CN116599166A - Reactive power optimization method, device, equipment and storage medium for power distribution network - Google Patents
Reactive power optimization method, device, equipment and storage medium for power distribution network Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—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
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—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
- H02J3/48—Controlling the sharing of the in-phase component
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
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- 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
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Abstract
The application discloses a reactive power optimization method, a device, equipment and a storage medium for a power distribution network, wherein a branch power flow model is established, and linearization treatment is carried out to obtain a distributed power generation reactive power optimization model of a mixed second-order cone; constructing an objective function and each constraint condition; and constructing a distributed opportunity constraint optimization model, inputting the pre-acquired power distribution network line parameters, the initial active power of the distributed power generation and the initial load predicted value into the distributed opportunity constraint optimization model, and training the distributed opportunity constraint optimization model by using constraint conditions and objective functions so as to minimize the active power loss of the system and the node voltage offset. According to the scheme, the distributed opportunity constraint optimization model can be trained, the active power loss and the node voltage offset of the system are continuously optimized, the reactive power adjustment capability and the uncertainty of active power output of distributed power generation are fully considered, and the running economy and reliability of the power system can be improved.
Description
Technical Field
The application relates to the technical field of power grid optimization, in particular to a reactive power optimization method, device, equipment and storage medium for a power distribution network.
Background
Along with the continuous improvement of the grid-connected proportion of the distributed power supply, the influence of wind energy and solar energy on the external environment is larger, and many new challenges are brought to a grid-connected system. From the power system structure and the power system operation characteristics, the system network loss and the voltage are closely related to reactive power. When the reactive power of the system is too high, the voltage is increased, even the situation that the voltage breaks down the insulation of equipment beyond the upper limit can occur, so that the network loss of the system is increased, the operation safety of the system and the equipment can be endangered, and the personal hazard can be seriously caused. When the reactive power of the system is too low, the voltage drops seriously along the direction of the transmission line, and the condition that the voltage is lower than the lower limit value to influence the normal operation of equipment can occur, if the control is not performed, a series of problems such as voltage collapse, large-area power failure and the like can be caused. Therefore, in order to ensure safe and reliable operation of the power system, reactive power compensation equipment in the system needs reactive power optimization control, so that reactive power of the system is reasonably distributed to maintain stability of system voltage.
However, the grid connection of the distributed power supply brings a lot of uncertainty to the system operation, so that the reactive power optimization control cannot correctly reflect the influence of wind power, photovoltaic output uncertainty and load fluctuation on the system, and the optimization result is conservative compared with the actual result, so that a certain potential safety hazard exists for the actual operation of the system.
Disclosure of Invention
In view of the above, the application provides a reactive power optimization method, a device, equipment and a storage medium for a power distribution network, which are used for solving the problems that the grid connection of a distributed power supply brings a lot of uncertainty to the operation of the system, so that the influence of wind power, photovoltaic output uncertainty and load fluctuation on the system cannot be reflected correctly by reactive power optimization control, and the optimization result is conserved compared with the actual result, and certain potential safety hazards exist for the actual operation of the system.
In order to achieve the above object, the following schemes are proposed:
in a first aspect, a reactive power optimization method for a power distribution network includes:
establishing a branch power flow model, and carrying out linearization treatment on the branch power flow model to obtain a distributed power generation reactive power optimization model of a mixed second-order cone;
constructing an objective function, and establishing constraint conditions according to the distributed power generation reactive power optimization model of the mixed second-order cone;
and constructing a distributed opportunity constraint optimization model, inputting the pre-acquired power distribution network line parameters, the initial active power of distributed power generation and the initial load predicted value into the distributed opportunity constraint optimization model, and training the distributed opportunity constraint optimization model by utilizing the constraint conditions and the objective function so as to minimize the active power loss and the node voltage offset of the system.
Preferably, the linearizing the branch tidal current model to obtain a distributed power generation reactive power optimization model of a mixed second order cone includes:
establishing a power flow equation of each power distribution network according to the branch power flow model;
determining each nonlinear term based on each power distribution network tide equation;
linearizing each nonlinear term to obtain a first equation and a second equation corresponding to the nonlinear term;
determining a first power flow equation from the power distribution network power flow equations, and performing linear relaxation on the first power flow equation to obtain a linear relaxation equation;
performing rotation cone constraint conversion on the linear relaxation equation to obtain a conversion equation;
and obtaining a distributed power generation reactive power optimization model of the mixed second-order cone according to the conversion equation and each power flow equation of the power distribution network.
Preferably, the linearizing the branch tidal current model to obtain a distributed power generation reactive power optimization model of a mixed second order cone includes:
establishing a first flow model equation based on the branch flow modelAnd a second tide model equationAnd third tide model equation->Fourth tide model equation- >
wherein ,Pjk and Qjk Active power and reactive power of a jk branch of the power distribution network are respectively represented; r is (r) ij and xij Respectively representing the series resistance and reactance of the ij branch; i ij Representing the square of the current amplitude of branch ij; u (u) j Representing the square of the voltage amplitude at node j; g j 、b j Respectively representing the parallel conductance and susceptance of a node j in the power distribution network; p (P) DGj 、Q DGj Active power and reactive power, respectively, connected to node j of the distributed generation; p (P) Lj 、Q Lj Respectively representing the active power and the reactive power of the load of the node j; b Cj Representing susceptance of the parallel capacitor at node j; t is t ij Representing the transformer ratio of the transformer at the ij branch, t when the branch ij contains no transformer ij Equal to 1;
determining a nonlinear term u according to the first, second and fourth flow model equations j b Cj and uj /t 2 ij ；
The nonlinear term u is processed by utilizing a tail-biting progression method j b Cj Expressed as:
wherein ,bCj，min Is the minimum value of susceptance of the parallel capacitance at node j; b Cj，1 Is the susceptance value of a set of parallel capacitor switches; m is m j Is the number of adjustable groups of parallel capacitors;is an introduced 0-1 variable, indicating whether the shunt capacitor switches;
setting upAs intermediate variables, and performing equivalent transformation by using a Big-M method, the method is as follows:
wherein M is a preset large constant;
And (3) carrying out linear relaxation on the third power flow model equation to obtain a linear relaxation equation:
and performing rotation cone constraint conversion on the linear relaxation equation to obtain a conversion equation:
and obtaining a distributed power generation reactive power optimization model of the mixed second-order cone according to the conversion equation, the first power flow model equation, the second power flow model equation, the third power flow model equation and the fourth power flow model equation.
