Renewable energy consumption capability optimization coordination method, device and equipment

Abstract

The invention relates to the technical field of intelligent control, in particular to a renewable energy consumption capability optimization coordination method, device and equipment, and the method comprises the steps: obtaining operation data, a typical load curve and an equipment maintenance plan of new energy power generation, and taking the operation data, the typical load curve and the equipment maintenance plan as optimization boundary conditions; acquiring an actual operation demand of a preset regional power grid and a corresponding constraint condition as a model constraint; obtaining and obtaining a model optimization target according to the new energy consumption amount, the construction cost, the operation cost and the wind curtailment and light curtailment penalty value; and according to the boundary condition, the model constraint and the model optimization target, carrying out model optimization coordination to obtain a renewable energy consumption capability optimization coordination model. It can be understood that according to the technical scheme shown in the invention, a plurality of boundary conditions, model constraints and model optimization targets are considered, so that the accuracy of planning obtained by the optimized model is improved.

Description

Technical Field

[0001] The present invention relates to the field of renewable energy technology, and in particular to a method, device and equipment for optimizing and coordinating renewable energy absorption capacity. Background Art

[0002] At present, the main factors affecting the grid-connected and absorption capacity of new energy include the following aspects: 1. The impact of system regulation capacity, mainly including unit regulation performance and startup mode; 2. The impact of system spare capacity. The setting of spare capacity needs to consider both the safe and stable operation of the system and its impact on the absorption capacity of new energy; 3. The penetration rate of new energy. The higher the penetration rate of new energy, the more natural resources can be fully utilized, but too high a penetration rate will also lead to excessive construction and waste of resources; 4. The impact of the transmission capacity of the power grid. The space for local absorption of new energy is limited, and the transmission capacity of the power grid will also restrict the absorption of new energy; 5. The impact of system load characteristics. The greater the peak-to-valley difference of the system, the greater the peak-shaving pressure and the more difficult it is to absorb; 6. The impact of the source-load timing coupling relationship. For a certain power system and a fixed scale of new energy installed capacity, the timing coupling relationship between new energy output and load changes is the main factor affecting the absorption of new energy.

[0003] However, currently wind power, photovoltaic power and other units are actually coupled through load balancing. The consumption of new energy is estimated separately, which is difficult to consider in a comprehensive manner. The constraints of the transmission capacity of the transmission channel on the consumption of wind and solar power are not considered, making the current planning of renewable energy consumption insufficiently accurate. Summary of the invention

[0004] In view of this, an object of the present invention is to provide a method, device and equipment for optimizing and coordinating renewable energy consumption capacity, so as to solve the problem of insufficient accuracy in the current planning of renewable energy consumption in the prior art.

[0005] According to a first aspect of an embodiment of the present invention, a method for optimizing and coordinating renewable energy absorption capacity is provided, comprising:

[0006] Obtain the operating data, typical load curves and equipment maintenance plans of renewable energy power generation as the boundary conditions for optimization;

[0007] Obtain the actual operation requirements and corresponding constraints of the preset regional power grid as model constraints;

[0008] Obtain and obtain the model optimization target based on the new energy consumption, construction cost, operation cost and penalty value for wind and solar power abandonment;

[0009] According to the boundary conditions, the model constraints and the model optimization objectives, model optimization coordination is performed to obtain a renewable energy absorption capacity optimization coordination model.

[0010] Preferably, obtaining the equipment maintenance plan includes:

[0011] Determine the equipment maintenance plan based on the pre-set unit and line maintenance rules and conditions.

[0012] Preferably, the acquisition of the operation data of the renewable energy power generation, the typical load curve and the equipment maintenance plan as the boundary conditions for optimization includes:

[0013] Perform cluster analysis on the historical operation data of renewable energy power generation to obtain a set of typical daily power curves of renewable energy;

[0014] The typical daily power curve set of the new energy source, the typical load curve, and the equipment maintenance plan are used as boundary conditions for optimization.

