CN115940152B - New energy installed capacity optimal allocation method, system, terminal and medium - Google Patents
New energy installed capacity optimal allocation method, system, terminal and medium Download PDFInfo
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Abstract
The application discloses a new energy installed capacity optimization allocation method, a system, a terminal and a medium, which relate to the technical field of new energy power generation and have the technical scheme that: acquiring power consumption load and new energy output fluctuation rate in each preassembly point year; establishing a new energy installation capacity allocation model by using the new energy installation capacity to meet the regulation and control range of the traditional unit according to the new energy output accumulation value of each time node; when a new energy installation allocation strategy generated by the new energy installation capacity allocation model faces a single pre-loading point at each time node and has the maximum fluctuation rate trough, an optimal allocation model is established for an optimal target by the minimum fluctuation risk value of the new energy installation allocation strategy; and inputting the power load and the new energy output fluctuation ratio into a new energy installed capacity allocation model and an optimized allocation model, and obtaining a new energy installed capacity optimization strategy by optimizing and solving. The application can effectively improve the reliability of the whole power system for coping with abnormal risks after the whole power system is connected with new energy.
Description
Technical Field
The application relates to the technical field of new energy power generation, in particular to a new energy installed capacity optimizing and distributing method, a system, a terminal and a medium.
Background
The new energy is generally renewable energy which is developed and utilized on the basis of new technology and comprises solar energy, biomass energy, wind energy, geothermal energy, wave energy, ocean current energy, tidal energy and the like. The new energy occupies a continuously increasing proportion in the electric power system, and because the new energy has stronger random fluctuation, the traditional unit is required to coordinate according to the adjustment allowance when the new energy is accessed into the power grid. In order to ensure the stability of the operation of the power grid, the adjustment margin of the traditional unit is limited, and the installed capacity and the installed place of the new energy to be accessed are required to be optimally planned.
At present, when planning the installed capacity of the new energy, the matching degree of the output of the new energy to be connected with the power load is mainly considered, namely, the difference value between the accumulated output of all the new energy to be connected with the same time node and the power load of the corresponding time node cannot exceed the regulation and control range of the traditional unit after the regulation allowance is considered. The existing new energy installed capacity planning method has a certain effect on the whole stable operation of the power system. However, since the new energy to be accessed is generally obtained by analyzing data of the same type and/or the same area, and the data of the power load is obtained by analyzing historical data, the two data have certain volatility and randomness, once the new energy to be accessed has abnormal downlink fluctuation on the basis of the existing data or abnormal uplink fluctuation on the basis of the existing data of the user load, the stability of the whole power system is easily reduced, and the reliability of the whole power system for coping with abnormal risks after the new energy is accessed is poor.
Therefore, how to study and design a new energy installation capacity optimizing and distributing method, system, terminal and medium capable of overcoming the defects is a problem which needs to be solved at present.
Disclosure of Invention
In order to solve the defects in the prior art, the application aims to provide a new energy installation capacity optimizing and distributing method, a system, a terminal and a medium, wherein the new energy installation capacity optimizing and distributing method, the system, the terminal and the medium are used for simulating and verifying whether a preliminary distributed new energy installation distribution strategy can resist risks when the abnormal downlink motion occurs in the new energy to be connected through an optimizing and distributing model, and finally the new energy installation distribution strategy with the minimum fluctuation risk value is selected as a final optimized new energy installation capacity optimizing strategy, so that the reliability of the whole electric power system for coping with abnormal risks after the new energy is connected can be effectively improved.
The technical aim of the application is realized by the following technical scheme:
in a first aspect, a new energy installation capacity optimizing and distributing method is provided, which includes the following steps:
acquiring the power load of the historical years and the fluctuation rate of new energy output in each preassembly point year;
establishing a new energy installation capacity allocation model according to the new energy installation capacity of the selected pre-loading point and the new energy output accumulation value of each time node meeting the regulation and control range of the traditional unit;
when a new energy installation allocation strategy generated by the new energy installation capacity allocation model faces a single pre-loading point at each time node and has the maximum fluctuation rate trough, an optimal allocation model is established for an optimal target by the minimum fluctuation risk value of the new energy installation allocation strategy;
and inputting the power load of the historical years and the fluctuation rate of the new energy output in each preassembled point year into a new energy installed capacity allocation model and an optimized allocation model, and optimizing and solving to obtain a new energy installed capacity optimization strategy.
