CN116090218B - Distribution network distributed photovoltaic admission capacity calculation method and system - Google Patents
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Abstract
The invention discloses a calculation method of distributed photovoltaic admission capacity of a power distribution network, which comprises the steps of obtaining data information of the power distribution network to be analyzed; calculating to obtain a load and a photovoltaic time sequence output interval based on an affine algorithm; constructing a distributed photovoltaic admission capacity calculation model of the power distribution network; linearizing the model by adopting a linear approximation method; and (5) solving the model to finish the calculation of the admittance capacity of the distributed photovoltaic of the power distribution network. The invention also discloses a system for realizing the distributed photovoltaic admission capacity calculation method of the power distribution network. According to the invention, an affine optimization type interval power flow algorithm is adopted to improve an interval optimal power flow model, so that uncertainty of load and photovoltaic output can be effectively treated; calculating the maximum admission capacity of the distributed photovoltaic by adopting a photovoltaic admission capacity evaluation model based on interval tide, and improving the admission capacity of the distribution network to the distributed photovoltaic; the calculation efficiency of the model is greatly improved by utilizing the linearization treatment of the model; the invention has high reliability, good accuracy and higher efficiency.
Description
Technical Field
The invention belongs to the technical field of electric automation, and particularly relates to a distributed photovoltaic admission capacity calculation method and system for a power distribution network.
Background
Along with the development of economic technology and the improvement of living standard of people, electric energy becomes an indispensable secondary energy source in the production and living of people, and brings endless convenience to the production and living of people. Therefore, ensuring stable and reliable supply of electric energy becomes one of the most important tasks of the electric power system.
Currently, distributed photovoltaic (Distributed Photovoltaic, DPV) systems have begun to be gradually incorporated into the power grid. However, with the large amount of access of the DPV, the power distribution network may have safety problems such as voltage fluctuation and out-of-limit, and the access of the distributed power supply is limited due to the lack of the structure and regulation means of the traditional power distribution network. Under the trend of power electronics of a power distribution network, how to accurately calculate the admission capacity of the DPV in the power distribution network is always one of the research key points of researchers.
The load and DPV output in the power distribution network have volatility, the traditional deterministic power flow model is not applicable to the uncertainty optimization problem, and the interval power flow algorithm provides an effective processing method. The section power flow adopts the uncertainty of the section number description system, namely a power flow model containing the section number is established, and the section power flow is solved by a corresponding section analysis method. And the upper and lower boundaries of the state quantity of the system are analyzed by the interval tide, all possible running states of the system are given, modeling is relatively simple, and the solving efficiency is higher. At present, a common interval tide algorithm comprises a direct optimization method and an iterative algorithm; the direct optimization method applies a nonlinear optimization method to the interval uncertainty trend, and the solving result is relatively accurate, but consumes more calculation time; and the iterative interval tide algorithm adopts different interval iterative algorithms to compress and solve intervals, and the convergence is insufficient.
Disclosure of Invention
The invention aims to provide a distributed photovoltaic admission capacity calculation method for a power distribution network, which is high in reliability, accuracy and efficiency.
The second purpose of the invention is to provide a system for realizing the distributed photovoltaic admission capacity calculation method of the power distribution network.
The invention provides a distributed photovoltaic admission capacity calculation method for a power distribution network, which comprises the following steps:
s1, acquiring data information of a power distribution network to be analyzed;
S2, calculating to obtain a load and photovoltaic time sequence output interval based on an affine algorithm according to the data information obtained in the step S1;
S3, constructing a distribution network distributed photovoltaic admission capacity calculation model by taking the maximum of the distribution network distributed photovoltaic admission capacity as an objective function and taking power flow constraint, branch voltage and current constraint, DPV reactive power output constraint, SOP operation constraint, ESOP operation constraint, reactive compensation constraint and OLTC constraint as constraint conditions;
S4, linearizing the model established in the step S3 by adopting a linear approximation method;
s5, solving and calculating the model obtained in the step S4, and completing the calculation of the admission capacity of the distributed photovoltaic of the power distribution network.
