CN113270895A - Day-ahead robust joint optimization method and system for electric energy and auxiliary service market - Google Patents

Day-ahead robust joint optimization method and system for electric energy and auxiliary service market Download PDF

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CN113270895A
CN113270895A CN202110818094.3A CN202110818094A CN113270895A CN 113270895 A CN113270895 A CN 113270895A CN 202110818094 A CN202110818094 A CN 202110818094A CN 113270895 A CN113270895 A CN 113270895A
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new energy
unit
auxiliary service
prediction error
constraint
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CN113270895B (en
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蔡帜
丁强
李强
戴赛
李立新
潘毅
崔晖
李宇轩
胡晓静
盛灿辉
张传成
屈富敏
李哲
张瑞雯
常江
徐晓彤
黄国栋
许丹
胡晨旭
李博
李静
李志宏
杨晓楠
王磊
苏明玉
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China Electric Power Research Institute Co Ltd CEPRI
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—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
    • 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]
    • 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

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Abstract

The invention belongs to the field of electric power automation, and discloses a method and a system for joint optimization of electric energy and day-ahead robustness of an auxiliary service market, which comprise the following steps: receiving a power market clearing request, and requesting to clear the power market; calling constraint conditions to solve a pre-established electric energy and auxiliary service day-ahead robust joint optimization model considering new energy prediction errors, and obtaining an electric power market clearing result; and outputting the clear result of the electric power market. The invention provides a day-ahead robust joint optimization method and system for an electric energy and auxiliary service market aiming at the problems of large prediction error and serious wind and light abandoning phenomenon of the current new energy, wherein the day-ahead electric energy and auxiliary service market joint optimization model considering the prediction error of the new energy considers the fluctuation error of the new energy, has strong robustness and can adapt to large fluctuation error of the new energy; the new energy consumption capacity of the power grid is effectively provided; the method can provide effective reference for the electric power market operation under the large-scale new energy access.

Description

Day-ahead robust joint optimization method and system for electric energy and auxiliary service market
Technical Field
The invention belongs to the technical field of electric power automation, and particularly relates to a day-ahead robust joint optimization method and system for an electric energy and auxiliary service market.
Background
The new energy generally refers to renewable energy developed and utilized on the basis of new technology, and includes common solar energy, wind energy and the like. The new energy power generation is to realize the power generation process by utilizing the prior art and through solar energy and wind energy. However, solar energy and wind energy are not controlled by human factors, the fluctuation is large, the processing wave pair of solar energy and wind energy power generation is also large, and accurate estimation is difficult.
In the aspect of processing the uncertainty problem of new energy, a random planning method and a robust optimization method are mainly used; compared with the two methods, the robust optimization has the advantages of being independent of probability distribution of uncertain parameters, capable of being applied to large-scale calculation and the like. In the prior art, whether the clean energy consumption can be realized or not is tested by Monte Carlo simulation extraction of scenes. However, the method cannot ensure that the complete consumption of the new energy can be realized when the power of the new energy has larger fluctuation errors. Therefore, the current new energy source prediction error is large, and the phenomenon of wind abandoning and light abandoning is serious.
Disclosure of Invention
The invention aims to provide a day-ahead robust joint optimization method and system for an electric energy and auxiliary service market, and aims to solve the technical problem that when the power of new energy has a large fluctuation error, the optimization method cannot realize complete consumption of the new energy, so that the phenomenon of wind and light abandonment is serious.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a method for assisting a service market in joint optimization of a day-ahead robust, comprising the following steps:
receiving a power market clearing request, and requesting to clear the power market;
calling constraint conditions to solve a pre-established electric energy and auxiliary service day-ahead robust joint optimization model considering new energy prediction errors, and obtaining an electric power market clearing result;
and outputting the clear result of the electric power market.
The invention further improves the following steps: in the step of calling the constraint conditions to solve the pre-established electric energy and auxiliary service day-ahead robust joint optimization model considering the new energy prediction error to obtain the electric power market clearing result, the optimization target of the electric energy and auxiliary service day-ahead robust joint optimization model considering the new energy prediction error is as follows:
Figure 392880DEST_PATH_IMAGE001
（1）
s.t.
Figure 575599DEST_PATH_IMAGE002
（2）
Figure 620916DEST_PATH_IMAGE003
（3）
in the formula:
Figure 812863DEST_PATH_IMAGE004
in terms of the cost per unit of frequency modulation capacity,p reg is the frequency-modulation capacity of the power grid,
Figure 909126DEST_PATH_IMAGE005
is prepared for use in unitThe cost of the volume is such that,p res in order to reserve the capacity of the power grid,
Figure 997167DEST_PATH_IMAGE006
in terms of the cost per unit of electrical energy,p 0 the output vector of the unit is taken as the output vector of the unit;pcollecting all possible unit state vectors; g (∙) is power system safe operation constraint, including power balance and transmission line capacity constraint;
Figure 529780DEST_PATH_IMAGE007
the new energy output vector is under the scene without new energy prediction error;p 0 the unit output under the scene without new energy prediction error is obtained;p w predicting an error vector for any possible new energy;p aux clearing the result for the auxiliary service;p c is composed ofp w The corresponding unit output;Wthe new energy output is set;Tis the total number of time periods.
