Renewable energy consumption capability assessment method, device and equipment

Abstract

The invention relates to the technical field of renewable energy sources, in particular to a renewable energy source consumption capability evaluation method, device and equipment, and the method comprises the steps: obtaining consumption capability evaluation data which at least comprises a typical load curve, a tie line plan and a wind and light unit power prediction value; obtaining model constraints, and according to the model constraints, constructing a mathematical model taking maximization of new energy consumption / installed capacity as a target; an optimization strategy based on artificial intelligence is adopted, and the preset maximum new energy consumption capacity / maximum wind power and photoelectric installed capacity of each node and region is obtained; and according to the consumption capability assessment data, the mathematical model and the maximum installed capacity, carrying out optimization solution to obtain a renewable energy consumption capability assessment result. It can be understood that by means of the technical scheme, accurate data can be obtained, modeling can be conducted according to constraints, and the accuracy of the evaluation result and the evaluation efficiency are greatly 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 evaluating renewable energy absorption capacity. Background Art

[0002] At present, the existing method of evaluating the renewable energy absorption capacity is to select a time section, fix the load and interconnection line, and after the startup combination is determined, the wind power output space is determined based on the difference between the net load and the minimum adjustable output of thermal power near the low point at night (no photovoltaic power generation), and converted into wind power capacity; then return to the daytime when there is photovoltaic power, subtract the thermal power space from the peak load to obtain the photovoltaic output space, which is converted into photovoltaic capacity, thereby obtaining the maximum loadable new energy installed capacity; then investigate the geographical, meteorological, construction site and other conditions in various parts of the preset funding area, obtain the distribution of wind and solar resources, and obtain the resource potential installed capacity; finally, the designed total installed capacity is distributed to various places, BPA data is simulated, and adjustments are made if there are any problems. However, there are many problems. For example, the analysis of a single time section makes it difficult to consider the balance of electricity and power in continuous operation. Wind power, photovoltaics and other units are actually coupled through load balance, and it is difficult to take them into overall consideration if they are estimated separately here. The constraints of the transmission capacity of the transmission channel on the absorption of wind and solar power are not considered. The installed capacity of wind power and photovoltaics is planned in the form of total volume, which makes it difficult to be refined to the city level. The installed capacity planning obtained is not convincing enough.

[0003] Therefore, the current method of evaluating the renewable energy absorption capacity of large power grids lacks standardized information processing and modeling capabilities, the calculation lines are rough, the results lack sensitivity analysis, and there is no accurate modeling and iterative optimization of renewable absorption capacity analysis auxiliary decision-making tools. As a result, the analysis accuracy of the renewable energy absorption capacity of large power grids is low and the workload is large, resulting in low accuracy and low efficiency in the evaluation results of renewable energy absorption capacity. Summary of the invention

[0004] In view of this, an object of the present invention is to provide a renewable energy absorption capacity assessment method, device and equipment to solve the problem of low accuracy and low efficiency of renewable energy absorption capacity assessment results in the prior art.

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

[0006] Obtaining absorptive capacity assessment data;

[0007] Obtaining model constraints, and constructing a mathematical model with the goal of maximizing new energy consumption / installed capacity based on the model constraints;

[0008] Adopting an AI-based optimization strategy to obtain the maximum renewable energy consumption capacity / maximum installed capacity of wind power and photovoltaic power for each preset node and region;

[0009] An optimization solution is performed based on the absorption capacity assessment data, the mathematical model and the maximum installed capacity to obtain a renewable energy absorption capacity assessment result.

[0010] Preferably, after obtaining the renewable energy consumption capacity assessment result, the method further includes:

[0011] Analyze the renewable energy absorption capacity assessment results using BPA related systems;

[0012] Determine whether there are any line / section constraints exceeding the limit based on the analysis results;

[0013] If so, the optimization solution is performed again according to the line / section constraints that exceed the limit until no line / section constraints exceed the limit.

