CN110247426B - Robust set combination method based on multi-band uncertain set - Google Patents

Robust set combination method based on multi-band uncertain set Download PDF

Info

Publication number
CN110247426B
CN110247426B CN201910503736.3A CN201910503736A CN110247426B CN 110247426 B CN110247426 B CN 110247426B CN 201910503736 A CN201910503736 A CN 201910503736A CN 110247426 B CN110247426 B CN 110247426B
Authority
CN
China
Prior art keywords
uncertain
uncertainty
constraint
wind power
time
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
CN201910503736.3A
Other languages
Chinese (zh)
Other versions
CN110247426A (en
Inventor
薄利明
郑惠萍
王玮茹
刘志诚
刘新元
郝捷
杨尉薇
曲莹
程雪婷
皮军
高宏
郭文博
陈艳波
张智
陈浩
刘锋
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 Electric Power Research Institute Of Sepc
Tsinghua University
North China Electric Power University
Original Assignee
State Grid Electric Power Research Institute Of Sepc
Tsinghua University
North China Electric Power University
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 Electric Power Research Institute Of Sepc, Tsinghua University, North China Electric Power University filed Critical State Grid Electric Power Research Institute Of Sepc
Priority to CN201910503736.3A priority Critical patent/CN110247426B/en
Publication of CN110247426A publication Critical patent/CN110247426A/en
Application granted granted Critical
Publication of CN110247426B publication Critical patent/CN110247426B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • 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—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • H02J3/386—
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • 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
    • 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

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Health & Medical Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Tourism & Hospitality (AREA)
  • Water Supply & Treatment (AREA)
  • Primary Health Care (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Development Economics (AREA)
  • Educational Administration (AREA)
  • Power Engineering (AREA)
  • Game Theory and Decision Science (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Supply And Distribution Of Alternating Current (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The invention discloses a robust unit combination method based on a multi-band uncertain set, which belongs to the technical field of power system dispatching automation. And combining the time correlation constraint with the multiband uncertainty set to accurately capture uncertainty variables of different time period fluctuation levels. In addition, the robust unit combination is solved by adopting a Benders decomposition method and a C & CG method. Test results show that the method can effectively reduce the conservatism of the uncertain set, and improve the economical efficiency of system operation while ensuring the robustness of the UC result.

