CN109474006B - Method for positioning and eliminating daily execution electric quantity out-of-limit factors of unit - Google Patents

Method for positioning and eliminating daily execution electric quantity out-of-limit factors of unit Download PDF

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CN109474006B
CN109474006B CN201811282391.5A CN201811282391A CN109474006B CN 109474006 B CN109474006 B CN 109474006B CN 201811282391 A CN201811282391 A CN 201811282391A CN 109474006 B CN109474006 B CN 109474006B
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electric quantity
wind power
formula
day
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CN109474006A (en
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沈晓东
刘俊勇
吴刚
刘彦
杨军峰
李旻
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Sichuan University
State Grid Corp of China SGCC
State Grid Sichuan Electric Power Co Ltd
Electric Power Research Institute of State Grid Sichuan Electric Power Co Ltd
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Sichuan University
State Grid Corp of China SGCC
State Grid Sichuan Electric Power Co Ltd
Electric Power Research Institute of State Grid Sichuan Electric Power Co Ltd
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    • H02J3/386—
    • 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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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Abstract

The invention discloses a method for positioning and eliminating out-of-limit factors of daily execution electric quantity of a unit, which comprises the following steps: s1, inputting initial data and initial parameters; s2, performing wind speed correlation processing according to the wind speed historical data; s3, carrying out uncertainty processing on wind power output, and carrying out deterministic conversion on a day-ahead scheduling random model; s4, solving a contract electric quantity decomposition model, and determining the daily execution electric quantity of the unit plan; s5, determining an electric quantity out-of-limit factor based on a Lagrange multiplier; s6, correcting daily execution electric quantity of the unit; s7, judging whether the contract electric quantity is out of limit according to the Lagrange multiplier; the method solves the problem of non-executability of medium and long term transaction results in the actual day scheduling plan under the condition of wind power and other renewable energy sources access in the prior art, and relieves the contradiction between the medium and long term transaction electric quantity completion progress requirement and the actual day scheduling plan arrangement.

Description

Method for positioning and eliminating daily execution electric quantity out-of-limit factors of unit
Technical Field
The invention belongs to the field of electric power markets, and particularly relates to a method for positioning and eliminating out-of-limit factors of daily execution electric quantity of a unit.
Background
In the transition period of current electric power market reformation, a perfect day-ahead market and spot market mechanism and a perfect platform are not established in China, and market trading is mainly medium-term and long-term trading. Under the current mechanism, partial units are allowed to participate in market trading, a power plant and a user can sign annual or monthly power contract, and a dispatching center is responsible for decomposing the power of the contract. In the new situation, when scheduling plans on a schedule day, a scheduling center needs to realize nested optimization with medium and long-term transaction results, and the difficulties are as follows:
(1) for medium and long-term transaction, how to reasonably decompose the daily execution electric quantity is obtained, and the validity of medium and long-term transaction results is ensured;
(2) how to solve the problem of contradiction between the requirement of the electric quantity completion progress of the medium-long term transaction and the formulation of the actual daily scheduling plan.
In the prior art, due to the influence of factors such as uncertainty of renewable energy sources, load prediction accuracy and fault maintenance of elements of a power grid, medium and long term trading daily planned electric quantity obtained by a monthly unit combination model often has the inexecutable condition in an actual daily dispatching plan, the contradiction between medium and long term trading electric quantity completion and daily dispatching plan arrangement is continuously intensified along with the advance of time, and the decomposed daily execution electric quantity has the inexecutable condition in the actual dispatching plan.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method for positioning and eliminating the out-of-limit factor of daily execution electric quantity of a unit, which is used for solving the problem of non-executability of medium and long term transaction results in an actual daily scheduling plan under the condition of access of renewable energy sources such as wind power and the like and relieving the contradiction between the medium and long term transaction electric quantity completion progress requirement and the actual daily scheduling plan arrangement.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
a method for positioning and eliminating the out-of-limit factor of daily execution electric quantity of a unit comprises the following steps:
s1: inputting initial data and initial parameters;
the initial data is historical wind speed data, and the initial parameters comprise medium and long term transaction parameters and unit technical parameters; inputting medium and long term transaction parameters and unit technical parameters into a contract electric quantity decomposition model; inputting technical parameters of the unit into a day-ahead scheduling model;
