CN114243750A - Photovoltaic absorption capacity assessment method and device considering time-space correlation and active management - Google Patents

Photovoltaic absorption capacity assessment method and device considering time-space correlation and active management Download PDF

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CN114243750A
CN114243750A CN202111319231.5A CN202111319231A CN114243750A CN 114243750 A CN114243750 A CN 114243750A CN 202111319231 A CN202111319231 A CN 202111319231A CN 114243750 A CN114243750 A CN 114243750A
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李强
杨俊义
唐伟佳
侯语涵
马欣
吴涵
袁越
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State Grid Jiangsu Electric Power Co Ltd
Hohai University HHU
Electric Power Research Institute of State Grid Jiangsu Electric Power Co Ltd
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Hohai University HHU
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Abstract

The invention discloses a photovoltaic absorption capacity evaluation method and device considering time-space correlation and active management, wherein the method comprises the steps of determining an ellipsoid uncertain set of photovoltaic output time uncertainty and an ellipsoid uncertain set of photovoltaic output space uncertainty based on the correlation of photovoltaic output with time and space; calculating the empirical distribution of uncertainty precalculated values of the time and space of the uncertain set of the photovoltaic output ellipsoids; and establishing a robust photovoltaic absorption capacity evaluation model considering photovoltaic output and time-space correlation, and solving to obtain the maximum installed capacity of the renewable energy. According to the photovoltaic absorption capacity evaluation method, the influence of the correlation between active management and the distributed power supply on the absorption capacity can be quantized by considering the time-space correlation and the photovoltaic absorption capacity evaluation method of the active management, and the power distribution network is stimulated to improve the self absorption level.

Description

Photovoltaic absorption capacity assessment method and device considering time-space correlation and active management
Technical Field
The invention belongs to the technical field of distributed energy consumption, and particularly relates to a photovoltaic consumption capacity evaluation method and device considering time-space correlation and active management.
Background
The distribution of distributed photovoltaics in a power distribution grid is generally more intensive. Under the influence of the same type of microclimate conditions, the output of distributed photovoltaic in the same power distribution network generally has stronger correlation, and the correlation can obviously reduce the fluctuation of the output of distributed photovoltaic clusters, so that the consumption level of the distributed photovoltaic of the power distribution network is influenced. The existing photovoltaic absorption capacity evaluation method does not consider the time-space correlation characteristic of photovoltaic output when calculating the maximum installation capacity of distributed photovoltaic in a distribution network, the calculated maximum installation amount of photovoltaic is conservative, the actual installation amount of photovoltaic is difficult to reflect, and solar energy resources in the distribution network are wasted.
Disclosure of Invention
The invention aims to provide a photovoltaic absorption capacity evaluation method and device considering time-space correlation and active management.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the invention provides a photovoltaic absorption capacity evaluation method considering time-space correlation and active management, which comprises the following steps of:
determining an ellipsoid uncertain set of photovoltaic output time uncertainty and an ellipsoid uncertain set of photovoltaic output space uncertainty based on the correlation of photovoltaic output with time and space;
calculating the empirical distribution of uncertainty precalculated values of the time and space of the uncertain set of the photovoltaic output ellipsoids;
establishing a robust photovoltaic absorption capacity evaluation model considering photovoltaic output and time-space correlation; the model takes the maximum installed capacity of renewable energy sources on the node to be selected as a target function, and takes OLTC constraint, PV output constraint, reactive power output constraint of reactive power elements, tie line power constraint, branch power flow constraint, network topology constraint and safety constraint as constraint conditions;
and solving the robust photovoltaic consumption capability evaluation model to obtain the maximum installed capacity of the renewable energy source and the maximum photovoltaic consumption capability.
Further, in the above-mentioned case,
the ellipsoid uncertain set of the photovoltaic output time uncertainty and the ellipsoid uncertain set of the photovoltaic output space uncertainty are expressed as follows:
Figure BDA0003344616850000011
Figure BDA0003344616850000012
Figure BDA0003344616850000021
Figure BDA0003344616850000022
Figure BDA0003344616850000023
wherein, COVSAnd COVTIs a photovoltaic output spatial covariance matrix and a temporal covariance matrix,
Figure BDA0003344616850000024
representing node j at time tPVThe average photovoltaic power generation amount of (a),
Figure BDA0003344616850000025
represents the photovoltaic power generation amount of the node j at the time t,
Figure BDA0003344616850000026
for the purpose of the spatial uncertainty budget,
Figure BDA0003344616850000027
for the purpose of the time uncertainty budget,
Figure BDA0003344616850000028
the covariance matrix of the output of any two photovoltaic power stations at time T and T +1 is shown, wherein T is 1,2, …, and T represent the number of time segments; sigmaPVn,PVn+1N is 1,2, …, N is the covariance matrix between the nth photovoltaic power plant and the (N + 1) th photovoltaic power plant, N is the number of photovoltaic power plants,
Figure BDA0003344616850000029
σPVn、σPVn+1is the standard deviation, pPVn,PVn+1And
Figure BDA00033446168500000210
is the Pearson correlation coefficient.
Further, the empirical distribution of the uncertainty budget values of the time and space of the uncertainty set of the photovoltaic output ellipsoids is calculated as follows:
calculating the average photovoltaic output value of each time period according to historical photovoltaic output data
Figure BDA00033446168500000211
Sum covarianceMatrix COVT；
Obtaining the gamma-shapedT(ii) an empirical distribution of;
Figure BDA00033446168500000212
deriving Γ from empirical distributionTAlpha percentile of (a);
and the number of the first and second groups,
obtaining an empirical relation of photovoltaic output correlation and geographic distance according to historical photovoltaic output data of a known geographic position;
calculating a distance matrix between target photovoltaic power stations according to the installation site of the target photovoltaic power station, and calculating a correlation coefficient matrix of a target photovoltaic power station group according to the obtained empirical relational expression;
sampling from N-dimensional Gaussian distribution according to the obtained correlation coefficient matrix to obtain the probability value of each sample;
calculating an actual sample value of the photovoltaic output according to the sample probability value and the inverse marginal distribution of the photovoltaic output;
Γ is calculated fromS(ii) an empirical distribution of;
Figure BDA00033446168500000213
deriving the parameter Γ from an empirical distributionSAlpha percentile of (c).
Further, a reference area with the same climate type as the target photovoltaic power station installation site is selected, and historical photovoltaic output data are obtained.
Further, the objective function of the robust photovoltaic absorption capability evaluation model considering the photovoltaic output and the time-space correlation is as follows:
Figure BDA0003344616850000031
wherein,
Figure BDA0003344616850000032
representing PV mounting node jPVUpper mounted photovoltaic capacity, psiPVRepresenting a set of PV installation nodes to be selected;
the constraint conditions are as follows:
A. the OLTC constraint:
Figure BDA0003344616850000033
Figure BDA0003344616850000034
Figure BDA0003344616850000035
Figure BDA0003344616850000036
Figure BDA0003344616850000037
Figure BDA0003344616850000038
Figure BDA0003344616850000039
Figure BDA00033446168500000310
Figure BDA00033446168500000311
Figure BDA00033446168500000312
Figure BDA00033446168500000313
Figure BDA00033446168500000314
wherein,
Figure BDA00033446168500000316
indicating access node j at time tOLTCAn integer variable for a tap position, 0 for no at this tap position, 1 for at this tap position,
Figure BDA00033446168500000317
the number of total taps is represented as,
Figure BDA00033446168500000315
is a binary variable that is a function of the variable,
Figure BDA00033446168500000318
is that
Figure BDA00033446168500000319
The length of the binary expression of (a),
Figure BDA00033446168500000430
indicating access node j at time tOLTCThe voltage of the secondary side of the transformer,
Figure BDA00033446168500000431
is the access node j at time tOLTCThe ratio of the number of the phase-change material,
Figure BDA00033446168500000432
indicating access node j at time tOLTCThe change in the tap is such that,
Figure BDA0003344616850000041
is the OLTC tap minimum turn ratio,
Figure BDA0003344616850000042
and
Figure BDA0003344616850000043
indicating access node j at time tOLTCThe increase or decrease of the adjustment state of (2),
Figure BDA0003344616850000044
an increase is indicated by a value of 1,
Figure BDA0003344616850000045
a value of 1 indicates a decrease in the number,
Figure BDA0003344616850000046
representing an access node jOLTCThe regulation range of the on-load voltage regulator,
Figure BDA0003344616850000047
representing an access node jOLTCMaximum regulation number of on-load voltage regulator, M and M0Representing parameters in a Big-M calculation method;
B. PV output constraint:
Figure BDA0003344616850000048
Figure BDA0003344616850000049
Figure BDA00033446168500000410
Figure BDA00033446168500000411
wherein,
Figure BDA00033446168500000412
and
Figure BDA00033446168500000413
representing PV mounting node jPVThe minimum and maximum photovoltaic capacity of the installation,
Figure BDA00033446168500000414
and
Figure BDA00033446168500000415
representing PV mounting node jPVThe photovoltaic active and photovoltaic reactive power output of the photovoltaic,
Figure BDA00033446168500000416
represents PV installation node j at time tPVZeta represents the minimum photovoltaic output level,
Figure BDA00033446168500000417
installing node j for PV at time tPVThe angle of the power factor of (a),
Figure BDA00033446168500000418
and
Figure BDA00033446168500000419
installing node j for PV at time tPVMinimum and maximum power factor angle of (d);
C. reactive power output constraint of reactive power element:
Figure BDA00033446168500000420
Figure BDA00033446168500000421
Figure BDA00033446168500000422
Figure BDA00033446168500000423
Figure BDA00033446168500000424
Figure BDA00033446168500000425
wherein,
Figure BDA00033446168500000426
installing node j for time tCONThe continuously variable VAR device of (a) reactive power,
Figure BDA00033446168500000427
and
Figure BDA00033446168500000428
respectively represent installation nodes jCONThe minimum value and the maximum value of the reactive power,
Figure BDA00033446168500000429
to install node jDISThe reactive power of the discrete adjustable VAR device of (a),
Figure BDA0003344616850000051
for each adjusted reactive power of a discrete adjustable VAR device,
Figure BDA0003344616850000052
and
Figure BDA0003344616850000053
is a binary auxiliary variable representing the installation of node j at time tDISIn discrete adjustable VAR deviceThe number of the drops is reduced,
Figure BDA0003344616850000054
represents the adjustment range of a discrete adjustable VAR device,
Figure BDA0003344616850000055
represents an installation node jDISAt the maximum number of adjustments of the discrete adjustable VAR apparatus,
Figure BDA0003344616850000056
for control-effective binary variables in discrete adjustable reactive devices, where the subscript s denotes the tap position, ΨCONRepresenting a set of mounted nodes of a candidate continuously variable VAR device, ΨDISRepresenting a set of discrete adjustable VAR device installation nodes to be selected;
D. tie line power constraint:
Figure BDA0003344616850000057
Figure BDA0003344616850000058
wherein,
Figure BDA0003344616850000059
and
Figure BDA00033446168500000510
represents the installation of node j at time tTRThe active power and the reactive power of the transformer are obtained,
Figure BDA00033446168500000511
and
Figure BDA00033446168500000512
represents the installation of node j at time tTRThe minimum and maximum active power of the transformer,
Figure BDA00033446168500000513
and
Figure BDA00033446168500000514
represents the installation of node j at time tTRMinimum and maximum reactive power of the transformer, psisubRepresenting a set of transformer installation nodes to be selected;
E. branch flow constraint:
Figure BDA00033446168500000515
Figure BDA00033446168500000516
Figure BDA00033446168500000517
Figure BDA00033446168500000518
where δ (j) is the set of all lines from node j, π (j) is the set of all lines connected to node j, Pjk,tAnd Qjk,tFor the active and reactive power of the line jk at time t, Pij,t、Qij,tFor the active and reactive power of line ij at time t, rijAnd xijAs resistance and reactance values of the line ij, bjIs the value of the conductance of the node j,
Figure BDA00033446168500000519
and
Figure BDA00033446168500000520
active and reactive powers of node j, ΨEFor all line sets, ΨnFor all line node sets, eijIs a binary auxiliary variable representing whether line ij is connected or not, if line ijAre connected, then e ij1, and vice versa, | · | | non-woven phosphor2Representing the euclidean norm.