Description

Robust set combination method based on multi-band uncertain set
Technical Field
The invention belongs to the technical field of power system dispatching automation, and particularly relates to a robust set combination method based on a multi-band uncertain set.
Background
With the rising of the wind power integration proportion and the promotion and deepening of the electric power market reformation, uncertain factors in the power grid gradually increase. To better understand the effect of various uncertainties on scheduling, power system generation plans are transformed from a deterministic problem to an uncertain problem. Incorporating uncertainty into the SCUC problem is challenging. Random optimization, opportunity constraint planning and robust optimization are applied to the field of power system scheduling. The random optimization generates uncertain scenes through probability distribution, a deterministic optimization problem is solved for each scene, and a weighted average value of the cost under each scene is minimized as a decision target. The random optimization can quantitatively analyze the scheduling process in an uncertain scene, but the probability distribution function is complex and is difficult to accurately obtain. Opportunistic constraint planning allows constraints to be violated with a certain probability, i.e. the constraint is satisfied with a certain confidence level, and the opportunistic constraint is generally non-convex mathematically and difficult to solve efficiently.
The robust optimization theory is applied to the scheduling field and achieves good effect. The robust optimization describes the fluctuation of the parameters through an uncertain set, and the solution of the robust optimization model is feasible as long as the values of the parameters are within the range of the uncertain set. Robust optimization uses a closed-convex set to describe the uncertainty of parameters, and calculates the optimal problem of the objective function under the worst condition. To further reduce the conservatism of the uncertain sets, the multiband uncertainty set formula has studied a general robust optimization problem.
The above studies have made significant work, but the above methods all assume that prediction errors between scheduling periods are independent of each other. This assumption of independence can be difficult to guarantee in practice in view of the continuity over the scheduling period, requiring an evaluation of its validity by actual data. The wind power/load prediction error has a time autocorrelation characteristic, and the phenomenon is noticed in the problems of random planning scene generation, optimal power flow and the like.
Disclosure of Invention
The invention aims to provide a robust set combination method based on a multi-band uncertain set, which is characterized by comprising the following steps of:
step A, in the unit combination problem considering uncertainty, wind power and load both adopt the same uncertainty set to carry out wind power uncertainty set modeling; the traditional wind power uncertain set is represented as:
Figure GDA0003635311660000021
establishing a multi-band uncertain set based on a traditional uncertain set; uncertain interval of wind power
Figure GDA0003635311660000022
Is divided into B frequency bands, and the multi-band uncertainty set formula is described as
Figure GDA0003635311660000023
Analyzing historical wind power data to obtain the correlation existing between wind power prediction errors in adjacent time periods; establishing wind power prediction error time correlation constraint, linearizing the time correlation constraint, and combining the uncertain set considering the prediction error time correlation constraint with the multi-frequency band uncertain set to obtain the wind power prediction error time correlation constraint
Figure GDA0003635311660000031
B, establishing a two-stage robust safety constraint unit combination model with the lowest operation cost in a prediction scene as a target based on a multi-band uncertain set considering the prediction error time correlation, wherein the target function is as follows:
Figure GDA0003635311660000032
the constraint conditions are as follows: X.I b +Y·P b ≤g b ，Q·I b +W·P b +R·P u (S)≤g u (S)，P b ≥0,P u ≥0,I b E {0,1 }; by Benders decomposition and C&The CG algorithm solves the model.
In the formula:
Figure GDA0003635311660000033
and w t Respectively obtaining a predicted value and an actual output of the wind power at a time interval t;
Figure GDA0003635311660000034
and
Figure GDA0003635311660000035
the prediction error of the time interval T is bounded by T1, …, T24. Gamma-shaped T An uncertainty budget that is a temporal smoothing effect; binary variable
Figure GDA0003635311660000036
Figure GDA0003635311660000037
The positive and negative uncertain interval is used for indicating whether the uncertain wind power falls into the b-th frequency band; total deviation weighted coefficient pi w between uncertain wind power and each frequency band predicted value b And uncertainty budget Γ T Limiting; in addition, the actual output of the wind power cannot fall in a positive and negative interval completely; therefore, a weight coefficient π w is set such that the indeterminate amount falls within the positive and negative intervals + And π w - ；
Figure GDA0003635311660000038
The change-marking variable v in (1) is:
Figure GDA0003635311660000039
summing upsilon on a time sequence, marking as Λ, and marking Λ as a variation budget of the time sequence; Λ represents the number of z fluctuations in time series, and the larger Λ is, the smaller is the predictive error phasor representing the uncertainty in time series, and conversely, the larger is the correlation. I.C. A b And P b Is composed ofUnit combinationSolving the main problem to obtain a unit combination and a unit output result; p is u (S) the output of the unit which deals with uncertainty in the second stage; n is a radical of hydrogen T ，c T Is composed ofUnit combinationCoefficient matrix in the objective function of the main problem, X, Y areUnit combinationCoefficient matrix of constraints in the main problem, g b Is a deterministic measure of the constraint in the main problem; q, W, R are coefficient matrixes of uncertainty constraint conditions, g u (S) is the amount of uncertainty in the uncertainty constraint.
The step A comprises the following steps:
step A1: the conservative property of the combination problem of the robust generator set considering wind power and load uncertainty is determined by an uncertain set;the traditional uncertain set of wind power is expressed as
Figure GDA0003635311660000041
In the formula
Figure GDA0003635311660000042
By passing
Figure GDA0003635311660000043
Controlling the uncertain quantity to be at the upper limit or the lower limit of the uncertain set; conventional uncertain set based on step A1
Figure GDA0003635311660000044