s2: according to the historical wind speed data, performing wind speed correlation processing, and according to the wind power output characteristics, obtaining corresponding wind power output;
s3: carrying out uncertainty processing on wind power output, and carrying out certainty conversion on a day-ahead scheduling random model;
s4: determining the daily execution electric quantity of the plan, namely obtaining the daily execution electric quantity value of the unit plan by solving a daily contract electric quantity decomposition model;
solving a daily contract electric quantity decomposition model according to the medium-long term transaction parameters and the unit technical parameters;
s5: determining out-of-limit factors, namely introducing a daily execution electric quantity constraint formula of a relaxation variable relaxation unit according to a day-ahead scheduling model, and reconstructing a target function;
the magnitude of the relaxation variable directly reflects the position of the out-of-limit factor and the magnitude of the out-of-limit electric quantity, namely the out-of-limit factor is the relaxation variable of which the optimization result is not 0;
s6: modifying the daily execution electric quantity of the unit, namely reconstructing daily contract electric quantity constraint of the unit according to the relaxation variable, repeating the step S4, solving a contract electric quantity decomposition model, and updating the daily execution electric quantity of the unit;
s7: and substituting the daily execution electric quantity after the unit is updated into a day-ahead scheduling model, judging whether the contract electric quantity is out of limit again according to a Lagrange multiplier, if not, namely the relaxation variable is 0, ending the method, otherwise, returning to the step S4, and eliminating the out-of-limit electric quantity.
Further, in step S1, the formula of the day-ahead scheduling model is:
Figure BDA0001848285300000031
in the formula, f is a day-ahead scheduling model objective function, and the system operation cost is minimized to be the objective function; SUi,tCost for generator start-up; SDi,tCost of generator shutdown; pi,tOutputting power for the unit; i is a unit variable; n is a radical ofEThe number of the units; t is a time variable; fi c(Pi,t) Generating a cost function for the unit i; i isi,tAnd the state variable is a unit start-stop state variable.
Further, in step S2, according to the historical wind speed data, performing wind speed correlation processing, and according to the wind power output characteristics, obtaining the corresponding wind power output, including the following steps:
s2-1: analyzing historical data of the wind power plant by using a Pearson correlation coefficient to obtain a correlation coefficient matrix;
s2-2: obtaining a wind speed sample meeting the requirement of a correlation coefficient matrix by utilizing Latin hypercube sampling;
s2-3: and converting the wind speed sample into the corresponding wind power output according to the wind power output characteristic.
Further, in step S3, performing uncertainty processing on the wind power output, and performing deterministic conversion on the day-ahead scheduling stochastic model, including the following steps:
s3-1: modeling the uncertainty of the wind power output by using opportunity constraint to obtain a day-ahead scheduling random model;
s3-2: and performing deterministic conversion on the day-ahead scheduling stochastic model, and converting the day-ahead scheduling stochastic model into a mixed integer linear programming model.
Further, in step S3-1, the influence of the wind power random output on the power balance constraint, the system rotation standby constraint and the line power flow constraint is considered, and an opportunity constraint planning theory is adopted for modeling, including modeling the power balance constraint, the system rotation standby constraint and the line power flow constraint.
Further, the formula of the power balance constraint stochastic model is as follows:
Figure BDA0001848285300000041
in the formula, Prob{. is a probability function; pi,tOutputting power for the unit; i is a unit variable; n is a radical ofEThe number of the units; pw,tOutputting power for the wind power plant; w is a wind farm variable; w is the number of wind power plants; DLd,tThe load is a predicted value; d is a load variable; n is a radical ofDIs the load number; t is a time variable; beta is a1A confidence level parameter for satisfying the load requirement for the unit output of the unit technical parameter;
the formula of the system rotation standby constraint random model is as follows:
Figure BDA0001848285300000042
Figure BDA0001848285300000043
in the formula, Prob{. is a probability function; pi minA lower limit of transmission power for the l line; pi maxThe upper limit of the transmission power of the first line;
Figure BDA0001848285300000044
rated output for the wind power plant;
Figure BDA0001848285300000045
the positive and negative standby requirements of the system are met in the time period t; alpha is alphau、αdA demand coefficient for wind power output for positive and negative rotation standby; beta is a2、β3Confidence level parameters for meeting the positive and negative spinning standby requirements;
the formula of the line power flow constraint random model is as follows:
Figure BDA0001848285300000046
Figure BDA0001848285300000047
in the formula, GSFl,i、GSFl,w、GSFl,dPower transfer distribution factors of the generator set, the wind power plant and the load on the l line are obtained based on the direct current flow; beta is a4、β5And the confidence level parameter is used for meeting the requirement of the line flow safety constraint.