The data information obtained in the step S1 in the step S2 is calculated based on an affine algorithm to obtain a load and a photovoltaic time sequence output interval, and specifically includes the following steps:
based on affine algorithm, calculating to obtain load and photovoltaic time sequence output interval as Wherein/>For load and photovoltaic time sequence output interval,/>As the average value of the output interval of the load at the t hour,/>For DPV, the mean value of the output interval at the t hour,/>Is the noise element of the output section of the load at the t hour,/>Is the noise element of the output section of the DPV at the t hour,/>For the radius of the output interval of load at the t hour,/>Is the radius of the output interval of the DPV at the t hour.
The step S3 of taking the maximum input capacity of the distributed photovoltaic of the power distribution network as an objective function specifically comprises the following steps:
the following equation is used as the objective function F:
In the middle of DPV capacity size for access node i; omega DPV is a candidate node set for DPV access of the power distribution network.
The step S3 takes power flow constraint, branch voltage current constraint, DPV reactive power output constraint, SOP operation constraint, ESOP operation constraint, reactive compensation constraint and OLTC constraint as constraint conditions, and specifically comprises the following steps:
The affine form power flow constraint described by the branch power flow model is adopted as the following formula:
In the middle of Is the active power flowing on branch ij; /(I)Is the current flowing on branch ij; omega l is the line set; r ij is the resistance of branch ij; /(I)Active power injected into node j; /(I)Active power absorbed by the load connected to node j; /(I)Reactive power flowing on branch ij; x ij is the reactance of branch ij; /(I)Reactive power for injection node j; /(I)Reactive power absorbed by the load connected to node j; omega G is the generator set; /(I)Injecting active power of a power grid into the generator node j; omega DPV is the DPV set; /(I)Injecting active power of the node j for the DPV; omega SOP is a set of nodes at two ends of the installation SOP; /(I)Injecting active power of the node j for SOP; /(I)Injecting reactive power of a power grid into the generator node j; /(I)Injecting reactive power of the node j for the DPV; /(I)Injecting reactive power of the node j for the SOP; omega SVC is the SVC access node set; /(I)Injecting reactive power of a node j for SVC; omega SCB is the SCB access node set; /(I)Injecting reactive power of a node j for the SCB; /(I)Is the voltage of node i;
The following formula is adopted as the branch voltage and current constraint:
In the middle of An upper limit value for the current of the branch ij; /(I)The lower limit value of the voltage of the node i; /(I)The upper limit value of the voltage of the node i;
The following equation is used as DPV reactive power output constraint:
In the middle of The per unit value of the DPV output interval; /(I)A minimum power factor for operation of the DPV inverter;
the following equation is used as SOP operation constraint:
In the middle of Active power loss of the converter at node i for SOP at time t; /(I)Is the loss coefficient of the SOP converter; /(I)Is the installed capacity of the SOP at node i;
The following equation is used as the ESOP operating constraint:
In the middle of Is the output power of the ESS; /(I)Charging power for the ESS; /(I)Discharge power for ESS; /(I)A 0-1 variable for representing the state of charge of the ESS; /(I)Maximum charging power for ESS; /(I)A 0-1 variable for indicating a discharge state of the ESS; /(I)Maximum discharge power for ESS; d ESS is the depth of discharge of ESS; e ESS is the ESS mounting capacity; /(I)The electric quantity of the ESS at the moment t; η ESS,C is the charging efficiency of ESS; η ESS,D is the discharge efficiency of the ESS;
the following equation is used as reactive compensation constraint:
In the middle of The SCB group number is an integer variable and represents the number of the j node to be used at the scene time t; /(I)Capacity for a single set of SCBs; /(I)The maximum SCB group number which can be input for the j node; /(I)A lower limit for the SVC to be able to deliver reactive power; /(I)An upper limit for the SVC to be able to deliver reactive power;
the following formula is used as OLTC constraint:
In the middle of Voltage for OLTC virtual bus; k ij,t is the gear ratio of OLTC; k ij,t is the tap position of the OLTC; Δk oltc is the adjustment step size for each gear of OLTC; /(I)Is the maximum gear of OLTC.
