CN111697563B - Method and system for predicting power capacity in new energy grid-connected power flow - Google Patents
Method and system for predicting power capacity in new energy grid-connected power flow Download PDFInfo
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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
- H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
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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
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
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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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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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
- 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
Abstract
The invention discloses a method and a system for predicting power supply capacity in new energy grid-connected power flow, and belongs to the technical field of power systems. The method comprises the following steps: determining the power type of large-scale new energy grid-connected output, and establishing a prediction objective function of power capacity in power flow aiming at the power type of the output; aiming at the power types of the output power, determining boundary conditions of the grid-connected total amount of the power types of the output power and influence coefficient vectors of complementary characteristics among the power types of the output power on the power flow; determining an output decomposition rule and a positioning rule of each output power type according to the output power type, and determining an extreme scene of power capacity prediction in a long-term power flow according to the output decomposition rule and the positioning rule; and predicting the target large-scale new energy grid connection to be predicted, and determining the power supply capacity in the long-term power flow of the target large-scale new energy grid connection to be predicted. The invention provides powerful technical support for planning a long-term power grid.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a method and a system for predicting power supply capacity in new energy grid-connected power flow.
Background
The output of new energy has randomness and strong fluctuation, and the large-scale new energy access brings new challenges to the system operation, and the problem of absorption becomes more severe. At present, wind power and solar power generation are main forms of new energy power grid connection. The wind power technology develops towards large-scale wind power plants, low wind speed wind power plants, wind power accurate prediction and power grid friendly wind power plants, and the wind power technology is suitable for extreme weather conditions and deep sea wind power. The solar photovoltaic power generation technology mainly develops a high-conversion-rate photovoltaic material, and the manufacturing and installation tend to be flaked and simplified; the solar tracking technology is developed, and the solar utilization rate is improved; the photovoltaic power station grid-connected control technology is developed towards a more controllable and more intelligent direction, the photo-thermal power generation capacity is improved, and the power generation cost is reduced.
As an intermittent power supply, wind power and solar photovoltaic power generation will affect system tidal current, reactive voltage, system stability, electric energy quality and the like in the grid-connected operation process; on the other hand, the generated output of new energy sources such as wind power, photovoltaic and the like has strong fluctuation, the output fluctuation among multiple power sources and multiple power stations with fluctuation has certain cross correlation characteristics, the weaker the cross correlation is, the stronger the complementarity is, and the output fluctuation range of a single power station can be favorably stabilized by collecting the output of the power stations. These new energy power supply characteristics also present new challenges to traditional grid planning methods.
Disclosure of Invention
In view of the above problems, the present invention provides a method for predicting power capacity in new energy grid-connected power flow, comprising:
determining the power type of large-scale new energy grid-connected output, and establishing a prediction objective function of power capacity in power flow aiming at the power type of the output;
aiming at the power types of the output power, determining boundary conditions of the grid-connected total amount of the power types of the output power and influence coefficient vectors of complementary characteristics among the power types of the output power on the power flow;
aiming at the output power type, determining an output decomposition rule and a positioning rule of each output power type, and determining an extreme scene of power capacity prediction in a long-term power flow according to the output decomposition rule and the positioning rule;
and predicting the target large-scale new energy grid connection to be predicted according to the prediction target function, the boundary condition of the total grid connection amount of the power types of the output, and the extreme scene of the power capacity prediction in the long-term power flow caused by the complementary characteristics among the power types of the output, so as to determine the power capacity in the long-term power flow of the target large-scale new energy grid connection to be predicted.
Optionally, the power type of the output includes: wind power stations, photovoltaic power stations, hydroelectric power stations, thermal power stations, nuclear power stations, and gas power stations.
Optionally, the establishing of the prediction objective function includes:
determining a power base function of the power type of the output, wherein the formula is as follows:
x＝[x W-1 ,...,x W-i ,...x S-j ,...,x H-k ,...,x T-m ,...，x N-O ，…，x NG-P ，…] T
wherein, X W-i For wind power station, X S-j For photovoltaic power stations, X H-k For hydroelectric power stations, X T-m For thermal power station, x N-O For nuclear power plants and x NG-P Is a gas power station;
determining a predicted primary objective function, wherein the formula is as follows:
wherein, maxG Wind+Solar (x) Maximum value G of power generation grid-connected capacity for wind power station and photovoltaic power station Thermal (x) Minimum value of power generation grid-connected capacity P for wind power station and photovoltaic power station Total To be connected to the gridTotal transmitted power, C Demand For grid-connected power demand values, G Hydro To the grid-connected capacity, C H-Plan Setting a value for the plan;
according to the formula (1) and the different requirements of the grid-connected power receiving requirement value in different time periods, determining a prediction objective function, wherein the formula is as follows:
wherein, G Wind For wind power station grid-connected capacity, C W-Set For the annual development target value G of the wind power station Solar For photovoltaic power station grid-connected capacity, C S-Set For the annual development target value and C of the photovoltaic power station Demand (m, t) is a set of time constants.
