CN116644854A - Regional distributed power supply energy permeability prediction method, device and storage medium - Google Patents

Regional distributed power supply energy permeability prediction method, device and storage medium Download PDF

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CN116644854A
CN116644854A CN202310631391.6A CN202310631391A CN116644854A CN 116644854 A CN116644854 A CN 116644854A CN 202310631391 A CN202310631391 A CN 202310631391A CN 116644854 A CN116644854 A CN 116644854A
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distributed power
power supply
regional
cost
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卢婧婧
颜华敏
任明珠
周珺
殷珉
肖远兵
李林锐
马晔晖
许婧琦
闫贻鹏
许铁峰
张�成
张红燕
黄怡
陆怡
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State Grid Shanghai Electric Power Co Ltd
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    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

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Abstract

The invention discloses a regional distributed power supply energy permeability prediction method, a regional distributed power supply energy permeability prediction device and a storage medium, wherein the regional distributed power supply energy permeability prediction method comprises the steps of firstly predicting the power cost of each distributed power supply according to a power cost model of each distributed power supply obtained through fitting power cost historical data of each distributed power supply, and predicting the power cost of thermal power according to the power cost historical data of thermal power; based on the thermal power electricity cost and the distributed power electricity cost, predicting the annual installed capacity of each distributed power supply according to a long-term prediction model of the installed capacity of each distributed power supply, and predicting the annual regional power consumption according to an annual regional power consumption prediction model based on a gray theory; and finally, predicting the regional distributed power source energy permeability based on the predicted annual installed capacity of each distributed power source and regional annual power consumption. The method can realize long-term prediction of the energy permeability, and has high prediction accuracy.