Preferably, the constructing an objective function includes:
defining a minimum active power loss function f 1 ：
Wherein, the l mark is used for representing the corresponding variable in the first sampling scene, N L Is the total number of sampling scenes; p is p l Is samplingProbability of scenario l occurrence;is the loss of the distribution network of branch ij, N B Is the total number of branches of the distribution network;
redefining a minimum voltage offset function f 2 ：
wherein ,NC Is the total number of nodes in the distribution network; u (U) i，spec Is the expected voltage of node i, set to 1.0p.u.; deltaU i，max Is the maximum allowable voltage deviation of node i;
defining the adjustment cost W of the control device, then:
wherein ,CT and CQ Penalty factors for on-load tap-changer transformer and capacitor regulation are respectively represented; setting C T 3 kW/time to 10 kW/time, C Q From 2 kW/time to 6 kW/time; n (N) t and Nq The number of on-load tap-changer transformers and capacitors, respectively; n is n t，i and nq，j The adjustment time of the ith on-load tap-changer transformer and the jth capacitor bank is respectively;
the objective function is expressed as:
wherein ,λp and λu Weighting factors respectively representing active loss and voltage deviation, reflecting economical operation and voltage stability of the system, satisfying lambda p +λ u ＝1。
Preferably, the establishing each constraint condition according to the distributed power generation reactive power optimization model of the mixed second order cone includes:
constructing branch power flow constraint conditions according to the distributed power generation reactive power optimization model of the mixed second order cone, and constructing additional constraint conditions of an on-load tap-changer transformer, additional constraint conditions of a parallel capacitor, node voltage constraint conditions, branch current constraint conditions, opportunity constraint conditions of node voltage and branch current and reactive power constraint conditions of distributed power generation;
and taking the branch power flow constraint condition, the additional constraint condition of the on-load tap-changer transformer, the additional constraint condition of the parallel capacitor, the node voltage constraint condition, the branch current constraint condition, the opportunity constraint condition of the node voltage and the branch current and the reactive constraint condition of the distributed power generation as all constraint conditions.
Preferably, the method for establishing the constraint conditions comprises the following steps:
The construction method of the branch power flow constraint condition comprises the following steps: linearizing the distributed power generation reactive power optimization model of the mixed second order cone, and according to different load demands and N of distributed power generation output characteristics L And (3) sampling scenes, wherein each branch tide equation in the first scene is as follows:
a first branch flow equation: wherein ,
the second branch flow equation:
third branch flow equation: wherein ,
fourth branch flow equation:
wherein ,NB,Trf Representing a set of branches with transformers;a kth stage transformer ratio representing the transformer branch ij; wherein />Is an introduced 0-1 variable, indicating whether the transformer branch ij is at the kth stage ratio;
the construction method of the additional constraint condition of the on-load tap-changer transformer comprises the following steps: setting according to the third branch flow equationAs intermediate variables, and defining the constraint equations as:
0≤n t,ij ≤N T,max ；
wherein ,nt,ij and NT,max Respectively represent the adjustment time of the transformer branch ijAnd the maximum allowed values thereof; n is n t,ij Representing the total number of adjustments of the transformer branch ij in the sampling scene; n is n j Representing the on-load tap-changer transformer cascading conversion ratio,a transformation ratio representing the scene of l-1;
the construction method of the additional constraint condition of the parallel capacitor comprises the following steps: according to the second branch tide equation, obtaining:
0≤n q,j ≤N Q,max ；
wherein ,nq，j and NQ，max Representing the number of actions of the capacitor j and the maximum value allowed by the capacitor j; n is n q，j For calculating a total number of actions for the capacitor j in the sampling scene; b Cj，1 A susceptance value representing a capacitor in a previous scene of l;
equation(s)As node voltage constraint, wherein +.>u j，min and uj，max Respectively representing the minimum value and the maximum value of the square of the voltage amplitude at the node j;
equation(s)Andas a branch current constraint condition;
wherein ,yij ＝g ij +b ij Representing the shunt admittance of branch ij; i ij，max A maximum value representing the square of the branch current;representing the forward leg current constraint of leg ij,representing the reverse branch current constraint condition of branch ij;
equation(s) and />As an opportunistic constraint on node voltage and branch current, wherein +.> Representing the voltage or current at node j, P l (|I ij |≤|I ij，max I) 1- ε represents the voltage or current of branch ij, and the probability of no out-of-limit in any l scene is not less than 1- ε, U j Is the voltage amplitude of node j, I ij Is the current of branch ij;
equation(s)As reactive constraints for distributed power generation, where pf D Representing the power factor, pf of distributed generation in scenario l D，min Representing an allowable minimum of the power factor of the distributed generation.
