100 million yuan, the 25-year operation and maintenance cost is 7.5 million yuan, and the total cost is 100 million yuan + 7.5 million yuan = 107.5 million yuan.

[0034] Assuming the total energy production of the thermal power project over its entire life cycle is 4.5 billion kWh, the on-grid electricity price is 0.3 yuan / kWh, and the total revenue is 4,500,000,000 × 0.3 = 1.35 billion yuan; the initial investment cost is 300 million yuan, the 25-year operation and maintenance cost is 175 million yuan, and the fuel cost is calculated based on the standard coal consumption for power generation and the coal price as 625 million yuan. The total cost is 300 million + 175 million + 625 million = 1.1 billion yuan.

[0035] Therefore, the total profit of the power energy project is (3.5 + 1.9 + 13.5) - (2.5 + 1.075 + 11) = 382.5 million yuan. The net present value, internal rate of return and other indicators are also derived accordingly, which serve as economic benefit evaluation indicators.

[0036] This invention constructs an energy supply chain based on the technical parameters of various energy resources, clarifies the synergistic effects of multiple energy sources, and then plans an integrated collaborative operation scheme based on the energy supply chain and power grid transmission parameters. This achieves a holistic technical analysis of the source, grid, and load. Furthermore, based on the energy supply chain and the integrated collaborative operation scheme, combined with the technical parameters of various energy resources, the energy production of each type of energy resource is calculated, thereby clarifying the output of each type of energy. This provides a comprehensive and more practical assessment of the technical feasibility and stability of the operation. Finally, based on the energy production and economic data of various energy resources, economic benefit evaluation indicators are calculated throughout the project's entire life cycle. This accurately reflects the actual economic value of the project, improves the accuracy of economic benefit assessment for power energy projects, and guides accurate investment decisions.

[0037] In one embodiment, steps 201 to 204 include: Step 201: Based on the pre-constructed energy supply diagram, simulate the first energy relationship between load demand and energy supply when various energy resources are supplied individually under their corresponding power generation technical parameters in different environments.

[0038] Optionally, the project evaluation system can call a pre-built energy supply map, select a single energy resource (such as a solar photovoltaic power generation project) within the target area, fix its power generation technical parameters (such as peak power, conversion efficiency, etc.), and simulate the energy supply of the energy resource when it operates alone under each environmental condition (such as a gradient change in solar irradiance from 200 W / m² to 1000 W / m²) through the energy supply map. This establishes a one-to-one correspondence between load demand and energy supply, i.e., the primary energy relationship. It should be noted that during this process, it is necessary to ensure that other energy resources are in a state of cessation of supply, and only the independent supply characteristics of the single energy source are analyzed.

[0039] In one embodiment, taking a solar photovoltaic power generation project in the target area as an example, its power generation technical parameters include a peak power of 100 MW and a conversion efficiency of 18%. A pre-defined correlation model between irradiance and power generation is included in the energy supply diagram. The project evaluation system simulates that when the irradiance is 200 W / m², the energy supplied by solar power alone is 100 MW × (200 W / m² / 1000 W / m²) × 18% = 3.6 MW; when the irradiance is 400 W / m², the supplied energy is 100 MW × (400 W / m² / 1000 W / m²) × 18% = 7.2 MW; and so on, until the energy supplied is 18 MW when the irradiance is 1000 W / m². Simultaneously, corresponding to the load demand of the target area at different times (e.g., 400 MW nighttime load and 800 MW daytime load), a primary energy relationship of "irradiance - solar power supply alone - load demand" is established, clarifying the characteristic that solar power alone cannot meet the base load when sunlight is insufficient.

[0040] Step 202: Based on the energy supply diagram, simulate the second energy relationship between load demand and energy supply when various energy resources are supplied in coordination under their corresponding power generation technical parameters in different environments.

[0041] Furthermore, the project evaluation system continues to be based on the energy supply map, selecting two or more energy resources (such as solar energy + wind energy + thermal energy) in the target area, fixing the power generation technology parameters of various energy sources, and traversing different combinations of environmental conditions (such as the cross-change of light intensity and wind speed).

[0042] Under each environmental combination, the total energy supply is simulated when multiple energy sources operate simultaneously, and the correspondence between this total supply and load demand is established, i.e., the second energy relationship. When coordinating supply, the operational constraints between energy resources (such as the minimum output limit of thermal power generation) need to be considered, but no coordination strategy is preset; only the matching between the total supply and load demand under natural operating conditions is recorded.

[0043] In one embodiment, solar, wind, and thermal power are selected for coordinated supply. Solar parameters are a peak power of 100 MW and a conversion efficiency of 18%; wind parameters are a rated power of 2 MW and a rated wind speed of 12 m / s; and thermal power parameters are a rated power of 300 MW and a minimum output of 100 MW. The energy supply diagram includes a correlation model between the combined solar and wind speed and the total power generation. The project evaluation system simulates a solar power supply of 100 × (800 / 1000) × 18% = 14.4 MW at 800 watts / m² and a wind speed of 12 m / s: full solar power supply of 100 × (800 / 1000) × 18% = 14.4 MW, full wind power supply of 2 MW, and thermal power supply at minimum output of 100 MW, for a total energy supply of 116.4 MW. When solar power supply is 400 watts / m² and a wind speed of 8 m / s: full solar power supply of 7.2 MW, full wind power supply of 1 MW (output at non-rated wind speed), and thermal power output of 150 MW, for a total supply of 158.2 MW. A second energy relationship is established by combining load demand with “solar energy - wind speed - combined supply - load demand” to reflect the changes in supply capacity when multiple energy sources operate together.

[0044] Step 203: Based on the first energy relationship and the second energy relationship, determine the supply coordination relationship of various energy resources under different environments.

[0045] Furthermore, the project evaluation system determines the supply synergy relationships of various energy resources under different environments based on the primary and secondary energy relationships, as detailed in steps 2031 to 2035. The supply synergy relationships include synergy effect types and synergy directions. Synergy effect types include positive synergy effects, no synergy effects, and negative synergy effects. Synergy directions include gain synergy directions and attenuation synergy directions.

[0046] Step 204: Based on the pre-built energy synergy structure tree and the supply synergy relationships of various energy resources under different environments, construct an energy supply chain path.

[0047] Furthermore, the project evaluation system loads a pre-built energy synergy structure tree, matches supply synergy relationships to corresponding edges in the structure tree, and assigns each edge a synergy effect type and synergy direction attribute. Further, the project evaluation system filters energy-related paths with stable positive synergy effects and eliminates paths dominated by negative synergy effects, ultimately forming an energy supply chain path with core energy resources as the hub and synergy paths dynamically adjusted according to the environment, as detailed in steps 2041 to 2045.

[0048] The embodiments of the present invention construct an energy supply chain that can adapt to the dynamic changes in the target area environment, thereby accurately identifying the synergistic characteristics between energy resources under different environmental conditions. Therefore, it can automatically select the optimal energy combination strategy based on real-time environmental parameters to ensure the dynamic balance between the energy supply and load demand under various environmental conditions.

[0049] In one embodiment, steps 2031 to 2035 include: Step 2031: Based on the load demand when supplied alone in the first energy relationship and the load demand when supplied collaboratively in the second energy relationship, determine the load satisfaction difference between collaborative supply and individual supply in the same load demand scenario under the same environmental parameters.