[0014] Preferably, the renewable energy absorption capacity assessment results include:

[0015] The first result: When the maximum renewable energy consumption capacity / maximum installed capacity of wind power and photovoltaic power for each node and region is determined, the feasible consumption of renewable energy for the whole year;

[0016] The second result: the maximum feasible installed capacity of new energy for each preset node and region.

[0017] Preferably, when calculating the first result, the model constraints at least include:

[0018] Consumption load balance constraints, system positive and negative reserve constraints, minimum start-up / shutdown time constraints of thermal power units, unit operation status and start-up / shutdown variable coupling constraints of thermal power units, output and state variable coupling constraints of thermal power units, peak load regulation capacity constraints of thermal power units, upper and lower limits of peak load regulation / output of thermal power units, climbing constraints of thermal power units and line flow constraints.

[0019] Preferably, the formula for the consumption load balance constraint is:

[0020]

[0021] The formula for the system positive and negative reserve constraints is:

[0022]

[0023] The formula for the minimum startup / shutdown time constraint of the thermal power unit is:

[0024]

[0025] The formula for the coupling constraint of the unit operation state and startup / shutdown variables of the thermal power unit is:

[0026]

[0027] The formula for the output of the thermal power unit and the state variable coupling constraint is:

[0028]

[0029] The formula for the peak load capacity constraint of the thermal power unit is:

[0030]

[0031] The upper and lower limits of the peak load / output of the thermal power unit are:

[0032]

[0033] The formula for the ramp constraint of the thermal power unit is:

[0034]

[0035] The formula for the line power flow constraint is:

[0036]

[0037] The constant definitions include: N1 represents other units except new energy units; N2 represents the set of new energy units; GSDF l,i represents the sensitivity factor of node i to line l; L represents the line set; Represents the power forecast value of the new energy unit i at time t; Load t represents the predicted system power value at time t; TU i Indicates the minimum startup time of unit i; TD i Represents the minimum downtime of unit i; PR t Indicates that the system is in standby state at time t; NR t Indicates the system negative reserve at time t; NL i,t represents the node load of node i at time t; d i,t represents the adjustable peak quantity of unit i at time t; represents the upper limit of adjustable output of unit i at time t; represents the lower limit of adjustable output of unit i at time t;

[0038] The variable definition includes: i,t Indicates the output of units other than new energy units; Expresses the peak load regulation of the unit; v i,t represents the state variable of unit i at time t; xi,t represents the startup variable of unit i at time t; y i,t represents the shutdown variable of unit i at time t; Indicates the output of new energy units.

[0039] Preferably, when calculating the second result, the model constraints at least include:

[0040] Installed capacity load balance constraints, system positive and negative reserve constraints, minimum start-up / shutdown time constraints of thermal power units, unit operation status and start-up / shutdown variable coupling constraints of thermal power units, output and state variable coupling constraints of thermal power units, peak-shaving capacity constraints of thermal power units, upper and lower limits of peak-shaving / output of thermal power units, climbing constraints of thermal power units, line flow constraints, new energy output constraints and new energy power abandonment constraints.

[0041] Preferably, the formula for the installed capacity load balance constraint is:

[0042]

[0043] The formula for the new energy output constraint is:

[0044]

[0045] The formula for the new energy curtailment constraint is:

[0046]

[0047] Among them, the constant definitions include: represents the normalized wind power at node i at time t; represents the normalized photovoltaic power of node i at time t;

[0048] The variable definition includes: represents the maximum installed capacity of wind power at node i; represents the maximum installed photovoltaic capacity of node i; represents the wind power output of node i at time t; represents the photovoltaic output of node i at time t; represents the wind abandonment amount at node i at time t; It represents the amount of abandoned light at node i at time t.

[0049] Preferably, the absorption capacity assessment data includes at least typical load curves, interconnection line plans and wind and solar power unit power forecast values.