Further, in step S3-2, the deterministic conversion of the day-ahead scheduling stochastic model and the conversion of the day-ahead scheduling stochastic model into the mixed integer linear programming model include the following steps:
s3-2-1: obtaining a prediction error sample taking correlation into account by utilizing Latin hypercube sampling, and superposing the sample and a predicted wind speed value to obtain a wind speed random sample matrix;
s3-2-2: converting the wind speed random sample matrix into a wind power output sample matrix according to the output characteristics of the wind power plant;
s3-2-3: obtaining total output of the wind power plant and a total power transfer sample vector of the wind power plant to a line based on the wind power plant output sample matrix and the generator power transfer distribution factor;
the calculation formula of the total output sample vector of the wind power plant is as follows:
Figure BDA0001848285300000051
in the formula, WGWThe total output sample vector of the wind power plant is obtained; wwOutput sample matrix of the wind power plant; w is a wind farm variable; w is the number of wind power plants;
the calculation formula of the total power transfer sample vector of the line by the wind power plant is as follows:
Figure BDA0001848285300000052
in the formula, WLWTransferring a sample vector for the total power of the lines of the wind power plant; GSFl,wA power transfer distribution factor of the wind power plant w to the l line; wwA sample matrix of wind power plant output;
s3-2-4: obtaining a discretization sequence of a corresponding probability distribution function by utilizing a statistical method according to the total output of the wind power plant and the sample vector of the total power transfer of the wind power plant to the line;
s3-2-5: and determining probability constraint according to the probability distribution function, and solving by using a dichotomy.
Further, in step S5, the formula of the daily execution capacity constraint is:
Figure BDA0001848285300000053
in the formula (I), the compound is shown in the specification,
Figure BDA0001848285300000054
for daily execution of the unitAn amount of electricity; pi,tOutputting power for the unit; i is a unit variable; t is a time variable; t is a scheduling period; n is a radical ofcSigning medium and long term contract unit set for medium and long term transaction parameters; deltatThe length of the time period is scheduled for a unit.
Further, in step S5, the formula of the objective function is:
the formula of the reconstructed daily execution electric quantity constraint is as follows:
Figure BDA0001848285300000061
in the formula (I), the compound is shown in the specification,
Figure BDA0001848285300000062
executing electric quantity for a unit day; pi,tOutputting power for the unit; i is a unit variable; t is a time variable; t is a scheduling period; n is a radical ofcSigning medium and long term contract unit set for medium and long term transaction parameters; deltatScheduling a time period length for a unit; mu.siExecuting electric quantity relaxation variables for the unit day;
the formula of the reconstructed objective function is as follows:
Figure BDA0001848285300000063
in the formula, F is a reconstructed objective function; f is an original day-ahead scheduling model objective function; m is a positive number; mu.siExecuting electric quantity relaxation variables for the unit day; i is a unit variable; n is a radical ofcThe number of the machine sets of the medium and long term transaction is signed.
Further, in step S6, the daily execution power amount update formula is:
Figure BDA0001848285300000064
in the formula (I), the compound is shown in the specification,
Figure BDA0001848285300000065
executing electric quantity for a unit day;
Figure BDA0001848285300000066
the daily execution electric quantity of the unit obtained by solving the contract electric quantity decomposition model for the last time;
Figure BDA0001848285300000067
executing electric quantity for the unit in the minimum day; mu.siAnd executing electric quantity relaxation variables for the unit day.
The invention has the beneficial effects that:
the invention provides a unit daily execution electric quantity out-of-limit factor positioning and eliminating method based on a Lagrange multiplier, which realizes nesting optimization of a daily scheduling plan and a medium-long term trading plan under the influence of wind power uncertainty and correlation, realizes unit daily execution electric quantity out-of-limit factor positioning and out-of-limit electric quantity elimination, improves the performability of medium-long term trading results and relieves the contradiction between the medium-long term trading electric quantity completion progress requirement and daily scheduling plan arrangement through unit daily execution electric quantity out-of-limit factor positioning and out-of-limit electric quantity elimination.
