CN113285459A - Droop slope optimization method and device, storage medium and electronic equipment - Google Patents

Droop slope optimization method and device, storage medium and electronic equipment Download PDF

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CN113285459A
CN113285459A CN202110827825.0A CN202110827825A CN113285459A CN 113285459 A CN113285459 A CN 113285459A CN 202110827825 A CN202110827825 A CN 202110827825A CN 113285459 A CN113285459 A CN 113285459A
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vsc
slope
droop
droop slope
optimization
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邱佳亮
齐冬莲
蔡树锦
刘铠滢
郑婵燕
于平澜
林岫菁
蔡丽敏
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Chaozhou Power Supply Bureau of Guangdong Power Grid Co Ltd
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Chaozhou Power Supply Bureau of Guangdong Power Grid Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/36—Arrangements for transfer of electric power between ac networks via a high-tension dc link
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/36—Arrangements for transfer of electric power between ac networks via a high-tension dc link
    • H02J2003/365—Reducing harmonics or oscillations in HVDC
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/60—Arrangements for transfer of electric power between AC networks or generators via a high voltage DC link [HVCD]

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Abstract

The embodiment of the invention discloses a droop slope optimization method, a droop slope optimization device, a storage medium and electronic equipment, wherein the droop slope optimization method comprises the following steps: acquiring target parameters of an AC/DC distribution network; the alternating current and direct current distribution network comprises at least two voltage source type converters (VSC); generating a CCG algorithm based on the columns and the constraints to construct a droop slope robust optimization model; and determining the target droop slope of the VSC according to the target parameter and the droop slope robust optimization model. By the technical scheme provided by the embodiment of the invention, the better droop slope of the VSC in the AC/DC distribution network can be determined, the second-order cone relaxation gap can be effectively reduced, and the random fluctuation consumption capability of the AC/DC distribution network on the new energy power is improved.

Description

Droop slope optimization method and device, storage medium and electronic equipment
Technical Field
The embodiment of the invention relates to the technical field of AC/DC distribution networks, in particular to a droop slope optimization method, a droop slope optimization device, a storage medium and electronic equipment.
Background
The alternating current-direct current hybrid distribution network is regarded as an effective solution to distributed new energy, a plurality of alternating current distribution systems are connected with the direct current distribution network through Voltage Source Converters (VSC), power interconnection among the plurality of alternating current distribution systems is achieved through tide regulation, line pressure overload can be effectively relieved, cross-region sharing of electric energy is achieved, and power supply reliability of the distribution systems is improved.
In an alternating current-direct current hybrid distribution network, droop control is a relatively mainstream VSC control mode. Aiming at the VSC droop control problem of the system power flow, a VSC droop reference point or a VSC droop slope is optimized by taking the minimum total network loss as a target in the related technology, but the prediction error of new energy is ignored.
Disclosure of Invention
The embodiment of the invention provides a droop slope optimization method and device, a storage medium and electronic equipment, so as to accurately and reasonably optimize a droop slope of a power distribution internet of things.
In a first aspect, an embodiment of the present invention provides a droop slope optimization method, including:
acquiring target parameters of an AC/DC distribution network; the alternating current and direct current distribution network comprises at least two voltage source type converters (VSC);
generating a CCG algorithm based on the columns and the constraints to construct a droop slope robust optimization model;
and determining the target droop slope of the VSC according to the target parameter and the droop slope robust optimization model.
In a second aspect, an embodiment of the present invention further provides a droop slope optimization apparatus, including:
the parameter acquisition module is used for acquiring target parameters of the AC/DC distribution network; the alternating current and direct current distribution network comprises at least two voltage source type converters (VSC);
the model building module is used for building a droop slope robust optimization model based on a column and constraint generation CCG algorithm;
and the droop slope determination module is used for determining the target droop slope of the VSC according to the target parameter and the droop slope robust optimization model.
In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a droop slope optimization method as provided by an embodiment of the present invention.
In a fourth aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the droop slope optimization method according to the embodiment of the present invention.
According to the droop slope optimization scheme provided by the embodiment of the invention, target parameters of an alternating current-direct current distribution network are obtained; the alternating current and direct current distribution network comprises at least two voltage source type converters (VSC); generating a CCG algorithm based on the columns and the constraints to construct a droop slope robust optimization model; and determining the target droop slope of the VSC according to the target parameter and the droop slope robust optimization model. By the technical scheme provided by the embodiment of the invention, the better droop slope of the VSC in the AC/DC distribution network can be determined, the second-order cone relaxation gap can be effectively reduced, and the random fluctuation consumption capability of the AC/DC distribution network on the new energy power is improved.