[0050] Optionally, the project evaluation system extracts load demand data from the first energy relationship when a single energy resource is supplied alone, and simultaneously extracts load demand data from the second energy relationship when multiple energy sources are supplied collaboratively under the same environmental parameters and load demand scenario. Further, the project evaluation system calculates the load satisfaction difference value by determining the difference between the load satisfaction amount under collaborative supply and the load satisfaction amount under individual supply. Here, the load satisfaction amount is the portion of the actual supplied energy used to meet load demand; if the supplied energy is greater than or equal to the load demand, the load satisfaction amount equals the load demand; if the supplied energy is less than the load demand, the load satisfaction amount equals the supplied energy.

[0051] In one embodiment, under a scenario with 400 watts / square meter of sunlight, a wind speed of 8 meters / second, and a load demand of 160 megawatts: In the first energy relationship, solar energy supplies 7.2 megawatts alone, wind energy supplies 1 megawatt alone, and thermal power supplies 100 megawatts alone. When supplied individually, the total load demand is 7.2 + 1 + 100 = 108.2 megawatts (all less than 160 megawatts, so they are directly added together). In the second energy relationship, the three energy sources supply a total of 158.2 megawatts collaboratively, and the load demand is 158.2 megawatts (less than 160 megawatts). Therefore, the load demand difference = collaborative supply load demand - individual supply load demand = 158.2 - 108.2 = 50 megawatts.

[0052] In a scenario with 800 watts / square meter of sunlight, 12 m / s wind speed, and a load demand of 120 MW: the total load capacity supplied alone is 14.4 + 2 + 100 = 116.4 MW, the total load capacity supplied collaboratively is 116.4 MW, and the load capacity difference is 116.4 - 116.4 = 0 MW.

[0053] Step 2032: Based on the energy supplied when supplied alone in the first energy relationship and the total energy supplied when supplied collaboratively in the second energy relationship, determine the energy supply difference between collaborative supply and individual supply in the same load demand scenario under the same environmental parameters.

[0054] Furthermore, based on the first energy relationship, the project evaluation system summarizes the total energy supply when various energy resources are supplied individually under the same environmental parameters and load demand scenario; at the same time, it extracts the total energy supply when supplied collaboratively under the second energy relationship scenario, and obtains the energy supply difference value by calculating the difference between the total energy supply through collaborative supply and the total energy supply when supplied individually.

[0055] In one embodiment, under a scenario with 400 watts / square meter of sunlight, a wind speed of 8 meters / second, and a load demand of 160 megawatts: the total energy supplied individually in the first energy relationship is 7.2 + 1 + 100 = 108.2 megawatts; the total energy supplied collaboratively in the second energy relationship is 158.2 megawatts. The energy supply difference = total energy supplied collaboratively - total energy supplied individually = 158.2 - 108.2 = 50 megawatts.

[0056] In an extreme low-temperature environment (-20℃) with a load demand of 100 MW: In the first energy relationship, the efficiency of solar power supply alone drops to 15%, and with 400 W / m² of sunlight, the supply is 100 × (400 / 1000) × 15% = 6 MW. Wind power supplies 0.5 MW, and thermal power supplies 100 MW, for a total of 106.5 MW supplied individually. In the second energy relationship, the total energy supply through collaboration is 6 + 0.5 + 100 = 106.5 MW (without additional adjustments). The energy supply difference is 106.5 - 106.5 = 0 MW (if the total supply drops to 105 MW due to equipment collaboration, the difference is 105 - 106.5 = -1.5 MW).

[0057] Step 2033: Based on the quotient between the load satisfaction difference value and the energy supply difference value, determine the synergistic response coefficient of the energy synergistic supply combination under the same environmental parameters, and determine the synergistic effect type of the energy synergistic supply combination under different environments based on the synergistic response coefficient.

[0058] Furthermore, the project evaluation system calculates the synergistic response coefficient. The calculation formula is: Synergistic Response Coefficient = Load Satisfaction Difference / Energy Supply Difference. It should be noted that the energy supply difference is not 0. If it is 0, it is directly determined that there is no synergistic effect.

[0059] Furthermore, the project evaluation system determines the type of synergy effect based on the synergy response coefficient: when the synergy response coefficient > 0 and the energy supply difference value > 0, it is determined to be a positive synergy effect; when the synergy response coefficient = 0 and the energy supply difference value = 0, it is determined to be a no-synergy effect; when the synergy response coefficient < 0 or the energy supply difference value < 0, it is determined to be a negative synergy effect.

[0060] In one embodiment, under the scenario of 400 watts / square meter of illumination and 8 m / s wind speed: the load meets the difference value of 50 MW, the energy supply difference value is 50 MW, the synergistic response coefficient = 50 / 50 = 1 > 0, and the energy supply difference value > 0, which is determined to be a positive synergistic effect.

[0061] In a scenario with 800 watts / square meter of sunlight and 12 meters / second of wind: the load difference is 0 MW and the energy supply difference is 0 MW, which is directly judged as having no synergistic effect.

[0062] In the scenario of extreme low temperature (-20℃) and total collaborative supply of 105 MW: the load satisfaction difference value = 105 - 106.5 = -1.5 MW, the energy supply difference value = -1.5 MW, the collaborative response coefficient = (-1.5) / (-1.5) = 1, but the energy supply difference value < 0, which is judged as a negative collaborative effect.

[0063] Step 2034: Based on the symbol combination of load satisfaction difference value and energy supply difference value, determine the collaborative direction of energy collaborative supply combination under the same environmental parameters.

[0064] Furthermore, the project evaluation system analyzes the sign combination of load satisfaction difference value and energy supply difference value: if the load satisfaction difference value > 0 and the energy supply difference value > 0, the coordination direction is the gain coordination direction; if the load satisfaction difference value < 0 and the energy supply difference value < 0, the coordination direction is the decay coordination direction; if the two signs are opposite, the actual energy adjustment logic needs to be combined (such as an abnormal decay if the supply increases but the load satisfaction decreases), but the direction is determined first by the consistency of the signs.

[0065] In one embodiment, under a scenario of 400 watts / square meter of illumination and 8 meters / second of wind: the load difference is 50 MW (positive), the energy supply difference is 50 MW (positive), and the sign combination is (+,+), which is determined to be the direction of gain coordination. Under a scenario of extreme low temperature and a total coordinated supply of 105 MW: the load difference is -1.5 MW (negative), the energy supply difference is -1.5 MW (negative), and the sign combination is (-,-), which is determined to be the direction of attenuation coordination.

[0066] Step 2035: Based on the synergistic effect type and synergistic direction of energy synergistic supply combination under different environments, determine the supply synergistic relationship of various energy resources under different environments.

[0067] Furthermore, the project evaluation system integrates synergy type (positive synergy, no synergy, negative synergy) and synergy direction (gain synergy direction, attenuation synergy direction) to form a supply synergy relationship table containing "environmental parameters - synergy type - synergy direction" for energy synergy supply combinations under different environmental parameter combinations, clarifying the synergy characteristics of various energy resources under specific environments.

