CN109636041B - Unit maintenance simulation method and system suitable for large-scale complex power grid - Google Patents
Unit maintenance simulation method and system suitable for large-scale complex power grid Download PDFInfo
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Abstract
The invention relates to the technical field of power grid maintenance, and provides a unit maintenance simulation method and a unit maintenance simulation system suitable for a large-scale complex power grid, wherein the method comprises the following steps: acquiring original maintenance data of the generator set; generating maintenance data of the hydroelectric generating set, and correcting the original maintenance data; acquiring hydroelectric power output parameters and new energy output parameters, and correcting an equivalent load curve in original overhaul data; acquiring a power transmission plan of a tie line, and carrying out load curve on each region in original maintenance data; and acquiring constraint conditions of the maintenance plans of each region, correcting the original maintenance data by using the acquired constraint conditions of the maintenance plans of each region, and generating and outputting optimized maintenance data, so that the ratio of the available unit capacity of the system to the system load in each period in an optimization cycle is as equal as possible, and the system has approximately the same reliability in each period.
Description
Technical Field
The invention belongs to the technical field of power grid maintenance, and particularly relates to a unit maintenance simulation method and system suitable for a large-scale complex power grid.
Background
In recent years, with the increasing scale of electric power systems, the addition of intermittent energy sources such as wind power and solar energy, the construction of large-scale multi-stage hydropower stations across watershed, the access of various types of power sources such as nuclear power, pumped storage power stations and gas turbines, the layout of long-distance alternating current and direct current hybrid power transmission of a power grid and other factors greatly increase the complexity of the operation of the power grid. How to optimize the system operation in the complex power supply and power grid environment, improve the energy-saving economy of the system and reduce the emission intensity of the system becomes an important problem for power grid planning. The operation optimization of the power grid relates to factors in various aspects such as system peak regulation, complex power supply structure coordination, line section tidal current safety and the like, the evaluation on the system safety can be realized by analyzing a typical operation mode, the system energy consumption, the cost and the emission are too rough, and the energy conservation, the economy and the carbon emission intensity of different scheduling operation schemes can be evaluated in a refined manner by simulating the operation of the power grid within a long time range.
The maintenance of the generator set is an important component of the operation optimization of the power grid, at present, the maintenance of the generator set is mainly carried out depending on load curves of various time periods of the national grid, the accuracy is low, and the problems that maintenance plans of different areas are not coordinated in space, time for maintenance and arrangement of different types of power supplies is not coordinated and the like are caused.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a unit overhaul simulation method suitable for a large-scale complex power grid, and aims to solve the problems that in the prior art, overhaul of a unit is mainly carried out depending on load curves of national grids in various time periods, the accuracy is low, overhaul plans in different areas are not coordinated in space, time for overhaul and arrangement of different types of power supplies is not coordinated and the like.
The technical scheme provided by the invention is as follows: a unit overhaul simulation method suitable for a large-scale complex power grid comprises the following steps:
acquiring original maintenance data of the generator set from a pre-generated complex power supply power grid planning scheme;
generating hydroelectric generating set maintenance data according to the water volumes of reservoirs in different areas, and correcting the original maintenance data by using the generated hydroelectric generating set maintenance data;
acquiring hydroelectric power output parameters and new energy output parameters respectively according to a pre-generated hydroelectric power output operation model and a pre-generated new energy output operation model, and correcting an equivalent load curve in the original maintenance data;
acquiring a tie line power transmission plan from the complex power supply power grid planning scheme, and using the acquired tie line power transmission plan to carry out load curve on each region in the original maintenance data;
and acquiring the constraint conditions of the maintenance plans of all the regions from the complex power supply power grid planning scheme, correcting the original maintenance data by using the acquired constraint conditions of the maintenance plans of all the regions, and generating and outputting optimized maintenance data.
As an improved scheme, the step of obtaining the original overhaul data of the generator set from the pre-generated complex power supply grid planning scheme further includes the following steps:
and generating a hydroelectric power output operation model and a new energy power output operation model in advance.