Preferably, the inputting the pre-acquired power distribution network line parameters, the initial active power of the distributed power generation and the initial load predicted value into the distributed opportunity constraint optimization model, and training the distributed opportunity constraint optimization model by using the constraint conditions and the objective function to minimize the active power loss of the system and the node voltage offset, includes:
Inputting the pre-acquired power distribution network line parameters, the initial active power of the distributed power generation and the initial load predicted value into the distributed opportunity constraint optimization model, and setting an iteration variable to be 1;
calculating an iteration convergence value of the active power of the distributed power generation and a load prediction iteration convergence value according to the objective function and each constraint condition;
defining error scene prediction formulasWherein l is a 0-1 variable, N is the number of samples, ε is the acceptable risk level for the selected scene, and γ is a confidence interval indicating that the probability of scene occurrence is not less than 1- γ;
generating N prediction error scenes through the error scene prediction formula;
adding the initial active power of the distributed power generation and the initial load predicted value to obtain N input values;
according to the calculated iterative convergence value of the active power of the distributed power generation and the calculated iterative convergence value of the load prediction, the power flow of the power distribution network is solved through forward-reverse substitution, and whether node voltage and branch current exceed preset limiting conditions is judged to determine the number N of over-limit scenes Y ；
If N Y =0, then determining a reactive power optimization result;
if N Y >0, determining a most typical scene from the over-limit scenes by using scene reduction based on a sub-module optimization method;
And in the most typical scene, node voltage, branch current and transformer regulation are solved, a reactive power optimization result is updated, and the steps of executing the distributed generation active power iteration convergence value and the load prediction iteration convergence value obtained according to calculation are returned until the minimum system active power loss and node voltage offset are obtained.
In a second aspect, a reactive power optimization device for a power distribution network includes:
the linearization processing module is used for establishing a branch power flow model, and linearizing the branch power flow model to obtain a distributed power generation reactive power optimization model of the mixed second-order cone;
the construction module is used for constructing an objective function and establishing constraint conditions according to the distributed power generation reactive power optimization model of the mixed second-order cone;
the training module is used for constructing a distributed opportunity constraint optimization model, inputting the pre-acquired power distribution network line parameters, the distributed power generation initial active power and the initial load predicted value into the distributed opportunity constraint optimization model, and training the distributed opportunity constraint optimization model by utilizing the constraint conditions and the objective function so as to minimize the system active power loss and the node voltage offset.
In a third aspect, a reactive power optimization device for a power distribution network includes a memory and a processor;
the memory is used for storing programs;
the processor is configured to execute the program to implement the steps of the reactive power optimization method for the power distribution network according to the first aspect.
In a fourth aspect, a storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the reactive power optimization method for a power distribution network according to the first aspect.
According to the technical scheme, the branch power flow model is established, and the branch power flow model is subjected to linearization treatment to obtain the distributed power generation reactive power optimization model of the mixed second-order cone; constructing an objective function, and establishing constraint conditions according to the distributed power generation reactive power optimization model of the mixed second-order cone; and constructing a distributed opportunity constraint optimization model, inputting the pre-acquired power distribution network line parameters, the initial active power of distributed power generation and the initial load predicted value into the distributed opportunity constraint optimization model, and training the distributed opportunity constraint optimization model by utilizing the constraint conditions and the objective function so as to minimize the active power loss and the node voltage offset of the system. According to the scheme, the distributed opportunity constraint optimization model can be trained, the active power loss and the node voltage offset of the system are continuously optimized, the reactive power adjustment capability and the uncertainty of active power output of distributed power generation are fully considered, and the running economy and reliability of the power system can be improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
Fig. 1 is an optional flowchart of a reactive power optimization method for a power distribution network according to an embodiment of the present application;
fig. 2 is an alternative flowchart of another reactive power optimization method for a power distribution network according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a reactive power optimization device for a power distribution network according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a reactive power optimization device for a power distribution network according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The rapid development of society, the generation of non-renewable energy sources such as coal, petroleum and the like are difficult to meet the high requirements of modern people on the electric energy quality, and the energy sources have the defects of shortage of resources, high pollution and the like, so that various industries of society face the problems of insufficient energy sources, poor working environment and the like. In recent years, with the continuous improvement of the living standard and thought of people, the importance of the environment is also improved, so that more and more people are aware of the importance of the vigorous development and utilization of renewable energy sources. However, with the continuous improvement of the grid-connected proportion of the distributed power supply, the influence of wind energy and solar energy on the external environment is larger, and many new challenges are brought to the power flow distribution, network loss, voltage quality, voltage stability and the like of a grid-connected system.
From the system structure and the system operation characteristics, the system network loss and the voltage are closely related to the reactive power. When the reactive power of the system is too high, the voltage is increased, even the situation that the voltage breaks down the insulation of equipment beyond the upper limit can occur, so that the network loss of the system is increased, the operation safety of the system and the equipment can be endangered, and the personal hazard can be seriously caused. When the reactive power of the system is too low, the voltage drops seriously along the direction of the transmission line, and the condition that the voltage is lower than the lower limit value to influence the normal operation of equipment can occur, if the control is not performed, a series of problems such as voltage collapse, large-area power failure and the like can be caused. Therefore, in order to ensure safe and reliable operation of the power system, reactive power compensation equipment in the system needs reactive power optimization control, so that reactive power of the system is reasonably distributed to maintain stability of system voltage.
Because the distributed power supply grid connection brings a lot of uncertainty to the system operation, the static reactive power optimization control can not correctly reflect the influence of wind power, photovoltaic output uncertainty and load fluctuation on the system, and the optimization result is conservative compared with the actual result, so that a certain potential safety hazard exists for the actual operation of the system. Therefore, reactive power optimization of an active distribution network considering distributed generation is a problem to be solved.