Drawings
FIG. 1 is a flow chart of a unit daily execution electric quantity out-of-limit factor positioning and eliminating method;
FIG. 2 is a flow chart of a method of wind speed correlation processing of historical wind speed data;
FIG. 3 is a flow chart of a method of performing uncertainty processing of wind power output;
FIG. 4 is a flow diagram of a method for deterministic transformation of stochastic models;
FIG. 5 is a diagram of an out-of-limit power removal scenario;
fig. 6 is a graph of the result of contract power decomposition.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
A method for positioning and eliminating the out-of-limit factor of the daily execution electric quantity of a unit is disclosed, as shown in figure 1, and comprises the following steps:
s1: inputting initial data and initial parameters;
the initial data is historical wind speed data, and the initial parameters comprise medium and long term transaction parameters and unit technical parameters; inputting medium and long term transaction parameters and unit technical parameters into a contract electric quantity decomposition model; inputting technical parameters of the unit into a day-ahead scheduling model;
the formula of the day-ahead scheduling model is as follows:
Figure BDA0001848285300000071
in the formula, f is a day-ahead scheduling model objective function, and the system operation cost is minimized to be the objective function; SUi,tCost for generator start-up; SDi,tCost of generator shutdown; pi,tOutputting power for the unit; i is a unit variable; n is a radical ofEThe number of the units; t is a time variable; fi c(Pi,t) Generating a cost function for the unit i; i isi,tIs a unit start-stop state variable;
the contract electricity quantity decomposition model is common general knowledge in the technical field, and is not described herein again, and its formula is:
and (3) describing an objective function formula by the variance of the progress difference of contract electric quantity of each unit:
Figure BDA0001848285300000081
the calculation formula of the contract electric quantity completion progress of the bidding unit i by the x day is as follows:
Figure BDA0001848285300000082
the calculation formula of the average completion progress of all the bidding units by the x day is as follows:
Figure BDA0001848285300000083
the daily contract electric quantity upper and lower limit constraint formula of the bidding unit is as follows:
Figure BDA0001848285300000084
the monthly contract electric quantity constraint formula of the unit is as follows:
Figure BDA0001848285300000085
the daily total power generation amount constraint formula of the bidding unit is as follows:
Figure BDA0001848285300000086
planning total contract electric quantity to be finished by each contract unit on a day:
Figure BDA0001848285300000087
in the formula, x is a planning day number; l is the total days of the month; ei,xThe Emax i, x and the Emin i, x are the daily decomposition electric quantity of the unit i and the upper and lower limit values thereof determined according to the technical parameters of the unit, the contract completion condition and the maintenance plan respectively; etrade i is the monthly contract electric quantity of the unit i; eo i, x-1 is the contract electric quantity finished by the unit i before the planning day x; li,xAnd
Figure BDA0001848285300000088
respectively completing the contract electric quantity completion progress and the average completion progress of all the machine sets for the bidding machine set i by the x day; eplan x is the total contract electric quantity to be completed by each contract unit on the planning day; dxThe required electric quantity for the planning day x;
s2: according to the historical wind speed data, performing wind speed correlation processing, and according to the wind power output characteristics, obtaining the corresponding wind power output, as shown in fig. 2, the method comprises the following steps:
s2-1: analyzing historical data of the wind power plant by using a Pearson correlation coefficient to obtain a correlation coefficient matrix;
s2-2: obtaining a wind speed sample meeting the requirement of a correlation coefficient matrix by utilizing Latin hypercube sampling;
s2-3: converting the wind speed sample into corresponding wind power output according to the wind power output characteristic;
s3: carrying out uncertainty processing on wind power output, and carrying out certainty conversion on a day-ahead scheduling random model, as shown in fig. 3, wherein the method comprises the following steps:
s3-1: modeling the uncertainty of the wind power output by using opportunity constraint to obtain a day-ahead scheduling random model;
and (3) considering the influence of the wind power random output on the power balance constraint, the system rotation standby constraint and the line power flow constraint, and adopting an opportunity constraint planning theory to carry out modeling, wherein the modeling comprises the modeling of the power balance constraint, the system rotation standby constraint and the line power flow constraint.
