AU2020103555A4 - Method and system for power supply prediction by variety - Google Patents
Method and system for power supply prediction by variety Download PDFInfo
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- AU2020103555A4 AU2020103555A4 AU2020103555A AU2020103555A AU2020103555A4 AU 2020103555 A4 AU2020103555 A4 AU 2020103555A4 AU 2020103555 A AU2020103555 A AU 2020103555A AU 2020103555 A AU2020103555 A AU 2020103555A AU 2020103555 A4 AU2020103555 A4 AU 2020103555A4
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P80/00—Climate change mitigation technologies for sector-wide applications
- Y02P80/20—Climate change mitigation technologies for sector-wide applications using renewable energy
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- 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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- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a method and system for power supply prediction by variety, which
comprise: acquiring power data in a region to be predicted; adopting a linear regression model and
an autoregressive moving average model to predict the power generation amount of each variety of
power generation equipment in a target year based on the power data; predicting the net increased
installed capacity of the power generation equipment of each variety based on a predicted value of
the power generation amount of the power generation equipment of each variety in the target year
and a pre-constructed optimization prediction model; each variety of the power generation
equipment comprising a hydropower generating set, a thermal power generating set, a nuclear power
generating set, a wind power generating set and a solar generating set. The invention can
quantitatively analyze the influence of the supply-side structural reform on the power generation
field by variety. By predicting the power supply of different varieties, it is possible to provide an
important decision-making basis for grid companies to formulate energy development planning and
power construction investment, thereby reducing the costs of grid companies and improving energy
use efficiency.
1/1
Acquiring power data in a
region to be predicted
S2
Adopting a linear regression model and an autoregressive
moving average model to predict the power generation
amount of each variety of power generation equipment in
a target year based on the power data
S3
Predicting the net increased installed capacity of
the power generation equipment of each variety based
on a predicted value of the power generation amount
of the power generation equipment of each variety in
the target year and a pre-constructed optimization
prediction model
Fig. I
Description
1/1
Acquiring power data in a region to be predicted
S2
Adopting a linear regression model and an autoregressive moving average model to predict the power generation amount of each variety of power generation equipment in a target year based on the power data
S3 Predicting the net increased installed capacity of the power generation equipment of each variety based on a predicted value of the power generation amount of the power generation equipment of each variety in the target year and a pre-constructed optimization prediction model
Fig. I
FIELD OF THE INVENTION The invention relates to the technical field of power engineering, in particular to a method and system for power supply prediction by variety.
BACKGROUND OF THE INVENTION Electric power is an important basic resource to support social development. Power supply plays an important role in national economic development. It is necessary to plan power supply early, make a reasonable decision, and provide a guarantee for national economic security and steady development. Correct prediction of future power supply growth trend is the premise of scientifically formulating power planning, and power supply prediction technology is the basis of formulating energy power development strategic planning. With the enhancement of environmental awareness, the permeability of new energy increases gradually, leading to the supply-side structural reform, which has an important influence on energy and power demand. The supply-side structural reform brings new requirements and challenges for the power supply prediction method. The traditional power supply prediction mainly starts from the demand side and lacks consideration of supply-side factors. How to quantitatively analyze the influence of supply side structural reform on power supply needs to be solved urgently.
