CN115189401A - Day-ahead-day coordinated optimization scheduling method considering source load uncertainty - Google Patents

Day-ahead-day coordinated optimization scheduling method considering source load uncertainty Download PDF

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CN115189401A
CN115189401A CN202210893504.5A CN202210893504A CN115189401A CN 115189401 A CN115189401 A CN 115189401A CN 202210893504 A CN202210893504 A CN 202210893504A CN 115189401 A CN115189401 A CN 115189401A
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徐敏
李万伟
白望望
冯智慧
张洪源
夏成璧
皮霞
魏占宏
贾玲玲
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Lanzhou University of Technology
Economic and Technological Research Institute of State Grid Gansu Electric Power Co Ltd
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Economic and Technological Research Institute of State Grid Gansu Electric Power Co Ltd
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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
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • 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
    • 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
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • 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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • 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
    • H02J3/381—Dispersed generators
    • 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
    • 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
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    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • H—ELECTRICITY
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    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
    • 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
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/58—The condition being electrical
    • H02J2310/60—Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving

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Abstract

The invention discloses a day-ahead-day coordinated optimization scheduling method considering source load uncertainty, which comprises the following steps: respectively describing the uncertainty of the day-ahead wind power output prediction and the day-ahead photovoltaic output prediction by using a multi-scene random programming method, and constructing a day-ahead wind and light output prediction combined scene set; based on a day-ahead wind-solar output prediction combined scene set, carrying out day-ahead optimized scheduling by combining a thermal power generating unit and a photo-thermal power station; respectively representing uncertainties of the solar wind power output prediction and the solar photovoltaic output prediction through a trapezoidal fuzzy number equivalent model, and constructing a solar source load fuzzy data set; taking the scheduling output of the thermal power generating unit and the photo-thermal power station before the day and the daily endogenous load fuzzy data set as the input quantity of daily scheduling, and establishing a two-layer scheduling model of the thermal power generating unit and the photo-thermal power station before the day; by the method, the problem of high-ratio new energy efficient consumption caused by prediction errors and wind-light output fluctuation can be effectively solved, and the fluctuation amount of the load after wind-light grid connection is effectively reduced, so that the comprehensive operation cost of the system is reduced.

Description

Day-ahead-day coordinated optimization scheduling method considering source load uncertainty
Technical Field
The invention belongs to the technical field of high-proportion new energy grid-connected scheduling, and particularly relates to a day-ahead-day coordinated optimization scheduling method for considering source load uncertainty.
Background
The new energy has volatility and intermittence, and the peak output power fluctuation is increased due to large-scale grid connection, so that a large amount of flexibility is needed for adjusting the power supply to carry out peak shaving. In a new energy base, wind and light resources in a region are reasonably utilized, new energy supply such as wind power, photovoltaic and CSP power generation is improved, construction of a multi-element complementary new energy supply system is accelerated, and the method is an effective way for solving green and efficient consumption of new energy.
The CSP power generation mainly uses a heat collector to collect a large range of sunlight, converts light energy into heat energy, heats the generated heat with a heat-transfer fluid (HTF), and uses the HTF to heat water to generate high-pressure steam, thereby driving a steam turbine to generate power. The CSP has the adjusting performance similar to that of a thermal power generating unit by introducing heat energy to a heat storage system (TES), and compared with the thermal power generating unit, the CSP is more flexible and controllable in output adjustment and higher in response speed.
The basic requirement of safe and stable operation of the power system is balance between power generation and load supply and demand. The reasonable formulation of a scheduling method and the utilization of wind-solar-thermal output complementary characteristics are the premise and the basis of the efficient consumption of large-scale new energy and the scientific planning and operation of a power system.
Therefore, how to combine the wind power plant, the photovoltaic power station and the CSP power station to form a multi-source combined power generation system to balance wind and light output fluctuation becomes a key problem of current research.
Disclosure of Invention
In view of the above problems, the present invention provides a day-ahead and day-inside coordinated optimization scheduling method for solving at least some of the above technical problems, which can effectively alleviate the problem of efficient consumption of high-proportion new energy due to prediction errors and wind-light output fluctuation. The multi-period coordinated scheduling can be combined with more accurate in-day prediction information, the rapid adjusting capability and the electric quantity translation characteristic of the CSP power station are fully utilized, and the fluctuation amount of the load after wind-light grid connection can be effectively reduced, so that the comprehensive operation cost of the system is reduced
The embodiment of the invention provides a day-ahead-day coordinated optimization scheduling method considering source load uncertainty, which comprises the following steps:
s1, respectively describing uncertainties of a day-ahead wind power output prediction and a day-ahead photovoltaic output prediction by using a multi-scene random programming method, and constructing a day-ahead wind and light output prediction combined scene set;
s2, based on the day-ahead wind and light output prediction combined scene set, performing day-ahead optimal scheduling by combining a thermal power generating unit and a photo-thermal power station;
s3, representing uncertainties of the solar wind power output prediction and the solar photovoltaic output prediction respectively through a trapezoidal fuzzy number equivalent model, and constructing a solar source load fuzzy data set; the daily intrinsic load fuzzy data set comprises a daily wind power output predicted value, a daily photovoltaic output predicted value and a daily intrinsic load predicted value;
s4, taking the scheduling output of the thermal power generating unit and the photo-thermal power station before the day and the fuzzy data set of the daily load as input quantities of daily scheduling, and establishing a two-layer scheduling model of the day before the day and the day;
and S5, realizing day-ahead-day coordinated optimization scheduling considering source load uncertainty through the day-ahead-day two-layer scheduling model.