The invention further improves the following steps: in the step of calling a constraint condition to solve a pre-established electric energy and auxiliary service day-ahead robust joint optimization model considering the new energy prediction error to obtain an electric power market clearing result, the constraint of the electric energy and auxiliary service day-ahead robust joint optimization model considering the new energy prediction error comprises the following steps:
and (3) system balance constraint:
Figure 994259DEST_PATH_IMAGE008
（4）
in the formula (I), the compound is shown in the specification,p d,t is at the moment of timetThe power of the load of (a) is,p i,t is composed oftThe output of the conventional unit at the moment,N i the number of the conventional machine sets is the same as that of the conventional machine sets,p w,t is composed oftThe output of new energy at any moment;
and (3) unit operation constraint:
Figure 194297DEST_PATH_IMAGE009
（5）
in the formula (I), the compound is shown in the specification,p i,min ，p i,max is a conventional unitiAn upper and lower output limit;
unit climbing restraint:
Figure 453240DEST_PATH_IMAGE010
（6）
in the formula (I), the compound is shown in the specification,p i,up ，p i,down is a conventional unitiThe upper and lower limits of the climbing speed;
frequency modulation, reserve capacity demand constraint:
Figure 784733DEST_PATH_IMAGE011
(7)
Figure 52903DEST_PATH_IMAGE012
(8)
in the formula (I), the compound is shown in the specification,R reg,t 、R res,t to be at the moment of timetFrequency modulation requirements and standby requirements;
and (3) limiting the branch and section:
Figure 841867DEST_PATH_IMAGE013
（9）
in the formula (I), the compound is shown in the specification,
Figure 553603DEST_PATH_IMAGE014
representing a scenesLower nodemAnd nodenAt the time of the branch in betweentThe power flow value of (a) is,f mn,max , f mn,min is a nodemAnd nodenThe upper and lower limits of the power flow value of the branch between,
Figure 60807DEST_PATH_IMAGE015
as a scenesLower sectionhAt the moment of timetIn the flow of (2) to (2),S h,max ,S h,min is a section ofhUpper and lower limits of tidal current;
and (3) robust constraint:
Figure 601510DEST_PATH_IMAGE016
（10）
in the formula:
Figure 776139DEST_PATH_IMAGE017
represents the firstwA new energy unit is arranged ontPredicted values within a time period; theta is a risk degree parameter and theta is a risk degree parameter,
Figure 376885DEST_PATH_IMAGE018
is as followswThe upper limit of the prediction error of the new energy set,
Figure 371386DEST_PATH_IMAGE019
is as followswLower prediction error limit of each new energy source unit.
The invention further improves the following steps: in the step of calling constraint conditions to solve a pre-established electric energy and auxiliary service day-ahead robust joint optimization model considering the new energy prediction error to obtain an electric power market clearing result, a robust linear programming problem with an ellipsoid set as parameter uncertain quantity is formed by considering the electric energy of the new energy prediction error and the optimization target and constraint of the auxiliary service day-ahead robust joint optimization model, and the electric energy and auxiliary service day-ahead robust joint optimization model is converted into a quadratic cone programming for solving through a dual method; and obtaining the clear result of the power market.
In a second aspect, the present invention further provides a system for joint optimization of electric energy and future robustness of an auxiliary service market, including:
the receiving module is used for receiving the electric power market clearing request and requesting to clear the electric power market;
the calling and solving module is used for calling constraint conditions to solve a pre-established electric energy and auxiliary service day-ahead robust joint optimization model considering the new energy prediction error so as to obtain an electric power market clearing result;
and the output module is used for outputting the clear result of the power market.
The invention further improves the following steps: the optimization target of the day-ahead robust joint optimization model considering the electric energy of the new energy prediction error and the auxiliary service is as follows:
Figure 230626DEST_PATH_IMAGE020
（1）
s.t.
Figure 994183DEST_PATH_IMAGE021
（2）
Figure 31409DEST_PATH_IMAGE022
（3）
in the formula:
Figure 247627DEST_PATH_IMAGE023
in terms of the cost per unit of frequency modulation capacity,p reg is the frequency-modulation capacity of the power grid,
Figure 395711DEST_PATH_IMAGE024
in terms of the cost per unit of spare capacity,p res in order to reserve the capacity of the power grid,
Figure 13775DEST_PATH_IMAGE025
in terms of the cost per unit of electrical energy,p 0 the output vector of the unit is taken as the output vector of the unit;pcollecting all possible unit state vectors;
Figure 221902DEST_PATH_IMAGE026
the method comprises the following steps of (1) performing safe operation constraint on a power system, wherein the safe operation constraint comprises power balance and transmission line capacity constraint;
Figure 925416DEST_PATH_IMAGE027
the new energy output vector is under the scene without new energy prediction error;p 0 the unit output under the scene without new energy prediction error is obtained;p w is any one ofA possible new energy prediction error vector;p aux clearing the result for the auxiliary service;p c is composed ofp w The corresponding unit output;Wthe new energy output is set;Tis the total number of time periods.
The invention further improves the following steps: the constraint of the electric energy and auxiliary service day-ahead robust joint optimization model considering the new energy prediction error comprises the following steps:
and (3) system balance constraint:
Figure 627924DEST_PATH_IMAGE028
（4）
in the formula (I), the compound is shown in the specification,p d,t is at the moment of timetThe power of the load of (a) is,p i,t is composed oftThe output of the conventional unit at the moment,N i the number of the conventional machine sets is the same as that of the conventional machine sets,p w,t is composed oftThe output of new energy at any moment;
and (3) unit operation constraint:
Figure 366073DEST_PATH_IMAGE029
（5）
in the formula (I), the compound is shown in the specification,p i,min ，p i,max is a conventional unitiAn upper and lower output limit;
unit climbing restraint:
Figure 745101DEST_PATH_IMAGE030
（6）
in the formula (I), the compound is shown in the specification,p i,up ，p i,down is a conventional unitiThe upper and lower limits of the climbing speed;
frequency modulation, reserve capacity demand constraint:
Figure 935911DEST_PATH_IMAGE031
(7)
Figure 691378DEST_PATH_IMAGE032
(8)
in the formula (I), the compound is shown in the specification,R reg,t 、R res,t to be at the moment of timetFrequency modulation requirements and standby requirements;
and (3) limiting the branch and section:
Figure 18454DEST_PATH_IMAGE033
（9）
in the formula (I), the compound is shown in the specification,
Figure 89090DEST_PATH_IMAGE034
representing a scenesLower nodemAnd nodenAt the time of the branch in betweentThe power flow value of (a) is,f mn,max , f mn,min is a nodemAnd nodenThe upper and lower limits of the power flow value of the branch between,
Figure 767196DEST_PATH_IMAGE035
as a scenesLower sectionhAt the moment of timetIn the flow of (2) to (2),S h,max ,S h,min is a section ofhUpper and lower limits of tidal current;
and (3) robust constraint:
Figure 60774DEST_PATH_IMAGE036
（10）
in the formula:
Figure 507936DEST_PATH_IMAGE037
represents the firstwA new energy unit is arranged ontPredicted values within a time period; theta is a risk degree parameter and theta is a risk degree parameter,
Figure 963188DEST_PATH_IMAGE038
is as followswThe upper limit of the prediction error of the new energy set,
Figure 394169DEST_PATH_IMAGE039
is as followswLower prediction error limit of each new energy source unit.
The invention further improves the following steps: considering the electric energy of the new energy prediction error and the robust linear programming problem of which the parameter uncertainty of the description is an ellipsoid set and which is formed by the optimization target and the constraint of the day-ahead robust joint optimization model of the auxiliary service, converting the electric energy into a quadratic cone programming for solving by a dual method; and obtaining the clear result of the power market.
In a third aspect, the present invention provides an electronic device comprising a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement the method for future robust joint optimization of power and ancillary services markets.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon at least one instruction that, when executed by a processor, performs the method for future robust joint optimization of electrical energy and ancillary services markets.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a day-ahead robust joint optimization method and system for an electric energy and auxiliary service market aiming at the problems of large prediction error and serious wind and light abandoning phenomenon of the current new energy, wherein the day-ahead electric energy and auxiliary service market joint optimization model considering the prediction error of the new energy considers the fluctuation error of the new energy, has strong robustness and can adapt to large fluctuation error of the new energy; the new energy consumption capacity of the power grid is effectively provided; the technical problem of serious wind and light abandoning phenomena is effectively solved. The method can provide effective reference for the electric power market operation under the large-scale new energy access.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic flow chart of a method for optimizing the joint robustness of the electric energy and auxiliary service market in the future according to the present invention;
FIG. 2 is a block diagram of a prior robust joint optimization system for the power and auxiliary service market in accordance with the present invention;
fig. 3 is a block diagram of an electronic device according to the present invention.
Detailed Description
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings. It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The following detailed description is exemplary in nature and is intended to provide further details of the invention. Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention.
Example 1
Referring to fig. 1, the present invention provides a method for optimizing the future robust joint of electric energy and auxiliary service market, which includes the following steps:
s1, receiving a power market clearing request and requesting power market clearing;
s2, calling constraint conditions to solve a pre-established electric energy and auxiliary service day-ahead robust joint optimization model considering the new energy prediction error, and obtaining an electric power market clearing result;
and S3, outputting the electric power market clearing result.
In the step of calling the constraint conditions to solve the pre-established electric energy and auxiliary service day-ahead robust joint optimization model considering the new energy prediction error to obtain the electric power market clearing result, the optimization target of the electric energy and auxiliary service day-ahead robust joint optimization model considering the new energy prediction error is as follows:
Figure 976592DEST_PATH_IMAGE040
（1）
s.t.
Figure 543839DEST_PATH_IMAGE021
（2）
Figure 435572DEST_PATH_IMAGE022
（3）
in the formula:
Figure 353849DEST_PATH_IMAGE042
in terms of the cost per unit of frequency modulation capacity,p reg is the frequency-modulation capacity of the power grid,
Figure 989230DEST_PATH_IMAGE043
in terms of the cost per unit of spare capacity,p res in order to reserve the capacity of the power grid,
Figure 145405DEST_PATH_IMAGE044
in terms of the cost per unit of electrical energy,p 0 the output vector of the unit is taken as the output vector of the unit;pcollecting all possible unit state vectors;
Figure 457306DEST_PATH_IMAGE045
the method comprises the following steps of (1) performing safe operation constraint on a power system, wherein the safe operation constraint comprises power balance and transmission line capacity constraint;
Figure 331721DEST_PATH_IMAGE046
the new energy output vector is under the scene without new energy prediction error;p 0 the unit output under the scene without new energy prediction error is obtained;p w predicting an error vector for any possible new energy;p aux clearing the result for the auxiliary service;p c is composed ofp w The corresponding unit output;Wthe new energy output is set;Tis the total number of time periods.
In the step of calling a constraint condition to solve a pre-established electric energy and auxiliary service day-ahead robust joint optimization model considering the new energy prediction error to obtain an electric power market clearing result, the constraint of the electric energy and auxiliary service day-ahead robust joint optimization model considering the new energy prediction error comprises the following steps:
and (3) system balance constraint:
Figure 36372DEST_PATH_IMAGE047
（4）
in the formula (I), the compound is shown in the specification,p d,t is at the moment of timetThe power of the load of (a) is,p i,t is composed oftThe output of the conventional unit at the moment,N i the number of the conventional machine sets is the same as that of the conventional machine sets,p w,t is composed oftThe output of new energy at any moment;
and (3) unit operation constraint:
Figure 312633DEST_PATH_IMAGE029
（5）
in the formula (I), the compound is shown in the specification,p i,min ，p i,max is a conventional unitiAn upper and lower output limit;
unit climbing restraint:
Figure 811747DEST_PATH_IMAGE048
（6）
in the formula (I), the compound is shown in the specification,p i,up ，p i,down is a conventional unitiThe upper and lower limits of the climbing speed;
frequency modulation, reserve capacity demand constraint:
Figure 173458DEST_PATH_IMAGE049
(7)
Figure 901374DEST_PATH_IMAGE032
(8)
in the formula (I), the compound is shown in the specification,R reg,t 、R res,t to be at the moment of timetFrequency modulation requirements and standby requirements;
and (3) limiting the branch and section:
Figure 32141DEST_PATH_IMAGE033
（9）
in the formula (I), the compound is shown in the specification,
Figure 702157DEST_PATH_IMAGE050
representing a scenesLower nodemAnd nodenAt the time of the branch in betweentThe power flow value of (a) is,f mn,max , f mn,min is a nodemAnd nodenThe upper and lower limits of the power flow value of the branch between,
Figure 551164DEST_PATH_IMAGE051
as a scenesLower sectionhAt the moment of timetIn the flow of (2) to (2),S h,max ,S h,min is a section ofhUpper and lower limits of tidal current;
and (3) robust constraint:
Figure 332038DEST_PATH_IMAGE052
（10）
in the formula:
Figure 582891DEST_PATH_IMAGE053
represents the firstwA new energy unit is arranged ontPredicted values within a time period; theta is a risk degree parameter and theta is a risk degree parameter,
Figure 407497DEST_PATH_IMAGE054
is as followswThe upper limit of the prediction error of the new energy set,
Figure 9379DEST_PATH_IMAGE055
is as followswLower prediction error limit of each new energy source unit.
In the step of calling constraint conditions to solve a pre-established electric energy and auxiliary service day-ahead robust joint optimization model considering the new energy prediction error to obtain an electric power market clearing result, a robust linear programming problem with an ellipsoid set as parameter uncertain quantity is formed by considering the electric energy of the new energy prediction error and the optimization target and constraint of the auxiliary service day-ahead robust joint optimization model, and the electric energy and auxiliary service day-ahead robust joint optimization model is converted into a quadratic cone programming for solving through a dual method; and obtaining the clear result of the power market.
Example 2
The embodiment 2 provides a day-ahead robust joint optimization method for an electric energy and auxiliary service market, which is different from the embodiment 1 in that:
in step S2, an optimization objective of the future robust joint optimization model considering the electric energy and the auxiliary service of the new energy prediction error is established:
in day-ahead scheduling, the decision-making objectives of the scheduling center are: on the premise of ensuring the operation safety and reliability of the power grid, the capability of the power grid for coping with new energy prediction errors and the operation economy under the worst condition are improved. The whole decision process can be described as a min-max optimization problem. The optimization goal of the day-ahead robust joint optimization model of the electric energy and the auxiliary service is that the market electricity purchasing cost is minimum, and the following formula is shown as follows:
Figure 328365DEST_PATH_IMAGE057
（11）
in the formula (I), the compound is shown in the specification,tin order to number the time period,Tis the total number of time periods;Nthe total number of the conventional units;p i,t is a conventional unitiTime of daytThe output of (a) the (b) is,p w,t is at the moment of timetThe power of the new energy source is obtained,a i,t ，a i,reg,t ，a i,res,t are respectively conventional unitsiAt the moment of timetElectrical energy, frequency modulation and standby cost coefficients;p i,reg,t is a conventional unitiAt the moment of timetThe frequency-modulation capacity of the frequency modulation device,p i,res,t is a conventional unitiAt the moment of timetSpare capacity of (c).
(6) In the robust constraint:
when the new energy unit operates at the maximum output point, the uncertainty of the output of the new energy comes from the prediction error. Given circumstances at confidence levelUnder the condition, the prediction error of the new energy unit at each moment has upper and lower limits, so the new energy output is integratedWCan be expressed as:
Figure 433724DEST_PATH_IMAGE058
（12）
in the formula (I), the compound is shown in the specification,
Figure 445543DEST_PATH_IMAGE059
,
Figure 3563DEST_PATH_IMAGE060
are respectively the firstwA new energy unit is arranged on t Lower and upper active power limits in each time segment.
Equation (10) describes the robust model in the sense of the Soyster linear robust optimization model. The model can ensure the robustness of a feasible solution under the worst condition, but the model is too conservative and does not meet the requirement of economy. The invention describes the feasible fields of uncertain variables using a set of ellipsoids. The method is applied to the situation of the invention, and can be understood that the probability that the same new energy unit reaches the upper limit/lower limit of the prediction error at a plurality of moments is smaller. New energy output setWComprises the following steps:
Figure 142551DEST_PATH_IMAGE052
（10）
in the formula:
Figure 102417DEST_PATH_IMAGE053
represents the firstwA new energy unit is arranged ontPredicted values within a time period; and theta is a risk degree parameter determined by the scheduling center, and represents the attitude of the scheduling center for dealing with the risk of the scheduling strategy.
Figure 285137DEST_PATH_IMAGE062
Is as followswThe upper limit of the prediction error of the new energy set,
Figure 596032DEST_PATH_IMAGE063
is as followswThe lower limit of the prediction error of each new energy source unit; when theta =0, the set W degenerates to a new energy prediction vector only
Figure 256821DEST_PATH_IMAGE064
The single element set of (2) represents that the risk of the new energy prediction error is completely not considered; when theta increases, setWThe represented ellipsoid set range is increased, the robustness of the model is enhanced, but the economy is weakened.
When the unit combination is formulated in the day-ahead scheduling, the influence of the new energy prediction error on the safety of each power saving network needs to be considered, namely after the unit combination is formulated in the day-ahead scheduling, the power system can always meet the safety operation constraint through the adjustment of the tie line plan and the unit output in the day-ahead scheduling on any possible prediction error.
Based on the above analysis, the mathematical description of the day-ahead schedule (equation 11) can be simplified to be expressed as:
Figure 586040DEST_PATH_IMAGE020
（1）
s.t.
Figure 939661DEST_PATH_IMAGE021
（2）
Figure 737853DEST_PATH_IMAGE022
（3）
in the formula:
Figure 733490DEST_PATH_IMAGE065
in terms of the cost per unit of frequency modulation capacity,p reg is the frequency-modulation capacity of the power grid,
Figure 402369DEST_PATH_IMAGE066
in terms of the cost per unit of spare capacity,p res in order to reserve the capacity of the power grid,
Figure 677624DEST_PATH_IMAGE067
in terms of the cost per unit of electrical energy,p 0 the output vector of the unit is taken as the output vector of the unit;pcollecting all possible unit state vectors; g (∙) is power system safe operation constraint, including power balance and transmission line capacity constraint;
Figure 697532DEST_PATH_IMAGE068
the new energy output vector is under the scene without new energy prediction error;p 0 the unit output under the scene without new energy prediction error is obtained;p w predicting an error vector for any possible new energy;p aux clearing the result for the auxiliary service;p c is composed ofp w The corresponding unit output;Wthe new energy output is set;Tis the total number of time periods.
In the objective function, if the uncertain parameters are symmetrically distributed, they are expected to be equal to the parameter average value regardless of the specific distribution rule, that is, the parameter average value
Figure 700124DEST_PATH_IMAGE069
. From a practical point of view, the prediction errors are generally distributed symmetrically, which means that the actual value of the new energy power should also conform to the symmetric distribution. The output of the unit is linearly related to the output of the new energy, so the output of the unit also follows symmetrical distribution.
Therefore, the model optimization target (1) and the constraint conditions (4) - (10) jointly form a robust linear programming problem with the described parameter uncertainty being an ellipsoid set, and new energy prediction power, prediction error upper and lower limits and data required by various market clearing are collected; converting the data into a quadratic cone program for solving by a dual method; and obtaining the clear result of the power market.
Example 3
Referring to fig. 2, the present invention further provides a system for joint optimization of the electric energy and the future robustness of the auxiliary service market, which includes:
the receiving module is used for receiving the electric power market clearing request and requesting to clear the electric power market;
the calling and solving module is used for calling constraint conditions to solve a pre-established electric energy and auxiliary service day-ahead robust joint optimization model considering the new energy prediction error so as to obtain an electric power market clearing result;
and the output module is used for outputting the clear result of the power market.
In the invention, the optimization target of the electric energy and auxiliary service day-ahead robust joint optimization model considering the new energy prediction error is as follows:
Figure 489088DEST_PATH_IMAGE020
（1）
s.t.
Figure 184512DEST_PATH_IMAGE021
（2）
Figure 426137DEST_PATH_IMAGE022
（3）
in the formula:
Figure 232419DEST_PATH_IMAGE065
in terms of the cost per unit of frequency modulation capacity,p reg is the frequency-modulation capacity of the power grid,
Figure 141469DEST_PATH_IMAGE066
in terms of the cost per unit of spare capacity,p res in order to reserve the capacity of the power grid,
Figure 985623DEST_PATH_IMAGE067
in terms of the cost per unit of electrical energy,p 0 the output vector of the unit is taken as the output vector of the unit;pcollecting all possible unit state vectors; g (∙) is power system safe operation constraint, including power balance and transmission line capacity constraint;
Figure 980124DEST_PATH_IMAGE071
for no new energy prediction errorNew energy output vector under scene;p 0 the unit output under the scene without new energy prediction error is obtained;p w predicting an error vector for any possible new energy;p aux clearing the result for the auxiliary service;p c is composed ofp w The corresponding unit output;Wthe new energy output is set;Tis the total number of time periods.
In the invention, the constraint of the electric energy and auxiliary service day-ahead robust joint optimization model comprises the following steps:
and (3) system balance constraint:
Figure 324517DEST_PATH_IMAGE072
（4）
in the formula (I), the compound is shown in the specification,p d,t is at the moment of timetThe power of the load of (a) is,p i,t is composed oftThe output of the conventional unit at the moment,N i the number of the conventional machine sets is the same as that of the conventional machine sets,p w,t is composed oftThe output of new energy at any moment;
and (3) unit operation constraint:
Figure 88074DEST_PATH_IMAGE029
（5）
in the formula (I), the compound is shown in the specification,p i,min ，p i,max is a conventional unitiAn upper and lower output limit;
unit climbing restraint:
Figure 859721DEST_PATH_IMAGE030
（6）
in the formula (I), the compound is shown in the specification,p i,up ，p i,down is a conventional unitiThe upper and lower limits of the climbing speed;
frequency modulation, reserve capacity demand constraint:
Figure 607097DEST_PATH_IMAGE031
(7)
Figure 755182DEST_PATH_IMAGE032
(8)
in the formula (I), the compound is shown in the specification,R reg,t 、R res,t to be at the moment of timetFrequency modulation requirements and standby requirements;
and (3) limiting the branch and section:
Figure 123977DEST_PATH_IMAGE073
（9）
in the formula (I), the compound is shown in the specification,
Figure 332105DEST_PATH_IMAGE074
representing a scenesLower nodemAnd nodenAt the time of the branch in betweentThe power flow value of (a) is,f mn,max , f mn,min is a nodemAnd nodenThe upper and lower limits of the power flow value of the branch between,
Figure 35619DEST_PATH_IMAGE076
as a scenesLower sectionhAt the moment of timetIn the flow of (2) to (2),S h,max ,S h,min is a section ofhUpper and lower limits of tidal current;
and (3) robust constraint:
Figure 721815DEST_PATH_IMAGE052
（10）
in the formula:
Figure 459964DEST_PATH_IMAGE077
represents the firstwA new energy unit is arranged ontPredicted values within a time period; theta is a risk degree parameter and theta is a risk degree parameter,
Figure 573413DEST_PATH_IMAGE054
is as followswThe upper limit of the prediction error of the new energy set,
Figure 764223DEST_PATH_IMAGE078
is as followswLower prediction error limit of each new energy source unit. In the invention, the optimization target and the constraint of the day-ahead robust joint optimization model of the electric energy and the auxiliary service jointly form a robust linear programming problem of which the described parameter uncertain quantity is an ellipsoid set, and the robust linear programming problem is converted into a quadratic cone programming for solving through a dual method; and obtaining the clear result of the power market.
Example 3
Referring to fig. 3, the present invention further provides an electronic device 100 for a method of joint optimization of electric energy and future robustness of an auxiliary service market; the electronic device 100 comprises a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.
The memory 101 may be used for storing the computer program 103, and the processor 102 implements the future robust joint optimization method steps of the electric energy and auxiliary service market according to embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data) created according to the use of the electronic apparatus 100, and the like. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other non-volatile solid state storage device.
The at least one Processor 102 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The processor 102 may be a microprocessor or the processor 102 may be any conventional processor or the like, and the processor 102 is a control center of the electronic device 100 and connects various parts of the whole electronic device 100 by various interfaces and lines.
The memory 101 of the electronic device 100 stores a plurality of instructions executable by the processor 102 to implement a method for a future robust joint optimization of power and ancillary services markets to implement:
receiving a power market clearing request, and requesting to clear the power market;
calling constraint conditions to solve a pre-established electric energy and auxiliary service day-ahead robust joint optimization model considering new energy prediction errors, and obtaining an electric power market clearing result;
and outputting the clear result of the electric power market.
Specifically, the processor 102 may refer to the description of the relevant steps in embodiment 1 for a specific implementation method of the instruction, which is not described herein again.
Example 4
The modules/units integrated by the electronic device 100 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, and Read-Only Memory (ROM).
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (10)

1. A day-ahead robust joint optimization method for an electric energy and auxiliary service market is characterized by comprising the following steps:
receiving a power market clearing request, and requesting to clear the power market;
calling constraint conditions to solve a pre-established electric energy and auxiliary service day-ahead robust joint optimization model considering new energy prediction errors, and obtaining an electric power market clearing result;
and outputting the clear result of the electric power market.
2. The method for the day-ahead robust joint optimization of the electric energy and auxiliary service market according to claim 1, wherein in the step of calling constraint conditions to solve a pre-established day-ahead robust joint optimization model considering the new energy prediction error and the auxiliary service to obtain the clearing result of the electric power market, the optimization objective of the day-ahead robust joint optimization model considering the new energy prediction error and the auxiliary service is as follows:
Figure 135327DEST_PATH_IMAGE001
（1）
s.t.
Figure 318046DEST_PATH_IMAGE002
（2）
Figure 114095DEST_PATH_IMAGE003
（3）
in the formula:
Figure 774884DEST_PATH_IMAGE005
in terms of the cost per unit of frequency modulation capacity,p reg is the frequency-modulation capacity of the power grid,
Figure 854835DEST_PATH_IMAGE007
in terms of the cost per unit of spare capacity,p res in order to reserve the capacity of the power grid,
Figure 942877DEST_PATH_IMAGE009
in terms of the cost per unit of electrical energy,p 0 the output vector of the unit is taken as the output vector of the unit;pcollecting all possible unit state vectors;
Figure 475489DEST_PATH_IMAGE011
the method comprises the following steps of (1) performing safe operation constraint on a power system, wherein the safe operation constraint comprises power balance and transmission line capacity constraint;
Figure 939969DEST_PATH_IMAGE013
the new energy output vector is under the scene without new energy prediction error;p 0 the unit output under the scene without new energy prediction error is obtained;p w predicting an error vector for any possible new energy;p aux clearing the result for the auxiliary service;p c is composed ofp w The corresponding unit output;Wthe new energy output is set;Tis the total number of time periods.
3. The method as claimed in claim 2, wherein in the step of obtaining the clearing result of the power market by calling a constraint condition to solve a pre-established power and auxiliary service robustness combined optimization model considering the new energy prediction error, the constraint of the power and auxiliary service robustness combined optimization model considering the new energy prediction error comprises:
and (3) system balance constraint:
Figure 874427DEST_PATH_IMAGE014
（4）
in the formula (I), the compound is shown in the specification,p d,t is at the moment of timetThe power of the load of (a) is,p i,t is composed oftThe output of the conventional unit at the moment,N i the number of the conventional machine sets is the same as that of the conventional machine sets,p w,t is composed oftThe output of new energy at any moment;
and (3) unit operation constraint:
Figure 133370DEST_PATH_IMAGE015
（5）
in the formula (I), the compound is shown in the specification,p i,min ，p i,max is a conventional unitiAn upper and lower output limit;
unit climbing restraint:
Figure 408405DEST_PATH_IMAGE016
（6）
in the formula (I), the compound is shown in the specification,p i,up ，p i,down is a conventional unitiThe upper and lower limits of the climbing speed;
frequency modulation, reserve capacity demand constraint:
Figure 410996DEST_PATH_IMAGE017
(7)
Figure 199961DEST_PATH_IMAGE018
(8)
in the formula (I), the compound is shown in the specification,R reg,t 、R res,t to be at the moment of timetFrequency modulation requirements and standby requirements;
and (3) limiting the branch and section:
Figure 895384DEST_PATH_IMAGE019
（9）
in the formula (I), the compound is shown in the specification,
Figure 402589DEST_PATH_IMAGE021
representing a scenesLower nodemAnd nodenAt the time of the branch in betweentThe power flow value of (a) is,f mn,max ,f mn,min is a nodemAnd nodenThe upper and lower limits of the power flow value of the branch between,
Figure 943292DEST_PATH_IMAGE023
as a scenesLower sectionhAt the moment of timetIn the flow of (2) to (2),S h,max ,S h,min is a section ofhUpper and lower limits of tidal current;
and (3) robust constraint:
Figure 852342DEST_PATH_IMAGE024
（10）
in the formula:
Figure 453087DEST_PATH_IMAGE026
represents the firstwA new energy unit is arranged ontPredicted values within a time period; theta is a risk degree parameter and theta is a risk degree parameter,
Figure 198321DEST_PATH_IMAGE028
is as followswThe upper limit of the prediction error of the new energy set,
Figure 542714DEST_PATH_IMAGE030
is as followswLower prediction error limit of each new energy source unit.
4. The method for the day-ahead robust joint optimization of the electric energy and auxiliary service market according to claim 3, characterized in that in the step of calling constraint conditions to solve a pre-established day-ahead robust joint optimization model considering the new energy prediction error and the auxiliary service to obtain the clearing result of the electric power market, the optimization target and the constraint of the electric energy considering the new energy prediction error and the day-ahead robust joint optimization model considering the auxiliary service jointly form a robust linear programming problem that the described parameter uncertainty is an ellipsoid set, and the robust linear programming problem is converted into a quadratic cone programming for solving through a dual method; and obtaining the clear result of the electric power market.
5. A system for day-ahead robust joint optimization of an electrical energy and ancillary services market, comprising:
the receiving module is used for receiving the electric power market clearing request and requesting to clear the electric power market;
the calling and solving module is used for calling constraint conditions to solve a pre-established electric energy and auxiliary service day-ahead robust joint optimization model considering the new energy prediction error so as to obtain an electric power market clearing result;
and the output module is used for outputting the clear result of the power market.
6. The system of claim 5, wherein the optimization objective of the future robust joint optimization model of the electric energy and auxiliary service considering the new energy prediction error is:
Figure 306271DEST_PATH_IMAGE001
（1）
s.t.
Figure 343497DEST_PATH_IMAGE002
（2）
Figure 559715DEST_PATH_IMAGE003
（3）
in the formula:
Figure 707799DEST_PATH_IMAGE031
in terms of the cost per unit of frequency modulation capacity,p reg is the frequency-modulation capacity of the power grid,
Figure 325863DEST_PATH_IMAGE032
in terms of the cost per unit of spare capacity,p res in order to reserve the capacity of the power grid,
Figure 533990DEST_PATH_IMAGE033
in terms of the cost per unit of electrical energy,p 0 the output vector of the unit is taken as the output vector of the unit;pcollecting all possible unit state vectors;
Figure 486771DEST_PATH_IMAGE034
the method comprises the following steps of (1) performing safe operation constraint on a power system, wherein the safe operation constraint comprises power balance and transmission line capacity constraint;
Figure 172968DEST_PATH_IMAGE035
the new energy output vector is under the scene without new energy prediction error;p 0 the unit output under the scene without new energy prediction error is obtained;p w predicting an error vector for any possible new energy;p aux clearing the result for the auxiliary service;p c is composed ofp w The corresponding unit output;Wthe new energy output is set;Tis the total number of time periods.
7. The system of claim 6, wherein the constraints of the future robust joint optimization model for the electric energy and auxiliary service that take into account new energy prediction errors comprise:
and (3) system balance constraint:
Figure 911117DEST_PATH_IMAGE014
（4）
in the formula (I), the compound is shown in the specification,p d,t is at the moment of timetThe power of the load of (a) is,p i,t is composed oftThe output of the conventional unit at the moment,N i the number of the conventional machine sets is the same as that of the conventional machine sets,p w,t is composed oftThe output of new energy at any moment;
and (3) unit operation constraint:
Figure 290145DEST_PATH_IMAGE036
（5）
in the formula (I), the compound is shown in the specification,p i,min ，p i,max is a conventional unitiAn upper and lower output limit;
unit climbing restraint:
Figure 480955DEST_PATH_IMAGE016
（6）
in the formula (I), the compound is shown in the specification,p i,up ，p i,down is a conventional unitiThe upper and lower limits of the climbing speed;
frequency modulation, reserve capacity demand constraint:
Figure 970842DEST_PATH_IMAGE017
(7)
Figure 563498DEST_PATH_IMAGE018
(8)
in the formula (I), the compound is shown in the specification,R reg,t 、R res,t to be at the moment of timetNeed for frequency modulationSumming the standby requirements;
and (3) limiting the branch and section:
Figure 864160DEST_PATH_IMAGE019
（9）
in the formula (I), the compound is shown in the specification,
Figure 542266DEST_PATH_IMAGE037
representing a scenesLower nodemAnd nodenAt the time of the branch in betweentThe power flow value of (a) is,f mn,max ,f mn,min is a nodemAnd nodenThe upper and lower limits of the power flow value of the branch between,
Figure 835844DEST_PATH_IMAGE038
as a scenesLower sectionhAt the moment of timetIn the flow of (2) to (2),S h,max ,S h,min is a section ofhUpper and lower limits of tidal current;
and (3) robust constraint:
Figure 283006DEST_PATH_IMAGE024
（10）
in the formula:
Figure 472679DEST_PATH_IMAGE039
represents the firstwA new energy unit is arranged ontPredicted values within a time period; theta is a risk degree parameter and theta is a risk degree parameter,
Figure 638081DEST_PATH_IMAGE040
is as followswThe upper limit of the prediction error of the new energy set,
Figure DEST_PATH_IMAGE041
is as followswLower prediction error limit of each new energy source unit.
8. The system of claim 7, wherein the optimization objectives and constraints of the electric energy and auxiliary service day-ahead robust joint optimization model considering the new energy prediction error jointly form a robust linear programming problem with an ellipsoid set as the described parameter uncertainty, and the problem is converted into a quadratic cone programming for solving by a dual method; and obtaining the clear result of the electric power market.
9. An electronic device comprising a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement the method for the future robust joint optimization of electrical energy and ancillary services markets according to any of claims 1 to 4.
10. A computer-readable storage medium storing at least one instruction which, when executed by a processor, implements a method for the future robust joint optimization of electrical energy and ancillary services markets as recited in any of claims 1 to 4.
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