SUMMARY OF THE INVENTION In order to solve the above-mentioned defects existing in the prior art, the invention provides a method and a system for power supply prediction by variety. The present invention can comprehensively and quantitatively analyze the influence of supply-side structural reform on economic and social development and power supply, while comprehensively considering factors such as technological progress, net increased installed capacity, carbon emission constraints, etc., to predict future power supply, which can provide important reference and guidance for energy development planning. The invention provides a method for power supply prediction by variety, which comprises: acquiring power data in a region to be predicted; adopting a linear regression model and an autoregressive moving average model to predict the power generation amount of each variety of power generation equipment in a target year based on the power data; predicting the net increased installed capacity of the power generation equipment of each variety based on a predicted value of the power generation amount of the power generation equipment of each variety in the target year and a pre-constructed optimization prediction model; and each variety of power generation equipment comprising hydropower generating set, a thermal power generating set, a nuclear power generating set, a wind power generating set, and a solar generating set. Preferably, adopting a linear regression model and an autoregressive moving average model to predict power generation amount of each variety of power generation equipment in a target year based on the power data comprises: predicting electricity consumption of the target year; calculating by adopting a linear regression model based on the predicted value of the electricity consumption to obtain the predicted value of the power generation amount of the target year; and distributing power generation amount prediction value of the target year based on annual utilization hours of each variety of the power generation equipment by adopting an autoregressive moving average model to obtain the power generation amount prediction value of each variety of the power generation equipment in the target year. Preferably, the constructing of the optimization prediction model comprises: constructing an objective function based on the lowest total installation cost, power generation cost, and total carbon dioxide emission cost of a thermoelectric set of each variety of power generation equipment; constructing constraint conditions for the objective function; and the constraint conditions comprising: hydropower installation constraint, thermal power installation constraint, nuclear power installation constraint, wind power installation capacity constraint, solar installation capacity constraint, and electric quantity balance constraint. Preferably, the objective function is as shown by an expression: f = min(C + C2 + CO where represents the objective function, C1 represents the total installation cost of each variety of power generation equipment, C 2 represents the total power generation cost of each variety of power generation equipment, and 3 represents the total carbon dioxide emission cost of the thermal power set.
Preferably, the total installation cost C1 of each variety of power generation equipment is
calculated as shown by the following expression:
C, =xlx S 1 +x2 Xs2 +X x3 +X4 xs4 +X55 xs
where 11 represents net increased installed capacity of hydropower power generation equipment,
X2 represents net increased installed capacity of thermal power generation equipment, X3
represents net increased installed capacity of nuclear power generation equipment, X4 represents
net increased installed capacity of wind power generation equipment, and X5 represents net
increased installed capacity of solar power generation equipment, and unit construction cost of each
variety installation is respectively as follows: s, represents hydropower installation unit
construction cost, S2 represents thermal power installation unit construction cost, 3 represents
nuclear power installation unit construction cost, S4 represents wind power installation unit
construction cost and 5 represents solar installation unit construction cost.
Preferably, after predicting the net increased installed capacity of the power generation equipment of each variety based on a predicted value of the power generation amount of the power generation equipment of each variety in the target year and a pre-constructed optimization prediction model, the method further comprises: calculating the installed capacity at the end of a period of each variety based on the net increased installed capacity of each variety of the power generation equipment and the installed capacity of each variety at the beginning of a period; obtaining an average installed capacity of each variety based on the installed capacity of each variety at the end of the period; and obtaining total utilization hours of the power generation equipment based on the average installed capacity of each variety. Preferably, the installed capacity of each variety at the end of the period is calculated as the following expression:
X, = x + x,
where 4 indicates the installed capacity at the end of the period of the i variety, X indicates
the installed capacity at the beginning of the period of the i variety, and i indicates the net
increased installed capacity of the i variety. Preferably, the power data comprises: electricity consumption, power generation amount, and average utilization hours of each variety of the power generation equipment. Based on the same inventive concept, the invention also provides a power supply prediction by variety system, comprising: an acquisition module used for acquiring power data in a region to be predicted; a power generation amount prediction module used for predicting power generation amount of each variety of power generation equipment in a target year by adopting a linear regression model and an autoregressive moving average model based on the power data; a net increased installed capacity prediction module used for predicting the net increased installed capacity of the power generation equipment of each variety based on a predicted value of the power generation amount of the power generation equipment of each variety in a target year and a pre constructed optimization prediction model; and each variety of the power generation equipment comprising a hydropower generating set, a thermal power generating set, a nuclear power generating set, a wind power generating set, and a solar generating set. Preferably, the power generation amount prediction module comprises: an electricity consumption prediction unit used for predicting electricity consumption of the target year; a first power generation amount prediction unit used for calculating by adopting a linear regression model based on a predicted value of the electricity consumption to obtain a power generation amount predicted value of the target year; and a second power generation amount prediction unit used for distributing the power generation amount prediction value of the target year based on the annual utilization hours of each variety of the power generation equipment by adopting an autoregressive moving average model to obtain the power generation amount prediction value of each variety of the power generation equipment in the target year. Compared with the closest prior art, the technical scheme provided by the invention has the following beneficial effects. The technical scheme provided by the invention comprises acquiring power data in a region to be predicted; adopting a linear regression model and an autoregressive moving average model to predict the power generation amount of each variety of power generation equipment in a target year based on the power data; predicting the net increased installed capacity of the power generation equipment of each variety based on a predicted value of the power generation amount of the power generation equipment of each variety in the target year and a pre-constructed optimization prediction model; the influence of supply-side structural reform on power generation fields of different varieties being quantitatively analyzed. Through predicting the net increased installed capacity of hydropower generating set, thermal power generating set, nuclear power generating set, wind power generating set, and solar generating set, the prediction of power supply of different varieties is realized. It provides an important decision-making basis for power grid companies to formulate energy development planning and power construction investment, thereby reducing the cost of power grid companies, encouraging and guiding social capital investment in power construction, and improving energy use efficiency.
BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 is a flow chart of a method for power supply prediction by variety according to the present invention.
DETAILED DESCRIPTION OF THE INVENTION For a better understanding of the present invention, reference will now be made to the accompanying drawings and examples to further illustrate the present invention. As shown in figure 1, the invention provides a method for power supply prediction by variety, which comprises the following steps: Si acquiring power data in a region to be predicted; S2 adopting a linear regression model and an autoregressive moving average model to predict the power generation amount of each variety of power generation equipment in a target year based on the power data; S3 predicting the net increased installed capacity of the power generation equipment of each variety based on a predicted value of the power generation amount of the power generation equipment of each variety in the target year and a pre-constructed optimization prediction model; and each variety of power generation equipment comprising hydropower generating set, a thermal power generating set, a nuclear power generating set, a wind power generating set and a solar energy generating set. S2 Adopting a linear regression model and an autoregressive moving average model to predict the power generation amount of each variety of power generation equipment in a target year based on the power data comprises steps as follows. Firstly, historical power data is collected; the power data include electricity consumption, power generation amount, average utilization hours of each variety of power generation equipment, historical net increased installed capacity and the like, wherein each variety refers to hydropower, thermal power, nuclear power, wind power and solar energy. The overall social cost includes the construction cost, levelized cost of energy and carbon emission cost of all varieties of power generation installations. The data are derived from related power data over the last 10 years. And then, a linear regression model is established for historical power generation and electricity consumption data, and combining the exogenous target year electricity consumption prediction result, a target year power generation amount prediction value is obtained. The target year in this embodiment refers to the next year and the following expression is the expression of the linear regression model: y=ax+b where Y represents the predicted value of the power generation amount of the target year, X represents the electricity consumption of the target year, and a , b represents a correlation coefficient of a linear function that can be obtained by historical data fitting. Finally, a prediction model is established based on a time sequence in order to predict the average utilization hours of the power generation amount of each variety and the unit installation cost levelized cost of energy and carbon emission cost of the power generation of each variety. The installation utilization hours of each variety are respectively set as follows: hydropower t1, thermal power t , nuclear power t3, wind power t4 and solar energy t5 the unit construction cost of each variety installation is respectively as follows: hydropower s, thermal power S2, nuclear power 3, wind power S4 and solar energy s5 ; the levelized cost of energy of each variety of power generation equipment is respectively as follows: hydropower PI , thermal power P2, nuclear power 3, wind power 4 and solar energy A ; the carbon dioxide emission cost unit electric quantity of thermal power generation is 19. According to the annual utilization hours, unit installed cost, levelized cost of energy and other data of each variety of historical power generation equipment, and the ARMA model can be used to respectively obtain the predicted value of the target year of each variety of power generation equipment.