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Fig. 1 is a flowchart of a droop slope optimization method according to an embodiment of the present invention;
FIG. 2 is a flow chart of a droop slope optimization method according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a droop slope optimization apparatus in another embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device in another embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present invention. It should be understood that the drawings and the embodiments of the present invention are illustrative only and are not intended to limit the scope of the present invention.
It should be understood that the various steps recited in the method embodiments of the present invention may be performed in a different order and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the invention is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present invention are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in the present invention are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that reference to "one or more" unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present invention are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
In the related art, because the new energy has uncertainty, when there is a large error between the actual output and the predicted output, the system power flow may be out of limit. Aiming at the problem, some robust optimization models exist at present, and the safe operation of the system under the worst scene is ensured by optimizing VSC interaction power. But the problems of over conservative solution, inaccurate second-order cone relaxation and the like exist. Moreover, the existing technical scheme performs robust optimization scheduling in a given uncertain set, and lacks quantitative analysis on the system new energy fluctuation interval.
Fig. 1 is a flowchart of a droop slope optimization method according to an embodiment of the present invention, where the droop slope optimization method according to an embodiment of the present invention may be applied to a power distribution internet of things system, and the droop slope optimization method may be implemented by a droop slope optimization apparatus, which may be implemented by hardware and/or software, and may be generally integrated in an electronic device. As shown in fig. 1, the method specifically includes the following steps:
step 110, acquiring target parameters of an AC/DC distribution network; the alternating current and direct current distribution network comprises at least two voltage source type converters (VSC).
In an embodiment of the present invention, the ac/dc distribution network may be an ac/dc hybrid distribution network system including a plurality of Voltage Source Converters (VSCs). The method comprises the steps of obtaining target parameters of an alternating current and direct current distribution network, wherein the target parameters can comprise alternating current and direct current voltage reference points of all VSCs in the alternating current and direct current distribution network and alternating current and direct current voltage amplitude values of all VSCs. The target parameters can also comprise related parameters such as initial droop slope and uncertain coefficient corresponding to each VSC in the AC/DC distribution network.
And step 120, generating a CCG algorithm based on the columns and the constraints to construct a droop slope robust optimization model.
And step 130, determining a target droop slope of the VSC according to the target parameter and the droop slope robust optimization model.
In the embodiment of the invention, a droop slope robust optimization model is constructed based on a column-and-constraint generation (CCG) algorithm. The droop slope robust optimization model can be decoupled into a slope optimization main problem and an extreme scene optimization sub problem. And determining a target droop slope of each VSC in the AC/DC distribution network based on the target parameters and the droop slope robust optimization model, wherein the target droop slope can be understood as a better droop slope, the size of a new energy fluctuation interval can be adaptively adjusted, the second-order cone relaxation gap is effectively reduced, and the random fluctuation absorption capacity of the AC distribution network system for the new energy power is improved.
Optionally, determining the target droop slope of the VSC according to the target parameter and the droop slope robust optimization model includes: and under the power-voltage double-droop control mode of the VSC, determining a target droop slope of the VSC according to the target parameter and the droop slope robust optimization model. Specifically, in order to enable the droop equation to be adaptive to the branch flow tide equation, the droop slope robust optimization model can be constructed into a convex problem, and the embodiment of the invention can be used for solving the problem of squaring of the VSC alternating-current side voltage
Figure DEST_PATH_IMAGE001
And determining the target droop slope of the VSC for controlling variables according to the target parameters and the droop slope robust optimization model in a power-voltage double droop control mode. Optionally, in the power-voltage dual droop control mode of the VSC, determining the target droop slope of the VSC according to the target parameter and the droop slope robust optimization model, including: taking any VSC in the AC/DC distribution network as a master VSC, and taking other VSCs in the AC/DC distribution network as slave VSCs; wherein, other VSCs are VSCs in the AC/DC distribution network except the main VSC; aiming at the master VSC, the square value of the constant direct-current voltage, the square value of each VSC alternating-current side voltage and the reactive output power of each VSC are adopted as control variables, aiming at the slave VSC, the square value of each VSC alternating-current side voltage, the reactive output power and the active output power are adopted as control variables, and the target droop slope of the VSC is determined according to the target parameters and the droop slope robust optimization model. Specifically, one VSC in an AC/DC distribution network is used as a main converter, and a constant DC voltage is adopted
