CN111478360A - Centralized-local integrated voltage control method and device for power distribution network with photovoltaic access - Google Patents

Centralized-local integrated voltage control method and device for power distribution network with photovoltaic access Download PDF

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CN111478360A
CN111478360A CN202010201173.5A CN202010201173A CN111478360A CN 111478360 A CN111478360 A CN 111478360A CN 202010201173 A CN202010201173 A CN 202010201173A CN 111478360 A CN111478360 A CN 111478360A
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node
voltage
reactive
inverter
photovoltaic
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王子凌
周金辉
苏义荣
孙保华
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Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd
NARI Nanjing Control System Co Ltd
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Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd
NARI Nanjing Control System Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • 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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/16—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by adjustment of reactive power
    • 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
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/50—Controlling the sharing of the out-of-phase component
    • 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
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/30—Reactive power compensation
    • 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
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention discloses a centralized-local comprehensive voltage control method and a device suitable for a power distribution network with high-proportion photovoltaic access, wherein the method comprises the following steps: in the centralized control stage, power distribution network photovoltaic and load prediction data are obtained at a first sampling interval, a pre-constructed centralized control model is utilized, the minimum loss of a power distribution network is taken as an optimization target, network power flow constraint, inverter droop curve slope constraint and inverter reactive output constraint are considered, and the inverter droop slope of each photovoltaic access node is optimally calculated and updated; in the local control stage, the real-time measured voltage of each photovoltaic access node is obtained in a first sampling interval at a second sampling interval, and a voltage-reactive droop curve is determined according to the current droop slope of the inverter of each photovoltaic access node so as to perform local control on the corresponding node. The invention realizes the optimization of reactive power control by combining a centralized control mode and a local control mode, can quickly respond to photovoltaic prediction errors and power fluctuation, and can realize effective coordination among equipment.

Description

Centralized-local integrated voltage control method and device for power distribution network with photovoltaic access
Technical Field
The invention relates to the technical field of power utilization information acquisition and analysis, in particular to a centralized-local comprehensive voltage control method and device suitable for a power distribution network with high-proportion photovoltaic access.
Background
In recent years, the installed photovoltaic grid-connected capacity of medium and low voltage power distribution networks is rapidly increased, and the photovoltaic power distribution networks have the development trend of 'decentralized development and nearby consumption'. Meanwhile, the high-proportion photovoltaic grid connection also has obvious adverse effect on the low-voltage distribution network, which is shown in the following steps: 1) the power flow is changed from unidirectional flow to bidirectional flow, and obvious reverse power flow is easy to occur particularly under the condition of strong illumination, so that the voltage is higher than the upper limit; 2) the uncertainty of network operation is obviously increased, and the node voltage fluctuates frequently and rapidly. How to effectively weaken these adverse effects and ensure the economic and safe operation of the network becomes a research hotspot.
The current mainstream photovoltaic inverter reactive power control can be roughly divided into a centralized type and an on-site type. Centralized control is usually implemented based on Optimal Power Flow (OPF), and corresponding prediction data of distributed power generation and load is acquired before each control, and reference output of controllable devices is given after optimization. Unlike centralized control, in-situ control can achieve regulation of photovoltaic reactive power by only in-situ real-time measurement and minimal computation, often without considering the global optimality of reactive power control.
The method for adjusting the reactive power of the photovoltaic inverter by simply adopting a centralized or local control mode has some defects. Although the centralized control can effectively coordinate the reactive outputs of different photovoltaic inverters, the response of the centralized control to the rapid change of the photovoltaic power is restricted by the data transmission time delay and the larger calculation amount, and meanwhile, the prediction error also obviously influences the effect of the centralized control.
In order to solve the problem of difficult local control coordination, some researches indicate that the coordination of local control parameters can be realized based on historical data and network topology and parameters, but the change of network structure or line parameters can cause the resetting of inverter parameters; there have also been some studies to improve the synergy of control in low voltage distribution networks using local area communication technology and distributed control, but the global optimization of control has not yet been considered.
Disclosure of Invention
The invention aims to provide a centralized-local comprehensive voltage control method and a centralized-local comprehensive voltage control device suitable for a power distribution network with high-proportion photovoltaic access, which are combined with a centralized control mode and a local control mode to realize optimization of reactive power control, can quickly respond to photovoltaic prediction errors and power fluctuation, and can realize effective coordination among devices.
The technical scheme adopted by the invention is as follows:
on one hand, the invention discloses a centralized-local comprehensive voltage control method for a power distribution network with photovoltaic access, which comprises the following steps:
acquiring power distribution network topology and line parameter data, photovoltaic and load prediction data at a set first sampling interval;
based on the acquired data, calculating and updating the inverter droop slope of each photovoltaic access node by using a pre-constructed centralized control model; the centralized control model is an optimization objective function which takes the minimum loss of a power distribution network as an optimization objective, considers network power flow constraint, inverter droop curve slope constraint and inverter reactive power output constraint and takes the photovoltaic access node inverter droop slope as an optimization variable; the inverter reactive output constraint and the droop curve slope constraint of each node are determined according to the predetermined node overvoltage boundary point and the predetermined node undervoltage boundary point; the overvoltage boundary point and the undervoltage boundary point respectively correspond to a historical extreme overvoltage scene and an undervoltage scene to meet the reactive power sufficiency;
acquiring the voltage of each photovoltaic access node at a set second sampling interval; the second sampling interval is less than the first sampling interval;
and for each photovoltaic access node, determining a voltage-reactive droop curve according to the current droop slope of the corresponding inverter, and performing local control according to the voltage of the corresponding photovoltaic access node and the voltage-reactive droop curve to adjust the reactive power output of the inverter.
The overvoltage scene and the undervoltage scene are the most serious historical overvoltage scene and the most serious historical undervoltage scene which meet the reactive power sufficiency.