The linearization of the model established in the step S3 is performed by adopting a linear approximation method in the step S4, and specifically comprises the following steps:
Replacing the square term of the variable in the model established in the step S3 by a linear variable:
decoupling a noise element term and a mean value term in affine form power flow constraint, and adopting a first-order Taylor inclusion function approximation to an affine multiplication form;
The variable multiplication form in the model is introduced into intermediate variable substitution, the quadratic form constraint is subjected to relaxation treatment, and the quadratic form expression is converted into a plurality of inequality expression constraints by utilizing a polyhedral approximation method.
The method for linearizing the model established in the step S3 by adopting the linear approximation method specifically comprises the following steps:
Using voltage square term and current square term contained in affine form power flow constraint and branch voltage current constraint And/>Instead, the following formula is converted:
Decoupling a noise element term and a mean value term in affine form power flow constraint, and approximating an affine multiplication form by adopting a first-order Taylor inclusion function to be as follows:
Wherein, Belonging to affine multiplication, the first order Taylor inclusion function approximation is adopted to obtain the following formula:
according to affine definition, decoupling the noise element term from the mean term, and obtaining the following formula:
ignoring noise elements, will Expressed as:
Directly decoupling SOP operation constraint while simultaneously calculating formula The treatment is as follows:
neglecting noise elements, decoupling affine forms into:
Obtaining
Converting ESOP operation constraint into:
converting SVC constraints into:
constrained to tide And SOP constraint typePerforming second-order cone relaxation treatment and linearizing constraint; the relaxation treatment is carried out to obtain:
then equivalent to
Then decomposing into two rotating second order cone constraints:
Then uniformly processing the polyhedral approximation method into the following linearized expression:
Constrained form of multiplying variable AndAnd (3) performing treatment:
Introduction of parameters And/>Will be constrainedConversion toSolving for the constraint of the introduced parameter term to obtain/>And/>Introducing absolute value linearization method to process the above formula to obtain/>
To constraint typeProcessing, introducing parameters/>And/>And adding constraints to the parameter items: /(I)
Linearizing OLTC operating constraints: by usingReplace/>Replace/>Instead ofAnd discrete variable equivalent K ij,t is adopted to obtain:
Wherein b ij,k,t is a variable from 0 to 1;
At this time, the OLTC virtual bus voltage equation is Order theObtain/>And
The invention also provides a system for realizing the distributed photovoltaic admission capacity calculation method of the power distribution network, which comprises a data acquisition module, an output interval calculation module, a model construction module, a model linearization module and a solving module; the data acquisition module, the output interval calculation module, the model construction module, the model linearization module and the solving module are sequentially connected in series; the data acquisition module is used for acquiring data information of the power distribution network to be analyzed and uploading the data to the force interval calculation module; the output interval calculation module is used for calculating and obtaining a load and photovoltaic time sequence output interval based on an affine algorithm according to the received data, and uploading the data to the model construction module; the model construction module is used for constructing a distributed photovoltaic admission capacity calculation model of the power distribution network according to received data, taking the maximum of the distributed photovoltaic admission capacity of the power distribution network as an objective function, taking power flow constraint, branch voltage and current constraint, DPV reactive power output constraint, SOP operation constraint, ESOP operation constraint, reactive compensation constraint and OLTC constraint as constraint conditions, and linearizing the model of the data uploading module; the model linearization module is used for linearizing the established model by adopting a linear approximation method according to the received data, and uploading the data to the solving module; and the solving module is used for solving and calculating the obtained model according to the received data to finish the calculation of the admission capacity of the distributed photovoltaic of the power distribution network.
According to the distributed photovoltaic admission capacity calculation method and system for the power distribution network, an affine optimization type interval power flow algorithm is adopted for solving the problem of high-permeability distributed photovoltaic access of the power distribution network, an interval optimal power flow model is improved, and uncertainty of load and photovoltaic output can be effectively treated; calculating the maximum admission capacity of the distributed photovoltaic by adopting a photovoltaic admission capacity evaluation model based on interval tide, and improving the admission capacity of the distribution network to the distributed photovoltaic by utilizing the cooperative optimization of various active management measures and flexible interconnection devices; the model is utilized to carry out linearization treatment, so that the calculation efficiency of the model can be greatly improved on the premise of minimizing errors; therefore, the invention has high reliability, good accuracy and higher efficiency.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
FIG. 2 is a graph illustrating exemplary scenario load and DPV timing output intervals for the method of the present invention.
