CN112054504B - Wind power-containing power system economic dispatching method based on improved affine spare allocation - Google Patents

Wind power-containing power system economic dispatching method based on improved affine spare allocation Download PDF

Info

Publication number
CN112054504B
CN112054504B CN201910492478.3A CN201910492478A CN112054504B CN 112054504 B CN112054504 B CN 112054504B CN 201910492478 A CN201910492478 A CN 201910492478A CN 112054504 B CN112054504 B CN 112054504B
Authority
CN
China
Prior art keywords
power
wind power
conventional unit
wind
standby
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201910492478.3A
Other languages
Chinese (zh)
Other versions
CN112054504A (en
Inventor
唐程辉
张凡
薛松
马莉
胡源
梁才
廖建辉
杨素
曲昊源
张晓萱
宋海旭
张笑峰
李景
徐杨
宋海云
范孟华
陈珂宁
林晓斌
高国伟
武泽辰
赵铮
冯昕欣
李维
李睿
李晓冬
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
State Grid Energy Research Institute Co Ltd
Original Assignee
State Grid Energy Research Institute Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by State Grid Energy Research Institute Co Ltd filed Critical State Grid Energy Research Institute Co Ltd
Priority to CN201910492478.3A priority Critical patent/CN112054504B/en
Publication of CN112054504A publication Critical patent/CN112054504A/en
Application granted granted Critical
Publication of CN112054504B publication Critical patent/CN112054504B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)
  • Control Of Eletrric Generators (AREA)

Abstract

The invention discloses an improved affine spare allocation-based economic dispatching method for a wind power-containing power system, which comprises the steps of determining parameters of a generator set, line parameters and a wind power scene in the system; based on an improved affine standby method, determining the actual standby power of a conventional unit after the real-time actual wind power is obtained by taking the difference between the actual wind power and the wind power dispatching power as a reference, and establishing an economic dispatching model of the power system; and determining and outputting a scheduling result of the conventional unit based on the linear programming solver solution model. The method comprises the steps of determining the actual reserve power of the conventional unit after the real-time actual wind power is obtained by taking the difference between the actual wind power and the wind power dispatching power as a reference, establishing an economic dispatching model of the power system, determining and outputting a dispatching result of the conventional unit based on a solving model of a linear programming solver, wherein the dispatching cost of the overall system is obviously lower than that of the conventional affine reserve allocation method.