Optionally, the first sampling interval is a minute-level interval, and the second sampling interval is a second-level interval. For example, the first sampling interval may be set to 5min, and the second sampling interval may be set to 20s, which are complementary in time scale, so that the in-situ control phase can adjust the reactive power according to the in-situ voltage change, and a fast response is realized.
Optionally, according to the historical photovoltaic and load data of the power distribution network and considering the voltage-reactive sensitivity, the invention determines the voltage and the reactive power corresponding to the overvoltage boundary point and the undervoltage boundary point of each photovoltaic access node, that is,
the predetermination of the overvoltage and undervoltage boundary points of each photovoltaic access node comprises:
acquiring node voltages of all photovoltaic access nodes in the power distribution network under the scene of the most serious historical overvoltage and undervoltage;
and calculating node voltage and reactive adjustable capacity corresponding to an overvoltage boundary/undervoltage boundary point in a node voltage adjusting range and simultaneously meeting reactive adjustable capacity abundance in an extreme overvoltage scene/undervoltage scene based on the acquired data and in consideration of voltage-reactive sensitivity.
Specifically, the node voltages under the historical most severe overvoltage scene and the undervoltage scene of the photovoltaic access node i are defined as
Figure BDA0002419433740000021
Node s/l represents an overvoltage fieldA node where maximum/minimum voltage appears in the power distribution network under a scene/under-voltage scene;
then the reactive power richness check parameter under the extreme over-limit sceneOVComprises the following steps:
Figure BDA0002419433740000031
in the formula
Figure BDA0002419433740000032
Representing the degree of out-of-limit, denominator of the node s
Figure BDA0002419433740000033
Represents the maximum voltage change, theta, of the node s that can be induced by all invertersPVA photovoltaic node set is accessed to the power distribution network,
Figure BDA0002419433740000034
is the node voltage, U, of the node s in the historical most severe overvoltage scenarioth-OVIn order to have an upper voltage limit that is allowed,
Figure BDA0002419433740000035
for the reactive sensitivity of the voltage change on node s that the inverter at node j can cause to node s,
Figure BDA0002419433740000036
the capacity is the reactive adjustable capacity of the photovoltaic access point j under the condition of the most serious historical overvoltage;
reactive power richness checking parameter under extreme lower limit sceneUVComprises the following steps:
Figure BDA0002419433740000037
in the formula
Figure BDA0002419433740000038
Represents the out-of-limit degree, denominator of the node l
Figure BDA0002419433740000039
Representing the maximum voltage change at node l that all inverters can cause,
Figure BDA00024194337400000310
is the node voltage, U, of the node l in the historical most severe under-voltage scenarioth-UVIn order to allow a lower limit of the voltage,
Figure BDA00024194337400000311
for the reactive sensitivity of the voltage change on node l that the inverter at node j can cause to node l,
Figure BDA00024194337400000312
the capacity is the reactive adjustable capacity under the condition of the most serious historical undervoltage;
the overvoltage boundary point satisfying the reactive power sufficiency under the overvoltage scene and the undervoltage scene respectively
Figure BDA00024194337400000313
And under voltage boundary point
Figure BDA00024194337400000314
The node voltage is calculated according to the following formula:
Figure BDA00024194337400000315
Figure BDA00024194337400000316
Figure BDA00024194337400000317
and
Figure BDA00024194337400000318
and respectively corresponding the reactive adjustable capacity of the node i under the historical most severe overvoltage scene and the undervoltage scene.
Alternatively, history is the most severeReactive adjustable capacity in voltage scene
Figure BDA00024194337400000319
Comprises the following steps:
Figure BDA0002419433740000048
in the formula, SinvTo the inverter capacity, PpvAnd (4) taking photovoltaic rated output.
Under the condition of historical extreme overvoltage, when the reactive power abundance verification parameters are obtained, the inverters j, j ∈ theta of all the photovoltaic access pointsPVAre all pressed
Figure BDA0002419433740000041
Absorbing reactive power.
Satisfy the idle power sufficiency under the overvoltage scene namelyOV< 1, the overvoltage risk can be eliminated at this time, ifOVIf the voltage is more than 1, the overvoltage risk can not be eliminated, and more reactive resources need to be configured in the network. The invention further calculates the overvoltage boundary point and the undervoltage boundary point only on the premise of meeting the reactive power margin, and can analyze the situation of not meeting the reactive power margin after inputting new reactive power resources. The judgment of reactive power sufficiency and the calculation idea of the boundary point under the undervoltage scene are consistent with those under the overvoltage scene.
Optionally, according to the overvoltage boundary point and the undervoltage boundary point of each photovoltaic access node, the droop slope constraint of the voltage-reactive power control curve of the inverter is determined by considering the voltage sensitivity theory, that is,
the determining of the droop slope constraint for the inverter comprises:
determining the lower limit value according to the voltage sensitivity theorym i：
Figure BDA0002419433740000042
In the formula (I), the compound is shown in the specification,
Figure BDA0002419433740000043
representing a sensitivity matrix SVQThe (i, j) th element means the voltage change of the node i caused by the unit reactive power change quantity of the node j; thetaPVRepresenting a set of photovoltaic access nodes in a power distribution network;
for overvoltage boundary point
Figure BDA0002419433740000044
Under voltage boundary point
Figure BDA0002419433740000045
i∈ΘPV,ΘPVThe upper limit value of the droop slope
Figure BDA0002419433740000046
Comprises the following steps:
Figure BDA0002419433740000047
the droop curve m of voltage-reactive power control of the photovoltaic inverter on node iiThe constraint is as follows:
Figure BDA0002419433740000051
optionally, for any node i, according to the overvoltage boundary point and the undervoltage boundary point, determining that the reactive output constraint of the inverter on the node is as follows:
Figure BDA0002419433740000052
optionally, the objective optimization function of the centralized control model is as follows:
Figure BDA0002419433740000053
in the formula (I), the compound is shown in the specification,
Figure BDA0002419433740000054
representing the total loss of the distribution network, GijRepresents the conductance of a node between i and j nodes, Ui、UjIs the voltage of node i, j, θijRepresenting the phase angle difference between the two nodes i and j;
the optimization solution of the objective function is carried out based on node photovoltaic and load prediction data;
the network power flow constraint is that the voltage and the power of each photovoltaic access node of the power distribution network meet the following formula:
Figure BDA0002419433740000055
in the formula: pi＝PPV,i-Pload,i,Qi＝QPV,i-Qload,iRespectively, net active and net reactive of node i; pPV,iAnd QPV,iRespectively the photovoltaic active and reactive of the node i; pload,iAnd Qload,iThe load active and reactive of the node i are respectively; gijAnd BijRespectively representing the conductance and susceptance between the two nodes i and j.