Figure BDA00033446168500000521
Representing the square of the line current and the node voltage, respectively;
F. and (3) network topology constraint:
Figure BDA0003344616850000061
Figure BDA0003344616850000062
Figure BDA0003344616850000063
-M·eij≤Fij≤M·eij ij∈ΨE；
Figure BDA00033446168500000611
wherein N isbusIs the number of nodes of the distribution network, FijIs a non-negative variable representing the virtual power flow transmitted on line ij in the virtual power flow, Wj1Is the power provided by the source point in the virtual network;
G. safety restraint:
Figure BDA0003344616850000064
Figure BDA0003344616850000065
H. photovoltaic output time uncertainty and photovoltaic output space uncertainty represent:
Figure BDA0003344616850000066
further, in the above-mentioned case,
converting the robust photovoltaic absorption capacity evaluation model of the photovoltaic output and time-space correlation into a robust optimization form, and taking the efficiency factor of the photovoltaic system
Figure BDA0003344616850000067
Taking the minimum absorption capacity as a target for decision variables, adopting a CPLEX solver to calculate in an iterative mode until the calculation is finished when the line current error obtained by iteration is smaller than a preset threshold value, outputting the maximum photovoltaic installed capacity which is the photovoltaic absorption capacity of the power distribution network,
wherein the decision variables include: continuous variable
Figure BDA0003344616850000068
Figure BDA0003344616850000069
And
discrete variable
Figure BDA00033446168500000610
Furthermore, in the solving process, the second-order cone constraint of the power grid flow is dynamically tightened by adopting a secant plane method,
the cut plane in the τ +1 th iteration is:
Figure BDA0003344616850000071
wherein,
Figure BDA0003344616850000072
representing the square of the current of line ij during time t in the # 1 th iteration,
Figure BDA0003344616850000073
and
Figure BDA0003344616850000074
respectively representing the active power, the reactive power and the square of the node voltage in tau iterations.
The invention also provides a photovoltaic absorption capacity evaluation device considering time-space correlation and active management, which comprises:
the first calculation module is used for determining an ellipsoid uncertain set of photovoltaic output time uncertainty and an ellipsoid uncertain set of photovoltaic output space uncertainty based on the correlation of photovoltaic output with time and space;
the second calculation module is used for calculating the empirical distribution of the uncertainty precalculated values of the time and space of the uncertain set of the photovoltaic output ellipsoids;
the building module is used for building a robust photovoltaic absorption capacity evaluation model considering photovoltaic output and time-space correlation; the model takes the maximum installed capacity of renewable energy sources on the node to be selected as a target function, and takes OLTC constraint, PV output constraint, reactive power output constraint of reactive power elements, tie line power constraint, branch power flow constraint, network topology constraint and safety constraint as constraint conditions;
and the number of the first and second groups,
and the output module is used for solving the robust photovoltaic consumption capability evaluation model to obtain the maximum installed capacity of the renewable energy source and the maximum photovoltaic consumption capability.
Further, the first calculation module is specifically configured to,
the ellipsoid uncertain set of the photovoltaic output time uncertainty and the ellipsoid uncertain set of the photovoltaic output space uncertainty are determined as follows:
Figure BDA0003344616850000075
Figure BDA0003344616850000076
Figure BDA0003344616850000077
Figure BDA0003344616850000078
Figure BDA0003344616850000079
wherein, COVSAnd COVTIs a photovoltaic output spatial covariance matrix and a temporal covariance matrix,
Figure BDA0003344616850000081
representing node j at time tPVThe average photovoltaic power generation amount of (a),
Figure BDA0003344616850000082
represents the photovoltaic power generation amount of the node j at the time t,
Figure BDA0003344616850000083
for the purpose of the spatial uncertainty budget,
Figure BDA0003344616850000084
for the purpose of the time uncertainty budget,
Figure BDA0003344616850000085
the covariance matrix of the output of any two photovoltaic power stations at time T and T +1 is shown, wherein T is 1,2, …, and T represent the number of time segments; sigmaPVn,PVn+1N is 1,2, …, N is the covariance matrix between the nth photovoltaic power plant and the (N + 1) th photovoltaic power plant, N is the number of photovoltaic power plants,
Figure BDA0003344616850000086
σPVn、σPVn+1is the standard deviation, pPVn,PVn+1And
Figure BDA0003344616850000087
is Pearson correlation coefficients.
Further, the building block is specifically configured to,
an objective function of a robust photovoltaic absorption capacity evaluation model considering photovoltaic output and time-space correlation is established as follows:
Figure BDA0003344616850000088
wherein,
Figure BDA0003344616850000089
representing PV mounting node jPVUpper mounted photovoltaic capacity, psiPVRepresenting a set of PV installation nodes to be selected;
the constraints are as follows:
A. the OLTC constraint:
Figure BDA00033446168500000810
Figure BDA00033446168500000811
Figure BDA00033446168500000812
Figure BDA00033446168500000813
Figure BDA00033446168500000814
Figure BDA00033446168500000815
Figure BDA00033446168500000816
Figure BDA00033446168500000817
Figure BDA00033446168500000818
Figure BDA00033446168500000819
Figure BDA0003344616850000091
Figure BDA0003344616850000092
wherein,
Figure BDA0003344616850000093
indicating access node j at time tOLTCAn integer variable for a tap position, 0 for no at this tap position, 1 for at this tap position,
Figure BDA0003344616850000094
the number of total taps is represented as,
Figure BDA0003344616850000095
is a binary variable that is a function of the variable,
Figure BDA0003344616850000096
is that
Figure BDA0003344616850000097
The length of the binary expression of (a),
Figure BDA0003344616850000098
indicating access node j at time tOLTCThe voltage of the secondary side of the transformer,
Figure BDA0003344616850000099
is the access node j at time tOLTCThe ratio of the number of the phase-change material,
Figure BDA00033446168500000910
indicating access node j at time tOLTCThe change in the tap is such that,
Figure BDA00033446168500000911
is the OLTC tap minimum turn ratio,
Figure BDA00033446168500000912
and
Figure BDA00033446168500000913
indicating access node j at time tOLTCThe increase or decrease of the adjustment state of (2),
Figure BDA00033446168500000914
an increase is indicated by a value of 1,
Figure BDA00033446168500000915
a value of 1 indicates a decrease in the number,
Figure BDA00033446168500000916
representing an access node jOLTCThe regulation range of the on-load voltage regulator,
Figure BDA00033446168500000917
representing an access node jOLTCMaximum regulation number of on-load voltage regulator, M and M0Representing parameters in a Big-M calculation method;
B. PV output constraint:
Figure BDA00033446168500000918
Figure BDA00033446168500000919
Figure BDA00033446168500000920
Figure BDA00033446168500000921
wherein,
Figure BDA00033446168500000922
and
Figure BDA00033446168500000923
representing PV mounting node jPVThe minimum and maximum photovoltaic capacity of the installation,
Figure BDA00033446168500000924
and
Figure BDA00033446168500000925
representing PV mounting node jPVThe photovoltaic active and photovoltaic reactive power output of the photovoltaic,
Figure BDA00033446168500000926
represents PV installation node j at time tPVZeta represents the minimum photovoltaic output level,
Figure BDA00033446168500000927
installing node j for PV at time tPVThe angle of the power factor of (a),
Figure BDA00033446168500000928
and
Figure BDA00033446168500000929
installing node j for PV at time tPVMinimum sum of power factor angle ofA maximum value;
C. reactive power output constraint of reactive power element:
Figure BDA00033446168500000930
Figure BDA00033446168500000931
Figure BDA00033446168500000932
Figure BDA00033446168500000933
Figure BDA0003344616850000101
Figure BDA0003344616850000102
wherein,
Figure BDA0003344616850000103
installing node j for time tCONThe continuously variable VAR device of (a) reactive power,
Figure BDA0003344616850000104
and
Figure BDA0003344616850000105
respectively represent installation nodes jCONThe minimum value and the maximum value of the reactive power,
Figure BDA0003344616850000106
to install node jDISThe reactive power of the discrete adjustable VAR device of (a),
Figure BDA0003344616850000107
for each adjusted reactive power of a discrete adjustable VAR device,
Figure BDA0003344616850000108
and
Figure BDA0003344616850000109
is a binary auxiliary variable representing the installation of node j at time tDISThe discrete adjustable VAR apparatus of (a) adjusts the state of increase and decrease,
Figure BDA00033446168500001010
represents the adjustment range of a discrete adjustable VAR device,
Figure BDA00033446168500001011
represents an installation node jDISAt the maximum number of adjustments of the discrete adjustable VAR apparatus,
Figure BDA00033446168500001012
for control-effective binary variables in discrete adjustable reactive devices, where the subscript s denotes the tap position, ΨCONRepresenting a set of mounted nodes of a candidate continuously variable VAR device, ΨDISRepresenting a set of discrete adjustable VAR device installation nodes to be selected;
D. tie line power constraint:
Figure BDA00033446168500001013
Figure BDA00033446168500001014
wherein,
Figure BDA00033446168500001015
and
Figure BDA00033446168500001016
represents the installation of node j at time tTRThe active power and the reactive power of the transformer are obtained,
Figure BDA00033446168500001017
and
Figure BDA00033446168500001018
represents the installation of node j at time tTRThe minimum and maximum active power of the transformer,
Figure BDA00033446168500001019
and
Figure BDA00033446168500001020
represents the installation of node j at time tTRMinimum and maximum reactive power of the transformer, psisubRepresenting a set of transformer installation nodes to be selected;
E. branch flow constraint:
Figure BDA00033446168500001021
Figure BDA00033446168500001022
Figure BDA00033446168500001023
Figure BDA00033446168500001024
where δ (j) is the set of all lines from node j, π (j) is the set of all lines connected to node j, Pjk,tAnd Qjk,tFor the active and reactive power of the line jk at time t, Pij,t、Qij,tFor the active and reactive power of line ij at time t, rijAnd xijAs resistance and reactance values of the line ij, bjIs the value of the conductance of the node j,
Figure BDA0003344616850000111
and
Figure BDA0003344616850000112
active and reactive powers of node j, ΨEFor all line sets, ΨnFor all line node sets, eijIs a binary auxiliary variable representing whether line ij is connected, if line ij is connected, e ij1, and vice versa, | · | | non-woven phosphor2Representing the euclidean norm.
