CN115000957B - Resource scheduling optimization method and system considering uncertainty of net load among moments - Google Patents
Resource scheduling optimization method and system considering uncertainty of net load among moments Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
- H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention belongs to the field of power system scheduling, and provides a resource scheduling optimization method and a resource scheduling optimization system considering uncertainty of a time-interval net load. Under the background that the uncertainty of the net load at the time point is described by intervals, the historical data of the net load fluctuation is combined, and four time net load extreme fluctuation trajectories are found out according to the time net load fluctuation. The system can guarantee robust handling of time-time net load fluctuations as long as it handles these four extreme fluctuation trajectories, thereby avoiding overly conservative or aggressive outcomes.
Description
Technical Field
The invention belongs to the field of power system scheduling, and particularly relates to a resource scheduling optimization method and system considering the uncertainty of a time-to-time net load.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Unlike traditional thermal power generating units, the power generation output of renewable energy sources such as fans and photovoltaic power generation is limited by natural conditions, and the power generation output has strong volatility and uncertainty. Therefore, the severe fluctuation and uncertainty of how to accept the high proportion of renewable energy is a serious challenge to the power system.
In order to solve the volatility and uncertainty caused by renewable energy in scheduling, a series of researches are carried out by scholars at home and abroad.
The uncertainty is usually described in the form of an interval, probability distribution, a set of probability distribution and the like, and then optimization models such as interval optimization, robust optimization, random optimization, opportunity constraint optimization, distributed robust optimization and the like are established. However, the above studies are mainly directed to the characterization and handling of uncertainty at the point of time. For the transition process of the net load (load minus the renewable energy output) between different time points, the trajectory connecting the predicted value at one time point and the predicted value at the next time point is generally described. The simple description of the transition process of the net load between the moments does not fully consider the uncertainty of the fluctuation of the net load between the moments, and the various possible fluctuation processes of the net load between the moments cannot be guaranteed to be effectively followed by the adjustable unit, so that the frequency of the system during normal operation is out of limit, even wind is abandoned, the load is cut, and the like can be caused.
With the continuous increase of the permeability of renewable energy sources, the uncertainty problem of the time-to-time net load fluctuation is also increasingly concerned.
The inventors found that the problems of the prior art are as follows:
on the basis of describing uncertainty of time points by intervals, extreme fluctuation scenes of the net load between the times are described by diagonals connecting end points of net load uncertainty intervals of two adjacent time points, and the handling of the extreme scenes can guarantee the handling of any net load fluctuation between the times. However, the net load limit fluctuation speed corresponding to these extreme scenarios is not directly related to the net load actual limit fluctuation speed obtained from the statistical information, and thus the selection of these extreme scenarios may be too conservative or aggressive. For example, if the net load fluctuation speed corresponding to an extreme trajectory exceeds the net load actual limit fluctuation speed, the trajectory is too aggressive, and the corresponding backup is provided, which may result in waste of resources and decrease the economy of system operation. On the contrary, if the net load fluctuation speed corresponding to a certain extreme trajectory is lower than the actual net load limit fluctuation speed, the trajectory is too conservative, and the spare provided for the trajectory is not enough to deal with the actual net load fluctuation, thereby threatening the safe operation of the system.
The single-period static economic dispatch is taken as a research object, the limit fluctuation speed of the net load is considered, and an extreme fluctuation trajectory under the condition that the system climbing resource demand is severest is given. However, the uncertainty of the previous time point is not considered, but only the uncertainty of the next time point is considered, that is, the previous time point only considers the predicted value and the uncertainty of the next time point is described in intervals, so the extreme trajectory is not suitable for multi-interval optimization. In addition, the proposed model does not consider the influence of branch power flow constraint under an extreme fluctuation scene, and spare transferability required for dealing with uncertainty of net load fluctuation cannot be guaranteed.
Disclosure of Invention
In order to solve at least one technical problem in the background art, the invention provides a resource scheduling optimization method and a resource scheduling optimization system considering the uncertainty of the net load among moments, which are based on the two angles of the fluctuation quantity and the fluctuation speed of the net load among moments and are combined with historical statistical data of the fluctuation of the net load, so that a plurality of extreme fluctuation trajectories under the limit fluctuation quantity and the limit fluctuation speed of the net load are described, and a unit combination model considering the uncertainty of the net load fluctuation is further provided.
In order to achieve the purpose, the invention adopts the following technical scheme:
a first aspect of the present invention provides a resource scheduling optimization method considering an inter-time payload uncertainty, comprising the steps of:
acquiring historical power system statistical data, describing the uncertainty of the net load at a moment point by intervals, and depicting and obtaining an extreme net load fluctuation trajectory from the two aspects of net load fluctuation quantity and fluctuation speed;
constructing a resource scheduling optimization model by combining branch power flow constraints under various extreme fluctuation trajectories of net loads with the minimum total cost as an optimization target;
and solving the resource scheduling optimization model to obtain an optimization scheme of resource scheduling.
A second aspect of the present invention provides a resource scheduling optimization system that takes into account an inter-time payload uncertainty, comprising:
the net load extreme fluctuation trajectory delineating module is used for acquiring historical power system statistical data, describing the net load uncertainty at a moment point in intervals, and delineating the net load extreme fluctuation trajectory from the two aspects of net load fluctuation quantity and fluctuation speed;
the resource scheduling optimization model building module is used for building a resource scheduling optimization model by combining branch power flow constraints under various net load extreme fluctuation trajectories with the minimum total cost as an optimization target;
and the resource scheduling optimization model solving module is used for solving the resource scheduling optimization model to obtain an optimization scheme of resource scheduling.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, aiming at the uncertainty of the fluctuation process of the net load between moments, the extreme fluctuation trajectory of the net load is excavated and described from two aspects of the fluctuation quantity and the fluctuation speed of the net load, and a unit combination model which carefully considers the uncertainty of the fluctuation of the net load between the moments is provided, so as to ensure the robust response to the fluctuation of the net load between the moments.
Under the background that the uncertainty of the net load at the time point is described by intervals, the invention combines the historical data of the net load fluctuation to the net load fluctuation at the time to discover four extreme fluctuation trajectories of the net load at the time. The system can ensure robust handling of time-time net load fluctuations as long as the four extreme fluctuation trajectories are handled, thereby avoiding overly conservative or aggressive results.