The construction of the centralized control model is realized based on the network topology of the power distribution network and the line parameter data.
Optionally, for the photovoltaic access node i, the current droop slope m of the corresponding inverter on the node is usediThe voltage-reactive droop curve is:
Figure BDA0002419433740000061
in the formula of UiIs the voltage of node i; qPV,iIs the inverter reactive output of section i,
Figure BDA0002419433740000062
and
Figure BDA0002419433740000063
the voltage and reactive adjustable capacity of the node i under the historical extreme overvoltage scene are obtained.
In a second aspect, the present invention further provides a centralized-local integrated voltage control apparatus for a distribution network with photovoltaic access, comprising:
the first data sampling module is configured to acquire distribution network photovoltaic and load prediction data at a set first sampling interval;
the centralized control module is configured for calculating and updating the inverter droop slope of each photovoltaic access node by utilizing a pre-constructed centralized control model based on the acquired data; the centralized control model is an optimization objective function which takes the minimum loss of a power distribution network as an optimization objective, considers network power flow constraint, inverter droop curve slope constraint and inverter reactive power output constraint and takes the photovoltaic access node inverter droop slope as an optimization variable; the inverter reactive output constraint and the droop curve slope constraint of each node are determined according to the predetermined node overvoltage boundary point and the predetermined node undervoltage boundary point; the overvoltage boundary point and the undervoltage boundary point respectively correspond to a historical extreme overvoltage scene and an undervoltage scene to meet the reactive power sufficiency;
the second data sampling module is configured to acquire the voltage of each photovoltaic access node at a set second sampling interval; the second sampling interval is less than the first sampling interval;
and the local control module is configured for determining a voltage-reactive droop curve according to the current droop slope of the corresponding inverter for each photovoltaic access node, and performing local control according to the voltage of the corresponding photovoltaic access node and the voltage-reactive droop curve to adjust the reactive power output of the inverter.
Advantageous effects
According to the invention, the droop slope of the node inverter meeting the network power flow constraint, the droop curve slope constraint of the inverter and the reactive output constraint of the inverter is optimized and updated by performing centralized control on the photovoltaic access node of the power distribution network in a larger time scale, and then the voltage-reactive droop curve is determined according to the updated droop slope of the node inverter in a smaller time scale to perform local control, so that the coordination of the centralized control and the local control is realized, the two are complementary and insufficient in control real-time performance and global optimization, the rapid response to the prediction error and the power fluctuation is realized, and the control effect is improved.
Compared with the traditional centralized control, the local control stage of the control strategy can immediately respond to the voltage change caused by photovoltaic and load prediction errors, and the reactive output is adjusted locally to avoid voltage out-of-limit; meanwhile, the real-time reactive power adjustment of the local stage is beneficial to restraining the rapid fluctuation of the network voltage.
Compared with the traditional local control, the centralized control stage of the control strategy can globally optimize the slope of the voltage-reactive droop curve, so that the network loss is reduced; the provided control strategy utilizes a voltage sensitivity theory to set key control parameters, limits the adjustment range of the slope and can more obviously reduce the network voltage fluctuation.
In conclusion, the method can be well suitable for voltage-reactive power control coordination of the power distribution network containing high-proportion photovoltaic.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of the two-stage voltage-reactive control architecture of the present invention;
FIG. 3 is a schematic diagram of an in-situ voltage-reactive control curve;
fig. 4 is a schematic diagram of a two-stage voltage-reactive control flow according to an embodiment of the present invention.
Detailed Description
The following further description is made in conjunction with the accompanying drawings and the specific embodiments.
The invention conception of the invention is as follows: in order to solve the defects that the current centralized control method cannot rapidly cope with photovoltaic prediction errors and power fluctuation and the problems that local control is difficult to realize effective coordination among devices, a two-stage centralized-local voltage-reactive control method for a photovoltaic inverter is provided. General local control is improved into variable slope droop control, control parameters are set by using a voltage sensitivity theory to ensure that a model in an extreme scene can effectively inhibit voltage out-of-limit and improve the response capability to voltage fluctuation; and in the centralized control stage, the minimum network loss is taken as a target, the voltage-reactive droop slope of the local control is optimized according to the photovoltaic and load prediction results, and in the local control stage, the local control regulation of the inverter nodes is performed according to the droop slope periodically optimized in the centralized control stage according to the real-time measurement data.
Example 1
The embodiment introduces a centralized-local integrated voltage control method for a distribution network with photovoltaic access, which is shown in fig. 1 and includes:
acquiring photovoltaic and load prediction data at a set first sampling interval;
based on the acquired data, calculating and updating the inverter droop slope of each photovoltaic access node by using a pre-constructed centralized control model; the centralized control model is an optimization objective function which takes the minimum loss of a power distribution network as an optimization objective, considers network power flow constraint, inverter droop curve slope constraint and inverter reactive power output constraint and takes the photovoltaic access node inverter droop slope as an optimization variable; the inverter reactive output constraint and the droop curve slope constraint of each node are determined according to the predetermined node overvoltage boundary point and the predetermined node undervoltage boundary point; the overvoltage boundary point and the undervoltage boundary point respectively correspond to a historical extreme overvoltage scene and an undervoltage scene to meet the reactive power sufficiency;
acquiring the voltage of each photovoltaic access node at a set second sampling interval; the second sampling interval is less than the first sampling interval;
and for each photovoltaic access node, determining a voltage-reactive droop curve according to the current droop slope of the corresponding inverter, and performing local control according to the voltage of the corresponding photovoltaic access node and the voltage-reactive droop curve to adjust the reactive power output of the inverter.