Figure 310482DEST_PATH_IMAGE002
Droop control, with the remaining VSCs as slave converters, using
Figure DEST_PATH_IMAGE003
And
Figure 493202DEST_PATH_IMAGE004
and (3) double droop control:
Figure DEST_PATH_IMAGE005
and then, determining a target droop slope of the VSC according to the target parameter and the droop slope robust optimization model. Wherein in the formula
Figure 804097DEST_PATH_IMAGE006
And
Figure 730465DEST_PATH_IMAGE007
is shown asmThe VSC active and reactive droop slopes;
Figure 341575DEST_PATH_IMAGE008
and
Figure 898458DEST_PATH_IMAGE009
representing the voltage amplitude of the AC and DC nodes;
Figure 962229DEST_PATH_IMAGE010
and
Figure 692288DEST_PATH_IMAGE011
is shown asmA VSC alternating current and direct current voltage reference point;
Figure 95587DEST_PATH_IMAGE012
and
Figure 885689DEST_PATH_IMAGE013
is shown asmPlatform VSC active and reactive output power, establish and pour into alternating current systemIs the positive direction;
Figure 374439DEST_PATH_IMAGE014
and
Figure 141145DEST_PATH_IMAGE015
is shown asmThe VSC active and reactive power reference points;
Figure 461267DEST_PATH_IMAGE016
representing the set of dc side and ac side nodes connected to the master VSC and the slave VSC, respectively.
In this embodiment of the present invention, determining the target droop slope of the VSC according to the target parameter and the droop slope robust optimization model may include: based on the CCG algorithm, the droop slope robust optimization model can be decoupled into a main slope optimization problem (master problem, MP) and an extreme scene optimization sub-problem (slave problem, SP), the main slope optimization problem and the extreme scene optimization sub-problem are iteratively solved based on target parameters, and the target droop slope of each VSC is obtained. The MP can optimize the droop slope by taking the minimum total network loss of the expected scene as an objective function on the premise of ensuring the safe operation of the system in the limited extreme scene. And the SP can identify the scene with the most severe steady-state safety constraint violation under a given droop slope and add it as an extreme scene to the extreme scene set of the main problem.
Optionally, the droop slope robust optimization model includes a slope optimization main model and an extreme scene optimization sub-model; the target parameter comprises an initial droop slope corresponding to the VSC; determining a target droop slope of the VSC according to the target parameter and the droop slope robust optimization model, including: determining the predicted output of a new energy system corresponding to the VSC in the AC/DC distribution network according to the target parameters and the slope optimization main model; determining a middle droop slope corresponding to the VSC based on the predicted output and the extreme scene optimization submodel; and when the intermediate droop slope does not meet a preset condition, taking the intermediate droop slope as the initial droop slope, returning to execute the optimization main model according to the target parameter and the slope, determining the predicted output of the new energy system corresponding to the VSC until the intermediate droop slope meets the preset condition, and taking the intermediate droop slope as the target droop slope of the VSC.
Specifically, a droop slope robust optimization model based on a CCG algorithm is established, and a slope main problem is solved. The AC-DC power distribution system is arranged to commonly contain z VSCs and N new energy power generation devices, and the new energy output uncertain set U can be expressed as:
Figure 625533DEST_PATH_IMAGE017
wherein,
Figure 663896DEST_PATH_IMAGE018
is as followsvActual output of the new energy system;
Figure 470178DEST_PATH_IMAGE019
for the first time of regulation and controlvPredicting the output of the new energy system in the day ahead;
Figure 848069DEST_PATH_IMAGE020
is as followsvThe initial uncertainty coefficient of the new energy can be equivalent to a prediction error.
According to the CCG algorithm, the droop slope robust optimization model can be decoupled into a slope optimization main problem (namely a slope optimization main model) and an extreme scene optimization sub problem (namely an extreme scene optimization sub model), and the target droop slope can be iteratively solved for the slope optimization main problem and the extreme scene optimization sub problem. Firstly, modeling is carried out on the MP problem, and the objective function is as follows:
Figure 979973DEST_PATH_IMAGE021
in the formula
Figure 443316DEST_PATH_IMAGE022
To a grid-connected nodeeInjecting power;wfor grid connectionCounting;nan expected number of scenes for photovoltaic output;K l is as followslExtreme set of scenarios in sub-iterations, comprisingl-1 extreme scenario;yis a system state vector;koptimizing a vector for the droop slope;Da set of desired scenes for the photovoltaic. It is worth noting that minimizing the sum of the main network injected distribution network power is equivalent to minimizing the distribution network total loss.