Fig. 3 is a schematic diagram of a grid structure according to an embodiment of the method of the present invention.
FIG. 4 is a schematic diagram of functional modules of the system of the present invention.
Detailed Description
A schematic process flow diagram of the method of the present invention is shown in fig. 1: the invention provides a distributed photovoltaic admission capacity calculation method for a power distribution network, which comprises the following steps:
s1, acquiring data information of a power distribution network to be analyzed;
s2, calculating to obtain a load and photovoltaic time sequence output interval based on an affine algorithm according to the data information obtained in the step S1; the method specifically comprises the following steps:
Introducing distributed photovoltaic output and load typical time sequence output data taking 24 hours as time scale, and calculating to obtain load and photovoltaic time sequence output interval as Wherein/>For load and photovoltaic time sequence output interval,/>As the average value of the output interval of the load at the t hour,/>For DPV, the mean value of the output interval at the t hour,/>Is the noise element of the output section of the load at the t hour,/>Is the noise element of the output section of the DPV at the t hour,/>For the radius of the output interval of load at the t hour,/>The radius of the output interval of the DPV at the t hour is set;
then, selecting a proper interval radius, and generating a load and photovoltaic output interval, as shown in fig. 2 (taking 10% interval radius as an example);
S3, constructing a distribution network distributed photovoltaic admission capacity calculation model by taking the maximum of the distribution network distributed photovoltaic admission capacity as an objective function and taking power flow constraint, branch voltage and current constraint, DPV reactive power output constraint, SOP operation constraint, ESOP operation constraint, reactive compensation constraint and OLTC constraint as constraint conditions; the method specifically comprises the following steps:
the following equation is used as the objective function F:
In the middle of DPV capacity size for access node i; omega DPV is a candidate node set accessed by the DPV of the power distribution network;
The affine form power flow constraint described by the branch power flow model is adopted as the following formula:
/>
In the middle of Is the active power flowing on branch ij; /(I)Is the current flowing on branch ij; omega l is the line set; r ij is the resistance of branch ij; /(I)Active power injected into node j; /(I)Active power absorbed by the load connected to node j; /(I)Reactive power flowing on branch ij; x ij is the reactance of branch ij; /(I)Reactive power for injection node j; /(I)Reactive power absorbed by the load connected to node j; omega G is the generator set; /(I)Injecting active power of a power grid into the generator node j; omega DPV is the DPV set; /(I)Injecting active power of the node j for the DPV; omega SOP is a set of nodes at two ends of the installation SOP; /(I)Injecting active power of the node j for SOP; /(I)Injecting reactive power of a power grid into the generator node j; /(I)Injecting reactive power of the node j for the DPV; /(I)Injecting reactive power of the node j for the SOP; omega SVC is the SVC access node set; /(I)Injecting reactive power of a node j for SVC; omega SCB is the SCB access node set; /(I)Injecting reactive power of a node j for the SCB; /(I)Is the voltage of node i;
In order to ensure the safety and the electric energy quality of the system, the following formula is adopted as the branch voltage and current constraint:
In the middle of An upper limit value for the current of the branch ij; /(I)The lower limit value of the voltage of the node i; /(I)The upper limit value of the voltage of the node i;
In order to fully utilize the reactive compensation function of the DG inverter, the DG inverter is assumed to work in a maximum power point tracking mode; and adopts the following formula as DPV reactive power output constraint:
/>
In the middle of The per unit value of the DPV output interval; /(I)A minimum power factor for operation of the DPV inverter;
the following equation is used as SOP operation constraint:
In the middle of Active power loss of the converter at node i for SOP at time t; /(I)Is the loss coefficient of the SOP converter; /(I)Is the installed capacity of the SOP at node i;
The following equation is used as the ESOP operating constraint:
In the middle of Is the output power of the ESS; /(I)Charging power for the ESS; /(I)Discharge power for ESS; /(I)A 0-1 variable for representing the state of charge of the ESS; /(I)Maximum charging power for ESS; /(I)A 0-1 variable for indicating a discharge state of the ESS; /(I)Maximum discharge power for ESS; d ESS is the depth of discharge of ESS; e ESS is the ESS mounting capacity; /(I)The electric quantity of the ESS at the moment t; η ESS,C is the charging efficiency of ESS; η ESS,D is the discharge efficiency of the ESS;