The overvoltage scene and the undervoltage scene are the most serious historical overvoltage scene and the most serious historical undervoltage scene which meet the reactive power sufficiency.
Examples 1 to 2
In this embodiment, a method for performing centralized-local integrated control on a power distribution network including a photovoltaic access is implemented based on embodiment 1, and the principle is shown in fig. 2, a centralized control stage is performed based on photovoltaic and load prediction data predicted in a short time, an optimization target of a centralized control model is that the loss of the power distribution network is minimum, an optimization control variable is a voltage-reactive droop slope of a photovoltaic inverter of each photovoltaic access node, and constraints of an optimization process of a target function include power flow constraints of the power distribution network, reactive control constraints of the inverter, droop slope constraints and the like. And in the centralized control stage, after the droop slope of each photovoltaic access node is obtained through optimized calculation, the control content of the local stage can be executed, the local control is carried out based on the local measured voltage of each photovoltaic access node, for each node, the voltage-reactive droop curve can be determined according to the determined droop slope in the centralized control stage, and then the reactive output of the photovoltaic inverter can be adjusted according to the local measured voltage according to the voltage-reactive droop curve.
In this embodiment, the first sampling interval is a minute-level interval, and the second sampling interval is a second-level interval. The two are complementary on a time scale, so that reactive power can be adjusted according to the change of the on-site voltage in the on-site control stage, and quick response is realized. In addition, the first sampling interval and the second sampling interval may be selected preferably according to other time intervals of different levels.
In the embodiment, according to the historical photovoltaic and load data of the power distribution network and by considering the voltage-reactive sensitivity, the voltage and the reactive power corresponding to the overvoltage boundary point and the undervoltage boundary point of each photovoltaic access node are determined, that is,
the predetermination of the overvoltage and undervoltage boundary points of each photovoltaic access node comprises:
acquiring node voltages of all photovoltaic access nodes in the power distribution network under the scene of the most serious historical overvoltage and undervoltage;
and calculating node voltage and reactive adjustable capacity corresponding to an overvoltage boundary/undervoltage boundary point in a node voltage adjusting range and simultaneously meeting reactive adjustable capacity abundance in an extreme overvoltage scene/undervoltage scene based on the acquired data and in consideration of voltage-reactive sensitivity.
Defining the node voltages of the photovoltaic access node i under the condition of the most serious historical overvoltage and undervoltage respectively as
Figure BDA0002419433740000091
Figure BDA0002419433740000092
The node s/l represents a node where the maximum/minimum voltage appears in the power distribution network under an overvoltage scene/an undervoltage scene;
then the reactive power richness check parameter under the extreme over-limit sceneOVComprises the following steps:
Figure BDA0002419433740000093
in the formula
Figure BDA0002419433740000094
Representing the degree of out-of-limit, denominator of the node s
Figure BDA0002419433740000095
Represents the maximum voltage change, theta, of the node s that can be induced by all invertersPVA photovoltaic node set is accessed to the power distribution network,
Figure BDA0002419433740000096
is the node voltage, U, of the node s in the historical most severe overvoltage scenarioth-OVIn order to have an upper voltage limit that is allowed,
Figure BDA0002419433740000097
for the reactive sensitivity of the voltage change on node s that the inverter at node j can cause to node s,
Figure BDA0002419433740000098
the capacity is the reactive adjustable capacity under the condition of the most serious historical overvoltage;
reactive power richness checking parameter under extreme lower limit sceneUVComprises the following steps:
Figure BDA0002419433740000099
in the formula
Figure BDA00024194337400000910
Represents the out-of-limit degree, denominator of the node l
Figure BDA00024194337400000911
Representing the maximum voltage change at node l that all inverters can cause,
Figure BDA00024194337400000912
is the node voltage, U, of the node l in the historical most severe under-voltage scenarioth-UVIn order to allow a lower limit of the voltage,
Figure BDA00024194337400000913
for the reactive sensitivity of the voltage change on node l that the inverter at node j can cause to node l,
Figure BDA00024194337400000914
the capacity is the reactive adjustable capacity under the condition of the most serious historical undervoltage;
the overvoltage boundary point satisfying the reactive power sufficiency under the overvoltage scene and the undervoltage scene respectively
Figure BDA00024194337400000915
And under voltage boundary point
Figure BDA00024194337400000916
The node voltage is calculated according to the following formula:
Figure BDA00024194337400000917
Figure BDA00024194337400000918
Figure BDA00024194337400000919
and
Figure BDA00024194337400000920
and respectively corresponding the reactive adjustable capacity of the node i under the historical most severe overvoltage scene and the undervoltage scene.
Reactive adjustable capacity under the scene of the most serious historical overvoltage
Figure BDA0002419433740000101
Comprises the following steps:
Figure BDA0002419433740000109
in the formula, SinvTo the inverter capacity, PpvAnd (4) taking photovoltaic rated output.
Under the condition of historical extreme overvoltage, when the reactive power abundance verification parameters are obtained, the inverters j, j ∈ theta of all the photovoltaic access pointsPVAre all pressed
Figure BDA0002419433740000102
Absorbing reactive power.