[0068] Continuing with the above embodiments, the supply synergy relationship is as follows: 400 watts / square meter of sunlight and 8 meters / second of wind speed in the environment: the combination of solar energy, wind energy and thermal power has a positive synergy effect, and the direction of the synergy is (increased thermal power output).

[0069] Environment with 800 watts / square meter of sunlight and 12 meters / second of wind: The combination of solar, wind and thermal power has no synergistic effect; Extreme low temperature environment (-20℃): The combination of solar energy + wind energy + thermal power has a negative synergistic effect, and the synergistic effect is weakened (solar and wind power output decreases).

[0070] The embodiments of the present invention can clarify the synergistic effect type (positive / no / negative) and specific synergistic direction (gain / attenuation) of energy combinations under different scenarios based on the linkage analysis of environmental parameters, load demand and energy supply data, forming a structured supply synergy relationship. This ensures that energy combinations with positive synergistic effects are prioritized in path design, while avoiding the adverse effects of negative synergistic effects. It provides a clear decision basis for the dynamic optimization of energy supply and ultimately achieves controllability of energy synergistic supply characteristics.

[0071] In one embodiment, steps 2041 to 2045 include: Step 2041: Based on the synergy effect types under different environments, traverse the energy synergy structure tree to obtain the target associations in the energy synergy structure tree that have positive synergy effects.

[0072] Optionally, the project evaluation system can traverse the energy synergy structure tree based on the synergy type under different environments, filter out all edges with positive synergy type, and determine the relationship corresponding to these edges as the target relationship.

[0073] In one embodiment, the energy synergy structure tree includes the following relationships and synergy effect types: Solar-Wind (400 W / m² sunshine, 8 m / s wind): No synergy effect; Solar-Coal (400 W / m² sunshine, 8 m / s wind): Positive synergy effect; Wind-Coal (400 W / m² sunshine, 8 m / s wind): Positive synergy effect; Solar-Wind (800 W / m² sunshine, 12 m / s wind): No synergy effect; Solar-Coal (Extreme low temperature -20°C): Negative synergy effect. After traversing the project evaluation system, "Solar-Coal (400 W / m² sunshine, 8 m / s wind)" and "Wind-Coal (400 W / m² sunshine, 8 m / s wind)" are selected as target relationships.

[0074] Step 2042: Group the target associations with the cooperative direction of gain cooperative direction into gain cooperative clusters, and group the target associations with the cooperative direction of attenuation cooperative direction into attenuation cooperative clusters.

[0075] Furthermore, regarding target relationships, the project evaluation system groups them according to the direction of collaboration. For each target relationship, if its corresponding collaboration direction is a gain collaboration direction, the relationship is assigned to a gain collaboration cluster; if the collaboration direction is a decay collaboration direction, it is assigned to a decay collaboration cluster. After grouping, a gain collaboration cluster set and a decay collaboration cluster set are formed, each set containing the corresponding relationship and its environmental parameter labels.

[0076] Continuing with the target associations in the above embodiments: the synergistic direction of "solar energy - thermal power (400 watts / m² sunshine, 8 m / s wind speed)" is a gain synergistic direction (increased thermal power output); the synergistic direction of "wind energy - thermal power (400 watts / m² sunshine, 8 m / s wind speed)" is also a gain synergistic direction (increased thermal power output); there is also an assumed "thermal power - energy storage (high load period)" association with a positive synergistic effect, and the synergistic direction is a gain synergistic direction; it is also assumed that there is a positive synergistic effect of "solar energy - wind energy (dust storm)" but the synergistic direction is a decay synergistic direction (both outputs decrease simultaneously). The first three are categorized into the gain synergistic cluster, and "solar energy - wind energy (dust storm)" is categorized into the decay synergistic cluster.

[0077] Step 2043: Based on the gain synergy cluster, start from any energy resource node in the energy synergy structure tree, and connect the associated nodes sequentially along the gain synergy direction to obtain the initial supply transmission chain.

[0078] Furthermore, the project evaluation system starts from any energy resource node (such as a thermal power node, as it is a basic energy source) in the energy synergy structure tree, and connects related nodes sequentially along the gain synergy direction based on gain synergy clusters. During the connection process, each node is connected to other nodes only through the association relationship in the gain synergy cluster, forming a continuous energy supply transmission path. This path is the initial supply transmission chain, which contains environmental parameter conditions such as node order and association relationship.

[0079] Continuing with the above embodiments, starting from the thermal power node, the gain coordination cluster includes "solar-thermal power," "wind-thermal power," and "thermal power-energy storage" relationships. The gain coordination direction is that solar and wind power supplement thermal power, and thermal power transmits energy to energy storage. The project evaluation system connects along the gain coordination direction: solar power node → thermal power node (through the "solar-thermal power" relationship), wind power node → thermal power node (through the "wind-thermal power" relationship), thermal power node → energy storage node (through the "thermal power-energy storage" relationship), forming an initial supply transmission chain: solar power → thermal power → energy storage; wind power → thermal power → energy storage.

[0080] Step 2044: Based on the analysis of the attenuation coordination cluster, the constraint of the attenuation coordination direction on the initial supply transmission chain is obtained to obtain the constraint boundary range, and target nodes that are not included in the initial supply transmission chain and do not belong to the constraint boundary range are screened out in the gain coordination cluster.

[0081] Furthermore, the project evaluation system analyzes the impact of the attenuation coordination direction on the initial supply transmission chain based on the attenuation coordination cluster, and determines the operational limitations of the initial supply transmission chain in the attenuation environment, i.e. the constraint boundary range (such as the maximum output limit or prohibition of access conditions for a certain type of energy node under specific conditions).

[0082] Furthermore, the project evaluation system filters out energy resource nodes in the gain synergy cluster that are not included in the initial supply transmission chain and whose environmental parameters and operating characteristics do not fall within the constraint boundary range, and identifies these nodes as target nodes.

[0083] Continuing with the above embodiments, in the attenuation coordination cluster, the attenuation coordination direction of "solar energy-wind energy (dust storm weather)" is a decrease in the output of both. The constraint boundary range is that solar energy output ≤ 3 MW and wind energy output ≤ 0.5 MW under dust storm weather, and direct access to the main chain is prohibited. In the gain coordination cluster, there are biomass energy nodes not included in the initial supply transmission chain. Their association "biomass energy-thermal energy (all environment)" is in the gain coordination direction, and biomass energy is not affected by attenuation under dust storm weather, thus not falling within the constraint boundary range. Therefore, the target node is the biomass energy node.

[0084] Step 2045: Based on the gain coordination direction, the target node is associated with the initial supply transmission chain to obtain the energy supply chain path.

[0085] Furthermore, the project evaluation system connects the target node to the corresponding position in the initial supply transmission chain based on its association relationship and gain coordination direction within the gain coordination cluster. During connection, it must be ensured that the target node forms a new gain-related path with the nodes in the initial chain, without violating the constraints of the boundary range. The final network structure containing all valid gain-related paths and nodes constitutes the energy supply chain path.

[0086] Continuing with the above embodiment, the target node is a biomass energy node, which has a "biomass energy-thermal power" gain correlation with the thermal power node. The biomass energy node is connected to the initial supply chain along the gain cooperation direction, forming a new path: biomass energy → thermal power → energy storage. The integrated energy supply chain path includes: solar energy → thermal power → energy storage; wind energy → thermal power → energy storage; and biomass energy → thermal power → energy storage. It is explicitly stated that under sandstorm conditions, when the output of solar and wind power is below the constraint boundary, the biomass energy → thermal power path will be prioritized.