Optionally, the boundary condition of the total grid-connected amount of the power types of the output includes:
C W-Set ≤∑X W-i ≤C W-max-resource ，o＝1,2,…,n wind-total ，∑X W-i ＝C W-Set ； (3)
wherein, C W-max-resource For the maximum bearing grid-connected capacity and n of the wind power station wind-total The number of the grid-connected wind power stations;
C S-Set ≤∑X S-j ≤C S-max-resource ，j＝1,2,…,n solar-total ，∑X S-j ＝C S-Set ； (4)
wherein, C S-max-resource For maximum load-bearing grid-connected capacity and n of photovoltaic power station solar-total The number of the grid-connected photovoltaic power stations;
∑X T-m ≤C T-max-resource ，m＝1,2,…,n Thermal-total ； (5)
wherein, C T-max-resource Upper limit sum n for thermal power station development thermal-total The number of the grid-connected thermal power stations is.
∑X H-k ＝C H-Plan ，k＝1,2,…,n Hydro-total ； (6)
Wherein n is Hydro-total The number of hydroelectric power stations.
Optionally, the influence coefficient vector of the complementary characteristics between the power types of the output power on the power flow is specifically as follows:
Factor＝[σ i ，ρ for-wind-total ，σ j ，ρ for-solar-total ，α k ，δ m ，θ feng ，θ ku ] T
wherein, σ i Internal output concurrency rate and rho of wind power station for-wind-total The overall adjusting effect coefficient, sigma, of the output of the wind power station for the time-space difference of the outputs of a plurality of wind power stations j Internal output coincidence rate rho of photovoltaic power station for-solar-total Adjusting the coefficient of effect, alpha, of the photovoltaic plant contribution to the totality of the time-space differences of the contribution of a plurality of photovoltaic plants k For the forced coefficient of output, delta, of hydroelectric power stations m Adjusting peak depth coefficient theta for thermal power generating unit feng Smoothing coefficient of output, theta, for complementary effects in summer hydroelectric, wind and photovoltaic power stations ku For the complementary effect output smoothing coefficient of winter hydropower station, wind power station and photovoltaic power station, i is 1,2, …, n wind-total 、j＝1,2,…,n solar-total 、m＝1,2,…,n Thermal-total 、k＝1,2,…,n Hydro-total 。
Optionally, the output decomposition rule is used for decomposing the output power curve according to the power type of the output.
Optionally, an extreme scenario of power source capacity prediction in the forward power flow is as follows:
G Thermal (x)×δm+C (H-Plan) ×(1-α k )＝(C W-Set ×σ i ×ρ (for-wind-total) +C S-Set ×σ j ×ρ (for-solar-total) )×θ ku 。 (7)
the invention also provides a system for predicting the power supply capacity in the new energy grid-connected power flow, which comprises the following steps:
the target function determining module is used for determining the power type of large-scale new energy grid-connected output and establishing a prediction target function of power capacity in power flow aiming at the power type of the output;
the condition limiting module is used for determining the boundary condition of the grid-connected total quantity of the power types of the output and the influence coefficient vector of the complementary characteristics among the power types of the output on the power flow aiming at the power types of the output;
the scene construction module is used for determining an output decomposition rule and a positioning rule of each output power type according to the output power type and determining an extreme scene of power capacity prediction in a long-term power flow according to the output decomposition rule and the positioning rule;
and the prediction module predicts the target large-scale new energy grid connection to be predicted according to the prediction target function, the boundary condition of the total grid connection amount of the power types of the output, and the extreme scene of power capacity prediction in the long-term power flow caused by complementary characteristics among the power types of the output, and determines the power capacity in the long-term power flow of the target large-scale new energy grid connection to be predicted.
Optionally, the power type of the output includes: wind power stations, photovoltaic power stations, hydroelectric power stations, thermal power stations, nuclear power stations, and gas power stations.
Optionally, the establishing of the prediction objective function includes:
determining a power base function of the power type of the output, wherein the formula is as follows:
x＝[x W-1 ,...,x W-i ,...x S-j ,...,x H-k ,...,x T-m ,...，x N-O ，…，x NG-P ，…] T
wherein X W-i For wind power station, X S-j For photovoltaic power stations, X H-k For hydroelectric power stations, X T-m For thermal power station, x N-O For nuclear power plants and x NG-P Is a gas power station;
determining a predicted primary objective function, wherein the formula is as follows:
wherein, maxG Wind+Solar (x) Maximum value G of power generation grid-connected capacity for wind power station and photovoltaic power station Thermal (x) Minimum value of power generation grid-connected capacity P for wind power station and photovoltaic power station Total Total transmitted power for grid connection, C Demand For grid-connected power demand values, G Hydro To the grid-connected capacity, C H-Plan Setting a value for the plan;
according to the formula (1) and the different requirements of the grid-connected power receiving requirement value in different time periods, determining a prediction objective function, wherein the formula is as follows:
wherein, G Wind For the grid-connected capacity, C of the wind power station W-Set For the horizontal annual development target value G of the wind power station Solar For photovoltaic power station grid-connected capacity, C S-Set For the annual development target value and C of the photovoltaic power station Demand (m, t) is a set of time constants.