Satisfy the idle power sufficiency under the overvoltage scene namelyOV< 1, the overvoltage risk can be eliminated at this time, ifOVIf the voltage is more than 1, the overvoltage risk can not be eliminated, and more reactive resources need to be configured in the network. The invention further calculates the overvoltage boundary point and the undervoltage boundary point only on the premise of meeting the reactive power margin, and can analyze the situation of not meeting the reactive power margin after inputting new reactive power resources. The judgment of reactive power sufficiency and the calculation idea of the boundary point under the undervoltage scene are consistent with those under the overvoltage scene.
Referring to fig. 3, the present embodiment determines the droop slope constraint of the inverter voltage-reactive control curve based on the over-voltage boundary point and the under-voltage boundary point of each photovoltaic access node, while considering the voltage sensitivity theory, that is,
the determining of the droop slope constraint for the inverter comprises:
determining the lower limit value according to the voltage sensitivity theorym i：
Figure BDA0002419433740000103
In the formula (I), the compound is shown in the specification,
Figure BDA0002419433740000104
representing a sensitivity matrix SVQThe (i, j) th element means the voltage change of the node i caused by the unit reactive power change quantity of the node j; thetaPVRepresenting a set of photovoltaic access nodes in a power distribution network;
for overvoltage boundary point
Figure BDA0002419433740000105
Under voltage boundary point
Figure BDA0002419433740000106
i∈ΘPV,ΘPVThe upper limit value of the droop slope
Figure BDA0002419433740000107
Comprises the following steps:
Figure BDA0002419433740000108
the droop curve m of voltage-reactive power control of the photovoltaic inverter on node iiThe constraint is as follows:
Figure BDA0002419433740000111
in this embodiment, for any node i, according to the overvoltage boundary point and the undervoltage boundary point, it is determined that the reactive output constraint of the inverter on the node is as follows:
Figure BDA0002419433740000112
in this embodiment, the establishment of the centralized control model and the implementation of the centralized control model are based on the network topology and the line parameter data of the power distribution network, and the objective optimization function is as follows:
Figure BDA0002419433740000113
in the formula (I), the compound is shown in the specification,
Figure BDA0002419433740000114
representing the total loss of the distribution network, GijRepresents the conductance of a node between i and j nodes, Ui、UjIs the voltage of node i, j, θijRepresenting the phase angle difference between the two nodes i and j; the optimization solution of the objective function is carried out based on node photovoltaic and load prediction data;
the network power flow constraint is that the voltage and the power of each photovoltaic access node of the power distribution network meet the following formula:
Figure BDA0002419433740000115
in the formula: pi＝PPV,i-Pload,i,Qi＝QPV,i-Qload,iRespectively, net active and net reactive of node i; pPV,iAnd QPV,iRespectively the photovoltaic active and reactive of the node i; pload,iAnd Qload,iThe load active and reactive of the node i are respectively; gijAnd BijRespectively representing the conductance and susceptance between the two nodes i and j.
Referring to fig. 3, for a photovoltaic access node i, the current droop slope m of the corresponding inverter on the node is usediThe voltage-reactive droop curve is:
Figure BDA0002419433740000116
in the formula of UiIs the voltage of node i; qPV,iIs the inverter reactive output of section i,
Figure BDA0002419433740000121
and
Figure BDA0002419433740000122
at the history extreme for node iVoltage and reactive adjustable capacity in an overvoltage scenario.
Examples 1 to 2
Based on the embodiment 1 and the same inventive concept as the embodiment 1-1, this embodiment specifically introduces a centralized-local integrated voltage control method suitable for a power distribution network containing a high proportion of photovoltaic cells.
Before centralized-local control needs to be executed, configuration initialization is firstly performed, including inputting network topology and line parameters for constructing a centralized control model, inputting historical photovoltaic and load data for determining objective function optimization constraints in a centralized control stage, and calculating an Overvoltage Boundary Point (OBP) and an Undervoltage Boundary Point (UBP) of each photovoltaic access node in a historical extreme scene according to the historical photovoltaic and load data. Then, starting a centralized control stage, acquiring photovoltaic and load prediction data at a first sampling interval t, and optimally calculating and updating the inverter droop slope of each photovoltaic access node by using a pre-constructed centralized control model and taking the minimum network loss of the power distribution network as an optimization target, considering network power flow constraint, inverter droop curve slope constraint and inverter reactive output constraint; in the local control stage, in a time period corresponding to the first sampling interval, the real-time measured voltage of each photovoltaic access node is obtained at the second sampling interval tau, and then a voltage-reactive droop curve is determined according to the current droop slope of the inverter of each photovoltaic access node so as to perform local control on the corresponding node and adjust reactive output of the photovoltaic inverter.
In fig. 4, t represents the time variation of the centralized control phase and τ represents the time variation of the local control phase. Tau represents a more subtle time sequence between t and t-1. The reasons for this are: the time for completing one-time centralized control is long, so that the centralized control is not suitable for quickly responding to the prediction error and random fluctuation of photovoltaic power generation; and the local control can realize reactive regulation only by local measurement and simple calculation, and can better adapt to the rapid change of the photovoltaic power. The control advantages of the time scale coordination concentration and the local control can be better exerted.
The following details the present embodiment with respect to the centralized-local two-stage integrated voltage control.
In-situ control model of in-situ control stage
1.1) control curves and control equations.
To overcome the shortcomings of the conventional in-situ control, the improved in-situ control curve provided by the present invention is shown in fig. 3, the logic of reactive power regulation is unchanged with reference to the prior art, but the conventional in-situ control is improved by setting an over-voltage boundary point (OBP) in fig. 3 at ①, and marking the point as an over-voltage boundary point (OBP)
Figure BDA0002419433740000123
i∈ΘPV,ΘPVIs a node set of photovoltaic access, the function of the node set is to ensure that the upper limit of the reactive suppression voltage is exceeded enough to be absorbed by an inverter under an extreme scene, ② OBP determines the slope m of the curveiThe dynamic adjustment is carried out along with the change of the network operation scene so as to realize the coordination of the inverter. The reactive control equation in fig. 3 is:
Figure BDA0002419433740000131
in the formula of UiIs the voltage of node i; qPV,iIs the inverter reactive output of node i.