Description

Regional distributed power supply energy permeability prediction method, device and storage medium
Technical Field
The present invention relates to power information technologies, and in particular, to a method and apparatus for predicting energy permeability of a regional distributed power source, and a storage medium.
Background
The distributed power supply is used as an important component of a novel power system taking new energy as a main body, supports the establishment of a clean low-carbon, safe and efficient energy system, and the development of the distributed power supply is promoted by the reform, promotion and upgrading and synergy processes in the energy field. The permeability of the regional distributed power supply is gradually increased, so that a scientific prediction model needs to be established, and the development trend of the permeability of the distributed power supply is macroscopically grasped.
Disclosure of Invention
The invention aims to: the invention provides a regional distributed power supply energy permeability prediction method, a regional distributed power supply energy permeability prediction device and a storage medium aiming at the problems existing in the prior art.
The technical scheme is as follows: the invention provides a regional distributed power supply energy permeability prediction method, which comprises the following steps:
predicting the power consumption cost of each distributed power supply according to a power consumption cost model of each distributed power supply obtained through fitting of power consumption cost historical data of each distributed power supply;
predicting the thermal power electricity cost according to the thermal power electricity cost historical data;
based on the thermal power electricity cost and the distributed power electricity cost, predicting the annual installed capacity of each distributed power supply according to a long-term prediction model of the installed capacity of the distributed power supply;
predicting regional annual electricity consumption according to a regional annual electricity consumption prediction model based on a gray theory;
and predicting the regional distributed power source energy permeability based on the predicted annual installed capacity of each distributed power source and regional annual power consumption.
Further, the power cost model of the distributed power supply is obtained by the following steps:
based on historical data of the distributed power supply electricity measurement cost changing along with time, fitting is carried out by adopting the following formula, so that an electricity measurement cost model of the distributed power supply is obtained:
C i,DG (t)＝αt β
wherein C is i,DG And (t) is the i-degree electric cost of the distributed power supply at the moment t, and alpha and beta are parameters to be fitted.
Further, the long-term prediction model of the installed capacity of the distributed power supply specifically comprises the following steps:
wherein X is i (t) represents the installed capacity, N, of the distributed power supply i at time t i (t) represents the maximum potential scale of the installed capacity of the distributed power supply i at the moment t, p and q are coefficients to be fitted, u and v are influence factors of environment on the development of the distributed power supply, and N max Representing maximum developable capacity, L i (t)＝C i,DG (t)/C f (t) i-degree electric cost C of distributed power supply at t moment i,DG Thermal power cost C at time (t) and t f Ratio of (t).
Further, the coefficients p and q to be fitted are obtained by fitting the historical data of the installed capacity of the distributed power supply by using a least square method, a maximum likelihood method or a parameter fitting method. The maximum developable capacity N max Obtained by literature search or by expert prediction.
Further, the regional annual electricity consumption prediction model is specifically a gray prediction GM (1, 1) model.
Further, the method for predicting the regional distributed power source energy permeability based on the predicted annual installed capacity and regional annual power consumption of each distributed power source comprises the following steps:
based on the predicted annual installed capacity of each distributed power source and regional annual electricity consumption, the energy permeability of the regional distributed power source is calculated by adopting the following formula:
η is the energy permeability of the regional distributed power supply, X i (t) is the i-year installed capacity of the distributed power supply at the moment t, F i For the capacity coefficient of the distributed power supply i, C y And the power consumption is regional annual.
The invention also provides a regional distributed power supply energy permeability prediction device, which is characterized in that:
one or more processors;
a memory for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the methods described above.
The present invention also provides a storage medium containing computer-executable instructions for performing the above-described method when executed by a computer processor.
The beneficial effects are that: compared with the prior art, the invention has the remarkable advantages that: the invention provides a regional distributed power source permeability prediction method, a regional distributed power source permeability prediction device and a storage medium.
Drawings
FIG. 1 is a schematic flow chart of a method for predicting energy permeability of a regional distributed power supply according to the present invention;
FIG. 2 is a block diagram of a regional distributed power source energy permeability prediction apparatus provided by the present invention;
FIG. 3 is a graph showing the trend of accumulated installed capacity of a distributed power supply in Shanghai region;
fig. 4 is a graph showing the trend of annual electricity consumption in Shanghai region;
fig. 5 is a graph of the permeability growth of a distributed power supply in the Shanghai region.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
The embodiment provides a regional distributed power energy permeability prediction method, as shown in fig. 1, comprising the following steps:
s1, predicting the power consumption cost of each distributed power supply according to a power consumption cost model of each distributed power supply obtained through power consumption cost historical data fitting of each distributed power supply.
The power cost model of the distributed power supply is obtained by the following steps: based on historical data of the distributed power supply electricity measurement cost changing along with time, fitting is carried out by adopting the following formula, so that an electricity measurement cost model of the distributed power supply is obtained:
C i,DG (t)＝αt β
wherein C is i,DG And (t) is the i-degree electric cost of the distributed power supply at the moment t, and alpha and beta are parameters to be fitted.
C i,DG (t) includes investment costs, operation and maintenance costs, and financial costs. Taking distributed photovoltaic as an example, the investment cost of distributed photovoltaic is composed of photovoltaic module cost, system balance cost and other costs. The photovoltaic module cost is determined by the raw material cost, in particular the silicon cost, the cell processing/manufacturing cost and the module assembly cost. The operation and maintenance cost of the distributed photovoltaic mainly comprises power station depreciation cost, labor cost, standby equipment cost, equipment maintenance cost and the like. The financial cost of distributed photovoltaics refers to the cost of financing during investment and tax after power generation.
S2, predicting the thermal power electricity cost according to the thermal power electricity cost historical data.
The thermal power electricity cost is generally kept unchanged, so that the thermal power electricity cost can be predicted according to thermal power electricity cost historical data during prediction. And the mathematical model of the thermal power electricity cost and time can be obtained by fitting the same fitting method as the distributed power electricity cost, and the future thermal power electricity cost can be predicted according to the mathematical model.