Description

Wind power-containing power system economic dispatching method based on improved affine spare allocation
Technical Field
The invention relates to the technical field of operation and control in a power system, in particular to an improved affine spare allocation-based economic dispatching method for a power system containing wind power.
Background
The power system scheduling operation comprises static economic scheduling and dynamic economic scheduling. The dynamic economic dispatching considers the mutual influence among all time periods, can reflect the operation requirement of the system more practically, at present, a lot of related researches are carried out, wind energy is used as an important renewable energy source, and the research on the dynamic economic dispatching problem containing the grid-connected wind power plant is an important problem; meanwhile, the wind energy is different from conventional energy sources such as thermal power and the like, and the wind energy has intermittency and unpredictability different from those of a conventional unit, so that difficulty and challenge are brought to the problem. Certain standby power needs to be reserved in the power system to stabilize the randomness of the wind power output. Specifically, a part of standby power of the conventional set is reserved in economic dispatching, and after real-time actual wind power is obtained, deviation of wind power output is balanced by calling the standby power of the conventional set.
The affine standby distribution method is a classical method considering wind power actual distribution. In the affine standby allocation method, the difference value between the actual wind power and the predicted wind power is usually used as a reference, the unbalanced power of the wind power is allocated to each conventional unit through corresponding participation factors, and if the actual wind power is greater than the predicted wind power, downward standby of the system is needed, that is, the conventional unit needs to reduce the output to call the reserved standby power. If the actual wind power is less than the predicted wind power, upward standby of the system is needed, that is, the conventional generator set needs to increase output to call the reserved standby power. In a worse case scenario, if the system backup fails to balance the system power imbalance caused by the randomness of the wind power output, it may result in wind curtailment or load shedding due to underestimation and overestimation of the wind power output. However, the social cost difference between the abandoned wind and the load shedding is large, so that the scheduling result obtained by the traditional method only taking the difference between the actual wind power and the predicted wind power as the reference is not an economic optimal solution.
Disclosure of Invention
In order to solve the technical problem, the technical scheme adopted by the invention is to provide an improved affine spare allocation-based economic dispatching method for a wind power-containing power system, which comprises the following steps of:
determining parameters, line parameters and wind power scenes of a generator set in the system;
based on an improved affine standby method, determining the actual standby power of a conventional unit after the real-time actual wind power is obtained by taking the difference between the actual wind power and the wind power dispatching power as a reference, and establishing an economic dispatching model of the power system;
and determining and outputting a scheduling result of the conventional unit based on the linear programming solver solution model.
In the above method, the parameters of the generator set in the system include: upper and lower output limits, fuel cost coefficients, reserve cost coefficients, maximum upward and downward ramp power, and maximum upward and downward reserve capacity;
the line parameters include: topological structure, maximum transmission capacity and direct current power flow distribution coefficient.
In the above method, the objective function of the economic dispatch problem is:
Figure BDA0002087494900000021
wherein f is the total cost of the system; f. ofcModeling is carried out in the first stage for the total cost of the conventional unit of the system, and a variable p is decided by the her-and-nowi,t、ru,i,tAnd rd,i,tDetermining; p is a radical ofi,tScheduling power r of a conventional unit i in a scheduling period tu,i,tAnd rd,i,tRespectively the upward standby power and the downward standby power of the conventional unit i in a scheduling period t;