1.2)miThe range of variation of (a).
In one aspect, the droop slope m of the voltage-reactive curve in FIG. 3iShould be limited to a reasonable range, i.e.
Figure BDA0002419433740000132
On one hand, the voltage-reactive curve cannot be too inclined, otherwise the reactive output is too sensitive to voltage disturbance, and the voltage stability problem is caused, and the lower limit of the slope can be determined by a voltage sensitivity theory:
Figure BDA0002419433740000133
in the formula:
Figure BDA0002419433740000134
representing a sensitivity matrix SVQThe (i, j) th element means the voltage change of the node i caused by the unit reactive change of the node j.
On the other hand, the slope of the curve cannot be too gentle, otherwise the suppression of the undervoltage is lost, and the inverter should be ensured to have sufficient regulation capability for the lower limit of the voltage. Therefore, fig. 3 also includes an under-voltage boundary point (UBP), which is denoted as
Figure BDA0002419433740000135
i∈ΘPV,ΘPVThe function is to ensure that the lower limit of the idle suppression voltage is enough to be injected by the inverter under the extreme scene. Slope of UBP-OBP line, i.e. mi。
miUpper limit of the variation range:
Figure BDA0002419433740000136
1.3) parameter setting of OBP and UBP.
How to set the OBP and UBP in fig. 3 is very critical, on one hand, the out-of-limit of the reactive suppression voltage with sufficient inverter output in an extreme scene can be ensured; on the other hand, the voltage regulation range of each node is matched with the actual voltage change range, so that reactive resources can be utilized more efficiently.
In order to ensure the effective voltage control, the abundance of the reactive adjustable capacity needs to be ensured firstlyOVAndUVthe method is characterized in that the method is a reactive power richness check parameter for an extremely higher and lower limit scene respectively. To be provided withOVTaking the verification of (1) as an example, the node voltage under the overvoltage scene with the most serious history is recorded as
Figure BDA0002419433740000141
Where the node where the maximum voltage occurs in the network (typically the endmost node of the network) is denoted by s. At this point, the photovoltaic is close to or equal to the rated power, at this point nonePower Capacity modulated
Figure BDA0002419433740000142
j∈ΘPV，
Figure BDA0002419433740000143
SinvTo the inverter capacity, PpvTaking the rated photovoltaic output, j is the photovoltaic access point) of all inverters
Figure BDA0002419433740000144
Absorbing reactive power, the result of the reactive power abundance is:
Figure BDA0002419433740000145
in the formula: the numerator represents the degree of violation of node s; the denominator represents the maximum voltage change at node s that all inverters can cause. If the node s no longer exceeds the upper voltage limit, the rest nodes do not exceed the upper voltage limit. Therefore, ifOVIf the voltage is more than 1, overvoltage risk cannot be eliminated, and more reactive resources need to be configured in the network; if it isOVAnd (5) the overvoltage risk can be eliminated, and the reactive power sufficiency is met. Under the premise of sufficient reactive power, further calculating the voltage value of an overvoltage boundary point according to a voltage sensitivity theory as follows:
Figure BDA0002419433740000146
calculation results
Figure BDA0002419433740000147
I.e. the OBP point in fig. 3. The calculation idea of the UBP is consistent with that of the OBP, and the calculation idea of the UBP is respectively as follows:
Figure BDA0002419433740000148
Figure BDA0002419433740000149
where l is the node where the historical minimum voltage occurs (usually also the endmost node of the network).
2) Low-voltage distribution network centralized control model
OBP and UBP in fig. 3 ensure that the network voltage does not go out of limit in extreme scenarios. However, the difference of the slopes in the graph can cause the difference of the reactive output, thereby causing the influence on the network loss, and the control in the centralized stage can perform coordinated optimization on the droop slopes of different inverters so as to reduce the network loss.
2.1) an objective function.
The optimization goal of the concentration phase is the network loss
Figure BDA0002419433740000151
Minimum:
Figure BDA0002419433740000152
in the formula (I), the compound is shown in the specification,
Figure BDA0002419433740000153
representing the total loss of the distribution network, GijRepresents the conductance of a node between i and j nodes, Ui、UjIs the voltage of node i, j, θijRepresenting the phase angle difference between the two nodes i and j;
the optimal solution of the objective function is based on the node photovoltaic and load prediction data, and takes into account the network power flow constraint, droop curve slope constraint, and inverter reactive output constraint as described below.
2.2) network flow constraints.
The network node voltage and power should satisfy the following equations (9) and (10) simultaneously:
Figure BDA0002419433740000154
Figure BDA0002419433740000155
in the formula: pi＝PPV,i-Pload,i,Qi＝QPV,i-Qload,iRespectively, net active and net reactive of node i; pPV,iAnd QPV,iRespectively the photovoltaic active and reactive of the node i; pload,iAnd Qload,iThe load active and reactive of the node i are respectively; gijAnd BijRespectively representing the conductance and susceptance between the two nodes i and j.
2.3) sag curve slope constraint.
Allowable lower limit m of voltage-reactive droop slopeiDetermined by equation (2), upper limit
Figure BDA0002419433740000156
Determined by the formula (3) and satisfies:
Figure BDA0002419433740000161
2.4) inverter reactive output constraint.
The reactive power regulating quantity of the inverter is calculated according to the formula (1), and the introduction of the OBP and the UBP can ensure that the reactive power output does not exceed the regulating capacity of the inverter. It should be noted that the control curve in fig. 3 is a piecewise function, and in order to optimize the piecewise equation appearing in the model, the node voltage should be limited to the linear interval in fig. 3:
Figure BDA0002419433740000162
the optimization model of the formula (8) can be solved by adopting a primal-dual interior point method, and the primal-dual interior point method has the advantages of rapid convergence and strong robustness, and is an interior point algorithm widely used at present.