Y=A +Ay,,+2 A - 2+3 A -3 +...+/,8 _, +s+ale-1 +a2et-28+...+aqe,-q
Where represents a predicted value of the target year, A and represent the ARMA model
correlation coefficient, z represents data corresponding to the historical year and "I represents
an error interference term. S3 Predicting the net increased installed capacity of the power generation equipment of each variety based on a predicted value of the power generation amount of the power generation equipment of each variety in the target year and a pre-constructed optimization prediction model comprises steps as follows: an optimization prediction model is established, the minimum overall social cost is taken as a prediction target, factors such as economic and social development, environmental constraints and technical progress are comprehensively considered, and a nonlinear optimization method is utilized to predict the net increased installed capacity by variety. The steps specifically comprise: step 1, setting a variable, including: setting the net increased installed capacity of each variety as hydropower XI , thermal power x2, nuclear power X3, wind power X4 and solar energy X5 step 2, constructing an objective function, including: as the objective function is the lowest cost of the overall society, the unit installed cost, levelized cost of energy, and carbon dioxide emission cost of the unit electric quantity of the thermal power generation of each variety being comprehensively considered; f = min(C + C2 + C3 ) where C1 represents the total installation cost for all varieties, C 2 represents the total power generation cost for all varieties and C3 represents the total carbon dioxide emission cost for the thermal power set; ( I ) the total installation cost of each variety of power generation equipment is shown in the following expression:
C =x 1 Xs1 +X2 Xs2 +X3 S3 +X4 xs4 +X5 s5
(2) the total power generation cost of each variety of power generation equipment is shown in the following expression:
C 2 =x1 xt 1 Xp +X2 xt 2 xp2 +x 3 xt 3 xp3 +x 4 xt 4 xp4 +x 5 x t5 xp 5
(3 ) the total carbon dioxide emission cost for thermal power generation is shown in the following expression:
C 3 =x2 x t2 xO
where X1 represents the net increased installed capacity of hydropower power generation
equipment, X2 represents the net increased installed capacity of thermal power generation equipment, X3 represents the net increased installed capacity of nuclear power generation equipment, X4 represents the net increased installed capacity of wind power generation equipment and XI represents the net increased installed capacity of solar power generation equipment; step 3, constructing a constraint condition, including: the constraint condition being net increased installed capacity respectively for each variety: (1) hydropower installation constraint, as shown in the following expression: x, : z, where z represents the maximum net increased installed capacity for hydropower;
(2) thermal power installation constraint, as shown in the following expression:
x2 z2
where Z 2 represents the maximum net increased installed capacity for thermal power;
(3) nuclear power installation constraint, as shown in the following expression:
x3 z3
where Z 3 represents the maximum net increased installed capacity for nuclear power;
(4) wind power installed capacity constraint, as shown in the following expression:
z4 X4 Z5
where z, Z5 respectively represents the minimum and maximum net increased installed capacity
for wind power; (5) solar energy installed capacity constraint, as shown in the following expression:
z6 X4 Z7
where Z6 and Z7 respectively represents the minimum and maximum net increased installed
capacity for solar energy; (6) electric quantity balance constraint, as shown in the following expression:
z:x i=1 t =y
X. t. where r represents the installed capacity at the end of the period of the i variety, representss
the utilization hours for the i variety, and Y represents the electricity consumption for the target year; and step 4, predicting a result according to the objective function and constraint condition constructed in step 3, adopting a linear regression optimization method to obtain the net increased installed capacity of each variety in the target year. According to the embodiment, the installed capacity and the average installed capacity at the end of the period of each variety are predicted by using the prediction result of step 4, comprising: according to the prediction result of the net increased installed capacity of each variety in the target year, combining the installed capacity of each variety at the beginning of the period, it being made possible that the installed capacity at the end of the period of each variety can be obtained according to the following expression:
X,=x + x i =1,2,3,4,5 x i where X indicates the installed capacity at the end of the period of the i variety, X indicates
the installed capacity at the beginning of the period of the i variety, and indicates the net
increased installed capacity of the i variety. This embodiment also calculates the average installed capacity of each variety based on the installed capacity at the beginning of the period and the installed capacity at the end of the period as the following expression:
_ x.± _ X+ x'; ' 2
where! indicates the average installed capacity of the i variety.