[0050] According to a second aspect of an embodiment of the present invention, there is provided a renewable energy consumption capacity assessment device, comprising:

[0051] A data acquisition module is used to acquire consumption capacity assessment data, wherein the consumption capacity assessment data at least includes a typical load curve, a tie line plan, and a wind and solar power unit power forecast value;

[0052] A model building module, used to obtain model constraints and build a mathematical model with the goal of maximizing the new energy consumption / installed capacity according to the model constraints;

[0053] The installed capacity acquisition module uses an artificial intelligence-based optimization strategy to obtain the maximum new energy consumption capacity / maximum installed capacity of wind power and photovoltaic power for each preset node and region;

[0054] The optimization and solution module is used to perform optimization and solution according to the absorption capacity evaluation data, the mathematical model and the maximum installed capacity to obtain the renewable energy absorption capacity evaluation result.

[0055] Preferably, the device further comprises:

[0056] The verification optimization module is used to analyze the renewable energy absorption capacity assessment results using the BPA related system; determine whether there are any line / section constraints that exceed the limit based on the analysis results; if so, optimize and solve again based on the line / section constraints that exceed the limit until there are no line / section constraints that exceed the limit.

[0057] Preferably, the renewable energy absorption capacity assessment results generated by the optimization solution module include at least: the first result: the feasible absorption capacity of new energy throughout the year when the maximum new energy absorption capacity / maximum installed capacity of wind power and photovoltaic power for each preset node and region is determined; the second result: the maximum feasible installed capacity of new energy for each preset node and region.

[0058] According to a third aspect of an embodiment of the present invention, there is provided a renewable energy absorption capacity assessment device, comprising:

[0059] A main controller, and a memory connected to the main controller;

[0060] The memory stores program instructions;

[0061] The main controller is used to execute program instructions stored in the memory to perform any of the above methods.

[0062] The technical solution provided by the embodiments of the present invention may have the following beneficial effects:

[0063] It is understandable that the technical solution shown in the present invention obtains the absorption capacity assessment data, and the absorption capacity assessment data at least includes typical load curves, interconnection line plans and wind and solar power unit power forecast values; obtains model constraints, and constructs a mathematical model with the goal of maximizing the absorption / installed capacity of new energy according to the model constraints; adopts an optimization strategy based on artificial intelligence to obtain the preset maximum absorption capacity of new energy / maximum installed capacity of wind power and photovoltaic power for each node and region; optimizes and solves according to the absorption capacity assessment data, the mathematical model and the maximum installed capacity to obtain the evaluation result of the absorption capacity of renewable energy. It is understandable that the technical solution shown in the present invention can be analyzed in detail for a single scenario, or it can be combined with multiple scenarios for joint analysis and optimization, taking into account the balance of power and electricity in the continuous operation scenario; considering the output of wind power and photovoltaic power at the same time, enhancing the credibility of the results; taking into account the overall regional and network transmission capacity, ensuring the availability of the results; being able to obtain accurate data and modeling according to constraints, greatly improving the accuracy of the evaluation results and the evaluation efficiency.

[0064] 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

[0065] 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.

[0066] Figure 1 is a schematic diagram of steps of a renewable energy absorption capacity evaluation method according to an exemplary embodiment;

[0067] Figure 2 is a flow chart of a renewable energy absorption capacity evaluation method according to an exemplary embodiment;

[0068] Figure 3 It is a schematic block diagram of a renewable energy absorption capacity assessment device according to an exemplary embodiment. DETAILED DESCRIPTION

[0069] 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.

[0070] Embodiment 1

[0071] Figure 1 is a schematic diagram of the steps of a renewable energy absorption capacity evaluation method according to an exemplary embodiment, see Figure 1 , providing a renewable energy absorption capacity assessment method, including:

[0072] Step S11, obtaining consumption capacity assessment data;

[0073] Preferably, the absorption capacity assessment data includes at least typical load curves, interconnection line plans and wind and solar power unit power forecast values.

[0074] Step S12: obtaining model constraints, and constructing a mathematical model with the goal of maximizing the new energy consumption / installed capacity according to the model constraints;

[0075] Step S13: adopt an optimization strategy based on artificial intelligence to obtain the maximum new energy consumption capacity / maximum installed capacity of wind power and photovoltaic power for each preset node and region;

[0076] Step S14: performing optimization calculations based on the absorption capacity assessment data, the mathematical model and the maximum installed capacity to obtain a renewable energy absorption capacity assessment result.