Further, the expression of the new energy installation capacity allocation model is specifically:
wherein,,indicating that the traditional unit is->The output of the time node; />Indicating that the traditional unit is->A lower adjustment margin of the time node; />Indicating that the traditional unit is->An upper adjustment margin of the time node; />Indicate->New energy installation of each pre-loading point allocates capacity; />Indicate->The pre-loading point is->The new energy output fluctuation rate of the time node; />Representing the selected number of the pre-loading points; />Representing the planned total capacity of the new energy installation.
Further, the process for establishing the optimal allocation model specifically includes:
selecting the smallest new energy output fluctuation rate in the year of the preassembly point as the risk output fluctuation rate of the corresponding preassembly point;
selecting a risk output fluctuation rate to replace the new energy output fluctuation rate of the corresponding pre-loading point at a single time node, and judging whether the new energy output accumulation value of the single time node meets the regulation and control range of the traditional unit according to the replaced new energy output fluctuation rate; if not, determining that the current replacement is a risk replacement;
taking the duty ratio of risk replacement in all the replacement in a single time node as a risk value;
and establishing an optimization distribution model by taking the minimum sum of the average value of the risk values corresponding to all the time nodes and the standard deviation of the risk values corresponding to all the time nodes as an optimization target.
Further, the process for establishing the optimal allocation model specifically includes:
selecting the smallest new energy output fluctuation rate in the year of the preassembly point as the risk output fluctuation rate of the corresponding preassembly point;
selecting a risk output fluctuation rate to replace the new energy output fluctuation rate of the corresponding pre-loading point at a single time node, and judging whether the new energy output accumulation value of the single time node meets the regulation and control range of the traditional unit according to the replaced new energy output fluctuation rate; if not, determining that the current replacement is a risk replacement;
taking the duty ratio of risk replacement in all the replacement in a single time node as a risk value;
and establishing an optimal distribution model by taking the minimum average value of risk values corresponding to all the time nodes as an optimal target.
Further, the process for establishing the optimal allocation model specifically includes:
the establishment process of the optimal allocation model specifically comprises the following steps:
selecting the smallest new energy output fluctuation rate in the year of the preassembly point as the risk output fluctuation rate of the corresponding preassembly point;
selecting a risk output fluctuation rate to replace the new energy output fluctuation rate of the corresponding pre-loading point at a single time node, and determining the accumulated difference value of the new energy output accumulation value of the single time node lower than the lower limit value of the regulation range of the traditional unit according to the replaced new energy output fluctuation rate;
and establishing an optimization distribution model by taking the minimum sum of the average value of the accumulated differences corresponding to all the time nodes and the standard deviation of the accumulated differences corresponding to all the time nodes as an optimization target.
Further, the new energy output fluctuation rate in the preassembly point year is obtained through training according to the output data of the new energy of the same type.
Further, the determining process of the new energy output fluctuation rate specifically comprises the following steps:
acquiring a new energy output average value within the same preassembly point year;
and determining the new energy output fluctuation rate of the single time node according to the ratio of the actual estimated output of the corresponding pre-loading point at the single time node to the new energy output average value.
In a second aspect, there is provided a new energy installation capacity optimizing and distributing system, comprising:
the data acquisition module is used for acquiring the power load of the historical years and the new energy output fluctuation rate in each preassembly point year;
the capacity allocation module is used for establishing a new energy installation capacity allocation model according to the new energy installation capacity allocation capacity of the selected pre-loading point and the new energy output accumulation value of each time node meeting the regulation and control range of the traditional unit;
the optimizing and distributing module is used for establishing an optimizing and distributing model by taking the minimum fluctuation risk value of the new energy installation and distributing strategy as an optimizing target when the maximum fluctuation rate trough occurs to the single pre-loading point facing each time node by using the new energy installation and distributing strategy generated by the new energy installation and capacity distributing model;
the strategy optimization module is used for inputting the historical annual power load and the new energy output fluctuation rate in each preassembly point year into a new energy installation capacity allocation model and an optimization allocation model, and optimizing and solving to obtain a new energy installation capacity optimization strategy.