futhe cost of the randomness of the system caused by the randomness of the wind power is all corresponding to the second stage, and the cost is changed by wait-and-see variable
Figure BDA0002087494900000022
It is decided that,
Figure BDA0002087494900000023
is a random variable of wind power output.
In the above method, the first-stage modeling is specifically as follows:
the total cost of the conventional unit of the system can be obtained by the following formula:
Figure BDA0002087494900000031
wherein T is the number of scheduling periods in the scheduling time domain, where T is 1,2 … T; i is the number of conventional units in the system, I is 1,2 … I; bf,iAnd cf,iPrimary term and constant term coefficients of the fuel cost of the conventional unit i are respectively; c. Cur,iAnd cdr,iRespectively reserving cost coefficients for the upward standby and the downward standby of the conventional unit i;
the constraint conditions are as follows:
power constraint after output accumulation standby constraint of conventional unit
Figure BDA0002087494900000032
Figure BDA0002087494900000033
Second, the upper limit of the reserve capacity of the conventional unit is restricted
Figure BDA0002087494900000034
Figure BDA0002087494900000035
Third, climbing restraint of conventional unit
Figure BDA0002087494900000036
Figure BDA0002087494900000037
Power balance constraint
Figure BDA0002087494900000038
Wind power random performance is constrained by the relationship between the upper and lower output limits corresponding to the system standby balance and the system standby
Figure BDA0002087494900000039
Figure BDA00020874949000000310
Sixthly, the random performance of the wind power is constrained by the upper and lower output limits corresponding to the standby balance of the system
Figure BDA0002087494900000041
In the formula,
Figure BDA0002087494900000042
andp irespectively representing the upper limit and the lower limit of the output of the conventional unit i;
Figure BDA0002087494900000043
and
Figure BDA0002087494900000044
upper limits for upward and downward standby of the conventional unit i, respectively;
Figure BDA0002087494900000045
and
Figure BDA0002087494900000046
the maximum upward and downward climbing power of the conventional unit i are respectively;
wtscheduling power for wind power, LtPredicting power for the system under the scheduling period t;
Figure BDA0002087494900000047
the upper limit of the output force is as follows,w tthe lower limit of the output is;
wris the installed capacity of wind power.
In the above method, the second stage modeling is specifically as follows:
the wind power randomness cost can be obtained by the following formula:
E[fu(wt)]＝cwcEwc+clsEls
in the formula, wherein fu(wt) Penalty cost expectation for second stage wind power randomness；EwcAnd ElsRespectively obtaining expected power values of abandoned wind and load shedding; c. CwcAnd clsThe penalty coefficients are wind curtailment and load shedding respectively.
In the method, the wind power randomness cost in the second stage is written as follows according to a wind power scene model:
Figure BDA0002087494900000048
Figure BDA0002087494900000049
Figure BDA00020874949000000410
Figure BDA00020874949000000411
in the formula, pisIs the probability of a wind power scenario s;
Figure BDA00020874949000000412
is the sum of the wind power of the scene s in the scheduling period t;
Figure BDA00020874949000000413
and
Figure BDA00020874949000000414
respectively the load shedding and the wind curtailment power of a scene s; s is the number of wind power scenes;
Figure BDA0002087494900000051
in the scenario of the sum of the wind power,
Figure BDA0002087494900000052
for the wind power of wind farm jRate scene, J is the number of wind farms in the system;
the conventional unit i determines the actual reserve power of a scene s under a scheduling period t according to a certain scale factor:
Figure BDA0002087494900000053
Figure BDA0002087494900000054
Figure BDA0002087494900000055
Figure BDA0002087494900000056
in the formula, aiNamely, the conventional unit i bears a scale factor of system backup caused by wind power randomness;
for the transmission line l under the scheduling period t of each scene s, the transmission capacity constraint is as follows:
Figure BDA0002087494900000057
in the formula: n is a radical ofbThe number of nodes in the system; l is the transmission line index; b is a node index;
Figure BDA0002087494900000058