[0087] The energy supply chain constructed in this embodiment of the invention is based on the positive synergistic effect of the correlation. It forms the main energy transmission network through gain synergistic clusters and defines the operational constraint boundary by using attenuation synergistic clusters to avoid negative synergistic effects. In this process, the coverage and flexibility of the supply chain are improved by target nodes. The final energy supply chain can automatically select the optimal correlation path according to different environmental parameters, ensuring that energy resources can be transmitted in an efficient and collaborative manner in various scenarios.

[0088] In one embodiment, steps 301 to 304 include: Step 301: Taking the energy resources corresponding to the starting point of energy production in the energy supply chain as source-end energy resources and the energy resources corresponding to the energy nodes connected to the load end as end-end energy resources, establish the grid-source correlation relationship between source-end production and grid-end transmission based on the source-end energy resources and the grid-end transmission constraints in the power grid transmission parameters.

[0089] Optionally, the project evaluation system can clearly define the starting point of energy production in the energy supply chain, identifying the corresponding energy resources as source-end energy resources, such as solar, wind, and biomass energy, which are directly used for energy production. Simultaneously, it can identify energy nodes directly connected to the load end, with the energy resources corresponding to these nodes being end-end energy resources, such as energy storage resources connected to energy storage nodes, thermal power nodes, etc.

[0090] Furthermore, the project evaluation system extracts grid-end transmission constraints from the power grid transmission parameters, including parameters such as the rated capacity of transmission lines, maximum transmission power, voltage limits, and line loss rates. Based on the production characteristics of source-end energy resources (such as the power generation periods of solar energy and the output fluctuation characteristics of wind energy) and grid-end transmission constraints, a correspondence is established between the production output of source-end energy resources and the power grid transmission capacity, i.e., the grid-source correlation, clarifying the requirements and limitations of source-end production on power grid transmission under different operating conditions.

[0091] In one embodiment, the source energy resources in the energy supply chain are solar, wind, and biomass energy, while the end energy resources are thermal power and energy storage. Grid transmission constraints in the power grid transmission parameters include: a rated capacity of 300 MW for a 220 kV transmission line, a maximum transmission power not exceeding 300 MW, a line loss rate of 2%, and a voltage fluctuation range of ±5%. The project evaluation system analyzes the characteristics of the source energy resources: solar energy has output during the day from 8:00 to 18:00, with a maximum output of 14.4 MW; wind energy has output when the wind speed is ≥3 m / s, with a maximum output of 2 MW; biomass energy can stably output, with a maximum output of 5 MW. Combined with grid transmission constraints, a grid-source correlation is established: solar energy output during the day needs to be controlled between 0-14.4 MW, and the sum of the output of solar energy, wind energy, and biomass energy transmitted through the transmission line should not exceed 300 MW; simultaneously, the total output of the source energy transmission must meet voltage fluctuation requirements, and when the total output of the source energy exceeds 280 MW, an early warning of line load is required.

[0092] Step 302: Based on the load demand characteristics and grid transmission constraints corresponding to the end energy resources, determine the grid-load supply and demand balance conditions, and based on the grid-source correlation and grid-load supply and demand balance conditions, determine the source-end production scheduling rules for source-end energy resources.

[0093] Furthermore, the project evaluation system analyzes the load-side demand characteristics corresponding to end-point energy resources, including the time-period distribution of load (e.g., high load during the day and low load at night), load fluctuation range, and minimum load demand. Combining this with grid-end transmission constraints in the power grid transmission parameters, the system calculates the conditions that the amount of energy transmitted from the grid to the load end must meet to satisfy load-side demand at different time periods—that is, the grid-load supply-demand balance condition. This condition must ensure that the amount of energy transmitted to the load end is equal to or within the allowable deviation range of the load-side demand.

[0094] Furthermore, based on the grid-source relationship and the determined grid-load supply and demand balance conditions, the project evaluation system formulates source-end energy resource production scheduling rules, clarifies the output adjustment strategies, priority order, output upper and lower limits of various source-end energy resources at different times, so as to ensure the balance between source-end production, grid transmission and load-end demand.

[0095] In one embodiment, the load demand characteristics corresponding to the end-point energy resources are as follows: the load demand is 800 MW during the daytime (10:00-16:00) and 400 MW during the nighttime (22:00-6:00 the next day), with load fluctuations not exceeding ±50 MW and a minimum demand of 350 MW. Combined with grid-end transmission constraints (rated capacity 300 MW), the grid-load supply-demand balance conditions are determined as follows: the energy transmitted to the load end through transmission lines during the daytime (10:00-16:00) must reach 800 MW (transmitted by multiple lines, with a single 220 kV line transmitting no more than 300 MW); the energy transmitted to the load end during the nighttime (22:00-6:00 the next day) must reach 400 MW. Based on the grid-source correlation and this balance condition, the project evaluation system determines the source-end production scheduling rules: During the day, priority is given to scheduling full solar power (14.4 MW) and full wind power (2 MW), with any shortfall supplemented by biomass energy (5 MW) and thermal power. The total output at the source end must meet the energy demand transmitted to the load end. At night, when there is no solar power output, wind power (if there is wind speed) and biomass energy are scheduled to maintain stable output, with the remainder supplemented by thermal power. The total output at the source end is transmitted on a single line, and does not exceed 300 MW. When the load end demand fluctuates, thermal power output is adjusted first for a rapid response, with the adjustment range not exceeding 50 MW / hour.

[0096] Step 303: Based on the source-end production scheduling rules, the source-end production cycle of energy resources is divided into multiple production stages to obtain the project's full life cycle sequence.

[0097] Furthermore, the project evaluation system analyzes the production cycle of energy resources at the source based on the source-end production scheduling rules. The production cycle covers the entire operation period of the project, such as 25 years.

[0098] Furthermore, the project evaluation system divides the source-end production cycle into multiple production stages based on factors such as the production characteristics of energy resources at the source end, seasonal variations in load-end demand, and the cyclical nature of environmental conditions. Each production stage has unique production characteristics; for example, it can be divided into spring, summer, autumn, and winter production stages according to season, or peak load stages, off-peak load stages, and low-peak load stages according to load demand. By dividing the production stages, the entire project lifecycle sequence is obtained, clarifying the time range of each stage, the corresponding load-end demand characteristics, and the key points of source-end energy resource scheduling.

[0099] In one embodiment, the source-end production scheduling rules clearly define the scheduling strategies for different time periods, with the project's entire lifecycle being a 25-year operating period. Based on the seasonal characteristics of load demand and the seasonal output characteristics of source-end energy resources, the system divides the production cycle into four production stages: the summer (June-August) peak stage, characterized by high daytime load and ample solar power output; the winter (December-February) peak stage, with higher nighttime load and more opportunities for wind power output; the spring and autumn (March-May, September-November) off-peak stages, with relatively stable load; and the year-round off-peak stage (0:00-6:00 daily), where load is at its lowest level. This results in the project's entire lifecycle sequence: each year is sequentially divided into a spring off-peak stage, a summer peak stage, an autumn off-peak stage, and a winter peak stage, with each stage containing a daily off-peak stage. The system clearly defines the time range, load demand range, and source-end scheduling priority for each stage (e.g., prioritizing solar power in summer and prioritizing wind and thermal power at night in winter).