The AC/DC branch current flow constraint is expressed by formulas (7) to (14):
Figure 53289DEST_PATH_IMAGE023
Figure 348004DEST_PATH_IMAGE024
in the formula
Figure 650809DEST_PATH_IMAGE025
And
Figure 335868DEST_PATH_IMAGE026
representing a set of AC/DC nodes;
Figure 516576DEST_PATH_IMAGE027
and
Figure 603481DEST_PATH_IMAGE028
representing an alternating current-direct current branch set;
Figure 342767DEST_PATH_IMAGE029
and
Figure 311860DEST_PATH_IMAGE030
are respectively an alternating currentjActive power and reactive power are injected into the nodes;
Figure 466898DEST_PATH_IMAGE031
and
Figure 736205DEST_PATH_IMAGE032
are respectively a current flowing throughijActive and reactive power of the branch;
Figure 380813DEST_PATH_IMAGE033
is a direct currentiActive power is injected into the node;
Figure 40464DEST_PATH_IMAGE034
is a direct current flowing throughijActive power of the branch;
Figure 61510DEST_PATH_IMAGE035
are respectively an alternating currentijThe resistance and the reactance of the branch circuit,jnode parallel susceptance and direct currentijA branch resistance;
Figure 654165DEST_PATH_IMAGE036
and
Figure 672937DEST_PATH_IMAGE037
are respectively AC or DCijBranch current amplitude sum of squaresjThe node voltage magnitude squared.
Wherein equations (13) and (14) can be further relaxed into the following second order cone constraints:
Figure 882201DEST_PATH_IMAGE038
the steady state safety constraint is expressed by formulas (17) - (18)
Figure 644621DEST_PATH_IMAGE039
Wherein,
Figure 855897DEST_PATH_IMAGE040
the voltage of the AC/DC node is the upper and lower limits;
Figure 842308DEST_PATH_IMAGE041
the AC/DC branch current upper and lower limits.
The VSC constraint is of the formulae (21) - (22)
Figure 476552DEST_PATH_IMAGE042
Wherein,
Figure 573821DEST_PATH_IMAGE043
is as followsmStation VSC apparent capacity.
Figure 672227DEST_PATH_IMAGE044
And
Figure 767222DEST_PATH_IMAGE045
the upper and lower droop slope limits can be determined by VSC stability analysis.
The droop constraints are the same as equations (1) - (4).
In the embodiment of the invention, the predicted output of the cooperation of the new energy corresponding to the VSC in the AC/DC distribution network can be determined by optimizing the main model and the target parameters according to the slopes corresponding to the formulas (1) to (22). And then, solving an optimization sub-problem under an extreme scene based on the predicted output, and determining a middle droop slope corresponding to the VSC. Specifically, the objective function of the extreme scene optimization submodel is as follows:
Figure 685499DEST_PATH_IMAGE046
wherein,
Figure 852038DEST_PATH_IMAGE047
and
Figure 477055DEST_PATH_IMAGE048
for a non-negative security constraint relaxation variable,
Figure 70847DEST_PATH_IMAGE049
and the capacity constraint relaxation variables are used for measuring out-of-limit amplitudes of the system steady-state safety constraint and the VSC capacity constraint respectively. When objective function of SP
Figure 210841DEST_PATH_IMAGE050
And in the process, steady-state safety constraint and VSC capacity constraint are always established in the new energy uncertain set, which shows that the droop slope obtained by the main problem is robust.
Steady state safety relaxation constraints are of formulas (24) - (28)
Figure 118754DEST_PATH_IMAGE051
The VSC capacity relaxation constraint is of the formulae (29) - (30)
Figure 427638DEST_PATH_IMAGE052
Droop constraints are the same as equations (1) - (4); the branch flow power flow constraints are the same as equations (7) - (14).
Based on a strong dual theory, the max-min double-layer programming problem can be subjected to dual transformation, and finally equivalently transformed into a single-layer mixed integer second-order cone programming problem to be solved. Therefore, the middle droop slope corresponding to the VSC can be determined based on the predicted output and the extreme scene optimization submodel, and whether the middle droop slope meets the preset condition or not is judged. Wherein the preset condition can be judgment
Figure 130015DEST_PATH_IMAGE053
And if not, taking the intermediate droop slope as the initial droop slope, returning to execute the optimization main model according to the target parameters and the slope, determining the predicted output of the new energy system corresponding to the VSC until the intermediate droop slope meets the preset condition, and taking the intermediate droop slope as the target droop slope of the VSC.