The embodiment of the application provides a reactive power optimization method for a power distribution network, which can be applied to various computer terminals or intelligent terminals, wherein an execution subject of the method can be a processor or a server of the computer terminal or the intelligent terminal, and a flow chart of the method is shown in fig. 1, and specifically comprises the following steps:
s1: and establishing a branch power flow model, and carrying out linearization treatment on the branch power flow model to obtain a distributed power generation reactive power optimization model of the mixed second-order cone.
The branch power flow (Distflow) model is built according to a radial distribution network system.
S2: and constructing an objective function, and establishing constraint conditions according to the distributed power generation reactive power optimization model of the mixed second order cone.
Specifically, the economical efficiency and the safety of the operation of the power grid are comprehensively considered, an objective function is built by taking the minimum system active power loss and the node voltage offset as targets, meanwhile, the adjustment cost caused by the action times of control equipment is also considered, and a plurality of constraint conditions are built based on a distributed power generation reactive power optimization model of a mixed second-order cone.
S3: and constructing a distributed opportunity constraint optimization model, inputting the pre-acquired power distribution network line parameters, the initial active power of distributed power generation and the initial load predicted value into the distributed opportunity constraint optimization model, and training the distributed opportunity constraint optimization model by utilizing the constraint conditions and the objective function so as to minimize the active power loss and the node voltage offset of the system.
In particular, the number of scenarios that need to be addressed increases due to the uncertainty of the active power of distributed generation, thus doubling the model optimization time. Therefore, in order to optimize the solution process, a scenario-simplified opportunistic constraint method is used to reduce the number of solutions, and the solutions are constrained through a typical scenario, so that training of the model is completed in an effort to minimize the system active power loss and node voltage offset.
In the method provided by the embodiment of the invention, the process of linearizing the branch tidal current model to obtain the distributed power generation reactive power optimization model of the mixed second-order cone is specifically described as follows:
establishing a power flow equation of each power distribution network according to the branch power flow model;
determining each nonlinear term based on each power distribution network tide equation;
linearizing each nonlinear term to obtain a first equation and a second equation corresponding to the nonlinear term;
determining a first power flow equation from the power distribution network power flow equations, and performing linear relaxation on the first power flow equation to obtain a linear relaxation equation;
performing rotation cone constraint conversion on the linear relaxation equation to obtain a conversion equation;
and obtaining a distributed power generation reactive power optimization model of the mixed second-order cone according to the conversion equation and each power flow equation of the power distribution network.
Specifically, the above-mentioned process may be specifically:
establishing a first flow model equation based on the branch flow modelAnd a second tide model equationAnd third tide model equation->Fourth tide model equation->
wherein ,Pjk and Qjk Active power and reactive power of a jk branch of the power distribution network are respectively represented; r is (r) ij and xij Respectively representing the series resistance and reactance of the ij branch; i ij Representing the square of the current amplitude of branch ij; u (u) j Representing the square of the voltage amplitude at node j; g j 、b j Respectively representing the parallel conductance and susceptance of a node j in the power distribution network; p (P) DGj 、Q DGj Active power and reactive power, respectively, connected to node j of the distributed generation; p (P) Lj 、Q Lj Respectively representing the active power and the reactive power of the load of the node j; b Cj Representing susceptance of the parallel capacitor at node j; t is t ij Representing the transformer ratio of the transformer at the ij branch, t when the branch ij contains no transformer ij Equal to 1;
determining a nonlinear term u according to the first, second and fourth flow model equations j b Cj and uj /t 2 ij ；
The nonlinear term u is processed by utilizing a tail-biting progression method j b Cj Expressed as:
wherein ,bCj，min Is the minimum value of susceptance of the parallel capacitance at node j; b Cj，1 Is the susceptance value of a set of parallel capacitor switches; m is m j Is the number of adjustable groups of parallel capacitors;is an introduced 0-1 variable, indicating whether the shunt capacitor switches;
setting upAs intermediate variables, and performing equivalent transformation by using a Big-M method, the method is as follows:
wherein M is a preset large constant;
and (3) carrying out linear relaxation on the third power flow model equation to obtain a linear relaxation equation:
And performing rotation cone constraint conversion on the linear relaxation equation to obtain a conversion equation:
and obtaining a distributed power generation reactive power optimization model of the mixed second-order cone according to the conversion equation, the first power flow model equation, the second power flow model equation, the third power flow model equation and the fourth power flow model equation.
Preferably, the process of constructing the objective function may specifically include:
defining a minimum active power loss function f 1 ：
Wherein, the l mark is used for representing the corresponding variable in the first sampling scene, N L Is the total number of sampling scenes; p is p l Is the probability of the sampling scene i occurring;is the loss of the distribution network of branch ij, N B Is the total number of branches of the distribution network;
redefining a minimum voltage offset function f 2 ：
wherein ,NC Is the total number of nodes in the distribution network; u (U) i，spec Is the expected voltage of node i, set to 1.0p.u.; deltaU i，max Is the maximum allowable voltage deviation of node i;
defining the adjustment cost W of the control device, then:
wherein ,CT and CQ Penalty factors for on-load tap-changer transformer and capacitor regulation are respectively represented; setting C T 3 kW/time to 10 kW/time, C Q From 2 kW/time to 6 kW/time; n (N) t and Nq Respectively are provided withThe number of on-load tap-changer transformers and capacitors; n is n t，i and nq，j The adjustment time of the ith on-load tap-changer transformer and the jth capacitor bank is respectively;
The objective function is expressed as:
wherein ,λp and λu Weighting factors respectively representing active loss and voltage deviation, reflecting economical operation and voltage stability of the system, satisfying lambda p +λ u ＝1。
The following embodiment specifically describes a process of establishing each constraint condition according to the distributed power generation reactive power optimization model of the mixed second order cone.