Optionally, the boundary condition of the total grid-connected amount of the power types of the output includes:
C W-Set ≤∑X W-i ≤C W-max-resource ，i＝1,2,…,n wind-total ，∑X W-i ＝C W-Set ；
wherein, C W-max-resource For the maximum bearing grid-connected capacity and n of the wind power station wind-total The number of the grid-connected wind power stations;
C S-Set ≤∑X S-j ≤C S-max-resource ，j＝1,2,…,n solar-total ，∑X S-j ＝C S-Set ；
wherein, C S-max-resource For maximum load-bearing grid-connected capacity and n of photovoltaic power station solar-total The number of the grid-connected photovoltaic power stations;
∑X T-m ≤C T-max-resource ，m＝1,2,…,n Thermal-total ；
wherein, C T-max-resource Upper limit for the development of thermal power stations andn thermal-total the number of the grid-connected thermal power stations is.
∑X H-k ＝C H-Plan ，k＝1,2,…,n Hydro-total ；
Wherein n is Hydro-total The number of hydroelectric power stations.
Optionally, the influence coefficient vector of the complementary characteristics between the power types of the output power on the power flow is specifically as follows:
Factor＝[σ i ，ρ for-wind-total ，σ j ，ρ for-solar-total ，α k ，δ m ，θ feng ，θ ku ] T
wherein, σ i Internal output concurrency rate and rho of wind power station for-wind-total Overall adjustment effect coefficient, sigma, of wind power station output for time-space difference of multiple wind power stations output j Internal output coincidence rate rho of photovoltaic power station for-solar-total Adjusting the coefficient of effect, alpha, of the photovoltaic plant contribution to the totality of the time-space differences of the contribution of a plurality of photovoltaic plants k For the forced coefficient of output, delta, of hydroelectric power stations m Adjusting peak depth coefficient theta for thermal power generating unit feng Smoothing coefficient of output, theta, for complementary effects in summer hydroelectric, wind and photovoltaic power stations ku Smoothing coefficient i ═ 1,2, …, n for complementary effect output of winter hydroelectric, wind and photovoltaic power stations wind-total 、j＝1,2,…,n solar-total 、m＝1,2,…,n Thermal-total 、k＝1,2,…,n Hydro-total 。
Optionally, the output decomposition rule is used for decomposing the output power curve according to the power type of the output.
Optionally, an extreme scenario of power source capacity prediction in the forward power flow is as follows:
G Thermal (x)×δm+C (H-Plan) ×(1-α k )＝(C W-Set ×σ i ×ρ (for-wind-total) +C S-Set ×σ j ×ρ (for-solar-total) )×θ ku 。
the method can combine primary energy characteristics such as fluctuation of active power output after large-scale new energy such as wind power, photovoltaic and the like is combined into a grid, can effectively solve the problems that the horizontal annual span of a planning target of a long-term power grid is large, and the total amount of the grid-connected power supply is difficult to estimate due to uncertainty and difficulty in most cases of related power supply construction planning, and provides a reasonable quantitative analysis method for grid-connected power supply capacity for long-term power flow prediction, thereby providing powerful technical support for long-term power grid planning.
Drawings
FIG. 1 is a flow chart of a method for predicting power capacity in a new energy grid-connected power flow in accordance with the present invention;
FIG. 2 is a graph of the output power of a method for predicting power capacity in a new energy grid-connected power flow in accordance with the present invention;
fig. 3 is a diagram of a system architecture for predicting power capacity in a new energy grid-connected power flow in accordance with the present invention.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, the same units/elements are denoted by the same reference numerals.
Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Further, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
The invention provides a method for predicting power capacity in new energy grid-connected power flow, which comprises the following steps of:
determining the power type of large-scale new energy grid-connected output, and establishing a prediction objective function of power capacity in power flow aiming at the power type of the output;
determining boundary conditions of the total grid-connected amount of the power types of the output power and influence coefficient vectors of complementary characteristics among the power types of the output power on the power flow aiming at the power types of the output power;
determining an output decomposition rule and a positioning rule of each output power type according to the output power type, and determining an extreme scene of power capacity prediction in a long-term power flow according to the output decomposition rule and the positioning rule;
and predicting the target large-scale new energy grid connection to be predicted according to the prediction target function, the boundary condition of the total grid connection amount of the power types of the output, and the extreme scene of the power capacity prediction in the long-term power flow caused by the complementary characteristics among the power types of the output, so as to determine the power capacity in the long-term power flow of the target large-scale new energy grid connection to be predicted.
The type of power source that is to be applied includes: wind power stations, photovoltaic power stations, hydroelectric power stations, thermal power stations, nuclear power stations, and gas power stations.
Establishing a prediction objective function, comprising:
determining a power base function of the power type of the output, wherein the formula is as follows:
x＝[x W-1 ,...,x W-i ,...x S-j ,...,x H-k ,...,x T-m ,...，x N-O ，…，x NG-P ，…] T
wherein, X W-i For wind power stations, X S-j For photovoltaic power stations, X H-k For hydroelectric power stations, X T-m For thermal power station, x N-O For nuclear power plants and x NG-P Is a gas power station;
determining a predicted primary objective function, wherein the formula is as follows:
wherein, maxG Wind+Solar (x) Maximum value G of power generation grid-connected capacity for wind power station and photovoltaic power station Thermal (x) Minimum value of power generation grid-connected capacity P for wind power station and photovoltaic power station Total Total transmitted power for grid connection, C Demand For grid-connected power demand values, G Hydro To the grid-connected capacity, C H-Plan Setting a value for the plan;
according to the formula (1) and the different requirements of the grid-connected power receiving requirement value in different time periods, determining a prediction objective function, wherein the formula is as follows:
wherein G Wind For wind power station grid-connected capacity, C W-Set For the annual development target value G of the wind power station Solar For photovoltaic power station grid-connected capacity, C S-Set For the annual development target value and C of the photovoltaic power station Demand (m, t) is a set of time constants.
The boundary conditions of the total grid-connected amount of the power types of the output comprise:
C W-Set ≤∑X W-i ≤C W-max-resource ，i＝1,2,…,n wind-total ，∑X W-i ＝C W-Set ； (3)
wherein, C W-max-resource For the maximum bearing grid capacity and n of the wind power station wind-total The number of the grid-connected wind power stations;
C S-Set ≤∑X S-j ≤C S-max-resource ，j＝1,2,…,n solar-total ，∑X S-j ＝C S-Set ； (4)
wherein, C S-max-resource For maximum load-bearing grid-connected capacity and n of photovoltaic power station solar-total The number of the grid-connected photovoltaic power stations;
∑X T-m ≤C T-max-resource ，m＝1,2,…,n Thermal-total ； (5)
wherein, C T-max-resource Upper limit sum n for thermal power station development thermal-total The number of the grid-connected thermal power stations is.
∑X H-k ＝C H-Plan ，k＝1,2,…,n Hydro-total ； (6)
Wherein n is Hydro-total The number of hydroelectric power stations.
Optionally, the influence coefficient vector of the complementary characteristics between the power types of the output on the power flow is specifically as follows:
Factor＝[σ i ，ρ for-wind-total ，σ j ，ρ for-solar-total ，α k ，δ m ，θ feng ，θ ku ] T
wherein, σ i Internal output concurrency rate and rho of wind power station for-wind-total Overall adjustment effect coefficient, sigma, of wind power station output for time-space difference of multiple wind power stations output j Internal output coincidence rate rho of photovoltaic power station for-solar-total Adjusting the coefficient of effect, alpha, of the photovoltaic plant contribution to the totality of the time-space differences of the contribution of a plurality of photovoltaic plants k For the forced coefficient of output, delta, of hydroelectric power stations m Adjusting peak depth coefficient theta for thermal power generating unit feng Output smoothing factor, theta, for complementary effects in summer hydropower stations, wind power stations, and photovoltaic power stations ku Smoothing coefficient i ═ 1,2, …, n for complementary effect output of winter hydroelectric, wind and photovoltaic power stations wind-total 、j＝1,2,…,n solar-total 、m＝1,2,…,n Thermal-total 、k＝1,2,…,n Hydro-total 。
Wind power needs to consider the internal output concurrency rate sigma of a certain wind power base i And the integral adjustment effect coefficient rho of the output of the wind power supply caused by the time-space difference of the outputs of the wind power bases in the whole planning power grid for-wind-total 。
The solar photovoltaic power supply needs to consider the internal output simultaneous rate sigma of a certain photovoltaic power supply base j And the integral adjustment effect coefficient rho of the photovoltaic power output caused by the time-space difference of the output of a plurality of photovoltaic power bases in the whole planned power grid for-solar-total 。
The general output adjusting characteristic of the hydropower station is that the remaining range is 100 percent adjustable except certain forced output, and each minute is adjustableThe clock can adjust 50% of the installed capacity, and the forced output coefficient of any hydropower station can be set as alpha k 。
Thermal power belongs to a stable power supply and can provide output power regulation response for other various power supplies. Peak-regulating depth coefficient delta of any thermal power generating unit m Considered at 40%.