According to the invention, branch power flow constraints under various extreme fluctuation trajectories of the net load are brought into the model, so that the standby transferability required by the fluctuation of the net load is ensured.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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 included to illustrate an exemplary embodiment of the invention and not to limit the invention.
FIG. 1 is a schematic flow chart of a resource scheduling optimization method of the present invention considering time payload uncertainty;
FIG. 2 is a conventional time-to-time payload transition from predictor to predictor;
FIG. 3 is a conventional inter-time payload extreme trajectory based on interval end point diagonals;
FIG. 4 is an inter-time net load extreme fluctuation trajectory of the present invention that takes into account extreme fluctuation speed;
FIG. 5 is a probability distribution of the fluctuating velocities of the time payload of the present invention;
FIG. 6 is a net load fluctuation trajectory of the present invention taking into account the number of limit fluctuations and the speed of the fluctuations;
FIG. 7 is a limit fluctuation speed of the net load of the present invention;
FIG. 8 is a comparison of total costs for various time periods of the present invention taking into account payload fluctuation uncertainty;
FIG. 9 is a graph of the effect of the net load limit surge speed of the present invention on the cost of the proposed model;
fig. 10 is a graph of the impact of line capacity variation on the overall cost of two models according to the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that 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. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
As shown in fig. 1, the present embodiment provides a resource scheduling optimization method considering an inter-time payload uncertainty, including the following steps:
step 1: acquiring historical power system statistical data, describing the uncertainty of the net load at a moment point by intervals, and describing two aspects of net load fluctuation quantity and fluctuation speed to obtain an extreme fluctuation trajectory of the net load;
the transition process of the net load between adjacent time points in the conventional model is often simply represented by a connection line of the net load predicted values of two adjacent time points, as shown in fig. 2. In the drawingsDfcst t-1、Dfcst tRespectively representt-1 time andta predicted value of the time payload. The method defaults that if the uncertainty at the time point is effectively coped with, the transition process between the times is naturally satisfied. The description does not take into account the uncertainty of the time-to-time payload fluctuation and cannot guarantee that various possible transition processes of the time-to-time payload are effectively followed by the adjustable units in the system.
The prior art describes the extreme fluctuation trajectory of the net load between moments by the diagonal connecting the end points of the interval of the adjacent moments while describing the uncertainty of the moments by the interval, as shown in fig. 3. It is believed that the system is capable of handling arbitrary waves of time-of-day net load if it can handle these extreme trajectoriesAnd (4) dynamic condition. In FIG. 3Dfcst t、Dmax t、Dmin tRespectively representing the predicted value of the net load at the time point t and the upper limit and the lower limit of the uncertainty interval. Extreme fluctuating trajectories based on diagonal lines in the diagram, i.e. slaveDmin t-1 toDmax tAnd a varying trajectory fromDmax t-1 toDmin tThe change trajectory of (2).
This extreme fluctuation trajectory based on diagonal lines takes into account the uncertainty of the inter-time payload fluctuation, compared to the inter-time payload transition process description from the predicted value to the predicted value. However, the selection of such extreme fluctuation trajectory is independent of the net load limit fluctuation speed and may be too conservative or aggressive. For example, if the net load fluctuation speed corresponding to an extreme trajectory exceeds the net load actual limit fluctuation speed, the trajectory is too aggressive, and the corresponding backup is provided, which may result in waste of resources and decrease the economy of system operation. On the contrary, if the net load fluctuation speed corresponding to a certain extreme trajectory is lower than the actual net load limit fluctuation speed, the trajectory is too conservative, and the spare provided for the trajectory is not enough to deal with the actual net load fluctuation, thereby threatening the safe operation of the system.
Based on the single slot economic scheduling, an extreme fluctuation trajectory based on the net load limit fluctuation speed is given, as shown in fig. 4. The net load on the extreme fluctuation trajectory in the graph is always in the extreme fluctuation speed state. It is believed that as long as the net load varies within the two extreme fluctuation trajectories, both can be effectively dealt with.
But the method is only suitable for the case that the sending point of the extreme fluctuation trajectory is known, and is not suitable for the multi-period optimization problem. In addition, the influence of branch power flow constraint under an extreme fluctuation trajectory is not considered in the model, and the transferability of backup needed for dealing with uncertainty of net load fluctuation cannot be guaranteed.
As one or more embodiments, in step 1, the mining and characterizing the net load extreme fluctuation trajectory from the two aspects of the net load fluctuation quantity and the fluctuation speed by describing the net load uncertainty at the time point by the interval in the embodiment includes:
and carrying out probability statistics on the fluctuation speed of the net load among the moments according to historical statistical data to obtain the probability distribution of the fluctuation speed of the net load among the moments.
Specifically, the historical power system statistics include: and performing data statistics on the upper fluctuation speed and the lower fluctuation speed of the net load at the extreme end of the net load at 24 moments on different typical days in different seasons to obtain a probability curve corresponding to the extreme fluctuation speed of the net load at the moments, and intercepting the extreme fluctuation speed of the net load at the moments under certain confidence coefficient according to different conservative degrees (requirements) of scheduling optimization decision makers for coping with the extreme fluctuation speed as input data.
In this embodiment, a normal distribution is taken as an example, as shown in fig. 5, and in other embodiments, other probability distributions may be used.
And intercepting the probability distribution according to the given confidence coefficient alpha, thereby obtaining the upper and lower fluctuation speeds of the net load limit under the given confidence coefficientUR max AndDR max the calculation is shown below.
Describing the net load uncertainty at a moment point by an interval, and depicting the net load fluctuation between the moments from two angles of fluctuation quantity and fluctuation speed to obtain an extreme fluctuation trajectory, which specifically comprises the following steps:
this embodiment assumes that the fluctuating trajectory of the payload is linear or piecewise linear.
Based on a given extreme fluctuation speed, four extreme fluctuation trajectories can be plotted: upper fluctuation trajectory corresponding to limit fluctuation amountsUpper fluctuation trajectory corresponding to limit fluctuation speeds2, lower fluctuation trajectory corresponding to limit fluctuation speeds3, and a lower fluctuation trajectory corresponding to the number of limit fluctuationss4, the specific information is shown in fig. 6.