[0077] It is understandable that the technical solution shown in the present invention obtains the absorption capacity assessment data, and the absorption capacity assessment data at least includes typical load curves, interconnection line plans and wind and solar power unit power forecast values; obtains model constraints, and constructs a mathematical model with the goal of maximizing the absorption / installed capacity of new energy according to the model constraints; adopts an optimization strategy based on artificial intelligence to obtain the preset maximum absorption capacity of new energy / maximum installed capacity of wind power and photovoltaic power for each node and region; optimizes and solves according to the absorption capacity assessment data, the mathematical model and the maximum installed capacity to obtain the evaluation result of the absorption capacity of renewable energy. It is understandable that the technical solution shown in the present invention can be analyzed in detail for a single scenario, or it can be combined with multiple scenarios for joint analysis and optimization, taking into account the balance of power and electricity in the continuous operation scenario; considering the output of wind power and photovoltaic power at the same time, enhancing the credibility of the results; taking into account the overall regional and network transmission capacity, ensuring the availability of the results; being able to obtain accurate data and modeling according to constraints, greatly improving the accuracy of the evaluation results and the evaluation efficiency.

[0078] It should be noted that after obtaining the renewable energy absorption capacity assessment results, it also includes:

[0079] Analyze the renewable energy absorption capacity assessment results using BPA related systems;

[0080] Determine whether there are any line / section constraints exceeding the limit based on the analysis results;

[0081] If so, the optimization solution is performed again according to the line / section constraints that exceed the limit until no line / section constraints exceed the limit.

[0082] In practice, see Figure 2 , the technical solution shown in this embodiment first needs to obtain data and perform data verification. At this time, the data obtained is the absorption capacity assessment data and the preset maximum new energy absorption capacity / maximum installed capacity of wind power and photovoltaic power for each node and region. After that, the data model is constructed. According to the model constraints, a mathematical model with the goal of maximizing the absorption / installed capacity of new energy is constructed. Preferably, the constructed model does not contain flow constraints. After that, optimization and solution are performed, and Cplex (digital optimization technology) or Gurobi (a new generation of large-scale mathematical programming optimizer) solver is called to solve and obtain the calculation results. The line / section flow calculation is performed on the calculation results, and it is determined whether there are line / section constraints that exceed the limit. If so, the optimization and solution are performed again according to the line / section constraints that exceed the limit until there are no line / section constraints that exceed the limit.

[0083] It should be noted that the renewable energy absorption capacity assessment results include:

[0084] The first result: When the maximum renewable energy consumption capacity / maximum installed capacity of wind power and photovoltaic power for each node and region is determined, the feasible consumption of renewable energy for the whole year;

[0085] The second result: the maximum feasible installed capacity of new energy for each preset node and region.

[0086] In practice, when solving the evaluation results of renewable energy consumption capacity, there are two sub-problems: 1. The feasible consumption of renewable energy throughout the year, assuming that the installed capacity of renewable energy at each node is determined; 2. The maximum feasible installed capacity of renewable energy at each node. Therefore, two results can be obtained.

[0087] It should be noted that, when calculating the first result, the model constraints at least include:

[0088] Consumption load balance constraints, system positive and negative reserve constraints, minimum start-up / shutdown time constraints of thermal power units, unit operation status and start-up / shutdown variable coupling constraints of thermal power units, output and state variable coupling constraints of thermal power units, peak load regulation capacity constraints of thermal power units, upper and lower limits of peak load regulation / output of thermal power units, climbing constraints of thermal power units and line flow constraints.