Complementary effect of hydroelectric power, wind power and photovoltaic power, complementary effect of hydroelectric power, wind power and photovoltaic power in the whole network range can be set for taking account of the effect, complementary effect output smooth coefficients of hydroelectric power, wind power and photovoltaic power in the whole network range of a power grid can be set, the adjustment performance of hydroelectric power is different due to the difference of a dry period and a rich period, the adjustment performance needs to be considered respectively, but certain simplification processing is carried out at the position due to the lack of relevant actual data, and the complementary effect output smooth coefficients theta of all hydroelectric power, wind power and photovoltaic power in the rich season (summer) are calculated according to the difference of the dry period and the rich period feng 0.8, smoothing coefficient theta of complementary effect output of hydropower in dry season (winter) and wind power and photovoltaic ku Considering 0.9, if actual data more accurate in this respect can be obtained in the future, the correlation coefficient can be directly replaced, and the calculation analysis result can be corrected.
And (5) decomposing the output power according to the output power curve of the power type of the output.
The output of large-scale wind power stations and photovoltaic power stations has certain fluctuation and intermittency, and the output power curve can be decomposed, taking wind power as an example, as shown in fig. 2.
Total output P of certain wind power base W (t) can be decomposed into three components, a slowly conventionally varying component P a (t) a rapidly fluctuating component P r (t), a more occasional significant change P r (t) satisfies the following formula:
P W (t)＝P a (t)+P t (t)+P r (t) (7)
the solar photovoltaic output can also be decomposed as above, and the characteristics of these components are described separately below:
P t (t) has zero mean value when the change is frequent, and when the wind power and photovoltaic grid-connected proportion is relatively highWhen the wind power and photovoltaic grid-connected ratio is increased, the statistical smoothing effect of the output of different wind power and photovoltaic bases can effectively eliminate the fluctuation component, so that the change component does not need to be adjusted by a conventional power supply.
P r (t), the change of the output of the fan is in a proportional relation with the cube of the wind speed, which shows that a small wind speed change can cause large fluctuation of the output, the photovoltaic output also has similar characteristics, when the large fluctuation of the output of each wind power and photovoltaic base is superposed, a large active output change can be brought, and the change is haphazard, but the power following adjustment of depth is required to be provided by a conventional power supply.
P a And (t) is a trend change component in the change of the wind power and the photovoltaic power, the change is slow, but the occurrence frequency is high, and a conventional power supply is required to provide frequent power following adjustment.
A conventional power supply function positioning principle;
the conventional power supplies for stabilizing power fluctuation for wind power and photovoltaic power bases are mainly fire power and hydroelectric power, although the hydroelectric power supply has a large adjustable range and a high adjusting speed, the hydroelectric bases are required to guarantee other tasks such as flood control and irrigation besides the power generation task, so that the total generated water for stabilizing the output change of new energy is limited; on the other hand, as the distribution of hydroelectric power sources is mainly limited by resource conditions, the specific distribution positions are possibly far away from the wind power and photovoltaic power sources, and the hydropower is used for bearing the daily follow-up adjustment of the output change of each new energy base, even if the stabilization of the overall fluctuation of the power grid can be realized, the problem of improving the utilization rate of the new energy power source output lines cannot be solved. Therefore, thermal power adjustment can be used as a main component for adjusting the component, and as each wind power and photovoltaic power base is changed according to the proportion, in order to improve the utilization rate of the transmission lines of each new energy base and avoid overload of some lines, and light load of some lines, the corresponding and matched conventional power adjustment capacity of each wind power and photovoltaic base can be estimated according to the scene. In the analysis process, the hydropower station has a regulation characteristic for many years, so the regulation capacity of the hydropower station is prioritized.
Extreme scenarios for power source capacity prediction in forward power flow are as follows:
G Thermal (x)×δm+C (H-Plan) ×(1-α k )＝(C W-Set ×σ i ×ρ (for-wind-total) +C S-Set ×σ j ×ρ (for-solar-total) )×θ ku 。 (8)
the present invention further provides a system 200 for predicting power capacity in a new energy grid-connected power flow, as shown in fig. 3, including:
the objective function determining module 201 is used for determining the power type of large-scale new energy grid-connected output and establishing a prediction objective function of power capacity in power flow aiming at the power type of output;
the condition limiting module 202 is used for determining boundary conditions of the grid-connected total amount of the power types of the output power and influence coefficient vectors of complementary characteristics among the power types of the output power on the power flow according to the power types of the output power;
the scene construction module 203 determines an output decomposition rule and a positioning rule of each output power type according to the output power type, and determines an extreme scene of power capacity prediction in a long-term power flow according to the output decomposition rule and the positioning rule;
the prediction module 204 predicts the target large-scale new energy grid connection to be predicted according to the prediction target function, the boundary condition of the total grid connection amount of the power types of the output, and the extreme scene of power capacity prediction in the long-term power flow caused by complementary characteristics among the power types of the output, and determines the power capacity in the long-term power flow of the target large-scale new energy grid connection to be predicted.
The type of power source that is to be applied includes: wind power stations, photovoltaic power stations, hydroelectric power stations, thermal power stations, nuclear power stations, and gas power stations.