The formula of the power balance constraint stochastic model is as follows:
Figure BDA0001848285300000091
in the formula, Prob{. is a probability function; pi,tOutputting power for the unit; i is a unit variable; n is a radical ofEThe number of the units; pw,tOutputting power for the wind power plant; w is a wind farm variable; w is the number of wind power plants; DLd,tThe load is a predicted value; d is a load variable; n is a radical ofDIs the load number; t is a time variable; beta is a1A confidence level parameter for satisfying the load requirement for the unit output of the unit technical parameter;
the formula of the system rotation standby constraint random model is as follows:
Figure BDA0001848285300000092
Figure BDA0001848285300000093
in the formula, Prob{. is a probability function; pi minA lower limit of transmission power for the l line; pi maxThe upper limit of the transmission power of the first line;
Figure BDA0001848285300000101
rated output for the wind power plant;
Figure BDA0001848285300000102
the positive and negative standby requirements of the system are met in the time period t; alpha is alphau、αdA demand coefficient for wind power output for positive and negative rotation standby; beta is a2、β3Confidence level parameters for meeting the positive and negative spinning standby requirements;
the formula of the line power flow constraint random model is as follows:
Figure BDA0001848285300000103
Figure BDA0001848285300000104
in the formula, GSFl,i、GSFl,w、GSFl,dPower transfer distribution factors of the generator set, the wind power plant and the load on the l line are obtained based on the direct current flow; beta is a4、β5A confidence level parameter for meeting the line power flow safety constraint requirement;
s3-2: performing deterministic conversion on the day-ahead scheduling stochastic model, and converting the day-ahead scheduling stochastic model into a mixed integer linear programming form, as shown in fig. 4, including the following steps:
s3-2-1: obtaining a prediction error sample taking correlation into account by utilizing Latin hypercube sampling, and superposing the sample and a predicted wind speed value to obtain a wind speed random sample matrix;
s3-2-2: converting the wind speed random sample matrix into a wind power output sample matrix according to the output characteristics of the wind power plant;
s3-2-3: obtaining total output of the wind power plant and a total power transfer sample vector of the wind power plant to a line based on the wind power plant output sample matrix and the generator power transfer distribution factor;
the calculation formula of the total output sample vector of the wind power plant is as follows:
Figure BDA0001848285300000105
in the formula, WGWThe total output sample vector of the wind power plant is obtained; wwOutput sample matrix of the wind power plant; w is a wind farm variable; w is the number of wind power plants;
the calculation formula of the total power transfer sample vector of the line by the wind power plant is as follows:
Figure BDA0001848285300000111
in the formula, WLWTransferring a sample vector for the total power of the lines of the wind power plant; GSFl,wA power transfer distribution factor of the wind power plant w to the l line; wwA sample matrix of wind power plant output;
s3-2-4: obtaining a discretization sequence of a corresponding probability distribution function by utilizing a statistical method according to the total output of the wind power plant and the sample vector of the total power transfer of the wind power plant to the line;
s3-2-5: confirming probability constraint according to a probability distribution function, and solving by using a dichotomy;
taking the power balance as an example, the formula for determining the probability constraint is as follows:
Figure BDA0001848285300000112
in the formula, FwtThe probability distribution function of the total output of the wind power plant.
The final deterministic formula obtained by solving by the dichotomy is as follows:
Figure BDA0001848285300000113
in the formula (I), the compound is shown in the specification,
Figure BDA0001848285300000114
and the probability distribution function of the total output of the wind power plant is an inverse function.
S4: determining the daily execution electric quantity of the plan, namely obtaining the daily execution electric quantity value of the unit plan by solving a daily contract electric quantity decomposition model;
solving a daily contract electric quantity decomposition model according to the medium-long term transaction parameters and the unit technical parameters;
s5: determining out-of-limit factors, namely introducing a daily execution electric quantity constraint formula of a relaxation variable relaxation unit according to a day-ahead scheduling model, and reconstructing a target function;
the formula for daily execution of electric quantity constraint is as follows:
Figure BDA0001848285300000115
in the formula (I), the compound is shown in the specification,
Figure BDA0001848285300000116
executing electric quantity for a unit day; pi,tOutputting power for the unit; i is a unit variable; t is a time variable; t is a scheduling period; n is a radical ofcSigning medium and long term contract unit set for medium and long term transaction parameters; deltatScheduling a time period length for a unit;
the magnitude of the relaxation variable directly reflects the position of the out-of-limit factor and the magnitude of the out-of-limit electric quantity, namely the out-of-limit factor is the relaxation variable of which the optimization result is not 0;