Further, the S1 specifically includes:
s11, respectively representing the day-ahead wind power output and the day-ahead photovoltaic output by using an invariable output prediction value and a variable output prediction error value of the day-ahead wind power output and the day-ahead photovoltaic output:
Figure BDA0003768487900000021
in the formula, P w,t Representing the wind power output before the day; p w,t,y Representing a constant output predicted value of the day-ahead wind power output; p w,t,c A variable output prediction error value representing a day-ahead wind power output; p v,t Representing a photovoltaic output day ahead; p v,t,y Representing a predicted constant output value of the photovoltaic output before the day; p is v,t,c A variable output prediction error value representing a photovoltaic output day ahead;
s12, respectively analyzing the prediction errors of the day-ahead wind power output and the day-ahead photovoltaic output by using standard normal probability distribution to obtain a day-ahead wind power output prediction error probability density function and a day-ahead photovoltaic output prediction error probability density function, wherein the probability density functions are expressed as follows:
Figure BDA0003768487900000031
in the formula, f (p) w,t,c ) Representing a prediction error probability density function of the day-ahead wind power output; delta w,t,c Representing the variance of the wind power output prediction error before the moment t day; mu.s w,t,c The expected value of the wind power output prediction error before the moment t is represented; f (p) v,t,c ) Representing a photovoltaic output prediction error probability density function before the day; delta v,t,c The variance of photovoltaic output prediction errors before the moment t day is represented; mu.s v,t,c Indicating day-ahead light at time tThe expected value of the volt-output prediction error;
and S13, sampling the day-ahead wind power output prediction error probability density function and the day-ahead photovoltaic output prediction error probability density function by adopting a Latin hypercube sampling method to generate a day-ahead wind and light output prediction combined scene set.
Further, the S13 further includes preprocessing the day-ahead wind-solar output prediction combination scene set, specifically including:
deleting scenes with prediction errors exceeding a preset range in the day-ahead wind-solar output prediction combination scene set;
performing scene reduction processing on the day-ahead wind-solar output prediction combination scene set by adopting a synchronous back-substitution elimination method;
and scene combination is carried out on the eliminated scene set of the wind-solar output prediction combination before the day by adopting a Cartesian product method.
Further, the performing scene reduction processing on the future wind-solar output prediction combined scene set by using a synchronous back-substitution elimination method specifically includes:
step1, for any scene lambda m (m =1,2, \8230l), calculating a scene λ at the shortest distance therefrom j (ii) a Expressed as:
Figure BDA0003768487900000032
wherein λ is m Represents the mth scene; lambda [ alpha ] j Represents the jth scene; l represents the number of scenes; d m,min Indicating for a certain scene λ m Scene λ with the shortest distance to it j The probability product of (a); delta. For the preparation of a coating j Representing a scene λ j The probability of occurrence; d (lambda) m ,λ j ) Representing a scene λ m And scene lambda j The Euclidean distance between;
step2, determining scenes lambda to be deleted in L scenes m 。
Figure BDA0003768487900000041
Wherein D is min Representing the smallest probability product among the L scenes.
Step3, modifying the number L = L-1 of the residual scenes, and accumulating the probability of the deleted scenes to the scenes closest to the deleted scenes to ensure that the sum of the probabilities of all the scenes is 1;
and Step4, repeating Step1 to Step3 until the number of the remaining scenes reaches the expected set value.
Further, the scene combination is performed on the reduced day-ahead wind-solar output prediction combination scene set by adopting a cartesian product method, which is expressed as:
Figure BDA0003768487900000042
in the formula, N s Representing a combined scene total number; n is a radical of hydrogen w Representing typical scene number of wind power output before the day; n is a radical of v Representing the number of typical scenes of photovoltaic output in the day ahead; p m,j Representing the probability of the occurrence of the combined scene; p m Representing the probability of occurrence of a typical scene of wind power output; p j Representing the probability of a typical scene of photovoltaic output occurring.
Further, in S2, the day-ahead optimal scheduling is performed in combination with the thermal power generating unit and the photothermal power station, including: the method comprises the following steps of a thermal power unit starting and stopping plan, a thermal power unit output plan and a photo-thermal power station output plan.
Further, the S3 specifically includes:
respectively using trapezoidal fuzzy parameters for the wind power output in the day, the photovoltaic output in the day and the load in the day
Figure BDA0003768487900000043
Figure BDA0003768487900000044
The quadruple of (a) is represented as:
Figure BDA0003768487900000045
in the formula, p wn,t Representing trapezoidal blur parameters
Figure BDA0003768487900000046
The trapezoidal membership parameter; p is a radical of vn,t Representing trapezoidal blur parameters
Figure BDA0003768487900000047
The trapezoidal membership parameter; p is a radical of formula ln,t Representing trapezoidal blur parameters
Figure BDA0003768487900000048
The trapezoidal membership parameter of (a); n =1,2,3,4;
wherein:
Figure BDA0003768487900000051
the specific formula for describing the intraday wind power output, the intraday photovoltaic output and the intraday load through the fuzzy parameters is as follows:
Figure BDA0003768487900000052
in the formula (I), the compound is shown in the specification,
Figure BDA0003768487900000053
representing the predicted value of the wind power output in the day;
Figure BDA0003768487900000054
representing a predicted value of photovoltaic output within a day;
Figure BDA0003768487900000055
representing the predicted value of the load in the day; k is a radical of wn 、k vn And k ln All are scaling factors, n =1,2,3,4.
Further, in S4, the day-ahead-day internal two-layer scheduling model includes a day-ahead scheduling plan model and an internal scheduling plan model;
the day-ahead scheduling plan model comprises a first upper layer model and a first lower layer model; the first upper layer model takes the minimum expected value of the residual load variance of the system as an objective function; the first lower-layer model reasonably arranges the output of a conventional thermal power generating unit and a photothermal power station according to a system residual load curve, and a dispatching plan with the lowest total operation cost of the thermal power generating unit and the photothermal power station is made;
the daily scheduling plan model comprises a second upper layer model and a second lower layer model; the second upper layer model takes the minimum expected value of the system residual load variance as an objective function; and the second lower-layer model takes the lowest total operation cost of the thermal power generating unit and the photothermal power station as a target function.
Further, the constraints of the day-ahead scheduling plan model include: the method comprises the following steps of power load power balance constraint, thermal power unit output constraint, wind power unit output constraint, photovoltaic output constraint, photo-thermal unit output constraint, thermal power rotation standby constraint, photo-thermal rotation standby constraint, thermal power unit climbing capability constraint and photo-thermal unit climbing capability constraint.
Further, the constraints of the intra-day dispatch plan model include: the constraint conditions of the day scheduling plan model comprise: the system comprises a power supply load power balance constraint, a thermal power rotation standby constraint and a photo-thermal rotation standby constraint.
Compared with the prior art, the day-ahead-day coordinated optimization scheduling method considering source load uncertainty has the following beneficial effects:
according to the invention, the scheduling output of the thermal power and CSP power station and the day-in source load fuzzy data set are used as the input quantity of day-in scheduling, and a day-in two-layer scheduling model for accounting source load uncertainty is established, so that the scheduling output and the rotary standby plan of the thermal power and CSP power station are determined. The model can effectively solve the problem of high-proportion new energy efficient consumption caused by prediction errors and wind-light output fluctuation. The multi-period coordinated scheduling can be combined with more accurate in-day prediction information, the rapid adjustment capability and the electric quantity translation characteristic of the CSP power station are fully utilized, and the fluctuation quantity of the load after wind-light grid connection can be effectively reduced, so that the comprehensive operation cost of the system is reduced.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a diagram of an operating framework of a wind, light and heat combined power generation system with EH taken into account according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a system optimized scheduling framework according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a prediction error of wind power generation power provided by an embodiment of the present invention.
Fig. 4 is a schematic diagram of a photovoltaic power generation power prediction error provided in the embodiment of the present invention.
Fig. 5 is a schematic diagram of latin hypercube sampling according to an embodiment of the present invention.
Fig. 6 is a schematic view of a wind power output scene provided by an embodiment of the present invention.
Fig. 7 is a schematic view of a photovoltaic output scene provided by an embodiment of the present invention.
Fig. 8 is a schematic diagram of a scene of typical wind and light output provided by an embodiment of the invention.
FIG. 9 is a flowchart illustrating a method for solving a model using Cplex according to an embodiment of the present invention.
Fig. 10 is a flowchart of calculating a two-layer optimization model according to an embodiment of the present invention.
Fig. 11 is a flowchart of an optimized scheduling algorithm for performing model solution by using an improved particle swarm CIPSO according to the embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Referring to fig. 1, an embodiment of the present invention provides a day-ahead-day coordinated optimization scheduling method for accounting source load uncertainty, which specifically includes the following steps:
s1, respectively describing uncertainties of a day-ahead wind power output prediction and a day-ahead photovoltaic output prediction by using a multi-scene random programming method, and constructing a day-ahead wind-solar output prediction combined scene set;
s2, based on the day-ahead wind and light output prediction combined scene set, performing day-ahead optimal scheduling by combining a thermal power generating unit and a photo-thermal power station;
s3, representing uncertainties of the solar wind power output prediction and the solar photovoltaic output prediction respectively through a trapezoidal fuzzy number equivalent model, and constructing a solar source load fuzzy data set; the daily intrinsic load fuzzy data set comprises a daily wind power output predicted value, a daily photovoltaic output predicted value and a daily intrinsic load predicted value;
s4, taking the scheduling output of the thermal power generating unit and the photo-thermal power station before the day and the fuzzy data set of the daily load as input quantities of daily scheduling, and establishing a two-layer scheduling model of the day before the day and the day;
and S5, realizing day-ahead-day coordinated optimization scheduling considering source load uncertainty through the day-ahead-day two-layer scheduling model.
The above steps will be described in detail below.
In step S1, the multi-source Power generation system operation framework is composed of a thermal Power plant, a photo-thermal (CSP) Power plant, an EH, a wind farm, and a photovoltaic Power plant. The electric energy generated by the combined system is merged into a large power grid to meet the load requirement, and the purpose of balance of supply and demand of the system is achieved. A diagram of an operating framework of a wind, light and heat combined generation system considering EH is shown in fig. 1. The double-layer optimization scheduling model (namely a day-day inner two-layer scheduling model) in the invention is composed of a day-ahead wind-solar output multi-scene random planning model (namely a day-ahead scheduling plan model) and a day-inner source-load fuzzy opportunity constraint optimization model (namely a day-inner scheduling plan model), and a system optimization scheduling framework is shown in figure 2.
And specific operation parameters of the thermal power generating unit, the photothermal power station, the wind power and photovoltaic installed capacity participating in system scheduling and the thermal power generating unit and the CSP power station are given. The CSP power station has the operating and maintenance cost coefficients k of the heat collection device and the heat storage device providing heat energy power generation j And k s (ii) a The rotating standby cost coefficients of the thermal power generating unit and the CSP power station are respectively k i And k c 。
Compared with uncertainty of wind power and photovoltaic output, the fluctuation quantity of the system load demand is small and relatively stable under the time scale of 24h before the day, so that the uncertainty is not considered at once. Uncertainty of wind power and photovoltaic output is a problem needing to be mainly solved in day-ahead scheduling, and the wind power and photovoltaic output is represented by the sum of a determined predicted value and a changed prediction error. Namely:
Figure BDA0003768487900000081
in the formula, P w,t Representing the wind power output before the day; p is w,t,y Representing a constant output predicted value of the day-ahead wind power output; p w,t,c A variable output prediction error value representing a day-ahead wind power output; p is v,t Representing a photovoltaic output day ahead; p is v,t,y Representing a predicted constant output value of the photovoltaic output before the day; p v,t,c A variable output prediction error value representing a photovoltaic output at a day-ahead time;
for the output prediction problem of large-scale wind power plants and photovoltaic power plants, the prediction error of new energy is generally analyzed by normal distribution. The prediction error probability density function is as follows:
Figure BDA0003768487900000082
in the formula, f (p) w,t,c ) Representing a prediction error probability density function of the day-ahead wind power output; delta w,t,c Representing the variance of the wind power output prediction error before the moment t day; mu.s w,t,c The expected value of the wind power output prediction error before the moment t is represented; f (p) v,t,c ) Representing a photovoltaic output prediction error probability density function before the day; delta v,t,c The variance of photovoltaic output prediction errors before the moment t day is represented; mu.s v,t,c The expected value of the photovoltaic output prediction error before the moment t is represented;
in the embodiment of the invention, the mean value and the variance of the wind power and photovoltaic prediction errors are obtained by performing data processing on the original wind and light actual output and the predicted output of the system. Fig. 3 and 4 show prediction errors of wind power and photovoltaic for 30 days, respectively, and for wind and photovoltaic prediction errors at twenty-four time points per day, assuming that the probability of occurrence of each time point is the same, discarding data exceeding the upper and lower limits of the prediction errors, and then obtaining the expectation and variance of the wind and photovoltaic prediction errors by using the data of 30 days as follows: mu.s w,t,c ＝0.012，μ v,t,c ＝0.008，δ w,t,c ＝1.003，δ v,t,c =1.001. Thus, the wind-solar power prediction error follows approximately a standard normal distribution.
Analyzing prediction errors of the day-ahead wind power output and the day-ahead photovoltaic output, and sampling probability distribution of the wind power output and the day-ahead photovoltaic output by utilizing Latin Hypercube Sampling (LHS) to generate a day-ahead wind-light output prediction combined scene set; in the embodiment of the invention, 120 wind power and photovoltaic scene sets can be randomly generated, and 100 scenes are reserved after the scenes with large wind and photovoltaic prediction errors are deleted through preliminary analysis and comparison. The LHS belongs to layered sampling, and the overall distribution condition of random variables is reflected by using sampling values. The essence of the method is that the points in each layer are forcibly extracted by layering the input probability distribution, so that the sampling points can cover all sampling areas, and the sampling schematic diagram is shown in fig. 5.
Suppose X k (k =1,2,3 \8230k) is a random variable for k questions whose cumulative probability distribution function can be expressed as:
Y k ＝F k (X k )
assuming that the total number of samples is N, in the interval [0,1 ]]Above, use LHS to convert Y k ＝F k (X k ) The longitudinal axis of the X-ray tube is divided into N intervals, the N intervals are equal, the width of the intervals is 1/N, each interval is independent and not interfered with each other, and Y is k For the sampled value of the midpoint of each interval, X k Is its corresponding abscissa, X k The calculation formula of the nth sampled value is as follows:
Figure BDA0003768487900000091
when all the random variables are sampled, an initial sampling matrix of K × N order can be obtained. Fig. 6 and 7 are obtained scene initial sampling scene sets.
Large-scale scenes increase the amount of computation, and the goal of scene cuts is to replace the large number of generated scenes with a small number of typical scenes. Thus, a set of "clustered" and representative typical scenarios can be obtained by clustering techniques. The embodiment of the invention adopts a synchronous back substitution elimination method to carry out a large amount of scene reduction, and the specific reduction steps are as follows:
step1, for any scene lambda m (m =1,2, \8230l), calculating a scene λ with the shortest distance to it j (ii) a Expressed as:
Figure BDA0003768487900000101
wherein λ is m Representing the mth scene; lambda [ alpha ] j Represents the jth scene; l represents the number of scenes; d m,min Indicating λ for a certain scene m Scene λ with the shortest distance to it j The probability product of (a); delta j Representing a scene lambda j The probability of occurrence; d (lambda) m ,λ j ) Representing a scene lambda m And scene lambda j The Euclidean distance between;
step2, determining scenes lambda to be deleted in L scenes m 。
Figure BDA0003768487900000102
Wherein D is min Representing the smallest probability product among the L scenes.
Step3, modifying the number of the residual scenes, wherein the number of the residual scenes is L = L-1, and accumulating the probability of the deleted scenes to the scenes closest to the deleted scenes to ensure that the sum of the probabilities of all the scenes is 1;
and Step4, repeating Step1 to Step3 until the number of the remaining scenes reaches the expected set value.
Fig. 8 is a typical scene of remaining wind-solar output after scene reduction, and 3 wind-solar output scenes are reserved respectively.
On the basis of assuming that the output of various new energy sources is mutually independent, scene combination is carried out on the typical scene before the day of the wind power output and the photovoltaic output after reduction by utilizing the Cartesian product idea, and then the total number of the combined scenes and the occurrence probability of the corresponding scenes are as follows:
Figure BDA0003768487900000103
in the formula, N s Representing a combined scene total number; n is a radical of hydrogen w Representing the number of typical scenes of wind power output in the day ahead; n is a radical of v Representing the number of typical scenes of photovoltaic output in the day ahead; p m,j Representing the probability of the occurrence of the combined scene; p m Representing the probability of occurrence of a typical scene of wind power output; p j Representing the probability of a typical scene of photovoltaic output occurring.
In above-mentioned step S2, combine thermal power generating unit and light and heat power plant to carry out optimization scheduling day and day, include: the method comprises the following steps of a thermal power unit starting and stopping plan, a thermal power unit output plan and a photo-thermal power station output plan. The power generation plan comprises a unit combination plan and an output plan, and is the power generation power of each unit which is arranged in advance by combining constraint conditions such as the upper and lower output limits of each unit, the maximum climbing power of each unit and the like on the premise of meeting power balance according to load prediction and considering actual conditions such as start-stop, minimum shutdown time and the like of each unit; the unit starting and stopping, namely the unit running state, is set as follows in the embodiment of the invention: 1 bit running and 0 bit stopping.
In step S3, the number of trapezoidal ambiguities is first expressed:
is provided with
Figure BDA0003768487900000111
Is a fuzzy number, if
Figure BDA0003768487900000112
The membership function of (a) is:
Figure BDA0003768487900000113
in the formula u 1 、u 2 、u 3 、u 4 Referred to as the membership parameter of the trapezoidal fuzzy number, u 1 ≤u 2 ≤u 3 ≤u 4 And u is i e.R (i =1,2,3,4, then a is called the trapezoidal fuzzy number, and is recorded as
Figure BDA0003768487900000114
Figure BDA0003768487900000115
Is a function of the degree of membership,
Figure BDA0003768487900000116
balance [ u ] 1 ,u 4 ]As a fuzzy number
Figure BDA0003768487900000117
The supporting interval of (1) is called [ u ] 2 ,u 3 ]As a fuzzy number
Figure BDA0003768487900000118
The peak interval of (2). 0 ≦ ω ≦ 1 is a constant, and when ω =1, u = (u =) 1 ,u 2 ,u 3 ,u 4 ω is a normal trapezoidal fuzzy number, and is denoted by u = (u) 1 ,u 2 ,u 3 ,u 4 ) And then:
Figure BDA0003768487900000119
trapezoidal fuzzy parameter
Figure BDA00037684879000001110
Can be used for
Represented by a quadruple.
Figure BDA00037684879000001111
In the formula, k 1 、k 2 、k 3 、k 4 Is a proportionality coefficient;
Figure BDA00037684879000001112
is a fuzzy representation of P, P f Is the predicted value of P.
The trapezoidal blur parameter map is shown in fig. 9.
In the embodiment of the invention, in the day-to-day optimization scheduling, the uncertainty problem of wind, light and load is represented by a normal trapezoidal fuzzy number.
Respectively using trapezoidal fuzzy parameters for the intraday wind power output, the intraday photovoltaic output and the intraday load
Figure BDA0003768487900000121
Figure BDA0003768487900000122
The four-tuple of (1) is expressed, then through clear equivalence class, the optimal solution is sought by using a variable confidence level method; the specific process is as follows:
Figure BDA0003768487900000123
in the formula, p wn,t Representing trapezoidal blur parameters
Figure BDA0003768487900000124
The trapezoidal membership parameter; p is a radical of formula vn,t Representing trapezoidal blur parameters
Figure BDA0003768487900000125
The trapezoidal membership parameter of (a); p is a radical of ln,t Representing trapezoidal blur parameters
Figure BDA0003768487900000126
The trapezoidal membership parameter; n =1,2,3,4;
wherein:
Figure BDA0003768487900000127
the specific formula for describing the intraday wind power output, the intraday photovoltaic output and the intraday load through the fuzzy parameters is as follows:
Figure BDA0003768487900000128
in the formula (I), the compound is shown in the specification,
Figure BDA0003768487900000129
representing the predicted value of the wind power output in the day;
Figure BDA00037684879000001210
representing a predicted value of photovoltaic output within a day;
Figure BDA00037684879000001211
representing the predicted value of the load in the day; k is a radical of wn 、k vn And k ln All are scaling factors, n =1,2,3,4.
In the step S4, the day ahead-day internal two-layer scheduling model includes a day ahead scheduling plan model and a day internal scheduling plan model; the following explains these two models separately:
1. day-ahead scheduling plan model
(1) Objective function
The day-ahead scheduling plan model is a day-ahead 24h scheduling plan and comprises a first upper layer model and a first lower layer model; the first upper model takes the minimum expected value of the residual load variance of the system as an objective function, and the objective function is expressed as follows:
Figure BDA0003768487900000131
in the formula, S represents the total number of a typical day-ahead wind power output scene and a typical day-ahead photovoltaic output scene; t denotes a scheduling period; p s Represents the probability of occurrence of the composite scene S in%; p l,t Representing the system load in unit MW in the period t; p l,t,s Representing the residual load of the system in the time period t in the combined scene S, wherein the unit is MW; p w,t,s Representing the daily wind power output of a system in a time period t in the combined scene S, wherein the unit is MW; p v,t,s Representing the photovoltaic output of the system in the time period t in the combined scene S, wherein the unit is MW; p is l,t Representing the system load for the period t.