If these four extreme fluctuation trajectory systems are all capable of handling, the system can handle net load fluctuations at any time.
When the net load is int-1, the end point on the uncertainty interval of the time is first in the limit speed directionUp wave and down wave at limit speed totWhen the upper end point of the uncertainty interval of the time is positioned, the absolute value of the upward fluctuation is maximum, namely the net load value corresponding to 5 points, so that the upward fluctuation trajectory corresponding to the limit fluctuation quantity can be determineds1. The trajectory pair systemt-1 totThe upper limit of the unit operation capacity between the moments is the most demanding.
When the net load is intThe lower end point of the uncertainty interval at the moment-1 fluctuates upward at the limiting speed and then fluctuates downward at the limiting speed totThe relative magnitude of the upward fluctuation is maximized at the upper end of the uncertainty interval of the time of day, i.e., the difference in net load between point 2 and point 6, from which the upper fluctuation trajectory corresponding to the limit fluctuation speed can be determineds2. The trajectory pair systemt-1 totThe uphill resource demand between moments is the most demanding.
When the net load is intThe end point on the uncertainty interval at the moment-1 fluctuates first downwards at the limiting speed and then upwards at the limiting speed totAt the lower end of the uncertainty interval of the time of day, the relative magnitude of the downward fluctuation is maximized, i.e., the difference in net load between point 1 and point 8, from which the lower fluctuation trajectory corresponding to the limit fluctuation speed can be determineds3. The trajectory pair systemt-1 totThe down hill climbing resource requirements between the moments are most demanding.
When the net load is intThe lower end point of the uncertainty interval at the moment-1 fluctuates downwards at the limiting speed and then upwards at the limiting speedtWhen the uncertainty interval of the time is lower than the end point, the absolute value of the downward fluctuation is the maximum, namely the net load value corresponding to 7 points, so that the downward fluctuation trajectory corresponding to the limit fluctuation quantity can be determineds4. The trajectory pair systemt-1 totThe lower limit of the unit operation capacity between the moments is the most strict.
And 2, step: constructing a resource scheduling optimization model by combining branch power flow constraints under various extreme fluctuation trajectories of net loads with the minimum total cost as an optimization target;
in step 2, the optimization goal of minimizing the total cost is specifically as follows:
the unit combination model considering the uncertainty of the time-interval net load fluctuation specifically expresses the following objective functions:
wherein the content of the first and second substances,C oper including the unit operating cost, start-up cost and shut-down cost,C res for up-regulation and down-regulation of system standby cost；C i,t (p i,t ,u i,t )Is composed ofFirst, theiThe operation cost function of each unit is expressed by a three-section piecewise linear function; in the formula (I), the compound is shown in the specification,i, trespectively indexing the unit and the optimization moment;N G the number of the units;N T the number of total time segments optimized for a study period;
、
are respectively a unitiThe start-up and shut-down costs of (1);p i,t as a unitiIn thattThe output of the time period;
、
the prices for standby are up and down respectively;
、
are respectively a unitiIn the first placetThe up and down standby number of the time interval;u i,t as a unitiIn a period of timetThe operation state of (1 for operation, 0 for shutdown);y i,t 、z i,t indicating machine setiIn a period of timetAnd whether to start or stop the binary variable.
The branch flow constraints under the various extreme fluctuation trajectories of the net load comprise: based on predicted value power balance constraint, power balance constraint under any fluctuation scene, climbing constraint considering uncertainty of net load fluctuation between moments, standby constraint, line capacity constraint and other constraints, the specific construction process is as follows:
1) Predicted value based power balance constraints:
equation (5) describes the total power balance of the system based on the predicted value, and equations (6) and (7) are given intAnd predicting the total wind power generation amount and the total expected demand of the load in a time period.
In the formula (I), the compound is shown in the specification,d,windexes of the load nodes and the fans are respectively;N D the number of load nodes;N W the number of the fans;
is a fanwIn thattA predicted output power for the time period;
is composed oftTime interval nodedA predicted value of the load on the system;
for wind power generationtA predicted output power for the time period;
is composed oftTotal predicted value of time interval load;
is net loaded attA predicted value of the time period.
2) Power balance constraint under arbitrary fluctuation scenarios:
equation (8) describes the power balancing at any time point in the optimization period under different fluctuation trajectories.
In the formula (I), the compound is shown in the specification,
is composed ofsUnit under fluctuation sceneiIn thattWithin a time periodτThe force at that moment;
is composed ofsUnder fluctuating conditions the load istWithin a time periodτThe demand of the moment;
is composed ofsWind power generation under fluctuating scenetWithin a time periodτGenerating capacity at a moment;
is composed ofsNet load under fluctuating scenariostWithin a time periodτThe demand of the moment;Tis the duration of a unit period, in hours.
3) Hill climbing constraints that take into account the uncertainty of the inter-time net load fluctuation:
as only four extreme fluctuation trajectories of the net load in a certain period are met, the system has the capability of coping with all possible fluctuations of the net load in the period, and the robust coping with the fluctuation of the net load in the period is realized.
Thus the index in equation (8)sGet onlys1、s2、s3、s4 four cases are enough.
a) For the tracks1, it needs to ensure that the power balance constraint of any point on the trajectory and the climbing constraint between any two points are satisfied. But since only the trajectory form of the linear variation is considered, only the power balance constraint at points 1, 5 and 3 and the hill climbing constraint from point 1 to point 5 and from point 5 to point 3 need to be considered.
The power balance at point 1 can be expressed as:
in the formula (I), the compound is shown in the specification,
to represents1 unit under fluctuating sceneiIn thatt-1 output at a time point;
to representt-a payload value corresponding to an upper limit of the payload interval at time 1;
to representt-a time of day payload prediction value;
to representt-the difference between the upper net load limit at time 1 and the predicted value, i.e. the amount of fluctuation in net load;
is at the same timet-1 time of day nodedTo take into account the amount of up-fluctuation of the net load.
The power balance at point 3 is similar to the expression at point 1, and only the subscript in equation (9) is usedt-1 is changed totAnd (4) finishing.