Establishing a prediction objective function, comprising:
determining a power base function of the power type of the output, wherein the formula is as follows:
x＝[x W-1 ,...,x W-i ,...x S-j ,...,x H-k ,...,x T-m ,...，x N-O ，…，x NG-P ，…] T
wherein, X W-i For wind power station, X S-j For photovoltaic power stations, X H-k For hydroelectric power stations, X T-m For thermal power plants, x N-O For nuclear power plants and x NG-P Is a gas power station;
determining a predicted primary objective function, wherein the formula is as follows:
wherein, maxG Wind+Solar (x) Maximum value G of power generation grid-connected capacity for wind power station and photovoltaic power station Thermal (x) Minimum value P of power generation grid-connected capacity of wind power station and photovoltaic power station Total Total transmitted power for grid connection, C Demand For grid-connected power demand values, G Hydro To the grid-connected capacity, C H-Plan Setting a value for the plan;
according to the formula (1) and the different requirements of the grid-connected power receiving requirement value in different time periods, determining a prediction objective function, wherein the formula is as follows:
wherein G Wind For wind power station grid-connected capacity, C W-Set For the horizontal annual development target value G of the wind power station Sikar For photovoltaic power station grid-connected capacity, C S-Set For the annual development target value and C of the photovoltaic power station Demand (m, t) is a set of time constants.
The boundary conditions of the total grid-connected amount of the power types of the output comprise:
C W-Set ≤∑X W-I ≤C W-max-resource ，i＝1,2,…,n wind-total ，∑X W-i ＝C W-Set ；
wherein, C W-max-resource For the maximum bearing grid-connected capacity and n of the wind power station wind-total The number of the grid-connected wind power stations;
C S-Set ≤∑X S-j ≤C S-max-resource ，j＝1,2,…,n solar-total ，∑X S-j ＝C S-Set ；
wherein, C S-max-resource For maximum load-bearing grid-connected capacity and n of photovoltaic power station solar-total The number of the grid-connected photovoltaic power stations;
∑X T-m ≤C T-max-resource ，m＝1,2,…,n Thermal-total ；
wherein, C T-max-resource Upper limit and n for thermal power plant development thermal-total The number of the grid-connected thermal power stations is.
∑X H-k ＝C H-Plan ，k＝1,2,…,n Hydro-total ；
Wherein n is Hydro-total The number of hydroelectric power stations.
The influence coefficient vector of the complementary characteristics among the power types of the output on the power flow is as follows:
Factor＝[σ i ，ρ for-wind-total ，σ j ，ρ for-solar-total ，α k ，δ m ，θ feng ，θ ku ] T
wherein, σ i Internal output concurrency rate and rho of wind power station for-wind-total Overall adjustment effect coefficient, sigma, of wind power station output for time-space difference of multiple wind power stations output j Internal output coincidence rate rho of photovoltaic power station for-solar-total Adjusting the coefficient of effect, alpha, of the photovoltaic plant contribution to the totality of the time-space differences of the contribution of a plurality of photovoltaic plants k For the forced coefficient of output, delta, of hydroelectric power stations m Adjusting peak depth coefficient theta for thermal power generating unit feng Smoothing coefficient of output, theta, for complementary effects in summer hydroelectric, wind and photovoltaic power stations ku Smoothing coefficient i ═ 1,2, …, n for complementary effect output of winter hydroelectric, wind and photovoltaic power stations wind-total 、j＝1,2,…,n solar-total 、m＝1,2,…,n Thermal-total 、k＝1,2,…,n Hydro-total 。
And (5) decomposing the output power according to the output power curve of the power type of the output.
Extreme scenarios for power source capacity prediction in forward power flow are as follows:
G Thermal (x)×δm+C (H-Plan) ×(1-α k )＝(C W-Set ×σ i ×ρ (for-wind-total) +C S-Set ×σ j ×ρ (for-solar-total) )×θ ku 。
the method can combine primary energy characteristics such as fluctuation of active power output after large-scale new energy such as wind power, photovoltaic and the like is combined into a grid, can effectively solve the problems that the horizontal annual span of a planning target of a long-term power grid is large, and the total amount of the grid-connected power supply is difficult to estimate due to uncertainty and difficulty in most cases of related power supply construction planning, and provides a reasonable quantitative analysis method for grid-connected power supply capacity for long-term power flow prediction, thereby providing powerful technical support for long-term power grid planning.
As will be appreciated by one skilled in the art, 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 so forth) having computer-usable program code embodied therein. The scheme in the embodiment of the application can be implemented by adopting various computer languages, such as object-oriented programming language Java and transliterated scripting language JavaScript.