In a third aspect, a computer terminal is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements a new energy installation capacity optimization allocation method according to any one of the first aspects when executing the program.
In a fourth aspect, there is provided a computer readable medium having stored thereon a computer program executable by a processor to implement a new energy installation capacity optimizing allocation method according to any one of the first aspects.
Compared with the prior art, the application has the following beneficial effects:
1. according to the new energy installation capacity optimizing and distributing method provided by the application, after the new energy installation capacity distribution model is used for carrying out preliminary distribution on the new energy installation distribution capacity and preassembly points, the new energy installation distribution strategy subjected to preliminary distribution is used for carrying out simulation verification on whether the resistance risk is high when the abnormal descending motion of the new energy to be connected is treated through the optimizing and distributing model, and finally the new energy installation distribution strategy with the minimum fluctuation risk value is selected as the new energy installation capacity optimizing strategy subjected to final optimization, so that the reliability of the whole electric power system for treating the abnormal risk after the new energy is connected can be effectively improved;
2. according to the application, the deviation condition of the actual estimated output of the pre-loading point at the single time node relative to the output of the whole year is considered, the new energy output fluctuation rate of the single time node is determined according to the ratio of the actual estimated output of the corresponding pre-loading point at the single time node to the new energy output average value, and the abnormal risk condition can be more accurately simulated;
3. according to the application, the minimum new energy output fluctuation rate in the year of the preassembly point is selected as the risk output fluctuation rate of the corresponding preassembly point, the risk output fluctuation rate is replaced with the new energy output fluctuation rate of the corresponding time node, new energy output fluctuation data representing abnormal risk conditions are obtained through updating, and the integral risk resistance is analyzed after multiple replacement operations, so that the fluctuation condition of a single time node after the new energy is accessed is considered, and the fluctuation condition of a single preassembly point after the new energy is accessed is considered.
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The accompanying drawings, which are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the drawings:
FIG. 1 is a flow chart in an embodiment of the application;
fig. 2 is a system block diagram in an embodiment of the application.
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present application, the present application will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present application and the descriptions thereof are for illustrating the present application only and are not to be construed as limiting the present application.
Example 1: the new energy installation capacity optimizing and distributing method, as shown in figure 1, comprises the following steps:
s1: acquiring the power load of the historical years and the fluctuation rate of new energy output in each preassembly point year;
s2: establishing a new energy installation capacity allocation model according to the new energy installation capacity of the selected pre-loading point and the new energy output accumulation value of each time node meeting the regulation and control range of the traditional unit;
s3: when a new energy installation allocation strategy generated by the new energy installation capacity allocation model faces a single pre-loading point at each time node and has the maximum fluctuation rate trough, an optimal allocation model is established for an optimal target by the minimum fluctuation risk value of the new energy installation allocation strategy;
s4: and inputting the power load of the historical years and the fluctuation rate of the new energy output in each preassembled point year into a new energy installed capacity allocation model and an optimized allocation model, and optimizing and solving to obtain a new energy installed capacity optimization strategy.
According to the application, after the new energy installation allocation capacity and the preassembly points are primarily allocated through the new energy installation capacity allocation model, whether the initially allocated new energy installation allocation strategy can resist risks when the abnormal downlink motion occurs to the new energy to be accessed is dealt with is simulated and verified through the optimized allocation model, and finally the new energy installation allocation strategy with the minimum fluctuation risk value is selected as the finally optimized new energy installation capacity optimization strategy, so that the reliability of the whole electric power system for coping with the abnormal risks after the new energy is accessed can be effectively improved.
In this embodiment, the expression of the new energy installation capacity allocation model is specifically:
wherein,,indicating that the traditional unit is->The output of the time node; />Indicating that the traditional unit is->A lower adjustment margin of the time node; />Indicating that the traditional unit is->An upper adjustment margin of the time node; />Indicate->New energy installation of each pre-loading point allocates capacity; />Indicate->The pre-loading point is->The new energy output fluctuation rate of the time node; />Representing the selected number of the pre-loading points; />Representing the planned total capacity of the new energy installation.