The traditional uncertain set still has a certain degree of conservation; this is because the uncertainty in the single-band uncertainty set defaults to the boundary of the uncertainty interval, however, it is the actual case that most of the uncertainty occurs within the uncertainty interval; the probability that a plurality of uncertain parameters reach the boundary thereof at the same time in different time is small; the multi-band uncertainty set is characterized by the worst case scenario that can be implemented within the uncertainty set with small deviations; uncertain interval of wind power
Figure GDA0003635311660000045
Divided into B frequency bands, and a multi-band uncertainty set formula is described as
Figure GDA0003635311660000046
Step A2: wind power historical data are analyzed to obtain the correlation between wind power prediction errors in adjacent time periods, and wind power prediction error time correlation constraint is established
Figure GDA0003635311660000047
Linearizing time correlation constraint, and combining uncertain set considering prediction error time correlation constraint with multi-band uncertain set to obtain
Figure GDA0003635311660000051
In the formula:
Figure GDA0003635311660000052
and w t Respectively obtaining a predicted value and an actual output of the wind power at a time interval t;
Figure GDA0003635311660000053
and
Figure GDA0003635311660000054
the prediction error of the time interval T is bounded by T1, …, T24. Gamma-shaped T An uncertainty budget that is a time smoothing effect; binary variable
Figure GDA0003635311660000055
The positive and negative uncertain interval is used for representing whether the uncertain wind power falls into the b-th frequency band; total deviation weighted coefficient pi w between uncertain wind power and each frequency band predicted value b And uncertainty budget Γ T Limiting; in addition, the actual output of the wind power cannot fall in a positive and negative interval completely; therefore, a weight coefficient π w is set such that the indeterminate amount falls within the positive and negative intervals + And π w - ；s 1 ＝[e 1,1 ,…e n,1 ,…e N,1 ,…,e 1,T-1 ,…,e n,T-1 ,…,e N,T-1 ]，s 2 ＝[e 1,2 ,…e n,2 ,…e N,2 ,…,e 1,T ,…,e n,T ,…,e N,T ]Is a relative error sequence, s 2 Is as s 1 Translating backwards for a sequence of time periods, where the time interval is consistent with the calculation step of the unit combination; n is the number of samples for wind power prediction, and N is 1,2, …, N; c(s) 1 ，s 2 ) Calculating a function for Pearson; gamma is the lower limit of the time correlation between the uncertain scene and the predicted scene, and the smaller gamma is, the larger the uncertain set is, and the more conservative the robust optimization result is;
Figure GDA0003635311660000056
the change-marking variable v in (1) is:
Figure GDA0003635311660000057
summing upsilon on the time sequence, marking as Λ, and marking Λ as the variation budget of the time sequence; Λ represents the number of z fluctuations in time series, and the larger Λ is, the smaller is the predictive error phasor representing the uncertainty in time series, and conversely, the larger is the correlation.
And B, providing a two-stage robust safety constraint unit combination model, wherein the objective function is that the start-up and shutdown cost and the operation cost under a prediction scene are the lowest:
Figure GDA0003635311660000058
the constraint conditions are as follows: X.I b +Y·P b ≤g b ，Q·I b +W·P b +R·P u (S)≤g u (S)，P b ≥0,P u ≥0,I b E {0,1}, where X.I b +Y·P b ≤g b To predict constraints in a scenario, Q.I b +W·P b +R·P u (S)≤g u (S) is a constraint condition under an uncertain scene; the constraint conditions include: the method comprises the following steps of system power balance constraint, thermal power unit and wind power plant output upper and lower limit constraint, thermal power unit startup and shutdown time constraint, thermal power unit climbing constraint, power adjustment constraint under an uncertain scene of the thermal power unit and network safety constraint in the system. Decomposition by Benders and C&The CG algorithm solves the model, and comprises the following steps:
step B1: the provided two-stage robust CCUC model decomposes an original model into a main problem of unit combination and a safety verification problem under various uncertainties through a Benders decomposition method.
Step B2: solving the main problem of the unit combination, wherein the objective function is as follows:
Figure GDA0003635311660000061
constraint condition is X.I b +Y·P b ≤g b All C obtained so far&CG optimal secant plane cut set, P b ≥0,I b The element is {0,1}, the main problem of the unit combination is a mixed integer linear programming problem, and Gurobi is adopted to solve. Main questions of unit combinationSubject to Unit Assembly I under basic conditions b Output P of the mixing unit b The solution constraints corresponding to the basic case and all the optimal cutting planes obtained so far; wherein, the first main iteration has no optimal cutting plane;
step B3: solving the security check, solving the security violation in the worst scenario for the security check problem; the security check problem objective function is:
Figure GDA0003635311660000062
the constraint conditions are as follows:
Figure GDA0003635311660000063
v≥0,P u is more than or equal to 0. If the security violation in the worst failure scenario exceeds a given security threshold, then feasibility C is generated&The CG optimizes the cutting plane and feeds back to the unit combination main problem of step B2 to seek a new unit combination scheme that can alleviate security violation.
The step B3 includes:
step B31: the safety check problem is a Max-Min problem which cannot be directly solved, the Min problem of the inner layer is a linear problem and can be converted into a single-layer problem by using pair-even transformation, and the objective function is as follows:
Figure GDA0003635311660000064
the constraint conditions are as follows: u. of T R≤0 T ，-1 T ≤u T ≤1，u T ≤0 T ；
Step B32: due to u T ·g u A quadratic term exists in the step (S), the Max problem in the step B31 is solved by applying a 'Big-M' method, and the worst scene corresponding to the maximum security violation is solved;
step B33: generating C corresponding to the worst scenario if the maximum security violation v of the worst scenario is above a given threshold&CG optimal cutting plane
Figure GDA0003635311660000071
And feeding back to the main problem of the unit combination.
In the formula I b And P b Solving the main problem of the unit combination to obtain a unit combination and a unit output result; p u (S) the unit output of uncertainty is responded in the second stage;
Figure GDA0003635311660000072
the unit combination and the unit output introduced into the safety verification problem in the first stage are obtained; g b Is a deterministic measure of the constraint in the main problem; q, W, R are coefficient matrixes of uncertainty constraint conditions, g u (S) an amount of uncertainty in the uncertainty constraint;