Constructing branch power flow constraint conditions according to the distributed power generation reactive power optimization model of the mixed second order cone, and constructing additional constraint conditions of an on-load tap-changer transformer, additional constraint conditions of a parallel capacitor, node voltage constraint conditions, branch current constraint conditions, opportunity constraint conditions of node voltage and branch current and reactive power constraint conditions of distributed power generation;
and taking the branch power flow constraint condition, the additional constraint condition of the on-load tap-changer transformer, the additional constraint condition of the parallel capacitor, the node voltage constraint condition, the branch current constraint condition, the opportunity constraint condition of the node voltage and the branch current and the reactive constraint condition of the distributed power generation as all constraint conditions.
Specifically, the construction method of the branch power flow constraint condition comprises the following steps: linearizing the distributed power generation reactive power optimization model of the mixed second order cone, and according to different load demands and N of distributed power generation output characteristics L And (3) sampling scenes, wherein each branch tide equation in the first scene is as follows:
a first branch flow equation: wherein ,
the second branch flow equation:
third branch flow equation: wherein ,
fourth branch flow equation:
wherein ,representing constraints of a branch with an on-load tap-changer transformer, N B,Trf Representing a set of branches with transformers; />A kth stage transformer ratio representing the transformer branch ij; /> wherein />Is an introduced 0-1 variable, indicating whether the transformer branch ij is at the kth stage ratio;
the construction method of the additional constraint condition of the on-load tap-changer transformer comprises the following steps: setting according to the third branch flow equationAs intermediate variables, and defining the constraint equations as:
0≤n t,ij ≤N T,max ；
wherein ,nt，ij and NT，max Respectively representing the adjustment time of the transformer branch ij and the maximum value allowed by the adjustment time; n is n t，ij Representing the total number of adjustments of the transformer branch ij in the sampling scene; n is n j Representing the on-load tap-changer transformer cascading conversion ratio,a transformation ratio representing the scene of l-1;
the construction method of the additional constraint condition of the parallel capacitor comprises the following steps: according to the second branch tide equation, obtaining:
0≤n q，j ≤N Q，max ；
wherein ,nq，j and NQ，max Representing the number of actions of the capacitor j and the maximum value allowed by the capacitor j; n is n q，j For calculating a total number of actions for the capacitor j in the sampling scene; b Cj，1 A susceptance value representing a capacitor in a previous scene of l;
equation(s)As node voltage constraint, wherein +.>u j，min and uj，max Respectively representing the minimum value and the maximum value of the square of the voltage amplitude at the node j;
equation(s)Andas a branch current constraint condition; />
wherein ,yij ＝g ij +b ij Representing the shunt admittance of branch ij; i ij，max A maximum value representing the square of the branch current;representing the forward leg current constraint of leg ij,representing the reverse branch current constraint condition of branch ij;
equation(s) and Pl (|I ij |≤|I ij，max I) is equal to or greater than 1-epsilon as an opportunity constraint for node voltage and branch current, wherein ∈r> Representing the voltage or current at node j, P l (|I ij |≤|I ij，max I) 1- ε represents the voltage or current of branch ij and the probability of not out of limit in any l scenarioNot less than 1-epsilon, U j Is the voltage amplitude of node j, I ij Is the current of branch ij;
equation(s)As reactive constraints for distributed power generation, where pf D Representing the power factor, pf of distributed generation in scenario l D,min Representing an allowable minimum of the power factor of the distributed generation.
The process of inputting the pre-acquired power distribution network line parameters, the initial active power of the distributed power generation and the initial load predicted value into the distributed opportunity constraint optimization model, and training the distributed opportunity constraint optimization model by utilizing the constraint conditions and the objective function so as to minimize the active power loss of the system and the node voltage offset is described in detail below.
Inputting the pre-acquired power distribution network line parameters, the initial active power of the distributed power generation and the initial load predicted value into the distributed opportunity constraint optimization model, and setting an iteration variable to be 1;
calculating an iteration convergence value of the active power of the distributed power generation and a load prediction iteration convergence value according to the objective function and each constraint condition;
defining error scene prediction formulasWherein l is a 0-1 variable, N is the number of samples, ε is the acceptable risk level for the selected scene, and γ is a confidence interval indicating that the probability of scene occurrence is not less than 1- γ;
selecting a typical scene, namely generating N prediction error scenes according to the error scene prediction formula according to a probability distribution function of the distributed generation active power and the load prediction error; adding the initial active power of the distributed power generation and the initial load predicted value to obtain N input values; according to the calculated iterative convergence value of the distributed power generation active power and the load prediction iterative convergence value, the power is obtained through forward-reverseSolving the power flow of the distribution network by replacing, judging whether the node voltage and the branch current exceed preset limiting conditions or not to determine the number N of the over-limit scenes Y ；
If N Y =0, then determining a reactive power optimization result;
if N Y >0, determining a most typical scene from the over-limit scenes by using scene reduction based on a sub-module optimization method;
and solving constraints such as node voltage, branch current and transformer regulation in the most typical scene, updating a reactive power optimization result, and returning to execute the steps of calculating the obtained iterative convergence value of the distributed generation active power and the iterative convergence value of load prediction until the minimum system active power loss and node voltage offset are obtained.
The process of calculating the distributed generation active power iteration convergence value and the load prediction iteration convergence value in the process can also be regarded as a process of solving a distributed generation reactive power optimization model of a mixed second-order cone, and a Yalmip+Cplex can be called on a Matlab platform to solve a MISOCP model.