As an optional implementation manner, the establishment process of the optimal allocation model specifically includes: selecting the smallest new energy output fluctuation rate in the year of the preassembly point as the risk output fluctuation rate of the corresponding preassembly point; selecting a risk output fluctuation rate to replace the new energy output fluctuation rate of the corresponding pre-loading point at a single time node, and judging whether the new energy output accumulation value of the single time node meets the regulation and control range of the traditional unit according to the replaced new energy output fluctuation rate; if not, determining that the current replacement is a risk replacement; taking the duty ratio of risk replacement in all the replacement in a single time node as a risk value; and establishing an optimization distribution model by taking the minimum sum of the average value of the risk values corresponding to all the time nodes and the standard deviation of the risk values corresponding to all the time nodes as an optimization target.
As another optional implementation manner, the process of establishing the optimal allocation model specifically includes: selecting the smallest new energy output fluctuation rate in the year of the preassembly point as the risk output fluctuation rate of the corresponding preassembly point; selecting a risk output fluctuation rate to replace the new energy output fluctuation rate of the corresponding pre-loading point at a single time node, and judging whether the new energy output accumulation value of the single time node meets the regulation and control range of the traditional unit according to the replaced new energy output fluctuation rate; if not, determining that the current replacement is a risk replacement; taking the duty ratio of risk replacement in all the replacement in a single time node as a risk value;
and establishing an optimal distribution model by taking the minimum average value of risk values corresponding to all the time nodes as an optimal target.
In addition, the process of establishing the optimal allocation model may specifically be: selecting the smallest new energy output fluctuation rate in the year of the preassembly point as the risk output fluctuation rate of the corresponding preassembly point; selecting a risk output fluctuation rate to replace the new energy output fluctuation rate of the corresponding pre-loading point at a single time node, and determining the accumulated difference value of the new energy output accumulation value of the single time node lower than the lower limit value of the regulation range of the traditional unit according to the replaced new energy output fluctuation rate; and establishing an optimization distribution model by taking the minimum sum of the average value of the accumulated differences corresponding to all the time nodes and the standard deviation of the accumulated differences corresponding to all the time nodes as an optimization target.
According to the application, the minimum new energy output fluctuation rate in the year of the preassembly point is selected as the risk output fluctuation rate of the corresponding preassembly point, the risk output fluctuation rate is replaced with the new energy output fluctuation rate of the corresponding time node, new energy output fluctuation data representing abnormal risk conditions are obtained through updating, and the integral risk resistance is analyzed after multiple replacement operations, so that the fluctuation condition of a single time node after the new energy is accessed is considered, and the fluctuation condition of a single preassembly point after the new energy is accessed is considered.
The new energy output fluctuation rate in the preassembly point year can be obtained by training the output data of the new energy in the same category, or can be obtained by simulating the actual application and analysis, and is not limited.
It should be noted that, the present application considers the deviation of the actual estimated output of the pre-loading point at a single time node relative to the output of the whole year to determine the output fluctuation rate of the new energy, which is different from the output fluctuation rate analysis in the prior art. In this embodiment, the determining process of the new energy output fluctuation rate specifically includes: acquiring a new energy output average value within the same preassembly point year; and determining the new energy output fluctuation rate of the single time node according to the ratio of the actual estimated output of the corresponding pre-loading point at the single time node to the new energy output average value.
Example 2: the new energy installation capacity optimizing and distributing system is used for realizing the new energy installation capacity optimizing and distributing method described in the embodiment 1, and comprises a data acquisition module, a capacity distributing module, an optimizing and distributing module and a strategy optimizing module as shown in fig. 2.
The data acquisition module is used for acquiring the power load of the historical years and the new energy output fluctuation rate in each preassembly point year; the capacity allocation module is used for establishing a new energy installation capacity allocation model according to the new energy installation capacity allocation capacity of the selected pre-loading point and the new energy output accumulation value of each time node meeting the regulation and control range of the traditional unit; the optimizing and distributing module is used for establishing an optimizing and distributing model by taking the minimum fluctuation risk value of the new energy installation and distributing strategy as an optimizing target when the maximum fluctuation rate trough occurs to the single pre-loading point facing each time node by using the new energy installation and distributing strategy generated by the new energy installation and capacity distributing model; the strategy optimization module is used for inputting the historical annual power load and the new energy output fluctuation rate in each preassembly point year into a new energy installation capacity allocation model and an optimization allocation model, and optimizing and solving to obtain a new energy installation capacity optimization strategy.
Working principle: according to the application, after the new energy installation allocation capacity and the preassembly points are primarily allocated through the new energy installation capacity allocation model, whether the initially allocated new energy installation allocation strategy can resist risks when the abnormal downlink motion occurs to the new energy to be accessed is dealt with is simulated and verified through the optimized allocation model, and finally the new energy installation allocation strategy with the minimum fluctuation risk value is selected as the finally optimized new energy installation capacity optimization strategy, so that the reliability of the whole electric power system for coping with the abnormal risks after the new energy is accessed can be effectively improved.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing detailed description of the application has been presented for purposes of illustration and description, and it should be understood that the application is not limited to the particular embodiments disclosed, but is intended to cover all modifications, equivalents, alternatives, and improvements within the spirit and principles of the application.
Claims (12)
1. The new energy installation capacity optimizing and distributing method is characterized by comprising the following steps:
acquiring the power load of the historical years and the fluctuation rate of new energy output in each preassembly point year;
establishing a new energy installation capacity allocation model according to the new energy installation capacity of the selected pre-loading point and the new energy output accumulation value of each time node meeting the regulation and control range of the traditional unit;
when a new energy installation allocation strategy generated by the new energy installation capacity allocation model faces a single pre-loading point at each time node and has the maximum fluctuation rate trough, an optimal allocation model is established for an optimal target by the minimum fluctuation risk value of the new energy installation allocation strategy;
inputting the power load of the historical years and the fluctuation rate of the new energy output in each preassembled point year into a new energy installed capacity allocation model and an optimized allocation model, and optimizing and solving to obtain a new energy installed capacity optimization strategy;
the expression of the new energy installation capacity allocation model is specifically as follows:
wherein P is t ct The output of the traditional unit at a t time node is represented;representing the lower adjustment margin of the traditional unit at a t time node; k (K) t up Representing the upper adjustment margin of a traditional unit at a t time node; p (P) i Representing the new energy installation allocation capacity of the ith pre-loading point; delta i t The new energy output fluctuation rate of the ith pre-loading point at the t time node is represented; n represents the selected number of the pre-loading points; p (P) 0 Representing the planned total capacity of the new energy installation;
the establishment process of the optimal allocation model specifically comprises the following steps:
selecting the smallest new energy output fluctuation rate in the year of the preassembly point as the risk output fluctuation rate of the corresponding preassembly point;
selecting a risk output fluctuation rate to replace the new energy output fluctuation rate of the corresponding pre-loading point at a single time node, and judging whether the new energy output accumulation value of the single time node meets the regulation and control range of the traditional unit according to the replaced new energy output fluctuation rate; if not, determining that the current replacement is a risk replacement;
taking the duty ratio of risk replacement in all the replacement in a single time node as a risk value;
and establishing an optimization distribution model by taking the minimum sum of the average value of the risk values corresponding to all the time nodes and the standard deviation of the risk values corresponding to all the time nodes as an optimization target.
2. The method for optimizing and distributing the installed capacity of the new energy according to claim 1, wherein the new energy output fluctuation rate in the preassembly period is obtained through training according to the output data of the new energy of the same type.
3. The method for optimizing and distributing the installed capacity of the new energy according to claim 1, wherein the determining process of the fluctuation rate of the output of the new energy is specifically as follows:
acquiring a new energy output average value within the same preassembly point year;
and determining the new energy output fluctuation rate of the single time node according to the ratio of the actual estimated output of the corresponding pre-loading point at the single time node to the new energy output average value.
4. The new energy installation capacity optimizing and distributing method is characterized by comprising the following steps:
acquiring the power load of the historical years and the fluctuation rate of new energy output in each preassembly point year;
establishing a new energy installation capacity allocation model according to the new energy installation capacity of the selected pre-loading point and the new energy output accumulation value of each time node meeting the regulation and control range of the traditional unit;
when a new energy installation allocation strategy generated by the new energy installation capacity allocation model faces a single pre-loading point at each time node and has the maximum fluctuation rate trough, an optimal allocation model is established for an optimal target by the minimum fluctuation risk value of the new energy installation allocation strategy;
inputting the power load of the historical years and the fluctuation rate of the new energy output in each preassembled point year into a new energy installed capacity allocation model and an optimized allocation model, and optimizing and solving to obtain a new energy installed capacity optimization strategy;
the expression of the new energy installation capacity allocation model is specifically as follows:
wherein P is t ct The output of the traditional unit at a t time node is represented;representing the lower adjustment margin of the traditional unit at a t time node; k (K) t up Representing the upper adjustment margin of a traditional unit at a t time node; p (P) i Represent the firstNew energy installation allocation capacity of i pre-loading points; delta i t The new energy output fluctuation rate of the ith pre-loading point at the t time node is represented; n represents the selected number of the pre-loading points; p (P) 0 Representing the planned total capacity of the new energy installation;
the establishment process of the optimal allocation model specifically comprises the following steps:
selecting the smallest new energy output fluctuation rate in the year of the preassembly point as the risk output fluctuation rate of the corresponding preassembly point;
selecting a risk output fluctuation rate to replace the new energy output fluctuation rate of the corresponding pre-loading point at a single time node, and judging whether the new energy output accumulation value of the single time node meets the regulation and control range of the traditional unit according to the replaced new energy output fluctuation rate; if not, determining that the current replacement is a risk replacement;
taking the duty ratio of risk replacement in all the replacement in a single time node as a risk value;
and establishing an optimal distribution model by taking the minimum average value of risk values corresponding to all the time nodes as an optimal target.
5. The method for optimizing and distributing the installed capacity of the new energy according to claim 4, wherein the fluctuation rate of the new energy output within the year of the preassembly point is obtained by training the output data of the new energy of the same category.
6. The method for optimizing and distributing the installed capacity of the new energy according to claim 4, wherein the determining process of the fluctuation rate of the output of the new energy is specifically as follows:
acquiring a new energy output average value within the same preassembly point year;
and determining the new energy output fluctuation rate of the single time node according to the ratio of the actual estimated output of the corresponding pre-loading point at the single time node to the new energy output average value.
7. The new energy installation capacity optimizing and distributing method is characterized by comprising the following steps:
acquiring the power load of the historical years and the fluctuation rate of new energy output in each preassembly point year;
establishing a new energy installation capacity allocation model according to the new energy installation capacity of the selected pre-loading point and the new energy output accumulation value of each time node meeting the regulation and control range of the traditional unit;
when a new energy installation allocation strategy generated by the new energy installation capacity allocation model faces a single pre-loading point at each time node and has the maximum fluctuation rate trough, an optimal allocation model is established for an optimal target by the minimum fluctuation risk value of the new energy installation allocation strategy;
inputting the power load of the historical years and the fluctuation rate of the new energy output in each preassembled point year into a new energy installed capacity allocation model and an optimized allocation model, and optimizing and solving to obtain a new energy installed capacity optimization strategy;
the expression of the new energy installation capacity allocation model is specifically as follows:
wherein P is t ct The output of the traditional unit at a t time node is represented;representing the lower adjustment margin of the traditional unit at a t time node; k (K) t up Representing the upper adjustment margin of a traditional unit at a t time node; pi represents the new energy installation allocation capacity of the ith pre-loading point; delta i t The new energy output fluctuation rate of the ith pre-loading point at the t time node is represented; n represents the selected number of the pre-loading points; p (P) 0 Representing the planned total capacity of the new energy installation;
the establishment process of the optimal allocation model specifically comprises the following steps:
selecting the smallest new energy output fluctuation rate in the year of the preassembly point as the risk output fluctuation rate of the corresponding preassembly point;
selecting a risk output fluctuation rate to replace the new energy output fluctuation rate of the corresponding pre-loading point at a single time node, and determining the accumulated difference value of the new energy output accumulation value of the single time node lower than the lower limit value of the regulation range of the traditional unit according to the replaced new energy output fluctuation rate;
and establishing an optimization distribution model by taking the minimum sum of the average value of the accumulated differences corresponding to all the time nodes and the standard deviation of the accumulated differences corresponding to all the time nodes as an optimization target.
8. The method for optimizing and distributing the installed capacity of the new energy according to claim 7, wherein the fluctuation rate of the new energy output within the year of the preassembly point is obtained by training the output data of the new energy of the same category.