the method has the advantages that the robust unit combination method based on the multi-band uncertain set is tested on the improved IEEE-118 node system, and the result shows that the method can effectively reduce the conservatism of the uncertain set and improve the economical efficiency of system operation.
Drawings
FIG. 1 is a flow chart of robust unit combination based on a multi-band uncertain set.
Fig. 2 is a schematic diagram of the division of the multiband wind power uncertainty set.
Fig. 3 considers the time correlation versus the worst scenario without considering the time correlation.
FIG. 4 operating costs under different uncertain and varying budgets
Detailed Description
The invention provides a robust unit combination method based on a multi-band uncertain set, which is further explained in detail with reference to the attached drawings.
Fig. 1 shows a flow chart of robust unit combination based on a multiband uncertain set. The method specifically comprises the following steps:
the step A comprises the following steps:
step A1: the conservatism of the robust unit combination problem considering wind power/load uncertainty is determined by the uncertainty set. In the unit combination problem considering the uncertainty, both the wind power and the load are uncertain quantities, and the same uncertainty set is generally adopted for the wind power and the load. And taking wind power as an example, performing uncertain set modeling. The traditional wind power uncertain set is represented as:
Figure GDA0003635311660000081
in the formula
Figure GDA0003635311660000082
By passing
Figure GDA0003635311660000083
Controlling the uncertainty amount to be at the upper or lower bound of the uncertainty set.
The invention provides a multi-band uncertain set, and the traditional uncertain set still has certain degree of conservatism. This is because the uncertainty in a single-segment uncertainty set defaults to the boundaries of the uncertainty interval. However, it is a practical matter that most uncertainty realizations may occur strictly within an uncertainty interval. It is less likely that multiple uncertain parameters will arrive at their boundaries at the same time at different times. The multi-band uncertainty set is characterized by a worst case scenario that can be implemented within the uncertainty set with a small deviation. Uncertain interval of wind power
Figure GDA0003635311660000084
Is divided into B segments as shown in fig. 2. The multi-band uncertainty set formula is described as:
Figure GDA0003635311660000085
each frequency band uncertain budget constraint is
Figure GDA0003635311660000086
In the formula:
Figure GDA0003635311660000091
and wt is the predicted value and actual output of wind power in the time period t respectively;
Figure GDA0003635311660000092
and
Figure GDA0003635311660000093
the upper and lower bounds of the prediction error for time period T, T-1, …, T-24, respectively. Gamma-shaped T An uncertainty budget that is a time smoothing effect; binary variable
Figure GDA0003635311660000094
The positive and negative uncertain interval is used for representing whether the uncertain wind power falls into the b-th frequency band; where B is 1, …, B as shown in fig. 2. Total deviation weighted coefficient pi w between uncertain wind power and each frequency band predicted value b And uncertainty budget Γ T And (4) limiting.
Step A2: a multi-band uncertainty set based on considering the prediction error time correlation is obtained based on step a 1.
The wind power prediction error mainly comes from the fact that meteorological conditions cannot be accurately predicted, and due to the fact that the continuity of the scheduling time period is considered, the prediction errors of adjacent time periods have certain correlation. Before wind power modeling is performed, correlations of different time periods are first analyzed.
the relative prediction error for the t period may be expressed as:
Figure GDA0003635311660000095
in the formula (I), the compound is shown in the specification,
Figure GDA0003635311660000096
w t respectively the predicted output and the actual output of the wind power.
Note that the relative error sequence is:
s 1 ＝[e 1,1 ,…e n,1 ,…e N,1 ,…,e 1,T-1 ,…,e n,T-1 ,…,e N,T-1 ] (5)
s 2 ＝[e 1,2 ,…e n,2 ,…e N,2 ,…,e 1,T ,…,e n,T ,…,e N,T ] (6)
in the formula, N is the number of samples for wind power prediction. s 2 Is s is 1 A sequence of time periods is translated backwards. The time interval here corresponds to the calculation step of the unit combination.
A temporal correlation constraint is added to the uncertain set.
Figure GDA0003635311660000097
Where c (s1, s2) is a Pearson calculation function, and γ is a lower limit of temporal correlation between the uncertain scene and the predicted scene. The smaller the gamma, the larger the uncertainty set, and the more conservative the robust optimization results.
E in equation (8) can be converted as follows:
Figure GDA0003635311660000101
the prediction error sequence can thus be expressed as e ═ z + -z - . So that s in formula (8) 1 And s 2 The time dependency constraint of (c) can be translated into a pair z + And z - Of (3) is performed. In formula (1), z + And z - Is symmetrical, hereinafter denoted by z + For purposes of example discussion.
Will z + Two prediction error sequences differing by one time interval in the middle are recorded as
Figure GDA0003635311660000102
Figure GDA0003635311660000103
From equations (9) and (10), a change flag variable u is defined + Comprises the following steps:
Figure GDA0003635311660000104
will be upsilon + The sum is denoted as Λ + 'Lambda' scale + Budgeting for variations of time series, like Γ T 。Λ + Is represented in time series by z + Number of oscillations,. lambda + The larger the prediction error correlation coefficient, the greater the amount of uncertainty in the time series
Figure GDA0003635311660000105
The smaller. Conversely, the larger the correlation coefficient. At the same time, the user can select the required time,
Figure GDA0003635311660000106
value of and Γ T And Λ + Related to z + And upsilon + Is irrelevant to the specific value of (a).
In summary of the analysis, the uncertain set considering the wind power prediction error time correlation can be represented as
Figure GDA0003635311660000111
Where Λ is the total uncertainty budget, upsilon + The superscripts u and d in each case denote z t To z t+1 From 0 to 1 and from 1 to 0. In the method, the uncertainty of the wind power is considered, the uncertainty of the load is also considered, and the uncertain set form of the load is the same as that of the wind power.
Then combining the uncertainty set considering the prediction error time correlation constraint with the multi-band uncertainty set to obtain:
Figure GDA0003635311660000112
and B, providing a two-stage robust safety constraint unit combination model, wherein the objective function is that the start-up and shutdown cost and the operation cost under a prediction scene are the lowest:
Figure GDA0003635311660000113
the constraint conditions are as follows:
s.t.X·I b +Y·P b ≤g b (15)
Q·I b +W·P b +R·P u (S)≤g u (S) (16)
wherein X.I b +Y·P b ≤g b To predict constraints in a scenario, Q.I b +W·P b +R·P u (S)≤g u And (S) is a constraint condition under an uncertain scene. The constraint conditions include: the method comprises the following steps of system power balance constraint, thermal power unit and wind power plant output upper and lower limit constraint, thermal power unit startup and shutdown time constraint, thermal power unit climbing constraint, power adjustment constraint under an uncertain scene of the thermal power unit and network safety constraint in the system. The concrete model is as follows:
the deterministic safety constrained crew combined model is a mixed integer programming problem. The aim is to minimize the total scheduling operating cost of the system, including the operating cost of the thermal power generating unit and the startup and shutdown costs.
The linearized unit combination model has been widely studied as a mixed integer linear programming problem. The objective is to minimize the total operating cost, including the coal consumption cost, start-stop cost, over the entire scheduling period.
(1) Objective function
Figure GDA0003635311660000121
In the formula, c ik The active output of the unit i is the operation cost coefficient of the kth section;
Figure GDA0003635311660000122
the output of the kth section of the unit i in the time period t is obtained;
Figure GDA0003635311660000123
respectively determining active output and startup and shutdown decision variables of the unit i in a t period;
Figure GDA0003635311660000124
respectively start and stop expenses.
(2) Constraint conditions
And (3) system power balance: the total generated energy of the running unit needs to meet the system load requirement. Here, the network loss is temporarily ignored, that is, the sum of the outputs of the thermal power generating unit and the wind power generating unit is equal to the total load.
Figure GDA0003635311660000125
In the formula:
Figure GDA0003635311660000126
wind power and load power in each time interval.
The upper and lower limits of the unit output are as follows: the output of each unit has maximum and minimum constraints.
And (3) limiting the output of the thermal power generating unit: the output of the thermal power generating unit is higher than the minimum generating power and lower than the maximum generating power.
Figure GDA0003635311660000127
Figure GDA0003635311660000128
Figure GDA0003635311660000129
In the formula: p i min 、P i max The output power of the thermal power generating unit is the upper limit and the lower limit,
Figure GDA00036353116600001210
is the power on each segment after segmentation.
And (4) limiting the output of the wind turbine generator, wherein the output of the fan is less than the maximum predicted value of the wind power.
Figure GDA0003635311660000131
In formula (22):
Figure GDA0003635311660000132
and w represents the w-th wind power plant as the predicted value of the wind power.
Minimum start-stop time of the unit: the unit can not be repeatedly started or stopped within a certain time period.
Figure GDA0003635311660000133
Figure GDA0003635311660000134
In the formula:
Figure GDA0003635311660000135
for the on-off time of the unit in unit time, T on,i T off,i Are minimum boot and downtime constraints.
And the start-up and shutdown cost of the unit is limited.
Figure GDA0003635311660000136
Figure GDA0003635311660000137
In the formula: su i ,sd i The start-up and shutdown costs of the unit i.
And (3) climbing restraint: the variation of the unit output must satisfy a certain limit in adjacent time intervals.
Figure GDA0003635311660000138
Figure GDA0003635311660000139
In the formula: UR i ,DR i And limiting the climbing power of the unit.
Network security constraint based on direct current power flow
Figure GDA00036353116600001310
In the formula:
Figure GDA00036353116600001311
for maximum current constraint of the line, SF l,m Is the node power transfer factor.
Constraints of uncertainty factors are taken into account.
And (5) system power balance constraint.
Figure GDA0003635311660000141
In the formula:
Figure GDA0003635311660000142
self-adaptive output adjustment of a thermal power generating unit i and a wind power plant w in response to an uncertain interval in a time period t is carried out;
Figure GDA0003635311660000143
an indeterminate load that is a load d at time t;
Figure GDA0003635311660000144
the following equation describes the contribution limits of the thermal power generating unit and the wind farm in consideration of uncertainty.
Figure GDA0003635311660000145
Figure GDA0003635311660000146
The contribution adjustment of a thermal power generating unit in response to uncertainty is limited by its correction capability under baseline conditions.
Figure GDA0003635311660000147
In the formula:
Figure GDA0003635311660000148
R i down and (4) up/down correcting the power limit of the unit i.
And (3) climbing restraint: in an uncertain interval, the change of the power adjustment of the unit in the adjacent time period and each moment needs to meet certain limit.
Figure GDA0003635311660000149
Figure GDA00036353116600001410
(5) And (4) network security constraint under direct current flow.
Figure GDA00036353116600001411
B, solving the two-stage robust security unit combination model by using a Benders decomposition method and a C & CG algorithm, and comprising the following steps of:
step B1: the two-stage robust security constraint unit combination model is used for decomposing an original model into a unit combination main problem and a security verification problem under various uncertainties through a Benders decomposition method and a C & CG algorithm.
Step B2: solving the main problem of the unit combination, wherein the objective function is as follows:
Figure GDA00036353116600001412
constraint condition is X.I b +Y·P b ≤g b All C obtained so far&CG optimum cutting plane cut set, P b ≥0,I b E {0,1 }. The main problem of the unit combination is a mixed integer linear programming problem, and CPLEX is adopted to solve. The unit combination obtains a unit combination I under the basic condition b Output P of the harmony unit b The solution constraints for the base case and all the optimal cutting planes obtained so far. There is no optimal cutting plane in the first main iteration.
Step B3: and solving the safety check problem. The security check problem solves the security violation in the worst scenario. The security check problem objective function is:
Figure GDA0003635311660000151
the constraint conditions are as follows:
Figure GDA0003635311660000152
v≥0,P u is more than or equal to 0. If the security violation in the worst failure scenario exceeds a given security threshold, then feasibility C is generated&The CG optimal cutting plane is fed back to the UC master problem of step B2 to seek a new fleet composition scheme that can mitigate security violations.
4. Step B3 includes:
step B31: the security check problem is the Max-Min problem and cannot be solved directly. The Min problem for the inner layer is a linear problem that can be converted to a single layer problem with a dual transform, the objective function being:
Figure GDA0003635311660000153
the constraint conditions are as follows: u. of T R≤0 T ，-1 T ≤u T ≤1，u T ≤0 T 。
Step B32: due to u T ·g u (S) Secondary item, use "The Big-M "method solves the Max problem in B31, and solves the worst scenario corresponding to the maximum security violation.
Step B33: if the maximum security violation v for the worst case scenario is above a given threshold. Generating C corresponding to worst scenario&CG optimal cutting plane
Figure GDA0003635311660000154
And feeding back to the main problem of the unit combination.
For a better understanding of the present invention and to show the advantages thereof over the prior art, reference is made to the accompanying drawings, which form a part hereof, and in which is shown by way of illustration specific embodiments.
Examples
In this section, the effectiveness and advantages of the proposed robust SCUC method are illustrated. These numerical conditions are based on a modified IEEE 118 bus system. The system consists of 54 units, a wind power station and 186 power transmission lines. The robust SCUC model was solved using YALMIP and Gurobi-8.0.1 on MATLAB 2017 b.
And comparing the three unit combination models in order to analyze and consider the influence of the wind power/load prediction error time correlation on the uncertain set.
Model 1) conventional safety constrained unit assembly (SCUC). And a deterministic optimization algorithm is adopted, and wind power and load prediction errors are not considered.
Model 2) robust fleet combination (RUC). And adopting a traditional uncertain set modeling method and considering uncertain budget.
Model 3) takes account of The Robust Unit Combination (TRUC) of the wind power and load prediction error time correlation.
Based on historical data provided by Elia and equation (4), an uncertainty parameter Γ is estimated T 12. And when the lambda is 3, solving the unit combination result of the three models, and performing a robustness test. The robustness test is to determine whether the formulated scheduling plan is feasible under the actual output of wind power and load, namely, load shedding or wind abandon does not occur.
TABLE 1 comparison of scheduling results for three models
Figure GDA0003635311660000161
As can be seen from table 1, the conventional SCUC model has the lowest running cost and the fastest calculation speed, but fails the robustness test in a practical scenario. Both the RUC model and the TRUC model can ensure the safe operation of the system. Compared with the RUC model, the TRUC model reduces the operation cost of the system and improves the solving speed on the basis of ensuring the safe operation of the system.
In order to further analyze and consider the influence of the prediction error time correlation on the robust solving result, the wind power worst scene is constrained by the prediction error time correlation. As shown in fig. 3. As can be seen from fig. 3, when the prediction error correlation is not considered, frequent fluctuation occurs in the worst wind power scenario between the predicted value and the prediction lower bound, and the number of starts needs to be increased due to insufficient hill climbing capability of the thermal power generating unit, which leads to an increase in the operation cost. According to the analysis of the time correlation of wind power output, the scene occurrence probability of frequent fluctuation of wind power on an uncertain interval is low. After the correlation of the prediction errors is considered, due to the existence of the variable quantity budget Λ, the fluctuation of the wind power prediction errors is limited, scenes with low occurrence probability are eliminated, the starting number of thermal power machines is reduced, and the economy of robust optimization is improved.
The uncertainty set considering the prediction error time dependency is mainly influenced by the uncertainty budget Γ T And the effect of the variation budget Λ. FIG. 4 shows the L-shape of the L-shaped lens in different F T And robust optimization cost of the TRUC model under Λ. As can be seen from fig. 4, with uncertainty Γ T The robustness of the unit combination solution is enhanced, and the optimization result is increased. When gamma is T When the lambda is kept unchanged, the operation cost of the system is increased along with the increase of the lambda, and the operation reliability of the system is improved along with the increase of the lambda. At the same time, it is found that when F is T Larger, the robust operating cost is less affected by Λ.
A new multi-band uncertainty set considering prediction error time correlation is based on a multi-band uncertainty set and an uncertainty set considering prediction error time correlation. For testing indeterminate setsCombination of Chinese herbs
Figure GDA0003635311660000171
The performance of (c). Tables 2-5 show the uncertainty budget Γ for different frequency bands b T And robust optimization cost under the variation budget Λ.
TABLE 2F T Running cost of 6 hours
Figure GDA0003635311660000172
Figure GDA0003635311660000181
TABLE 3 gamma T Running cost of 8 ═ h
Figure GDA0003635311660000182
TABLE 4F T Operating cost of 10 hours
Figure GDA0003635311660000183
TABLE 5 gamma T Operating cost of 12 hours
Figure GDA0003635311660000184
Table 2-table 5 show that the robust operating cost depends on Γ for different frequency bands b2, 3,4,5 respectively T And a change in Λ. When a fixed number of bands and Γ are given T The robust optimization cost gradually decreases as the variation budget Λ decreases. Comparing the operating costs for the four frequency bands in table 5, the operating cost of the system as a whole decreases as the number of frequency bands increases. Through the analysis, the method can be obtained by considering the time correlation constraint of the wind power and load prediction error in the multi-band uncertain set, and further reducing the uncertain setAnd (4) conservation. In order to further analyze and consider the influence of the prediction error time correlation constraint on the multi-band uncertain set, the frequency band number b is 5, Γ is 12, and Λ is 3, and the wind power worst scene when the prediction error time correlation is considered and not considered is compared.
The test result of the example system shows that the model provided by the invention reduces the conservative property of robust optimization and improves the economical efficiency of system operation.
The above embodiments describe the technical solution of the present invention in detail. It will be clear that the invention is not limited to the described embodiments. Based on the embodiments of the present invention, those skilled in the art can make various changes, but any changes equivalent or similar to the present invention are within the protection scope of the present invention.

Claims (4)

1. A robust set combination method based on a multi-band uncertain set is characterized by comprising the following steps:
step A, in the unit combination problem considering uncertainty, wind power and load both adopt the same uncertainty set to carry out wind power uncertainty set modeling; the traditional wind power uncertain set is represented as:
Figure FDA0003665973770000011
establishing a multi-band uncertain set based on a traditional uncertain set; the uncertain interval of wind power
Figure FDA0003665973770000012
Divided into B frequency bands, and the multi-band uncertainty set formula is described as
Figure FDA0003665973770000013
Analyzing wind power historical data to obtain the correlation existing between wind power prediction errors in adjacent time periods; establishing wind power prediction error time correlation constraint, linearizing the time correlation constraint, and combining an uncertain set considering the prediction error time correlation constraint with a multi-frequency band uncertain set to obtain the time correlation constraint
Figure FDA0003665973770000014
And B, establishing a two-stage robust safety constraint unit combination model with the lowest running cost as a target in a prediction scene based on a multi-band uncertain set considering the prediction error time correlation, wherein the target function is as follows:
Figure FDA0003665973770000021
the constraint conditions are as follows: X.I b +Y·P b ≤g b ，Q·I b +W·P b +R·P u (S)≤g u (S)，P b ≥0,P u ≥0,I b E {0,1 }; by Benders decomposition and C&Solving the model by a CG algorithm;
in the formula:
Figure FDA0003665973770000022
and w t Respectively obtaining a predicted value and an actual output of the wind power at a time interval t;
Figure FDA0003665973770000023
and
Figure FDA0003665973770000024
upper and lower bounds for the prediction error of time period T, T-1, …, T-24, respectively; binary variable
Figure FDA0003665973770000025
The method is used for representing whether the uncertain wind power falls into a positive uncertain interval and a negative uncertain interval of the b-th frequency band; total deviation weighted coefficient pi w between uncertain wind power and each frequency band predicted value b And uncertainty budget Γ T Limiting; in addition, the actual output of the wind power cannot fall in a positive and negative interval completely; therefore, a weight coefficient π w is set such that the indeterminate amount falls within the positive and negative intervals + And π w - ；
Figure FDA0003665973770000026
The change flag variable v is defined as:
Figure FDA0003665973770000027
summing upsilon on the time sequence, marking as Λ, and marking Λ as the variation budget of the time sequence; Λ represents the number of z fluctuation times on the time series, the larger the Λ is, the smaller the correlation of prediction errors representing uncertain quantities on the time series is, and conversely, the larger the correlation is; i is b And P b Solving the main problem of the unit combination to obtain a unit combination and unit output result; p is u (S) the unit output of uncertainty is responded in the second stage; n is a radical of T ，c T Coefficient matrix in the objective function of main problem for unit combination, X, Y are coefficient matrix of constraint condition in main problem for unit combination, g b Determining the quantity of the constraint condition in the main problem of unit combination; q, W, R are coefficient matrixes of uncertainty constraint conditions, g u (S) is the amount of uncertainty in the uncertainty constraint.
2. The robust set combination method based on the multiband uncertain set as claimed in claim 1, wherein the step A comprises:
step A1: the conservatism of the combination problem of the robust generator set considering the wind power and load uncertainty is determined by the uncertainty set; the traditional uncertain set of wind power is expressed as
Figure FDA0003665973770000031
In the formula
Figure FDA0003665973770000032
By passing
Figure FDA0003665973770000033
Controlling the uncertainty to be at the upper limit or the lower limit of the uncertainty set; conventional uncertain set W based on step A1 1 u (ii) a The conventional uncertain set still has a certain degreeConservation; this is because the uncertainty in a single-segment uncertainty set defaults to the boundary of the uncertainty interval, however, the reality is that most uncertainties occur within the uncertainty interval; the probability that a plurality of uncertain parameters reach the boundary thereof at the same time in different time is small; the multi-band uncertainty set is characterized by the worst case scenario that can be implemented within the uncertainty set with small deviations; uncertain interval of wind power
Figure FDA0003665973770000034
Divided into B frequency bands, and a multi-band uncertainty set formula is described as
Figure FDA0003665973770000035
Step A2: wind power historical data are analyzed to obtain the correlation between wind power prediction errors in adjacent time periods, and wind power prediction error time correlation constraint is established
Figure FDA0003665973770000036
Linearizing time correlation constraint, and combining uncertain set considering prediction error time correlation constraint with multi-band uncertain set to obtain
Figure FDA0003665973770000041
In the formula:
Figure FDA0003665973770000042
and w t Respectively obtaining a predicted value and an actual output of the wind power at a time interval t;
Figure FDA0003665973770000043
and
Figure FDA0003665973770000044
are the upper and lower bounds of the prediction error of the time interval T, T-1, …, T24; binary variable
Figure FDA0003665973770000045
The method is used for representing whether the uncertain wind power falls into a positive uncertain interval and a negative uncertain interval of the b-th frequency band; total deviation weighted coefficient pi w between uncertain wind power and each frequency band predicted value b And uncertainty budget Γ T Limiting; wherein gamma is T Not determining a budget for time; in addition, the actual output of the wind power cannot fall in a positive and negative interval completely; therefore, a weight coefficient π w is set such that the indeterminate amount falls within the positive and negative intervals + And π w - ；s 1 ＝[e 1,1 ,…e n,1 ,…e N,1 ,…,e 1,T-1 ,…,e n,T-1 ,…,e N,T-1 ]，s 2 ＝[e 1,2 ,…e n,2 ,…e N,2 ,…,e 1,T ,…,e n,T ,…,e N,T ]For relative error sequence, wind power prediction error time correlation constraint
Figure FDA0003665973770000046
s 2 Is s is 1 Moving backwards for a time period sequence, wherein the time interval is consistent with the calculation step length of the unit combination; n is the number of samples for wind power prediction, and N is 1,2, …, N; c(s) 1 ，s 2 ) Calculating a function for Pearson; gamma is the lower limit of the time correlation between the uncertain scene and the predicted scene;
Figure FDA0003665973770000047
the change-marking variable v in (1) is:
Figure FDA0003665973770000048
summing upsilon on the time sequence, marking as Λ, and marking Λ as the variation budget of the time sequence; Λ represents the number of z fluctuations in time series, and the larger Λ is, the smaller the correlation of prediction errors representing an indeterminate amount in time series is, and conversely, the larger the correlation is.
3. A method of manufacturing a composite material according to claim 1The robust unit combination method based on the multi-band uncertain set is characterized in that in the step B, a two-stage robust safety constraint unit combination model is given, and the objective function is that the start-up and shut-down cost and the operation cost under a prediction scene are the lowest:
Figure FDA0003665973770000051
the constraint conditions are as follows: X.I b +Y·P b ≤g b ，Q·I b +W·P b +R·P u (S)≤g u (S)，P b ≥0,P u ≥0,I b E {0,1}, where X.I b +Y·P b ≤g b To predict constraints in a scenario, Q.I b +W·P b +R·P u (S)≤g u (S) is a constraint condition in an uncertain scene; the constraint conditions include: the method comprises the following steps of carrying out system power balance constraint, thermal power unit and wind power plant output upper and lower limit constraint, thermal power unit startup and shutdown time constraint, thermal power unit climbing constraint, thermal power unit uncertain scene power adjustment constraint and network safety constraint in a system; decomposition by Benders and C&The CG algorithm is used for solving, and comprises the following steps:
step B1: the original model of the proposed two-stage robust security constraint unit combination model is decomposed into a unit combination main problem and a security verification problem under various uncertainties through a Benders decomposition method;
step B2: solving the main problem of the unit combination, wherein the objective function is as follows:
Figure FDA0003665973770000052
constraint condition is X.I b +Y·P b ≤g b All C obtained so far&CG optimum cutting plane cut set, P b ≥0,I b The method belongs to {0,1}, the UC main problem is a mixed integer linear programming problem, and Gurobi is adopted to solve; the main problem of the unit combination is obtained as the unit combination I under the basic condition b Output P of the harmony unit b The solution constraints corresponding to the basic case and all the optimal cutting planes obtained so far; wherein, the first main iteration has no optimal cutting plane;
step B3: solving a safety check problem, wherein the safety check problem solves the safety violation in the worst scenario; the security check problem objective function is:
Figure FDA0003665973770000053
the constraint conditions are as follows:
Figure FDA0003665973770000054
v≥0,P u not less than 0; v is a security violation for the worst case scenario; if the security violation in the worst failure scenario exceeds a given security threshold, then feasibility C is generated&The CG optimizes the cutting plane and feeds back to the unit combination main problem of step B2 to seek a new unit combination scheme that can alleviate security violation.
4. The robust assembly method of claim 3, wherein the B3 comprises:
step B31: the safety check problem is a Max-Min problem which cannot be directly solved, the Min problem of the inner layer is a linear problem which can be converted into a single-layer problem by using dual transformation, and an objective function is as follows:
Figure FDA0003665973770000061
the constraint conditions are as follows: u. of T R≤0 T ，-1 T ≤u T ≤1，u T ≤0 T (ii) a u represents a dual variable;
step B32: due to u T ·g u A quadratic term exists in the step (S), the Max problem in the step B31 is solved by applying a 'Big-M' method, and the worst scene corresponding to the maximum security violation is solved;
step B33: if the maximum security violation v for the worst case scenario is above a given threshold, C is generated corresponding to the worst case scenario&CG optimal cutting plane
Figure FDA0003665973770000062
Feeding back to the main problem of the unit combination; in the formula I b And P b Is a unit combinationSolving the main problem to obtain a unit combination and a unit output result; p u (S) the unit output of uncertainty is responded in the second stage;
Figure FDA0003665973770000063
the unit combination and the unit output introduced into the safety verification problem in the first stage are obtained; q, W, R are coefficient matrixes of uncertainty constraint conditions, g u (S) is the amount of uncertainty in the uncertainty constraint.
CN201910503736.3A 2019-06-12 2019-06-12 Robust set combination method based on multi-band uncertain set Active CN110247426B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910503736.3A CN110247426B (en) 2019-06-12 2019-06-12 Robust set combination method based on multi-band uncertain set

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910503736.3A CN110247426B (en) 2019-06-12 2019-06-12 Robust set combination method based on multi-band uncertain set

Publications (2)

Publication Number Publication Date
CN110247426A CN110247426A (en) 2019-09-17
CN110247426B true CN110247426B (en) 2022-09-06

Family

ID=67886586

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910503736.3A Active CN110247426B (en) 2019-06-12 2019-06-12 Robust set combination method based on multi-band uncertain set

Country Status (1)

Country Link
CN (1) CN110247426B (en)

Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111082466B (en) * 2019-12-27 2021-03-02 国网宁夏电力有限公司经济技术研究院 New energy access and grid frame extension optimization method considering wind power uncertainty
CN112260268B (en) * 2020-09-22 2023-08-29 国网冀北电力有限公司经济技术研究院 Method for obtaining system flexibility domain
CN112668751B (en) * 2020-11-26 2022-06-17 广西大学 Method and device for establishing unit optimization scheduling model
CN112886572A (en) * 2021-01-21 2021-06-01 三峡大学 Evaluation method for renewable energy consumption capability of power grid
CN113659636A (en) * 2021-08-20 2021-11-16 东北电力大学 N-k distribution robust unit control method and device under distribution uncertainty condition
CN115270008B (en) * 2022-09-29 2023-01-10 西南财经大学 Maximum influence owner searching method and system, storage medium and terminal
CN116780649B (en) * 2023-06-16 2024-03-01 国网浙江省电力有限公司嘉兴供电公司 Multi-energy complementary utilization distributed robust optimization operation method

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN202602291U (en) * 2012-05-09 2012-12-12 华锐风电科技(集团)股份有限公司 Wind generating set filter circuit and power generation system
CN107742901A (en) * 2017-11-14 2018-02-27 清华大学 Consider the wind-electricity integration Unit Combination method and device of compressed-air energy storage
US10078328B1 (en) * 2014-08-19 2018-09-18 Dan Slater Solar array remote acoustic sensing (SARAS)
CN108599268A (en) * 2018-04-17 2018-09-28 上海电力学院 A kind of Optimization Scheduling a few days ago considering the constraint of wind power plant space time correlation
CN109066772A (en) * 2018-08-01 2018-12-21 广州供电局有限公司 Wind-powered electricity generation climbing rate stabilizes methods, devices and systems
CN109617134A (en) * 2018-12-21 2019-04-12 华北电力大学 The robust Unit Combination method of meter and Uncertainty prediction error temporal correlation
CN109785183A (en) * 2018-12-26 2019-05-21 国网山西省电力公司电力科学研究院 A kind of consideration wind-powered electricity generation and the probabilistic Robust Scheduling method of load prediction

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN202602291U (en) * 2012-05-09 2012-12-12 华锐风电科技(集团)股份有限公司 Wind generating set filter circuit and power generation system
US10078328B1 (en) * 2014-08-19 2018-09-18 Dan Slater Solar array remote acoustic sensing (SARAS)
CN107742901A (en) * 2017-11-14 2018-02-27 清华大学 Consider the wind-electricity integration Unit Combination method and device of compressed-air energy storage
CN108599268A (en) * 2018-04-17 2018-09-28 上海电力学院 A kind of Optimization Scheduling a few days ago considering the constraint of wind power plant space time correlation
CN109066772A (en) * 2018-08-01 2018-12-21 广州供电局有限公司 Wind-powered electricity generation climbing rate stabilizes methods, devices and systems
CN109617134A (en) * 2018-12-21 2019-04-12 华北电力大学 The robust Unit Combination method of meter and Uncertainty prediction error temporal correlation
CN109785183A (en) * 2018-12-26 2019-05-21 国网山西省电力公司电力科学研究院 A kind of consideration wind-powered electricity generation and the probabilistic Robust Scheduling method of load prediction

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Experimental research and parameter optimization for speed governing system of isolated network operation area;Xinjie Hao et al;《2017 IEEE Conference on Energy Internet and Energy System Integration (EI2)》;20180104;1-6 *
风电场混合储能的小波包-SOC 分区功率控制;陈科彬等;《仪器仪表学报》;20170831;第38卷(第8期);2078-2085 *

Also Published As

Publication number Publication date
CN110247426A (en) 2019-09-17

Similar Documents

Publication Publication Date Title
CN110247426B (en) Robust set combination method based on multi-band uncertain set
CN109617134B (en) Robust unit combination method considering uncertainty prediction error time-space correlation
CN106655246B (en) Solving method of robust double-layer optimization model based on wind power prediction and demand response
De Jonghe et al. Value of price responsive load for wind integration in unit commitment
Tigas et al. Wide scale penetration of renewable electricity in the Greek energy system in view of the European decarbonization targets for 2050
CN107239863B (en) Robust unit combination method for power grid safety constraint
Niu et al. Sizing and coordinating fast-and slow-response energy storage systems to mitigate hourly wind power variations
Almeida et al. Optimal active-reactive power dispatch under competition via bilevel programming
CN107947244A (en) The grid-connected power controls in advance method of new energy power station
Guo et al. A stochastic-process-based method for assessing frequency regulation ability of power systems with wind power fluctuations
Wang et al. Flexible transmission expansion planning for integrating wind power based on wind power distribution characteristics
Pereira et al. Could electricity demand contribute to diversifying the mix and mitigating CO2 emissions? A fresh daily analysis of the French electricity system
Kushwaha et al. A novel framework to assess synthetic inertia & primary frequency response support from energy storage systems
Boutsika et al. Sizing an energy storage system to minimize wind power imbalances from the hourly average
Dratsas et al. A real-time redispatch method to evaluate the contribution of storage to capacity adequacy
CN118432044A (en) Source network collaborative planning method and system for modifying flexibility of power system unit
Wu et al. Preventive control strategy for an island power system that considers system security and economics
Toh et al. Incorporating forecast uncertainties into EENS for wind turbine studies
Yorino et al. Dynamic load dispatch for power system robust security against uncertainties
CN110688725B (en) Robust unit combination method considering operation risk and demand response
Ding et al. A dynamic period partition method for time-of-use pricing with high-penetration renewable energy
CN106203742A (en) A kind of grid equipment Energy efficiency evaluation based on energy-conservation return rate and selection method
CN104331748A (en) Method for forecasting continuous power curve of wind power plant group in planning target year
Wilson et al. Use of meteorological data for improved estimation of risk in capacity adequacy studies
Becker et al. What can transmission do for a fully renewable Europe?

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