Referring to fig. 4, the process involved in the implementation of the integrated-in-place two-stage integrated voltage control in practical applications is described as an application example.
First, initialize
This stage is used to:
acquiring topological information, line parameters and photovoltaic and load data of historical extreme scenes of a power distribution network;
calculating parameters of overvoltage boundary points and undervoltage boundary points of the photovoltaic inverters at each node on the basis of the network voltage sensitivity matrix so as to ensure the voltage out-of-limit inhibition capability of the inverters in an extreme scene;
and determining a centralized control model for determining the topology of the power distribution network.
The above-mentioned overvoltage boundary point and undervoltage boundary point parameters are calculated with reference to equations (4) to (7).
Second, centralized control stage
In the phase, firstly, a minute-scale (control interval is delta t photovoltaic and load prediction values, and then, the local voltage-reactive droop slope is optimized according to an optimization model formed by the formulas (1) - (3) and (8) - (12).
Third, in-situ control stage
In the phase, the voltage of a grid-connected node measured by the photovoltaic inverter is firstly obtained, the sampling time interval is delta tau, and the quantity of delta tau in a single delta t is taumaxIf the integrated control time length T is set, the quantity of delta T in T is Tmax。
Then continuously adjusting the reactive power output of the inverter according to the optimized voltage-reactive droop curve in the centralized control stage; when τ is τmaxThen check if t equals tmaxIf yes, ending the flow of centralized-local control, otherwise, acquiring the photovoltaic and load prediction values at the moment of t +1 and executing centralized control again.
Example 2
This embodiment is a distribution network that contains photovoltaic access concentrates-synthesizes voltage control device on spot, includes:
the first data sampling module is configured to acquire distribution network photovoltaic and load prediction data at a set first sampling interval;
the centralized control module is configured for calculating and updating the inverter droop slope of each photovoltaic access node by utilizing a pre-constructed centralized control model based on the acquired data; the centralized control model is an optimization objective function which takes the minimum loss of a power distribution network as an optimization objective, considers network power flow constraint, inverter droop curve slope constraint and inverter reactive power output constraint and takes the photovoltaic access node inverter droop slope as an optimization variable; the inverter reactive output constraint and the droop curve slope constraint of each node are determined according to the predetermined node overvoltage boundary point and the predetermined node undervoltage boundary point; the overvoltage boundary point and the undervoltage boundary point respectively correspond to a historical extreme overvoltage scene and an undervoltage scene to meet the reactive power sufficiency;
the second data sampling module is configured to acquire the voltage of each photovoltaic access node at a set second sampling interval; the second sampling interval is less than the first sampling interval;
and the local control module is configured for determining a voltage-reactive droop curve according to the current droop slope of the corresponding inverter for each photovoltaic access node, and performing local control according to the voltage-reactive droop curve to adjust the reactive power output of the inverter.
In the embodiment, the invention realizes the power distribution network centralized-local comprehensive voltage control method and device suitable for high-proportion photovoltaic access, the overall coordination of the reactive power-voltage droop curve of the inverter is realized in the centralized stage, and the local control quickly responds to the prediction error and the photovoltaic power fluctuation according to the real-time measurement result; the organic combination of the two can obtain more excellent control effect compared with the traditional control mode.
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.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (10)

1. A centralized-local comprehensive voltage control method for a power distribution network with photovoltaic access is characterized by comprising the following steps:
acquiring photovoltaic and load prediction data of the power distribution network at a set first sampling interval;
based on the acquired data, calculating and updating the inverter droop slope of each photovoltaic access node by using a pre-constructed centralized control model; the centralized control model is an optimization objective function which takes the minimum loss of a power distribution network as an optimization objective, considers network power flow constraint, inverter droop curve slope constraint and inverter reactive power output constraint and takes the photovoltaic access node inverter droop slope as an optimization variable; the inverter reactive output constraint and the droop curve slope constraint of each node are determined according to the predetermined node overvoltage boundary point and the predetermined node undervoltage boundary point; the overvoltage boundary point and the undervoltage boundary point respectively correspond to a historical extreme overvoltage scene and an undervoltage scene to meet the reactive power sufficiency;
acquiring the voltage of each photovoltaic access node at a set second sampling interval; the second sampling interval is less than the first sampling interval;
and for each photovoltaic access node, determining a voltage-reactive droop curve according to the current droop slope of the corresponding inverter, and performing local control according to the voltage of the corresponding photovoltaic access node and the voltage-reactive droop curve to adjust the reactive power output of the inverter.
2. The method of claim 1, wherein the first sampling interval is a minute-scale interval and the second sampling interval is a second-scale interval.
3. A method according to claim 1 or 2, characterized in that the first sampling interval is set to 5min and the second sampling interval is set to 20 s.
4. The method of claim 1, wherein the predetermining of the over-voltage and under-voltage boundary points for each photovoltaic access node comprises:
acquiring node voltages of all photovoltaic access nodes in the power distribution network under the scene of the most serious historical overvoltage and undervoltage;
and calculating node voltage and reactive adjustable capacity corresponding to an overvoltage boundary/undervoltage boundary point in a node voltage adjusting range and simultaneously meeting reactive adjustable capacity abundance in an extreme overvoltage scene/undervoltage scene based on the acquired data and in consideration of voltage-reactive sensitivity.