Further, the total utilization hours of the power generation equipment are calculated by using the average installed capacity of each variety according to the following expression:
Y t=
where t indicates the number of utilization hours of the power generation equipment, and Y indicates the total power generation amount for the target year. According to the invention, based on the background of supply-side structural reform, the influence of supply-side structural reform on the power generation field by variety can be quantitatively analyzed by carrying out power supply prediction by variety by taking the minimum overall social cost as a prediction target and comprehensively considering the change factors such as technological progress, net increased installation constraint, carbon emission constraint and the like. Based on the same inventive concept, the embodiment of the invention also provides a power supply prediction by variety system, which comprises: an acquisition module used for acquiring power data in a region to be predicted; a power generation amount prediction module used for predicting the power generation amount of each variety of power generation equipment in a target year by adopting a linear regression model and an autoregressive moving average model based on the power data; and a net increased installed capacity prediction module used for predicting the net increased installed capacity of the power generation equipment of each variety based on the predicted value of the power generation amount of the power generation equipment of each variety in a target year and a pre-constructed optimization prediction model; Each variety of power generation equipment comprises a hydropower generating set, a thermal power generating set, a nuclear power generating set, a wind power generating set and a solar generating set. Preferably, the power generation amount prediction module includes: an electricity consumption prediction unit used for predicting the electricity consumption of the target year; a first power generation amount prediction unit used for calculating by adopting a linear regression model based on the predicted value of the electricity consumption to obtain a power generation amount predicted value of a target year; and a second power generation amount prediction unit used for distributing the power generation amount prediction value of the target year based on the annual utilization hours of each variety of power generation equipment by adopting an autoregressive moving average model to obtain the power generation amount prediction value of each variety ofpower generation equipment in the target year. 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. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, CD-ROM, optical storage, and the like) having computer-usable program code therein. The present application is described with reference to the flowchart and/or block diagrams of methods, equipment (systems), and computer program products according to embodiments of the present application. It is to be understood that each flow and/or block of the flowcharts and/or block diagrams, and combinations of flows and/or blocks in the flowcharts 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 equipment to produce a machine, such that an instruction, which is executed via a processor of a computer or other programmable data processing equipment, produces means for implementing functions specified in a flow or multiple flows in the flowchart and/or a block or multiple blocks in a block diagram. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing equipment 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 functions specified in a flow or multiple flows in the flowchart and/or a block or multiple blocks in a block diagram. These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on a computer or other programmable equipment to produce a computer implemented process such that an instruction, which is executed on a computer or other programmable equipment, provides steps for implementing functions specified in a flow or multiple flows in the flowchart and/or a block or multiple blocks in a block diagram. The above are only embodiments of the present invention and are not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention is included in the scope of the claims of the present invention pending approval.
Claims (10)
1. A method for power supply prediction by variety, characterized by comprising: acquiring power data in a region to be predicted; adopting a linear regression model and an autoregressive moving average model to predict power generation amount of each variety of power generation equipment in a target year based on the power data; predicting a net increased installed capacity of the power generation equipment of each variety based on a predicted value of the power generation amount of the power generation equipment of each variety in the target year and a pre-constructed optimization prediction model; and each variety of power generation equipment comprising hydropower generating set, a thermal power generating set, a nuclear power generating set, a wind power generating set, and a solar generating
set.
2. The method according to claim 1, characterized in that adopting a linear regression model and an autoregressive moving average model to predict power generation amount of each variety of power generation equipment in a target year based on the power data comprises: predicting electricity consumption of the target year; calculating by adopting a linear regression model based on the predicted value of the electricity consumption to obtain the predicted value of the power generation amount of the target year; and distributing power generation amount prediction value of the target year based on annual utilization hours of each variety of the power generation equipment by adopting an autoregressive moving average model to obtain the power generation amount prediction value of each variety of the power generation equipment in the target year.