the formula of the objective function is:
the formula of the reconstructed daily execution electric quantity constraint is as follows:
Figure BDA0001848285300000121
in the formula (I), the compound is shown in the specification,
Figure BDA0001848285300000122
executing electric quantity for a unit day; pi,tOutputting power for the unit; i is a unit variable; t is a time variable; t is a scheduling period; n is a radical ofcSigning medium and long term contract unit set for medium and long term transaction parameters; deltatScheduling a time period length for a unit; mu.siExecuting electric quantity relaxation variables for the unit day;
the formula of the reconstructed objective function is as follows:
Figure BDA0001848285300000123
in the formula, F is a reconstructed objective function; f is an original day-ahead scheduling model objective function; m is a positive number, and is generally a larger value; mu.siExecuting electric quantity relaxation variables for the unit day; i is a unit variable; n is a radical ofcThe number of the machine sets of the medium and long term transaction is signed;
s6: modifying the daily execution electric quantity of the unit, namely reconstructing daily contract electric quantity constraint of the unit according to the relaxation variable, repeating the step S4, solving a contract electric quantity decomposition model, and updating the daily execution electric quantity of the unit;
the updating formula of daily execution electric quantity is as follows:
Figure BDA0001848285300000124
in the formula (I), the compound is shown in the specification,
Figure BDA0001848285300000125
executing electric quantity for a unit day;
Figure BDA0001848285300000126
the daily execution electric quantity of the unit obtained by solving the contract electric quantity decomposition model for the last time;
Figure BDA0001848285300000127
executing electric quantity for the unit in the minimum day; mu.siExecuting electric quantity relaxation variables for the unit day;
s7: and substituting the daily execution electric quantity after the unit is updated into a day-ahead scheduling model, judging whether the contract electric quantity is out of limit again according to a Lagrange multiplier, if not, namely the relaxation variable is 0, ending the method, otherwise, returning to the step S4, and eliminating the out-of-limit electric quantity.
Calculation example: the set for signing medium and long term transaction contract in the IEEE118 node calculation example adopted in the invention has 19 sets, wherein the relevant parameters of the long term transaction are shown in a table 1 of the relevant parameters of the medium and long term transaction of the bidding set; in order to fully verify the feasibility of the method provided by the invention, the contract electric quantity decomposition conditions under two scenes are compared by an example:
scene one: the condition that the contract electric quantity decomposition result is unexecutable in an actual scheduling plan is not considered;
scene two: considering the inexecutable condition of the contract electric quantity decomposition result in the actual scheduling plan, the method provided by the invention determines the position of the out-of-limit factor and eliminates the out-of-limit electric quantity.
In the first scenario, the contract electric quantity decomposition result cannot be executed in actual scheduling, and on the basis, the invention considers the situation that the contract electric quantity decomposition result possibly exceeds the limit. In the first iteration process, the method determines the out-of-limit factors causing the unexecutable contract electric quantity in the initial contract electric quantity decomposition result and the out-of-limit electric quantity size thereof. As can be seen from fig. 5, the daily execution power constraints of the units G40, G47, G48, and G51 all cause the irrevocable result of the contract power decomposition, where the unit G51 is the most serious out-of-limit condition, and the out-of-limit power is 331MW · h.
According to the method, the out-of-limit electric quantity is eliminated through two iterations according to the out-of-limit result, the final contract electric quantity decomposition result and the scene-one contract decomposition result of the method are shown in fig. 6, as can be seen from fig. 6, the unit electric quantity which causes the unexecutable contract electric quantity decomposition result is limited, the electric quantity is reduced, the reduced electric quantity is completed by the units such as G21, G27 and G28, and the total electric quantity of the bidding unit on the planning day is ensured.
TABLE 1
Figure BDA0001848285300000131
Figure BDA0001848285300000141
The invention provides a unit daily execution electric quantity out-of-limit factor positioning and eliminating method based on a Lagrange multiplier, which realizes nesting optimization of a daily scheduling plan and a medium-long term trading plan under the influence of wind power uncertainty and correlation, realizes unit daily execution electric quantity out-of-limit factor positioning and out-of-limit electric quantity elimination, improves the performability of medium-long term trading results and relieves the contradiction between the medium-long term trading electric quantity completion progress requirement and daily scheduling plan arrangement through unit daily execution electric quantity out-of-limit factor positioning and out-of-limit electric quantity elimination.