In the above scheme, as shown in fig. 2, the present scheme linearizes the established branch tidal current model to obtain the distributed power generation reactive power optimization model of the hybrid second-order cone, and then, on the basis of the reactive power of the traditional on-load voltage regulation and parallel compensation, the reactive power regulation means is expanded in consideration of the distributed power generation regulation cost and the number of traditional reactive power regulation constraints. Finally, a scene cutting method is adopted to simplify the solving process, so that optimization of network loss and minimization of adjustment cost are realized. The method fully considers the uncertainty of reactive power regulation capability and active power output of distributed power generation, can improve the economical efficiency and reliability of power system operation, effectively inhibits the action times of reactive power regulation equipment, and realizes the minimum total cost of the system operation target.
Corresponding to the method shown in fig. 1, the embodiment of the present invention further provides a reactive power optimization device for a power distribution network, which is used for implementing the method shown in fig. 1, where the reactive power optimization device for a power distribution network provided by the embodiment of the present invention may be introduced in a computer terminal or various mobile devices with reference to fig. 3, and as shown in fig. 3, the device may include:
the linearization processing module 10 is used for establishing a branch power flow model, and linearizing the branch power flow model to obtain a distributed power generation reactive power optimization model of the mixed second-order cone;
the construction module 20 is used for constructing an objective function and establishing constraint conditions according to the distributed power generation reactive power optimization model of the mixed second order cone;
the training module 30 is configured to construct a distributed opportunity constraint optimization model, input the pre-acquired power distribution network line parameters, the distributed power generation initial active power and the initial load predicted value into the distributed opportunity constraint optimization model, and train the distributed opportunity constraint optimization model by using the constraint conditions and the objective function, so as to minimize the system active power loss and the node voltage offset.
According to the technical scheme, the branch power flow model is established, and the branch power flow model is subjected to linearization treatment to obtain the distributed power generation reactive power optimization model of the mixed second-order cone; constructing an objective function, and establishing constraint conditions according to the distributed power generation reactive power optimization model of the mixed second-order cone; and constructing a distributed opportunity constraint optimization model, inputting the pre-acquired power distribution network line parameters, the initial active power of distributed power generation and the initial load predicted value into the distributed opportunity constraint optimization model, and training the distributed opportunity constraint optimization model by utilizing the constraint conditions and the objective function so as to minimize the active power loss and the node voltage offset of the system. According to the scheme, the distributed opportunity constraint optimization model can be trained, the active power loss and the node voltage offset of the system are continuously optimized, the reactive power adjustment capability and the uncertainty of active power output of distributed power generation are fully considered, and the running economy and reliability of the power system can be improved.
Further, the embodiment of the application provides reactive power optimization equipment for a power distribution network. Optionally, fig. 4 shows a block diagram of a hardware structure of the reactive power optimization device of the power distribution network, and referring to fig. 4, the hardware structure of the reactive power optimization device of the power distribution network may include: at least one processor 01, at least one communication interface 02, at least one memory 03 and at least one communication bus 04.
In the embodiment of the present application, the number of the processor 01, the communication interface 02, the memory 03 and the communication bus 04 is at least one, and the processor 01, the communication interface 02 and the memory 03 complete communication with each other through the communication bus 04.
The processor 01 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of the present application, or the like.
The memory 03 may include a high-speed RAM memory, and may further include a nonvolatile memory (non-volatile memory) or the like, such as at least one magnetic disk memory.
The storage stores a program, and the processor can call the program stored in the storage, and the program is used for executing the following reactive power optimization method of the power distribution network, which comprises the following steps of:
establishing a branch power flow model, and carrying out linearization treatment on the branch power flow model to obtain a distributed power generation reactive power optimization model of a mixed second-order cone;
constructing an objective function, and establishing constraint conditions according to the distributed power generation reactive power optimization model of the mixed second-order cone;
and constructing a distributed opportunity constraint optimization model, inputting the pre-acquired power distribution network line parameters, the initial active power of distributed power generation and the initial load predicted value into the distributed opportunity constraint optimization model, and training the distributed opportunity constraint optimization model by utilizing the constraint conditions and the objective function so as to minimize the active power loss and the node voltage offset of the system.
Alternatively, the refinement function and the extension function of the program may refer to the description of the reactive power optimization method of the power distribution network in the method embodiment.
The embodiment of the application also provides a storage medium, which can store a program suitable for being executed by a processor, and when the program runs, the device where the storage medium is controlled to execute the following reactive power optimization method of the power distribution network, comprising the following steps:
establishing a branch power flow model, and carrying out linearization treatment on the branch power flow model to obtain a distributed power generation reactive power optimization model of a mixed second-order cone;
constructing an objective function, and establishing constraint conditions according to the distributed power generation reactive power optimization model of the mixed second-order cone;
and constructing a distributed opportunity constraint optimization model, inputting the pre-acquired power distribution network line parameters, the initial active power of distributed power generation and the initial load predicted value into the distributed opportunity constraint optimization model, and training the distributed opportunity constraint optimization model by utilizing the constraint conditions and the objective function so as to minimize the active power loss and the node voltage offset of the system.
In particular, the storage medium may be a computer-readable storage medium, which may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read only memory), an EPROM, a hard disk, or a ROM.
Alternatively, the refinement function and the extension function of the program may refer to the description of the reactive power optimization method of the power distribution network in the method embodiment.