5. The method as claimed in claim 1 or 4, wherein the node voltage of photovoltaic access node i under the condition of the most serious overvoltage and undervoltage history is defined as
Figure FDA0002419433730000011
The node s/l represents a node where the maximum/minimum voltage appears in the power distribution network under an overvoltage scene/an undervoltage scene;
then the reactive power richness check parameter under the extreme over-limit sceneOVComprises the following steps:
Figure FDA0002419433730000021
in the formula
Figure FDA0002419433730000022
Representing the degree of out-of-limit, denominator of the node s
Figure FDA0002419433730000023
Represents the maximum voltage change, theta, of the node s that can be induced by all invertersPVA photovoltaic node set is accessed to the power distribution network,
Figure FDA0002419433730000024
is the node voltage, U, of the node s in the historical most severe overvoltage scenarioth-OVIn order to have an upper voltage limit that is allowed,
Figure FDA0002419433730000025
for the reactive sensitivity of the voltage change on node s that the inverter at node j can cause to node s,
Figure FDA0002419433730000026
the capacity is the reactive adjustable capacity under the condition of the most serious historical overvoltage;
reactive power richness checking parameter under extreme lower limit sceneUVComprises the following steps:
Figure FDA0002419433730000027
in the formula
Figure FDA0002419433730000028
Represents the out-of-limit degree, denominator of the node l
Figure FDA0002419433730000029
Representing the maximum voltage change at node l that all inverters can cause,
Figure FDA00024194337300000210
is the node voltage, U, of the node l in the historical most severe under-voltage scenarioth -UVIn order to allow a lower limit of the voltage,
Figure FDA00024194337300000211
for the reactive sensitivity of the voltage change on node l that the inverter at node j can cause to node l,
Figure FDA00024194337300000212
the capacity is the reactive adjustable capacity under the condition of the most serious historical undervoltage;
the overvoltage boundary point satisfying the reactive power sufficiency under the overvoltage scene and the undervoltage scene respectively
Figure FDA00024194337300000213
And under voltage boundary point
Figure FDA00024194337300000214
The node voltage is calculated according to the following formula:
Figure FDA00024194337300000215
Figure FDA00024194337300000216
Figure FDA00024194337300000217
and
Figure FDA00024194337300000218
and respectively corresponding the reactive adjustable capacity of the node i under the historical most severe overvoltage scene and the undervoltage scene.
6. The method of claim 1, wherein the determining of the droop slope constraint for the inverter comprises:
determining the lower limit value according to the voltage sensitivity theorym i：
Figure FDA0002419433730000031
In the formula (I), the compound is shown in the specification,
Figure FDA0002419433730000032
representing a sensitivity matrix SVQThe (i, j) th element means the voltage change of the node i caused by the unit reactive power change quantity of the node j; thetaPVRepresenting a set of photovoltaic access nodes in a power distribution network;
for overvoltage boundary point
Figure FDA0002419433730000033
i∈ΘPV,ΘPVBoundary point of undervoltage
Figure FDA0002419433730000034
i∈ΘPV,ΘPVThe upper limit value of the droop slope
Figure FDA0002419433730000035
Comprises the following steps:
Figure FDA0002419433730000036
the droop curve m of voltage-reactive power control of the photovoltaic inverter on node iiThe constraint is as follows:
Figure FDA0002419433730000037
7. the method of claim 6, wherein for any node i, based on the over-voltage boundary point and the under-voltage boundary point, determining the on-node inverter reactive output constraint is:
Figure FDA0002419433730000038
8. the method of claim 1, wherein the centralized control model has an objective optimization function of:
Figure FDA0002419433730000039
in the formula (I), the compound is shown in the specification,
Figure FDA00024194337300000310
representing the total loss of the distribution network, GijRepresents the conductance of a node between i and j nodes, Ui、UjIs the voltage of node i, j, θijRepresenting the phase angle difference between the two nodes i and j;
the optimization solution of the objective function is carried out based on node photovoltaic and load prediction data;
the network power flow constraint is that the voltage and the power of each photovoltaic access node of the power distribution network meet the following formula:
Figure FDA0002419433730000041
in the formula: pi＝PPV,i-Pload,i,Qi＝QPV,i-Qload,iRespectively, net active and net reactive of node i; pPV,iAnd QPV,iRespectively the photovoltaic active and reactive of the node i; pload,iAnd Qload,iThe load active and reactive of the node i are respectively; gijAnd BijRespectively representing the conductance and susceptance between the two nodes i and j.
9. Method according to claim 1, characterised in that for a photovoltaic access node i, the current droop slope m of the corresponding inverter on the node is determinediThe voltage-reactive droop curve is:
Figure FDA0002419433730000042
in the formula of UiIs the voltage of node i; qPV,iIs the inverter reactive output of section i,
Figure FDA0002419433730000043
and
Figure FDA0002419433730000044
the voltage and reactive adjustable capacity of the node i under the historical extreme overvoltage scene are obtained.
10. A centralized-local integrated voltage control device for a distribution network with photovoltaic access is characterized by comprising:
the first data sampling module is configured to acquire distribution network photovoltaic and load prediction data at a set first sampling interval;
the centralized control module is configured for calculating and updating the inverter droop slope of each photovoltaic access node by utilizing a pre-constructed centralized control model based on the acquired data; the centralized control model is an optimization objective function which takes the minimum loss of a power distribution network as an optimization objective, considers network power flow constraint, inverter droop curve slope constraint and inverter reactive power output constraint and takes the photovoltaic access node inverter droop slope as an optimization variable; the inverter reactive output constraint and the droop curve slope constraint of each node are determined according to the predetermined node overvoltage boundary point and the predetermined node undervoltage boundary point; the overvoltage boundary point and the undervoltage boundary point respectively correspond to a historical extreme overvoltage scene and an undervoltage scene to meet the reactive power sufficiency;
the second data sampling module is configured to acquire the voltage of each photovoltaic access node at a set second sampling interval; the second sampling interval is less than the first sampling interval;
and the local control module is configured for determining a voltage-reactive droop curve according to the current droop slope of the corresponding inverter for each photovoltaic access node, and performing local control according to the voltage of the corresponding photovoltaic access node and the voltage-reactive droop curve to adjust the reactive power output of the inverter.
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Application publication date: 20200731