Claims (6)

1. A method for positioning and eliminating out-of-limit factors of daily execution electric quantity of a unit is characterized by comprising the following steps:
s1: inputting initial data and initial parameters;
the initial data is historical wind speed data, and the initial parameters comprise medium and long-term transaction parameters and unit technical parameters; inputting medium and long term transaction parameters and unit technical parameters into a contract electric quantity decomposition model; inputting technical parameters of the unit into a day-ahead scheduling model;
s2: according to the historical wind speed data, performing wind speed correlation processing, and according to the wind power output characteristics, obtaining corresponding wind power output;
s3: carrying out uncertainty processing on wind power output, and carrying out certainty conversion on a day-ahead scheduling random model;
s4: determining the daily execution electric quantity of the plan, namely obtaining the daily execution electric quantity value of the unit plan by solving a daily contract electric quantity decomposition model;
solving a daily contract electric quantity decomposition model according to the medium-long term transaction parameters and the unit technical parameters;
s5: determining out-of-limit factors, namely introducing a daily execution electric quantity constraint formula of a relaxation variable relaxation unit according to a day-ahead scheduling model, and reconstructing a target function;
the magnitude of the relaxation variable directly reflects the position of an out-of-limit factor and the magnitude of out-of-limit electric quantity, namely the out-of-limit factor is the relaxation variable of which the optimization result is not 0;
s6: modifying the daily execution electric quantity of the unit, namely reconstructing daily contract electric quantity constraint of the unit according to the relaxation variable, repeating the step S4, solving a contract electric quantity decomposition model, and updating the daily execution electric quantity of the unit;
s7: substituting the daily execution electric quantity after the unit is updated into a day-ahead scheduling model, judging whether the contract electric quantity is out of limit again according to a Lagrange multiplier, if not, namely the relaxation variable is 0, ending the method, otherwise, returning to the step S4, and eliminating the out-of-limit electric quantity;
in step S3, the wind power output uncertainty processing and the deterministic conversion of the day-ahead scheduling stochastic model are performed, including the following steps:
s3-1: modeling the uncertainty of the wind power output by using opportunity constraint to obtain a day-ahead scheduling random model;
s3-2: carrying out deterministic conversion on a day-ahead scheduling stochastic model, and converting the day-ahead scheduling stochastic model into a mixed integer linear programming model;
in the step S3-1, the influence of the wind power random output on the power balance constraint, the system rotation standby constraint and the line power flow constraint is considered, and an opportunity constraint planning theory is adopted for modeling, wherein the modeling comprises the modeling of the power balance constraint, the system rotation standby constraint and the line power flow constraint;
the formula of the power balance constraint stochastic model is as follows:
Figure FDA0003232342100000021
in the formula, Prob{. is a probability function; pi,tOutputting power for the unit; i is a unit variable; n is a radical ofEThe number of the units; pw,tOutputting power for the wind power plant; w is a wind farm variable; w is the number of wind power plants; DLd,tThe load is a predicted value; d is a load variable; n is a radical ofDIs the load number; t is a time variable; beta is a1A confidence level parameter for satisfying the load requirement for the unit output of the unit technical parameter;
the formula of the system rotation standby constraint random model is as follows:
Figure FDA0003232342100000022
Figure FDA0003232342100000023
in the formula Ii,tIs a unit start-stop state variable; prob{. is a probability function; pi minThe lower limit of the output of the unit; pi maxThe upper limit of the output of the unit is set;
Figure FDA0003232342100000024
rated output for the wind power plant;
Figure FDA0003232342100000025
the positive and negative standby requirements of the system are met in the time period t; alpha is alphau、αdA demand coefficient for wind power output for positive and negative rotation standby; beta is a2、β3Confidence level parameters for meeting the positive and negative spinning standby requirements;
the formula of the line power flow constraint random model is as follows:
Figure FDA0003232342100000031
Figure FDA0003232342100000032
in the formula, GSFl,i、GSFl,w、GSFl,dPower transfer distribution factors of the generator set, the wind power plant and the load on the l line are obtained based on the direct current flow; beta is a4、β5A confidence level parameter for meeting the line power flow safety constraint requirement;
in the step S3-2, the deterministic conversion of the day-ahead scheduling stochastic model and the conversion of the day-ahead scheduling stochastic model into the mixed integer linear programming model include the following steps:
s3-2-1: obtaining a prediction error sample taking correlation into account by utilizing Latin hypercube sampling, and superposing the sample and a predicted wind speed value to obtain a wind speed random sample matrix;
s3-2-2: converting the wind speed random sample matrix into a wind power output sample matrix according to the output characteristics of the wind power plant;
s3-2-3: obtaining total output of the wind power plant and a total power transfer sample vector of the wind power plant to a line based on the wind power plant output sample matrix and the generator power transfer distribution factor;
the calculation formula of the total output sample vector of the wind power plant is as follows:
Figure FDA0003232342100000033
in the formula, WGWThe total output sample vector of the wind power plant is obtained; wwOutput sample matrix of the wind power plant; w is a wind farm variable; w is the number of wind power plants;
the calculation formula of the total power transfer sample vector of the line by the wind power plant is as follows:
Figure FDA0003232342100000034
in the formula (I), the compound is shown in the specification,WLWtransferring a sample vector for the total power of the lines of the wind power plant; GSFl,wA power transfer distribution factor of the wind power plant w to the l line; wwA sample matrix of wind power plant output;
s3-2-4: obtaining a discretization sequence of a corresponding probability distribution function by utilizing a statistical method according to the total output of the wind power plant and the sample vector of the total power transfer of the wind power plant to the line;
s3-2-5: and determining probability constraint according to the probability distribution function, and solving by using a dichotomy.