The first lower layer model reasonably arranges the output of the conventional thermal power generating unit and the CSP power station according to the system residual load curve, and makes a scheduling plan with the lowest total operating cost of the thermal power generating unit and the CSP power station. The objective function is as follows:
Figure BDA0003768487900000132
in the formula, N represents the number of thermal power generating units, and N =4; f 1 、F 2 Operating costs of a thermal power generating unit and a CSP power station are respectively calculated; f 3 Rotating the system for standby cost; a is i 、b i 、c i -a fuel cost factor for the thermal power unit i; p is a radical of formula it,s Generating power, MW, of the thermal power generating unit i at the moment t; u. of it The operating state of the thermal power generating unit i at the moment t, u it =1 unit operation, u it =0 indicates a unit shutdown;
Figure BDA0003768487900000133
heat energy is respectively provided for the CSP power station by the heat collecting device and the heat storage devicePower generated, MW; k is a radical of j 、k s The heat collection device and the heat storage device provide heat energy for the CSP power station to generate power; k is a radical of i 、k c The cost coefficient of the backup of the thermal power generating unit and the photo-thermal power station is high.
(2) Constraint conditions are as follows: the method comprises the following steps of (1) power supply (thermal power, wind power and photo-thermal) load power balance constraint, thermal power unit output constraint, wind power unit output constraint, photovoltaic output constraint, photo-thermal unit output constraint, thermal power rotation standby constraint, photo-thermal rotation standby constraint, thermal power unit climbing capacity constraint and photo-thermal unit climbing capacity constraint;
a. power balance constraint
Figure BDA0003768487900000141
In the formula, p csp,t,s The CSP power station output in the scene S at the moment t.
b. Unit output constraint
Figure BDA0003768487900000142
In the formula: p is a radical of formula csp_max 、p csp_min Respectively representing the upper limit and the lower limit of the CSP power station output; p is a radical of formula i_min 、p i_max Respectively representing the upper limit and the lower limit of the output of the thermal power generating unit; p is a radical of formula wind_max 、p v_max And the upper limit of the wind-solar electric field output is set.
c. Set rotation backup constraints
Figure BDA0003768487900000143
In the formula of U it,s 、D it,s 、
Figure BDA0003768487900000144
Respectively providing positive and negative rotation reserve capacities for the thermal power generating unit and the CSP power station; p is a radical of formula c,t,s A prediction error for a system load; wherein: p is a radical of c,t,s ＝p l,t,s And L are load prediction error rates.
d. Unit climbing capacity constraint
Figure BDA0003768487900000145
In the formula, r di 、r ui Respectively the maximum up-down climbing rate of the thermal power generating unit i; r is a radical of hydrogen d_csp 、r u_csp Respectively the maximum up-down climbing rate of the CSP power station.
2. Intraday scheduling plan model
(1) Objective function
The in-day scheduling plan model comprises a second upper layer model and a second lower layer model; the second upper layer model takes the minimum expected value of the system residual load variance as an objective function; the second lower-layer model takes the lowest total operation cost of the thermal power generating unit and the CSP power station as an objective function.
Figure BDA0003768487900000151
In the formula (I), the compound is shown in the specification,
Figure BDA0003768487900000152
fuzzy number of load demand in day;
Figure BDA0003768487900000153
the output power of the wind power is fuzzy number in a day;
Figure BDA0003768487900000154
and the photovoltaic output fuzzy number in the day.
Figure BDA0003768487900000155
In the formula, F 5 For the operation cost of the thermal power generating unit, the starting and stopping states of the thermal power generating unit are determined in the day-ahead scheduling, so that only the fuel cost is considered in the day-ahead scheduling.
(2) Constraint conditions are as follows: the method comprises the following steps of (1) power source (thermal power, wind and light heat) load power balance constraint, thermal power rotation standby constraint and light and heat rotation standby constraint;
a. power balance constraint
Figure BDA0003768487900000156
Where α is the confidence level.
b. Rotational back-up constraint
Figure BDA0003768487900000157
According to the invention, the CSP power station heat storage capacity constraint, the TES heat storage and release power constraint, the simultaneous heat storage and release in the same time period and the like are restricted.
In the embodiment of the invention, the whole optimized scheduling model is divided into two layers, and the upper layer model mainly solves the problem of power fluctuation caused by wind-solar grid connection, and specifically comprises the following steps: and the upper-layer optimization scheduling model performs rolling optimization by using renewable energy output prediction data, and takes the minimum expected value of the residual load variance as a target function. The lower layer model is mainly based on the maximization of the system economic benefit, and specifically, the lower layer economic optimization scheduling model comprises two types of decision variables: 0-1 variables and integer variables, the model to solve is a typical mixed integer programming model, which can be solved using mixed integer programming or commercial software.
As shown in a flow chart 9 of a Cplex solution model, firstly, data are initialized, wind, light, heat, load and physical and economic parameters of the thermal power generating unit are input, the starting and stopping states of the thermal power generating unit are defined as outer-layer decision variables, and the output of the thermal power generating unit and the output of a CSP power station are defined as inner-layer decision scalars; then writing target functions and constraint conditions in a column, and calling a CPLEX solver to solve on an MATLAB platform by using YALMIP; and outputting an optimal unit starting and stopping scheme, a rotary standby plan and an operation cost.
The calculation flow chart of the double-layer optimization model is shown in fig. 10, firstly, wind power output, photovoltaic output and a load predicted value are read, then, an upper layer model is solved through CIPSO, whether a feasible solution exists or not is judged, and if not, the upper layer model is continuously solved; if the feasible solution exists, the wind-solar heat output value and the load predicted value are read as lower-layer input, YALMIP is used for calling a CPLEX solver to solve on the MATLAB platform, whether the feasible solution exists or not is judged again, and if the feasible solution does not exist, YALMIP is continuously used for calling the CPLEX solver to solve on the MATLAB platform; and if the feasible solution exists, outputting the scheduling results of the upper layer model and the lower layer model.