As an improved scheme, the step of generating the hydroelectric output operation model in advance specifically comprises the following steps:
analyzing the random characteristic and the fluctuation characteristic of wind power output according to historical operating data of the wind power plant, and providing a key index of the random characteristic of the wind power plant and a calculation method thereof;
establishing a wind speed randomness model, a wind speed fluctuation model and a space correlation model among multiple regions, and generating a wind speed time sequence which accords with wind speed statistical characteristics and space correlation;
and modeling the output characteristics of the wind power plant fans by using the power characteristic curve of the wind turbine generator to generate the output time sequence of each wind power plant fan.
As an improved scheme, the step of generating the new energy output operation model in advance specifically includes the following steps:
analyzing the distribution characteristic and the random characteristic of the new energy output according to the actual operation data of the new energy system, and separately modeling the deterministic part and the random part of the new solar energy output;
aiming at the determined part of the output of the new energy, simulating the solar radiation intensity of any place and any time on the earth under the condition of no shielding by establishing a global solar radiation model, and calculating the upper limit value of the output predicted by the new energy;
modeling the random part of the new energy output, establishing a probability distribution model of the new energy shielding factor, and obtaining a random part simulation value of the new energy output;
and generating an output time sequence of the new energy system by overlapping the results of the deterministic part and the stochastic part.
As an improved scheme, the constraint conditions of the maintenance plan comprise maintenance constraint, system operation safety constraint and equal standby rate constraint.
Another object of the present invention is to provide a unit overhaul simulation system suitable for a large-scale complex power grid, the system comprising:
the system comprises an original maintenance data acquisition module, a power supply planning module and a power supply planning module, wherein the original maintenance data acquisition module is used for acquiring original maintenance data of a generator set from a pre-generated complex power supply power grid planning scheme;
the hydroelectric generating set maintenance data generating module is used for generating hydroelectric generating set maintenance data according to the water inflow amount of reservoirs in different areas;
the first correction module is used for correcting the original overhaul data by using the generated hydroelectric generating set overhaul data;
the second correction module is used for respectively acquiring hydroelectric power output parameters and new energy output parameters according to a pre-generated hydroelectric power output operation model and a pre-generated new energy output operation model, and correcting the equivalent load curve in the original maintenance data;
the tie line power transmission plan acquisition module is used for acquiring a tie line power transmission plan from the complex power supply power grid planning scheme;
the third correction module is used for using the acquired tie line power transmission plan to carry out load curve on each area in the original maintenance data;
the constraint condition acquisition module is used for acquiring constraint conditions of maintenance plans of all areas from the complex power supply power grid planning scheme;
the fourth correction module is used for correcting the original maintenance data by using the obtained constraint conditions of the maintenance plans of all the areas;
and the maintenance data generation and output module is used for generating and outputting optimized maintenance data according to the correction of the original maintenance data by the first correction module, the second correction module, the third correction module and the fourth correction module.
As an improvement, the system further comprises:
the hydropower output operation model generation module is used for generating a hydropower output operation model in advance;
and the new energy output operation model generation module is used for generating a new energy output operation model in advance.
As an improved scheme, the hydroelectric power output operation model generation module specifically includes:
the key index proposing module is used for analyzing the random wind power output characteristic and the fluctuation characteristic according to the historical operating data of the wind power plant and proposing the key index of the random wind power plant characteristic and the calculating method thereof;
the wind speed time sequence generation module is used for establishing a wind speed randomness model, a fluctuation model and a multi-region space correlation model and generating a wind speed time sequence which accords with wind speed statistical characteristics and space correlation;
and the output time sequence generation module is used for modeling the output characteristics of the wind power plant fans by utilizing the power characteristic curve of the wind turbine generator to generate the output time sequence of each wind power plant fan.
As an improved scheme, the new energy output operation model generation module specifically includes:
the split modeling control module is used for analyzing the distribution characteristic and the random characteristic of the new energy output according to the actual operation data of the new energy system and modeling the deterministic part and the random part of the solar new energy output separately;
the upper limit value calculation module is used for simulating the solar radiation intensity of any place and any time on the earth under the condition of no shielding by establishing a global solar radiation model aiming at the determined part of the new energy output, and calculating the upper limit value of the new energy output predicted;
the random part analog value calculation module is used for modeling the new energy power generation randomness part for the random part of the new energy output, establishing a probability distribution model of a new energy shielding factor and obtaining a random part analog value of the new energy output;
and the new energy output time sequence generation module is used for generating the output time sequence of the new energy system by overlapping the results of the deterministic portion and the stochastic portion.
As an improved scheme, the constraint conditions of the maintenance plan comprise maintenance constraint, system operation safety constraint and equal standby rate constraint.
In the embodiment of the invention, the original maintenance data of the generator set is obtained from the pre-generated complex power supply power grid planning scheme; generating hydroelectric generating set maintenance data according to the water volumes of reservoirs in different areas, and correcting the original maintenance data by using the generated hydroelectric generating set maintenance data; acquiring hydroelectric power output parameters and new energy output parameters respectively according to a pre-generated hydroelectric power output operation model and a pre-generated new energy output operation model, and correcting an equivalent load curve in the original maintenance data; acquiring a tie line power transmission plan from the complex power supply power grid planning scheme, and using the acquired tie line power transmission plan to carry out load curve on each region in the original maintenance data; and acquiring the constraint conditions of the maintenance plans of each region from the complex power supply power grid planning scheme, correcting the original maintenance data by using the acquired constraint conditions of the maintenance plans of each region, and generating and outputting optimized maintenance data, so that the ratio of the available unit capacity of the system to the system load in each period of the optimization cycle is as equal as possible, and the system has approximately same reliability in each period of time.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
FIG. 1 is a flow chart of an implementation of a unit overhaul simulation method suitable for a large-scale complex power grid provided by the invention;
FIG. 2 is a flow chart of the implementation of the pre-generated hydroelectric power output operation model provided by the present invention;
FIG. 3 is a flow chart of an implementation of a pre-generated new energy output operation model provided by the present invention;
FIG. 4 is a block diagram of a unit overhaul simulation system suitable for a large-scale complex power grid provided by the invention;
FIG. 5 is a block diagram of a module for generating a hydroelectric power output operation model according to the present invention;
fig. 6 is a block diagram of a new energy output operation model generation module provided in the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are merely for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
Fig. 1 is a flow chart of an implementation of a unit overhaul simulation method suitable for a large-scale complex power grid, which specifically includes the following steps:
in step S101, original maintenance data of the generator set is obtained from a pre-generated complex power supply grid planning scheme.
In step S102, hydroelectric generating set overhaul data is generated according to the water volumes of reservoirs in different areas, and the generated hydroelectric generating set overhaul data is used to correct the original overhaul data.
In step S103, a hydroelectric output parameter and a new energy output parameter are respectively obtained from the pre-generated hydroelectric output operation model and the new energy output operation model, and the equivalent load curve in the original overhaul data is corrected.
In step S104, a tie-line power transmission plan is acquired from the complex power supply grid planning plan, and the load curves of the respective regions in the raw inspection data are plotted using the acquired tie-line power transmission plan.
In step S105, constraint conditions of the maintenance plans of each region are obtained from the complex power supply grid planning scheme, the obtained constraint conditions of the maintenance plans of each region are used to correct the original maintenance data, and optimized maintenance data is generated and output.
In the generated optimized maintenance data, the difference of the annual daily utilization rate is as small as possible, and the following mathematical calculation formula is satisfied:
in the formula: NT is the total time interval in the maintenance optimization cycle;
respectively, the system standby rates in i and j time periods.
In the embodiment of the present invention, before executing the above step S101, the following steps need to be executed:
generating a hydropower output operation model and a new energy output operation model in advance;
as shown in fig. 2, the step of generating the hydroelectric power output operation model in advance specifically includes the following steps:
in step S201, wind power output stochastic characteristics and fluctuation characteristics are analyzed according to historical operating data of the wind farm, and a wind farm stochastic characteristic key index and a calculation method thereof are provided.
In step S202, a spatial correlation model between wind speed randomness, volatility and multiple regions is established, and a wind speed time series conforming to the wind speed statistical characteristics and the spatial correlation is generated.
In step S203, the output characteristics of the wind turbines of the wind farm are modeled by using the power characteristic curve of the wind turbine, and an output time sequence of each wind farm is generated.
In this embodiment, the simulation concept of wind power output operation simulation is as follows: firstly, fitting scale parameters and shape parameters of wind speed Weibull distribution, wind speed sequence autocorrelation coefficients of a wind power plant and a wind speed correlation coefficient matrix among multiple wind power plants according to historical measured wind speed data of the multiple wind power plants (which can also be obtained by converting historical output data of the wind power plants); and meanwhile, fitting an average wind speed per hour per day curve and an average wind speed per month per unit curve of the wind power plant according to the historical actual measurement wind speed data of the multi-wind power plant and by considering the seasonality and the regularity of the wind speed of the wind power plant. Then, a random differential equation simulation model is established by adopting a key technology of a random differential equation and considering the random characteristic and the spatial correlation of the wind speed, and a wind speed time sequence conforming to the random characteristic of the historical data is generated in a simulation mode. And then, simultaneously considering the power characteristic curve of the wind turbine generator and random outage, thereby generating the output time sequence of the wind turbines of each wind power plant.
The method for generating the wind power plant output time sequence by utilizing the random differential equation simulation is described as follows:
if the probability density function f (x) is non-negative, continuous and of finite variance in its domain of definition (l, u), its mathematical expectation is E (x) = u, for a random differential equation
Wherein theta is not less than 0,W t Is the standard Brownian motion, v (X) t ) Is a non-negative function defined on (l, u):
the following conclusions are made:
the stochastic process X is ergodic and the probability density function is f (X).
The random process X is mean-regressive and its autocorrelation function follows:
corr(X s+t ,X s )＝e -θt ,s,t≥0
the method is utilized to simulate the time sequence of the wind speed, and the wind speed is set to accord with Weibull distribution with scale parameters and shape parameters of c and k respectively:
then, u is the average wind speed:
f (x) is a distribution function corresponding to F (x), Γ (a) is a gamma function, and theta is taken as an autocorrelation attenuation coefficient of the wind power plant:
Γ (x, a), x ≧ 0 is the incomplete gamma function:
if a plurality of wind speed related wind power plant wind speeds are generated, multidimensional related Brownian motion W needs to be generated firstly t Each dimension W t The correlation coefficient matrix among all dimensions is equal to the wind speed correlation coefficient matrix of the wind power plant. Then, W is reused t And generating wind speed sequences of each wind power plant by each dimensional component.
The wind speed sequence of the wind power plant is not a completely random process, the wind speed levels of areas where the wind power plants are located in different seasons are different due to climate reasons and have a certain rule (such as small winter and large summer), the average wind speeds at different times in the day are different due to different surface temperatures of the areas where the wind power plants are located in the day (such as large evening and small day), and in order to consider the seasonality and the regularity in the day of the wind speed of the wind power plants, the randomly generated wind speed sequence is subjected to
And (6) correcting. Final wind powerThe field output can be determined by:
wherein: p it The output of the wind power plant i at the moment t is obtained; n is it The probability of available wind at the moment t of the wind power plant i is obtained; eta i The wind power plant wake effect coefficient; c i (v) The characteristic curve of the output of the wind turbine generator is shown; k is a radical of ih And k im The correction coefficients are the daily characteristic and the seasonal characteristic of the wind speed.
C i (v) Generally, this is given by:
wherein v is in ，v rated And v out The cut-in wind speed, the rated wind speed and the cut-out wind speed of the wind turbine generator are respectively. And R is the rated output of wind power.
In the embodiment of the present invention, as shown in fig. 3, the step of generating the new energy output operation model in advance specifically includes the following steps:
in step S301, analyzing the distribution characteristic and the random characteristic of the new energy output according to the actual operation data of the new energy system, and separately modeling the deterministic part and the random part of the solar new energy output;
in step S302, for the determined portion of the new energy output, the solar radiation intensity at any time and any place on the earth without any occlusion is simulated by establishing a global solar radiation model, and the upper limit value of the predicted output of the new energy is calculated;
in step S303, modeling the random part of the new energy output, establishing a probability distribution model of the new energy blocking factor, and obtaining a random part simulation value of the new energy output;
in step S304, a power output time series of the new energy system is generated by superimposing the results of the deterministic and stochastic parts.
Wherein, the new energy output model is as follows:
P stc is the rated output of the solar panel, which is defined as the output of the solar panel under standard conditions (intensity of solar radiation I under standard conditions) stc ＝1000W/m 2 Temperature T stc =25 ℃), the rated output of the solar panel is defined, and when the actual solar radiation intensity is greater than the solar radiation intensity under the standard condition, the output of the solar panel is greater than the rated output; i is 0t The solar radiation in the plane outside the atmosphere is only related to the relative position change of the sun and the earth under the condition that the scattering effect of the atmosphere on the sunlight and the weakening effect of random factors such as cloud shielding on the solar radiation intensity are not considered. k is a radical of t Is clear sky index and is defined as total radiation I of the ground surface plane t Intensity of solar radiation in plane outside atmosphere I 0t The ratio between: k is a radical of t ＝I t /I 0t Wherein, I t The intensity of irradiation (including direct irradiation and scattering) at the surface level during the t-th period. k is a radical of t The method is mainly influenced by factors such as cloud shading, weather change, altitude and the like. R is t The ratio of the solar radiation intensity on the inclined plane to the total irradiation intensity of the ground surface plane in the t-th time period is represented, the numerical value of the ratio is approximately equal to the ratio of the cosine of the incident angle of the sun on the inclined plane to the cosine of the incident angle of the sun on the ground surface plane, and the change rule of the ratio is related to the placing inclination angle of the photovoltaic panel and the tracking mode (fixed mode, horizontal tracking, inclined axis tracking or double-axis tracking) of the photovoltaic panel; i (R) t ,k t ,I 0t ) Representing the total irradiation on the photovoltaic panel after considering factors such as solar irradiation (direct, diffuse and reflected), clear sky index and tracking type of the photovoltaic panel. T represents the atmospheric pressure, α T Is the power temperature coefficient of the solar panel.
The key of the new energy output simulation is to I 0t 、k t 、R t And a mode of TAnd (3) simulating. Horizontal plane non-shading illumination I 0t The clear sky index k is related to the geographic position, season, time of day and the like of the solar photovoltaic panel and can be calculated by a global solar irradiation model t The photovoltaic power generation system is mainly influenced by factors such as cloud layer shielding, weather change and altitude, has strong intermittence and randomness, is a main source of uncertainty of photovoltaic output, and needs to be realized by random simulation. Meanwhile, clear sky indexes of a plurality of photovoltaic power stations with similar geographic positions have strong correlation, so that a random differential equation model is adopted for sampling in consideration of the correlation. R is t The photovoltaic panel is mainly placed in a tracking mode, and the incident angle of sunlight of the photovoltaic panel under different tracking modes needs to be deduced. The temperature T has a certain randomness, but the simulation and data collection are difficult, and alpha T The value of (a) is generally small and therefore the effect of temperature on the photovoltaic output is also relatively small, and in the model presented herein, the effect of temperature on the photovoltaic output will be ignored.
The simulation idea of the new energy output is as follows:
1) And acquiring data, wherein the basic information of the light area comprises geographic information of the light area, an autocorrelation coefficient of solar radiation intensity, basic parameters of a clear sky index probability model and the like.
2) Is provided with N light areas, generates a plurality of non-independent normal distribution time sequences X containing the correlation among different light areas and the autocorrelation of each light area m (t)＝x m1,t ,x m2,t ,…,x mN,t Where m represents the number of simulations.
3) Firstly, the probability distribution of the day type of each light area and the maximum clear sky index corresponding to different day types are obtained. Sampling is carried out according to the probability distribution of the day type to obtain clear sky index time sequences K of different light areas th,m (t)＝k m1,t ,k m2,t ,…,k mN,t Calculating the CDF of the solar radiation intensity under different clear sky indexes according to the probability model of the clear sky index, and using the CDF Kth,m(t) And (4) showing.
4) By using X m (t) and CDF Kth,m(t) Sampling to obtain time series I of solar radiation intensities in different light regions m (t)＝i m1,t ,i m2,t ,…,i mN,t 。
5) By means of I m And (t) calculating the photovoltaic power station output of each light area according to the output formulas of the photovoltaic arrays of different types.
6) The above procedure is repeated until the number M of simulations equals the given number M. Averaging the M calculations to obtain an average value P AV And (t) taking a group of calculation results closest to the average value as a final simulation result P (t) and outputting the final simulation result P (t).
In the embodiment of the present invention, the constraint conditions of the maintenance plan include maintenance constraint, system operation safety constraint, and equal availability constraint, wherein:
(1) The overhaul constraints comprise overhaul declaration constraints, overhaul resource constraints, overhaul mutual exclusion constraints, overhaul simultaneous constraints and overhaul frequency constraints;
(2) The system operation safety constraint comprises a system standby constraint, a partition standby constraint, a system peak regulation constraint and a partition peak regulation constraint.
The maintenance needs to consider the space coordination of maintenance plans in different areas and the time coordination optimization problem of different types of power supply maintenance schedules. In short, the overhaul result needs to ensure that the spare capacity of the units in different provinces is sufficient on the space scale; in the water enriching period on the time scale, the hydroelectric generating set is prevented from being overhauled, and water is prevented from being abandoned; when the wind power output is large in winter and spring, enough capacity of the peak shaving unit is ensured.
Fig. 4 shows a block diagram of a unit overhaul simulation system suitable for a large-scale complex power grid provided by the present invention, and for convenience of description, only the parts related to the embodiment of the present invention are shown in the figure.
The unit maintenance simulation system suitable for the large-scale complex power grid comprises:
the original maintenance data acquisition module 11 is used for acquiring original maintenance data of the generator set from a pre-generated complex power supply grid planning scheme;
the hydroelectric generating set maintenance data generating module 12 is used for generating hydroelectric generating set maintenance data according to the water inflow amount of reservoirs in different areas;
the first correction module 13 is configured to correct the original overhaul data by using the generated hydroelectric generating set overhaul data;
the second correction module 14 is configured to obtain a hydroelectric output parameter and a new energy output parameter respectively according to the pre-generated hydroelectric output operation model and the new energy output operation model, and correct the equivalent load curve in the original overhaul data;
a tie line power transmission plan obtaining module 15, configured to obtain a tie line power transmission plan from the complex power supply grid planning scheme;
a third correction module 16, configured to apply the obtained tie line power transmission plan to load curves of each area in the original maintenance data;
a constraint condition obtaining module 17, configured to obtain constraint conditions of a maintenance plan of each region from the complex power supply grid planning scheme;
a fourth correcting module 18, configured to correct the original maintenance data using the obtained constraint conditions of the maintenance plans of the respective areas;
and the maintenance data generation and output module 19 is configured to generate and output optimized maintenance data according to the correction of the original maintenance data by the first correction module, the second correction module, the third correction module and the fourth correction module.
In this embodiment, the system further comprises:
a hydroelectric power output operation model generation module 20 for generating a hydroelectric power output operation model in advance;
and the new energy output operation model generation module 21 is used for generating a new energy output operation model in advance.
As shown in fig. 5, the hydroelectric power output operation model generation module 20 specifically includes:
the key index proposing module 22 is used for analyzing the random wind power output characteristic and the fluctuation characteristic according to the historical operating data of the wind power plant and proposing the key index of the random wind power plant characteristic and the calculating method thereof;
the wind speed time sequence generation module 23 is configured to establish a wind speed randomness model, a wind speed volatility model and a multi-region space correlation model, and generate a wind speed time sequence conforming to wind speed statistical characteristics and space correlation;
and the output time sequence generation module 24 is used for modeling the output characteristics of the wind power plant fans by using the power characteristic curve of the wind turbine generator to generate the output time sequence of each wind power plant fan.
As shown in fig. 6, the new energy output operation model generation module 21 specifically includes:
the separated modeling control module 25 is used for analyzing the distribution characteristic and the random characteristic of the new energy output according to the actual operation data of the new energy system and separately modeling the deterministic part and the random part of the solar new energy output;
the upper limit value calculation module 26 is used for simulating the solar radiation intensity of any place and any time on the earth under the condition of no shielding by establishing a global solar radiation model aiming at the determined part of the new energy output, and calculating the upper limit value of the new energy output predicted;
the random part analog value calculation module 27 is used for modeling the new energy power generation randomness part for the random part of the new energy output, establishing a probability distribution model of the new energy shielding factor and obtaining a random part analog value of the new energy output;
and a new energy output time sequence generation module 28, configured to generate an output time sequence of the new energy system by superimposing the results of the deterministic portion and the stochastic portion.
The functions of the above modules are described in the above embodiments, and are not described herein again.
In the embodiment of the invention, the original maintenance data of the generator set is obtained from the pre-generated complex power supply power grid planning scheme; generating hydroelectric generating set maintenance data according to the water volumes of reservoirs in different areas, and correcting the original maintenance data by using the generated hydroelectric generating set maintenance data; acquiring hydroelectric power output parameters and new energy output parameters respectively according to a pre-generated hydroelectric power output operation model and a pre-generated new energy output operation model, and correcting an equivalent load curve in the original maintenance data; acquiring a tie line power transmission plan from the complex power supply power grid planning scheme, and using the acquired tie line power transmission plan to carry out load curve on each region in the original maintenance data; and acquiring the constraint conditions of the maintenance plans of each region from the complex power supply power grid planning scheme, correcting the original maintenance data by using the acquired constraint conditions of the maintenance plans of each region, and generating and outputting optimized maintenance data, so that the ratio of the available unit capacity of the system to the system load in each period of the optimization cycle is as equal as possible, and the system has approximately same reliability in each period of time.
The above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (4)
1. A unit overhaul simulation method suitable for a large-scale complex power grid is characterized by comprising the following steps:
acquiring original maintenance data of the generator set from a pre-generated complex power supply grid planning scheme;
generating hydroelectric generating set maintenance data according to the water volumes of reservoirs in different areas, and correcting the original maintenance data by using the generated hydroelectric generating set maintenance data;
respectively acquiring hydroelectric output parameters and new energy output parameters according to a pre-generated hydroelectric output operation model and a pre-generated new energy output operation model, and correcting an equivalent load curve in the original overhaul data;
acquiring a tie line power transmission plan from the complex power supply power grid planning scheme, and using the acquired tie line power transmission plan to carry out load curve on each region in the original maintenance data;
acquiring constraint conditions of maintenance plans of all regions from the complex power supply power grid planning scheme, correcting the original maintenance data by using the acquired constraint conditions of the maintenance plans of all regions, and generating and outputting optimized maintenance data;
the method comprises the following steps of obtaining original overhaul data of the generator set from a pre-generated complex power supply grid planning scheme:
generating a hydropower output operation model and a new energy output operation model in advance;
the step of generating the hydroelectric output operation model in advance specifically comprises the following steps:
analyzing the random characteristic and the fluctuation characteristic of wind power output according to historical operating data of the wind power plant, and providing a key index of the random characteristic of the wind power plant and a calculation method thereof;
establishing a space correlation model among wind speed randomness, fluctuation and multiple regions, and generating a wind speed time sequence which accords with wind speed statistical characteristics and space correlation;
modeling the output characteristics of the wind power plant fans by using the power characteristic curve of the wind turbine generator to generate output time sequences of the wind power plant fans;
the step of generating the new energy output operation model in advance specifically comprises the following steps:
analyzing the distribution characteristic and the random characteristic of the new energy output according to the actual operation data of the new energy system, and separately modeling the deterministic part and the random part of the new solar energy output;
aiming at the determined part of the output of the new energy, simulating the solar radiation intensity of any place and any time on the earth under the condition of no shielding by establishing a global solar radiation model, and calculating the upper limit value of the output predicted by the new energy;
modeling the random part of the new energy output, establishing a probability distribution model of the new energy shielding factor, and obtaining a random part simulation value of the new energy output;
and generating an output time sequence of the new energy system by overlapping the results of the deterministic part and the stochastic part.
2. The crew overhaul simulation method for a large-scale complex power grid according to claim 1, wherein the constraints of the overhaul plan comprise overhaul constraints, system operation safety constraints and equal standby rate constraints.
3. A unit overhaul simulation system suitable for large-scale complex power grids, the system comprising:
the system comprises an original maintenance data acquisition module, a maintenance data acquisition module and a maintenance data acquisition module, wherein the original maintenance data acquisition module is used for acquiring original maintenance data of a generator set from a pre-generated complex power supply power grid planning scheme;
the hydroelectric generating set maintenance data generating module is used for generating hydroelectric generating set maintenance data according to the water inflow amount of reservoirs in different areas;
the first correction module is used for correcting the original overhaul data by using the generated hydroelectric generating set overhaul data;
the second correction module is used for respectively acquiring hydroelectric power output parameters and new energy output parameters according to a pre-generated hydroelectric power output operation model and a pre-generated new energy output operation model, and correcting the equivalent load curve in the original maintenance data;
the tie line power transmission plan acquisition module is used for acquiring a tie line power transmission plan from the complex power supply power grid planning scheme;
the third correction module is used for correcting the load curve of each area in the original maintenance data by using the acquired tie line power transmission plan;
the constraint condition acquisition module is used for acquiring constraint conditions of maintenance plans of all areas from the complex power supply power grid planning scheme;
the fourth correction module is used for correcting the original maintenance data by using the obtained constraint conditions of the maintenance plans of all the areas;
the maintenance data generation and output module is used for generating and outputting optimized maintenance data according to the correction of the original maintenance data by the first correction module, the second correction module, the third correction module and the fourth correction module;
the system further comprises:
the hydropower output operation model generation module is used for generating a hydropower output operation model in advance;
the new energy output operation model generation module is used for generating a new energy output operation model in advance;
the hydroelectric power output operation model generation module specifically comprises:
the key index proposing module is used for analyzing the random characteristic and the fluctuation characteristic of wind power output according to the historical operating data of the wind power plant and proposing the key index of the random characteristic of the wind power plant and a calculating method thereof;
the wind speed time sequence generation module is used for establishing a wind speed randomness model, a fluctuation model and a multi-region space correlation model and generating a wind speed time sequence which accords with wind speed statistical characteristics and space correlation;
the output time sequence generation module is used for modeling the output characteristics of the wind power plant fans by utilizing the power characteristic curve of the wind turbine generator to generate output time sequences of the wind power plant fans;
the new energy output operation model generation module specifically comprises:
the separated modeling control module is used for analyzing the distribution characteristic and the random characteristic of the new energy output according to the actual operation data of the new energy system and separately modeling the deterministic part and the random part of the solar new energy output;
the upper limit value calculation module is used for simulating the solar radiation intensity of any place and any time on the earth under the condition of no shielding by establishing a global solar radiation model aiming at the determined part of the new energy output, and calculating the upper limit value of the new energy output predicted;
the random part analog value calculation module is used for modeling the new energy power generation randomness part for the random part of the new energy output, establishing a probability distribution model of a new energy shielding factor and obtaining a random part analog value of the new energy output;
and the new energy output time sequence generation module is used for generating an output time sequence of the new energy system by overlapping the results of the deterministic part and the stochastic part.
4. The crew overhaul simulation system for a large-scale complex power grid according to claim 3, wherein the constraints of the overhaul plan comprise overhaul constraints, system operation safety constraints, and equal standby rate constraints.
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