[0100] Step 304: Based on the project's full lifecycle sequence, network-source relationships, and source-end production scheduling rules, an integrated collaborative operation solution is obtained.

[0101] Furthermore, the project evaluation system integrates the entire project lifecycle sequence, grid-source relationships, and source-end production scheduling rules. During this integration, it is crucial to ensure that the production scheduling of source-end energy resources conforms to the transmission constraints within the grid-source relationships at each production stage of the project's lifecycle, while simultaneously satisfying the grid-load supply-demand balance condition. Further, the project evaluation system coordinates and optimizes each component, clarifying the specific output plans for various source-end energy resources, grid transmission path selection and power allocation, and the operation modes of end-point energy resources under different production stages and environmental conditions. The resulting integrated collaborative operation plan, encompassing the entire lifecycle and the coordinated operation of all stages, constitutes the integrated collaborative operation scheme.

[0102] In one embodiment, the entire project lifecycle sequence (seasonal phases + off-peak phases), grid-source relationships (transmission capacity, loss constraints, etc.), and source-end production scheduling rules (priority, output adjustment, etc.) are integrated. During the summer peak season (June-August) from 10:00 to 16:00 during the day, according to the scheduling rules, solar power generates 14.4 MW at full capacity, wind power generates 2 MW at full capacity, biomass power generates 5 MW, and thermal power generates 778.6 MW, for a total output of 800 MW. This is transmitted through multiple 220 kV lines, with each line transmitting no more than 300 MW, satisfying grid-end transmission constraints and load-end demands. During the winter peak season (December-February) from 22:00 to 24:00 at night, with a wind speed of 8 m / s, wind power generates 1 MW, biomass power generates 5 MW, and thermal power generates 394 MW, for a total output of 400 MW. This is transmitted to the load end through transmission lines. Therefore, the output plans and transmission arrangements for each phase are integrated to form an integrated collaborative operation scheme.

[0103] This invention uses the grid-source correlation as a transmission constraint and the grid-load supply-demand balance as the objective. It regulates the output adjustment of energy sources through source-end production scheduling rules and achieves targeted scheduling at different stages in conjunction with the project's entire life cycle sequence. This enables the final integrated collaborative operation scheme to rationally arrange the production and transmission of various energy resources according to the environmental conditions and load characteristics at different stages of the project's entire life cycle, ensuring the safe and stable operation of the power grid, meeting the energy demand of the load side, and improving the efficient operation of power energy projects throughout their entire life cycle.

[0104] In one embodiment, steps 401 to 404 include: Step 401: Match the power generation technology parameters of each energy source in the energy supply chain with each production stage in the project's entire life cycle sequence to construct the production correlation relationship of each energy source.

[0105] Optionally, the project evaluation system extracts power generation technology parameters of energy resources at each source end of the energy supply chain, such as peak power, conversion efficiency, and effective power generation periods for solar energy; rated power, cut-in / rated / cut-out wind speeds, and annual utilization hours for wind energy; and stable output power for biomass energy. Simultaneously, it retrieves the project's entire lifecycle sequence to clarify each production stage (such as summer peak season, winter peak season, spring / autumn off-peak season, and year-round low-peak season) and its time range and environmental characteristics.

[0106] Furthermore, the project evaluation system matches the power generation technology parameters of each energy source with the production stages in the entire life cycle sequence. Based on the environmental conditions (such as summer sunlight intensity and winter wind speed) and the adaptability of the technology parameters at different stages, it constructs a correspondence between "energy source - production stage - technology parameters", that is, the production correlation relationship, and clarifies the technical characteristics that each energy source can play in different production stages.

[0107] In one embodiment, the power generation technical parameters of the source energy resources are as follows: solar peak power of 100 MW, conversion efficiency of 18%, and effective power generation period from 8:00 to 18:00 during the day; wind power rated power of 2 MW, rated wind speed of 12 m / s, and annual utilization hours of 2500 hours; and biomass energy stable output power of 5 MW. The project's entire life cycle sequence includes the summer peak period (June-August, with abundant sunshine), the winter peak period (December-February, with higher wind speeds), the spring and autumn off-peak periods (March-May and September-November, with stable environments), and the annual low-peak period (0:00-6:00 daily, with insufficient sunshine). The production linkage is as follows: solar energy operates at a peak power of 100 MW and a conversion efficiency of 18% during the summer peak period from 8:00 to 18:00; wind energy operates at a rated power of 2 MW during the winter peak period (when the wind speed is ≥12 m / s); biomass energy outputs a stable 5 MW at all production stages; and solar energy outputs 0 during the off-peak period of the year due to the lack of sunlight.

[0108] Step 402: Match production capacity with transmission capacity based on production association and network source association, and determine the stage technical constraints of each source energy in each generation stage.

[0109] Furthermore, the project evaluation system, based on the production-source relationship and the grid-source relationship (including grid-end transmission constraints such as the rated capacity of transmission lines, maximum transmission power, and voltage limits), performs a matching analysis of the production capacity of each source energy source with the grid transmission capacity at each production stage. Based on the technical parameters of the source energy source (such as maximum output and efficiency) and grid-end transmission constraints (such as the transmission limit of a single line), it determines the upper and lower limits of output, and the operating time limit of each source energy source in a specific production stage. These constraints are the stage technical constraints, ensuring that the production of the source energy source does not exceed its own technical capacity and the grid transmission capacity.

[0110] In one embodiment, the grid-source correlation specifies that the rated capacity of the 220 kV transmission line is 300 MW, and the total power output of a single line during transmission from the source end does not exceed 300 MW. Combined with the production correlation, the project evaluation system analyzes the stage technical constraints: During the summer peak season (8:00-18:00), the maximum output of solar energy, according to technical parameters, is 14.4 MW (100 MW × 18%), and there are no separate restrictions in the grid-source correlation; therefore, the stage technical constraint is output 0-14.4 MW. During the winter peak season (wind speed ≥ 12 m / s), the rated power of wind energy is 2 MW, and there are no separate restrictions on grid transmission; therefore, the stage technical constraint is output 0-2 MW. Biomass energy has a stable output of 5 MW in all stages; therefore, the stage technical constraint is output 5 MW (without upper or lower limit fluctuations). Simultaneously, during the summer peak season, the total power output from the source end (solar energy + wind energy + biomass energy) transmitted through a single line must be ≤ 300 MW; therefore, the sum of the three stage technical constraints is ≤ 300 MW.

[0111] Step 403: Based on the load-end demand characteristics of each production stage and the source-end production demand of each source-end energy in each generation stage, determine the stage demand benchmark for each source-end energy in each generation stage.

[0112] Furthermore, the project evaluation system extracts the load-end demand characteristics corresponding to end-point energy resources (such as load demand volume and fluctuation range at each stage), and combines this with source-end production scheduling rules (clarifying the scheduling priority and output replenishment order of various source-end energy sources) to analyze the output requirements of each source-end energy source for load-end demand in each production stage, i.e., source-end production demand. Further, based on the total load-end demand and the scheduling priority of source-end energy sources, the project evaluation system decomposes the load-end demand into each source-end energy source, determining the minimum or target output that each source-end energy source needs to achieve in a specific production stage, i.e., the stage demand benchmark. This benchmark must meet the total load-end demand requirement for that stage.