As shown in a flow chart 11 of an optimized scheduling algorithm for model solution by using an improved particle swarm CIPSO, firstly, the speed and the position of a particle swarm are initialized, the inertial weight learning factor of the particle is changed, the speed and the position of the particle are updated, and a new generation of N particles is generated; judging whether the constraint condition is met, if not, memorizing the particle position, and then judging whether the particle diversity meets a threshold value; if the constraint condition is met, directly judging whether the particle diversity meets a threshold value; if the particle diversity does not meet the threshold value, selecting the first N larger particles according to the particle concentration value, and then judging whether the mark concentration of the current each particle allelic component reaches the threshold value; if the particle diversity meets the threshold, directly judging whether the mark concentration of the current each particle allelic component reaches the threshold;
if the marker concentration of the current allelic component of each particle reaches a threshold value, extracting an immune gene to carry out immunization and immunoselection, and judging whether the maximum iteration times is reached or not after N new-generation particles are generated; if the marker concentration of the current each particle allelic component does not reach the threshold value, directly judging whether the maximum iteration frequency is reached; if the maximum iteration times are reached, ending; if not, the loop execution is continued from the change of the particle inertia weight learning factor.
The embodiment of the invention provides a day-ahead and day-inside coordinated optimization scheduling method considering source load uncertainty, wherein multi-time scale and double-layer optimized coordinated scheduling can utilize more accurate day-inside prediction information, and the problem of unbalanced system supply and demand and power fluctuation caused by source load two-side prediction errors and uncertainty of a system can be corrected in time by utilizing the rapid adjusting capability of a CSP power station, so that the green and efficient consumption level of high-proportion new energy is improved, and the safe and stable operation of a power grid is ensured. The method combines a wind power plant, a photovoltaic power station and a CSP power station to form a multi-source combined power generation system; during the period of sufficient wind and light or low load, the CSP power station stores redundant heat energy into a heat storage system (TES) by using the HTF, and during the period of small wind and light output or high load of the system, the TES releases heat energy to increase the CSP power station output and balance the fluctuation of the wind and light output.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (10)

1. A day-ahead-day coordinated optimization scheduling method considering source load uncertainty is characterized by comprising the following steps:
s1, respectively describing uncertainties of a day-ahead wind power output prediction and a day-ahead photovoltaic output prediction by using a multi-scene random programming method, and constructing a day-ahead wind and light output prediction combined scene set;
s2, based on the day-ahead wind and light output prediction combined scene set, combining a thermal power generating unit and a photo-thermal power station to carry out day-ahead optimized scheduling;
s3, representing uncertainties of the solar wind power output prediction and the solar photovoltaic output prediction respectively through a trapezoidal fuzzy number equivalent model, and constructing a solar load fuzzy data set; the daily intrinsic load fuzzy data set comprises a daily wind power output predicted value, a daily photovoltaic output predicted value and a daily intrinsic load predicted value;
s4, taking the scheduling output of the thermal power generating unit and the photo-thermal power station before the day and the daily internal load fuzzy data set as input quantities of daily scheduling, and establishing a two-layer scheduling model of the thermal power generating unit and the photo-thermal power station before the day;
and S5, realizing day-ahead-day coordinated optimization scheduling considering source load uncertainty through the day-ahead-day two-layer scheduling model.
2. The method for coordinated optimization scheduling in a day-ahead and day-inside in consideration of source load uncertainty as claimed in claim 1, wherein said S1 specifically comprises:
s11, respectively representing the day-ahead wind power output and the day-ahead photovoltaic output by using an invariable output prediction value and a variable output prediction error value of the day-ahead wind power output and the day-ahead photovoltaic output:
Figure FDA0003768487890000011
in the formula, P w,t Representing the wind power output before the day; p w,t,y Representing a constant output predicted value of the day-ahead wind power output; p w,t,c A variable output prediction error value representing a wind power output day ahead; p v,t Representing a photovoltaic output day ahead; p v,t,y Representing a predicted constant output value of the photovoltaic output before the day; p v,t,c A variable output prediction error value representing a photovoltaic output day ahead;
s12, respectively analyzing the prediction errors of the day-ahead wind power output and the day-ahead photovoltaic output by using standard normal probability distribution to obtain a day-ahead wind power output prediction error probability density function and a day-ahead photovoltaic output prediction error probability density function, wherein the probability density functions are expressed as follows:
Figure FDA0003768487890000021
in the formula, f (p) w,t,c ) Representing a prediction error probability density function of the day-ahead wind power output; delta. For the preparation of a coating w,t,c Representing the variance of the wind power output prediction error before the moment t day; mu.s w,t,c The expected value of the wind power output prediction error before the moment t is represented; f (p) v,t,c ) Representing a photovoltaic output prediction error probability density function before the day; delta v,t,c The variance of photovoltaic output prediction errors before the moment t day is represented; mu.s v,t,c The expected value of the photovoltaic output prediction error before the moment t is represented;
and S13, sampling the day-ahead wind power output prediction error probability density function and the day-ahead photovoltaic output prediction error probability density function by adopting a Latin hypercube sampling method to generate a day-ahead wind and light output prediction combined scene set.
3. The method according to claim 2, wherein the S13 further comprises preprocessing the set of the combined scenario of the sunrise wind-solar output prediction, specifically comprising:
in the scene set of the day-ahead wind-solar output prediction combination, deleting the scenes with prediction errors exceeding a preset range;
performing scene reduction processing on the day-ahead wind-solar output prediction combination scene set by adopting a synchronous back-substitution elimination method;
and scene combination is carried out on the eliminated scene set of the wind-solar output prediction combination before the day by adopting a Cartesian product method.