is the transmission capacity limit of the transmission line l; k is a radical ofl,bIs the distribution coefficient in the dc power flow; i (b) is the number of conventional units connected to the bus bar b; j (b) is the number of wind farms connected to bus b; l isb,tIs the load demand of node b under the scheduling period t;
Figure BDA0002087494900000059
is the scene s under the scheduling period tActual standby power of conventional unit i.
The method comprises the steps of determining the actual reserve power of the conventional unit after the real-time actual wind power is obtained by taking the difference between the actual wind power and the wind power dispatching power as a reference, establishing an economic dispatching model of the power system, determining and outputting a dispatching result of the conventional unit based on a solving model of a linear programming solver, wherein the dispatching cost of the overall system is obviously lower than that of the conventional affine reserve allocation method.
Drawings
FIG. 1 is a flow chart provided by the present invention;
FIG. 2 is an explanatory diagram of the influence of the randomness of the wind power output on the system provided by the invention;
FIG. 3 is a graph of the calculation results of predicted wind power, wind power scenario and wind power dispatch power provided by the present invention.
Detailed Description
The invention provides a wind power system economic dispatching method based on improved affine spare allocation, aiming at the defect that social cost is improved by taking a difference value between actual wind power and predicted wind power as a reference in the conventional affine spare allocation method. The invention is described in detail below with reference to specific embodiments and the accompanying drawings.
An improved affine spare allocation-based economic dispatching method for a wind power-containing power system comprises the following steps:
s1, determining parameters of a generator set, line parameters and wind power scenes in the system; wherein,
the parameters of the generator set in the system comprise: upper and lower output limits, fuel cost coefficients, reserve cost coefficients, maximum upward and downward ramp power, and maximum upward and downward reserve capacity;
the line parameters comprise a topological structure, maximum transmission capacity and a direct current power flow distribution coefficient;
the wind power scene is mainly based on a wind power scene generation method in Applied Energy journal of Efficient power plant conditioning and temporal windings (Efficient output scene generation technology of a multi-renewable Energy power station considering space-time correlation) provided by Chenghui Tang, Yishen Wang et al in 1 July 2018;
s2, based on the improved affine standby method, with the difference between the actual wind power and the wind power dispatching power as a reference, determining the actual standby power of the conventional generator set after the real-time actual wind power is obtained, and establishing an economic dispatching model of the power system, specifically comprising:
the power system economic dispatching model is as follows:
the embodiment takes a rolling economy scheduling problem as an example, and decides the output, system reserve, wind curtailment power and load shedding power of a conventional unit. A two-stage model is employed to model decision variables and wind power randomness costs. The objective function of the economic dispatch problem is:
Figure BDA0002087494900000071
wherein f is the total cost of the system; f. ofcModeling is carried out in the first stage for the total cost of the conventional unit of the system, and a variable p is decided by the her-and-nowi,t、ru,i,tAnd rd,i,tDetermining; p is a radical ofi,tScheduling power r of a conventional unit i in a scheduling period tu,i,tAnd rd,i,tRespectively the upward standby power and the downward standby power of the conventional unit i in a scheduling period t; f. ofuThe cost of the randomness of the system caused by the randomness of the wind power is all corresponding to the second stage, and the cost is changed by wait-and-see variable
Figure BDA0002087494900000072
It is decided that,
Figure BDA0002087494900000073
is a random variable of wind power output.
The first stage is as follows:
the total cost of the conventional unit of the system can be obtained by the following formula:
Figure BDA0002087494900000074