[0089] The formula for the load balance constraint of the consumption amount is:

[0090]

[0091] The formula for the system positive and negative reserve constraints is:

[0092]

[0093] The formula for the minimum startup / shutdown time constraint of the thermal power unit is:

[0094]

[0095] The formula for the coupling constraint of the unit operation state and startup / shutdown variables of the thermal power unit is:

[0096]

[0097] The formula for the output of the thermal power unit and the state variable coupling constraint is:

[0098]

[0099] The formula for the peak load capacity constraint of the thermal power unit is:

[0100]

[0101] The upper and lower limits of the peak load / output of the thermal power unit are:

[0102]

[0103] The formula for the ramp constraint of the thermal power unit is:

[0104]

[0105] The formula for the line power flow constraint is:

[0106]

[0107] The constant definitions include: N1 represents other units except new energy units; N2 represents the set of new energy units; GSDF l,i represents the sensitivity factor of node i to line l; L represents the line set; Represents the power forecast value of the new energy unit i at time t; Load t represents the predicted system power value at time t; TU i Indicates the minimum startup time of unit i; TD i Represents the minimum downtime of unit i; PR t Indicates that the system is in standby state at time t; NR t Indicates the system negative reserve at time t; NL i,t represents the node load of node i at time t; d i,t represents the adjustable peak quantity of unit i at time t; represents the upper limit of adjustable output of unit i at time t; represents the lower limit of adjustable output of unit i at time t;

[0108] The variable definition includes: i,t Indicates the output of units other than new energy units; Expresses the peak load regulation of the unit; v i,t represents the state variable of unit i at time t; x i,t represents the startup variable of unit i at time t; y i,t represents the shutdown variable of unit i at time t; Indicates the output of new energy units.

[0109] It should be noted that, when calculating the second result, the model constraints at least include:

[0110] Installed capacity load balance constraints, system positive and negative reserve constraints, minimum start-up / shutdown time constraints of thermal power units, unit operation status and start-up / shutdown variable coupling constraints of thermal power units, output and state variable coupling constraints of thermal power units, peak-shaving capacity constraints of thermal power units, upper and lower limits of peak-shaving / output of thermal power units, climbing constraints of thermal power units, line flow constraints, new energy output constraints and new energy power abandonment constraints.

[0111] It should be noted that the formula for the installed capacity load balance constraint is:

[0112]

[0113] The formula for the new energy output constraint is:

[0114]

[0115] The formula for the new energy curtailment constraint is:

[0116]

[0117] Among them, the constant definitions include: represents the normalized wind power at node i at time t; represents the normalized photovoltaic power of node i at time t;

[0118] The variable definition includes: represents the maximum installed capacity of wind power at node i; represents the maximum installed photovoltaic capacity of node i; represents the wind power output of node i at time t; represents the photovoltaic output of node i at time t; represents the wind abandonment amount at node i at time t; It represents the amount of abandoned light at node i at time t.

[0119] It can be understood that the technical solution provided in this embodiment studies long time scales, different new energy levels, multiple operating modes, and different unit combination scenarios, and reasonably constructs optimization models and constraint processing. It can assist in solving planning optimization methods that are difficult problems such as peak-valley startup conflicts. At the same time, it uses artificial intelligence technology to realize the research on unified standardized algorithms for the maximum absorption capacity and maximum installed capacity of new energy at different time scales and spatial levels.

[0120] Embodiment 2

[0121] Figure 3 is a schematic block diagram of a renewable energy absorption capacity evaluation device according to an exemplary embodiment, see Figure 3 , a renewable energy consumption capacity assessment device is provided, comprising:

[0122] The data acquisition module 101 is used to acquire the consumption capacity evaluation data, wherein the consumption capacity evaluation data at least includes a typical load curve, a tie line plan and a wind and solar power unit power prediction value;

[0123] A model building module 102 is used to obtain model constraints and build a mathematical model with the goal of maximizing the new energy consumption / installed capacity according to the model constraints;

[0124] The installed capacity acquisition module 103 adopts an optimization strategy based on artificial intelligence to obtain the maximum new energy consumption capacity / maximum installed capacity of wind power and photovoltaic power for each preset node and region;

[0125] The optimization and solution module 104 is used to perform optimization and solution according to the absorption capacity evaluation data, the mathematical model and the maximum installed capacity to obtain the renewable energy absorption capacity evaluation result.