the following equation is used as reactive compensation constraint:
In the middle of The SCB group number is an integer variable and represents the number of the j node to be used at the scene time t; /(I)Capacity for a single set of SCBs; /(I)The maximum SCB group number which can be input for the j node; /(I)A lower limit for the SVC to be able to deliver reactive power; /(I)An upper limit for the SVC to be able to deliver reactive power;
the following formula is used as OLTC constraint:
In the middle of Voltage for OLTC virtual bus; k ij,t is the gear ratio of OLTC; k ij,t is the tap position of the OLTC; Δk oltc is the adjustment step size for each gear of OLTC; /(I)A maximum adjustment gear for OLTC;
s4, linearizing the model established in the step S3 by adopting a linear approximation method; the affine form variables and constraints in the model are evaluated, wherein the affine form variables and constraints comprise nonlinear constraints such as power flow constraints and SOP running constraints, so that the model is a non-convex nonlinear NP difficult problem, and an optimal solution is difficult to obtain; therefore, decoupling noise element items and mean value items in affine power flow formula and affine constraint, performing second order cone relaxation and linearization on power flow constraint and SOP constraint by using first order Taylor inclusion function approximation in affine multiplication form, and performing linearization treatment on other constraint conditions so as to facilitate quick calculation of an optimal solution by a model; the method specifically comprises the following steps:
Replacing the square term of the variable in the model established in the step S3 by a linear variable:
decoupling a noise element term and a mean value term in affine form power flow constraint, and adopting a first-order Taylor inclusion function approximation to an affine multiplication form;
Introducing a variable multiplication form in the model into an intermediate variable for replacement, performing relaxation treatment on quadratic form constraint, and converting the quadratic form expression into a plurality of inequality expression constraint by utilizing a polyhedral approximation method;
the specific implementation method comprises the following steps:
Using voltage square term and current square term contained in affine form power flow constraint and branch voltage current constraint And/>Instead, the following formula is converted:
/>
Decoupling a noise element term and a mean value term in affine form power flow constraint, and approximating an affine multiplication form by adopting a first-order Taylor inclusion function to be as follows:
Wherein, Belonging to affine multiplication, the first order Taylor inclusion function approximation is adopted to obtain the following formula: /(I)
According to affine definition, decoupling the noise element term from the mean term, and obtaining the following formula:
ignoring noise elements, will Expressed as:
Directly decoupling SOP operation constraint while simultaneously calculating formula The treatment is as follows:
neglecting noise elements, decoupling affine forms into:
/>
Obtaining
Converting ESOP operation constraint into:
the reactive power sent by the OLTC and the SCB is discontinuous, the integer variable in the constraint can be regarded as a certainty problem which does not change along with the interval, and the variable in the constraint can be replaced by a corresponding mean value form; converting SVC constraints into:
constrained to tide And SOP constraint typePerforming second-order cone relaxation treatment and linearizing constraint; the relaxation treatment is carried out to obtain:
then equivalent to
Then decomposing into two rotating second order cone constraints:
Then uniformly processing the polyhedral approximation method into the following linearized expression:
Constrained form of multiplying variable AndAnd (3) performing treatment:
Introduction of parameters And/>Will be constrainedConversion toSolving for the constraint of the introduced parameter term to obtain/>And/>Introducing absolute value linearization method to process the above formula to obtain/>
To constraint typeProcessing, introducing parameters/>And/>And adding constraints to the parameter items: /(I)
Linearizing OLTC operating constraints: by usingReplace/>Replace/>Instead ofAnd discrete variable equivalent K ij,t is adopted to obtain:
Wherein b ij,k,t is a variable from 0 to 1;
At this time, the OLTC virtual bus voltage equation is Order theObtain/>And
S5, solving and calculating the model obtained in the step S4, and completing the calculation of the admission capacity of the distributed photovoltaic of the power distribution network.