According to the droop slope optimization scheme provided by the embodiment of the invention, target parameters of an alternating current-direct current distribution network are obtained; the alternating current and direct current distribution network comprises at least two voltage source type converters (VSC); generating a CCG algorithm based on the columns and the constraints to construct a droop slope robust optimization model; and determining the target droop slope of the VSC according to the target parameter and the droop slope robust optimization model. By the technical scheme provided by the embodiment of the invention, the better droop slope of the VSC in the AC/DC distribution network can be determined, the second-order cone relaxation gap can be effectively reduced, and the random fluctuation consumption capability of the AC/DC distribution network on the new energy power is improved.
In some embodiments, before determining the corresponding intermediate droop slope for the VSC based on the predicted contribution and the extreme scene optimization submodel, the method further comprises: constructing a branch loss optimal power flow model; determining the maximum loss of each branch in the AC/DC distribution network based on the branch loss optimal power flow model; increasing branch current limit inequality constraint in the extreme scene optimization submodel based on the maximum loss; determining a middle droop slope corresponding to the VSC based on the predicted contribution and the extreme scene optimization submodel, including: and determining a middle droop slope corresponding to the VSC based on the extreme scene optimization sub-model after the predicted output and the constraint of the added branch current limit inequality.
Specifically, after the predicted output of the new energy system corresponding to the VSC in the AC/DC distribution network is determined according to the target parameter and the slope optimization main model, a branch loss optimal power flow model is constructed before a middle droop slope corresponding to the VSC is determined based on the predicted output and the extreme scene optimization submodel. Wherein, the branch circuit loss optimal power flow model is shown as formulas (31) to (32):
Figure 757305DEST_PATH_IMAGE054
wherein,
Figure 265647DEST_PATH_IMAGE055
and
Figure 865256DEST_PATH_IMAGE056
AC and DC at a given droop slopeijMaximum loss of the branch. Determining the maximum loss of each branch in the AC/DC distribution network based on the branch loss optimal power flow model, and adding the maximum loss of each branch in the extreme scene optimization submodel as shown in the formulas (33) to (34) after solving to obtain the maximum loss of each branchThe branch current limit inequality constraint of (1):
Figure 535272DEST_PATH_IMAGE057
equations (33) - (34) are constantly true inequalities when the second order cone relaxation is accurate. Therefore, the branch loss limit value strategy does not influence the optimizing range of the original robust optimization model. And then determining a middle droop slope corresponding to the VSC based on an extreme scene optimization sub-model after the constraint of the prediction output and the added branch current limit inequality.
In some embodiments, the target parameter comprises an uncertainty coefficient corresponding to the VSC; before determining the intermediate droop slope corresponding to the VSC based on the predicted contribution and the extreme scene optimization submodel, the method further includes: when the predicted output of a new energy system corresponding to the VSC in the AC/DC distribution network cannot be determined according to the target parameters and the slope optimization main model, establishing a new energy fluctuation interval correction model; and updating the uncertain coefficient based on the new energy fluctuation interval correction model, and returning to execute the main slope optimization model according to the target parameter and the main slope optimization model to determine the predicted output of the new energy system corresponding to the VSC in the AC/DC distribution network.
Specifically, when the predicted output of the new energy system corresponding to the VSC in the ac/dc distribution network cannot be determined according to the target parameter and the slope optimization master model, it is indicated that the determination of the uncertain coefficient corresponding to the VSC is not accurate enough, and therefore, a new energy fluctuation interval correction model is established. Taking the uncertain parameters of the new energy in each sub-network as an example, the maximum joint new energy fluctuation interval objective function of the system is as follows:
Figure 915437DEST_PATH_IMAGE058
wherein,
Figure 899574DEST_PATH_IMAGE059
which represents the coefficient of uncertainty of the relaxation,is the variable to be optimized.
In the model, under the convex polyhedron uncertainty set, each new energy output extreme scene is always positioned at the boundary of the uncertainty interval. Thus, in the first placelIn the secondary iteration, defining an extreme scene set incidence matrix
Figure 681585DEST_PATH_IMAGE060
The elements of (a) are as follows:
Figure 725764DEST_PATH_IMAGE061
the extreme scenario set relaxation constraint can be expressed as:
Figure 327647DEST_PATH_IMAGE062
in the formula
Figure 177791DEST_PATH_IMAGE063
Is shown aslNew energy uncertainty factor in 1 iteration, known parameter. And the other constraints are the same as the droop constraint, the alternating current and direct current branch constraint, the steady-state safety constraint and the VSC constraint in the main slope problem. And updating the uncertain coefficient by the method, returning to execute the main optimization model according to the target parameter and the slope, and determining the predicted capacity of the new energy system corresponding to the VSC in the AC/DC distribution network.