The power balance at point 5 can be expressed as:
wherein the content of the first and second substances,
to represents1 unit under fluctuating sceneiIn thattWithin a time periodτ1 * The force at the time point (i.e., point 5);
to represents1 in a fluctuating scenetWithin a time periodτ1 * Net load value at time point 5;
to representtThe net load limit climb speed within a time period;
represents1 in a fluctuating scene fromt-the duration of point 1 to point 5 at time 1,
can be calculated from the following formula:
wherein the content of the first and second substances,
to representtThe net load limit climb-down speed over a period of time. In the formula (11)
The variable is a unique variable and can be conveniently calculated in advance.
The unit ramp constraints from point 1 to point 5 can be expressed as follows:
wherein the content of the first and second substances,UR i andDR i respectively indicating unitsiUp-and down-climb speed of (a).
The unit ramp constraints from point 5 to point 3 may be expressed as follows:
wherein the content of the first and second substances,
to represents1 unit under fluctuating sceneiIn thattThe force applied at the moment.
In addition, in the case of the present invention,sthe output of each generator when the power at points 1, 5 and 3 is balanced under the fluctuation scene 1 is also limited by the capacity thereof, which is specifically expressed as follows:
wherein the content of the first and second substances,
and
respectively indicating unitsiLower and upper limits of the output.
b) For the tracks2, the power balance constraint of any point on the trajectory and the climbing constraint between any two points need to be ensured. But since only the trajectory form of the linear variation is considered, only the power balance constraint at points 2, 6 and 3 and the hill climbing constraint from point 2 to point 6 and from point 6 to point 3 need to be considered.
The power balance at point 2 can be expressed as:
in the formula (I), the compound is shown in the specification,
represents2 Unit under fluctuating sceneiIn thatt-1 output at a time point;
to representt-a payload value corresponding to a lower limit of the payload interval at time 1;
to representtThe difference between the lower net load limit at time 1 and the predicted value, i.e. the amount of fluctuation under net load.
The power at point 3 is balanced ats1, already listed under the fluctuation scene, will not be described again.
The power balance at point 6 can be expressed as:
wherein, the first and the second end of the pipe are connected with each other,
to represents2 unit under fluctuating sceneiIn thattWithin a time periodτ2 * The force at the time point (i.e., point 6);
represents2 in a fluctuating scenetWithin a time periodτ2 * Net load value at time point 6;
to represents2 in a fluctuating scenario fromt-the duration of point 2 to point 6 at time 1,
can be calculated from the following formula:
in the formula (I), the compound is shown in the specification,
the variable is a unique variable and can be conveniently calculated in advance.
The unit ramp constraints from point 2 to point 6 may be expressed as follows:
the unit ramp constraints from point 6 to point 3 may be expressed as follows:
wherein the content of the first and second substances,
to represents2 unit under fluctuating sceneiIn thattThe force applied at the moment.
sThe output of each generator when the power at points 2, 6 and 3 is balanced under the fluctuation scene is limited by the capacity constraint, and the specific expression is as follows:
c) For the tracks3, the power balance constraint of any point on the trajectory and the climbing constraint between any two points need to be ensured. But since only the trajectory form of the linear variation is considered, only the power balance constraint at points 1, 8 and 4 and the hill climbing constraint from point 1 to point 8 and from point 8 to point 4 need to be considered.
The power at point 1 is balanced ats1 waveThe dynamic scenarios are listed and will not be described in detail.
The power balance at point 4 can be expressed as:
in the formula (I), the compound is shown in the specification,
to represents3 unit under fluctuating sceneiIn thattThe force of the time point;
to representtThe time corresponds to the payload value of the lower limit of the payload interval.
The power balance at point 8 can be expressed as:
wherein the content of the first and second substances,
to represents3 unit under fluctuating sceneiIn thattWithin a time periodτ3 * The force at the time point (i.e., point 8);
to represents3 in a fluctuating scenetWithin a time periodτ3 * The net load value at time point 8;
to represents3 under the fluctuation scenet-the duration of point 1 to point 8 at time 1,
can be calculated from the following formula:
in the formula (I), the compound is shown in the specification,
the variable is a unique variable and can be conveniently calculated in advance.
The unit ramp constraints from point 1 to point 8 may be expressed as follows:
wherein the content of the first and second substances,
to represents3 unit under fluctuating sceneiIn thatt-force at time 1.
The unit ramp constraint from point 8 to point 4 can be expressed as follows:
s3 the output of each generator is limited by the capacity constraint when the power at the points 1, 8 and 4 is balanced under the fluctuation scene, and the specific expression is as follows:
d) For the tracks4, the power balance constraint of any point on the trajectory and the climbing constraint between any two points need to be ensured. But since only the trajectory form of the linear variation is considered, only the power balance constraint at points 2, 7 and 4 and the hill climbing constraint from point 2 to point 7 and from point 7 to point 4 need to be considered.
The power at point 2 is balanced ats2, which have already been listed in the fluctuation scenario, are not described in detail.
The power at point 4 is balanced ats3, already listed under the fluctuation scene, are not described in detail.
The power balance at point 7 can be expressed as:
in the formula (I), the compound is shown in the specification,
to represents4 unit under fluctuating sceneiIn thattWithin a time periodτ1 * The force at the time point (i.e., point 7);
represents4 fluctuation scenetWithin a time periodτ1 * Net load value at time point 7;
to represents4 under the condition of fluctuationt-the duration of point 2 to point 7 at time 1,
can be calculated from the following formula:
in the formula (I), the compound is shown in the specification,
the unique variable can be conveniently calculated in advance.
The unit ramp constraints from point 2 to point 7 may be expressed as follows:
wherein the content of the first and second substances,
to represents4 unit under fluctuating sceneiIn thatt-force at time 1.
The unit ramp constraints from point 7 to point 4 may be expressed as follows:
wherein the content of the first and second substances,
to represents4 unit under fluctuating sceneiIn thattThe force applied at the moment.
sThe output of each generator when the power at the points 2, 7 and 4 is balanced under the condition of 4 fluctuations is limited by the capacity of the generator, and the specific expression is as follows:
4) Standby constraint:
the formula (32) gives the unitiIn thattFor the provision of an up-regulation in the time interval for the handling of a net load fluctuation, equation (33) describes the unitiIn thattAnd the time interval is a down standby for coping with the configuration of the net load fluctuation.