[0126] Preferably, the device further comprises:

[0127] The verification optimization module is used to analyze the renewable energy absorption capacity assessment results using the BPA related system; determine whether there are any line / section constraints that exceed the limit based on the analysis results; if so, optimize and solve again based on the line / section constraints that exceed the limit until there are no line / section constraints that exceed the limit.

[0128] Preferably, the renewable energy absorption capacity assessment results generated by the optimization solution module include at least: the first result: the feasible absorption capacity of new energy throughout the year when the maximum new energy absorption capacity / maximum installed capacity of wind power and photovoltaic power for each preset node and region is determined; the second result: the maximum feasible installed capacity of new energy for each preset node and region.

[0129] It can be understood that the technical solution shown in the present invention obtains the absorption capacity assessment data through the data acquisition module 101, and the absorption capacity assessment data at least includes the typical load curve, the interconnection line plan and the wind and solar power unit power forecast value; obtains the model constraints through the model construction module 102, and constructs a mathematical model with the goal of maximizing the new energy absorption / installed capacity according to the model constraints; adopts an artificial intelligence-based optimization strategy through the installed capacity acquisition module 103 to obtain the maximum new energy absorption capacity / wind power and photovoltaic maximum installed capacity of each node and region; and performs optimization and solution according to the absorption capacity assessment data, the mathematical model and the maximum installed capacity through the optimization solution module 104 to obtain the renewable energy absorption capacity assessment result. It can be understood that the technical solution shown in the present invention can be used for detailed analysis of a single scenario, or it can be used to integrate multiple scenarios for joint analysis and optimization, taking into account the power balance under the continuous operation scenario; taking into account the output of wind power and photovoltaic power at the same time, thereby enhancing the credibility of the results; taking into account the regional and network transmission capabilities globally, thereby ensuring the availability of the results; and being able to obtain accurate data and perform modeling based on constraints, thereby greatly improving the accuracy of the evaluation results and the efficiency of the evaluation.

[0130] Embodiment 3

[0131] According to a third aspect of an embodiment of the present invention, there is provided a renewable energy absorption capacity assessment device, comprising:

[0132] A main controller, and a memory connected to the main controller;

[0133] The memory stores program instructions;

[0134] The main controller is used to execute program instructions stored in the memory to perform any of the above methods.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0142] 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.

[0143] 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 evaluating renewable energy absorption capacity, characterized in that: include: Obtaining absorptive capacity assessment data; Obtaining model constraints, and constructing a mathematical model with the goal of maximizing new energy consumption / installed capacity based on the model constraints; Adopting an AI-based optimization strategy to obtain the maximum renewable energy consumption capacity / maximum installed capacity of wind power and photovoltaic power for each preset node and region; An optimization solution is performed based on the absorption capacity assessment data, the mathematical model and the maximum installed capacity to obtain a renewable energy absorption capacity assessment result.

2. The method according to claim 1, characterized in that After obtaining the renewable energy consumption capacity assessment result, the method further includes: Analyze the renewable energy absorption capacity assessment results using BPA related systems; Determine whether there are any line / section constraints exceeding the limit based on the analysis results; If so, the optimization solution is performed again according to the line / section constraints that exceed the limit until no line / section constraints exceed the limit.

3. The method according to claim 1, characterized in that The renewable energy absorption capacity assessment results include: The first result: When the maximum renewable energy consumption capacity / maximum installed capacity of wind power and photovoltaic power for each node and region is determined, the feasible consumption of renewable energy for the whole year; The second result: the maximum feasible installed capacity of new energy for each preset node and region.

4. The method according to claim 3, characterized in that When calculating the first result, the model constraints include at least: Consumption load balance constraints, system positive and negative reserve constraints, minimum start-up / shutdown time constraints of thermal power units, unit operation status and start-up / shutdown variable coupling constraints of thermal power units, output and state variable coupling constraints of thermal power units, peak load regulation capacity constraints of thermal power units, upper and lower limits of peak load regulation / output of thermal power units, climbing constraints of thermal power units and line flow constraints.