Figure BDA0003344616850000113
Representing the square of the line current and the node voltage, respectively;
F. and (3) network topology constraint:
Figure BDA0003344616850000114
Figure BDA0003344616850000115
Figure BDA0003344616850000116
-M·eij≤Fij≤M·eij ij∈ΨE；
Figure BDA0003344616850000117
wherein N isbusIs the number of nodes of the distribution network, FijIs a non-negative variable representing the virtual power flow transmitted on line ij in the virtual power flow, Wj1Is the power provided by the source point in the virtual network;
G. safety restraint:
Figure BDA0003344616850000118
Figure BDA0003344616850000119
H. photovoltaic output time uncertainty and photovoltaic output space uncertainty represent:
Figure BDA00033446168500001110
compared with the prior art, the invention has the following advantages:
according to the photovoltaic power distribution network photovoltaic power generation and absorption capacity calculation method, the photovoltaic output time-space correlation and the power distribution network active management are considered when the power distribution network photovoltaic absorption capacity is calculated, and the photovoltaic fluctuation is utilized to the maximum extent to improve the power distribution network photovoltaic absorption capacity by fully considering the time and space correlation among a plurality of photovoltaic power stations. Meanwhile, active management strategies such as on-load tap changer regulation, network reconstruction, photovoltaic inverter reactive power output control and reactive compensation which are common in operation are comprehensively considered, and the photovoltaic consumption level of the active power distribution network can be calculated more accurately.
Drawings
FIG. 1 is a flow chart of a photovoltaic absorption capacity evaluation method considering time-space correlation and active management according to the present invention;
FIG. 2 is a diagram of a 59-node power distribution system in Suzhou, Jiangsu province in accordance with an embodiment of the present invention;
FIG. 3 is a typical time-of-day load and photovoltaic output curve for a 59-node power distribution system in an embodiment of the present invention;
fig. 4 illustrates photovoltaic absorption capability of each node in a 59-node power distribution system before and after active management (ANM) enabled by using a DC-PV-HCAM model according to an embodiment of the present invention.
Detailed Description
The invention is further described below. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The invention provides a photovoltaic absorption capacity evaluation method considering time-space correlation and active management, which specifically comprises the following steps:
a reference area is selected that has sufficient historical photovoltaic output data and a similar climate type as the target photovoltaic installation site. In addition, the latitude and longitude should be as close as possible to the target photovoltaic installation site to be consistent with the actual photovoltaic parameters.
And (4) inspecting the correlation of the distributed photovoltaic power generation capacity with time and geographical distribution through historical photovoltaic output data. The separate spatial and temporal correlations further configure the uncertain set of photovoltaic exit ellipsoids to reduce overall conservation.
And calculating parameters of the photovoltaic output ellipsoid uncertainty set, corresponding uncertainty budgets and observation probabilities one to one, and respectively obtaining the empirical distribution of uncertainty budget values from two aspects of time and space.
And establishing a robust photovoltaic absorption capacity evaluation model (RC-PV-HCAM) considering the time-space correlation.
And converting the original problem into a problem of solving a single maximum value by using a robust equivalent model of RC-PV-HCAM to solve to obtain the maximum absorption capacity of the active power distribution network, and dynamically tightening second-order cone constraints by using a secant plane method in the solving process.
In the invention, the uncertain set of photovoltaic output ellipsoids is expressed as:
Figure BDA0003344616850000121
in the formula,
Figure BDA0003344616850000122
represents a power distribution network node j at the moment tPVThe average photovoltaic power generation amount is obtained from long-term historical photovoltaic power generation amount data or a predicted value,
Figure BDA0003344616850000123
representing the photovoltaic power generation amount of a power distribution network node j at the moment t; Σ denotes a covariance matrix obtained from historical data; gamma-shapedαCommonly referred to as uncertainty budget, represents the coverage of observations over the total samples.
Equation (1) can be decomposed into:
Figure BDA0003344616850000124
in the formula,
Figure BDA0003344616850000125
and
Figure BDA0003344616850000126
is the inverse of the historical photovoltaic output space and time covariance matrix;
Figure BDA0003344616850000127
commonly referred to as spatial uncertainty budget, the specific calculation method is shown by equation (8);
Figure BDA0003344616850000128
referred to as the time uncertainty budget, the specific calculation method is shown by equation (9).
Wherein the historical photovoltaic output time and spatial covariance matrix is expressed as:
Figure BDA0003344616850000131
Figure BDA0003344616850000132
Figure BDA0003344616850000133
Figure BDA0003344616850000134
Figure BDA0003344616850000135
in the formula,
Figure BDA0003344616850000136
the method is characterized in that the method is a covariance matrix of output of any two photovoltaic power stations at time T and T +1, wherein T is 1,2 and …, T comprises time correlation among different time periods, and T represents the number of the time periods; sigmaPVn,PVn+1N is 1,2, …, where N is a covariance matrix between the nth photovoltaic power station and the (N + 1) th photovoltaic power station, where spatial correlation between different photovoltaic power stations is included, and N is the number of photovoltaic power stations;
Figure BDA0003344616850000137
σPVn，σPVn+1is the corresponding standard deviation; rhoPVn,PVn+1And
Figure BDA0003344616850000138
is the Pearson correlation coefficient. The correlation coefficient is equal to 1 if a pair of photovoltaic power stations can always observe the same output at the same moment. Conversely, a correlation coefficient equal to zero means that the photovoltaic contribution is linearly independent. Equation (7) is a calculation equation of the Pearson correlation coefficient.
In the invention, the uncertainty of the time-space correlation is described by an ellipsoid-type uncertainty set defined by covariance. The uncertainty budget controls the conservatism of the uncertain set. A higher uncertainty budget usually means that the uncertainty set contains more observations, but it is also possible that a large number of invalid regions are thus covered, making the uncertainty set too conservative. An ideal uncertainty set should be well balanced between the volume and coverage of the uncertainty set, and shrinking the volume of the uncertainty set may result in a more ideal uncertainty set.
In the invention, the generation steps of the ellipsoid uncertainty set with time uncertainty are as follows:
(A1) calculating the average photovoltaic output value of each time period according to the historical photovoltaic output
Figure BDA0003344616850000139
Sum covariance matrix COVT；
(A2) Obtaining gamma from formula (1)T(ii) an empirical distribution of;
Figure BDA00033446168500001310
(A3) deriving Γ from empirical distributionTAlpha percentile of (c).
In the invention, the generation steps of the ellipsoid uncertainty set with the space uncertainty are as follows:
(B1) obtaining an empirical relation of photovoltaic output correlation and geographic distance by using historical photovoltaic output data of a known geographic position;
(B2) calculating a distance matrix between the photovoltaic power stations to be selected by using the positions of the photovoltaic power stations to be selected, and calculating a correlation coefficient matrix of the target photovoltaic station group according to the empirical relational expression obtained in the step (B1);
(B3) sampling from N-dimensional Gaussian distribution by using the correlation coefficient matrix obtained in the step (B2) to obtain a probability value of each sample;
(B4) inverse marginal distribution F-1X by sample probability value and photovoltaic outputi(xi) Calculating an actual sample value of photovoltaic output;
(B5) Γ is calculated fromS(ii) an empirical distribution of;
Figure BDA0003344616850000141
(B6) deriving the parameter Γ from an empirical distributionSAlpha percentile of (c).
In the invention, a robust photovoltaic absorption capability evaluation model (RC-PV-HCAM) considering time-space correlation is established as follows:
the selected objective function is that the installed capacity of the renewable energy sources on the node to be selected is maximum:
Figure BDA0003344616850000142
in the formula,
Figure BDA0003344616850000143
represents an installation node jPVTop mounted photovoltaic capacity psiPVRepresenting a set of installation nodes to be selected.
On-load tap changers (OLTC) can adjust the voltage on the secondary side of the transformer by changing the position of a tap. The active management of the OLTC implementation can be implemented by OLTC constraints, which are:
Figure BDA0003344616850000144
Figure BDA0003344616850000145
Figure BDA0003344616850000146
Figure BDA0003344616850000147
Figure BDA0003344616850000148
Figure BDA0003344616850000149
Figure BDA00033446168500001410
Figure BDA00033446168500001411
Figure BDA0003344616850000151
Figure BDA0003344616850000152
Figure BDA0003344616850000153
Figure BDA0003344616850000154
in the formula,
Figure BDA0003344616850000155
is an access node j representing time tOLTCAn integer variable for a tap position, 0 for no at this tap position, 1 for at this tap position,
Figure BDA0003344616850000156
the number of total taps is represented as,
Figure BDA0003344616850000157
is a binary variable that is a function of the variable,
Figure BDA0003344616850000158
is that
Figure BDA0003344616850000159
The length of the binary expression of (a),
Figure BDA00033446168500001510
indicating access node j at time tOLTCThe voltage of the secondary side of the transformer,
Figure BDA00033446168500001511
is the access node j at time tOLTCThe ratio of the number of the phase-change material,
Figure BDA00033446168500001512
indicating access node j at time tOLTCThe change in the tap is such that,
Figure BDA00033446168500001513
is the OLTC tap minimum turn ratio,
Figure BDA00033446168500001514
and
Figure BDA00033446168500001515
indicating access node j at time tOLTCThe increase or decrease of the adjustment state of (2),
Figure BDA00033446168500001516
an increase is indicated by a value of 1,
Figure BDA00033446168500001517
a value of 1 indicates a decrease in the number,
Figure BDA00033446168500001518
representing an access node jOLTCThe regulation range of the on-load voltage regulator,
Figure BDA00033446168500001519
representing an access node jOLTCMaximum regulation number of on-load voltage regulator, NOLTCThe representation refers to the length of the OLTC tap at different positions, M and M0The parameters in the calculation method of Big-M are shown, and equations (13), (14), (16) and (17) are relaxation constraints for making substitution equivalent.
PV output constraint:
Figure BDA00033446168500001520
Figure BDA00033446168500001521
Figure BDA00033446168500001522
Figure BDA00033446168500001523
equations (24) - (26) represent the expected output of photovoltaic power generation at time t,
Figure BDA00033446168500001524
and
Figure BDA00033446168500001525
represents node jPVThe minimum and maximum photovoltaic capacity of the installation,
Figure BDA00033446168500001526
and
Figure BDA00033446168500001527
representing the photovoltaic active and photovoltaic reactive power output,
Figure BDA00033446168500001528
representing the equivalent power output coefficient, and zeta represents the minimum photovoltaic output level;
Figure BDA00033446168500001529
is node jPVThe angle of the power factor of (c) or (c),
Figure BDA00033446168500001530
and
Figure BDA00033446168500001531
the minimum and maximum power factor angle values.
Reactive power output constraint of reactive power element:
Figure BDA00033446168500001532
Figure BDA0003344616850000161
Figure BDA0003344616850000162
Figure BDA0003344616850000163
Figure BDA0003344616850000164
Figure BDA0003344616850000165
in the formula,
Figure BDA0003344616850000166
in order to continuously adjust the reactive power of the VAR device,
Figure BDA0003344616850000167
and
Figure BDA0003344616850000168
respectively represent the minimum value and the maximum value thereof,
Figure BDA0003344616850000169
for the reactive power of a discrete adjustable VAR device,
Figure BDA00033446168500001610
for each adjusted reactive power of a discrete adjustable VAR device,
Figure BDA00033446168500001611
and
Figure BDA00033446168500001612
is a binary auxiliary variable representing node j at time tDISThe increase and decrease of the adjustment state of the discrete adjustable reactive power device,
Figure BDA00033446168500001613
the adjustment range of the discrete adjustable reactive device is shown,
Figure BDA00033446168500001614
a maximum number of adjustments is indicated,
Figure BDA00033446168500001615
for control-effective binary variables in discrete adjustable reactive devices, where the subscript s denotes the tap position, ΨCONRepresenting a set of mounted nodes of a candidate continuously variable VAR device, ΨDISAnd representing a set of discrete tunable VAR device installation nodes to be selected. Equations (27), (28) represent continuously adjustable and continuously discrete VAR device constraints; equations (29) - (32) represent the daily switching operating constraints of the discrete tunable reactive device.
Tie line power constraint:
Figure BDA00033446168500001616
Figure BDA00033446168500001617
in the formula,
Figure BDA00033446168500001618
and
Figure BDA00033446168500001619
the active and the reactive power of the transformer are represented,
Figure BDA00033446168500001620
and
Figure BDA00033446168500001621
representing the minimum and maximum active power of the transformer,
Figure BDA00033446168500001622
and
Figure BDA00033446168500001623
representing minimum and maximum reactive power, Ψ, of the transformersubAnd representing a set of transformer installation nodes to be selected.
Branch flow constraint:
Figure BDA00033446168500001624
Figure BDA00033446168500001625
Figure BDA00033446168500001626
Figure BDA0003344616850000171
where δ (j) is the set of all lines from node j, π (j) is the set of all lines connected to node j, Pjk,t、Qjk,tFor the active and reactive power of the line jk at time t, Pij,t、Qij,tActive power, reactive power, r, for line ij at time tij、xijIs the resistance value, reactance value, b of line ijjIs the value of the conductance of the node j,
Figure BDA0003344616850000172
is a node j hasPower, reactive power, psiEFor all line sets, ΨnIs a collection of all nodes. e.g. of the typeijIs a binary auxiliary variable representing whether line ij is connected, if line ij is connected, e ij1 and vice versa; i | · | purple wind2Representing the euclidean norm.
Figure BDA0003344616850000173
Representing the square of the line current and the node voltage, respectively.
If it is
Figure BDA0003344616850000174
If it is
Figure BDA0003344616850000175
If it is
Figure BDA0003344616850000176
If it is
Figure BDA0003344616850000177
And (3) network topology constraint:
Figure BDA0003344616850000178
Figure BDA0003344616850000179
Figure BDA00033446168500001710
-M·eij≤Fij≤M·eij ij∈ΨE (42)
Figure BDA00033446168500001711
in the formula, NbusIs the number of nodes of the distribution network, FijIs a non-negative variable representing the virtual power flow transmitted on line ij in the virtual power flow,
Figure BDA00033446168500001712
is the power provided by the "source" point in the virtual network (the transformer node in the distribution network). Equation (39) is a radial constraint and equations (40) - (43) are connectivity constraints.
Safety restraint:
Figure BDA00033446168500001713
Figure BDA00033446168500001714
safety constraints include line thermal constraints and node voltage constraints.
Figure BDA00033446168500001715
Is the maximum value of the current of line ij,
Figure BDA00033446168500001716
the minimum and maximum voltage values at node j.
Converting the comprehensive consumption capacity evaluation model into a robust optimization form by using the efficiency factor of the photovoltaic system
Figure BDA0003344616850000181
And determining the photovoltaic output efficiency by taking the minimum consumption capacity as a decision variable.
The decision variables are other continuous variables such as photovoltaic installed capacity and the like
Figure BDA0003344616850000182
Figure BDA0003344616850000183
And get away fromVariation of dispersion
Figure BDA0003344616850000184
At this time, the RC-PV-HCAM can be represented by a vector as:
Figure BDA0003344616850000185
in the formula, pi represents the weight of the parameter.
In the present invention, the robust peer-to-peer model of the RC-PV-HCAM described above is rewritten into matrix-vector form as equations (47) - (55):
Figure BDA0003344616850000186
s.t.ax＝b (48)
Figure BDA0003344616850000187
ey＝g (50)
hy≤k (51)
mx+ny＝u (52)
Figure BDA0003344616850000188
x∈X (54)
Ω＝{δ|δTΣ-1δ≤Γ} (55)
wherein a, b, c, d, e, g, h, k, m, n, u, w, η and X are the corresponding parameter vectors and sets;
Figure BDA0003344616850000189
representing the mean value, δ, of the uncertainty parameter ηRepresents a small perturbation of the uncertain parameter, Σ being the covariance matrix of the uncertain parameter. Equation (55) is an ellipsoid uncertainty set representation of the uncertainty variable. Equation (48) represents an equation relationship of continuous variables including equations (13), (16), (28), (35), (36), (40), (41); equation (49) includes the temporal coupling relationship between successive variables, and between uncertain photovoltaic outputs, including equations (14), (23), (24), (25), (26), (27), (33), (34), (37), (38), (42), (43), (44), (45); equations (50) and (51) represent equations and inequalities of discrete variables, respectively, including equations (39) and (19), (20), (21), (22), (29), (30), (31), (32); equation (52) represents the power flow balance constraint and the relationship between the continuous and discrete variables; equation (53) represents all second order cone constraints; equation (54) represents the value range of the continuous variable.
The model adopts a second-order cone equation to describe the power flow of the power distribution network. In the actual calculation process, because the problem that the original power flow variable is difficult to be tightly wrapped by the second-order cone exists when the maximum photovoltaic loading capacity target is described in the second-order cone power flow, the second-order cone equation needs to be further dynamically tightened by a secant plane method during iterative solution.