2. The method for positioning and eliminating off-limit factor of daily execution electric quantity of a unit according to claim 1, wherein in step S1, the formula of the day-ahead scheduling model is:
Figure FDA0003232342100000041
in the formula, f is a day-ahead scheduling model objective function, and the system operation cost is minimized to be the objective function; SUi,tCost for generator start-up; SDi,tCost of generator shutdown; pi,tOutputting power for the unit; i is a unit variable; n is a radical ofEThe number of the units; t is a time variable; fi c(Pi,t) Generating a cost function for the unit i; i isi,tAnd the state variable is a unit start-stop state variable.
3. The method for positioning and eliminating the out-of-limit factor of daily executed electric quantity of the unit according to claim 1, wherein in the step S2, wind speed correlation processing is performed according to historical wind speed data, and corresponding wind power output is obtained according to wind power output characteristics, and the method comprises the following steps:
s2-1: analyzing historical data of the wind power plant by using a Pearson correlation coefficient to obtain a correlation coefficient matrix;
s2-2: obtaining a wind speed sample meeting the requirement of a correlation coefficient matrix by utilizing Latin hypercube sampling;
s2-3: and converting the wind speed sample into the corresponding wind power output according to the wind power output characteristic.
4. The method for positioning and eliminating out-of-limit factors of daily execution electric quantity of a unit according to claim 1, wherein in step S5, the formula of daily execution electric quantity constraint is as follows:
Figure FDA0003232342100000042
in the formula (I), the compound is shown in the specification,
Figure FDA0003232342100000043
executing electric quantity for a unit day; pi,tOutputting power for the unit; i is a unit variable; t is a time variable; t is a scheduling period; n is a radical ofcSigning medium and long term contract unit set for medium and long term transaction parameters; deltatThe length of the time period is scheduled for a unit.
5. The method for positioning and eliminating off-limit factor of daily execution electric quantity of a unit according to claim 1, wherein in step S5, the formula of the objective function is:
the formula of the reconstructed daily execution electric quantity constraint is as follows:
Figure FDA0003232342100000051
in the formula (I), the compound is shown in the specification,
Figure FDA0003232342100000052
executing electric quantity for a unit day; pi,tOutputting power for the unit; i is a unit variable; t is a time variable; t is a scheduling period; n is a radical ofcSigning medium and long term contract unit set for medium and long term transaction parameters; deltatScheduling a time period length for a unit; mu.siExecuting electric quantity relaxation variables for the unit day;
the formula of the reconstructed objective function is as follows:
Figure FDA0003232342100000053
in the formula, F is a reconstructed objective function; f is an original day-ahead scheduling model objective function; m is a positive number; mu.siExecuting electric quantity relaxation variables for the unit day; i is a unit variable; n is a radical ofcThe number of the machine sets of the medium and long term transaction is signed.
6. The method for positioning and eliminating off-limit factors of daily execution electric quantity of a unit according to claim 1, wherein in step S6, the updating formula of daily execution electric quantity is:
Figure FDA0003232342100000054
in the formula (I), the compound is shown in the specification,
Figure FDA0003232342100000055
executing electric quantity for a unit day;
Figure FDA0003232342100000056
the daily execution electric quantity of the unit obtained by solving the contract electric quantity decomposition model for the last time;
Figure FDA0003232342100000057
executing electric quantity for the unit in the minimum day; mu.siAnd executing electric quantity relaxation variables for the unit day.
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