[0113] In one embodiment, the load demand characteristics are as follows: during the peak summer period from 10:00 to 16:00, the load demand is 800 MW. The source-side production scheduling rule is to prioritize the scheduling of solar, wind and biomass energy, with the shortfall being supplemented by thermal power. The project evaluation system determines the phased demand baselines: During the summer peak season (10:00-16:00), solar power needs to operate at its maximum output of 14.4 MW (phased demand baseline 14.4 MW), wind power at its maximum output of 2 MW (phased demand baseline 2 MW), and biomass energy at 5 MW (phased demand baseline 5 MW), totaling 21.4 MW, which serves as the basic output to meet the load demand; During the winter peak season (22:00-24:00), the load demand is 400 MW. The dispatch rule is that wind power and biomass energy will operate at stable output, with the remainder supplemented by thermal power. Therefore, the phased demand baseline for wind power is 1 MW when the wind speed is ≥8 m / s, and the phased demand baseline for biomass energy is 5 MW; During the off-season, the load demand is 400 MW, with no solar power output, a phased demand baseline of 0 MW for wind power (if the wind speed is insufficient), and a phased demand baseline of 5 MW for biomass energy.

[0114] Step 404: Based on the source-end production scheduling rules and combined with the stage technical constraints and stage demand benchmarks of each source-end energy in each generation stage, the project calculation is carried out to obtain the energy production of each source-end energy in the entire life cycle of the project.

[0115] Furthermore, the project evaluation system calculates the project based on the source-end production scheduling rules and the stage technical constraints and stage demand benchmarks of each source-end energy in each generation stage, to obtain the energy production of each source-end energy in the entire life cycle of the project, as in step 4043 of step 4041.

[0116] This invention achieves the adaptation of technical parameters to life cycle stages through production correlation, ensures that production does not exceed technical and transmission limits based on stage technical constraints, and anchors the output target to meet load-end demand using stage demand benchmarks, thus obtaining accurate energy production data. Therefore, it can comprehensively reflect the actual contribution of energy from different sources throughout the entire life cycle, ensuring the accuracy of project economic analysis.

[0117] In one embodiment, the process of step 4043 in step 4041 includes: Step 4041: Based on the stage technology constraints and stage demand benchmarks of each source energy in each generation stage, determine the theoretical production volume of each source energy in each generation stage.

[0118] Optionally, for each energy source, in each production phase, the project evaluation system compares the phase technical constraints with the phase demand baseline. If the phase demand baseline is within the output range of the phase technical constraints (i.e., the demand baseline does not exceed the upper limit of the constraints and is not lower than the lower limit of the constraints), the project evaluation system uses the phase demand baseline as the actual output for that phase.

[0119] Furthermore, if the stage demand baseline exceeds the upper limit of the stage technical constraint, the project evaluation system uses the upper limit of the constraint as the actual output; if the stage demand baseline is lower than the lower limit of the stage technical constraint, the project evaluation system uses the lower limit of the constraint as the actual output. After determining the actual output, the actual output is multiplied by the operating time of the production stage to obtain the theoretical production of each source energy in that production stage.

[0120] In one embodiment, the technical constraint for solar energy during the summer peak season is an output of 0-14.4 MW, with a demand baseline of 14.4 MW. Since the demand baseline is within the constraint, the actual output is taken as 14.4 MW. This season lasts 92 days × 6 hours = 552 hours per year. Therefore, the theoretical production of solar energy during the summer peak season is 14.4 MW × 552 hours = 7948.8 MWh. The technical constraint for wind energy during the winter peak season is an output of 0-2 MW, with a demand baseline of 1 MW (when wind speed ≥ 8 m / s). Since the demand baseline is within the constraint, the actual output is taken as 1 MW. The annual operating duration of this phase is 90 days × 2 hours = 180 hours, and the number of days with wind speed ≥ 12 m / s accounts for 50%, meaning the effective operating time is 180 hours × 50% = 90 hours. Therefore, the theoretical production capacity of wind energy during the winter peak phase is 2 MW × 90 hours = 180 MWh (calculated based on a rated power of 2 MW due to wind speed reaching the rated value). The technical constraint for biomass energy across all phases is an output of 5 MW, the phase demand baseline is 5 MW, and the actual output is taken as 5 MW. The annual operating duration of the spring and autumn off-peak phase is 183 days × 12 hours = 2196 hours, and the theoretical production capacity during this phase is 5 MW × 2196 hours = 10980 MWh.

[0121] Step 4042: Based on the source-end production scheduling rules, perform a feasibility verification on the theoretical production volume of each source-end energy in each generation stage, and obtain the feasibility verification result.

[0122] Furthermore, the project evaluation system performs a feasibility check on the theoretical production volume of each energy source in each production stage based on the source-end production scheduling rules. The check includes: whether the theoretical production volume conforms to the priority order in the scheduling rules (e.g., whether priority-scheduled energy is operating at full capacity as required), whether it meets the grid-load supply-demand balance condition (i.e., whether the sum of the theoretical production volumes of all energy sources can meet the load-end demand in the corresponding stage), and whether there are scheduling conflicts with other energy sources (e.g., whether adjustments to the output of one type of energy source affect the theoretical production volume of another type). If all checked items conform to the scheduling rules, the feasibility check result is passed; if any item does not conform, the check result is failed.

[0123] Continuing with the above embodiments, the source-end production scheduling rules require that during the summer peak period, priority should be given to scheduling solar, wind, and biomass energy to operate at full capacity.

[0124] During the summer peak season, solar power operates at full capacity based on demand, with a theoretical production capacity of 7948.8 MWh. Wind power, if available during this period, operates at its maximum output of 2 MW, with a theoretical production capacity of 2 MW × 552 hours = 1104 MWh. Biomass power operates at 5 MW, with a theoretical production capacity of 5 MW × 552 hours = 2760 MWh. The sum of the theoretical production capacities of these three sources is 7948.8 + 1104 + 2760 = 11812.8 MWh. Combined with the supplementary output from thermal power, this can meet the 800 MW load demand during this period, and it conforms to the priority scheduling order with no scheduling conflicts. Therefore, the feasibility verification result is passed.

[0125] If it is assumed that due to equipment maintenance, the theoretical production of wind power exceeds the production corresponding to the actual operating time at a certain stage, the verification result will be failed. However, this situation does not exist in this embodiment, so the verification passes.

[0126] Step 4043: If the feasibility verification result is passed, the theoretical production of each source energy in each generation stage is summarized to obtain the energy production of each source energy in the entire life cycle of the project.

[0127] Furthermore, when the feasibility verification result is passed, the project evaluation system summarizes the theoretical production of each energy source in each production stage. That is, it adds up the theoretical production of the same energy source in different production stages (such as summer peak, winter peak, spring and autumn off-peak, and year-round low), to obtain the annual energy production of that energy source. Then, it multiplies the annual energy production by the number of years in the project's entire life cycle (such as 25 years) to finally obtain the energy production of each energy source over the entire project life cycle.