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 flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (6)
1. A method for predicting power source capacity in a new energy grid-connected power flow, the method comprising:
determining the power type of large-scale new energy grid-connected output, and establishing a prediction objective function of power capacity in power flow aiming at the power type of the output;
determining boundary conditions of the total grid-connected amount of the power types of the output power and influence coefficient vectors of complementary characteristics among the power types of the output power on the power flow aiming at the power types of the output power;
determining an output decomposition rule and a positioning rule of each output power type according to the output power type, and determining an extreme scene of power capacity prediction in a long-term power flow according to the output decomposition rule and the positioning rule;
predicting the target large-scale new energy grid connection to be predicted according to the prediction target function, the boundary condition of the total grid connection amount of the power types of the output, and the extreme scene of the power capacity prediction in the long-term power flow caused by the complementary characteristics among the power types of the output, and determining the power capacity in the long-term power flow of the target large-scale new energy grid connection to be predicted;
wherein, the establishment of the prediction objective function comprises:
determining a power base function of the power type of the output, wherein the formula is as follows:
x＝[x W-1 ，...，x W-i ，...x S-j ，...，x H-k ，...，x T-m ，...，x N-O ，...，x NG-P ，...] T
wherein, X W-i For wind power stations, X S-j For photovoltaic power stations, X H-k For hydroelectric power stations, X T-m For thermal power station, x N-O For nuclear power plants and x NG-P Is a gas power station;
determining a predicted primary objective function, wherein the formula is as follows:
wherein, maxG Wind+Solar (x) Maximum value of power generation grid-connected capacity for wind power station and photovoltaic power station、Gmin Thermal (x) Minimum value of power generation grid-connected capacity P for wind power station and photovoltaic power station Total Total transmitted power for grid connection, C Demand For grid-connected power demand values, G Hydro To the grid-connected capacity, C H-Plan Setting a value for the plan;
according to the formula (1) and the different requirements of the grid-connected power receiving requirement value in different time periods, determining a prediction objective function, wherein the formula is as follows:
wherein G Wind For the grid-connected capacity, C of the wind power station W-Set For the annual development target value G of the wind power station Solar For photovoltaic power station grid-connected capacity, C S-Set For the annual development target value and C of the photovoltaic power station Demand (m, t) is a set of time constants;
the boundary condition of the total grid-connected amount of the power types of the output power comprises the following steps:
C W-Set ≤∑X W-i ≤C W-max-resource ，i＝1，2，…，n wind-total ，∑X W-i ＝C W-Set ； (3)
wherein, C W-max-resource For the maximum bearing grid-connected capacity and n of the wind power station wind-total The number of grid-connected wind power stations;
C S-Set ≤∑X S-j ≤C S-max-resource ，j＝1，2，…，n solar-total ，∑X S-j ＝C S-Set ； (4)
wherein, C S-max-resource For maximum load-bearing grid-connected capacity and n of photovoltaic power station solar-total The number of the grid-connected photovoltaic power stations;
∑X T-m ≤C T-max-resource ，m＝1，2，…，n Thermal-total ； (5)
wherein, C T-max-resource Upper limit sum n for thermal power station development thermal-total The number of the grid-connected thermal power stations is;
∑X H-k ＝C H-Plan ，k＝1，2，…，n Hydro-total ； (6)
wherein n is Hydro-total The number of hydroelectric power stations;
the influence coefficient vector of the complementary characteristics among the power types of the output on the power flow is as follows:
Factor
＝[σ i ，ρ for-wind-total ，σ j ，ρ for-solar-total ，α k ，δ m ，θ feng ，θ ku ] T
wherein, σ i Internal output concurrency rate and rho of wind power station for-wind-total The overall adjusting effect coefficient, sigma, of the output of the wind power station for the time-space difference of the outputs of a plurality of wind power stations j Internal output coincidence rate rho of photovoltaic power station for-solar-total Adjusting the coefficient of effect, alpha, of the photovoltaic plant contribution to the totality of the time-space differences of the contribution of a plurality of photovoltaic plants k For the forced coefficient of output, delta, of hydroelectric power stations m Adjusting peak depth coefficient theta for thermal power generating unit feng Smoothing coefficient of output, theta, for complementary effects in summer hydroelectric, wind and photovoltaic power stations ku Smoothing coefficient i ═ 1,2, …, n for complementary effect output of winter hydroelectric, wind and photovoltaic power stations wind-total 、j＝1，2，…，n solar-total 、m＝1，2，…，n Thermal-total 、k＝1，2，…，n Hydro-total ；
Extreme scenarios for power supply capacity prediction in the forward power flow are as follows:
G Thermal (x)×δm+C (H-Plan) ×(1-α k )＝(C W-Set ×σ i ×ρ (for-wind-total) +C S-Set ×σ j ×ρ (for-solar-total) )×θ ku (7)。
2. the method of claim 1, the power type of the output comprising: wind power stations, photovoltaic power stations, hydroelectric power stations, thermal power stations, nuclear power stations, and gas power stations.
3. The method of claim 1, wherein the output decomposition rule decomposes an output power curve according to a power type of the output.
4. A system for predicting power source capacity in a new energy grid-connected power flow, the system comprising:
the target function determining module is used for determining the power type of large-scale new energy grid-connected output and establishing a prediction target function of power capacity in power flow aiming at the power type of the output;
the condition limiting module is used for determining the boundary condition of the grid-connected total amount of the power types of the output power and the influence coefficient vector of the complementary characteristics among the power types of the output power on the power flow aiming at the power types of the output power;
the scene construction module is used for determining an output decomposition rule and a positioning rule of each output power type according to the output power type and determining an extreme scene of power capacity prediction in a long-term power flow according to the output decomposition rule and the positioning rule;
the prediction module predicts the target large-scale new energy grid connection to be predicted according to the prediction target function, the boundary condition of the total grid connection amount of the power types of the output, and the extreme scene of power capacity prediction in the long-term power flow caused by complementary characteristics among the power types of the output, and determines the power capacity in the long-term power flow of the target large-scale new energy grid connection to be predicted;
wherein, the establishment of the prediction objective function comprises:
determining a power base function of the power type of the output, wherein the formula is as follows:
x＝[x W-1 ，...，x W-i ，...x S-j ，...，x H-k ，...，x T-m ，...，x N-O ，...，x NG-P ，...] T
wherein, X W-i For wind power station, X S-j For photovoltaic power stations, X H-k For hydroelectric power stations, X T-m For thermal power station, x N-O For nuclear power plants and x NG-P Is a gas power station;
determining a predicted primary objective function, wherein the formula is as follows:
wherein, maxG Wind+Solar (x) For the maximum value of the power generation grid-connected capacity of the wind power station and the photovoltaic power station, Gmin Thermal (x) Minimum value of power generation grid-connected capacity P for wind power station and photovoltaic power station Total Total transmitted power for grid connection, C Demand For grid-connected power demand values, G Hydro Is the grid-connected capacity, C H-Plan Setting a value for the plan;
according to the formula (1) and the different requirements of the grid-connected power receiving requirement value in different time periods, determining a prediction objective function, wherein the formula is as follows:
wherein, G Wind For wind power station grid-connected capacity, C W-Set For the annual development target value G of the wind power station Solar For photovoltaic power station grid-connected capacity, C S-Set For the annual development target value and C of the photovoltaic power station Demand (m, t) is a set of time constants;
the boundary condition of the total grid-connected amount of the power types of the output power comprises the following steps:
C W-Set ≤∑X W-i ≤C W-max-resource ，i＝1，2，…，n wind-total ，∑X W-i ＝C W-Set ；
wherein, C W-max-resource For the maximum bearing grid capacity and n of the wind power station wind-total The number of grid-connected wind power stations;
C S-Set ≤∑X S-j ≤C S-max-resource ，j＝1，2，…，n solar-total ，∑X S-j ＝C S-Set ；
wherein, C S-max-resource For maximum load-bearing grid-connected capacity and n of photovoltaic power station solar-total The number of the grid-connected photovoltaic power stations;
∑X T-m ≤C T-max-resource ，m＝1，2，…，n Thermal-total ；
wherein, C T-max-resource Upper limit sum n for thermal power station development thermal-total The number of the grid-connected thermal power stations is;
∑X H-k ＝C H-Plan ，k＝1，2，…，n Hydro-total ；
wherein n is Hydro-total The number of hydroelectric power stations;
the influence coefficient vector of the complementary characteristics among the power types of the output on the power flow is as follows:
Factor
＝[σ i ，ρ for-wind-total ，σ j ，ρ for-solar-total ，α k ，δ m ，θ feng ，θ ku ] T
wherein, σ i Internal output concurrency rate and rho of wind power station for-wind-total The overall adjusting effect coefficient, sigma, of the output of the wind power station for the time-space difference of the outputs of a plurality of wind power stations j Internal output coincidence rate rho of photovoltaic power station for-solar-total Adjusting the coefficient of effect, alpha, of the photovoltaic plant contribution to the totality of the time-space differences of the contribution of a plurality of photovoltaic plants k For the forced coefficient of output, delta, of hydroelectric power stations m Adjusting peak depth coefficient theta for thermal power generating unit feng Smoothing coefficient of output, theta, for complementary effects in summer hydroelectric, wind and photovoltaic power stations ku Smoothing coefficient i ═ 1,2, …, n for complementary effect output of winter hydroelectric, wind and photovoltaic power stations wind-total 、j＝1，2，…，n solar-total 、m＝1，2，…，n Thermal-total 、k＝1，2，…，n Hydro-total ；
Extreme scenarios for power supply capacity prediction in the forward power flow are as follows:
G Thermal (x)×δm+C (H-Plan) ×(1-α k )＝(C W-Set ×σ i ×ρ (for-wind-total) +C S-Set ×σ j ×ρ (for-solar-total) )×θ ku (7)。
5. the system of claim 4, the type of power source that provides the output, comprising: wind power stations, photovoltaic power stations, hydroelectric power stations, thermal power stations, nuclear power stations, and gas power stations.
6. The system of claim 4, wherein the output resolution rule performs resolution based on an output power curve for a power type of output.
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