Further, the cutting plane method is as follows:
the cut plane in the τ +1 th iteration can be written as:
Figure BDA0003344616850000191
in the formula,
Figure BDA0003344616850000192
representing the square of the current of line ij during time t in the # 1 th iteration,
Figure BDA0003344616850000193
and
Figure BDA0003344616850000194
respectively representing the active power, the reactive power and the node voltage squared in tau iterations, and are considered as known parameters in tau +1 iterations. At this moment, above-mentioned distribution network photovoltaic absorption ability aassessment mouldThe model can be calculated in an iterative mode by using a CPLEX and other common commercial solvers until the line current error obtained by iteration is smaller than a preset threshold epsilon, and the calculation is finished, and the output maximum photovoltaic installed capacity is the photovoltaic absorption capacity of the power distribution network.
Examples
The RC-PV-HCAM model provided by the invention is verified by adopting a rural power distribution network with Jiangsu 59 nodes. The analysis considers the following two scenarios:
case A: and calculating the absorption capacity of the power distribution network by using a deterministic comprehensive PV horizontal absorption capacity assessment model (DC-PV-HCAM). Examine the effect of active management (ANM) on PV absorption capacity.
Case B: and calculating the consumption capacity of the power distribution network by using the RC-PV-HCAM. Case B takes into account the uncertainty and relevance of multiple PV contributions based on Case a. The effect of randomness and dependence of the photovoltaic contribution on the photovoltaic absorption capacity is illustrated by a comparison of Case a and Case B.
The data are as follows: the structure of a 59-node rural power distribution system is shown in fig. 2. Two 300kVar SVCs are respectively arranged at nodes 18 and 42; there are 5 150kVar CBs at nodes 7, 24, 33, 38, and 59, and the capacitor tap is 50 kVar. The OLTC is located at node 1, with a total of 20 equally spaced taps, limited to adjustment only once per day. The voltage range of each node is set to [0.93, 1.07] p.u. The total load of the distribution network is 3.85MW and 0.97MVar, and the total electric quantity of the load of the distribution network in the daytime is 11.13 MWh. The test system comprises 15 candidate PV installation nodes, the minimum photovoltaic capacity of each candidate node is set to be 100kW, and the maximum photovoltaic power limiting rate of a distribution network is set to be 10%. The threshold epsilon of the secant plane method during the optimization solution is set to be 1%, and the relative clearance of the CPLEX is set to be 5%.
To be as close as practical, three types of loads are considered: industrial, commercial and residential loads. Figure 3 gives a typical daily load curve for three loads and the historical average and per unit value of the maximum solar photovoltaic power generation. In consideration of the power characteristics of the photovoltaic power generation output, the load and photovoltaic output values for 32 periods (15-minute intervals) in total from 8 am to 4 pm are used in the present embodiment. And obtaining a time-space covariance matrix of the photovoltaic output, uncertain budget and corresponding coverage rate of the uncertain budget according to the data.
Firstly, calculating the absorption capacity of the active power distribution network by using the DC-PV-HCAM, and comparing the absorption capacity of the power distribution network under the condition of existence of the ANM under the condition of average photovoltaic output scene and the maximum photovoltaic output scene.
The calculation results of the DC-PV-HCAM absorption capacity are shown in table 1, and fig. 4 shows the comparison of the installed photovoltaic capacity of each node before and after the ANM is enabled.
TABLE 1 distribution network photovoltaic absorption capability obtained by DC-PV-HCAM under different scenes
Figure BDA0003344616850000201
As can be seen from table 1, the absorption capacity of the power distribution network is significantly increased after the ANM is used, and the photovoltaic absorption capacity is significantly lower than the average photovoltaic output under the maximum photovoltaic output scene.
Table 2 shows the network reconstruction results calculated by the DC-PV-HCAM in different scenarios, and in the scenario where the ANM is not enabled, the disconnected line is the line shown by the dotted line in fig. 2. After the ANM is enabled, the network structure is also changed in order to maximize the photovoltaic efficiency, with the lines disconnected being 30-31, 40-59 and 14-15, 38-39, respectively.
TABLE 2 distribution network topology obtained from DC-PV-HCAM under different scenarios
Figure BDA0003344616850000202
And (4) on the basis of ANM, based on the evaluation result after the randomness and the correlation of the photovoltaic output are considered under different uncertain budgets of RC-PV-HCAM test.
First, assuming that the correlation coefficient is 1, the uncertainty ellipse is degenerated into a straight line. The photovoltaic absorption capacity after considering the photovoltaic output randomness is shown in table 3. Where the uncertainty budget has been translated into a corresponding uncertainty coverage. As can be seen from table 3, the photovoltaic absorption capacity increases with decreasing uncertainty coverage. In fact, a larger uncertainty budget means that more scenes deviating from the average photovoltaic contribution need to be considered, and an ellipsoid uncertainty set with a larger variance is selected for decision making. Therefore, the capacity evaluation result is more conservative.
The results in table 3 are based on the fully correlated photovoltaic contribution assumption, in fact only taking into account the fluctuations of a single photovoltaic contribution. The solution for RC-PV-HCAM will converge to the solution at the maximum photovoltaic output scenario in DC-PV-HCAM when the risk level is large enough. And as the uncertainty level is reduced, the worst scene gradually deviates to the original average photovoltaic output scene, and the solution of the RC-PV-HCAM also gradually converges to the solution in the DC-PV-HCAM under the average photovoltaic output scene.
TABLE 3 photovoltaic absorption Capacity (MW) of each node at different uncertainty levels without regard to relevance
Figure BDA0003344616850000211
The above results do not account for the influence of the correlation on the photovoltaic absorption capacity, and only account for the randomness of the photovoltaic output. In consideration of the smoothing effect brought by the correlation, the photovoltaic cluster output with the correlation can reduce the fluctuation of the total photovoltaic output, and the photovoltaic absorption capacity is possibly improved. Therefore, the correlation is taken into consideration, and the RC-PV-HCAM calculation is used for calculating the photovoltaic absorption capacity under different uncertain levels after the correlation is considered. The calculation results are shown in table 4.
TABLE 4 photovoltaic absorption Capacity (MW) of each node considering relevance at different uncertainty levels
Figure BDA0003344616850000212
Figure BDA0003344616850000221
It can be seen from table 4 that the photovoltaic absorption capacity can be improved indeed in consideration of the spatio-temporal correlation. Under the same uncertain level, the photovoltaic consumption of each node is improved to different degrees after the correlation is considered. The magnitude of the increase in absorptive capacity decreases with decreasing uncertainty level. Unlike the scenario where correlation is not considered in table 3, the results in table 4 consider the correlation of photovoltaic output, which has the most significant impact on the output of the photovoltaic cluster in the scenario of maximum photovoltaic output uncertainty. The sum of the output of the photovoltaic power generation system is more gentle than that of a single photovoltaic power generation system after being directly amplified, or the photovoltaic power generation system has the correlation of less than 1, so that the photovoltaic power generation system plays a role of 1+1<2, and the photovoltaic power generation system is more beneficial to photovoltaic consumption.
In terms of calculation performance, the DC-PV-HCAM calculation time is about 60min, and the secant plane method converges after 2-3 iterations. The RC-PV-HCAM calculation time is about 80-100min, and the secant plane method can be converged after 2-3 times of iteration. Considering that the estimation of the absorption capability is part of the planning process, and generally takes off-line calculation as the main part, the calculation efficiency is acceptable.
The invention also provides a photovoltaic absorption capacity evaluation device considering time-space correlation and active management, which comprises:
the first calculation module is used for determining an ellipsoid uncertain set of photovoltaic output time uncertainty and an ellipsoid uncertain set of photovoltaic output space uncertainty based on the correlation of photovoltaic output with time and space;
the second calculation module is used for calculating the empirical distribution of the uncertainty precalculated values of the time and space of the uncertain set of the photovoltaic output ellipsoids;
the building module is used for building a robust photovoltaic absorption capacity evaluation model considering photovoltaic output and time-space correlation; the model takes the maximum installed capacity of renewable energy sources on the node to be selected as a target function, and takes OLTC constraint, PV output constraint, reactive power output constraint of reactive power elements, tie line power constraint, branch power flow constraint, network topology constraint and safety constraint as constraint conditions;
and the number of the first and second groups,
and the output module is used for solving the robust photovoltaic consumption capability evaluation model to obtain the maximum installed capacity of the renewable energy source and the maximum photovoltaic consumption capability.
In the present invention, the first calculation module is specifically configured to,
the ellipsoid uncertain set of the photovoltaic output time uncertainty and the ellipsoid uncertain set of the photovoltaic output space uncertainty are determined as follows:
Figure BDA0003344616850000222
Figure BDA0003344616850000223
Figure BDA0003344616850000231
Figure BDA0003344616850000232
Figure BDA0003344616850000233
wherein, COVSAnd COVTIs a photovoltaic output spatial covariance matrix and a temporal covariance matrix,
Figure BDA0003344616850000234
representing node j at time tPVThe average photovoltaic power generation amount of (a),
Figure BDA0003344616850000235
represents the photovoltaic power generation amount of the node j at the time t,
Figure BDA0003344616850000236
for the purpose of the spatial uncertainty budget,
Figure BDA0003344616850000237
for the purpose of the time uncertainty budget,
Figure BDA0003344616850000238
the covariance matrix of the output of any two photovoltaic power stations at time T and T +1 is shown, wherein T is 1,2, …, and T represent the number of time segments; sigmaPVn,PVn+1N is 1,2, …, N is the covariance matrix between the nth photovoltaic power plant and the (N + 1) th photovoltaic power plant, N is the number of photovoltaic power plants,
Figure BDA0003344616850000239
σPVn、σPVn+1is the standard deviation, pPVn,PVn+1And
Figure BDA00033446168500002310
is the Pearson correlation coefficient.
In the present invention, the building blocks are used in particular,
an objective function of a robust photovoltaic absorption capacity evaluation model considering photovoltaic output and time-space correlation is established as follows:
Figure BDA00033446168500002311
wherein,
Figure BDA00033446168500002312
representing PV mounting node jPVUpper mounted photovoltaic capacity, psiPVRepresenting a set of PV installation nodes to be selected;
the constraints are as follows:
A. the OLTC constraint:
Figure BDA00033446168500002313
Figure BDA00033446168500002314
Figure BDA00033446168500002315
Figure BDA00033446168500002316
Figure BDA00033446168500002317
Figure BDA0003344616850000241
Figure BDA0003344616850000242
Figure BDA0003344616850000243
Figure BDA0003344616850000244
Figure BDA0003344616850000245
Figure BDA0003344616850000246
Figure BDA0003344616850000247
wherein,
Figure BDA0003344616850000248
indicating access node j at time tOLTCInteger variable for tap position, 0 for not at this tap position, 1 forIn the position of this tap it is possible to,
Figure BDA0003344616850000249
the number of total taps is represented as,
Figure BDA00033446168500002410
is a binary variable that is a function of the variable,
Figure BDA00033446168500002411
is that
Figure BDA00033446168500002412
The length of the binary expression of (a),
Figure BDA00033446168500002413
indicating access node j at time tOLTCThe voltage of the secondary side of the transformer,
Figure BDA00033446168500002414
is the access node j at time tOLTCThe ratio of the number of the phase-change material,
Figure BDA00033446168500002415
indicating access node j at time tOLTCThe change in the tap is such that,
Figure BDA00033446168500002416
is the OLTC tap minimum turn ratio,
Figure BDA00033446168500002417
and
Figure BDA00033446168500002418
indicating access node j at time tOLTCThe increase or decrease of the adjustment state of (2),
Figure BDA00033446168500002419
an increase is indicated by a value of 1,
Figure BDA00033446168500002420
a value of 1 indicates a decrease in the number,
Figure BDA00033446168500002421
representing an access node jOLTCThe regulation range of the on-load voltage regulator,
Figure BDA00033446168500002422
representing an access node jOLTCMaximum regulation number of on-load voltage regulator, M and M0Representing parameters in a Big-M calculation method;
B. PV output constraint:
Figure BDA00033446168500002423
Figure BDA00033446168500002424
Figure BDA00033446168500002425
Figure BDA00033446168500002426
wherein,
Figure BDA00033446168500002427
and
Figure BDA00033446168500002428
representing PV mounting node jPVThe minimum and maximum photovoltaic capacity of the installation,
Figure BDA00033446168500002429
and
Figure BDA00033446168500002430
representing PV mounting node jPVThe photovoltaic active and photovoltaic reactive power output of the photovoltaic,
Figure BDA00033446168500002431
represents PV installation node j at time tPVZeta represents the minimum photovoltaic output level,
Figure BDA0003344616850000251
installing node j for PV at time tPVThe angle of the power factor of (a),
Figure BDA0003344616850000252
and
Figure BDA0003344616850000253
installing node j for PV at time tPVMinimum and maximum power factor angle of (d);
C. reactive power output constraint of reactive power element:
Figure BDA0003344616850000254
Figure BDA0003344616850000255
Figure BDA0003344616850000256
Figure BDA0003344616850000257
Figure BDA0003344616850000258
Figure BDA0003344616850000259
wherein,
Figure BDA00033446168500002510
installing node j for time tCONThe continuously variable VAR device of (a) reactive power,
Figure BDA00033446168500002511
and
Figure BDA00033446168500002512
respectively represent installation nodes jCONThe minimum value and the maximum value of the reactive power,
Figure BDA00033446168500002513
to install node jDISThe reactive power of the discrete adjustable VAR device of (a),
Figure BDA00033446168500002514
for each adjusted reactive power of a discrete adjustable VAR device,
Figure BDA00033446168500002515
and
Figure BDA00033446168500002516
is a binary auxiliary variable representing the installation of node j at time tDISThe discrete adjustable VAR apparatus of (a) adjusts the state of increase and decrease,
Figure BDA00033446168500002517
represents the adjustment range of a discrete adjustable VAR device,
Figure BDA00033446168500002518
represents an installation node jDISAt the maximum number of adjustments of the discrete adjustable VAR apparatus,
Figure BDA00033446168500002519
for control-effective binary variables in discrete adjustable reactive devices, where the subscript s denotes the tap position, ΨCONRepresenting a set of mounted nodes of a candidate continuously variable VAR device, ΨDISRepresenting a set of discrete adjustable VAR device installation nodes to be selected;
D. tie line power constraint:
Figure BDA00033446168500002520
Figure BDA00033446168500002521
wherein,
Figure BDA00033446168500002522
and
Figure BDA00033446168500002523
represents the installation of node j at time tTRThe active power and the reactive power of the transformer are obtained,
Figure BDA00033446168500002524
and
Figure BDA00033446168500002525
represents the installation of node j at time tTRThe minimum and maximum active power of the transformer,
Figure BDA00033446168500002526
and
Figure BDA00033446168500002527
represents the installation of node j at time tTRMinimum and maximum reactive power of the transformer, psisubRepresenting a set of transformer installation nodes to be selected;
E. branch flow constraint:
Figure BDA0003344616850000261
Figure BDA0003344616850000262
Figure BDA0003344616850000263
Figure BDA0003344616850000264
where δ (j) is the set of all lines from node j, π (j) is the set of all lines connected to node j, Pjk,tAnd Qjk,tFor the active and reactive power of the line jk at time t, Pij,t、Qij,tFor the active and reactive power of line ij at time t, rijAnd xijAs resistance and reactance values of the line ij, bjIs the value of the conductance of the node j,
Figure BDA0003344616850000265
and
Figure BDA0003344616850000266
active and reactive powers of node j, ΨEFor all line sets, ΨnFor all line node sets, eijIs a binary auxiliary variable representing whether line ij is connected, if line ij is connected, e ij1, and vice versa, | · | | non-woven phosphor2Representing the euclidean norm.
Figure BDA0003344616850000267
Representing the square of the line current and the node voltage, respectively;
F. and (3) network topology constraint:
Figure BDA0003344616850000268
Figure BDA0003344616850000269
Figure BDA00033446168500002610
-M·eij≤Fij≤M·eij ij∈ΨE；
Figure BDA00033446168500002611
wherein N isbusIs the number of nodes of the distribution network, FijIs a non-negative variable representing the virtual power flow transmitted on line ij in the virtual power flow, Wj1Is the power provided by the source point in the virtual network;
G. safety restraint:
Figure BDA00033446168500002612
Figure BDA00033446168500002613
H. photovoltaic output time uncertainty and photovoltaic output space uncertainty represent:
Figure BDA0003344616850000271
it is to be noted that the apparatus embodiment corresponds to the method embodiment, and the implementation manners of the method embodiment are all applicable to the apparatus embodiment and can achieve the same or similar technical effects, so that the details are not described herein.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (10)

1. A photovoltaic absorption capacity evaluation method considering time-space correlation and active management is characterized by comprising the following steps:
determining an ellipsoid uncertain set of photovoltaic output time uncertainty and an ellipsoid uncertain set of photovoltaic output space uncertainty based on the correlation of photovoltaic output with time and space;
calculating the empirical distribution of uncertainty precalculated values of the time and space of the uncertain set of the photovoltaic output ellipsoids;
establishing a robust photovoltaic absorption capacity evaluation model considering photovoltaic output and time-space correlation; the model takes the maximum installed capacity of renewable energy sources on the node to be selected as a target function, and takes OLTC constraint, PV output constraint, reactive power output constraint of reactive power elements, tie line power constraint, branch power flow constraint, network topology constraint and safety constraint as constraint conditions;
and solving the robust photovoltaic consumption capability evaluation model to obtain the maximum installed capacity of the renewable energy source and the maximum photovoltaic consumption capability.
2. The method for photovoltaic absorption capacity evaluation considering time-space correlation and active management according to claim 1,
the ellipsoid uncertain set of the photovoltaic output time uncertainty and the ellipsoid uncertain set of the photovoltaic output space uncertainty are expressed as follows:
Figure FDA0003344616840000011
Figure FDA0003344616840000012
Figure FDA0003344616840000013
Figure FDA0003344616840000014
Figure FDA0003344616840000015
wherein, COVSAnd COVTIs a photovoltaic output spatial covariance matrix and a temporal covariance matrix,
Figure FDA0003344616840000016
representing node j at time tPVThe average photovoltaic power generation amount of (a),
Figure FDA0003344616840000017
represents the photovoltaic power generation amount of the node j at the time t,
Figure FDA0003344616840000018
for the purpose of the spatial uncertainty budget,
Figure FDA0003344616840000019
for the purpose of the time uncertainty budget,
Figure FDA00033446168400000110
the covariance matrix of the output of any two photovoltaic power stations at time T and T +1 is shown, wherein T is 1,2, …, and T represent the number of time segments; sigmaPVn,PVn+1N is 1,2, …, N is the covariance matrix between the nth photovoltaic power plant and the (N + 1) th photovoltaic power plant, N is the number of photovoltaic power plants,
Figure FDA0003344616840000021
σPVn、σPVn+1is the standard deviation, pPVn,PVn+1And
Figure FDA0003344616840000022
is the Pearson correlation coefficient.
3. The method of claim 2, wherein the empirical distribution of the temporal and spatial uncertainty estimates for the uncertain set of photovoltaic output ellipsoids is calculated as follows:
calculating the average photovoltaic output value of each time period according to historical photovoltaic output data
Figure FDA0003344616840000023
Sum covariance matrix COVT；
Obtaining the gamma-shapedT(ii) an empirical distribution of;
Figure FDA0003344616840000024
deriving Γ from empirical distributionTAlpha percentile of (a);
and the number of the first and second groups,
obtaining an empirical relation of photovoltaic output correlation and geographic distance according to historical photovoltaic output data of a known geographic position;
calculating a distance matrix between target photovoltaic power stations according to the installation site of the target photovoltaic power station, and calculating a correlation coefficient matrix of a target photovoltaic power station group according to the obtained empirical relational expression;
sampling from N-dimensional Gaussian distribution according to the obtained correlation coefficient matrix to obtain the probability value of each sample;
calculating an actual sample value of the photovoltaic output according to the sample probability value and the inverse marginal distribution of the photovoltaic output;
Γ is calculated fromS(ii) an empirical distribution of;
Figure FDA0003344616840000025
from the warpObtaining parameter gamma in the distributionSAlpha percentile of (c).
4. The method of claim 3, wherein a reference area having the same climate type as the target photovoltaic power plant installation site is selected and historical photovoltaic output data is obtained.
5. The photovoltaic absorptive capacity evaluation method considering the spatio-temporal correlation and the active management according to claim 3, wherein the objective function of the robust photovoltaic absorptive capacity evaluation model considering the photovoltaic output and the spatio-temporal correlation is as follows:
Figure FDA0003344616840000026
wherein,
Figure FDA0003344616840000027
representing PV mounting node jPVUpper mounted photovoltaic capacity, psiPVRepresenting a set of PV installation nodes to be selected;
the constraint conditions are as follows:
A. the OLTC constraint:
Figure FDA0003344616840000028
Figure FDA0003344616840000031
Figure FDA0003344616840000032
Figure FDA0003344616840000033
Figure FDA0003344616840000034
Figure FDA0003344616840000035
Figure FDA0003344616840000036
Figure FDA0003344616840000037
Figure FDA0003344616840000038
Figure FDA0003344616840000039
Figure FDA00033446168400000310
Figure FDA00033446168400000311
wherein,
Figure FDA00033446168400000312
indicating access node j at time tOLTCInteger variable for tap position, 0 for not at this tap position, 1 tableShown in this position of the tap,
Figure FDA00033446168400000313
the number of total taps is represented as,
Figure FDA00033446168400000314
is a binary variable that is a function of the variable,
Figure FDA00033446168400000315
is that
Figure FDA00033446168400000316
The length of the binary expression of (a),
Figure FDA00033446168400000317
indicating access node j at time tOLTCThe voltage of the secondary side of the transformer,
Figure FDA00033446168400000318
is the access node j at time tOLTCThe ratio of the number of the phase-change material,
Figure FDA00033446168400000319
indicating access node j at time tOLTCThe change in the tap is such that,
Figure FDA00033446168400000320
is the OLTC tap minimum turn ratio,
Figure FDA00033446168400000321
and
Figure FDA00033446168400000322
indicating access node j at time tOLTCThe increase or decrease of the adjustment state of (2),
Figure FDA00033446168400000323
an increase is indicated by a value of 1,
Figure FDA00033446168400000324
a value of 1 indicates a decrease in the number,
Figure FDA00033446168400000325
representing an access node jOLTCThe regulation range of the on-load voltage regulator,
Figure FDA00033446168400000326
representing an access node jOLTCMaximum regulation number of on-load voltage regulator, M and M0Representing parameters in a Big-M calculation method;
B. PV output constraint:
Figure FDA00033446168400000327
Figure FDA00033446168400000328
Figure FDA0003344616840000041
Figure FDA0003344616840000042
wherein,
Figure FDA0003344616840000043
and
Figure FDA0003344616840000044
representing PV mounting node jPVThe minimum and maximum photovoltaic capacity of the installation,
Figure FDA0003344616840000045
and
Figure FDA0003344616840000046
representing PV mounting node jPVThe photovoltaic active and photovoltaic reactive power output of the photovoltaic,
Figure FDA0003344616840000047
represents PV installation node j at time tPVZeta represents the minimum photovoltaic output level,
Figure FDA0003344616840000048
installing node j for PV at time tPVThe angle of the power factor of (a),
Figure FDA0003344616840000049
and
Figure FDA00033446168400000410
installing node j for PV at time tPVMinimum and maximum power factor angle of (d);
C. reactive power output constraint of reactive power element:
Figure FDA00033446168400000411
Figure FDA00033446168400000412
Figure FDA00033446168400000413
Figure FDA00033446168400000414
Figure FDA00033446168400000415
Figure FDA00033446168400000416
wherein,
Figure FDA00033446168400000417
installing node j for time tCONThe continuously variable VAR device of (a) reactive power,
Figure FDA00033446168400000418
and
Figure FDA00033446168400000419
respectively represent installation nodes jCONThe minimum value and the maximum value of the reactive power,
Figure FDA00033446168400000420
to install node jDISThe reactive power of the discrete adjustable VAR device of (a),
Figure FDA00033446168400000421
for each adjusted reactive power of a discrete adjustable VAR device,
Figure FDA00033446168400000422
and
Figure FDA00033446168400000423
is a binary auxiliary variable representing the installation of node j at time tDISThe discrete adjustable VAR apparatus of (a) adjusts the state of increase and decrease,
Figure FDA00033446168400000424
represents the adjustment range of a discrete adjustable VAR device,
Figure FDA00033446168400000425
represents an installation node jDISAt the maximum number of adjustments of the discrete adjustable VAR apparatus,
Figure FDA00033446168400000426
for control-effective binary variables in discrete adjustable reactive devices, where the subscript s denotes the tap position, ΨCONRepresenting a set of mounted nodes of a candidate continuously variable VAR device, ΨDISRepresenting a set of discrete adjustable VAR device installation nodes to be selected;
D. tie line power constraint:
Figure FDA00033446168400000427
Figure FDA00033446168400000428
wherein,
Figure FDA0003344616840000051
and
Figure FDA0003344616840000052
represents the installation of node j at time tTRThe active power and the reactive power of the transformer are obtained,
Figure FDA0003344616840000053
and
Figure FDA0003344616840000054
represents the installation of node j at time tTRThe minimum and maximum active power of the transformer,
Figure FDA0003344616840000055
and
Figure FDA0003344616840000056
represents the installation of node j at time tTRMinimum and maximum reactive power of the transformer, psisubRepresenting a set of transformer installation nodes to be selected;
E. branch flow constraint:
Figure FDA0003344616840000057
Figure FDA0003344616840000058
Figure FDA0003344616840000059
Figure FDA00033446168400000510
where δ (j) is the set of all lines from node j, π (j) is the set of all lines connected to node j, Pjk,tAnd Qjk,tFor the active and reactive power of the line jk at time t, Pij,t、Qij,tFor the active and reactive power of line ij at time t, rijAnd xijAs resistance and reactance values of the line ij, bjIs the value of the conductance of the node j,
Figure FDA00033446168400000511
and
Figure FDA00033446168400000512
active and reactive powers of node j, ΨEFor all line sets, ΨnFor all line node sets, eijIs a binary auxiliary variable representing whether line ij is connected, if line ij is connected, eij＝1，Vice versa, | · purple sweet2Representing the euclidean norm.
Figure FDA00033446168400000513
Representing the square of the line current and the node voltage, respectively;
F. and (3) network topology constraint:
Figure FDA00033446168400000514
Figure FDA00033446168400000515
Figure FDA00033446168400000516
-M·eij≤Fij≤M·eij ij∈ΨE；
Figure FDA00033446168400000517
wherein N isbusIs the number of nodes of the distribution network, FijIs a non-negative variable, representing the virtual power flow transmitted on line ij in the virtual power flow,
Figure FDA00033446168400000518
is the power provided by the source point in the virtual network;
G. safety restraint:
Figure FDA0003344616840000061
Figure FDA0003344616840000062
H. photovoltaic output time uncertainty and photovoltaic output space uncertainty represent:
Figure FDA0003344616840000063
6. the method for photovoltaic absorption capacity evaluation considering time-space correlation and active management according to claim 5,
converting the robust photovoltaic absorption capacity evaluation model of the photovoltaic output and time-space correlation into a robust optimization form, and taking the efficiency factor of the photovoltaic system
Figure FDA0003344616840000064
Taking the minimum absorption capacity as a target for decision variables, adopting a CPLEX solver to calculate in an iterative mode until the calculation is finished when the line current error obtained by iteration is smaller than a preset threshold value, outputting the maximum photovoltaic installed capacity which is the photovoltaic absorption capacity of the power distribution network,
wherein the decision variables include: continuous variable
Figure FDA0003344616840000065
Figure FDA0003344616840000066
Discrete variable
Figure FDA0003344616840000067
7. The method for evaluating the photovoltaic absorption capacity by considering the time-space correlation and the active management as claimed in claim 6, wherein in the solving process, a secant plane method is adopted to dynamically tighten the second-order cone constraint of the power flow of the power grid,
the cut plane in the τ +1 th iteration is:
Figure FDA0003344616840000068
wherein,
Figure FDA0003344616840000069
representing the square of the current of line ij during time t in the # 1 th iteration,
Figure FDA00033446168400000610
and
Figure FDA00033446168400000611
respectively representing the active power, the reactive power and the square of the node voltage in tau iterations.
8. A photovoltaic absorption capacity evaluation apparatus considering a time-space correlation and an active management, comprising:
the first calculation module is used for determining an ellipsoid uncertain set of photovoltaic output time uncertainty and an ellipsoid uncertain set of photovoltaic output space uncertainty based on the correlation of photovoltaic output with time and space;
the second calculation module is used for calculating the empirical distribution of the uncertainty precalculated values of the time and space of the uncertain set of the photovoltaic output ellipsoids;
the building module is used for building a robust photovoltaic absorption capacity evaluation model considering photovoltaic output and time-space correlation; the model takes the maximum installed capacity of renewable energy sources on the node to be selected as a target function, and takes OLTC constraint, PV output constraint, reactive power output constraint of reactive power elements, tie line power constraint, branch power flow constraint, network topology constraint and safety constraint as constraint conditions;
and the number of the first and second groups,
and the output module is used for solving the robust photovoltaic consumption capability evaluation model to obtain the maximum installed capacity of the renewable energy source and the maximum photovoltaic consumption capability.
9. The photovoltaic absorption capability assessment apparatus according to claim 8, wherein said first calculation module is specifically configured to,
the ellipsoid uncertain set of the photovoltaic output time uncertainty and the ellipsoid uncertain set of the photovoltaic output space uncertainty are determined as follows:
Figure FDA0003344616840000071
Figure FDA0003344616840000072
Figure FDA0003344616840000073
Figure FDA0003344616840000074
Figure FDA0003344616840000075
wherein, COVSAnd COVTIs a photovoltaic output spatial covariance matrix and a temporal covariance matrix,
Figure FDA0003344616840000076
representing node j at time tPVThe average photovoltaic power generation amount of (a),
Figure FDA0003344616840000077
represents the photovoltaic power generation amount of the node j at the time t,
Figure FDA0003344616840000078
for the purpose of the spatial uncertainty budget,
Figure FDA0003344616840000079
for the purpose of the time uncertainty budget,
Figure FDA00033446168400000710
the covariance matrix of the output of any two photovoltaic power stations at time T and T +1 is shown, wherein T is 1,2, …, and T represent the number of time segments; sigmaPVn,PVn+1N is 1,2, …, N is the covariance matrix between the nth photovoltaic power plant and the (N + 1) th photovoltaic power plant, N is the number of photovoltaic power plants,
Figure FDA00033446168400000711
σPVn、σPVn+1is the standard deviation, pPVn,PVn+1And
Figure FDA00033446168400000712
is the Pearson correlation coefficient.
10. The method for photovoltaic absorption capacity assessment considering time-space correlation and active management according to claim 8, wherein said building block is specifically configured to,
an objective function of a robust photovoltaic absorption capacity evaluation model considering photovoltaic output and time-space correlation is established as follows:
Figure FDA0003344616840000081
wherein,
Figure FDA0003344616840000082
representing PV mounting node jPVUpper mounted photovoltaic capacity, psiPVRepresenting a set of PV installation nodes to be selected;
the constraints are as follows:
A. the OLTC constraint:
Figure FDA0003344616840000083
Figure FDA0003344616840000084
Figure FDA0003344616840000085
Figure FDA0003344616840000086
Figure FDA0003344616840000087
Figure FDA0003344616840000088
Figure FDA0003344616840000089
Figure FDA00033446168400000810
Figure FDA00033446168400000811
Figure FDA00033446168400000812
Figure FDA00033446168400000813
Figure FDA00033446168400000814
wherein,
Figure FDA00033446168400000815
indicating access node j at time tOLTCAn integer variable for a tap position, 0 for no at this tap position, 1 for at this tap position,
Figure FDA00033446168400000816
the number of total taps is represented as,
Figure FDA00033446168400000817
is a binary variable that is a function of the variable,
Figure FDA00033446168400000818
is that
Figure FDA00033446168400000819
The length of the binary expression of (a),
Figure FDA00033446168400000820
indicating access node j at time tOLTCThe voltage of the secondary side of the transformer,
Figure FDA00033446168400000821
is the access node j at time tOLTCThe ratio of the number of the phase-change material,
Figure FDA00033446168400000822
indicating access at time tNode jOLTCThe change in the tap is such that,
Figure FDA0003344616840000091
is the OLTC tap minimum turn ratio,
Figure FDA0003344616840000092
and
Figure FDA0003344616840000093
indicating access node j at time tOLTCThe increase or decrease of the adjustment state of (2),
Figure FDA0003344616840000094
an increase is indicated by a value of 1,
Figure FDA0003344616840000095
a value of 1 indicates a decrease in the number,
Figure FDA0003344616840000096
representing an access node jOLTCThe regulation range of the on-load voltage regulator,
Figure FDA0003344616840000097
representing an access node jOLTCMaximum regulation number of on-load voltage regulator, M and M0Representing parameters in a Big-M calculation method;
B. PV output constraint:
Figure FDA0003344616840000098
Figure FDA0003344616840000099
Figure FDA00033446168400000910
Figure FDA00033446168400000911
wherein,
Figure FDA00033446168400000912
and
Figure FDA00033446168400000913
representing PV mounting node jPVThe minimum and maximum photovoltaic capacity of the installation,
Figure FDA00033446168400000914
and
Figure FDA00033446168400000915
representing PV mounting node jPVThe photovoltaic active and photovoltaic reactive power output of the photovoltaic,
Figure FDA00033446168400000916
represents PV installation node j at time tPVZeta represents the minimum photovoltaic output level,
Figure FDA00033446168400000917
installing node j for PV at time tPVThe angle of the power factor of (a),
Figure FDA00033446168400000918
and
Figure FDA00033446168400000919
installing node j for PV at time tPVMinimum and maximum power factor angle of (d);
C. reactive power output constraint of reactive power element:
Figure FDA00033446168400000920
Figure FDA00033446168400000921
Figure FDA00033446168400000922
Figure FDA00033446168400000923
Figure FDA00033446168400000924
Figure FDA00033446168400000925
wherein,
Figure FDA00033446168400000926
installing node j for time tCONThe continuously variable VAR device of (a) reactive power,
Figure FDA00033446168400000927
and
Figure FDA00033446168400000928
respectively represent installation nodes jCONThe minimum value and the maximum value of the reactive power,
Figure FDA00033446168400000929
to install node jDISThe reactive power of the discrete adjustable VAR device of (a),
Figure FDA00033446168400000930
for each adjusted reactive power of a discrete adjustable VAR device,
Figure FDA00033446168400000931
and
Figure FDA00033446168400000932
is a binary auxiliary variable representing the installation of node j at time tDISThe discrete adjustable VAR apparatus of (a) adjusts the state of increase and decrease,
Figure FDA0003344616840000101
represents the adjustment range of a discrete adjustable VAR device,
Figure FDA0003344616840000102
represents an installation node jDISAt the maximum number of adjustments of the discrete adjustable VAR apparatus,
Figure FDA0003344616840000103
for control-effective binary variables in discrete adjustable reactive devices, where the subscript s denotes the tap position, ΨCONRepresenting a set of mounted nodes of a candidate continuously variable VAR device, ΨDISRepresenting a set of discrete adjustable VAR device installation nodes to be selected;
D. tie line power constraint:
Figure FDA0003344616840000104
Figure FDA0003344616840000105
wherein,
Figure FDA0003344616840000106
and
Figure FDA0003344616840000107
represents the installation of node j at time tTRThe active power and the reactive power of the transformer are obtained,
Figure FDA0003344616840000108
and
Figure FDA0003344616840000109
represents the installation of node j at time tTRThe minimum and maximum active power of the transformer,
Figure FDA00033446168400001010
and
Figure FDA00033446168400001011
represents the installation of node j at time tTRMinimum and maximum reactive power of the transformer, psisubRepresenting a set of transformer installation nodes to be selected;
E. branch flow constraint:
Figure FDA00033446168400001012
Figure FDA00033446168400001013
Figure FDA00033446168400001014
Figure FDA00033446168400001015
where δ (j) is the set of all lines from node j, π (j) is the set of all lines connected to node j, Pjk,tAnd Qjk,tFor the active and reactive power of the line jk at time t, Pij,t、Qij,tFor the active and reactive power of line ij at time t, rijAnd xijAs resistance and reactance values of the line ij, bjIs the value of the conductance of the node j,
Figure FDA00033446168400001016
and
Figure FDA00033446168400001017
active and reactive powers of node j, ΨEFor all line sets, ΨnFor all line node sets, eijIs a binary auxiliary variable representing whether line ij is connected, if line ij is connected, eij1, and vice versa, | · | | non-woven phosphor2Representing the euclidean norm.
Figure FDA00033446168400001018
Representing the square of the line current and the node voltage, respectively;
F. and (3) network topology constraint:
Figure FDA0003344616840000111
Figure FDA0003344616840000112
Figure FDA0003344616840000113
-M·eij≤Fij≤M·eij ij∈ΨE；
Figure FDA0003344616840000114
wherein N isbusIs the number of nodes of the distribution network, FijIs a non-negative variable, representing the virtual power flow transmitted on line ij in the virtual power flow,
Figure FDA0003344616840000115
is the power provided by the source point in the virtual network;
G. safety restraint:
Figure FDA0003344616840000116
Figure FDA0003344616840000117
H. photovoltaic output time uncertainty and photovoltaic output space uncertainty represent:
Figure FDA0003344616840000118
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117411086A (en) * 2023-11-16 2024-01-16 南京工业大学 Novel electric power system distribution robust optimal scheduling method and system containing fused magnesium

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109378861A (en) * 2018-12-28 2019-02-22 东南大学 The robust Optimization Scheduling of the active distribution network of meter and temporal correlation
CN113241759A (en) * 2021-05-13 2021-08-10 国网上海市电力公司 Power distribution network and multi-microgrid robust scheduling method, electronic equipment and storage medium

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109378861A (en) * 2018-12-28 2019-02-22 东南大学 The robust Optimization Scheduling of the active distribution network of meter and temporal correlation
CN113241759A (en) * 2021-05-13 2021-08-10 国网上海市电力公司 Power distribution network and multi-microgrid robust scheduling method, electronic equipment and storage medium

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
MOSTAFA NAZEMI, ET AL: "Multivariate Uncertainty Characterization for Resilience Planning in Electric Power Systems", 2020 IEEE/IAS 56TH INDUSTRIAL AND COMMERCIAL POWER SYSTEMS TECHNICAL CONFERENCE (I&CPS), 25 August 2020 (2020-08-25), pages 1 - 8 *
吴巍等: "考虑风电功率条件相关性的广义椭球不确定集合建模", 中国电机工程学报, vol. 37, no. 9, 5 May 2017 (2017-05-05), pages 2500 - 2507 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117411086A (en) * 2023-11-16 2024-01-16 南京工业大学 Novel electric power system distribution robust optimal scheduling method and system containing fused magnesium

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