5) And (3) line capacity constraint:
the line capacity constraint based on the expected payload is expressed as follows:
in the formula (I), the compound is shown in the specification,lindexing the line;
as a linelThe capacity of (a);
、
a power generation load transfer factor.
The line capacity constraint under any fluctuation scenario is expressed as follows:
in the formula (I), the compound is shown in the specification,
is composed ofsUnder fluctuating scenetWithin a time periodτTime linelThe trend of (2);
as a unitiIn thatsUnder fluctuating scenetWithin a time periodτThe output of the machine set at any moment;
to representsUnder fluctuating scenetWithin a time periodτTime nodedThe net load demand of (c).
As long as the equations (35-36) are in extreme fluctuation scenarioss1、s2、 s 3 andsand 4, the requirement is met, so that the power flow transmitted by the line at any time under any fluctuation trajectory can be ensured not to exceed the limit value of the line capacity.
6) Other constraints are:
the proposed model also contains a unit start-stop logic constraint, a minimum start-stop time constraint, an initial state constraint, a unit output power constraint, etc., which are all taken into account in the model.
For the sake of brevity, detailed description is omitted, and references Wang Shibai, han Xueshan, yang Ming, li Benxin and Zhu Xingxu are specifically expressed, power system interval economic dispatch [ J ] taking intermittent characteristics into account, china motor engineering report, 2016,36 (11): 2966-2977.
And step 3: and solving the resource scheduling optimization model to obtain an optimization scheme of resource scheduling.
Similar to the conventional unit combination model, the unit combination model provided in this embodiment, which carefully considers uncertainty of fluctuation of a payload between times, still belongs to a Mixed Integer Linear Programming (MILP) problem, and the solving method is similar to the conventional unit combination solving method.
For example, a conventional commercial solver may be used, or classical heuristic algorithms such as simulated annealing, genetic algorithms, and neural networks may be used.
In order to verify the effectiveness of the model provided by the invention, calculation is carried out by taking an IEEE-RTS single-region system as an example. The IEEE-RTS single-region system comprises 24 nodes, 26 generator sets and 38 transmission lines, wherein 17 nodes are connected with loads, and the peak load is 2550MW. The parameters and load data of 26 generator sets are described in the literature. The unit starting cost and line data reference documents adopt a three-section type to linearize the unit cost in a sectional mode, and 10% of the highest marginal cost of each unit is taken as respective up-regulation and down-regulation standby prices. Six fans are added in the single-area system and are respectively positioned on nodes 3, 10, 13, 14, 15 and 18 of the system, and the capacity of each fan is 100 MW. For convenience, the output power curve of the fan has the same shape as the load demand curve.
The load disturbance interval is assumed to be +/-5% of the predicted node load value, and the wind power disturbance interval is +/-10% of the predicted wind power value. The total disturbance range of a node is the superposition of the load on the node and the wind-power disturbance interval. The limit fluctuation speed of the time-of-day payload is obtained based on historical statistical data. Here, the limit fluctuation speed of the net load between two sets of time instants is given. The first set of diagonal lines, each having a net load fluctuation speed between time points greater than the net load fluctuation amount between time points, are shown by line 1 in fig. 7. The net load limit fluctuation velocities in the second group of time intervals are all 500 MW/h, as shown by line 2 in fig. 7, which is greater than the limit fluctuation velocity corresponding to the diagonal of the net load fluctuation amount in some time intervals and less than the limit fluctuation velocity corresponding to the diagonal of the net load fluctuation amount in some time intervals.
The computer is configured to be a Win10 system, an Intel Core i5-11400k series, a main frequency of 3.0GHz, a memory of 8G and a dual gap of 0.1 percent.
To compare the differences between the different models, consider the following 3 cases:
case a: according to the traditional unit combination model, the form from a predicted value to a predicted value is considered in the time-interval net load transition process, namely the uncertainty of the time-interval net load fluctuation is not considered.
Case B: in the traditional unit combination model, the transition process of the time-interval net load adopts an extreme fluctuation trajectory based on a diagonal line, namely, the uncertainty of the time-interval net load fluctuation is considered.
Case C: the unit combination model adopts four extreme fluctuation trajectories for the time-interval net load fluctuation trajectory. Further subdivision, only scenes with the most severe requirements on climbing resources are considereds2、s3 is referred to as case C1; the model that considers four extreme fluctuation trajectories simultaneously is referred to as case C2.
When the first set of time-interval net load limit fluctuation speed information, i.e., the limit fluctuation speed corresponding to the black curve in fig. 7, is used, the above three cases are optimized, and the various costs are shown in table 1.
TABLE 1 cost comparison of the three models
As can be seen from table 1, the total cost for case B rose from $530273.2 to $538098.9, increasing by 1.47% compared to case a. This is because case B considers the extreme fluctuation of the diagonal-based inter-time payload, whereas case a considers only the inter-time payload transition from predictor to predictor. Under the condition that the net load fluctuation amount at the time point is the same, case B considers the uncertainty of the net load fluctuation between the times, and puts higher requirements on the climbing capacity of the unit, so that the operation cost of the system is increased, and the total cost is increased.
Both case B and case C1 take the net load limit fluctuation speed of time into account, and the net load limit fluctuation speed is selected differently, so that the limit fluctuation speed of case C1 is larger. As can be seen from table 1, the standby cost and the operating cost of case C1 increase, the start-stop cost decreases, and the total cost of case C1 increases by 2.71% compared to case B. This is because case C1 considers a scene of a severe net load fluctuation between times, has a more stringent requirement on the climbing capability of the unit, and needs to start more units and configure more spares, thereby causing the operation base point of the unit to further deviate from the most economical operation point, and causing both the system operation cost and the spare cost to increase. More units are always in an operating state, the number of times of starting and stopping the units is reduced, the starting and stopping cost of the system is reduced to some extent, and the total cost of the system is still obviously increased.