The method of the invention is further described in connection with one embodiment as follows:
Taking a 10kV system of a node of a certain rural power grid 51 as an example, as shown in fig. 3. The total load in the system is 6875+j2440kVA, and three feeders are all arranged. Assuming that the DPV is to be installed at nodes 32, 34, 43, 47, the inverter power minimum factor is 0.9; the SOP/ESOP candidate positions are nodes 14, 32 and 48, the SOP capacity is 600 kV.A, the loss coefficient is 0.02, the ESS installation capacity is 2000 kW.h, the maximum charging and discharging power is 300kW, the charging and discharging efficiency is 90%, and the discharging depth is 95%; the OLTC tap can adjust the gear to +/-4 multiplied by 1.25%; the installation position of the SVC is the node 14 and 48, and the adjustable range is-500 kVar-500 kVar; the SCBs were installed at nodes 9, 31 for a total of 5 groups, each group having a capacity of 100kVar. The effect of source load uncertainty on DPV admission capacity was investigated and the results are shown in table 1.
TABLE 1 influence of source load uncertainty on DPV Admission Capacity schematic Table
When the section radius increases from 1% to 5%, the total amount of DPV to be installed decreases. As the interval radius increases, i.e. the DPV output and load ripple increases, the uncertainty of the system operation increases and the DPV acceptance of the distribution network decreases. The result verifies that the power distribution network DPV admission capacity evaluation model can effectively cope with source load uncertainty.
Taking active management measures such as an OLTC (on-line thermal insulation) adjusting device and a reactive compensation device into consideration, selecting an interval radius of 10%, and obtaining an evaluation result shown in table 2:
Table 2 evaluation results schematic table
As can be seen from table 2, the active management measure may improve the capacity of the distribution network to accommodate DPVs to some extent. The combination of the two active management measures has better effect on the improvement of the admission capacity than the single measure, but is not obvious in general.
On the basis of taking active management measures, different area radiuses are selected by considering the SOP/ESOP of the flexible interconnection device, and an evaluation result is shown in table 3:
TABLE 3 schematic representation of evaluation results
It can be seen from table 3 that SOP and E-SOP do improve the photovoltaic admission capacity of the distribution network, and that the latter is better than the former. Compared with the active management measures, the SOP and ESOP have stronger improvement on the photovoltaic access capacity, and the improvement effect is more remarkable as the uncertainty of the source load is increased.
According to the method for evaluating the distributed photovoltaic admission capacity of the power distribution network based on the interval power flow, which is provided by the embodiment, aiming at the problem of high-permeability distributed photovoltaic access of the power distribution network, an affine optimization type interval power flow algorithm is adopted, an interval optimal power flow model is improved, and the uncertainty of load and photovoltaic output can be effectively treated; calculating the maximum admission capacity of the distributed photovoltaic by adopting a photovoltaic admission capacity evaluation model based on interval tide, and improving the admission capacity of the distribution network to the distributed photovoltaic by utilizing the cooperative optimization of various active management measures and flexible interconnection devices; by using the linearization processing of the model, the calculation efficiency of the model can be greatly improved on the premise of minimizing the error.
FIG. 4 is a schematic diagram of functional modules of the system of the present invention: the system for realizing the distributed photovoltaic admission capacity calculation method of the power distribution network comprises a data acquisition module, an output interval calculation module, a model construction module, a model linearization module and a solving module; the data acquisition module, the output interval calculation module, the model construction module, the model linearization module and the solving module are sequentially connected in series; the data acquisition module is used for acquiring data information of the power distribution network to be analyzed and uploading the data to the force interval calculation module; the output interval calculation module is used for calculating and obtaining a load and photovoltaic time sequence output interval based on an affine algorithm according to the received data, and uploading the data to the model construction module; the model construction module is used for constructing a distributed photovoltaic admission capacity calculation model of the power distribution network according to received data, taking the maximum of the distributed photovoltaic admission capacity of the power distribution network as an objective function, taking power flow constraint, branch voltage and current constraint, DPV reactive power output constraint, SOP operation constraint, ESOP operation constraint, reactive compensation constraint and OLTC constraint as constraint conditions, and linearizing the model of the data uploading module; the model linearization module is used for linearizing the established model by adopting a linear approximation method according to the received data, and uploading the data to the solving module; and the solving module is used for solving and calculating the obtained model according to the received data to finish the calculation of the admission capacity of the distributed photovoltaic of the power distribution network.