3. The method according to claim 1, characterized in that the constructing of the optimization prediction model comprises: constructing an objective function based on the lowest total installation cost, power generation cost, and total carbon dioxide emission cost of a thermoelectric set of each variety of power generation equipment; constructing constraint conditions for the objective function; and the constraint conditions comprising: hydropower installation constraint, thermal power installation constraint, nuclear power installation constraint, wind power installation capacity constraint, solar installation capacity constraint, and electric quantity balance constraint.
4. The method according to claim 3, characterized in that the objective function is as shown by an expression:
f = min(C + C2 + C) where frepresents the objectivetfnction, 1 represents the total installation cost of each variety C of power generation equipment, C2 represents the total power generation cost of each variety of power generation equipment, and 3 represents the total carbon dioxide emission cost of thermal power set.
5. The method according to claim 4, characterized in that the total installation cost C1 of each
variety of power generation equipment is calculated as shown by the following expression:
C, = x x S 1 +x2 x s2 +x3 x s +X4 x)4 +x5 x 5
where 11 represents net increased installed capacity of hydropower power generation equipment,
X2 represents net increased installed capacity of thermal power generation equipment, X3
represents net increased installed capacity of nuclear power generation equipment, X4 represents
net increased installed capacity of wind power generation equipment, and X5 represents net
increased installed capacity of solar power generation equipment, and unit construction cost of each
variety installation is respectively as follows: s, represents hydropower installation unit
construction cost, S2 represents thermal power installation unit construction cost, 3 represents
nuclear power installation unit construction cost, S4 represents wind power installation unit
construction cost and 5 represents solar installation unit construction cost.
6. The method according to claim 1, characterized in that after predicting the net increased installed capacity of the power generation equipment of each variety based on a predicted value of the power generation amount of the power generation equipment of each variety in the target year and a pre constructed optimization prediction model, the method further comprises: calculating the installed capacity at the end of a period of each variety based on the net increased installed capacity of each variety of the power generation equipment and the installed capacity of each variety at the beginning of a period; obtaining an average installed capacity of each variety based on the installed capacity of each variety at the end of the period; and obtaining total utilization hours of the power generation equipment based on the average installed capacity of each variety.
7. The method according to claim 1, characterized in that the installed capacity of each variety at the end of the period is calculated as the following expression:
X, = x + x,
where X indicates the installed capacity at the end of the period of the i variety, X indicates
the installed capacity at the beginning of the period of the i variety, and indicates the net
increased installed capacity of the i variety.
8. The method according to claim 1, characterized in that the power data comprises: electricity consumption, power generation amount, and average utilization hours of each variety of the power generation equipment.
9. A power supply prediction by variety system, characterized by comprising: an acquisition module used for acquiring power data in a region to be predicted;
a power generation amount prediction module used for predicting power generation amount of each variety of power generation equipment in a target year by adopting a linear regression model and an autoregressive moving average model based on the power data; a net increased installed capacity prediction module used for predicting net increased installed capacity of the power generation equipment of each variety based on a predicted value of the power generation amount of the power generation equipment of each variety in a target year and a pre constructed optimization prediction model; and each variety of the power generation equipment comprising a hydropower generating set, a thermal power generating set, a nuclear power generating set, a wind power generating set, and a solar generating set.
10. The system according to claim 9, characterized in that the power generation amount prediction module comprises: an electricity consumption prediction unit used for predicting electricity consumption of the target year; a first power generation amount prediction unit used for calculating by adopting a linear regression model based on a predicted value of the electricity consumption to obtain a power generation amount predicted value of the target year; and a second power generation amount prediction unit used for distributing the power generation amount prediction value of the target year based on the annual utilization hours of each variety of the power generation equipment by adopting an autoregressive moving average model to obtain the power generation amount prediction value of each variety of the power generation equipment in the target year.