Case C2 is based on case C1, and simultaneously takes into account the extreme fluctuation scenario of the payload between four times, that is, more severe requirements are imposed on the unit climbing capability and the unit commissioning capacity. Therefore, more spare equipment needs to be reserved, the operation base point of the unit is further deviated from the most economic operation point, and the operation cost, the spare cost and the total cost of the unit are obviously increased.
In order to highlight the superiority of the model presented herein, a detailed comparison was developed for the case B and case C1 models, using a second set of time-interval payload limit fluctuation speed information, i.e., the limit fluctuation speed corresponding to curve 2 in fig. 7.
The total cost at each time after optimization is shown in fig. 8.
As can be seen from fig. 8, the single-session cost of case B is greater than the single-session cost of case C1 at some times, while the single-session cost of case B is less than the single-session cost of case C1 at other times. Further analysis was performed on the 6 th and 7 th periods as representative, and the results of the 6 th and 7 th periods are shown in tables 2 and 3, respectively.
TABLE 2 various cost comparisons of the two models at time 6
TABLE 3 various cost comparisons for the two models at time 7
During period 6, the time-to-time payload transitions from 1327.5 MW to 1350.0 MW. Case B takes the diagonal line of net load fluctuation as the extreme fluctuation trajectory, and the extreme upper and lower fluctuation speeds are 253.1 MW/h and 208.1MW/h, respectively, while case C1 has an extreme fluctuation speed of 500 MW/h.
It can be seen that case C1 should cope with the more severe time-interval payload fluctuation, and more spares need to be configured, and the total cost of case C1 during this time interval is higher than that of case B. At this time, the extreme fluctuation trajectory of the payload determined artificially according to the diagonal line in case B cannot cope with some extreme fluctuation scenes of the actual payload, and the standby resources configured by the system are insufficient, thereby threatening the safe operation of the system.
During period 7, the time-to-time payload transitions from 1350.0 MW to 1665.0 MW. Case B takes the diagonal line of net load fluctuation as the extreme fluctuation trajectory, and the extreme upper and lower fluctuation speeds are 574.7 MW/h and 55.38 MW/h, while the extreme fluctuation speed of case C is 500 MW/h. It can be seen that case B needs to cope with the more severe time-interval payload fluctuation, and needs to be configured with more spares, and the total cost of the case B system is higher than that of case C in the time interval. In case B, the limit fluctuation speed corresponding to the extreme trajectory determined manually according to the diagonal line is significantly higher than the actual limit fluctuation speed of the payload, and the spare resource redundancy configured by the system reduces the economic level of the system operation.
In summary, in case B, a diagonal line is used as an extreme fluctuation scene of the net load, which is too conservative or too aggressive, and the uncertainty of the fluctuation of the net load between times cannot be reasonably reflected, so that the climbing capacity in the system is insufficient or excessive, and the safe and economic operation level of the system is reduced. The model provided by the text uses the net load limit fluctuation speed obtained based on historical statistical data, the uncertainty of the net load fluctuation between moments can be described more precisely, the unit climbing capacity required by the net load fluctuation is configured reasonably, and the safe and economic operation of the power system is ensured.
The net load limit fluctuation speed at a time has a remarkable influence on the climbing capability requirement of the unit in the system, and further the total cost of the system is influenced. The trend of the total cost and the spare cost at different net load limit ramp rates is shown in fig. 9.
As can be seen from fig. 9, as the speed of the net load limit fluctuation increases, the system needs to allocate more spare resources to cope with the increase, and the spare cost and the total cost in the system increase. When the net load limit surge speed increases from 520 MW/h to 560 MW/h, the system requires a new set of switches to increase the backup supply to cope with the net load surge, which can cause a significant increase in the total and backup costs.
Line capacity constraints can affect the capacity of the spare transport that a unit within the system configures to account for the uncertainty in the net load fluctuations between times. For this reason, taking case C2 as an example, the case without considering the line capacity constraint under any fluctuation scenario, i.e. constraints (35) - (36), is referred to as case C2 relax Thereafter, case C2 and case C2 were compared relax The result of (1). To analyze the effects of line capacity, the line capacity is multiplied by a scaling factor. As the coefficient gradually increased from 0.7 to 1.0, the total cost of the two models varied as shown in fig. 10.
As can be seen from fig. 10, for any one of the curves, when the line capacity is from 0.7
Gradually increases to 0.8
The total cost of both models is gradually reduced. This reflects the limiting effect of line capacity constraints on the optimal configuration of system resources. When the circuit is onCapacity from 0.8
Gradually increase to 0.9
Case C2 relax The line capacity in the model is in a relaxed state and the total cost of the system no longer changes, while the total cost of case C2 continues to decrease gradually. This reflects that line capacity constraints can affect the spare capacity of units within the system configured to handle the uncertainty of the inter-time payload fluctuation. Comparing the two lines in fig. 10, it can be seen that the total cost of case C2 is always higher than that of case C2 relax The difference of the total cost of the system reflects the limit of the line capacity constraint on the resource optimization configuration under the fluctuation scene. That is, the line capacity constraint in the fluctuation scenario is not considered, the obtained result is too optimistic, and the scheduling result may cause line out-of-limit in some extreme scenarios, and the safe operation of the system is jeopardized.
The calculation times of the proposed model and the existing models are shown in table 4. As can be seen from table 4, the computation times for all models are of the same order of magnitude. The model provided by the invention only upgrades the original climbing constraint, and the complexity of the model is not obviously increased.
TABLE 4 comparison of calculated times for the three models
Aiming at the uncertainty of the net load fluctuation between moments, the invention excavates and describes the extreme fluctuation trajectory of the net load from two angles of the net load fluctuation quantity and the fluctuation speed, and provides a unit combination model for carefully considering the uncertainty of the net load fluctuation between the moments. In the model, the net load uncertainty at a time point is described by intervals, and four net load extreme fluctuation trajectories are described for the net load fluctuation at time. If the system can cope with the four extreme trajectories, the system can guarantee robust coping to the time-interval net load fluctuation. The net load limit fluctuation speed is obtained based on historical statistical data, and the problem that the net load limit fluctuation speed value is too subjective at moments based on diagonal lines is solved. Meanwhile, branch power flow constraints under various net load extreme fluctuation trajectories are also brought into the model, and the standby transferability required by net load fluctuation is ensured. Finally, based on an IEEE-RTS24 node example, the validity of the proposed model is verified.