Claims (3)
1. A calculation method for distributed photovoltaic access capacity of a power distribution network comprises the following steps:
s1, acquiring data information of a power distribution network to be analyzed;
S2, calculating to obtain a load and photovoltaic time sequence output interval based on an affine algorithm according to the data information obtained in the step S1;
S3, constructing a distribution network distributed photovoltaic admission capacity calculation model by taking the maximum of the distribution network distributed photovoltaic admission capacity as an objective function and taking power flow constraint, branch voltage and current constraint, DPV reactive power output constraint, SOP operation constraint, ESOP operation constraint, reactive compensation constraint and OLTC constraint as constraint conditions;
The method specifically comprises the following steps of:
the following equation is used as the objective function F:
Wherein P i DPV is the DPV capacity of the access node i; omega DPV is a candidate node set accessed by the DPV of the power distribution network;
the method takes power flow constraint, branch voltage and current constraint, DPV reactive power output constraint, SOP operation constraint, ESOP operation constraint, reactive compensation constraint and OLTC constraint as constraint conditions, and specifically comprises the following steps:
The affine form power flow constraint described by the branch power flow model is adopted as the following formula:
In the middle of Is the active power flowing on branch ij; /(I)Is the current flowing on branch ij; omega l is the line set; r ij is the resistance of branch ij; /(I)Active power injected into node j; /(I)Active power absorbed by the load connected to node j; /(I)Reactive power flowing on branch ij; x ij is the reactance of branch ij; /(I)Reactive power for injection node j; reactive power absorbed by the load connected to node j; omega G is the generator set; /(I) Injecting active power of a power grid into the generator node j; omega DPV is the DPV set; /(I)Injecting active power of the node j for the DPV; omega SOP is a set of nodes at two ends of the installation SOP; /(I)Injecting active power of the node j for SOP; /(I)Injecting reactive power of a power grid into the generator node j; injecting reactive power of the node j for the DPV; /(I) Injecting reactive power of the node j for the SOP; omega SVC is the SVC access node set; /(I)Injecting reactive power of a node j for SVC; omega SCB is the SCB access node set; /(I)Injecting reactive power of a node j for the SCB; /(I)Is the voltage of node i;
The following formula is adopted as the branch voltage and current constraint:
In the middle of An upper limit value for the current of the branch ij; v i min is the lower limit of the voltage of node i; v i max is the upper limit of the voltage at node i;
The following equation is used as DPV reactive power output constraint:
In the middle of The per unit value of the DPV output interval; /(I)A minimum power factor for operation of the DPV inverter;
the following equation is used as SOP operation constraint:
In the middle of Active power loss of the converter at node i for SOP at time t; /(I)Is the loss coefficient of the SOP converter; /(I)Is the installed capacity of the SOP at node i;
The following equation is used as the ESOP operating constraint:
In the middle of Is the output power of the ESS; /(I)Charging power for the ESS; /(I)Discharge power for ESS; beta t C is a 0-1 variable for indicating the state of charge of the ESS; /(I)Maximum charging power for ESS; /(I)A 0-1 variable for indicating a discharge state of the ESS; /(I)Maximum discharge power for ESS; d ESS is the depth of discharge of ESS; e ESS is the ESS mounting capacity; /(I)The electric quantity of the ESS at the moment t; η ESS,C is the charging efficiency of ESS; η ESS,D is the discharge efficiency of the ESS;
the following equation is used as reactive compensation constraint:
In the middle of The SCB group number is an integer variable and represents the number of the j node to be used at the scene time t; /(I)Capacity for a single set of SCBs; /(I)The maximum SCB group number which can be input for the j node; /(I)A lower limit for the SVC to be able to deliver reactive power; an upper limit for the SVC to be able to deliver reactive power;
the following formula is used as OLTC constraint:
In the middle of Voltage for OLTC virtual bus; k ij,t is the gear ratio of OLTC; k ij,t is the tap position of the OLTC; Δk oltc is the adjustment step size for each gear of OLTC; /(I)A maximum adjustment gear for OLTC;
S4, linearizing the model established in the step S3 by adopting a linear approximation method; the method comprises the following steps:
Replacing the square term of the variable in the model established in the step S3 by a linear variable:
decoupling a noise element term and a mean value term in affine form power flow constraint, and adopting a first-order Taylor inclusion function approximation to an affine multiplication form;
Introducing a variable multiplication form in the model into an intermediate variable for replacement, performing relaxation treatment on quadratic form constraint, and converting the quadratic form expression into a plurality of inequality expression constraint by utilizing a polyhedral approximation method;