[0128] In one embodiment, solar energy has a theoretical production capacity of 7948.8 MWh per year during the summer peak season. Over the project's 25-year lifespan, the total energy production is 7948.8 MWh / year × 25 years = 198720 MWh. Wind energy has a theoretical production capacity of 180 MWh per year during the winter peak season, resulting in a total energy production of 180 MWh / year × 25 years = 4500 MWh over 25 years.

[0129] The theoretical production of biomass energy at each stage is as follows: 5 MW × 552 hours = 2760 MWh during the summer peak stage, 5 MW × 180 hours = 900 MWh during the winter peak stage, 10980 MWh during the spring and autumn off-peak stages, and 5 MW × (365 days × 6 hours) = 5 MW × 2190 hours = 10950 MWh during the yearly trough stage. The total theoretical production per year is 2760 + 900 + 10980 + 10950 = 25590 MWh. The total energy production over 25 years is 25590 MWh / year × 25 years = 639750 MWh.

[0130] The embodiments of the present invention ensure the accuracy and rationality of the data by reasonably determining the theoretical production volume and verifying its feasibility. The final full life cycle energy production data can truly reflect the actual contribution of various energy sources, thus ensuring the accuracy of the project's economic analysis.

[0131] The intelligent evaluation system for power energy project investment based on artificial intelligence provided by this invention will be described below. The intelligent evaluation system for power energy project investment based on artificial intelligence described below can be referred to in correspondence with the intelligent evaluation method for power energy project investment based on artificial intelligence described above.

[0132] Reference Figure 2 , Figure 2 This is a schematic diagram of the structure of the AI-based intelligent evaluation system for power energy project investment provided by the present invention. The AI-based intelligent evaluation system for power energy project investment includes: The data acquisition module 210 is used to collect power energy data of power energy projects deployed in the target area; the power energy data includes various energy resources and their power generation technology parameters; The supply chain construction module 220 is used to simulate the supply coordination relationship of various energy resources under different environments based on the load demand of the target area and the various energy resources and their power generation technology parameters, and to construct the energy supply chain. The collaborative planning module 230 is used to perform collaborative operation planning based on the power grid transmission parameters and energy supply chain routes of the target area, so as to obtain an integrated collaborative operation scheme. The project calculation module 240 is used to calculate the energy production of various energy resources throughout the project's entire life cycle based on the power generation technology parameters and integrated collaborative operation scheme of energy resources in the energy supply chain. The revenue calculation module 250 is used to calculate revenue based on the energy production of various energy resources and their corresponding energy economic data, and to obtain economic benefit evaluation indicators for power energy projects.

[0133] This invention constructs an energy supply chain based on the technical parameters of various energy resources, clarifies the synergistic effects of multiple energy sources, and then plans an integrated collaborative operation scheme based on the energy supply chain and power grid transmission parameters. This achieves a holistic technical analysis of the source, grid, and load. Furthermore, based on the energy supply chain and the integrated collaborative operation scheme, combined with the technical parameters of various energy resources, the energy production of each type of energy resource is calculated, thereby clarifying the output of each type of energy. This provides a comprehensive and more practical assessment of the technical feasibility and stability of the operation. Finally, based on the energy production and economic data of various energy resources, economic benefit evaluation indicators are calculated throughout the project's entire life cycle. This accurately reflects the actual economic value of the project, improves the accuracy of economic benefit assessment for power energy projects, and guides accurate investment decisions.

[0134] Please see Figure 3 , Figure 3 An embodiment diagram of an electronic device provided in accordance with the present invention. For example... Figure 3 As shown, an embodiment of the present invention provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor 320. When the processor 320 executes the computer program 311, it performs the following steps: Collect power energy data from power energy projects deployed in the target area; the power energy data includes various energy resources and their power generation technology parameters; Based on the load demand of the target area and combined with various energy resources and their power generation technology parameters, the supply coordination relationship of various energy resources under different environments is simulated to construct an energy supply chain path; Based on the power grid transmission parameters and energy supply chain routes of the target area, an integrated collaborative operation plan is obtained. Based on the power generation technology parameters and integrated collaborative operation scheme of energy resources in the energy supply chain, the project calculation is carried out to obtain the energy production of various energy resources throughout the project's entire life cycle. Based on the energy production of various energy resources and their corresponding energy economic data, the economic benefit evaluation indicators of power energy projects are obtained through revenue calculation.

[0135] Please see Figure 4 , Figure 4 An embodiment diagram of a computer-readable storage medium provided in accordance with an embodiment of the present invention is shown. Figure 4 As shown, this embodiment provides a computer-readable storage medium 400 on which a computer program 311 is stored. When the computer program 311 is executed by a processor, it performs the following steps: Collect power energy data from power energy projects deployed in the target area; the power energy data includes various energy resources and their power generation technology parameters; Based on the load demand of the target area and combined with various energy resources and their power generation technology parameters, the supply coordination relationship of various energy resources under different environments is simulated to construct an energy supply chain path; Based on the power grid transmission parameters and energy supply chain routes of the target area, an integrated collaborative operation plan is obtained. Based on the power generation technology parameters and integrated collaborative operation scheme of energy resources in the energy supply chain, the project calculation is carried out to obtain the energy production of various energy resources throughout the project's entire life cycle. Based on the energy production of various energy resources and their corresponding energy economic data, the economic benefit evaluation indicators of power energy projects are obtained through revenue calculation.

[0136] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the artificial intelligence-based intelligent evaluation method for power energy project investment provided by the above methods, the method comprising: Collect power energy data from power energy projects deployed in the target area; the power energy data includes various energy resources and their power generation technology parameters; Based on the load demand of the target area and combined with various energy resources and their power generation technology parameters, the supply coordination relationship of various energy resources under different environments is simulated to construct an energy supply chain path; Based on the power grid transmission parameters and energy supply chain routes of the target area, an integrated collaborative operation plan is obtained. Based on the power generation technology parameters and integrated collaborative operation scheme of energy resources in the energy supply chain, the project calculation is carried out to obtain the energy production of various energy resources throughout the project's entire life cycle. Based on the energy production of various energy resources and their corresponding energy economic data, the economic benefit evaluation indicators of power energy projects are obtained through revenue calculation.