wherein T is the number of scheduling periods in the scheduling time domain, where T is 1,2 … T; i is the number of conventional units in the system, I is 1,2 … I; bf,iAnd cf,iPrimary term and constant term coefficients of the fuel cost of the conventional unit i are respectively; c. Cur,iAnd cdr,iAnd respectively reserving cost coefficients for the upward reserve and the downward reserve of the conventional unit i.
The constraint conditions are as follows:
power constraint after output accumulation standby constraint of conventional unit
Figure BDA0002087494900000075
Second, the upper limit of the reserve capacity of the conventional unit is restricted
Figure BDA0002087494900000081
Third, climbing restraint of conventional unit
Figure BDA0002087494900000082
Power balance constraint
Figure BDA0002087494900000083
Wind power random performance is constrained by the relationship between the upper and lower output limits corresponding to the system standby balance and the system standby
Figure BDA0002087494900000084
Sixthly, the random performance of the wind power is constrained by the upper and lower output limits corresponding to the standby balance of the system
Figure BDA0002087494900000085
In the formula,
Figure BDA0002087494900000086
andp irespectively representing the upper limit and the lower limit of the output of the conventional unit i;
Figure BDA0002087494900000087
and
Figure BDA0002087494900000088
upper limits for upward and downward standby of the conventional unit i, respectively;
Figure BDA0002087494900000089
and
Figure BDA00020874949000000810
the maximum upward and downward climbing power of the conventional unit i are respectively;
wtscheduling power for wind power, LtPredicting power for the system under the scheduling period t;
Figure BDA00020874949000000811
the upper limit of the output force is as follows,w tthe lower limit of the output is;
wris the installed capacity of wind power.
And a second stage:
the wind power randomness cost can be obtained by the following formula:
E[fu(wt)]＝cwcEwc+clsEls (9)
in the formula, wherein fu(wt) The penalty cost expectation of the wind power randomness of the second stage is obtained; ewcAnd ElsRespectively obtaining expected power values of abandoned wind and load shedding; c. CwcAnd clsThe penalty coefficients are wind curtailment and load shedding respectively.
As shown in FIG. 2, in the worse case, if the sum of the actual wind power falls within
Figure BDA0002087494900000098
If the system is external, the randomness of the wind power cannot be balanced by the system standby; at this time, load shedding or wind curtailment has to be adopted to ensure the power balance of the system. However, considering that the processing difficulty of the system power transmission blocking comes from the connection of the wind power plants on different system nodes, in order to better consider the influence of the wind power randomness on the system power balance and the power transmission blocking, a better method is to obtain the actual wind power of each wind power plant; wind scenes are a classical model for this purpose. Wind power scene based on wind power plant j
Figure BDA0002087494900000099
It is also possible to obtain a scenario of the sum of the wind power, i.e.
Figure BDA0002087494900000091
J is the number of wind farms in the system. The impact of wind power randomness on system backup and transmission blocking can be considered through correlations in wind scenarios.
Thus, the wind power randomness cost E [ f ] in the second stageu(wt)]The method can be written as follows according to a wind power scene model:
Figure BDA0002087494900000092
Figure BDA0002087494900000093
in the formula, pisIs the probability of a wind power scenario s;
Figure BDA0002087494900000094
is the sum of the wind power of the scene s in the scheduling period t;
Figure BDA0002087494900000095
and
Figure BDA0002087494900000096
respectively the load shedding and the wind curtailment power of a scene s; and S is the number of wind power scenes.
The improved affine backup allocation provided by this embodiment is that, based on a difference between actual wind power and wind power scheduling power, a conventional unit i determines actual backup power of a scene s in a scheduling period t according to a certain scale factor:
Figure BDA0002087494900000097
Figure BDA0002087494900000101
Figure BDA0002087494900000102
Figure BDA0002087494900000103
in the formula, aiNamely, the conventional unit i bears the scale factor of the system backup caused by the wind power randomness.
For the transmission line l under the scheduling period t of each scene s, the transmission capacity constraint is as follows:
Figure BDA0002087494900000104
in the formula: n is a radical ofbThe number of nodes in the system; l is the transmission line index; b is a node index;