Example two
The present embodiment provides a resource scheduling optimization system considering an inter-time payload uncertainty, including:
the net load extreme fluctuation trajectory delineating module is used for acquiring historical power system statistical data, describing the net load uncertainty at a moment point in intervals, and delineating the net load extreme fluctuation trajectory from the two aspects of net load fluctuation quantity and fluctuation speed;
the resource scheduling optimization model building module is used for building a resource scheduling optimization model by combining branch power flow constraints under various net load extreme fluctuation trajectories with the minimum total cost as an optimization target;
and the resource scheduling optimization model solving module is used for solving the resource scheduling optimization model to obtain an optimization scheme of resource scheduling.
The method for mining and describing the net load uncertainty at the time point by the interval comprises the following steps of:
carrying out probability statistics on the fluctuation speed of the net load between the moments based on historical power system statistical data to obtain probability distribution of the fluctuation speed of the net load between the moments;
intercepting the probability distribution according to a given confidence coefficient to obtain the upper and lower fluctuation speeds of a net load limit under the given confidence coefficient;
and describing the net load uncertainty at the time point by intervals, and describing an upper fluctuation trajectory and a lower fluctuation trajectory corresponding to the limit fluctuation quantity and an upper fluctuation trajectory and a lower fluctuation trajectory corresponding to the limit fluctuation speed on the basis of the upper fluctuation speed and the lower fluctuation speed of the net load limit and the quantity of the net load fluctuation.
The method for describing the net load uncertainty at the time point by the interval and describing the upper and lower fluctuation loci corresponding to the limit fluctuation quantity and the upper and lower fluctuation loci corresponding to the limit fluctuation speed on the basis of the upper and lower fluctuation speeds of the net load limit and the net load fluctuation quantity comprises the following steps:
when the net load is intThe end point on the uncertainty interval at the moment-1 fluctuates upward at the limiting speed and then fluctuates downward at the limiting speed totWhen the upper end point of the uncertainty interval of the moment is reached, the absolute numerical value of the upward fluctuation is maximum, and an upward fluctuation trajectory corresponding to the limit fluctuation quantity is determined;
when the net load is intThe lower endpoint of the uncertainty interval at time 1 fluctuates upward at a limit speed and then downward at the limit speed totWhen the upper end point of the uncertainty interval of the moment is reached, the relative value of the upward fluctuation is maximum, and an upper fluctuation trajectory corresponding to the limit fluctuation speed is determined;
when the net load is intThe end point on the uncertainty interval at the moment-1 fluctuates first downwards at the limiting speed and then upwards at the limiting speed totWhen the lower end point of the uncertainty interval of the moment is reached, the relative numerical value of downward fluctuation is maximum, and a lower fluctuation trajectory corresponding to the limit fluctuation speed is determined;
when the net load is intThe lower end point of the uncertainty interval at the moment-1 fluctuates downwards at the limiting speed and then upwards at the limiting speedtAnd when the lower end point of the uncertainty interval of the time is reached, the absolute numerical value of the downward fluctuation is maximum, and a lower fluctuation trajectory corresponding to the limit fluctuation quantity is determined.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A resource scheduling optimization method considering uncertainty of time-to-time net load is characterized by comprising the following steps:
acquiring historical power system statistical data, describing the uncertainty of the net load at a moment point by intervals, and describing two aspects of net load fluctuation quantity and fluctuation speed to obtain an extreme fluctuation trajectory of the net load;
the method for describing the net load uncertainty at the time point by the interval, and mining and describing the net load extreme fluctuation trajectory from the two aspects of the net load fluctuation quantity and the fluctuation speed comprises the following steps:
carrying out probability statistics on the fluctuation speed of the net load between the moments based on historical power system statistical data to obtain probability distribution of the fluctuation speed of the net load between the moments;
intercepting the probability distribution according to a given confidence coefficient to obtain the upper and lower fluctuation speeds of the net load limit under the given confidence coefficient;
describing the uncertainty of the net load at a time point by intervals, and describing an upper fluctuation trajectory and a lower fluctuation trajectory which correspond to the limit fluctuation quantity and an upper fluctuation trajectory and a lower fluctuation trajectory which correspond to the limit fluctuation speed on the basis of the upper fluctuation speed and the lower fluctuation speed of the net load limit and the fluctuation quantity of the net load;
constructing a resource scheduling optimization model by taking the minimum total cost as an optimization target and combining branch flow constraints under various extreme fluctuation trajectories of net loads;
and solving the resource scheduling optimization model to obtain an optimization scheme of resource scheduling.
2. The method according to claim 1, wherein the describing the payload uncertainty at the time point in intervals, and describing the upper and lower fluctuation trajectories corresponding to the limit fluctuation amount and the upper and lower fluctuation trajectories corresponding to the limit fluctuation amount based on the upper and lower fluctuation speeds of the payload limit and the payload fluctuation amount, comprises:
when the upper end point of the uncertainty interval of the net load at the time t-1 fluctuates upwards at the limit speed, and then fluctuates downwards at the limit speed to the upper end point of the uncertainty interval at the time t, the absolute value of the upward fluctuation is maximum, and an upper fluctuation trajectory corresponding to the limit fluctuation quantity is determined;
when the lower endpoint of the uncertainty interval of the net load at the time t-1 fluctuates upwards at the limit speed and then fluctuates downwards at the limit speed to the upper endpoint of the uncertainty interval at the time t, the relative value of the upward fluctuation is maximum, and an upper fluctuation trajectory corresponding to the limit fluctuation speed is determined;
when the upper end point of the uncertainty interval of the net load at the time t-1 fluctuates downwards at the limit speed firstly and then fluctuates upwards at the limit speed to the lower end point of the uncertainty interval at the time t, the relative value of the downward fluctuation is maximum, and a lower fluctuation trajectory corresponding to the limit fluctuation speed is determined;
when the lower endpoint of the uncertainty interval of the net load at the time t-1 fluctuates downwards at the limit speed firstly and then fluctuates upwards at the limit speed to the lower endpoint of the uncertainty interval at the time t, the absolute value of the downward fluctuation is maximum, and a lower fluctuation trajectory corresponding to the limit fluctuation quantity is determined.
3. The method of claim 1, wherein the optimization objective is the minimum of the operation cost, the startup cost, the shutdown cost of the unit, and the up-regulation and down-regulation standby cost of the system, and the function of the optimization objective is:
wherein the Coper comprises the operation cost, the starting cost and the shutdown cost of the unit, and Cres is the up-regulation and down-regulation standby cost of the system; ci, t (pi, t, ui, t) is an operation cost function of the ith unit, and i and t are indexes of the unit and the optimization moment respectively; NG is the number of units; NT is in one study periodThe optimized total time period number;
、
the starting cost and the shutdown cost of the unit i are respectively; pi and t are the output of the unit i in the time period t;
、
the prices for standby are respectively up-regulated and down-regulated;
、
respectively setting the up and down standby number of the unit i in the t time period; ui and t are the running states of the unit i in the time period t; yi, t, zi, t represent the binary variable whether the unit i starts or stops in the time period t.
4. The method of claim 1, wherein the branch power flow constraints under the various extreme fluctuation trajectories of the payload include: power balance constraints based on predicted values, power balance constraints under any fluctuation scene, climbing constraints considering uncertainty of net load fluctuation between moments, standby constraints and line capacity constraints.
5. The method according to claim 4, wherein the constructing of the hill climbing constraint considering uncertainty of time-of-day payload fluctuation comprises:
and based on the upper and lower fluctuation trajectories corresponding to the limit fluctuation quantity and the upper and lower fluctuation trajectories corresponding to the limit fluctuation speed, obtaining a power balance constraint expression at a corresponding time point and a climbing constraint expression at different times by taking the power balance constraint at any point on the trajectories and the climbing constraint between any two points as targets.
6. The method of claim 1, wherein the historical power system statistics comprise generator set parameters and load data, transmission line data, and node data.
7. A resource scheduling optimization system that considers an inter-time payload uncertainty, comprising:
the net load extreme fluctuation trajectory delineating module is used for acquiring historical power system statistical data, describing the net load uncertainty at a moment point in intervals, and delineating the net load extreme fluctuation trajectory from the two aspects of net load fluctuation quantity and fluctuation speed;
the method for describing the net load uncertainty at the time point by the interval, and mining and describing the net load extreme fluctuation trajectory from the two aspects of the net load fluctuation quantity and the fluctuation speed comprises the following steps:
carrying out probability statistics on the fluctuation speed of the net load between the moments based on historical power system statistical data to obtain probability distribution of the fluctuation speed of the net load between the moments;
intercepting the probability distribution according to a given confidence coefficient to obtain the upper and lower fluctuation speeds of a net load limit under the given confidence coefficient;
describing the uncertainty of the net load at a time point by intervals, and describing an upper fluctuation trajectory and a lower fluctuation trajectory which correspond to the limit fluctuation quantity and an upper fluctuation trajectory and a lower fluctuation trajectory which correspond to the limit fluctuation speed on the basis of the upper fluctuation speed and the lower fluctuation speed of the net load limit and the fluctuation quantity of the net load;
the resource scheduling optimization model building module is used for building a resource scheduling optimization model by combining branch power flow constraints under various net load extreme fluctuation trajectories with the minimum total cost as an optimization target;
and the resource scheduling optimization model solving module is used for solving the resource scheduling optimization model to obtain an optimization scheme of resource scheduling.
8. The resource scheduling optimization system considering the time-to-time payload uncertainty as claimed in claim 7, wherein the step of describing the payload uncertainty at the time-to-time points in intervals, mining and characterizing the payload extreme fluctuation trajectory from the aspects of the payload fluctuation quantity and the fluctuation speed comprises:
carrying out probability statistics on the fluctuation speed of the net load between the moments based on historical power system statistical data to obtain probability distribution of the fluctuation speed of the net load between the moments;
intercepting the probability distribution according to a given confidence coefficient to obtain the upper and lower fluctuation speeds of a net load limit under the given confidence coefficient;
and describing the net load uncertainty at the time point by intervals, and describing an upper fluctuation trajectory and a lower fluctuation trajectory corresponding to the limit fluctuation quantity and an upper fluctuation trajectory and a lower fluctuation trajectory corresponding to the limit fluctuation speed on the basis of the upper fluctuation speed and the lower fluctuation speed of the net load limit and the quantity of the net load fluctuation.
9. The resource scheduling optimization system considering the uncertainty of the payload between the time instants as claimed in claim 7, wherein the describing the uncertainty of the payload at the time instants in intervals, and based on the upper and lower fluctuation speeds of the payload limit and the amount of the payload fluctuation, the upper and lower fluctuation trajectories corresponding to the amount of the limit fluctuation and the upper and lower fluctuation trajectories corresponding to the amount of the limit fluctuation are obtained by plotting, comprising:
when the upper end point of the uncertainty interval of the net load at the time t-1 fluctuates upwards at the limit speed, and then fluctuates downwards at the limit speed to the upper end point of the uncertainty interval at the time t, the absolute value of the upward fluctuation is maximum, and an upper fluctuation trajectory corresponding to the limit fluctuation quantity is determined;
when the lower endpoint of the uncertainty interval of the net load at the time t-1 fluctuates upwards at the limit speed and then fluctuates downwards at the limit speed to the upper endpoint of the uncertainty interval at the time t, the relative value of the upward fluctuation is maximum, and an upper fluctuation trajectory corresponding to the limit fluctuation speed is determined;
when the upper end point of the uncertainty interval of the net load at the time t-1 fluctuates downwards at the limit speed firstly and then fluctuates upwards at the limit speed to the lower end point of the uncertainty interval at the time t, the relative value of the downward fluctuation is maximum, and a lower fluctuation trajectory corresponding to the limit fluctuation speed is determined;
when the lower endpoint of the uncertainty interval of the net load at the time t-1 fluctuates downwards at the limit speed firstly and then fluctuates upwards at the limit speed to the lower endpoint of the uncertainty interval at the time t, the absolute value of the downward fluctuation is maximum, and a lower fluctuation trajectory corresponding to the limit fluctuation quantity is determined.
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