In some embodiments, further comprising: determining a maximum new energy fluctuation interval corresponding to the AC/DC distribution network based on the new energy fluctuation interval correction model; and evaluating the robustness of the AC/DC distribution network based on the maximum new energy fluctuation interval. Specifically, the maximum new energy fluctuation interval of the distribution network, which can ensure the robust operation of the system, can be solved based on the formulas (35) - (37). It should be noted that the new energy uncertainty coefficient is a system parameter rather than a controllable variable, so the correction model is only used for analyzing the consumption capability of the ac/dc distribution network system on new energy fluctuation under the sag control, evaluating the robust performance of the system, and not issuing a scheduling instruction.
For example, fig. 2 is a flowchart of a droop slope optimization method according to an embodiment of the present invention. Fig. 2 can be understood by combining the above embodiments, and will not be described herein again.
Fig. 3 is a schematic structural diagram of a droop slope optimization apparatus according to another embodiment of the present invention. As shown in fig. 3, the apparatus includes: a parameter acquisition module 310, a model construction module 320, and a droop slope determination module 330. Wherein,
a parameter obtaining module 310, configured to obtain target parameters of an ac/dc distribution network; the alternating current and direct current distribution network comprises at least two voltage source type converters (VSC);
the model building module 320 is used for building a droop slope robust optimization model based on a column and constraint generation CCG algorithm;
and a droop slope determination module 330, configured to determine a target droop slope of the VSC according to the target parameter and the droop slope robust optimization model.
The droop slope optimization device provided by the embodiment of the invention is used for acquiring target parameters of an AC/DC distribution network; the alternating current and direct current distribution network comprises at least two voltage source type converters (VSC); generating a CCG algorithm based on the columns and the constraints to construct a droop slope robust optimization model; and determining the target droop slope of the VSC according to the target parameter and the droop slope robust optimization model. By the technical scheme provided by the embodiment of the invention, the better droop slope of the VSC in the AC/DC distribution network can be determined, the second-order cone relaxation gap can be effectively reduced, and the random fluctuation consumption capability of the AC/DC distribution network on the new energy power is improved.
Optionally, the droop slope robust optimization model includes a slope optimization main model and an extreme scene optimization sub-model; the target parameter comprises an initial droop slope corresponding to the VSC;
the droop slope determination module, comprising:
the predicted output determining unit is used for determining the predicted output of a new energy system corresponding to the VSC in the AC/DC distribution network according to the target parameter and the slope optimization main model;
the intermediate droop slope determining unit is used for determining an intermediate droop slope corresponding to the VSC based on the predicted output and the extreme scene optimization submodel;
and the cyclic execution unit is used for taking the intermediate droop slope as the initial droop slope when the intermediate droop slope does not meet a preset condition, returning to execute the optimization main model according to the target parameter and the slope, determining the predicted output of the new energy system corresponding to the VSC until the intermediate droop slope meets the preset condition, and taking the intermediate droop slope as the target droop slope of the VSC.
Optionally, the apparatus further comprises:
the power flow model building module is used for building a branch loss optimal power flow model before determining a middle droop slope corresponding to the VSC based on the predicted output and the extreme scene optimization submodel;
the maximum loss determining module is used for determining the maximum loss of each branch in the AC/DC distribution network based on the branch loss optimal power flow model;
the constraint increasing module is used for increasing branch current limit inequality constraint in the extreme scene optimizing submodel based on the maximum loss;
the intermediate droop slope determination unit is configured to:
and determining a middle droop slope corresponding to the VSC based on the extreme scene optimization sub-model after the predicted output and the constraint of the added branch current limit inequality.
Optionally, the target parameter includes an uncertainty coefficient corresponding to the VSC;
further comprising:
the correction model building module is used for building a new energy fluctuation interval correction model when the predicted output of a new energy system corresponding to the VSC in the AC/DC distribution network cannot be determined according to the target parameter and the slope optimization main model before the middle droop slope corresponding to the VSC is determined based on the predicted output and the extreme scene optimization submodel;
and the uncertain coefficient updating module is used for updating the uncertain coefficient based on the new energy fluctuation interval correction model, returning to execute the main optimization model according to the target parameter and the slope, and determining the predicted output of the new energy system corresponding to the VSC in the AC/DC distribution network.