Figure BDA0002087494900000105
is the transmission capacity limit of the transmission line l; k is a radical ofl,bIs the distribution coefficient in the dc power flow; i (b) is the number of conventional units connected to the bus bar b; j (b) is the number of wind farms connected to bus b; l isb,tIs the load demand of node b under the scheduling period t;
Figure BDA0002087494900000106
is the actual reserve power of the conventional unit i in the scene s at the scheduling period t. The constraint condition (16) ensures that no output resistor blockage occurs in all scheduling periods under all scenes.
Thus, conventional unit costs (including fuel costs and standby costs) and wind power randomness costs are considered in the first and second stages, respectively. The economic dispatching method for the wind power-containing power system based on the improved affine spare allocation, which is provided by the embodiment, comprises the following steps:
an objective function: the compositions of the formulae (1), (2), (9), (10) and (11).
Constraint conditions are as follows: (3) - (8), (12) to (16).
And S3, determining and outputting a scheduling result of the conventional unit, namely scheduling power and a system standby curve, based on the linear programming solver solution model.
The present embodiment will be described below by way of specific examples.
The method for economically scheduling the wind power-containing power system based on improved affine spare allocation is verified in an IEEE 118 standard node system, 2 wind power plants are arranged in the system, each wind power plant is 400MW in capacity, and the wind power plants are connected to 59 th and 80 th nodes respectively. The data of the wind farm is from National Renewable Energy Laboratory (NREL) in the united states, the scheduling time domain is one hour, and consists of 12 scheduling periods, each of which is 5 minutes in length. The wind power distribution is characterized using 20 wind power scenarios. The penalty coefficients of the load shedding and the wind abandoning are respectively 1000$/MWh and 80 $/MWh. The spare reservation cost coefficients of the system in the upward direction and the downward direction are both 10 $/MWh.
Fig. 3 shows the predicted wind power (thick black lines) and wind power scenario (thin black lines). The wind power dispatching power (black dotted line) is obtained by solving the proposed wind power system economic dispatching method based on improved affine spare allocation based on a CPLEX tool kit under a matlab environment. It can be seen that the wind power dispatching power in all dispatching cycles is usually lower than the predicted wind power, and the main reason is that the load shedding penalty cost coefficient is far greater than the wind abandon penalty coefficient.
And solving the economic dispatching method of the wind power-containing power system based on the conventional affine standby distribution based on the same 20 wind power scenes. By using the 20 wind power scenes, the conventional unit scheduling power and the reserved reserve are tested based on the improved affine spare allocation method and the conventional affine spare allocation method of the embodiment of the monte carlo test. The actual system costs for these two methods are shown in table 1 below. It can be seen that the proposed method has higher fuel cost because the wind power dispatching power is generally lower than the wind power predicted power, so the conventional unit dispatching power is higher and the fuel cost is higher in the improved affine spare allocation method of the present embodiment, however, since the present embodiment has much lower load shedding and wind abandoning loss cost, the overall system cost of the method of the present embodiment is significantly lower than that of the conventional affine spare allocation method.
TABLE 1 social cost calculation results for the method of this example and the conventional affine spare assignment method
Method of the present embodiment Conventional affine spare allocation method
Fuel cost/$ 36635 35951
Spare reservation cost/$ 2634 2643
Load shedding cost/$ 3120 4165
Wind curtailment cost/$ 5210 6213
Total cost/$ 47599 48972
The present invention is not limited to the above-mentioned preferred embodiments, and any structural changes made under the teaching of the present invention shall fall within the protection scope of the present invention, which has the same or similar technical solutions as the present invention.