S3, based on the thermal power electricity cost and the distributed power electricity cost, predicting the annual installed capacity of each distributed power source according to a long-term prediction model of the installed capacity of the distributed power source.
The long-term prediction model of the installed capacity of the distributed power supply specifically comprises the following steps:
wherein X is i (t) represents the installed capacity, N, of the distributed power supply i at time t i (t) represents the maximum potential scale of the installed capacity of the distributed power supply i at the moment t, p and q are coefficients to be fitted, u and v are influence factors of environment on the development of the distributed power supply, and N max Representing maximum developable capacity, L i (t)＝C i,DG (t)/C f (t) i-degree electric cost C of distributed power supply at t moment i,DG Thermal power cost C at time (t) and t f Ratio of (t).
In the model, the coefficients p and q to be fitted are obtained by fitting the historical data of the installed capacity of the distributed power supply by using a least square method, a maximum likelihood method or a parameter fitting method, and the coefficients p and q are both between 0.00 and 1.00. Maximum developable capacity N max Obtained by literature search or by expert prediction.
And S4, predicting regional annual electricity consumption according to a regional annual electricity consumption prediction model based on a gray theory.
The regional annual electricity consumption prediction model is specifically a gray prediction GM (1, 1) model, and the specific establishment process is as follows:
the electricity consumption sequence in the history year is x (0) ＝{x (0) (j) Constructing a new sequence as: x is x (1) ＝{x (1) (k)}，
Construction sequence x (1) First order differential equation with year t:
wherein the values of a and b are obtained from the following matrix
y n ＝[x (0) (2),x (0) (3),...,x (0) (n)] T
Solving the first order differential equation can be:
for the new sequence value of the predicted k+1 time, the predicted value of the annual energy consumption of the k+1 time can be obtained by subtracting>Namely:
s5, predicting the regional distributed power source energy permeability based on the predicted annual installed capacity and regional annual power consumption of each distributed power source.
The energy permeability of the distributed power supply in the base region is calculated by the following formula:
η is the energy permeability of the regional distributed power supply, X i (t) is the i-year installed capacity of the distributed power supply at the moment t, F i For the capacity coefficient of the distributed power supply i, C y And the power consumption is regional annual.
Example two
Fig. 2 is a schematic structural diagram of an energy permeability prediction device of a regional distributed power supply according to an embodiment of the present invention, where the embodiment of the present invention provides services for implementing the method of the first embodiment. Fig. 2 illustrates a block diagram of an exemplary device 12 suitable for use in implementing embodiments of the present invention. The device 12 shown in fig. 2 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention. Device 12 is in the form of a general purpose computing device. Components of device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects the various system components including the system memory 28 and the processing units 16.
Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. Device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 2, commonly referred to as a "hard disk drive"). Although not shown in fig. 2, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of embodiments of the invention. Device 12 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by device 12 and includes both volatile and nonvolatile media, removable and non-removable media. A program/utility 40 having a set (at least one) of program modules 42 may be stored in, for example, memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods of the embodiments described herein.
Device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with device 12, and/or any devices (e.g., network card, modem, etc.) that enable device 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. Also, device 12 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, via network adapter 20. As shown in fig. 2, network adapter 20 communicates with other modules of device 12 over bus 18. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with device 12, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, for example, implementing the method provided by the first embodiment of the present invention.
Example III
The present invention provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are configured to perform the method of embodiment one,
the computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
Of course, the storage medium containing the computer executable instructions provided in the embodiments of the present invention is not limited to the above method operations, but may also perform the related operations in the method provided in any embodiment of the present invention.
The present invention is simulated and verified as follows.
The simulation takes Shanghai area as an example, and the change trend of the permeability of the distributed power supply is predicted and analyzed.
The power consumption cost model of the distributed power supply is obtained by fitting the power consumption cost historical data of the distributed power supply is as follows:
C DG ＝3.508t (-0.7758)
the electricity-to-electricity cost of thermal power in the region of 2021 Shanghai is 0.4155 yuan, and the electricity-to-electricity cost of thermal power is assumed to be unchanged in the future. Based on expert predictions, the maximum developable capacity of a distributed power supply in Shanghai is estimated to be 8000MW. The reference experience values of u and v are 5 and 1.2. The coefficient p=0.0020, q= 0.2833, which is found from the fitting.
Since 2009, the installed capacity history data of the distributed power supply in Shanghai region is shown in table 1
Table 1 distributed power supply installed capacity history data
Based on the history data of the installed capacity of the distributed power source, according to a long-term prediction model of the installed capacity of the distributed power source, the predicted annual installed capacity change trend of the distributed power source is shown in figure 3.
The annual power consumption history data of Shanghai region are shown in Table 2
Table 2 annual power usage history data
The historical data is input into the regional annual power consumption prediction model to obtain the annual power consumption prediction data of the Shanghai region up to 2060 years, as shown in fig. 4.
Assuming that capacity coefficients of various distributed power supplies are 0.2, according to the formulaThe permeability growth curve of the distributed power supply in Shanghai area is obtained as shown in fig. 5.
From the energy permeability increase curve of the distributed power supply, it can be seen that the distributed power supply in Shanghai region can bear 5.99% of electricity consumption by year 2030 when the peak reaches carbon, and can supply 8.82% of electricity consumption by year 2060 when the peak reaches carbon neutralization. Generally, from 2009 distributed power supply commercial use to 2030 carbon peak, the distributed power supply permeability increases rapidly, belonging to an expanding increment period; however, after 2030, a large amount of distributed power supplies cannot be continuously installed due to objective geographical reasons, the permeability is increased slowly, meanwhile, the problem of power grid stability caused by high-proportion distributed power supply access is remarkable, and the distributed power supplies are developed into a period of optimizing the stock.
In summary, the invention respectively predicts regional annual power consumption and distributed installed capacity, draws a distributed power permeability growth curve, has high prediction accuracy, provides a new research angle for the research of the development trend of the distributed power, and provides a basis for the decision of the long-term development and application planning of the distributed power.

Claims (10)

1. The regional distributed power supply energy permeability prediction method is characterized by comprising the following steps of:
predicting the power consumption cost of each distributed power supply according to a power consumption cost model of each distributed power supply obtained through fitting of power consumption cost historical data of each distributed power supply;
predicting the thermal power electricity cost according to the thermal power electricity cost historical data;
based on the thermal power electricity cost and the distributed power electricity cost, predicting the annual installed capacity of each distributed power supply according to a long-term prediction model of the installed capacity of the distributed power supply;
predicting regional annual electricity consumption according to a regional annual electricity consumption prediction model based on a gray theory;
and predicting the regional distributed power source energy permeability based on the predicted annual installed capacity of each distributed power source and regional annual power consumption.
2. The regional distributed power source energy permeability prediction method of claim 1, wherein: the power cost model of the distributed power supply is obtained by the following steps:
based on historical data of the distributed power supply electricity measurement cost changing along with time, fitting is carried out by adopting the following formula, so that an electricity measurement cost model of the distributed power supply is obtained:
C i,DG (t)＝αt β
wherein C is i,DG And (t) is the i-degree electric cost of the distributed power supply at the moment t, and alpha and beta are parameters to be fitted.
3. The regional distributed power source energy permeability prediction method of claim 1, wherein: the long-term prediction model of the installed capacity of the distributed power supply specifically comprises the following steps:
wherein X is i (t) represents the installed capacity, N, of the distributed power supply i at time t i (t) represents the maximum potential scale of the installed capacity of the distributed power supply i at the moment t, p and q are coefficients to be fitted, u and v are influence factors of environment on the development of the distributed power supply, and N max Representing maximum developable capacity, L i (t)＝C i,DG (t)/C f (t) i-degree electric cost C of distributed power supply at t moment i,DG Thermal power cost C at time (t) and t f Ratio of (t).
4. A method of regional distributed power energy permeability prediction according to claim 3, wherein: the coefficient p to be fitted is obtained by fitting the historical data of the installed capacity of the distributed power supply by using a least square method, a maximum likelihood method or a parameter fitting method.
5. The regional distributed power energy permeability prediction method of claim 4, wherein: the coefficient q to be fitted is obtained by fitting the historical data of the installed capacity of the distributed power supply by using a least square method, a maximum likelihood method or a parameter fitting method.
6. The regional distributed power energy permeability prediction method of claim 4, wherein: the maximum developable capacity N max Obtained by literature search or by expert prediction.
7. The regional distributed power source energy permeability prediction method of claim 1, wherein: the regional annual electricity consumption prediction model is specifically a gray prediction GM (1, 1) model.
8. The regional distributed power source energy permeability prediction method of claim 1, wherein: the method for predicting the regional distributed power source energy permeability based on the predicted annual installed capacity and regional annual power consumption of each distributed power source comprises the following steps:
based on the predicted annual installed capacity of each distributed power source and regional annual electricity consumption, the energy permeability of the regional distributed power source is calculated by adopting the following formula:
η is the energy permeability of the regional distributed power supply, X i (t) is the i-year installed capacity of the distributed power supply at the moment t, F i For the capacity coefficient of the distributed power supply i, C y And the power consumption is regional annual.
9. An apparatus for predicting energy permeability of a regional distributed power source, comprising:
one or more processors;
a memory for storing one or more programs;
when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-8.
10. A storage medium containing computer executable instructions which, when executed by a computer processor, are for performing the method of any of claims 1-8.
CN202310631391.6A 2023-05-31 2023-05-31 Regional distributed power supply energy permeability prediction method, device and storage medium Pending CN116644854A (en)

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