In addition, functional modules in various embodiments of the present disclosure may be integrated together to form a single portion, or each module may exist alone, or two or more modules may be integrated to form a single portion. The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored on a computer readable storage medium. Based on such understanding, the technical solution of the present disclosure may be embodied in essence or a part contributing to the prior art or a part of the technical solution, or in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a live device, or a network device, etc.) to perform all or part of the steps of the methods of the embodiments of the present disclosure.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for reactive power optimization of a power distribution network, comprising:
establishing a branch power flow model, and carrying out linearization treatment on the branch power flow model to obtain a distributed power generation reactive power optimization model of a mixed second-order cone;
constructing an objective function, and establishing constraint conditions according to the distributed power generation reactive power optimization model of the mixed second-order cone;
and constructing a distributed opportunity constraint optimization model, inputting the pre-acquired power distribution network line parameters, the initial active power of distributed power generation and the initial load predicted value into the distributed opportunity constraint optimization model, and training the distributed opportunity constraint optimization model by utilizing the constraint conditions and the objective function so as to minimize the active power loss and the node voltage offset of the system.
2. The method of claim 1, wherein the linearizing the branch tidal current model to obtain a distributed power generation reactive power optimization model of a hybrid second-order cone comprises:
establishing a power flow equation of each power distribution network according to the branch power flow model;
determining each nonlinear term based on each power distribution network tide equation;
linearizing each nonlinear term to obtain a first equation and a second equation corresponding to the nonlinear term;
determining a first power flow equation from the power distribution network power flow equations, and performing linear relaxation on the first power flow equation to obtain a linear relaxation equation;
performing rotation cone constraint conversion on the linear relaxation equation to obtain a conversion equation;
and obtaining a distributed power generation reactive power optimization model of the mixed second-order cone according to the conversion equation and each power flow equation of the power distribution network.
3. The method of claim 1, wherein the linearizing the branch tidal current model to obtain a distributed power generation reactive power optimization model of a hybrid second-order cone comprises:
establishing a first flow model equation based on the branch flow model And a second tide model equationAnd third tide model equation->Fourth tide model equation->
wherein ,Pjk and Qjk Active power and reactive power of a jk branch of the power distribution network are respectively represented; r is (r) ij and xij Respectively representing the series resistance and reactance of the ij branch; i ij Representing the square of the current amplitude of branch ij; u (u) j Representing the square of the voltage amplitude at node j; g j 、b j Respectively representing the parallel conductance and susceptance of a node j in the power distribution network; p (P) DGj 、Q DGj Active power and reactive power, respectively, connected to node j of the distributed generation; p (P) Lj 、Q Lj Respectively representing the active power and the reactive power of the load of the node j; b Cj Representing susceptance of the parallel capacitor at node j; t is t ij Representing the transformer ratio of the transformer at the ij branch, t when the branch ij contains no transformer ij Equal to 1;
determining a nonlinear term u according to the first, second and fourth flow model equations j b Cj and uj /t 2 ij ；
The nonlinear term u is processed by utilizing a tail-biting progression method j b Cj Expressed as:
wherein ,bCj,min Is the electricity of the parallel capacitor at node jA minimum of sodium; b Cj,1 Is the susceptance value of a set of parallel capacitor switches; m is m j Is the number of adjustable groups of parallel capacitors;is an introduced 0-1 variable, indicating whether the shunt capacitor switches;
Setting upAs intermediate variables, and performing equivalent transformation by using a Big-M method, the method is as follows:
wherein M is a preset large constant;
and (3) carrying out linear relaxation on the third power flow model equation to obtain a linear relaxation equation:
and performing rotation cone constraint conversion on the linear relaxation equation to obtain a conversion equation:
and obtaining a distributed power generation reactive power optimization model of the mixed second-order cone according to the conversion equation, the first power flow model equation, the second power flow model equation, the third power flow model equation and the fourth power flow model equation.
4. The method of claim 1, wherein constructing the objective function comprises:
defining a minimum active power loss function f 1 ：
Wherein, the l mark is used for representing the corresponding variable in the first sampling scene, N L Is the total number of sampling scenes; p is p l Is the probability of the sampling scene i occurring;is the loss of the distribution network of branch ij, N B Is the total number of branches of the distribution network;
redefining a minimum voltage offset function f 2 ：
wherein ,NC Is the total number of nodes in the distribution network; u (U) i,spec Is the expected voltage of node i, set to 1.0p.u.; deltaU i,max Is the maximum allowable voltage deviation of node i;
defining the adjustment cost W of the control device, then:
wherein ,CT and CQ Penalty factors for on-load tap-changer transformer and capacitor regulation are respectively represented; setting C T 3 kW/time to 10 kW/time, C Q From 2 kW/time to 6 kW/time; n (N) t and Nq The number of on-load tap-changer transformers and capacitors, respectively; n is n t,i and nq,j The adjustment time of the ith on-load tap-changer transformer and the jth capacitor bank is respectively;
the objective function is expressed as:
wherein ,λp and λu Weighting factors respectively representing active loss and voltage deviation, reflecting economical operation and voltage stability of the system, satisfying lambda p +λ u ＝1。
5. The method of claim 1, wherein the establishing respective constraints from the distributed power generation reactive power optimization model of the hybrid second order cone comprises:
constructing branch power flow constraint conditions according to the distributed power generation reactive power optimization model of the mixed second order cone, and constructing additional constraint conditions of an on-load tap-changer transformer, additional constraint conditions of a parallel capacitor, node voltage constraint conditions, branch current constraint conditions, opportunity constraint conditions of node voltage and branch current and reactive power constraint conditions of distributed power generation;
and taking the branch power flow constraint condition, the additional constraint condition of the on-load tap-changer transformer, the additional constraint condition of the parallel capacitor, the node voltage constraint condition, the branch current constraint condition, the opportunity constraint condition of the node voltage and the branch current and the reactive constraint condition of the distributed power generation as all constraint conditions.