Optionally, the apparatus further comprises:
the fluctuation interval determining module is used for determining a maximum new energy fluctuation interval corresponding to the AC/DC distribution network based on the new energy fluctuation interval correction model;
and the robustness evaluation module is used for evaluating the robustness of the AC/DC distribution network based on the maximum new energy fluctuation interval.
Optionally, the droop slope determining module includes:
and the droop slope determining unit is used for determining the target droop slope of the VSC according to the target parameter and the droop slope robust optimization model in a power-voltage double droop control mode of the VSC.
Optionally, the droop slope determining unit is configured to:
taking any VSC in the AC/DC distribution network as a master VSC, and taking other VSCs in the AC/DC distribution network as slave VSCs; wherein, other VSCs are VSCs in the AC/DC distribution network except the main VSC;
aiming at the master VSC, the square value of the constant direct-current voltage, the square value of each VSC alternating-current side voltage and the reactive output power of each VSC are adopted as control variables, aiming at the slave VSC, the square value of each VSC alternating-current side voltage, the reactive output power and the active output power are adopted as control variables, and the target droop slope of the VSC is determined according to the target parameters and the droop slope robust optimization model.
The device can execute the methods provided by all the embodiments of the invention, and has corresponding functional modules and beneficial effects for executing the methods. For technical details which are not described in detail in the embodiments of the present invention, reference may be made to the methods provided in all the aforementioned embodiments of the present invention.
Embodiments of the present invention also provide a storage medium containing computer-executable instructions, which when executed by a computer processor, perform a droop slope optimization method, the method comprising:
acquiring target parameters of an AC/DC distribution network; the alternating current and direct current distribution network comprises at least two voltage source type converters (VSC);
generating a CCG algorithm based on the columns and the constraints to construct a droop slope robust optimization model;
and determining the target droop slope of the VSC according to the target parameter and the droop slope robust optimization model.
Storage medium-any of various types of memory devices or storage devices. The term "storage medium" is intended to include: mounting media such as CD-ROM, floppy disk, or tape devices; computer system memory or random access memory such as DRAM, DDRRAM, SRAM, EDORAM, Lanbas (Rambus) RAM, etc.; non-volatile memory such as flash memory, magnetic media (e.g., hard disk or optical storage); registers or other similar types of memory elements, etc. The storage medium may also include other types of memory or combinations thereof. In addition, the storage medium may be located in a first computer system in which the program is executed, or may be located in a different second computer system connected to the first computer system through a network (such as the internet). The second computer system may provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media that may reside in different locations, such as in different computer systems that are connected by a network. The storage medium may store program instructions (e.g., embodied as a computer program) that are executable by one or more processors.
Of course, the storage medium provided by the embodiment of the present invention contains computer-executable instructions, and the computer-executable instructions are not limited to the above-mentioned droop slope optimization operation, and may also perform related operations in the droop slope optimization method provided by any embodiment of the present invention.
The embodiment of the invention provides electronic equipment, and the droop slope optimizing device provided by the embodiment of the invention can be integrated in the electronic equipment. Fig. 4 is a block diagram of an electronic device according to an embodiment of the present invention. The electronic device 400 may include: a memory 401, a processor 402 and a computer program stored on the memory 401 and executable by the processor, wherein the processor 402 implements the droop slope optimization method according to the present invention when executing the computer program.
The electronic equipment provided by the embodiment of the invention obtains target parameters of an AC/DC distribution network; the alternating current and direct current distribution network comprises at least two voltage source type converters (VSC); generating a CCG algorithm based on the columns and the constraints to construct a droop slope robust optimization model; and determining the target droop slope of the VSC according to the target parameter and the droop slope robust optimization model. By the technical scheme provided by the embodiment of the invention, the better droop slope of the VSC in the AC/DC distribution network can be determined, the second-order cone relaxation gap can be effectively reduced, and the random fluctuation consumption capability of the AC/DC distribution network on the new energy power is improved.
The droop slope optimization device, the storage medium and the electronic device provided in the above embodiments may execute the droop slope optimization method provided in any embodiment of the present invention, and have corresponding functional modules and beneficial effects for executing the method. For technical details not described in detail in the above embodiments, reference may be made to the droop slope optimization method provided in any embodiment of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims (10)

1. A droop slope optimization method, comprising:
acquiring target parameters of an AC/DC distribution network; the alternating current and direct current distribution network comprises at least two voltage source type converters (VSC);
generating a CCG algorithm based on the columns and the constraints to construct a droop slope robust optimization model;
and determining the target droop slope of the VSC according to the target parameter and the droop slope robust optimization model.