[0015] Preferably, the corresponding constraints at least include:

[0016] Power balance constraints, frequency regulation standby constraints, operation standby constraints, unit operation constraints, and network security constraints.

[0017] Preferably, the method further comprises:

[0018] The model optimization objective is expressed by the following formula:

[0019]

[0020] in, is the consumption of new energy unit i at time t, is the predicted value of new energy unit i at time t.

[0021] Preferably, the obtaining of the operating data of the renewable energy power generation includes:

[0022] Obtain BPA input files and manually entered maintenance data;

[0023] The BPA input file and the manually entered maintenance data are analyzed to obtain the operation data of the new energy power generation.

[0024] Preferably, the data analysis includes:

[0025] The BPA input file is parsed to form a point-line model, the point-line model is cut, and the cross section and new energy are defined.

[0026] Preferably, the operating data at least includes:

[0027] System demand data, unit data, topology data, section data, node / regional power generation potential data.

[0028] According to a second aspect of an embodiment of the present invention, there is provided a renewable energy consumption capacity optimization and coordination device, comprising:

[0029] The boundary condition acquisition module is used to obtain the operating data, typical load curves and equipment maintenance plans of renewable energy power generation as the boundary conditions for optimization;

[0030] A model constraint acquisition module is used to obtain the actual operation requirements and corresponding constraint conditions of the preset regional power grid as model constraints;

[0031] The optimization target acquisition module is used to obtain the model optimization target based on the new energy consumption, construction cost, operation cost and wind and solar power abandonment penalty value;

[0032] The optimization and coordination module is used to perform model optimization and coordination according to the boundary conditions, the model constraints and the model optimization objectives to obtain an optimization and coordination model for the renewable energy absorption capacity.

[0033] Preferably, the boundary condition acquisition module is also used to perform cluster analysis on the historical operation data of renewable energy power generation to obtain a set of typical daily power curves of renewable energy; and use the set of typical daily power curves of renewable energy together with the typical load curve and the equipment maintenance plan as optimized boundary conditions.

[0034] Preferably, it also includes: an operation data acquisition module, which is used to acquire BPA input files and manually entered maintenance data; and perform data analysis on the BPA input files and manually entered maintenance data to obtain the operation data of the new energy power generation.

[0035] Preferably, a renewable energy consumption capacity optimization and coordination device comprises:

[0036] A main controller, and a memory connected to the main controller;

[0037] The memory stores program instructions;

[0038] The main controller is used to execute program instructions stored in the memory to perform any of the above methods.

[0039] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0040] It is understandable that the technical solution provided by the present invention can obtain the operating data, typical load curves and equipment maintenance plans of renewable energy power generation as the boundary conditions for optimization; obtain the actual operating requirements and corresponding constraints of the preset regional power grid as model constraints; obtain and obtain the model optimization target based on the renewable energy consumption, construction cost, operating cost and wind and solar abandonment penalty value; perform model optimization coordination according to the boundary conditions, the model constraints and the model optimization target to obtain the optimization coordination model of renewable energy consumption capacity. It is understandable that the technical solution shown in the present invention takes into account multiple boundary conditions, model constraints and model optimization targets, so that the accuracy of the planning obtained by the optimized model is improved.

[0041] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0043] Figure 1 is a schematic diagram of steps of a method for optimizing and coordinating renewable energy absorption capacity according to an exemplary embodiment;

[0044] Figure 2 is a schematic diagram of a data acquisition process according to an exemplary embodiment;

[0045] Figure 3 It is a schematic block diagram of a renewable energy absorption capacity optimization and coordination device according to an exemplary embodiment. DETAILED DESCRIPTION

[0046] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0047] Embodiment 1

[0048] Figure 1 is a schematic diagram of the steps of a method for optimizing and coordinating renewable energy consumption capacity according to an exemplary embodiment, see Figure 1 , providing a method for optimizing and coordinating renewable energy consumption capacity, including:

[0049] Step S11, obtaining operation data, typical load curve and equipment maintenance plan of renewable energy power generation as boundary conditions for optimization;

[0050] Step S12: obtaining the actual operation requirements and corresponding constraints of the preset regional power grid as model constraints;

[0051] Step S13: Obtain and obtain the model optimization target based on the new energy consumption, construction cost, operation cost and wind and solar power abandonment penalty value;

[0052] Step S14: performing model optimization coordination according to the boundary conditions, the model constraints and the model optimization objectives to obtain a renewable energy consumption capacity optimization coordination model.

[0053] In specific practice, the technical solution provided in this embodiment is based on artificial intelligence technology, fully considering the typical regional grid characteristics of the preset regional power grid, the transmission capacity of key transmission channels and the system safety and stability requirements, and comprehensively considering various optimization objectives such as renewable energy absorption capacity, construction cost, and operating cost, and carrying out the construction of renewable energy absorption capacity optimization coordination algorithm and model.

[0054] The essence of optimizing the peak-shaving operation of power systems containing large-scale renewable energy is to optimize the operating mode of the power system, coordinate the start-up and shutdown plans of various types of units within the operating cycle, reasonably allocate the working positions and power generation outputs of each unit, ensure the supply and demand balance and network security of the power system, and realize the maximum absorption of renewable energy.

[0055] It is understandable that the technical solution provided by this embodiment can obtain the operating data, typical load curve and equipment maintenance plan of renewable energy power generation as the boundary conditions for optimization; obtain the actual operating requirements and corresponding constraints of the preset regional power grid as model constraints; obtain and obtain the model optimization target based on the renewable energy consumption, construction cost, operating cost and wind and solar abandonment penalty value; perform model optimization coordination according to the boundary conditions, the model constraints and the model optimization target to obtain the optimization coordination model of renewable energy consumption capacity. It is understandable that the technical solution shown in the present invention takes into account multiple boundary conditions, model constraints and model optimization targets, so that the accuracy of the planning obtained by the optimized model is improved.

[0056] It should be noted that the acquisition of the operating data of renewable energy power generation, typical load curves and equipment maintenance plans as optimization boundary conditions includes:

[0057] Perform cluster analysis on the historical operation data of renewable energy power generation to obtain a set of typical daily power curves of renewable energy;

[0058] The typical daily power curve set of the new energy source, the typical load curve, and the equipment maintenance plan are used as boundary conditions for optimization.

[0059] It should be noted that obtaining the equipment maintenance plan includes:

[0060] Determine the equipment maintenance plan based on the pre-set unit and line maintenance rules and conditions.

[0061] In specific practice, the boundary conditions can be set through information settings such as sections and units, new energy output (single machine, partition) capacity settings, energy storage (first phase according to boundary condition variables) and other boundary conditions settings, batch adjustments by bus, by partition, by type (such as unit, load type), and curve generation and combination adjustment for different time periods.

[0062] It should be noted that the corresponding constraints at least include:

[0063] Power balance constraints, frequency regulation standby constraints, operation standby constraints, unit operation constraints, and network security constraints.

[0064] In specific practice, constraints can be roughly divided into the following types of constraints: system constraints, unit constraints, safety constraints and new energy resource constraints, and system constraints include: system power balance constraints, system accident reserve constraints, system positive and negative reserve constraints. The system power balance constraint is that the power supply must match the demand in each period; the system accident reserve constraint is that the system needs to reserve a certain capacity to prepare for equipment failure; the system positive and negative reserve constraints include upper and lower reserve constraints. The unit constraints include: upper and lower limit constraints on unit output, the unit output needs to be between the minimum technical output and the rated capacity; unit climbing constraints, the unit output change between adjacent periods needs to be within the climbing limit; unit minimum start-up and shutdown time constraints, the unit needs to meet the minimum start-up and shutdown time constraints; unit state constraints, the unit is shut down in the maintenance state, and the capacity is limited when the unit is blocked. The safety constraints include: line thermal stability limit constraints, the line flow needs to be below the thermal stability limit; section safety constraints, the section flow needs to be within the safety limit. The new energy resource constraints include: a node / region new energy resource limit, which is a value that the new energy optimization amount of the node / region cannot exceed.

[0065] It should be noted that the method described above further includes:

[0066] The model optimization objective is expressed by the following formula:

[0067]

[0068] in, is the consumption of new energy unit i at time t, is the predicted value of new energy unit i at time t.

[0069] In actual practice, the above formula can maximize the new energy consumption capacity while taking into account the balance of power supply and demand and network security constraints within the existing unit regulation capacity.

[0070] The expected display results of the optimization coordination model obtained through the above formula include the overall situation of the three major balances (electricity, power, and peak load regulation), an overview of boundary conditions such as the maximum absorption capacity of the bus (area), regional start-up and shutdown conditions, and energy storage.

[0071] It should be noted that Figure 2 is a schematic diagram of a data acquisition process according to an exemplary embodiment. Figure 2 , the obtaining of the operation data of the new energy power generation includes:

[0072] Obtain BPA input files and manually entered maintenance data;

[0073] The BPA input file and the manually entered maintenance data are analyzed to obtain the operation data of the new energy power generation.

[0074] It should be noted that the data analysis includes:

[0075] The BPA input file is parsed to form a point-line model, the point-line model is cut, and the cross section and new energy are defined.

[0076] It should be noted that the operating data at least includes:

[0077] System demand data, unit data, topology data, section data, node / regional power generation potential data.

[0078] In specific practice, the system requirement data is shown in Table 1:

[0079]

[0080] Table 1

[0081] The unit data is shown in Table 2:

[0082]

[0083]

[0084] Table 2

[0085] The cross-sectional data are shown in Table 3:

[0086] Data Item Detailed Description Section Limit Positive and negative limits of the cross section Basic information of section Section name, ID, etc. Section includes equipment Information about lines and main transformers included in the section Section flow calculation formula Section flow calculation method

[0087] Table 3

[0088] The node / region power generation potential data is the new energy resource data, as shown in Table 4:

[0089]

[0090] Table 4

[0091] The operation data may also include line data, including basic line information and line thermal stability limit.

[0092] In practical applications, the scientificity and effectiveness of this embodiment are demonstrated, including: regional new energy distribution planning strategy based on system vulnerability indicators; renewable energy consumption capacity planning optimization decision-making algorithm that supports multiple operating modes, multiple scenarios, multi-dimensional space, and multiple constraints; flow calculation results and BPA comparison verification; optimization result analysis: optimization results are analyzed by bus / partition; constraint triggering situation analysis: key constraints are analyzed by bus / partition.

[0093] The key points and risk management of this embodiment include: BPA data integrity; curve data rationality and maintainability; unified maintenance and management of model versions; batch adjustment strategy setting (iterative optimization strategy), such as adjustment according to the bus short-circuit capacity ratio; time and space joint optimization: regional and annual analysis requires more comprehensive model and data management, which greatly increases maintenance work, such as information collection on start-up and shutdown duration, climbing ability, etc.

[0094] The key points and difficulties of this embodiment are as follows:

[0095] 1. Determine the boundary of the optimization model for renewable energy absorption capacity. The power of new energy is intermittent and sudden, and its historical change law needs to be extracted as the boundary condition of the absorption assessment model. At the same time, it is also necessary to consider the peak and valley change characteristics of the preset target area load, as well as the maintenance plan arrangement of the units and lines, to ensure that the model boundary has a certain representativeness and the availability of the optimization results.

[0096] 2. Establishment and solution of the optimization model for renewable energy absorption capacity. Since the optimization solution involves a large number of variables and constraints, it is a large-scale nonlinear optimization problem. It is necessary to perform linear modeling for specific constraints and introduce 0-1 variables to characterize its nonlinear characteristics.

[0097] 3. System implementation and data interaction. Due to the need to interact with BPA, the input and output data of BPA need to be analyzed and converted in a targeted manner. At the same time, the BPA analysis results need to be added as constraints into the optimization model. How to convert BPA limit-exceeding information into constraints is also a major difficulty of this project.

[0098] 4. Multi-source big data mining and analysis. The platform's data application includes not only data analysis of power grid core operation data, renewable energy data, etc., but also comprehensive analysis of the total amount and distribution of consumption capacity under current boundary conditions, as well as bottlenecks affecting consumption capacity, to fully tap consumption potential; multi-source heterogeneous big data processing for consumption capacity needs practical polishing.

[0099] 5. Network security verification: Based on the actual model of the power grid, comprehensive consideration is given to line and section transmission capacity limits, line / main transformer N-1, custom fault set scanning and other security verifications, and the closed-loop security verification is completed in collaboration with the optimization decision-making function.

[0100] Embodiment 2

[0101] Figure 3 is a schematic block diagram of a renewable energy consumption capacity optimization and coordination device according to an exemplary embodiment, see Figure 3 , providing a renewable energy consumption capacity optimization and coordination device, comprising:

[0102] The boundary condition acquisition module 101 is used to acquire the operation data, typical load curve and equipment maintenance plan of the new energy power generation as the boundary conditions for optimization;

[0103] The model constraint acquisition module 102 is used to acquire the actual operation requirements and corresponding constraint conditions of the preset regional power grid as model constraints;

[0104] The optimization target acquisition module 103 is used to obtain and obtain the model optimization target based on the new energy consumption, construction cost, operation cost and wind and solar power abandonment penalty value;

[0105] The optimization and coordination module 104 is used to perform model optimization and coordination according to the boundary conditions, the model constraints and the model optimization objectives to obtain a renewable energy consumption capacity optimization and coordination model.

[0106] It can be understood that the technical solution provided in this embodiment can obtain the operation data, typical load curve and equipment maintenance plan of new energy power generation through the boundary condition acquisition module 101 as the boundary conditions for optimization; obtain the actual operation requirements and corresponding constraints of the preset regional power grid through the model constraint acquisition module 102 as the model constraints; obtain and obtain the model optimization target according to the new energy consumption, construction cost, operation cost and wind and solar abandonment penalty value through the optimization target acquisition module 103; and perform model optimization and coordination according to the boundary conditions, the model constraints and the model optimization targets through the optimization coordination module 104 to obtain the optimization and coordination model of renewable energy consumption capacity optimization. It can be understood that the technical solution shown in this embodiment takes into account multiple boundary conditions, model constraints and model optimization targets, so that the accuracy of the planning obtained by the optimized model is improved.

[0107] Preferably, the boundary condition acquisition module is also used to perform cluster analysis on the historical operation data of renewable energy power generation to obtain a set of typical daily power curves of renewable energy; and use the set of typical daily power curves of renewable energy together with the typical load curve and the equipment maintenance plan as optimized boundary conditions.

[0108] Preferably, it also includes: an operation data acquisition module, which is used to acquire BPA input files and manually entered maintenance data; and perform data analysis on the BPA input files and manually entered maintenance data to obtain the operation data of the new energy power generation.

[0109] Preferably, the renewable energy absorption capacity optimization and coordination device can also be provided with a functional interface, and the functional interface design mainly includes the following three categories: 1. Data maintenance units, loads, new energy, energy storage, etc., with import and export functions from BPA or in standard formats. 2. Calculation verification function, with flow calculation function, with the function of starting optimization and generating result section data, and with safety verification function. 3. Visualization and result export: visualization of various calculation results, supporting the export of calculation results into standard calculation software input files.

[0110] Embodiment 3

[0111] Provided is a renewable energy consumption capacity optimization and coordination device, comprising:

[0112] A main controller, and a memory connected to the main controller;

[0113] The memory stores program instructions;

[0114] The main controller is used to execute program instructions stored in the memory to perform any of the above methods.

[0115] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0116] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0117] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0118] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0119] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0120] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0121] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0122] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0123] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for optimizing and coordinating renewable energy consumption capacity, characterized in that: include: Obtain the operating data, typical load curves and equipment maintenance plans of renewable energy power generation as the boundary conditions for optimization; Obtain the actual operation requirements and corresponding constraints of the preset regional power grid as model constraints; Obtain and obtain the model optimization target based on the new energy consumption, construction cost, operation cost and penalty value for wind and solar power abandonment; According to the boundary conditions, the model constraints and the model optimization objectives, model optimization coordination is performed to obtain a renewable energy absorption capacity optimization coordination model.

2. The method according to claim 1, characterized in that The obtaining of equipment maintenance plan includes: Determine the equipment maintenance plan based on the pre-set unit and line maintenance rules and conditions.

3. The method according to claim 2, characterized in that The acquisition of the operation data, typical load curve and equipment maintenance plan of the renewable energy power generation as the boundary conditions for optimization includes: Perform cluster analysis on the historical operation data of renewable energy power generation to obtain a set of typical daily power curves of renewable energy; The typical daily power curve set of the new energy source, the typical load curve, and the equipment maintenance plan are used as boundary conditions for optimization.

4. The method according to claim 1, characterized in that: The corresponding constraints include at least: Power balance constraints, frequency regulation standby constraints, operation standby constraints, unit operation constraints, and network security constraints.

5. The method according to claim 1, characterized in that Also includes: The model optimization objective is expressed by the following formula: in, is the consumption of new energy unit i at time t, is the predicted value of new energy unit i at time t.

6. The method according to claim 1, characterized in that The obtaining of the operation data of the new energy power generation includes: Obtain BPA input files and manually entered maintenance data; The BPA input file and the manually entered maintenance data are analyzed to obtain the operation data of the new energy power generation.

7. The method according to claim 6, characterized in that The data analysis includes: The BPA input file is parsed to form a point-line model, the point-line model is cut, and the cross section and new energy are defined.

8. The method according to claim 6, characterized in that The operation data at least includes: System demand data, unit data, topology data, section data, node / regional power generation potential data.

9. A renewable energy consumption capacity optimization and coordination device, characterized in that: include: The boundary condition acquisition module is used to obtain the operating data, typical load curves and equipment maintenance plans of renewable energy power generation as the boundary conditions for optimization; A model constraint acquisition module is used to obtain the actual operation requirements and corresponding constraint conditions of the preset regional power grid as model constraints; The optimization target acquisition module is used to obtain the model optimization target based on the new energy consumption, construction cost, operation cost and wind and solar power abandonment penalty value; The optimization and coordination module is used to perform model optimization and coordination according to the boundary conditions, the model constraints and the model optimization objectives to obtain an optimization and coordination model for the renewable energy absorption capacity.

10. The device according to claim 9, characterized in that The boundary condition acquisition module is also used to perform cluster analysis on the historical operation data of renewable energy power generation to obtain a set of typical daily power curves of renewable energy; and use the set of typical daily power curves of renewable energy together with the typical load curve and the equipment maintenance plan as optimized boundary conditions.

11. The device according to claim 10, characterized in that Also includes: Run the data acquisition module to obtain BPA input files and manually entered maintenance data; The BPA input file and the manually entered maintenance data are analyzed to obtain the operation data of the new energy power generation.

12. A renewable energy consumption capacity optimization and coordination device, characterized in that: include: A main controller, and a memory connected to the main controller; The memory stores program instructions; The main controller is used to execute program instructions stored in the memory and perform the method according to any one of claims 1 to 8.

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