the specific implementation method comprises the following steps:
Using voltage square term and current square term contained in affine form power flow constraint and branch voltage current constraint AndInstead, the following formula is converted:
Decoupling a noise element term and a mean value term in affine form power flow constraint, and approximating an affine multiplication form by adopting a first-order Taylor inclusion function to be as follows:
Wherein, Belonging to affine multiplication, the first order Taylor inclusion function approximation is adopted to obtain the following formula:
according to affine definition, decoupling the noise element term from the mean term, and obtaining the following formula:
ignoring noise elements, will Expressed as:
Directly decoupling SOP operation constraint while simultaneously calculating formula The treatment is as follows:
neglecting noise elements, decoupling affine forms into:
Obtaining
Converting ESOP operation constraint into:
converting SVC constraints into:
constrained to tide And SOP constraint typePerforming second-order cone relaxation treatment and linearizing constraint; the relaxation treatment is carried out to obtain:
then equivalent to
Then decomposing into two rotating second order cone constraints:
Then uniformly processing the polyhedral approximation method into the following linearized expression:
Constrained form of multiplying variable AndAnd (3) performing treatment:
Introduction of parameters And/>Will constrain/>Conversion to/>Solving for the constraint of the introduced parameter term to obtain/>And/>Introducing absolute value linearization method to process the above formula to obtain/>
To constraint typeProcessing, introducing parameters/>And/>And adding constraints to the parameter items: /(I)
Linearizing OLTC operating constraints: by usingReplace/> Replace/> Replace/>And discrete variable equivalent K ij,t is adopted to obtain:
Wherein b ij,k,t is a variable from 0 to 1;
At this time, the OLTC virtual bus voltage equation is Order theObtain/>And
S5, solving and calculating the model obtained in the step S4, and completing the calculation of the admission capacity of the distributed photovoltaic of the power distribution network.
2. The method for calculating the distributed photovoltaic admittance capacity of the power distribution network according to claim 1, wherein the data information obtained in step S1 in step S2 is calculated based on an affine algorithm to obtain a load and a photovoltaic time sequence output interval, and specifically comprises the following steps:
based on affine algorithm, calculating to obtain load and photovoltaic time sequence output interval as Wherein/>For load and photovoltaic time sequence output interval,/>As the average value of the output interval of the load at the t hour,/>For DPV, the mean value of the output interval at the t hour,/>Is the noise element of the output section of the load at the t hour,/>Is the noise element of the output section of the DPV at the t hour,/>For the radius of the output interval at the t-th hour of the load,Is the radius of the output interval of the DPV at the t hour.
3. A system for realizing the distributed photovoltaic admission capacity calculation method of the power distribution network according to claim 1 or 2, which is characterized by comprising a data acquisition module, an output interval calculation module, a model construction module, a model linearization module and a solving module; the data acquisition module, the output interval calculation module, the model construction module, the model linearization module and the solving module are sequentially connected in series; the data acquisition module is used for acquiring data information of the power distribution network to be analyzed and uploading the data to the force interval calculation module; the output interval calculation module is used for calculating and obtaining a load and photovoltaic time sequence output interval based on an affine algorithm according to the received data, and uploading the data to the model construction module; the model construction module is used for constructing a distributed photovoltaic admission capacity calculation model of the power distribution network according to received data, taking the maximum of the distributed photovoltaic admission capacity of the power distribution network as an objective function, taking power flow constraint, branch voltage and current constraint, DPV reactive power output constraint, SOP operation constraint, ESOP operation constraint, reactive compensation constraint and OLTC constraint as constraint conditions, and linearizing the model of the data uploading module; the model linearization module is used for linearizing the established model by adopting a linear approximation method according to the received data, and uploading the data to the solving module; and the solving module is used for solving and calculating the obtained model according to the received data to finish the calculation of the admission capacity of the distributed photovoltaic of the power distribution network.
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