2. The method of claim 1, wherein the droop slope robust optimization model comprises a slope optimization main model and an extreme scene optimization submodel; the target parameter comprises an initial droop slope corresponding to the VSC;
determining a target droop slope of the VSC according to the target parameter and the droop slope robust optimization model, including:
determining the predicted output of a new energy system corresponding to the VSC in the AC/DC distribution network according to the target parameters and the slope optimization main model;
determining a middle droop slope corresponding to the VSC based on the predicted output and the extreme scene optimization submodel;
and when the intermediate droop slope does not meet a preset condition, taking the intermediate droop slope as the initial droop slope, returning to execute the optimization main model according to the target parameter and the slope, determining the predicted output of the new energy system corresponding to the VSC until the intermediate droop slope meets the preset condition, and taking the intermediate droop slope as the target droop slope of the VSC.
3. The method of claim 2, further comprising, prior to determining the corresponding intermediate droop slope for the VSC based on the predicted contribution and the extreme scene optimization submodel:
constructing a branch loss optimal power flow model;
determining the maximum loss of each branch in the AC/DC distribution network based on the branch loss optimal power flow model;
increasing branch current limit inequality constraint in the extreme scene optimization submodel based on the maximum loss;
determining a middle droop slope corresponding to the VSC based on the predicted contribution and the extreme scene optimization submodel, including:
and determining a middle droop slope corresponding to the VSC based on the extreme scene optimization sub-model after the predicted output and the constraint of the added branch current limit inequality.
4. The method of claim 2, wherein the target parameters comprise uncertainty coefficients corresponding to the VSCs;
before determining the intermediate droop slope corresponding to the VSC based on the predicted contribution and the extreme scene optimization submodel, the method further includes:
when the predicted output of a new energy system corresponding to the VSC in the AC/DC distribution network cannot be determined according to the target parameters and the slope optimization main model, establishing a new energy fluctuation interval correction model;
and updating the uncertain coefficient based on the new energy fluctuation interval correction model, and returning to execute the main slope optimization model according to the target parameter and the main slope optimization model to determine the predicted output of the new energy system corresponding to the VSC in the AC/DC distribution network.
5. The method of claim 4, further comprising:
determining a maximum new energy fluctuation interval corresponding to the AC/DC distribution network based on the new energy fluctuation interval correction model;
and evaluating the robustness of the AC/DC distribution network based on the maximum new energy fluctuation interval.
6. The method of claim 1, wherein determining the target droop slope of the VSC based on the target parameter and the droop slope robust optimization model comprises:
and under the power-voltage double-droop control mode of the VSC, determining a target droop slope of the VSC according to the target parameter and the droop slope robust optimization model.
7. The method of claim 6, wherein determining the target droop slope of the VSC in a power-voltage dual droop control mode of the VSC according to the target parameter and the droop slope robust optimization model comprises:
taking any VSC in the AC/DC distribution network as a master VSC, and taking other VSCs in the AC/DC distribution network as slave VSCs; wherein, other VSCs are VSCs in the AC/DC distribution network except the main VSC;
aiming at the master VSC, the square value of the constant direct-current voltage, the square value of each VSC alternating-current side voltage and the reactive output power of each VSC are adopted as control variables, aiming at the slave VSC, the square value of each VSC alternating-current side voltage, the reactive output power and the active output power are adopted as control variables, and the target droop slope of the VSC is determined according to the target parameters and the droop slope robust optimization model.
8. A droop slope optimization apparatus, comprising:
the parameter acquisition module is used for acquiring target parameters of the AC/DC distribution network; the alternating current and direct current distribution network comprises at least two voltage source type converters (VSC);
the model building module is used for building a droop slope robust optimization model based on a column and constraint generation CCG algorithm;
and the droop slope determination module is used for determining the target droop slope of the VSC according to the target parameter and the droop slope robust optimization model.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processing device, carries out the droop slope optimization method according to any one of claims 1-7.
10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the droop slope optimization method of any one of claims 1-7 when executing the computer program.
CN202110827825.0A 2021-07-22 2021-07-22 Droop slope optimization method and device, storage medium and electronic equipment Pending CN113285459A (en)

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Application publication date: 20210820