4. The method for day-ahead-day coordinated optimization scheduling considering source-load uncertainty, according to claim 3, wherein the performing scene reduction processing on the day-ahead wind-solar output prediction combined scene set by using a synchronous back-substitution elimination method specifically comprises:
step1, for any scene lambda m (m =1,2.. L), calculating a scene λ whose distance from it is shortest j (ii) a Expressed as:
Figure FDA0003768487890000031
wherein λ is m Represents the mth scene; lambda j Represents the jth scene; l represents the number of scenes; d m,min Indicating for a certain scene λ m Scene λ with the shortest distance to it j The probability product of (a); delta j Representing a scene lambda j The probability of occurrence; d (lambda) m ,λ j ) Representing a scene lambda m And scene lambda j The Euclidean distance between;
step2, determining scenes lambda to be deleted in L scenes m 。
Figure FDA0003768487890000032
Wherein D is min Representing the smallest probability product among the L scenes.
Step3, modifying the number of the residual scenes, wherein the number of the residual scenes is L = L-1, and accumulating the probability of the deleted scenes to the scenes closest to the deleted scenes to ensure that the sum of the probabilities of all the scenes is 1;
and Step4, repeating Step1 to Step3 until the number of the remaining scenes reaches the expected set value.
5. The method for coordinated optimization scheduling in the day-ahead and in-day with source-load uncertainty taken into account of claim 3, wherein the scene combination of the subtracted day-ahead wind-solar output prediction combination scene set by using the Cartesian product method is represented as:
Figure FDA0003768487890000033
in the formula, N s Representing a combined scene total number; n is a radical of w Representing typical scene number of wind power output before the day; n is a radical of v Representing the number of typical scenes of photovoltaic output in the day ahead; p m,j Representing the probability of the occurrence of the combined scene; p m Representing the probability of the occurrence of a typical scene of wind power output; p is j Representing the probability of a typical scene of photovoltaic output occurring.
6. The method for day-ahead and day-inside coordinated optimization scheduling considering source-load uncertainty as claimed in claim 1, wherein in S2, the combining thermal power generating units and photo-thermal power stations for day-ahead optimization scheduling comprises: the system comprises a thermal power unit start-stop plan, a thermal power unit output plan and a photo-thermal power station output plan.
7. The method for coordinated optimization scheduling in a day-ahead and day-inside in consideration of source load uncertainty as claimed in claim 1, wherein said S3 specifically comprises:
respectively using trapezoidal fuzzy parameters for the intraday wind power output, the intraday photovoltaic output and the intraday load
Figure FDA0003768487890000034
Figure FDA0003768487890000035
The quadruple of (a) is represented as:
Figure FDA0003768487890000041
in the formula, p wn,t Representing trapezoidal blur parameters
Figure FDA0003768487890000042
The trapezoidal membership parameter of (a); p is a radical of vn,t Representing trapezoidal blur parameters
Figure FDA0003768487890000043
The trapezoidal membership parameter; p is a radical of ln,t Representing trapezoidal blur parameters
Figure FDA0003768487890000044
The trapezoidal membership parameter of (a); n =1,2,3,4;
wherein:
Figure FDA0003768487890000045
the specific formula for describing the intraday wind power output, the intraday photovoltaic output and the intraday load through the fuzzy parameters is as follows:
Figure FDA0003768487890000046
in the formula (I), the compound is shown in the specification,
Figure FDA0003768487890000047
representing the predicted value of the wind power output in the day;
Figure FDA0003768487890000048
representing a predicted photovoltaic output value in a day;
Figure FDA0003768487890000049
representing the predicted value of the load in the day; k is a radical of formula wn 、k vn And k ln All are scale factors, n =1,2,3,4.
8. The method for coordinated optimization scheduling in day-ahead and day-interior in consideration of source-load uncertainty as claimed in claim 1, wherein in S4, said day-ahead and day-interior two-layer scheduling model includes a day-ahead scheduling plan model and a day-interior scheduling plan model;
the day-ahead scheduling plan model comprises a first upper layer model and a first lower layer model; the first upper layer model takes the minimum expected value of the system residual load variance as an objective function; the first lower-layer model reasonably arranges the output of a conventional thermal power generating unit and a photothermal power station according to a system residual load curve, and a dispatching plan with the lowest total operation cost of the thermal power generating unit and the photothermal power station is made;
the daily scheduling plan model comprises a second upper layer model and a second lower layer model; the second upper layer model takes the minimum expected value of the residual load variance of the system as an objective function; and the second lower-layer model takes the lowest total operation cost of the thermal power generating unit and the photothermal power station as a target function.
9. The method of claim 8, wherein the constraints of the day-ahead scheduling plan model include: the method comprises the following steps of power load power balance constraint, thermal power unit output constraint, wind power unit output constraint, photovoltaic output constraint, photo-thermal unit output constraint, thermal power rotation standby constraint, photo-thermal rotation standby constraint, thermal power unit climbing capability constraint and photo-thermal unit climbing capability constraint.
10. The method of claim 8, wherein the constraints of the intra-day dispatch plan model include: the system comprises a power supply load power balance constraint, a thermal power rotation standby constraint and a photo-thermal rotation standby constraint.
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CN116542475B (en) * 2023-05-09 2023-11-07 河海大学 Two-stage optimization scheduling method considering working mode of photo-thermal power station collector

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