[0137] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0138] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0139] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An artificial intelligence-based intelligent evaluation method for power energy project investment, characterized in that, The method comprises the following steps: collecting power energy data of a power energy project deployed in a target area; the power energy data comprises various energy resources and their power generation technical parameters; based on the load demand of the target area and the power generation technical parameters of various energy resources, simulating the supply synergy relationship of various energy resources under different environments, and constructing an energy supply link; based on the power grid transmission parameters of the target area and the energy supply link, performing operation synergy planning to obtain an integrated synergy operation scheme; based on the power generation technical parameters of the energy resources in the energy supply link and the integrated synergy operation scheme, performing project calculation to obtain the energy production of various energy resources in the whole life cycle of the project; based on the energy production of various energy resources and their corresponding energy economic data, performing benefit calculation to obtain an economic benefit evaluation index of the power energy project. 2.The method of claim 1, wherein, The step of simulating the supply synergy relationship of various energy resources under different environments based on the load demand and the power generation technical parameters of various energy resources comprises the following steps: based on a pre-constructed energy supply graph, simulating the first energy relationship between the load demand and the supplied energy amount when various energy resources are supplied individually under their corresponding power generation technical parameters in different environments; based on the energy supply graph, simulating the second energy relationship between the load demand and the supplied energy amount when various energy resources are supplied cooperatively under their corresponding power generation technical parameters in different environments; based on the first energy relationship and the second energy relationship, determining the supply synergy relationship of various energy resources under different environments; based on a pre-constructed energy synergy structure tree and the supply synergy relationship of various energy resources under different environments, constructing the energy supply link; wherein the energy supply graph represents the energy relationship between the load end and the supply end when various energy resources are supplied individually or cooperatively under their power generation technical parameters in different environments; the nodes in the energy synergy structure tree are various energy resources, and the edges represent the correlation relationship between various energy resources in different environments. 3.The method of claim 2, wherein, The step of determining the supply synergy relationship of various energy resources under different environments based on the first energy relationship and the second energy relationship comprises the following steps: based on the load demand when supplied individually in the first energy relationship and the corresponding load demand when supplied cooperatively in the second energy relationship, determining the load satisfaction difference value of an energy cooperative supply combination in the same load demand scenario between cooperative supply and individual supply under the same environmental parameters; based on the supplied energy amount when supplied individually in the first energy relationship and the total supplied energy amount when supplied cooperatively in the second energy relationship, determining the energy supply difference value of the energy cooperative supply combination in the same load demand scenario between cooperative supply and individual supply under the same environmental parameters; based on the quotient value between the load satisfaction difference value and the energy supply difference value, determining the synergy response coefficient of the energy cooperative supply combination under the same environmental parameters, and based on the synergy response coefficient, determining the synergy effect type of the energy cooperative supply combination under different environments; determining a synergy direction of the energy collaborative supply combination under the same environmental parameter based on a sign combination of the load satisfaction difference value and the energy supply difference value; determining a supply synergy relationship of each type of energy resource under different environments based on the synergy effect type and the synergy direction of the energy collaborative supply combination under different environments. 4.The method of claim 3, wherein, The synergy effect type includes positive synergy effect, no synergy effect and negative synergy effect; the synergy direction includes gain synergy direction and attenuation synergy direction; The energy supply chain link is constructed based on the supply synergy relationship of each type of energy resource under different environments in the pre-constructed energy synergy structure tree, including: traversing the synergy effect type under different environments in the energy synergy structure tree to obtain a target correlation relationship with a positive synergy effect in the energy synergy structure tree; grouping the target correlation relationship with the gain synergy direction into a gain synergy cluster, and grouping the target correlation relationship with the attenuation synergy direction into an attenuation synergy cluster; based on the gain synergy cluster, sequentially connecting the associated nodes in the gain synergy direction from any energy resource node of the energy synergy structure tree to obtain an initial supply transmission chain; based on the attenuation synergy cluster, analyzing the constraint of the attenuation synergy direction on the initial supply transmission chain to obtain a constraint boundary range, and screening a target node in the gain synergy cluster that is not included in the initial supply transmission chain and does not belong to the constraint boundary range; based on the gain synergy direction, associating and connecting the target node to the initial supply transmission chain to obtain the energy supply chain link. 5.The method of claim 1, wherein, based on the power grid transmission parameter of the target area and the energy supply chain link, performing operation synergy planning to obtain an integrated synergy operation scheme, including: taking an energy resource corresponding to an energy production starting point in the energy supply chain link as a source-end energy resource, and taking an energy resource corresponding to an energy node connected to a load end as a terminal energy resource, establishing a grid-source association relationship between source-end production and grid-end transmission based on the source-end energy resource of the source-end energy resource and the grid-end transmission constraint in the power grid transmission parameter; determining a grid-load supply and demand balance condition based on the load-end demand characteristics corresponding to the terminal energy resource and the grid-end transmission constraint, and determining a source-end production scheduling rule of the source-end energy resource based on the grid-source association relationship and the grid-load supply and demand balance condition; dividing a source-end production cycle of the source-end energy resource into multiple production stages based on the source-end production scheduling rule to obtain a project full life cycle sequence; integrating the project full life cycle sequence, the grid-source association relationship and the source-end production scheduling rule to obtain the integrated synergy operation scheme. 6.The method of claim 5, wherein the method further comprises: based on the power generation technology parameter of the energy resource in the energy supply chain link and the integrated synergy operation scheme, performing project calculation to obtain an energy production amount of each type of energy resource in the project full life cycle, including: matching the power generation technology parameter of each source-end energy in the energy supply chain link with each production stage in the project full life cycle sequence to construct a production association relationship of each source-end energy; Based on the production association relationship and the network source association relationship, production capacity and transmission capacity matching is performed to determine the stage technical constraint of each source end energy in each generation stage; Based on the load end demand characteristics in each production stage and the source end production demand of each source end energy in each generation stage, the stage demand benchmark of each source end energy in each generation stage is determined; Based on the source end production scheduling rule, the stage technical constraint and the stage demand benchmark of each source end energy in each generation stage are combined to perform project calculation, and the energy production of each source end energy in the whole life cycle of the project is obtained. 7.The method of claim 6, wherein, The project calculation based on the source end production scheduling rule, the stage technical constraint and the stage demand benchmark of each source end energy in each generation stage to obtain the energy production of each source end energy in the whole life cycle of the project, comprises: Based on the stage technical constraint and the stage demand benchmark of each source end energy in each generation stage, the theoretical production of each source end energy in each generation stage is determined; Based on the source end production scheduling rule, the theoretical production of each source end energy in each generation stage is subjected to feasibility check to obtain a feasibility check result; If the feasibility check result passes, the theoretical production of each source end energy in each generation stage is summarized to obtain the energy production of each source end energy in the whole life cycle of the project.

8. An artificial intelligence-based intelligent evaluation system for power energy project investment, characterized in that, The method for intelligent evaluation of investment in a power energy project based on artificial intelligence according to any one of claims 1 to 7; The intelligent evaluation system of investment in a power energy project based on artificial intelligence comprises: A data acquisition module is configured to acquire power energy data of a power energy project deployed in a target region; the power energy data comprises various energy resources and their power generation technical parameters; A supply chain link construction module is configured to simulate supply coordination relationships of various energy resources under different environments based on load demand of the target region and the power generation technical parameters of various energy resources, and to construct energy supply chain links; A collaborative planning module is configured to perform operation coordination planning based on power grid transmission parameters of the target region and the energy supply chain links to obtain an integrated collaborative operation scheme; A project calculation module is configured to perform project calculation based on power generation technical parameters of energy resources in the energy supply chain links and the integrated collaborative operation scheme to obtain energy production of various energy resources in the whole life cycle of the project; A benefit calculation module is configured to perform benefit calculation based on energy production of various energy resources and their corresponding energy economic data to obtain economic benefit evaluation indexes of the power energy project.

9. An electronic device comprising: A memory is configured to store a computer software program; A processor is configured to read and execute the computer software program; when the processor executes the computer software program, the method for intelligent evaluation of investment in a power energy project based on artificial intelligence according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer readable storage medium having stored therein a computer software program, characterized in that, When the computer software program is executed by the processor, the method for intelligent evaluation of investment in a power energy project based on artificial intelligence according to any one of claims 1 to 7 is implemented.

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