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| CN201911423579.1 | 2019-12-31 | ||
| CN201911423579.1A CN113131523A (en) | 2019-12-31 | 2019-12-31 | Method and system for predicting variety-based power supply |
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113516289A (en) * | 2021-05-20 | 2021-10-19 | 北京中创碳投科技有限公司 | Carbon dioxide emission prediction method, device, equipment and medium |
| CN113705874A (en) * | 2021-08-19 | 2021-11-26 | 国家电网有限公司 | New energy power grid evolution prediction method and device, computer equipment and storage medium |
| CN114204553A (en) * | 2021-12-09 | 2022-03-18 | 软通动力信息技术(集团)股份有限公司 | Power generation proportioning method, device and equipment |
| CN114611845A (en) * | 2022-05-12 | 2022-06-10 | 浙江省发展规划研究院 | Method and apparatus for predicting carbon emission, electronic device, and medium |
| CN115660208A (en) * | 2022-11-10 | 2023-01-31 | 国网冀北电力有限公司计量中心 | Power grid enterprise monthly electricity purchase optimization method considering consumption responsibility weight |
| CN116433440A (en) * | 2023-04-11 | 2023-07-14 | 北京西清能源科技有限公司 | Data autoregressive enhanced carbon emission measuring and calculating method, system and electronic equipment |
| CN117011731A (en) * | 2023-10-07 | 2023-11-07 | 合肥工业大学 | Intelligent analysis method for safety of power distribution network in power grid power system establishment |
| CN118211816A (en) * | 2024-05-21 | 2024-06-18 | 中国石油大学(华东) | Comprehensive energy system evaluation method based on cooperative control strategy |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115776110B (en) * | 2022-10-28 | 2023-10-03 | 国网黑龙江省电力有限公司 | Electricity generation prediction model, electricity purchasing optimization model and electricity purchasing optimization model system |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107862414A (en) * | 2017-11-23 | 2018-03-30 | 国家电网公司 | Electric grid investment Forecasting Methodology and system |
| CN110309949A (en) * | 2019-05-14 | 2019-10-08 | 中国电力科学研究院有限公司 | A kind of region electric energy substitution Potential Prediction method and system |
-
2019
- 2019-12-31 CN CN201911423579.1A patent/CN113131523A/en active Pending
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113516289A (en) * | 2021-05-20 | 2021-10-19 | 北京中创碳投科技有限公司 | Carbon dioxide emission prediction method, device, equipment and medium |
| CN113705874A (en) * | 2021-08-19 | 2021-11-26 | 国家电网有限公司 | New energy power grid evolution prediction method and device, computer equipment and storage medium |
| CN113705874B (en) * | 2021-08-19 | 2024-03-15 | 国家电网有限公司 | New energy power grid evolution prediction method, device, computer equipment and storage medium |
| CN114204553A (en) * | 2021-12-09 | 2022-03-18 | 软通动力信息技术(集团)股份有限公司 | Power generation proportioning method, device and equipment |
| CN114611845A (en) * | 2022-05-12 | 2022-06-10 | 浙江省发展规划研究院 | Method and apparatus for predicting carbon emission, electronic device, and medium |
| CN115660208A (en) * | 2022-11-10 | 2023-01-31 | 国网冀北电力有限公司计量中心 | Power grid enterprise monthly electricity purchase optimization method considering consumption responsibility weight |
| CN115660208B (en) * | 2022-11-10 | 2024-06-07 | 国网冀北电力有限公司计量中心 | Power grid enterprise monthly electricity purchasing optimization method considering responsibility weight |
| CN116433440A (en) * | 2023-04-11 | 2023-07-14 | 北京西清能源科技有限公司 | Data autoregressive enhanced carbon emission measuring and calculating method, system and electronic equipment |
| CN117011731A (en) * | 2023-10-07 | 2023-11-07 | 合肥工业大学 | Intelligent analysis method for safety of power distribution network in power grid power system establishment |
| CN117011731B (en) * | 2023-10-07 | 2023-12-12 | 合肥工业大学 | Intelligent analysis method for safety of power distribution network in power grid power system establishment |
| CN118211816A (en) * | 2024-05-21 | 2024-06-18 | 中国石油大学(华东) | Comprehensive energy system evaluation method based on cooperative control strategy |
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