6. The method of claim 5, wherein the method of establishing each of the constraints comprises:
the construction method of the branch power flow constraint condition comprises the following steps: linearizing the distributed power generation reactive power optimization model of the mixed second order cone, and according to different load demands and N of distributed power generation output characteristics L And (3) sampling scenes, wherein each branch tide equation in the first scene is as follows:
a first branch flow equation: wherein ,
the second branch flow equation:
third branch flow equation: wherein ,
fourth branch flow equation:
wherein ,NB,Trf Representing a set of branches with transformers;a kth stage transformer ratio representing the transformer branch ij; wherein />Is an introduced 0-1 variable, indicating whether the transformer branch ij is at the kth stage ratio;
the construction method of the additional constraint condition of the on-load tap-changer transformer comprises the following steps: setting according to the third branch flow equationAs intermediate variables, and defining the constraint equations as:
0≤n t,ij ≤N T,max ；
wherein ,nt,ij and NT,max Respectively representing the adjustment time of the transformer branch ij and the maximum value allowed by the adjustment time; n is n t,ij Representing the total number of adjustments of the transformer branch ij in the sampling scene; n is n j Representing the on-load tap-changer transformer cascading conversion ratio, A transformation ratio representing the scene of l-1;
the construction method of the additional constraint condition of the parallel capacitor comprises the following steps: according to the second branch tide equation, obtaining:
0≤n q,j ≤N Q,max ；
wherein ,nq,j and NQ,max Representing the number of actions of the capacitor j and the maximum value allowed by the capacitor j; n is n q,j For calculating a total number of actions for the capacitor j in the sampling scene; b Cj,1 A susceptance value representing a capacitor in a previous scene of l;
equation(s)As node voltage constraint, wherein +.>u j,min and uj,max Respectively representing the minimum value and the maximum value of the square of the voltage amplitude at the node j;
equation(s)Andas a branch current constraint condition;
wherein ,yij ＝g ij +b ij Representing the shunt admittance of branch ij; i ij,max A maximum value representing the square of the branch current;representing the forward leg current constraint of leg ij,representing the reverse branch current constraint condition of branch ij;
equation(s) and Pl (|I ij |≤|I ij,max I) is equal to or greater than 1-epsilon as an opportunity constraint for node voltage and branch current, wherein ∈r> Representing the voltage or current at node j, pl (|i) ij |≤|I ij,max I) 1- ε represents the voltage or current of branch ij, and the probability of no out-of-limit in any l scene is not less than 1- ε, U j Is the voltage amplitude of node j, I ij Is the current of branch ij;
equation(s)As reactive constraints for distributed power generation, where pf D Representing the power factor, pf of distributed generation in scenario l D,min Representing an allowable minimum of the power factor of the distributed generation.
7. The method of claim 1, wherein inputting the pre-acquired power distribution network line parameters, the initial active power of the distributed power generation, and the initial load forecast values into the distributed opportunity constraint optimization model, and training the distributed opportunity constraint optimization model with the constraint conditions and objective functions in order to minimize system active power loss and node voltage offset, comprises:
inputting the pre-acquired power distribution network line parameters, the initial active power of the distributed power generation and the initial load predicted value into the distributed opportunity constraint optimization model, and setting an iteration variable to be 1;
calculating an iteration convergence value of the active power of the distributed power generation and a load prediction iteration convergence value according to the objective function and each constraint condition;
defining error scene prediction formulasWherein l is a 0-1 variable, N is the number of samples, ε is the acceptable risk level for the selected scene, and γ is a confidence interval indicating that the probability of scene occurrence is not less than 1- γ;
generating N prediction error scenes through the error scene prediction formula;
Adding the initial active power of the distributed power generation and the initial load predicted value to obtain N input values;
according to the calculated iterative convergence value of the active power of the distributed power generation and the load prediction iterative convergence value, the power flow of the power distribution network is solved through forward-reverse substitution, and the node voltage is judgedAnd whether the branch current exceeds a preset limit condition to determine the number N of the over-limit scenes Y ；
If N Y =0, then determining a reactive power optimization result;
if N Y >0, determining a most typical scene from the over-limit scenes by using scene reduction based on a sub-module optimization method;
and in the most typical scene, node voltage, branch current and transformer regulation are solved, a reactive power optimization result is updated, and the steps of executing the distributed generation active power iteration convergence value and the load prediction iteration convergence value obtained according to calculation are returned until the minimum system active power loss and node voltage offset are obtained.
8. A reactive power optimization device for a power distribution network, comprising:
the linearization processing module is used for establishing a branch power flow model, and linearizing the branch power flow model to obtain a distributed power generation reactive power optimization model of the mixed second-order cone;
The construction module is used for constructing an objective function and establishing constraint conditions according to the distributed power generation reactive power optimization model of the mixed second-order cone;
the training module is used for constructing a distributed opportunity constraint optimization model, inputting the pre-acquired power distribution network line parameters, the distributed power generation initial active power and the initial load predicted value into the distributed opportunity constraint optimization model, and training the distributed opportunity constraint optimization model by utilizing the constraint conditions and the objective function so as to minimize the system active power loss and the node voltage offset.
9. The reactive power optimization equipment for the power distribution network is characterized by comprising a memory and a processor;
the memory is used for storing programs;
the processor is configured to execute the program to implement the steps of the reactive power optimization method for a power distribution network according to any one of claims 1 to 7.
10. A storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the power distribution network reactive power optimization method according to any of claims 1-7.
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| CN117117973B (en) * | 2023-10-24 | 2024-01-12 | 国网浙江省电力有限公司宁波供电公司 | Distributed power supply scheduling method and device based on time scale and storage medium |
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