Claims (1)

1. The economic dispatching method of the wind power-containing power system based on improved affine spare allocation is characterized by comprising the following steps of:
determining parameters, line parameters and wind power scenes of a generator set in the system;
based on an improved affine standby method, determining the actual standby power of a conventional unit after the real-time actual wind power is obtained by taking the difference between the actual wind power and the wind power dispatching power as a reference, and establishing an economic dispatching model of the power system;
determining and outputting a scheduling result of the conventional unit based on a linear programming solver solution model;
the parameters of the generator set in the system comprise: upper and lower output limits, fuel cost coefficients, reserve cost coefficients, maximum upward and downward ramp power, and maximum upward and downward reserve capacity;
the line parameters include: topological structure, maximum transmission capacity and direct current power flow distribution coefficient;
the objective function of the economic dispatch model is:
minE[f]＝fc(pi,t,ru,i,t,rd,i,t)+E[fu(wt)]
wherein f is the total cost of the system; f. ofcModeling is carried out in the first stage for the total cost of the conventional unit of the system, and a variable p is decided by the her-and-nowi,t、ru,i,tAnd rd,i,tDetermining; p is a radical ofi,tScheduling power r of a conventional unit i in a scheduling period tu,i,tAnd rd,i,tRespectively the upward standby power and the downward standby power of the conventional unit i in a scheduling period t;
fu(wt) Is the penalty cost expectation of the second stage wind power randomness, and is composed of a variable wtDetermination of wtScheduling power for the wind power;
the first stage modeling is specifically as follows:
the total cost of the conventional unit of the system can be obtained by the following formula:
Figure FDA0003478742850000011
wherein T is the number of scheduling periods in the scheduling time domain, where T is 1,2 … T; i is the number of conventional units in the system, I is 1,2 … I; bf,iAnd cf,iPrimary term and constant term coefficients of the fuel cost of the conventional unit i are respectively; c. Cur,iAnd cdr,iRespectively reserving cost coefficients for the upward standby and the downward standby of the conventional unit i;
the constraint conditions are as follows:
power constraint after output accumulation standby constraint of conventional unit
Figure FDA0003478742850000021
Figure FDA0003478742850000022
Second, the upper limit of the reserve capacity of the conventional unit is restricted
Figure FDA0003478742850000023
Figure FDA0003478742850000024
Third, climbing restraint of conventional unit
Figure FDA0003478742850000025
Figure FDA0003478742850000026
Power balance constraint
Figure FDA0003478742850000027
Wind power random performance is constrained by the relationship between the upper and lower output limits corresponding to the system standby balance and the system standby
Figure FDA0003478742850000028
Figure FDA0003478742850000029
Sixthly, the random performance of the wind power is constrained by the upper and lower output limits corresponding to the standby balance of the system
Figure FDA00034787428500000210
In the formula,
Figure FDA00034787428500000211
andpirespectively representing the upper limit and the lower limit of the output of the conventional unit i;
Figure FDA00034787428500000212
and
Figure FDA00034787428500000213
upper limits for upward and downward standby of the conventional unit i, respectively;
Figure FDA00034787428500000214
and
Figure FDA00034787428500000215
the maximum upward and downward climbing power of the conventional unit i are respectively;
wtscheduling power for wind power, LtPredicting power for the system under the scheduling period t;
Figure FDA0003478742850000031
the upper limit of the output force is as follows,wtthe lower limit of the output is;
wris the installed capacity of wind power;
the second stage modeling is specifically as follows:
the wind power randomness cost can be obtained by the following formula:
E[fu(wt)]＝cwcEwc+clsEls
in the formula, wherein fu(wt) The penalty cost expectation of the wind power randomness of the second stage is obtained; ewcAnd ElsRespectively obtaining expected power values of abandoned wind and load shedding; c. CwcAnd clsPunishment coefficients of abandoned wind and load shedding are respectively;
the wind power randomness cost in the second stage is written as follows according to a wind power scene model:
Figure FDA0003478742850000032
Figure FDA0003478742850000033
Figure FDA0003478742850000034
Figure FDA0003478742850000035
in the formula, pisIs the probability of a wind power scenario s;
Figure FDA0003478742850000036
is the sum of the wind power of the scene s in the scheduling period t;
Figure FDA0003478742850000037
and
Figure FDA0003478742850000038
respectively the load shedding and the wind curtailment power of a scene s; s is the number of wind power scenes;
Figure FDA0003478742850000039
in the scenario of the sum of the wind power,
Figure FDA00034787428500000310
the method comprises the following steps of (1) setting a wind power scene of a wind power plant J, wherein J is the number of wind power plants in a system;
the conventional unit i determines the actual reserve power of a scene s under a scheduling period t according to a certain scale factor:
Figure FDA00034787428500000311
Figure FDA00034787428500000312
Figure FDA00034787428500000313
Figure FDA0003478742850000041
in the formula, aiNamely, the conventional unit i bears a scale factor of system backup caused by wind power randomness;
for the transmission line l under the scheduling period t of each scene s, the transmission capacity constraint is as follows:
Figure FDA0003478742850000042
in the formula: n is a radical ofbThe number of nodes in the system; l is a transmission lineIndexing; b is a node index; plIs the transmission capacity limit of the transmission line l; k is a radical ofl,bIs the distribution coefficient in the dc power flow; i (b) is the number of conventional units connected to the bus bar b; j (b) is the number of wind farms connected to bus b; l isb,tIs the load demand of node b under the scheduling period t;
Figure FDA0003478742850000043
is the actual reserve power of the conventional unit i in the scene s at the scheduling period t.
CN201910492478.3A 2019-06-06 2019-06-06 Wind power-containing power system economic dispatching method based on improved affine spare allocation Active CN112054504B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910492478.3A CN112054504B (en) 2019-06-06 2019-06-06 Wind power-containing power system economic dispatching method based on improved affine spare allocation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910492478.3A CN112054504B (en) 2019-06-06 2019-06-06 Wind power-containing power system economic dispatching method based on improved affine spare allocation