5. The method according to claim 4, characterized in that The formula for the load balance constraint of the consumption is: The formula for the system positive and negative reserve constraints is: The formula for the minimum startup / shutdown time constraint of the thermal power unit is: The formula for the coupling constraint of the unit operation state and startup / shutdown variables of the thermal power unit is: The formula for the output of the thermal power unit and the state variable coupling constraint is: The formula for the peak load capacity constraint of the thermal power unit is: The upper and lower limits of the peak load / output of the thermal power unit are: The formula for the ramp constraint of the thermal power unit is: The formula for the line power flow constraint is: The constant definitions include: N1 represents other units except new energy units; N2 represents the set of new energy units; GSDF l,i represents the sensitivity factor of node i to line l; L represents the line set; Represents the power forecast value of the new energy unit i at time t; Load t represents the predicted system power value at time t; TU i Indicates the minimum startup time of unit i; TD i Represents the minimum downtime of unit i; PR t Indicates that the system is in standby state at time t; NR t Indicates the system negative reserve at time t; NL i,t represents the node load of node i at time t; d i,t represents the adjustable peak quantity of unit i at time t; represents the upper limit of adjustable output of unit i at time t; represents the lower limit of adjustable output of unit i at time t; The variable definition includes: i,t Indicates the output of units other than new energy units; Expresses the peak load regulation of the unit; v i,t represents the state variable of unit i at time t; x i,t represents the startup variable of unit i at time t; y i,t represents the shutdown variable of unit i at time t; Indicates the output of new energy units.

6. The method according to claim 5, characterized in that When calculating the second result, the model constraints include at least: Installed capacity load balance constraints, system positive and negative reserve constraints, minimum start-up / shutdown time constraints of thermal power units, unit operation status and start-up / shutdown variable coupling constraints of thermal power units, output and state variable coupling constraints of thermal power units, peak-shaving capacity constraints of thermal power units, upper and lower limits of peak-shaving / output of thermal power units, climbing constraints of thermal power units, line flow constraints, new energy output constraints and new energy power abandonment constraints.

7. The method according to claim 6, characterized in that The formula for the installed capacity load balance constraint is: The formula for the new energy output constraint is: The formula for the new energy curtailment constraint is: Among them, the constant definitions include: represents the normalized wind power at node i at time t; represents the normalized photovoltaic power of node i at time t; The variable definition includes: represents the maximum installed capacity of wind power at node i; represents the maximum installed photovoltaic capacity of node i; represents the wind power output of node i at time t; represents the photovoltaic output of node i at time t; represents the wind abandonment amount at node i at time t; It represents the amount of abandoned light at node i at time t.

8. The method according to claim 1, characterized in that The absorption capacity assessment data at least includes typical load curves, interconnection line plans and wind and solar power unit power forecast values.

9. A renewable energy absorption capacity assessment device, characterized in that: include: A data acquisition module is used to obtain the consumption capacity assessment data; A model building module, used to obtain model constraints and build a mathematical model with the goal of maximizing the new energy consumption / installed capacity according to the model constraints; The installed capacity acquisition module uses an artificial intelligence-based optimization strategy to obtain the maximum new energy consumption capacity / maximum installed capacity of wind power and photovoltaic power for each preset node and region; The optimization and solution module is used to perform optimization and solution according to the absorption capacity evaluation data, the mathematical model and the maximum installed capacity to obtain the renewable energy absorption capacity evaluation result.

10. The device according to claim 9, characterized in that Also includes: A verification and optimization module, used for analyzing the renewable energy absorption capacity assessment result using a BPA-related system; Determine whether there are any line / section constraints exceeding the limit based on the analysis results; If so, the optimization solution is performed again according to the line / section constraints that exceed the limit until no line / section constraints exceed the limit.

11. The device according to claim 9, characterized in that The renewable energy absorption capacity assessment results generated by the optimization solution module include at least: the first result: when the maximum new energy absorption capacity / maximum installed capacity of wind power and photovoltaic power for each preset node and region is determined, the feasible absorption capacity of new energy throughout the year; the second result: the maximum feasible installed capacity of new energy for each preset node and region.

12. A renewable energy absorption capacity assessment 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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