9. The method for optimizing and distributing the installed capacity of the new energy according to claim 7, wherein the determining process of the fluctuation rate of the output of the new energy is specifically as follows:
acquiring a new energy output average value within the same preassembly point year;
and determining the new energy output fluctuation rate of the single time node according to the ratio of the actual estimated output of the corresponding pre-loading point at the single time node to the new energy output average value.
10. The utility model provides a new forms of energy installation capacity optimization distribution system which characterized in that includes:
the data acquisition module is used for acquiring the power load of the historical years and the new energy output fluctuation rate in each preassembly point year;
the capacity allocation module is used for establishing a new energy installation capacity allocation model according to the new energy installation capacity allocation capacity of the selected pre-loading point and the new energy output accumulation value of each time node meeting the regulation and control range of the traditional unit;
the optimizing and distributing module is used for establishing an optimizing and distributing model by taking the minimum fluctuation risk value of the new energy installation and distributing strategy as an optimizing target when the maximum fluctuation rate trough occurs to the single pre-loading point facing each time node by using the new energy installation and distributing strategy generated by the new energy installation and capacity distributing model;
the strategy optimization module is used for inputting the historical annual power load and the new energy output fluctuation rate in each preassembly point year into a new energy installed capacity allocation model and an optimized allocation model, and optimizing and solving to obtain a new energy installed capacity optimization strategy;
the expression of the new energy installation capacity allocation model is specifically as follows:
wherein P is t ct The output of the traditional unit at a t time node is represented;representing the lower adjustment margin of the traditional unit at a t time node; k (K) t up Representing the upper adjustment margin of a traditional unit at a t time node; p (P) i Representing the new energy installation allocation capacity of the ith pre-loading point; delta i t The new energy output fluctuation rate of the ith pre-loading point at the t time node is represented; n represents the selected number of the pre-loading points; p (P) 0 Representing the planned total capacity of the new energy installation;
the establishment process of the optimal allocation model specifically comprises the following steps:
selecting the smallest new energy output fluctuation rate in the year of the preassembly point as the risk output fluctuation rate of the corresponding preassembly point;
selecting a risk output fluctuation rate to replace the new energy output fluctuation rate of the corresponding pre-loading point at a single time node, and judging whether the new energy output accumulation value of the single time node meets the regulation and control range of the traditional unit according to the replaced new energy output fluctuation rate; if not, determining that the current replacement is a risk replacement;
taking the duty ratio of risk replacement in all the replacement in a single time node as a risk value;
establishing an optimization distribution model by taking the minimum sum of the average value of the risk values corresponding to all the time nodes and the standard deviation of the risk values corresponding to all the time nodes as an optimization target;
or, the establishment process of the optimal allocation model specifically comprises the following steps:
selecting the smallest new energy output fluctuation rate in the year of the preassembly point as the risk output fluctuation rate of the corresponding preassembly point;
selecting a risk output fluctuation rate to replace the new energy output fluctuation rate of the corresponding pre-loading point at a single time node, and judging whether the new energy output accumulation value of the single time node meets the regulation and control range of the traditional unit according to the replaced new energy output fluctuation rate; if not, determining that the current replacement is a risk replacement;
taking the duty ratio of risk replacement in all the replacement in a single time node as a risk value;
establishing an optimization distribution model by taking the minimum average value of risk values corresponding to all time nodes as an optimization target;
or, the establishment process of the optimal allocation model specifically comprises the following steps:
selecting the smallest new energy output fluctuation rate in the year of the preassembly point as the risk output fluctuation rate of the corresponding preassembly point;
selecting a risk output fluctuation rate to replace the new energy output fluctuation rate of the corresponding pre-loading point at a single time node, and determining the accumulated difference value of the new energy output accumulation value of the single time node lower than the lower limit value of the regulation range of the traditional unit according to the replaced new energy output fluctuation rate;
and establishing an optimization distribution model by taking the minimum sum of the average value of the accumulated differences corresponding to all the time nodes and the standard deviation of the accumulated differences corresponding to all the time nodes as an optimization target.
11. A computer terminal comprising a memory, a processor and a computer program stored in the memory and operable on the processor, wherein the processor implements a new energy installation capacity optimizing allocation method according to any one of claims 1-3, 4-6 or 7-9 when executing the program.
12. A computer readable medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement a new energy installation capacity optimizing allocation method according to any one of claims 1 to 3, 4 to 6 or 7 to 9.
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