Publications (2)

Publication Number Publication Date
CN112054504A CN112054504A (en) 2020-12-08
CN112054504B true CN112054504B (en) 2022-03-04

Family

ID=73608688

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910492478.3A Active CN112054504B (en) 2019-06-06 2019-06-06 Wind power-containing power system economic dispatching method based on improved affine spare allocation

Country Status (1)

Country Link
CN (1) CN112054504B (en)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106786570A (en) * 2017-03-10 2017-05-31 国网山东省电力公司经济技术研究院 A kind of interval of power system containing wind-powered electricity generation economic load dispatching method
CN109659937A (en) * 2019-01-11 2019-04-19 国网能源研究院有限公司 Power system economic dispatching method based on wind power randomness cost

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106786570A (en) * 2017-03-10 2017-05-31 国网山东省电力公司经济技术研究院 A kind of interval of power system containing wind-powered electricity generation economic load dispatching method
CN109659937A (en) * 2019-01-11 2019-04-19 国网能源研究院有限公司 Power system economic dispatching method based on wind power randomness cost

Non-Patent Citations (7)

* Cited by examiner, † Cited by third party
Title
Future Wind Power Scenario Synthesis Through Power Spectral Density Analysis;Duehee Lee等;《IEEE Transactions on Smart Grid》;20140131;第5卷(第1期);490-500页 *
含抽水蓄能机组的风电消纳鲁棒机组组合;夏沛等;《电力系统自动化》;20180829(第19期);59-71页 *
基于场景预测的风电场经济调度模型;刘永前等;《分布式能源》;20160815(第01期);17-24页 *
基于风电场总功率条件分布的电力系统经济调度二次规划方法;唐程辉等;《电工技术学报》;20190513(第10期);2069-2078页 *
机组组合问题的仿射可调整鲁棒优化模型与算法;李利利等;《电力工程技术》;20170528(第03期);33-37页 *
考虑可消纳风电区间的多区电力系统分散协调鲁棒调度方法;翟俊义等;《电网技术》;20180112(第03期);77-85页 *
计及风电功率预测误差的备用容量计算新方法;肖逸等;《电力系统保护与控制》;20190501(第09期);72-79页 *

Also Published As

Publication number Publication date
CN112054504A (en) 2020-12-08

Similar Documents

Publication Publication Date Title
Li et al. A coordinated dispatch method with pumped-storage and battery-storage for compensating the variation of wind power
Zhang et al. Optimal allocation of PV generation and battery storage for enhanced resilience
Crăciun et al. Frequency support functions in large PV power plants with active power reserves
Roald et al. Analytical reformulation of security constrained optimal power flow with probabilistic constraints
Xu et al. A day-ahead economic dispatch method considering extreme scenarios based on wind power uncertainty
CN104242356B (en) Consider Robust Interval wind-powered electricity generation dispatching method and the device of wind energy turbine set collection cable malfunction
Moreno et al. Transmission network investment with probabilistic security and corrective control
CN102637289A (en) Method for assessing safety value of planning scheme for electrical power system containing large-scale wind power
CN109038660A (en) A kind of wind-electricity integration System Reactive Power planing method considering quiet Enhancement of Transient Voltage Stability
CN106529737A (en) Planning and distribution method for peak load regulation power source on supply side of power distribution network
Ghasemi et al. A stochastic planning model for improving resilience of distribution system considering master-slave distributed generators and network reconfiguration
CN108830451B (en) Aggregation potential evaluation method and system for user-side distributed energy storage
CN110429591B (en) Power transmission network utilization rate evaluation method based on power system time sequence coupling
CN112994011B (en) Multi-source power system day-ahead optimal scheduling method considering voltage risk constraint
CN117913914A (en) Integrated project grid-connected multi-period scheduling method and device based on electric quantity classification
CN114444851A (en) Virtual power plant optimal scheduling method and system considering rotating standby service
CN112054504B (en) Wind power-containing power system economic dispatching method based on improved affine spare allocation
CN117856261A (en) Method and device for calculating power grid regulation capability gap based on time sequence simulation
CN117060400A (en) Urban power distribution network toughness recovery method, system, equipment and medium
CN113659566B (en) Capacity configuration optimization method of CVaR-based multi-energy complementary power generation system
CN113285482B (en) Method and system for determining proportion of renewable energy sources to be connected into power grid
CN113725904B (en) Power grid transformation method, device and equipment considering decommissioning of aging generator set
CN114759580A (en) Power system tight balance handling method, system and storage medium
CN111934309B (en) Random economic scheduling method containing transmission blocking opportunity constraint
Farrokhseresht et al. Economic impact of wind integration on Primary Frequency Response

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant